climsight.log

2025-02-26 11:06:32 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:33 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:36 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:37 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:37 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:37 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:38 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:38 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:40 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:41 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:42 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:43 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:44 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:46 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:47 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:48 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:50 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:51 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:52 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:53 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:54 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:55 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:56 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:06:57 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:07:01 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:07:02 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:07:04 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:08:03 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:08:03 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-26 11:08:03 - rag - WARNING - RAG database is not valid. Not loading it. Please run 'python db_generation.py first.
2025-02-26 11:08:03 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-26 11:08:03 - rag - WARNING - RAG database is not valid. Not loading it. Please run 'python db_generation.py first.
2025-02-26 11:08:03 - fiona._err - INFO - GDAL signalled an error: err_no=4, msg='./data/natural_earth/land/ne_10m_land.shp: No such file or directory'
2025-02-26 11:08:03 - root - ERROR - Unexpected error in is_point_onland: Failed to open dataset (flags=68): ./data/natural_earth/land/ne_10m_land.shp
2025-02-26 11:09:12 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:09:12 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-26 11:09:12 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 11:09:12 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-26 11:09:12 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-26 11:09:12 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 11:09:12 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-26 11:09:12 - root - INFO - Is the point on land? Yes.
2025-02-26 11:09:13 - climsight_engine - INFO - start agent_request
2025-02-26 11:09:14 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:09:14 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:09:14 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:09:14 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:09:14 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:14 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:14 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:23 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:23 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:24 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:24 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:26 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:26 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:31 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:31 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:32 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:33 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:34 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:36 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:36 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:37 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:38 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:39 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:40 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:43 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:45 - __main__ - INFO - reading config from: config.yml
2025-02-26 11:11:45 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-26 11:11:45 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 11:11:45 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-26 11:11:45 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-26 11:11:45 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 11:11:45 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-26 11:11:45 - root - INFO - Is the point on land? Yes.
2025-02-26 11:11:46 - climsight_engine - INFO - start agent_request
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - climsight_engine - INFO - IPCC RAG agent in work.
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - climsight_engine - INFO - General RAG agent in work.
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:47 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:48 - root - ERROR - list index out of range. Continue with: current_land_use = None
Traceback (most recent call last):
  File "/Users/ikuznets/work/projects/climsight/code/climsight/src/climsight/climsight_engine.py", line 669, in zero_rag_agent
    current_land_use = land_use_data["elements"][0]["tags"]["landuse"]
IndexError: list index out of range
2025-02-26 11:11:49 - climsight_engine - INFO - Data agent in work.
2025-02-26 11:11:49 - climsight_engine - INFO - data_agent_response: {'input_params': {'simulation1': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":3.26,\n    "Total Precipitation (mm\\/month)":82.56,\n    "Wind U (m s**-1)":1.76,\n    "Wind V (m s**-1)":1.97,\n    "Wind Speed (m\\/s)":2.64,\n    "Wind Direction (\\u00b0)":221.75\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":2.26,\n    "Total Precipitation (mm\\/month)":71.88,\n    "Wind U (m s**-1)":1.6,\n    "Wind V (m s**-1)":1.8,\n    "Wind Speed (m\\/s)":2.41,\n    "Wind Direction (\\u00b0)":221.66\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":1.13,\n    "Total Precipitation (mm\\/month)":51.66,\n    "Wind U (m s**-1)":0.11,\n    "Wind V (m s**-1)":0.79,\n    "Wind Speed (m\\/s)":0.79,\n    "Wind Direction (\\u00b0)":187.68\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":3.63,\n    "Total Precipitation (mm\\/month)":61.74,\n    "Wind U (m s**-1)":1.13,\n    "Wind V (m s**-1)":-0.04,\n    "Wind Speed (m\\/s)":1.13,\n    "Wind Direction (\\u00b0)":272.16\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":6.97,\n    "Total Precipitation (mm\\/month)":47.09,\n    "Wind U (m s**-1)":0.5,\n    "Wind V (m s**-1)":-0.31,\n    "Wind Speed (m\\/s)":0.59,\n    "Wind Direction (\\u00b0)":302.02\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":12.09,\n    "Total Precipitation (mm\\/month)":73.19,\n    "Wind U (m s**-1)":0.58,\n    "Wind V (m s**-1)":-0.48,\n    "Wind Speed (m\\/s)":0.75,\n    "Wind Direction (\\u00b0)":309.9\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":14.83,\n    "Total Precipitation (mm\\/month)":75.22,\n    "Wind U (m s**-1)":1.31,\n    "Wind V (m s**-1)":-0.86,\n    "Wind Speed (m\\/s)":1.57,\n    "Wind Direction (\\u00b0)":303.36\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":16.7,\n    "Total Precipitation (mm\\/month)":79.41,\n    "Wind U (m s**-1)":1.74,\n    "Wind V (m s**-1)":-0.25,\n    "Wind Speed (m\\/s)":1.76,\n    "Wind Direction (\\u00b0)":278.13\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":17.77,\n    "Total Precipitation (mm\\/month)":64.26,\n    "Wind U (m s**-1)":1.18,\n    "Wind V (m s**-1)":-0.51,\n    "Wind Speed (m\\/s)":1.29,\n    "Wind Direction (\\u00b0)":293.41\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":15.77,\n    "Total Precipitation (mm\\/month)":56.15,\n    "Wind U (m s**-1)":0.65,\n    "Wind V (m s**-1)":0.35,\n    "Wind Speed (m\\/s)":0.74,\n    "Wind Direction (\\u00b0)":241.5\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":10.67,\n    "Total Precipitation (mm\\/month)":64.33,\n    "Wind U (m s**-1)":0.47,\n    "Wind V (m s**-1)":1.1,\n    "Wind Speed (m\\/s)":1.2,\n    "Wind Direction (\\u00b0)":203.31\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.39,\n    "Total Precipitation (mm\\/month)":75.99,\n    "Wind U (m s**-1)":0.97,\n    "Wind V (m s**-1)":2.3,\n    "Wind Speed (m\\/s)":2.49,\n    "Wind Direction (\\u00b0)":202.9\n  }\n]', 'simulation2': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":3.63,\n    "Total Precipitation (mm\\/month)":93.32,\n    "Wind U (m s**-1)":1.97,\n    "Wind V (m s**-1)":1.55,\n    "Wind Speed (m\\/s)":2.51,\n    "Wind Direction (\\u00b0)":231.79\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":2.16,\n    "Total Precipitation (mm\\/month)":92.49,\n    "Wind U (m s**-1)":1.86,\n    "Wind V (m s**-1)":2.14,\n    "Wind Speed (m\\/s)":2.83,\n    "Wind Direction (\\u00b0)":221.07\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":3.4,\n    "Total Precipitation (mm\\/month)":56.26,\n    "Wind U (m s**-1)":1.82,\n    "Wind V (m s**-1)":1.87,\n    "Wind Speed (m\\/s)":2.61,\n    "Wind Direction (\\u00b0)":224.21\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.8,\n    "Total Precipitation (mm\\/month)":64.48,\n    "Wind U (m s**-1)":1.04,\n    "Wind V (m s**-1)":1.26,\n    "Wind Speed (m\\/s)":1.63,\n    "Wind Direction (\\u00b0)":219.4\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":8.26,\n    "Total Precipitation (mm\\/month)":60.96,\n    "Wind U (m s**-1)":0.06,\n    "Wind V (m s**-1)":-0.06,\n    "Wind Speed (m\\/s)":0.09,\n    "Wind Direction (\\u00b0)":316.02\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":11.61,\n    "Total Precipitation (mm\\/month)":78.52,\n    "Wind U (m s**-1)":0.2,\n    "Wind V (m s**-1)":-0.7,\n    "Wind Speed (m\\/s)":0.73,\n    "Wind Direction (\\u00b0)":344.2\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":15.12,\n    "Total Precipitation (mm\\/month)":70.26,\n    "Wind U (m s**-1)":1.25,\n    "Wind V (m s**-1)":-0.58,\n    "Wind Speed (m\\/s)":1.38,\n    "Wind Direction (\\u00b0)":294.98\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":17.39,\n    "Total Precipitation (mm\\/month)":79.89,\n    "Wind U (m s**-1)":1.47,\n    "Wind V (m s**-1)":-0.24,\n    "Wind Speed (m\\/s)":1.49,\n    "Wind Direction (\\u00b0)":279.41\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":17.7,\n    "Total Precipitation (mm\\/month)":57.64,\n    "Wind U (m s**-1)":1.44,\n    "Wind V (m s**-1)":-0.14,\n    "Wind Speed (m\\/s)":1.44,\n    "Wind Direction (\\u00b0)":275.54\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":16.5,\n    "Total Precipitation (mm\\/month)":63.81,\n    "Wind U (m s**-1)":0.84,\n    "Wind V (m s**-1)":0.42,\n    "Wind Speed (m\\/s)":0.94,\n    "Wind Direction (\\u00b0)":243.1\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.24,\n    "Total Precipitation (mm\\/month)":100.19,\n    "Wind U (m s**-1)":1.56,\n    "Wind V (m s**-1)":1.43,\n    "Wind Speed (m\\/s)":2.12,\n    "Wind Direction (\\u00b0)":227.38\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":5.64,\n    "Total Precipitation (mm\\/month)":76.48,\n    "Wind U (m s**-1)":0.28,\n    "Wind V (m s**-1)":1.11,\n    "Wind Speed (m\\/s)":1.14,\n    "Wind Direction (\\u00b0)":194.3\n  }\n]', 'simulation3': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":4.03,\n    "Total Precipitation (mm\\/month)":85.71,\n    "Wind U (m s**-1)":1.43,\n    "Wind V (m s**-1)":1.88,\n    "Wind Speed (m\\/s)":2.36,\n    "Wind Direction (\\u00b0)":217.35\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":4.24,\n    "Total Precipitation (mm\\/month)":108.92,\n    "Wind U (m s**-1)":2.97,\n    "Wind V (m s**-1)":2.68,\n    "Wind Speed (m\\/s)":4.0,\n    "Wind Direction (\\u00b0)":227.93\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":3.66,\n    "Total Precipitation (mm\\/month)":88.63,\n    "Wind U (m s**-1)":2.83,\n    "Wind V (m s**-1)":1.17,\n    "Wind Speed (m\\/s)":3.06,\n    "Wind Direction (\\u00b0)":247.64\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.3,\n    "Total Precipitation (mm\\/month)":64.4,\n    "Wind U (m s**-1)":2.05,\n    "Wind V (m s**-1)":0.92,\n    "Wind Speed (m\\/s)":2.24,\n    "Wind Direction (\\u00b0)":245.82\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":8.84,\n    "Total Precipitation (mm\\/month)":51.08,\n    "Wind U (m s**-1)":0.36,\n    "Wind V (m s**-1)":-0.13,\n    "Wind Speed (m\\/s)":0.38,\n    "Wind Direction (\\u00b0)":290.17\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":12.42,\n    "Total Precipitation (mm\\/month)":65.8,\n    "Wind U (m s**-1)":0.03,\n    "Wind V (m s**-1)":-0.88,\n    "Wind Speed (m\\/s)":0.88,\n    "Wind Direction (\\u00b0)":358.15\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":16.25,\n    "Total Precipitation (mm\\/month)":77.68,\n    "Wind U (m s**-1)":1.21,\n    "Wind V (m s**-1)":-0.33,\n    "Wind Speed (m\\/s)":1.26,\n    "Wind Direction (\\u00b0)":285.41\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":18.08,\n    "Total Precipitation (mm\\/month)":56.84,\n    "Wind U (m s**-1)":1.49,\n    "Wind V (m s**-1)":-0.57,\n    "Wind Speed (m\\/s)":1.59,\n    "Wind Direction (\\u00b0)":290.9\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":18.46,\n    "Total Precipitation (mm\\/month)":43.19,\n    "Wind U (m s**-1)":1.38,\n    "Wind V (m s**-1)":-0.41,\n    "Wind Speed (m\\/s)":1.44,\n    "Wind Direction (\\u00b0)":286.34\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":16.21,\n    "Total Precipitation (mm\\/month)":71.5,\n    "Wind U (m s**-1)":1.08,\n    "Wind V (m s**-1)":0.29,\n    "Wind Speed (m\\/s)":1.12,\n    "Wind Direction (\\u00b0)":254.81\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.88,\n    "Total Precipitation (mm\\/month)":51.78,\n    "Wind U (m s**-1)":0.57,\n    "Wind V (m s**-1)":0.73,\n    "Wind Speed (m\\/s)":0.93,\n    "Wind Direction (\\u00b0)":217.79\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.63,\n    "Total Precipitation (mm\\/month)":84.15,\n    "Wind U (m s**-1)":1.83,\n    "Wind V (m s**-1)":2.0,\n    "Wind Speed (m\\/s)":2.72,\n    "Wind Direction (\\u00b0)":222.47\n  }\n]', 'simulation5': '[\n  {\n    "Temperature (\\u00b0C)":0.37,\n    "Total Precipitation (mm\\/month)":10.76,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":-0.42,\n    "Wind Speed (m\\/s)":-0.13,\n    "Wind Direction (\\u00b0)":10.04\n  },\n  {\n    "Temperature (\\u00b0C)":-0.1,\n    "Total Precipitation (mm\\/month)":20.61,\n    "Wind U (m s**-1)":0.26,\n    "Wind V (m s**-1)":0.34,\n    "Wind Speed (m\\/s)":0.42,\n    "Wind Direction (\\u00b0)":-0.59\n  },\n  {\n    "Temperature (\\u00b0C)":2.27,\n    "Total Precipitation (mm\\/month)":4.6,\n    "Wind U (m s**-1)":1.71,\n    "Wind V (m s**-1)":1.08,\n    "Wind Speed (m\\/s)":1.82,\n    "Wind Direction (\\u00b0)":36.53\n  },\n  {\n    "Temperature (\\u00b0C)":2.17,\n    "Total Precipitation (mm\\/month)":2.74,\n    "Wind U (m s**-1)":-0.09,\n    "Wind V (m s**-1)":1.3,\n    "Wind Speed (m\\/s)":0.5,\n    "Wind Direction (\\u00b0)":-52.76\n  },\n  {\n    "Temperature (\\u00b0C)":1.29,\n    "Total Precipitation (mm\\/month)":13.87,\n    "Wind U (m s**-1)":-0.44,\n    "Wind V (m s**-1)":0.25,\n    "Wind Speed (m\\/s)":-0.5,\n    "Wind Direction (\\u00b0)":14.0\n  },\n  {\n    "Temperature (\\u00b0C)":-0.48,\n    "Total Precipitation (mm\\/month)":5.33,\n    "Wind U (m s**-1)":-0.38,\n    "Wind V (m s**-1)":-0.22,\n    "Wind Speed (m\\/s)":-0.02,\n    "Wind Direction (\\u00b0)":34.3\n  },\n  {\n    "Temperature (\\u00b0C)":0.29,\n    "Total Precipitation (mm\\/month)":-4.96,\n    "Wind U (m s**-1)":-0.06,\n    "Wind V (m s**-1)":0.28,\n    "Wind Speed (m\\/s)":-0.19,\n    "Wind Direction (\\u00b0)":-8.38\n  },\n  {\n    "Temperature (\\u00b0C)":0.69,\n    "Total Precipitation (mm\\/month)":0.48,\n    "Wind U (m s**-1)":-0.27,\n    "Wind V (m s**-1)":0.01,\n    "Wind Speed (m\\/s)":-0.27,\n    "Wind Direction (\\u00b0)":1.28\n  },\n  {\n    "Temperature (\\u00b0C)":-0.07,\n    "Total Precipitation (mm\\/month)":-6.62,\n    "Wind U (m s**-1)":0.26,\n    "Wind V (m s**-1)":0.37,\n    "Wind Speed (m\\/s)":0.15,\n    "Wind Direction (\\u00b0)":-17.87\n  },\n  {\n    "Temperature (\\u00b0C)":0.73,\n    "Total Precipitation (mm\\/month)":7.66,\n    "Wind U (m s**-1)":0.19,\n    "Wind V (m s**-1)":0.07,\n    "Wind Speed (m\\/s)":0.2,\n    "Wind Direction (\\u00b0)":1.6\n  },\n  {\n    "Temperature (\\u00b0C)":0.57,\n    "Total Precipitation (mm\\/month)":35.86,\n    "Wind U (m s**-1)":1.09,\n    "Wind V (m s**-1)":0.33,\n    "Wind Speed (m\\/s)":0.92,\n    "Wind Direction (\\u00b0)":24.07\n  },\n  {\n    "Temperature (\\u00b0C)":-1.75,\n    "Total Precipitation (mm\\/month)":0.49,\n    "Wind U (m s**-1)":-0.69,\n    "Wind V (m s**-1)":-1.19,\n    "Wind Speed (m\\/s)":-1.35,\n    "Wind Direction (\\u00b0)":-8.6\n  }\n]', 'simulation6': '[\n  {\n    "Temperature (\\u00b0C)":0.77,\n    "Total Precipitation (mm\\/month)":3.15,\n    "Wind U (m s**-1)":-0.33,\n    "Wind V (m s**-1)":-0.09,\n    "Wind Speed (m\\/s)":-0.28,\n    "Wind Direction (\\u00b0)":-4.4\n  },\n  {\n    "Temperature (\\u00b0C)":1.98,\n    "Total Precipitation (mm\\/month)":37.04,\n    "Wind U (m s**-1)":1.37,\n    "Wind V (m s**-1)":0.88,\n    "Wind Speed (m\\/s)":1.59,\n    "Wind Direction (\\u00b0)":6.27\n  },\n  {\n    "Temperature (\\u00b0C)":2.53,\n    "Total Precipitation (mm\\/month)":36.97,\n    "Wind U (m s**-1)":2.72,\n    "Wind V (m s**-1)":0.38,\n    "Wind Speed (m\\/s)":2.27,\n    "Wind Direction (\\u00b0)":59.96\n  },\n  {\n    "Temperature (\\u00b0C)":1.67,\n    "Total Precipitation (mm\\/month)":2.66,\n    "Wind U (m s**-1)":0.92,\n    "Wind V (m s**-1)":0.96,\n    "Wind Speed (m\\/s)":1.11,\n    "Wind Direction (\\u00b0)":-26.34\n  },\n  {\n    "Temperature (\\u00b0C)":1.87,\n    "Total Precipitation (mm\\/month)":3.99,\n    "Wind U (m s**-1)":-0.14,\n    "Wind V (m s**-1)":0.18,\n    "Wind Speed (m\\/s)":-0.21,\n    "Wind Direction (\\u00b0)":-11.85\n  },\n  {\n    "Temperature (\\u00b0C)":0.33,\n    "Total Precipitation (mm\\/month)":-7.39,\n    "Wind U (m s**-1)":-0.55,\n    "Wind V (m s**-1)":-0.4,\n    "Wind Speed (m\\/s)":0.13,\n    "Wind Direction (\\u00b0)":48.25\n  },\n  {\n    "Temperature (\\u00b0C)":1.42,\n    "Total Precipitation (mm\\/month)":2.46,\n    "Wind U (m s**-1)":-0.1,\n    "Wind V (m s**-1)":0.53,\n    "Wind Speed (m\\/s)":-0.31,\n    "Wind Direction (\\u00b0)":-17.95\n  },\n  {\n    "Temperature (\\u00b0C)":1.38,\n    "Total Precipitation (mm\\/month)":-22.57,\n    "Wind U (m s**-1)":-0.25,\n    "Wind V (m s**-1)":-0.32,\n    "Wind Speed (m\\/s)":-0.17,\n    "Wind Direction (\\u00b0)":12.77\n  },\n  {\n    "Temperature (\\u00b0C)":0.69,\n    "Total Precipitation (mm\\/month)":-21.07,\n    "Wind U (m s**-1)":0.2,\n    "Wind V (m s**-1)":0.1,\n    "Wind Speed (m\\/s)":0.15,\n    "Wind Direction (\\u00b0)":-7.07\n  },\n  {\n    "Temperature (\\u00b0C)":0.44,\n    "Total Precipitation (mm\\/month)":15.35,\n    "Wind U (m s**-1)":0.43,\n    "Wind V (m s**-1)":-0.06,\n    "Wind Speed (m\\/s)":0.38,\n    "Wind Direction (\\u00b0)":13.31\n  },\n  {\n    "Temperature (\\u00b0C)":1.21,\n    "Total Precipitation (mm\\/month)":-12.55,\n    "Wind U (m s**-1)":0.1,\n    "Wind V (m s**-1)":-0.37,\n    "Wind Speed (m\\/s)":-0.27,\n    "Wind Direction (\\u00b0)":14.48\n  },\n  {\n    "Temperature (\\u00b0C)":0.24,\n    "Total Precipitation (mm\\/month)":8.16,\n    "Wind U (m s**-1)":0.86,\n    "Wind V (m s**-1)":-0.3,\n    "Wind Speed (m\\/s)":0.23,\n    "Wind Direction (\\u00b0)":19.57\n  }\n]'}, 'content_message': '\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2020-2029. The nextGEMS pre-final simulations for years 2030x..This simulation serves as a historical reference to extract its values from other simulations. :\n {simulation1}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2030-2039. The nextGEMS pre-final simulations for years 2030x. :\n {simulation2}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2040-2049. The nextGEMS pre-final simulations for years 2040x. :\n {simulation3}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2030-2039 compared to the simulation for 2020-2029. :\n {simulation5}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2040-2049 compared to the simulation for 2020-2029. :\n {simulation6}'}
2025-02-26 11:11:49 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:49 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:49 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:50 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-26 11:11:50 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-26 11:11:50 - rag - INFO - Chunks returned from RAG: {'context': "Global\n38 Warming Levels (GLWs), and the relatively high uncertainties associated with future irrigation trends for the\n39 second half of the century (see e.g. Viviroli et al., 2020), assessment of risks associated with GLWs greater\n40 than 2.0°C GWL was not conducted. {Figure CCP5.6}\n\n41\n\n42 Figure AI.39: The effect of regional sea level rise on extreme sea level events at coastal locations.\n43 (a) Schematic illustration of extreme sea level events and their average recurrence in the recent past (1986\xad\n44 2005) and the future. As a consequence of mean sea level rise, local sea levels that historically occurred once\n45 per century (historical centennial events, HCEs) are projected to recur more frequently in the future. (b) The\n46 year in which HCEs are expected to recur once per year on average under RCP8.5 and RCP2.6, at the 439\n47 individual coastal locations where the observational record is sufficient. The absence of a circle indicates an\n48 inability to perform an assessment due to a lack of data but does not indicate absence of exposure and risk.\n49 The darker the circle, the earlier this transition is expected. The likely range is ±10 years for locations where\n50 this transition is expected before 2100. White circles (33% of locations under RCP2.6 and 10% under\n51 RCP8.5) indicate that HCEs are not expected to recur once per year before 2100. (c) An indication at which\n52 locations this transition of HCEs to annual events is projected to occur more than 10 years later under\n53 RCP2.6 compared to RCP8.5. As the scenarios lead to small differences by 2050 in many locations results\n54 are not shown here for RCP4.5 but they are available in Chapter 4. {4.2.3, Figure 4.10, Figure 4.12}\n\n55\n\n56 Figure AI.40: Relative trends in projected regional shoreline change.\n\nDo Not Cite, Quote or Distribute  AI-67    Total pages: 74\nFINAL DRAFT                       Annex I  IPCC WGII Sixth Assessment Report\n\n 1 Advance/retreat relative to 2010. Frequency distributions of\n\n(land-use change, pollution, overexploitation, fragmentation and destruction) and climate change\n36   (high confidence). In the case of the Amazon forest, this could lead to large-scale ecological transformations\n37   and shifts from a closed, wet forest into a drier and lower-biomass vegetation (medium confidence). If these\n38   pressures are not successfully addressed, the combined and interactive effects between climate change,\n39   deforestation and degradation, and forest fires are projected to lead to over 60% reduction of area covered by\n40   forest in response to 2.5°C global warming level (medium confidence). Some habitat-forming coastal\n41   ecosystems including many coral reefs, kelp forests and seagrass meadows, will undergo irreversible phase\n42   shifts due to marine heatwaves with global warming levels >1.5°C and are at high risk this century even in\n43   <1.5°C scenarios that include periods of temperature overshoot beyond 1.5°C (high confidence). Under\n44   SSP1-2.6, coral reefs are at risk of widespread decline, loss of structural integrity and transitioning to net\n45   erosion by mid-century due to increasing intensity and frequency of marine heatwaves (very high\n46   confidence). Due to these impacts, the rate of sea-level rise is very likely to exceed that of reef growth by\n47   2050, absent adaptation. In response to heatwaves, bleaching of the Great Barrier Reef is projected to occur\n48   annually if warming increases above 2.0°C resulting in widespread decline and loss of structural integrity\n49   (very high confidence). Global warming of 3.0-3.5°C increases the likelihood of extreme and lethal heat\n50   events in west and North Africa (medium confidence) and across Asia. Drought risks are projected to\n51   increase in many regions over the 21st century (very high confidence). {2.5.2, 2.5.4, 3.4.2, 3.4.3, 9.5.3, 9.10,\n52   10.2.1, 10.3.7, 11.3.1, 11.3.2, Box 11.2, Table 11.14, 13.3.1, 13.4.1, 14.5.3, Box 14.3, CCP7.3.6}\n53\n54   TS.C.2.2\n\nto Past Climate Changes\n        1      CCB ADAPT               Adaptation Science\n        1      CWGB ATTRIB             Attribution in the IPCC Sixth Assessment Report\n               (WGI & WGII)\n        2      CCB NATURAL             Nature-Based Solutions for climate change mitigation and adaptation\n        2      CCB EXTREMES            Ramifications of climatic extremes for marine, terrestrial, freshwater and polar\n                                       natural systems\n        2      CCB ILLNESS             Human health, biodiversity and climate: serious risks posed by vector- and\n                                       water-borne diseases\n        3      CCB SLR                 Sea Level Rise\n        4      CCB DISASTER            Disasters as the Public Face of Climate Change\n        5      CCB MOVING              The Moving Plate: Sourcing Food when Species Distributions Change\n               PLATE\n        5      CWGB                    Mitigation and Adaptation via the Bioeconomy\n               BIOECONOMY\n               (WGII & WGIII)\n        6      CWGB URBAN              Cities and Climate Change in the Age of the Anthropocene\n               (WGII & WGIII)\n         7     CCB COVID               COVID-19\n         7     CCB MIGRATE             Climate-Related Migration\n         7     CCB HEALTH              Co-Benefits Of Climate Solutions For Human Health And Wellbeing\n        16     CCB INTEREG             Inter-Regional Flows Of Risks And Responses To Risk\n        16     CWGB SRM                Solar Radiation Modification\n               (WGII & WGIII)\n        16     CWGB ECONOMIC           Estimating global economic impacts from climate change and the social cost of\n               (WGII & WGIII)          carbon\n        17     CCB LOSS                Loss and Damage\n        17     CCB DEEP                Effective adaptation and decision-making under deep uncertainties\n        17     CCB FINANCE             Finance for Adaptation and Resilience\n\ndue to increases in processes such as wildfires, tree mortality, insect pest outbreaks,\n 3   peatland drying and permafrost thaw (high confidence). These phenomena exacerbate self-reinforcing\n 4   feedbacks between emissions from high-carbon ecosystems (that currently store ~3030–4090 GtC) and\n 5   increasing global temperatures. Complex interactions of climate change, land use change, carbon dioxide\n 6   fluxes, and vegetation changes, combined with insect outbreaks and other disturbances, will regulate the\n 7   future carbon balance of the biosphere, processes incompletely represented in current earth system models.\n 8   The exact timing and magnitude of climate-biosphere feedbacks and potential tipping points of carbon loss\n 9   are characterized by large uncertainty, but studies of feedbacks indicate increased ecosystem carbon losses\n10   can cause large future temperature increases (medium confidence). {2.5.2.7; 2.5.2, 2.5.3, Figure 2.10, Figure\n11   2.11, Table 2.4, Table 2.5, Table 2.S.2; Table 2.S.4, Table 5.4, Figure 5.29, AR6 WGI 5.4}\n12\n13   TS.C.13.3 Extinction of species is an irreversible impact of climate change, the risk of which increases\n14   steeply with rises in global temperature (high confidence) (see TS.C.1). Even the lowest estimates of\n15   species' extinctions (9% lost) are 1000x natural background rates (medium confidence). Projected species'\n16   extinctions at future global warming levels are consistent with projections from AR4, but assessed on many\n17   more species with much greater geographic coverage and a broader range of climate models, giving higher\n18   confidence.{2.5.1.3; Figure 2.6; Figure 2.7; Figure 2.8; CCB DEEP, CCP1}\n19\n20   TS.C.13.4 Solar Radiation Modification (SRM) approaches have potential to offset warming and\n21   ameliorate other climate hazards, but their potential to reduce risk or introduce novel risks to people\n22   and ecosystems is not well understood (high confidence). SRM effects on climate hazards are", 'location': 'Address: house_number: 17, road: Lindenallee, quarter: Dreibergen, suburb: Wulsdorf, city_district: Stadtbezirk Bremerhaven-Süd, city: Bremerhaven, state: Bremen, ISO3166-2-lvl4: DE-HB, postcode: 27572, country: Germany, country_code: de', 'question': 'I want to setup a solar panels at top of my house, what should i keep in mind in term of climate change in next 30 years up to 2050 ?'}
2025-02-26 11:11:50 - rag - INFO - Chunks returned from RAG: {'context': "achieve positive outcomes in terms of both health and adaptation to climate change, so-called co-benefits [21]. One example is the expansion of urban green space and urban blue\ninfrastructure. This includes roadside trees and street greenery,\ngreening of facades and green roofs, and larger green spaces\n(parks, playgrounds) that promote recreation, air pollution control, and microclimate [22]. To simultaneously serve biodiversity\nconser\xadvation, this greenery should be as diverse as possible.\nSocio\xad\xadeconomically disadvantaged people in particular benefit\nfrom nature-based health interventions in cities [23]. In view of\nincreasing urbanisation worldwide – it is predicted that 68% of\nthe world's population will already live in cities by 2050 [24] – the\ndevelopment of urban, diverse greenery should therefore not be\nunderestimated as an important public health measure.\n\nlikely to lead to more and more intense weather extremes\nin the coming years. The increase in heatwaves and dry\nspells has a strong impact on health [26].\nAccording to the German coordination office of the Intergovernmental Panel on Climate Change (IPCC), global\n\nHome back\n\n10\n\nforward\n\n\x0cJournal of Health Monitoring\n\nair temperatures will continue to rise until at least mid-century under all emissions scenarios considered. Global\nwarming of 1.5°C and even 2°C is likely to be exceeded during the 21st century unless drastic reductions in CO2 and\nother greenhouse gas emissions occur in the coming\nyears [27]. Many changes in the climate system are amplified in direct relation to increasing global warming [25].\nNatural factors and internal variability will modulate human-induced forcing, especially at regional scales and in\nthe near future. It is important to consider these modulations when planning for the full range of potential impacts.\nAs global warming continues, projections indicate that simultaneous and multiple modifications of climatic impact\ndrivers (CIDs) will increasingly occur in nearly all\n\nderived. The oldest\nscenario family (special report on emissions scenarios,\nSRES) reflects the state of knowledge at the turn of the\nmillennium, the subsequent generation of scenarios (representative concentration pathways, RCP) was developed\nfor the IPCC's Fifth Assessment Report, and now considers other factors such as climate change mitigation and\nadaptation [29]. The latest generation is called shared socioeconomic pathways (SSP) and focuses on changing socioeconomic factors, such as population, economic growth,\neducation, urbanisation, and the pace of technological development [30]. In doing so, the SSP identify five different\nways in which the world could develop without climate\npolicies and how different levels of climate action could\nbe achieved. In doing so, the climate mitigation targets of\nthe RCP are combined with the SSP. The RCP set pathways\nfor greenhouse gas concentrations and thus the amount\nof warming that could occur by the end of the century. The\nSSP, on the other hand, provide the framework within\nwhich emissions reductions are achieved (or not\nachieved) [31].\nThe five socioeconomic development paths of the SSP\nscenarios (SSP1 to SSP5), are associated with additional\nradiative forcing (1.9 to 8.5 W/m2). Scenarios with low or\nvery low greenhouse gas emissions (SSP1-1.9 and SSP12.6) lead to detectable positive impacts on greenhouse gas\nconcentrations as well as air quality in a matter of years\ncompared to scenarios with high and very high greenhouse\n\ngas emissions (SSP3-7.0 or SSP5-8.5). When comparing\nthese contrasting scenarios, discernible differences between global air temperature trends begin to emerge from\nnatural variability within about 20 years.\n4. Impact of climate change on health\nThe Climate Impact and Risk Assessment 2021 for Germany lists eight climate risks in the field of human health,\nwhich are also in line with the structure of this status report and the following sections [32]: heat stress, UV-related\nhealth damage, allergic\n\non Human Systems\nBesides exposure to extreme heat, over 99% of the global population breathes air that does not concur\nwith WHO air quality standards.Air pollution is responsible for several million premature deaths every\nyear. It has been associated with three of the leading causes of death worldwide including stroke, ischemic\nheart disease, and primary cancer of the trachea, bronchus, and lung.\nHumans are becoming more susceptible to infectious disease as the suitability of dengue and malaria\ntransmission is increasing with changes to the global climate.\nHumans are increasingly susceptible to infectious disease transmission due to underlying factors such as\nglobal connectivity, climate change and landscape change. Vectorial capacity has increased for dengue\nfever, malaria and other vector-borne diseases, and higher global average temperatures are expanding the\ngeographic areas that are conducive to transmission.\nFor instance, the climatic suitability for the transmission of dengue increased by around 12.0% from the\n1951-1960 to 2012–2021 periods, causing febrile illnesses and, in severe cases, organ failure and death.\nChildren under the age of five were particularly at risk. The length of the transmission season for malaria,\nleading from absent or very mild symptoms to severe disease and even death, increased by 31.3% and\n13.8% in the highlands of the Americas and Africa, respectively, between the 1951–1960 and 2012–2021\ndecades.This increased transmission of infectious disease from climate change therefore threatens progress\ntoward SDG 3.3, whose aim is to put an end to epidemics of communicable diseases.\n\nFigure 35. How disease moves from the wild into human populations. Source: IPCC AR6 Frequently Asked Questions.\n\n49\n\n\x0cMoving Forward: Synergistic\nClimate and SDG Policy\nThis report has underscored the fact that global temperature in 2011-2020 was already 1.1°C above pre-industrial levels and catastrophic, intensifying, and widespread extreme events have become\n\nPanel on Climate Change (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution\nof Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Pörtner, H.-O.,\nRoberts, D.C., Tignor, M., Poloczanska, E.S., Mintenbeck, K., Alegría, A. et al. (eds.). Cambridge, UK and New York, NY,\nUSA: Cambridge University Press. https://www.ipcc.ch/report/ar6/wg2/.\nIntergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (2019). Global Assessment Report on\nBiodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem\nServices. Brondizio, E.S., Settele, J., Díaz, S. and Ngo, H.T. (eds.). Bonn, Germany. https://www.ipbes.net/globalassessment.\nInternational Monetary Fund (2023). 2023 Review of Resource Adequacy of the Poverty Reduction and Growth Trust, Resilience\nand Sustainability Trust, and Debt Relief Trusts. Washington, D.C. https://www.imf.org/en/Publications/Policy-Papers/\nIssues/2023/04/25/2023-Review-of-Resource-Adequacy-of-the-Poverty-Reduction-and-Growth-Trust-Resilienceand-532788.\nJain, P. and Bardhan, S. (2023). Does development assistance reduce climate vulnerability in developing countries? an empirical\ninvestigation. Climate and Development 15 (2), 148–161. https://doi.org/10.1080/17565529.2022.2065236.\nJoselow, M.(2023). Climate change is fueling an insurance crisis. There’s no easy fix, 29 June. https://www.washingtonpost.\ncom/politics/2023/06/27/climate-change-is-fueling-an-insurance-crisis-there-no-easy-fix/?s=03. Accessed 18 October\n2023.\nKhan, M., Robinson, S., Weikmans, R., Ciplet, D. and Roberts, J.T. (2020). Twenty-five years of adaptation finance through a\nclimate justice lens. Climatic Change 161(2), 251–269. https://doi.org/10.1007/s10584-019-02563-x.\nKirchhofer, X. and A. Fozzard (2021). Climate Change Budget Tagging: A Review of International Experience. Washington,\nD.C.: World Bank.", 'location': 'Address: house_number: 17, road: Lindenallee, quarter: Dreibergen, suburb: Wulsdorf, city_district: Stadtbezirk Bremerhaven-Süd, city: Bremerhaven, state: Bremen, ISO3166-2-lvl4: DE-HB, postcode: 27572, country: Germany, country_code: de', 'question': 'I want to setup a solar panels at top of my house, what should i keep in mind in term of climate change in next 30 years up to 2050 ?'}
2025-02-26 11:11:51 - climsight_engine - INFO - zero_agent_response: {'input_params': {'elevation': '3.0', 'soil': 'Fluvisols', 'biodiv': 'Cyclothone', 'distance_to_coastline': '3254.264053473585', 'nat_hazards': Empty DataFrame
Columns: [year, disastertype]
Index: [], 'population':     Time  TotalPopulationAsOf1July  PopulationDensity  PopulationGrowthRate
0   1980              77786.703000         223.165900             -0.145000
1   1990              78072.678100         223.986330              0.237400
2   2000              80995.587100         232.372000              0.241300
3   2010              81294.847800         233.230560             -0.021600
4   2020              82291.120600         236.088820              0.245600
5   2030              83132.605000         238.502990             -0.082500
6   2040              81965.316200         235.154110             -0.195300
7   2050              80013.692600         229.555010             -0.288400
8   2060              77399.456100         222.054900             -0.362600
9   2070              74806.608900         214.616160             -0.299400
10  2080              72785.056300         208.816440             -0.266400
11  2090              70893.830000         203.390610             -0.247800
12  2100              69481.298222         199.338133             -0.171333
13  2101                       NaN                NaN                   NaN}, 'content_message': 'Elevation above sea level: {elevation} \nCurrent soil type: {soil} \nOccuring species: {biodiv} \nDistance to the closest coastline: {distance_to_coastline} \nNatural hazards: {nat_hazards} \nPopulation in Germany data: {population} \n'}
2025-02-26 11:11:51 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:11:55 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 11:11:55 - root - INFO - Rendered RAG response: For Bremerhaven, Germany, when considering setting up solar panels in light of climate change over the next 30 years, keep in mind the following:

1. **Rising Temperatures**: Global air temperatures are expected to rise until mid-century, potentially exceeding 1.5°C to 2°C above pre-industrial levels. This may increase the efficiency of solar panels due to more sunlight exposure but also necessitates considerations for cooling systems to prevent overheating.

2. **Weather Extremes**: The frequency and intensity of weather extremes, such as heatwaves and dry spells, are likely to increase. Solar panel systems should be designed to withstand these conditions, ensuring they are durable and resistant to potential damage.

3. **Air Quality**: While air pollution is a concern, scenarios with low greenhouse gas emissions can lead to better air quality. Cleaner air may improve the efficiency of solar panels by reducing particulate matter that can obstruct sunlight.

4. **Technological Advancements**: Keep an eye on advancements in solar technology and energy storage, which may improve efficiency and resilience in the face of climate change.

These considerations are based on general climate change projections and may not be specific to Bremerhaven.
2025-02-26 11:11:55 - climsight_engine - INFO - general_rag_agent_response: For Bremerhaven, Germany, when considering setting up solar panels in light of climate change over the next 30 years, keep in mind the following:

1. **Rising Temperatures**: Global air temperatures are expected to rise until mid-century, potentially exceeding 1.5°C to 2°C above pre-industrial levels. This may increase the efficiency of solar panels due to more sunlight exposure but also necessitates considerations for cooling systems to prevent overheating.

2. **Weather Extremes**: The frequency and intensity of weather extremes, such as heatwaves and dry spells, are likely to increase. Solar panel systems should be designed to withstand these conditions, ensuring they are durable and resistant to potential damage.

3. **Air Quality**: While air pollution is a concern, scenarios with low greenhouse gas emissions can lead to better air quality. Cleaner air may improve the efficiency of solar panels by reducing particulate matter that can obstruct sunlight.

4. **Technological Advancements**: Keep an eye on advancements in solar technology and energy storage, which may improve efficiency and resilience in the face of climate change.

These considerations are based on general climate change projections and may not be specific to Bremerhaven.
2025-02-26 11:11:55 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:13:42 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 500 Internal Server Error"
2025-02-26 11:13:42 - openai._base_client - INFO - Retrying request to /chat/completions in 0.499250 seconds
2025-02-26 11:13:48 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 11:13:48 - root - INFO - Rendered RAG response: For Bremerhaven, Germany, when considering the installation of solar panels in the context of climate change over the next 30 years up to 2050, you should keep the following in mind:

1. **Increased Temperature and Heatwaves**: Global warming is expected to lead to more frequent and intense heatwaves. This could impact the efficiency of solar panels, as extreme heat can reduce their performance. Proper ventilation and cooling systems should be considered to maintain efficiency.

2. **Changes in Weather Patterns**: There might be changes in local weather patterns, including alterations in sunshine duration and cloud cover. It's essential to select solar panels that are efficient in a range of conditions and to consider potential fluctuations in solar energy generation.

3. **Sea Level Rise and Extreme Weather Events**: Although Bremerhaven is a coastal city, the specific impact of sea level rise on your property would depend on its elevation and proximity to the coast. It's advisable to ensure that your solar infrastructure is robust against potential extreme weather events, such as storms or flooding.

4. **Regulations and Incentives**: Stay informed about local and national policies which might affect solar energy installations, including incentives for renewable energy adoption or potential changes in building codes due to climate adaptation measures.

This information is based on general climate projections and considerations for the region surrounding Bremerhaven, Germany.
2025-02-26 11:13:48 - climsight_engine - INFO - ipcc_rag_agent_response: For Bremerhaven, Germany, when considering the installation of solar panels in the context of climate change over the next 30 years up to 2050, you should keep the following in mind:

1. **Increased Temperature and Heatwaves**: Global warming is expected to lead to more frequent and intense heatwaves. This could impact the efficiency of solar panels, as extreme heat can reduce their performance. Proper ventilation and cooling systems should be considered to maintain efficiency.

2. **Changes in Weather Patterns**: There might be changes in local weather patterns, including alterations in sunshine duration and cloud cover. It's essential to select solar panels that are efficient in a range of conditions and to consider potential fluctuations in solar energy generation.

3. **Sea Level Rise and Extreme Weather Events**: Although Bremerhaven is a coastal city, the specific impact of sea level rise on your property would depend on its elevation and proximity to the coast. It's advisable to ensure that your solar infrastructure is robust against potential extreme weather events, such as storms or flooding.

4. **Regulations and Incentives**: Stay informed about local and national policies which might affect solar energy installations, including incentives for renewable energy adoption or potential changes in building codes due to climate adaptation measures.

This information is based on general climate projections and considerations for the region surrounding Bremerhaven, Germany.
2025-02-26 11:13:48 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:13:48 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-26 11:13:50 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 11:14:03 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 11:14:03 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 11:14:04 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-26 11:14:05 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 11:14:08 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 11:14:09 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 11:14:25 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-26 11:14:25 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-26 11:14:25 - climsight_engine - INFO - combine_agent in work
2025-02-26 11:14:25 - climsight_engine - INFO - smart_agent_response: {'output': "The temperature for the solar panels at Bremerhaven, Germany, is expected to increase over the next 30 years. For example, the average temperature in July is projected to rise from 14.83°C in the 2020s to 16.25°C in the 2040s. According to the Wikipedia article, high temperatures negatively impact solar panel efficiency, while colder temperatures can improve performance. This increase in temperature may lead to reduced efficiency, especially during the summer months.\n\nThe precipitation levels are also expected to change, with January's average precipitation increasing from 82.56 mm/month in the 2020s to 85.71 mm/month in the 2040s. Solar modules must withstand damage from rain, hail, and heavy snow load. The increase in precipitation, particularly in winter months, could pose challenges for solar panel maintenance and efficiency.\n\nThe u_wind component, which represents the east-west wind speed, shows variability, with January's average increasing from 1.76 m/s in the 2020s to 1.43 m/s in the 2040s. Wind can affect the structural integrity of solar panels, and changes in wind patterns may require adjustments in panel mounting and support.\n\nThe v_wind component, representing the north-south wind speed, also shows changes, with February's average increasing from 1.8 m/s in the 2020s to 2.68 m/s in the 2040s. Similar to u_wind, changes in wind speed and direction may impact the stability and efficiency of solar panels.\n\nIn summary, the expected increase in temperature and changes in precipitation and wind patterns over the next 30 years may affect the efficiency and maintenance of solar panels in Bremerhaven. Consideration of these factors in the design and installation of solar panels will be crucial to optimize performance and longevity."}
2025-02-26 11:14:25 - climsight_engine - INFO - Wikipedia_tool_reponse: • Temperature:
  - Quantitative: The performance of a photovoltaic (PV) module depends on the temperature of the p–n junction, with VOC showing a significant inverse correlation with temperature, and Pmax decreasing as temperature increases. Temperature coefficients are used to describe these variations: β (VOC variation), α (ISC variation), and δ (Pmax variation).
  - Qualitative: High temperatures negatively impact efficiency, while colder temperatures can improve solar panel performance due to reduced electrical resistance. However, winter conditions can introduce challenges such as snow accumulation and reduced daylight hours.

• Precipitation:
  - Qualitative: Solar modules must withstand damage from rain, hail, and heavy snow load. Most solar panels on the U.S. market are UL listed, meaning they have been tested to withstand hail.

• Wind:
  - Qualitative: No specific information available.

• Elevation Above Sea Level:
  - Qualitative: No specific information available.

• Population:
  - Qualitative: No specific information available.

• Natural Hazards:
  - Qualitative: Solar panels must withstand cycles of heat and cold, and potential-induced degradation (PID) can occur due to stray currents, causing power loss.

• Soil Type:
  - Qualitative: No specific information available.
2025-02-26 11:14:25 - climsight_engine - INFO - Ecocrop_search_response: 
2025-02-26 11:14:33 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 11:14:33 - climsight_engine - INFO - Final_answer: When considering the installation of solar panels on your house in Bremerhaven, Germany, up to the year 2050, several climate-related factors should be taken into account to ensure optimal performance and longevity of the system.

**Temperature Trends**: Over the next 30 years, Bremerhaven is expected to experience rising temperatures. For instance, the average temperature in July is projected to increase from 14.83°C in the 2020s to 16.25°C in the 2040s. While higher temperatures can lead to more sunlight exposure, they can also reduce the efficiency of solar panels. Photovoltaic systems generally perform better in cooler conditions due to lower electrical resistance. Therefore, implementing adequate cooling mechanisms and selecting panels with a low temperature coefficient will help mitigate efficiency losses during peak summer months.

**Precipitation Changes**: The region will likely see an increase in precipitation, particularly in winter months. For example, January's average precipitation is expected to rise from 82.56 mm/month in the 2020s to 85.71 mm/month in the 2040s. Solar panels must be robust enough to withstand rain, hail, and potential snow loads. Ensuring that the panels are angled to facilitate water runoff and minimize snow accumulation will be crucial for maintaining their efficiency and preventing damage.

**Wind Patterns**: Wind speeds, particularly the north-south component, are expected to increase. For instance, February's north-south wind speed is projected to rise from 1.8 m/s in the 2020s to 2.68 m/s in the 2040s. It is essential to ensure that the mounting system for your solar panels is sturdy enough to withstand changing wind conditions. This may involve reinforcing the structural integrity of the panels and considering aerodynamic designs that reduce wind resistance.

**Technological and Regulatory Considerations**: As technology evolves, more efficient solar panels and energy storage solutions may become available. Staying updated with advancements in solar technology will allow you to capitalize on more resilient and efficient systems. Additionally, Germany's commitment to renewable energy and potential government incentives for solar installations can provide financial benefits. Keeping abreast of local and national policies can help you maximize these opportunities.

In summary, the installation of solar panels in Bremerhaven will require careful consideration of rising temperatures, increased precipitation, and changing wind patterns. By addressing these factors, you can ensure the efficient and durable operation of your solar power system in the face of a changing climate.
2025-02-26 11:14:33 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-26 11:14:33 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 11:14:33 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:11:58 - __main__ - INFO - reading config from: config.yml
2025-02-26 16:11:58 - __main__ - INFO - reading config from: config.yml
2025-02-26 16:12:03 - __main__ - INFO - reading config from: config.yml
2025-02-26 16:12:08 - __main__ - INFO - reading config from: config.yml
2025-02-26 16:12:08 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-26 16:12:08 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 16:12:08 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-26 16:12:08 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-26 16:12:08 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 16:12:08 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-26 16:12:09 - root - INFO - Is the point on land? Yes.
2025-02-26 16:12:10 - climsight_engine - INFO - start agent_request
2025-02-26 16:12:10 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:10 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:10 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:10 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:10 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:13 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:12:13 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:13 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:13 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:13 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:13 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:13 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:13 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:13 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:13 - streamlit_interface - WARNING - Error by getting high resolution climate data from data pocket: 'high_res_climate'
2025-02-26 16:12:27 - __main__ - INFO - reading config from: config.yml
2025-02-26 16:12:27 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-26 16:12:27 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-26 16:12:27 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-26 16:12:27 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-26 16:12:27 - climsight_engine - INFO - start agent_request
2025-02-26 16:12:27 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:27 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:27 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:27 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:27 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:28 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:12:28 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:28 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:28 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:28 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:28 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:28 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:28 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:28 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:28 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:28 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:28 - climsight_engine - INFO - IPCC RAG agent in work.
2025-02-26 16:12:28 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:28 - climsight_engine - INFO - General RAG agent in work.
2025-02-26 16:12:29 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-26 16:12:29 - rag - INFO - Chunks returned from RAG: {'context': 'deficits\nand high temperature curtailed both planting area and yields with compounding negative\nimpacts on final production, particularly for smallholders and more vulnerable households in\nthe Dry Corridor. During the second part of the season, tropical storms and unexpected heavy\nrain events disrupted the normal growth of crops in certain areas near the Central America\nPacific coast. In Haiti, irregular seasonal rainfall, including periods of high-intensity precipitation,\ncontributed to decrease the production of primary crops In 2023, record maize production in\nBrazil compensated for below-average harvests due to prolonged dry spells elsewhere in South\nAmerica, especially in Argentina, where drought conditions were expected to result in a 15%\ndecrease in cereal production compared with the five-year average. The return of El Niño in\n2023 led to adverse consequences through the entire crop cycle of maize in Central America\nand northern parts of South America, where water deficits and high temperature curtailed both\nplanting area and yields with compounding negative impacts on final production, particularly\nfor smallholders and more vulnerable households in the Dry Corridor. During the second part\nof the season, tropical storms and unexpected heavy rain events disrupted the normal growth\nof crops in certain areas near the Central America Pacific coast. In Haiti, irregular seasonal\nrainfall, including periods of high-intensity precipitation, contributed to decrease the production\nof primary crops.113\nOceania is expected to experience the sharpest annual reduction rate in cereal production\nworldwide, with a 31.1% decline in 2023 compared with 2022, although this largely reflects a\nreversion to near-average conditions after exceptionally high production in 2022, with 2023\nonly slightly below five-year averages.114\nIn September, Storm Daniel brought heavy rainfall to coastal and north-eastern Libya, flooding\nnearly 3 000 ha of cropland, particularly in the Al Marj and\n\nThe prolonged flooding made it difficult for people to access basic needs such\nas food, clean water and health care, and contributed to the near collapse of local livelihoods.\nBetween April and July 2023, 7.8 million people, almost two thirds of the total population of\nSouth Sudan, were expected to experience severe acute food insecurity.110\nAfghanistan experienced a substantial reduction in snowmelt and rainfall, resulting in another\npoor crop season. This led to widespread acute food insecurity, particularly in the north and\nnorth-eastern regions. Between May and October 2023, 15.3 million Afghans were estimated\nto face severe acute food insecurity.111 In Yemen, 53% of the population were already classified\nas in a crisis level of acute food insecurity or worse between October and December 2022.\nHigh food and fuel prices, together with floods from March to September 2023, and protracted\nconflict, further aggravated food insecurity.\nIn Indonesia, a meteorological drought linked to El Niño and the positive phase of the Indian\nOcean Dipole (IOD) (see Short-term climate drivers) occurred during the dry season, affecting 23\n450 ha of paddy cultivation and causing 6 964 ha of crop failure as of August 2023. A decrease\nof 645 000 t of rice production was predicted by October 2023,112 and crop planting in late 2023\nwas delayed.\nIn 2023, record maize production in Brazil compensated for below-average harvests due to\nprolonged dry spells elsewhere in South America, especially in Argentina, where drought\nconditions were expected to result in a 15% decrease in cereal production compared with the\nfive-year average. The return of El Niño in 2023 led to adverse consequences through the entire\ncrop cycle of maize in Central America and northern parts of South America, where water deficits\nand high temperature curtailed both planting area and yields with compounding negative\nimpacts on final production, particularly for smallholders and more vulnerable households in\nthe Dry\n\nof Madagascar, southern Malawi, Mozambique and Zimbabwe. Flooding associated\nwith the cyclone submerged extensive agricultural areas and inflicted severe damage on\ncrops, which has exacerbated a slow economic recovery.106\n900\n\n18\n796.9\n743.7\n\n793.4\n14\n12\n\n701.4\n656.6\n12.1%\n\n597.8\n\n%\n\n10\n\n563.9\n\n588.9\n\n8\n\n8.6%\n\n7.7% 7.9%\n\n783.1 800\n738.9\n735.1\n700\n674.6 690.6\n600\n\n612.8\n9.5%\n8.9%\n7.9%\n\n10.1%\n9.8% 500\n9.3%\n9.2%\n\n8.4% 8.5% 8.7% 400\n\n6\n\n300\n\n4\n\n200\n\n2\n\n100\n0\n\n0\n2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022*\n\nPrevalence of undernourishment\n(percentage, left axis)\n\nNumber of undernourished\n(millions, right axis)\n\nMillions\n\n16\n\nFigure 25. Global prevalence of\nundernourishment (as a percentage) and\nnumber of undernourished (in millions) since\n2005\nSource: The entire series has been updated\nto reflect new information released since\nthe publication of The State of Food\nSecurity and Nutrition in the World\n2023: Urbanization, Agrifood Systems\nTransformation and Healthy Diets Across\nthe Rural–Urban Continuum.107\n\n\x0cFood security\n\nGlobally, annual economic losses from climate- and weather-related disasters have increased\nsince the 2000s.108 Between 2007 and 2022, 88 post-disaster needs assessment surveys\nconducted in 60 countries worldwide showed that over 65% of losses caused by droughts occur\nin the agriculture sector, including crop and livestock production losses. For floods, storms and\ncyclones, about 20% of losses are in agriculture.109\nIn early 2023, South Sudan continued to experience exceptional flooding, with water levels\nremaining high even during the dry season. Despite relatively dry conditions locally, flooding\npersisted owing to high flows from further upstream in the Nile basin and very slow drainage\nfrom earlier floods. The prolonged flooding made it difficult for people to access basic needs such\nas food, clean water and health care, and contributed to the near collapse of local livelihoods.\nBetween April and July\n\nthe vulnerability of many who had already been uprooted by\ncomplex multi-causal situations of conflict and violence.\nThe development and implementation of local disaster risk reduction strategies has increased since\nthe adoption of the Sendai Framework for Disaster Risk Reduction.\nOne of the essential components for reducing the impact of disasters is to have effective multi-hazard\nearly warning systems.\n\nThe events described above, and many others besides, occur in a broader context. Extreme\nweather and climate events interact with, and in some cases trigger or exacerbate, situations\nconcerning water and food security, population mobility and environmental degradation.101,102\n\nFOOD SECURITY\nThe number of people who are acutely food insecure worldwide has more than doubled,\nfrom 149 million people before the coronavirus disease (COVID-19) pandemic to 333 million\npeople in 2023 (in 78 countries monitored by the World Food Programme (WFP)).103 Although\nglobal hunger levels remained unchanged from 2021 to 2022, they are still far above preCOVID 19 pandemic levels: in 2022, 9.2% of the global population (735.1 million people) were\nundernourished, compared with 7.9% (612.8 million people) in 2019 (see Figure 25).104 The\ncurrent global food and nutrition crisis is the largest in modern human history.105 Protracted\nconflicts, economic downturns and high food prices are at the root of high global food insecurity\nlevels. High food prices are exacerbated by the high costs of agricultural inputs, driven by\nongoing and widespread conflict around the world, and high global food insecurity levels are\naggravated by the effects of climate and weather extremes. In southern Africa, for example,\nweather extremes, including the passage of Cyclone Freddy in February 2023, have affected\nareas of Madagascar, southern Malawi, Mozambique and Zimbabwe. Flooding associated\nwith the cyclone submerged extensive agricultural areas and inflicted severe damage on\ncrops, which has exacerbated a', 'location': 'Address: house_number: 11, road: Turmstraße, suburb: Moabit, borough: Mitte, city: Berlin, ISO3166-2-lvl4: DE-BE, postcode: 10559, country: Germany, country_code: de', 'question': 'grow corn'}
2025-02-26 16:12:29 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-26 16:12:29 - rag - INFO - Chunks returned from RAG: {'context': 'on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n 9 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n10 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n11\n\n12 Figure AI.21: Climatic and environmental stresses on global production of maize.\n13 The global effects of five climatic and environmental stresses on maize yield. The combined effect of each\n14 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n15 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n16 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n17 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n18\n\n19 Figure AI.22: Projected changes in global maize production.\n20 For maize production time series are shown as relative changes to the 1983-2013 reference period under\n21 SSP126 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop\n22 model combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model\n23 response (and a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the\n24 historical variability (1983-2013; ensemble median) and open circles highlight the "time of climate impact\n25 emergence" (TCIE), the year in which the smoothed median response exceeds the historical envelope. For\n26 context, the TCIE calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop\n\nis indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop models for current growing regions (>10 ha). Hatching indicates areas\n29 where less than 70% of the climate-crop model combinations agree on the sign of impact. Regional\n30 production time series (e) are similar to (a), but stratified for the four major KoeppenGeiger climate zones\n31 (temperature limited, temperate/humid, subtropical, and tropical). The percentage of the total global\n32 production contributed by each zone is indicated in the top right corner of the inlets. All data are shown for\n33 the default [CO2] {Jägermeyr et al. 2021; 5.4.3.2}\n\n34\n\n35 Figure AI.23: Projected changes in global wheat production.\n36 Production time series are shown as relative changes to the 1983-2013 reference period under SSP126\n37 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop model\n38 combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model response (and\n39 a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the historical variability\n40 (1983-2013; ensemble median) and open circles highlight the "time of climate impact emergence" (TCIE),\n41 the year in which the smoothed median response exceeds the historical envelope. For context, the TCIE\n42 calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on GC6 (>2099 if no\n43 TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under SSP585 across\n44 climate and crop models for current growing regions (>10 ha). Hatching indicates areas where less than 70%\n45 of the climate-crop model combinations agree on the sign of impact. Regional production time series (e) are\n46 similar to (a), but stratified for the four major KoeppenGeiger climate zones (temperature limited,\n47\n\ntransboundary governance and ecosystem-\n55   based management, livelihood diversification, capacity development and improved knowledge-sharing will\n\n\n     Do Not Cite, Quote or Distribute                      TS-64                                         Total pages: 96\n     FINAL DRAFT                                    Technical Summary             IPCC WGII Sixth Assessment Report\n\n 1   reduce conflict and promote the fair distribution of sustainably-harvested wild products and revenues\n 2   (medium confidence). {5.8.4, 5.14.3, CCP5.4.2, CCB MOVING PLATE}\n 3\n 4   TS.D.5.5 Adaptation options that promote intensification of production have been widely adopted in\n 5   agriculture for climate change adaptation, but with potential negative effects (high confidence).\n 6   Agricultural intensification addresses short-term food security and livelihood goals but has trade-offs in\n 7   equity, biodiversity, and ecosystem services (high confidence). Irrigation is widely used and effective for\n 8   yield stability, but with several negative outcomes, including water demand (high confidence), groundwater\n 9   depletion (high confidence); alteration of local to regional climates (high confidence); increasing soil salinity\n10   (medium confidence) widening inequalities and loss of rural smallholder livelihoods with weak governance\n11   (medium confidence). Conventional breeding assisted by genomics introduces traits that adapt crops to\n12   climate change (high confidence). Genetic improvements through modern biotechnology have the potential\n13   to increase climate resilience in food production systems (high confidence), but with biophysical ceilings,\n14   and technical, agroecosystem, socio-economic and political variables strongly influence and limit uptake of\n15   climate-resilient crops, particularly for smallholders (medium confidence).{4.6.2, Box 4.3, 4.7.1, 5.4.4,\n16   5.12.5, 5.13.4, 5.14.1, 10.2.2, 12.5.4, 13.5.1, 13.5.2, 13.5.14, 14.5.4, 15.3.4, 17.5.1}\n17\n18\n\n5.3; 5.5.3; 5.4.1; Figure FAQ 5.1; Figure 9.22; 15.3.4; 15.3.3}\n\n44\n\n45 Figure AI.18: Climatic and environmental stresses on global production of wheat.\n46 The global effects of five climatic and environmental stresses on wheat yield. The combined effect of each\n47 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n48 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n49 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n50 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n51 wheat was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n52\n\n53 Figure AI.19: Climatic and environmental stresses on global production of soybean.\n54 The global effects of five climatic and environmental stresses on soybean yield. The combined effect of each\n55 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n56 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n\nDo Not Cite, Quote or Distribute  AI-63    Total pages: 74\nFINAL DRAFT                       Annex I  IPCC WGII Sixth Assessment Report\n\n 1 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n 2 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n 3 soybean was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n 4\n\n 5 Figure AI.20: Climatic and environmental stresses on global production of rice.\n 6 The global effects of five climatic and environmental stresses on rice yield. The combined effect of each\n 7 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but', 'location': 'Address: house_number: 11, road: Turmstraße, suburb: Moabit, borough: Mitte, city: Berlin, ISO3166-2-lvl4: DE-BE, postcode: 10559, country: Germany, country_code: de', 'question': 'grow corn'}
2025-02-26 16:12:30 - climsight_engine - INFO - Data agent in work.
2025-02-26 16:12:30 - climsight_engine - INFO - data_agent_response: {'input_params': {'simulation1': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.11,\n    "Total Precipitation (mm\\/month)":54.45,\n    "Wind U (m s**-1)":1.5,\n    "Wind V (m s**-1)":1.04,\n    "Wind Speed (m\\/s)":1.83,\n    "Wind Direction (\\u00b0)":235.2\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":0.84,\n    "Total Precipitation (mm\\/month)":54.82,\n    "Wind U (m s**-1)":1.32,\n    "Wind V (m s**-1)":1.05,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":231.5\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":-0.26,\n    "Total Precipitation (mm\\/month)":35.39,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":0.31,\n    "Wind Speed (m\\/s)":0.38,\n    "Wind Direction (\\u00b0)":214.38\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":3.17,\n    "Total Precipitation (mm\\/month)":47.41,\n    "Wind U (m s**-1)":1.05,\n    "Wind V (m s**-1)":0.02,\n    "Wind Speed (m\\/s)":1.05,\n    "Wind Direction (\\u00b0)":268.97\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":7.96,\n    "Total Precipitation (mm\\/month)":43.47,\n    "Wind U (m s**-1)":0.43,\n    "Wind V (m s**-1)":-0.33,\n    "Wind Speed (m\\/s)":0.54,\n    "Wind Direction (\\u00b0)":307.75\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":14.16,\n    "Total Precipitation (mm\\/month)":61.14,\n    "Wind U (m s**-1)":0.27,\n    "Wind V (m s**-1)":-0.22,\n    "Wind Speed (m\\/s)":0.35,\n    "Wind Direction (\\u00b0)":309.07\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":16.91,\n    "Total Precipitation (mm\\/month)":63.65,\n    "Wind U (m s**-1)":1.13,\n    "Wind V (m s**-1)":-0.42,\n    "Wind Speed (m\\/s)":1.21,\n    "Wind Direction (\\u00b0)":290.47\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":19.66,\n    "Total Precipitation (mm\\/month)":52.2,\n    "Wind U (m s**-1)":1.49,\n    "Wind V (m s**-1)":-0.13,\n    "Wind Speed (m\\/s)":1.49,\n    "Wind Direction (\\u00b0)":274.82\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.28,\n    "Total Precipitation (mm\\/month)":46.9,\n    "Wind U (m s**-1)":1.02,\n    "Wind V (m s**-1)":-0.41,\n    "Wind Speed (m\\/s)":1.1,\n    "Wind Direction (\\u00b0)":292.13\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.32,\n    "Total Precipitation (mm\\/month)":31.19,\n    "Wind U (m s**-1)":0.54,\n    "Wind V (m s**-1)":0.2,\n    "Wind Speed (m\\/s)":0.58,\n    "Wind Direction (\\u00b0)":249.8\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":10.92,\n    "Total Precipitation (mm\\/month)":32.04,\n    "Wind U (m s**-1)":0.28,\n    "Wind V (m s**-1)":0.49,\n    "Wind Speed (m\\/s)":0.57,\n    "Wind Direction (\\u00b0)":209.54\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.01,\n    "Total Precipitation (mm\\/month)":45.29,\n    "Wind U (m s**-1)":0.54,\n    "Wind V (m s**-1)":1.33,\n    "Wind Speed (m\\/s)":1.44,\n    "Wind Direction (\\u00b0)":202.03\n  }\n]', 'simulation2': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.58,\n    "Total Precipitation (mm\\/month)":65.59,\n    "Wind U (m s**-1)":1.66,\n    "Wind V (m s**-1)":1.05,\n    "Wind Speed (m\\/s)":1.96,\n    "Wind Direction (\\u00b0)":237.71\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":0.85,\n    "Total Precipitation (mm\\/month)":65.52,\n    "Wind U (m s**-1)":1.53,\n    "Wind V (m s**-1)":1.28,\n    "Wind Speed (m\\/s)":1.99,\n    "Wind Direction (\\u00b0)":230.08\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.68,\n    "Total Precipitation (mm\\/month)":39.18,\n    "Wind U (m s**-1)":1.5,\n    "Wind V (m s**-1)":1.09,\n    "Wind Speed (m\\/s)":1.85,\n    "Wind Direction (\\u00b0)":233.85\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.93,\n    "Total Precipitation (mm\\/month)":51.78,\n    "Wind U (m s**-1)":0.73,\n    "Wind V (m s**-1)":0.62,\n    "Wind Speed (m\\/s)":0.96,\n    "Wind Direction (\\u00b0)":229.85\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":9.48,\n    "Total Precipitation (mm\\/month)":46.12,\n    "Wind U (m s**-1)":0.09,\n    "Wind V (m s**-1)":-0.2,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":335.89\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":13.6,\n    "Total Precipitation (mm\\/month)":56.86,\n    "Wind U (m s**-1)":0.07,\n    "Wind V (m s**-1)":-0.36,\n    "Wind Speed (m\\/s)":0.37,\n    "Wind Direction (\\u00b0)":348.76\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":17.56,\n    "Total Precipitation (mm\\/month)":69.81,\n    "Wind U (m s**-1)":0.88,\n    "Wind V (m s**-1)":-0.48,\n    "Wind Speed (m\\/s)":1.01,\n    "Wind Direction (\\u00b0)":298.61\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":20.13,\n    "Total Precipitation (mm\\/month)":50.79,\n    "Wind U (m s**-1)":1.15,\n    "Wind V (m s**-1)":-0.28,\n    "Wind Speed (m\\/s)":1.18,\n    "Wind Direction (\\u00b0)":283.53\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.06,\n    "Total Precipitation (mm\\/month)":55.68,\n    "Wind U (m s**-1)":1.09,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":1.1,\n    "Wind Direction (\\u00b0)":277.88\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.89,\n    "Total Precipitation (mm\\/month)":29.57,\n    "Wind U (m s**-1)":0.58,\n    "Wind V (m s**-1)":0.18,\n    "Wind Speed (m\\/s)":0.6,\n    "Wind Direction (\\u00b0)":252.67\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.34,\n    "Total Precipitation (mm\\/month)":67.94,\n    "Wind U (m s**-1)":1.25,\n    "Wind V (m s**-1)":0.98,\n    "Wind Speed (m\\/s)":1.59,\n    "Wind Direction (\\u00b0)":232.11\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":4.67,\n    "Total Precipitation (mm\\/month)":56.26,\n    "Wind U (m s**-1)":0.48,\n    "Wind V (m s**-1)":0.59,\n    "Wind Speed (m\\/s)":0.77,\n    "Wind Direction (\\u00b0)":219.05\n  }\n]', 'simulation3': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.76,\n    "Total Precipitation (mm\\/month)":63.92,\n    "Wind U (m s**-1)":1.26,\n    "Wind V (m s**-1)":0.99,\n    "Wind Speed (m\\/s)":1.6,\n    "Wind Direction (\\u00b0)":231.73\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":3.36,\n    "Total Precipitation (mm\\/month)":68.89,\n    "Wind U (m s**-1)":2.18,\n    "Wind V (m s**-1)":1.63,\n    "Wind Speed (m\\/s)":2.72,\n    "Wind Direction (\\u00b0)":233.14\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.97,\n    "Total Precipitation (mm\\/month)":65.48,\n    "Wind U (m s**-1)":2.26,\n    "Wind V (m s**-1)":0.78,\n    "Wind Speed (m\\/s)":2.39,\n    "Wind Direction (\\u00b0)":250.96\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.5,\n    "Total Precipitation (mm\\/month)":43.33,\n    "Wind U (m s**-1)":1.62,\n    "Wind V (m s**-1)":0.47,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":253.83\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":10.04,\n    "Total Precipitation (mm\\/month)":36.31,\n    "Wind U (m s**-1)":0.17,\n    "Wind V (m s**-1)":-0.19,\n    "Wind Speed (m\\/s)":0.25,\n    "Wind Direction (\\u00b0)":319.07\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":14.11,\n    "Total Precipitation (mm\\/month)":78.01,\n    "Wind U (m s**-1)":0.04,\n    "Wind V (m s**-1)":-0.61,\n    "Wind Speed (m\\/s)":0.61,\n    "Wind Direction (\\u00b0)":356.67\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":18.44,\n    "Total Precipitation (mm\\/month)":72.4,\n    "Wind U (m s**-1)":0.96,\n    "Wind V (m s**-1)":-0.29,\n    "Wind Speed (m\\/s)":1.0,\n    "Wind Direction (\\u00b0)":286.64\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":20.93,\n    "Total Precipitation (mm\\/month)":52.27,\n    "Wind U (m s**-1)":1.11,\n    "Wind V (m s**-1)":-0.38,\n    "Wind Speed (m\\/s)":1.17,\n    "Wind Direction (\\u00b0)":288.78\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":21.38,\n    "Total Precipitation (mm\\/month)":26.08,\n    "Wind U (m s**-1)":0.97,\n    "Wind V (m s**-1)":-0.35,\n    "Wind Speed (m\\/s)":1.03,\n    "Wind Direction (\\u00b0)":289.62\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.64,\n    "Total Precipitation (mm\\/month)":45.71,\n    "Wind U (m s**-1)":0.78,\n    "Wind V (m s**-1)":0.12,\n    "Wind Speed (m\\/s)":0.79,\n    "Wind Direction (\\u00b0)":261.01\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.78,\n    "Total Precipitation (mm\\/month)":31.73,\n    "Wind U (m s**-1)":0.41,\n    "Wind V (m s**-1)":0.4,\n    "Wind Speed (m\\/s)":0.57,\n    "Wind Direction (\\u00b0)":226.23\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.09,\n    "Total Precipitation (mm\\/month)":53.14,\n    "Wind U (m s**-1)":1.35,\n    "Wind V (m s**-1)":1.16,\n    "Wind Speed (m\\/s)":1.78,\n    "Wind Direction (\\u00b0)":229.25\n  }\n]', 'simulation5': '[\n  {\n    "Temperature (\\u00b0C)":0.47,\n    "Total Precipitation (mm\\/month)":11.14,\n    "Wind U (m s**-1)":0.16,\n    "Wind V (m s**-1)":0.01,\n    "Wind Speed (m\\/s)":0.13,\n    "Wind Direction (\\u00b0)":2.51\n  },\n  {\n    "Temperature (\\u00b0C)":0.01,\n    "Total Precipitation (mm\\/month)":10.7,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":0.23,\n    "Wind Speed (m\\/s)":0.3,\n    "Wind Direction (\\u00b0)":-1.42\n  },\n  {\n    "Temperature (\\u00b0C)":2.94,\n    "Total Precipitation (mm\\/month)":3.79,\n    "Wind U (m s**-1)":1.29,\n    "Wind V (m s**-1)":0.78,\n    "Wind Speed (m\\/s)":1.47,\n    "Wind Direction (\\u00b0)":19.47\n  },\n  {\n    "Temperature (\\u00b0C)":2.76,\n    "Total Precipitation (mm\\/month)":4.37,\n    "Wind U (m s**-1)":-0.32,\n    "Wind V (m s**-1)":0.6,\n    "Wind Speed (m\\/s)":-0.09,\n    "Wind Direction (\\u00b0)":-39.12\n  },\n  {\n    "Temperature (\\u00b0C)":1.52,\n    "Total Precipitation (mm\\/month)":2.65,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":0.13,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":28.14\n  },\n  {\n    "Temperature (\\u00b0C)":-0.56,\n    "Total Precipitation (mm\\/month)":-4.28,\n    "Wind U (m s**-1)":-0.2,\n    "Wind V (m s**-1)":-0.14,\n    "Wind Speed (m\\/s)":0.02,\n    "Wind Direction (\\u00b0)":39.69\n  },\n  {\n    "Temperature (\\u00b0C)":0.65,\n    "Total Precipitation (mm\\/month)":6.16,\n    "Wind U (m s**-1)":-0.25,\n    "Wind V (m s**-1)":-0.06,\n    "Wind Speed (m\\/s)":-0.2,\n    "Wind Direction (\\u00b0)":8.14\n  },\n  {\n    "Temperature (\\u00b0C)":0.47,\n    "Total Precipitation (mm\\/month)":-1.41,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":-0.31,\n    "Wind Direction (\\u00b0)":8.71\n  },\n  {\n    "Temperature (\\u00b0C)":-0.22,\n    "Total Precipitation (mm\\/month)":8.78,\n    "Wind U (m s**-1)":0.07,\n    "Wind V (m s**-1)":0.26,\n    "Wind Speed (m\\/s)":0.0,\n    "Wind Direction (\\u00b0)":-14.25\n  },\n  {\n    "Temperature (\\u00b0C)":0.57,\n    "Total Precipitation (mm\\/month)":-1.62,\n    "Wind U (m s**-1)":0.04,\n    "Wind V (m s**-1)":-0.02,\n    "Wind Speed (m\\/s)":0.02,\n    "Wind Direction (\\u00b0)":2.87\n  },\n  {\n    "Temperature (\\u00b0C)":0.42,\n    "Total Precipitation (mm\\/month)":35.9,\n    "Wind U (m s**-1)":0.97,\n    "Wind V (m s**-1)":0.49,\n    "Wind Speed (m\\/s)":1.02,\n    "Wind Direction (\\u00b0)":22.57\n  },\n  {\n    "Temperature (\\u00b0C)":-2.34,\n    "Total Precipitation (mm\\/month)":10.97,\n    "Wind U (m s**-1)":-0.06,\n    "Wind V (m s**-1)":-0.74,\n    "Wind Speed (m\\/s)":-0.67,\n    "Wind Direction (\\u00b0)":17.02\n  }\n]', 'simulation6': '[\n  {\n    "Temperature (\\u00b0C)":0.65,\n    "Total Precipitation (mm\\/month)":9.47,\n    "Wind U (m s**-1)":-0.24,\n    "Wind V (m s**-1)":-0.05,\n    "Wind Speed (m\\/s)":-0.23,\n    "Wind Direction (\\u00b0)":-3.47\n  },\n  {\n    "Temperature (\\u00b0C)":2.52,\n    "Total Precipitation (mm\\/month)":14.07,\n    "Wind U (m s**-1)":0.86,\n    "Wind V (m s**-1)":0.58,\n    "Wind Speed (m\\/s)":1.03,\n    "Wind Direction (\\u00b0)":1.64\n  },\n  {\n    "Temperature (\\u00b0C)":3.23,\n    "Total Precipitation (mm\\/month)":30.09,\n    "Wind U (m s**-1)":2.05,\n    "Wind V (m s**-1)":0.47,\n    "Wind Speed (m\\/s)":2.01,\n    "Wind Direction (\\u00b0)":36.58\n  },\n  {\n    "Temperature (\\u00b0C)":2.33,\n    "Total Precipitation (mm\\/month)":-4.08,\n    "Wind U (m s**-1)":0.57,\n    "Wind V (m s**-1)":0.45,\n    "Wind Speed (m\\/s)":0.64,\n    "Wind Direction (\\u00b0)":-15.14\n  },\n  {\n    "Temperature (\\u00b0C)":2.08,\n    "Total Precipitation (mm\\/month)":-7.16,\n    "Wind U (m s**-1)":-0.26,\n    "Wind V (m s**-1)":0.14,\n    "Wind Speed (m\\/s)":-0.29,\n    "Wind Direction (\\u00b0)":11.32\n  },\n  {\n    "Temperature (\\u00b0C)":-0.05,\n    "Total Precipitation (mm\\/month)":16.87,\n    "Wind U (m s**-1)":-0.23,\n    "Wind V (m s**-1)":-0.39,\n    "Wind Speed (m\\/s)":0.26,\n    "Wind Direction (\\u00b0)":47.6\n  },\n  {\n    "Temperature (\\u00b0C)":1.53,\n    "Total Precipitation (mm\\/month)":8.75,\n    "Wind U (m s**-1)":-0.17,\n    "Wind V (m s**-1)":0.13,\n    "Wind Speed (m\\/s)":-0.21,\n    "Wind Direction (\\u00b0)":-3.83\n  },\n  {\n    "Temperature (\\u00b0C)":1.27,\n    "Total Precipitation (mm\\/month)":0.07,\n    "Wind U (m s**-1)":-0.38,\n    "Wind V (m s**-1)":-0.25,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":13.96\n  },\n  {\n    "Temperature (\\u00b0C)":1.1,\n    "Total Precipitation (mm\\/month)":-20.82,\n    "Wind U (m s**-1)":-0.05,\n    "Wind V (m s**-1)":0.06,\n    "Wind Speed (m\\/s)":-0.07,\n    "Wind Direction (\\u00b0)":-2.51\n  },\n  {\n    "Temperature (\\u00b0C)":0.32,\n    "Total Precipitation (mm\\/month)":14.52,\n    "Wind U (m s**-1)":0.24,\n    "Wind V (m s**-1)":-0.08,\n    "Wind Speed (m\\/s)":0.21,\n    "Wind Direction (\\u00b0)":11.21\n  },\n  {\n    "Temperature (\\u00b0C)":0.86,\n    "Total Precipitation (mm\\/month)":-0.31,\n    "Wind U (m s**-1)":0.13,\n    "Wind V (m s**-1)":-0.09,\n    "Wind Speed (m\\/s)":0.0,\n    "Wind Direction (\\u00b0)":16.69\n  },\n  {\n    "Temperature (\\u00b0C)":0.08,\n    "Total Precipitation (mm\\/month)":7.85,\n    "Wind U (m s**-1)":0.81,\n    "Wind V (m s**-1)":-0.17,\n    "Wind Speed (m\\/s)":0.34,\n    "Wind Direction (\\u00b0)":27.22\n  }\n]'}, 'content_message': '\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2020-2029. The nextGEMS pre-final simulations for years 2030x..This simulation serves as a historical reference to extract its values from other simulations. :\n {simulation1}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2030-2039. The nextGEMS pre-final simulations for years 2030x. :\n {simulation2}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2040-2049. The nextGEMS pre-final simulations for years 2040x. :\n {simulation3}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2030-2039 compared to the simulation for 2020-2029. :\n {simulation5}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2040-2049 compared to the simulation for 2020-2029. :\n {simulation6}'}
2025-02-26 16:12:30 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:30 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:30 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:31 - climsight_engine - INFO - zero_agent_response: {'input_params': {'elevation': '37.0', 'current_land_use': 'residential', 'soil': 'Chernozems', 'biodiv': 'Pristurus', 'distance_to_coastline': '143450.78316623147', 'nat_hazards':        year          disastertype
13415  2002                 storm
13428  2006                 storm
13434  2010                 storm
33496  2003  extreme temperature 
33517  2006  extreme temperature 
33528  2009  extreme temperature 
33536  2009  extreme temperature 
33552  2012  extreme temperature 
33569  2012  extreme temperature , 'population':     Time  TotalPopulationAsOf1July  PopulationDensity  PopulationGrowthRate
0   1980              77786.703000         223.165900             -0.145000
1   1990              78072.678100         223.986330              0.237400
2   2000              80995.587100         232.372000              0.241300
3   2010              81294.847800         233.230560             -0.021600
4   2020              82291.120600         236.088820              0.245600
5   2030              83132.605000         238.502990             -0.082500
6   2040              81965.316200         235.154110             -0.195300
7   2050              80013.692600         229.555010             -0.288400
8   2060              77399.456100         222.054900             -0.362600
9   2070              74806.608900         214.616160             -0.299400
10  2080              72785.056300         208.816440             -0.266400
11  2090              70893.830000         203.390610             -0.247800
12  2100              69481.298222         199.338133             -0.171333
13  2101                       NaN                NaN                   NaN}, 'content_message': 'Elevation above sea level: {elevation} \nCurrent landuse: {current_land_use} \nCurrent soil type: {soil} \nOccuring species: {biodiv} \nDistance to the closest coastline: {distance_to_coastline} \nNatural hazards: {nat_hazards} \nPopulation in Germany data: {population} \n'}
2025-02-26 16:12:31 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:31 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:12:31 - root - INFO - Rendered RAG response: For the specific location in Berlin, Germany, there is no direct information about growing corn. Generally, climatic and environmental stresses on global maize (corn) yield are presented using a Yield Constraint Score (YCS) on a scale from low to high stress. Higher temperatures can increase ozone production and uptake by plants, exacerbating yield loss and quality damage. Data are available at a global scale in 1 × 1° grid squares, but not specifically for Berlin.
2025-02-26 16:12:31 - climsight_engine - INFO - ipcc_rag_agent_response: For the specific location in Berlin, Germany, there is no direct information about growing corn. Generally, climatic and environmental stresses on global maize (corn) yield are presented using a Yield Constraint Score (YCS) on a scale from low to high stress. Higher temperatures can increase ozone production and uptake by plants, exacerbating yield loss and quality damage. Data are available at a global scale in 1 × 1° grid squares, but not specifically for Berlin.
2025-02-26 16:12:31 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:33 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:12:33 - root - INFO - Rendered RAG response: The provided reports do not contain specific information about growing corn in Berlin or Germany. However, it is important to note that corn growth can be influenced by climate conditions such as temperature and rainfall. The reports mention the impact of climate events like El Niño on corn production in other regions, which could serve as general context for understanding potential challenges in corn cultivation. For specific guidance on growing corn in Berlin, local agricultural resources or extension services would be more applicable.
2025-02-26 16:12:33 - climsight_engine - INFO - general_rag_agent_response: The provided reports do not contain specific information about growing corn in Berlin or Germany. However, it is important to note that corn growth can be influenced by climate conditions such as temperature and rainfall. The reports mention the impact of climate events like El Niño on corn production in other regions, which could serve as general context for understanding potential challenges in corn cultivation. For specific guidance on growing corn in Berlin, local agricultural resources or extension services would be more applicable.
2025-02-26 16:12:33 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:12:33 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-26 16:12:36 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:12:45 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:12:45 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 16:12:48 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-26 16:12:49 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:12:49 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 16:12:49 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-26 16:12:50 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:12:51 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:12:52 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:12:53 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:12:57 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-26 16:12:57 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-26 16:12:57 - climsight_engine - INFO - combine_agent in work
2025-02-26 16:12:57 - climsight_engine - INFO - smart_agent_response: {'output': 'The temperature for growing corn at Berlin is expected to range from 2.11°C in January to 20.28°C in September for the years 2020-2029, with future projections showing an increase to 2.76°C in January and 21.38°C in September for 2040-2049. According to the Wikipedia article, corn requires warm days above 10°C for flowering. The projected increase in temperature suggests a favorable trend for corn growth, especially during the critical growing months of June to September.\n\nThe precipitation for growing corn at Berlin is expected to range from 31.19 mm in October to 63.65 mm in July for the years 2020-2029, with future projections showing a range from 26.08 mm in September to 78.01 mm in June for 2040-2049. According to the ECOCROP database, corn requires an optimal rainfall of 600-1200 mm annually. The projected precipitation levels indicate potential challenges in meeting the optimal rainfall requirements, particularly in the drier months, which may necessitate irrigation strategies.'}
2025-02-26 16:12:57 - climsight_engine - INFO - Wikipedia_tool_reponse: • Temperature:
  - Quantitative: Requires warm days above 10 °C (50 °F) for flowering.
  - Qualitative: Maize is cold-intolerant and must be planted in the spring in temperate zones.

• Wind:
  - Qualitative: Maize pollen is dispersed by wind. Maize is prone to being uprooted by severe winds due to its shallow roots.

• Soil Type:
  - Qualitative: Maize is intolerant of nutrient-deficient soils and depends on adequate soil moisture.
2025-02-26 16:12:57 - climsight_engine - INFO - Ecocrop_search_response: Data from ECOCROP database for corn:
Scientific Name: Zea mays ssp. mays
Authority: L.
Family: Liliopsida:Commelinidae:Cyperales:Gramineae
Common names: corn
Life form: grass
Habitat: erect
Life span: annual
Physiology: single stem
Category: cereals & pseudocereals, forage/pasture, vegetables, materials, environmental
Plant attributes: grown on large scale
optimal minimum temperature (°C): 18.0
optimal maximum temperature (°C): 33.0
absolute minimum temperature (°C): 10.0
absolute maximum temperature (°C): 47.0
optimal minimum rainfall (mm): 600.0
optimal maximum rainfall (mm): 1200.0
absolute minimum rainfall (mm): 400.0
absolute maximum rainfall (mm): 1800.0
optimal minimum soil PH: 5.0
optimal maximum soil PH: 7.0
absolute minimum soil PH: 4.5
absolute maximum soil PH: 8.5
absolute minimum latitude: 40.0
absolute maximum latitude: 48.0
absolute maximum altitude (meter above sea level): 4000.0
optimal minimum light intensity: very bright
optimal maximum light intensity: very bright
absolute minimum light intensity: very bright
absolute maximum light intensity: clear skies
Optimal soil depth: medium (50-150 cm)
Absolute soil depth: shallow (20-50 cm)
Optimal soil texture: medium, organic
Absolute soil texture: heavy, medium, light
Optimal soil fertility: high
Absolute soil fertility: low
Optimal soil salinity: low (<4 dS/m)
Absolute soil salinity: medium (4-10 dS/m)
Optimal soil drainage: well (dry spells)
Absolute soil drainage: well (dry spells), excessive (dry/moderately dry)
Photoperiod: short day (<12 hours), neutral day (12-14 hours)
Climate zone: tropical wet & dry (Aw), steppe or semiarid (Bs), subtropical humid (Cf), subtropical dry summer (Cs), subtropical dry winter (Cw), temperate oceanic (Do), temperate continental (Dc), temperate with humid winters (Df), temperate with dry winters (Dw)
Abiotic susceptibility: hail
Introduction risk: mono culture may cause erosion problems, can become a weed
Minimum crop cycle (days): 65.0
Maximum crop cycle (days): 365.0

2025-02-26 16:13:05 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:13:05 - climsight_engine - INFO - Final_answer: Growing corn at the specified location in Berlin, Germany, presents both opportunities and challenges. The climate conditions are projected to become increasingly favorable for corn cultivation, with temperatures during the growing season (June to September) ranging from 14.16°C to 20.28°C for the years 2020-2029, and projections for 2040-2049 indicating an increase up to 21.38°C in September. This aligns well with corn's need for warm temperatures, as it requires at least 10°C for flowering and thrives optimally between 18°C and 33°C. However, the absolute minimum temperature tolerance for corn is 10°C, which suggests that early planting in spring should be carefully timed to avoid cold stress. 

Precipitation patterns, however, pose a potential challenge. The annual rainfall in Berlin is not expected to reach the optimal minimum of 600 mm necessary for corn, with monthly precipitation ranging from about 31.19 mm in October to 78.01 mm in June. This implies that supplemental irrigation may be necessary, particularly during drier months, to meet the crop's water demand and ensure healthy growth.

The soil type in the area, classified as Chernozems, is typically fertile and should support corn growth well if maintained properly. Corn requires well-drained, nutrient-rich soils, and Chernozems usually have high organic matter content and good fertility, which are favorable conditions. However, soil management practices to maintain fertility, such as crop rotation or the application of organic fertilizers, would be beneficial to sustain productivity over time.

Natural hazards such as storms and extreme temperatures have been recorded in the region, which could impact crop resilience. Corn, being susceptible to strong winds due to its shallow roots, may require protective measures during such events. 

Overall, while the temperature trends appear favorable, the precipitation shortfall must be addressed through irrigation to realize successful corn cultivation. Optimal soil management, along with strategic planting and harvesting schedules tailored to the changing climate, will be crucial in maximizing yield and minimizing climatic risks. Engaging with local agricultural extension services could provide additional insights and support tailored to Berlin's specific conditions.
2025-02-26 16:13:05 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-26 16:13:05 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:13:05 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:20:20 - __main__ - INFO - reading config from: config.yml
2025-02-26 16:20:21 - __main__ - INFO - reading config from: config.yml
2025-02-26 16:20:30 - __main__ - INFO - reading config from: config.yml
2025-02-26 16:20:30 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-26 16:20:30 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 16:20:30 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-26 16:20:30 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-26 16:20:30 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 16:20:30 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-26 16:20:31 - root - INFO - Is the point on land? Yes.
2025-02-26 16:20:36 - climsight_engine - INFO - start agent_request
2025-02-26 16:20:36 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:36 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:36 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:36 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:36 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:37 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:20:37 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:37 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:37 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:37 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:37 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:37 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:37 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:37 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:37 - climsight_engine - INFO - IPCC RAG agent in work.
2025-02-26 16:20:37 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:37 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:37 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:37 - climsight_engine - INFO - General RAG agent in work.
2025-02-26 16:20:38 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-26 16:20:38 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-26 16:20:38 - rag - INFO - Chunks returned from RAG: {'context': 'on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n 9 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n10 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n11\n\n12 Figure AI.21: Climatic and environmental stresses on global production of maize.\n13 The global effects of five climatic and environmental stresses on maize yield. The combined effect of each\n14 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n15 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n16 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n17 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n18\n\n19 Figure AI.22: Projected changes in global maize production.\n20 For maize production time series are shown as relative changes to the 1983-2013 reference period under\n21 SSP126 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop\n22 model combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model\n23 response (and a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the\n24 historical variability (1983-2013; ensemble median) and open circles highlight the "time of climate impact\n25 emergence" (TCIE), the year in which the smoothed median response exceeds the historical envelope. For\n26 context, the TCIE calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop\n\nis indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop models for current growing regions (>10 ha). Hatching indicates areas\n29 where less than 70% of the climate-crop model combinations agree on the sign of impact. Regional\n30 production time series (e) are similar to (a), but stratified for the four major KoeppenGeiger climate zones\n31 (temperature limited, temperate/humid, subtropical, and tropical). The percentage of the total global\n32 production contributed by each zone is indicated in the top right corner of the inlets. All data are shown for\n33 the default [CO2] {Jägermeyr et al. 2021; 5.4.3.2}\n\n34\n\n35 Figure AI.23: Projected changes in global wheat production.\n36 Production time series are shown as relative changes to the 1983-2013 reference period under SSP126\n37 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop model\n38 combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model response (and\n39 a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the historical variability\n40 (1983-2013; ensemble median) and open circles highlight the "time of climate impact emergence" (TCIE),\n41 the year in which the smoothed median response exceeds the historical envelope. For context, the TCIE\n42 calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on GC6 (>2099 if no\n43 TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under SSP585 across\n44 climate and crop models for current growing regions (>10 ha). Hatching indicates areas where less than 70%\n45 of the climate-crop model combinations agree on the sign of impact. Regional production time series (e) are\n46 similar to (a), but stratified for the four major KoeppenGeiger climate zones (temperature limited,\n47\n\ntransboundary governance and ecosystem-\n55   based management, livelihood diversification, capacity development and improved knowledge-sharing will\n\n\n     Do Not Cite, Quote or Distribute                      TS-64                                         Total pages: 96\n     FINAL DRAFT                                    Technical Summary             IPCC WGII Sixth Assessment Report\n\n 1   reduce conflict and promote the fair distribution of sustainably-harvested wild products and revenues\n 2   (medium confidence). {5.8.4, 5.14.3, CCP5.4.2, CCB MOVING PLATE}\n 3\n 4   TS.D.5.5 Adaptation options that promote intensification of production have been widely adopted in\n 5   agriculture for climate change adaptation, but with potential negative effects (high confidence).\n 6   Agricultural intensification addresses short-term food security and livelihood goals but has trade-offs in\n 7   equity, biodiversity, and ecosystem services (high confidence). Irrigation is widely used and effective for\n 8   yield stability, but with several negative outcomes, including water demand (high confidence), groundwater\n 9   depletion (high confidence); alteration of local to regional climates (high confidence); increasing soil salinity\n10   (medium confidence) widening inequalities and loss of rural smallholder livelihoods with weak governance\n11   (medium confidence). Conventional breeding assisted by genomics introduces traits that adapt crops to\n12   climate change (high confidence). Genetic improvements through modern biotechnology have the potential\n13   to increase climate resilience in food production systems (high confidence), but with biophysical ceilings,\n14   and technical, agroecosystem, socio-economic and political variables strongly influence and limit uptake of\n15   climate-resilient crops, particularly for smallholders (medium confidence).{4.6.2, Box 4.3, 4.7.1, 5.4.4,\n16   5.12.5, 5.13.4, 5.14.1, 10.2.2, 12.5.4, 13.5.1, 13.5.2, 13.5.14, 14.5.4, 15.3.4, 17.5.1}\n17\n18\n\n5.3; 5.5.3; 5.4.1; Figure FAQ 5.1; Figure 9.22; 15.3.4; 15.3.3}\n\n44\n\n45 Figure AI.18: Climatic and environmental stresses on global production of wheat.\n46 The global effects of five climatic and environmental stresses on wheat yield. The combined effect of each\n47 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n48 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n49 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n50 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n51 wheat was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n52\n\n53 Figure AI.19: Climatic and environmental stresses on global production of soybean.\n54 The global effects of five climatic and environmental stresses on soybean yield. The combined effect of each\n55 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n56 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n\nDo Not Cite, Quote or Distribute  AI-63    Total pages: 74\nFINAL DRAFT                       Annex I  IPCC WGII Sixth Assessment Report\n\n 1 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n 2 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n 3 soybean was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n 4\n\n 5 Figure AI.20: Climatic and environmental stresses on global production of rice.\n 6 The global effects of five climatic and environmental stresses on rice yield. The combined effect of each\n 7 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but', 'location': 'Address: railway: Berlin Hauptbahnhof (Tief), road: Europaplatz, quarter: Europacity, suburb: Moabit, borough: Mitte, city: Berlin, ISO3166-2-lvl4: DE-BE, postcode: 10557, country: Germany, country_code: de', 'question': 'grow corn'}
2025-02-26 16:20:38 - rag - INFO - Chunks returned from RAG: {'context': 'deficits\nand high temperature curtailed both planting area and yields with compounding negative\nimpacts on final production, particularly for smallholders and more vulnerable households in\nthe Dry Corridor. During the second part of the season, tropical storms and unexpected heavy\nrain events disrupted the normal growth of crops in certain areas near the Central America\nPacific coast. In Haiti, irregular seasonal rainfall, including periods of high-intensity precipitation,\ncontributed to decrease the production of primary crops In 2023, record maize production in\nBrazil compensated for below-average harvests due to prolonged dry spells elsewhere in South\nAmerica, especially in Argentina, where drought conditions were expected to result in a 15%\ndecrease in cereal production compared with the five-year average. The return of El Niño in\n2023 led to adverse consequences through the entire crop cycle of maize in Central America\nand northern parts of South America, where water deficits and high temperature curtailed both\nplanting area and yields with compounding negative impacts on final production, particularly\nfor smallholders and more vulnerable households in the Dry Corridor. During the second part\nof the season, tropical storms and unexpected heavy rain events disrupted the normal growth\nof crops in certain areas near the Central America Pacific coast. In Haiti, irregular seasonal\nrainfall, including periods of high-intensity precipitation, contributed to decrease the production\nof primary crops.113\nOceania is expected to experience the sharpest annual reduction rate in cereal production\nworldwide, with a 31.1% decline in 2023 compared with 2022, although this largely reflects a\nreversion to near-average conditions after exceptionally high production in 2022, with 2023\nonly slightly below five-year averages.114\nIn September, Storm Daniel brought heavy rainfall to coastal and north-eastern Libya, flooding\nnearly 3 000 ha of cropland, particularly in the Al Marj and\n\nThe prolonged flooding made it difficult for people to access basic needs such\nas food, clean water and health care, and contributed to the near collapse of local livelihoods.\nBetween April and July 2023, 7.8 million people, almost two thirds of the total population of\nSouth Sudan, were expected to experience severe acute food insecurity.110\nAfghanistan experienced a substantial reduction in snowmelt and rainfall, resulting in another\npoor crop season. This led to widespread acute food insecurity, particularly in the north and\nnorth-eastern regions. Between May and October 2023, 15.3 million Afghans were estimated\nto face severe acute food insecurity.111 In Yemen, 53% of the population were already classified\nas in a crisis level of acute food insecurity or worse between October and December 2022.\nHigh food and fuel prices, together with floods from March to September 2023, and protracted\nconflict, further aggravated food insecurity.\nIn Indonesia, a meteorological drought linked to El Niño and the positive phase of the Indian\nOcean Dipole (IOD) (see Short-term climate drivers) occurred during the dry season, affecting 23\n450 ha of paddy cultivation and causing 6 964 ha of crop failure as of August 2023. A decrease\nof 645 000 t of rice production was predicted by October 2023,112 and crop planting in late 2023\nwas delayed.\nIn 2023, record maize production in Brazil compensated for below-average harvests due to\nprolonged dry spells elsewhere in South America, especially in Argentina, where drought\nconditions were expected to result in a 15% decrease in cereal production compared with the\nfive-year average. The return of El Niño in 2023 led to adverse consequences through the entire\ncrop cycle of maize in Central America and northern parts of South America, where water deficits\nand high temperature curtailed both planting area and yields with compounding negative\nimpacts on final production, particularly for smallholders and more vulnerable households in\nthe Dry\n\nof Madagascar, southern Malawi, Mozambique and Zimbabwe. Flooding associated\nwith the cyclone submerged extensive agricultural areas and inflicted severe damage on\ncrops, which has exacerbated a slow economic recovery.106\n900\n\n18\n796.9\n743.7\n\n793.4\n14\n12\n\n701.4\n656.6\n12.1%\n\n597.8\n\n%\n\n10\n\n563.9\n\n588.9\n\n8\n\n8.6%\n\n7.7% 7.9%\n\n783.1 800\n738.9\n735.1\n700\n674.6 690.6\n600\n\n612.8\n9.5%\n8.9%\n7.9%\n\n10.1%\n9.8% 500\n9.3%\n9.2%\n\n8.4% 8.5% 8.7% 400\n\n6\n\n300\n\n4\n\n200\n\n2\n\n100\n0\n\n0\n2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022*\n\nPrevalence of undernourishment\n(percentage, left axis)\n\nNumber of undernourished\n(millions, right axis)\n\nMillions\n\n16\n\nFigure 25. Global prevalence of\nundernourishment (as a percentage) and\nnumber of undernourished (in millions) since\n2005\nSource: The entire series has been updated\nto reflect new information released since\nthe publication of The State of Food\nSecurity and Nutrition in the World\n2023: Urbanization, Agrifood Systems\nTransformation and Healthy Diets Across\nthe Rural–Urban Continuum.107\n\n\x0cFood security\n\nGlobally, annual economic losses from climate- and weather-related disasters have increased\nsince the 2000s.108 Between 2007 and 2022, 88 post-disaster needs assessment surveys\nconducted in 60 countries worldwide showed that over 65% of losses caused by droughts occur\nin the agriculture sector, including crop and livestock production losses. For floods, storms and\ncyclones, about 20% of losses are in agriculture.109\nIn early 2023, South Sudan continued to experience exceptional flooding, with water levels\nremaining high even during the dry season. Despite relatively dry conditions locally, flooding\npersisted owing to high flows from further upstream in the Nile basin and very slow drainage\nfrom earlier floods. The prolonged flooding made it difficult for people to access basic needs such\nas food, clean water and health care, and contributed to the near collapse of local livelihoods.\nBetween April and July\n\nthe vulnerability of many who had already been uprooted by\ncomplex multi-causal situations of conflict and violence.\nThe development and implementation of local disaster risk reduction strategies has increased since\nthe adoption of the Sendai Framework for Disaster Risk Reduction.\nOne of the essential components for reducing the impact of disasters is to have effective multi-hazard\nearly warning systems.\n\nThe events described above, and many others besides, occur in a broader context. Extreme\nweather and climate events interact with, and in some cases trigger or exacerbate, situations\nconcerning water and food security, population mobility and environmental degradation.101,102\n\nFOOD SECURITY\nThe number of people who are acutely food insecure worldwide has more than doubled,\nfrom 149 million people before the coronavirus disease (COVID-19) pandemic to 333 million\npeople in 2023 (in 78 countries monitored by the World Food Programme (WFP)).103 Although\nglobal hunger levels remained unchanged from 2021 to 2022, they are still far above preCOVID 19 pandemic levels: in 2022, 9.2% of the global population (735.1 million people) were\nundernourished, compared with 7.9% (612.8 million people) in 2019 (see Figure 25).104 The\ncurrent global food and nutrition crisis is the largest in modern human history.105 Protracted\nconflicts, economic downturns and high food prices are at the root of high global food insecurity\nlevels. High food prices are exacerbated by the high costs of agricultural inputs, driven by\nongoing and widespread conflict around the world, and high global food insecurity levels are\naggravated by the effects of climate and weather extremes. In southern Africa, for example,\nweather extremes, including the passage of Cyclone Freddy in February 2023, have affected\nareas of Madagascar, southern Malawi, Mozambique and Zimbabwe. Flooding associated\nwith the cyclone submerged extensive agricultural areas and inflicted severe damage on\ncrops, which has exacerbated a', 'location': 'Address: railway: Berlin Hauptbahnhof (Tief), road: Europaplatz, quarter: Europacity, suburb: Moabit, borough: Mitte, city: Berlin, ISO3166-2-lvl4: DE-BE, postcode: 10557, country: Germany, country_code: de', 'question': 'grow corn'}
2025-02-26 16:20:39 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:20:39 - root - INFO - Rendered RAG response: None
2025-02-26 16:20:39 - climsight_engine - INFO - general_rag_agent_response: None
2025-02-26 16:20:39 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:39 - root - ERROR - list index out of range. Continue with: current_land_use = None
Traceback (most recent call last):
  File "/Users/ikuznets/work/projects/climsight/code/climsight/src/climsight/climsight_engine.py", line 669, in zero_rag_agent
    current_land_use = land_use_data["elements"][0]["tags"]["landuse"]
IndexError: list index out of range
2025-02-26 16:20:39 - climsight_engine - INFO - Data agent in work.
2025-02-26 16:20:39 - climsight_engine - INFO - data_agent_response: {'input_params': {'simulation1': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.1,\n    "Total Precipitation (mm\\/month)":54.45,\n    "Wind U (m s**-1)":1.5,\n    "Wind V (m s**-1)":1.04,\n    "Wind Speed (m\\/s)":1.83,\n    "Wind Direction (\\u00b0)":235.33\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":0.82,\n    "Total Precipitation (mm\\/month)":54.9,\n    "Wind U (m s**-1)":1.32,\n    "Wind V (m s**-1)":1.05,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":231.6\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":-0.29,\n    "Total Precipitation (mm\\/month)":35.17,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":0.3,\n    "Wind Speed (m\\/s)":0.37,\n    "Wind Direction (\\u00b0)":215.23\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":3.16,\n    "Total Precipitation (mm\\/month)":47.51,\n    "Wind U (m s**-1)":1.05,\n    "Wind V (m s**-1)":0.02,\n    "Wind Speed (m\\/s)":1.05,\n    "Wind Direction (\\u00b0)":268.92\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":7.95,\n    "Total Precipitation (mm\\/month)":43.6,\n    "Wind U (m s**-1)":0.43,\n    "Wind V (m s**-1)":-0.34,\n    "Wind Speed (m\\/s)":0.54,\n    "Wind Direction (\\u00b0)":308.22\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":14.17,\n    "Total Precipitation (mm\\/month)":61.8,\n    "Wind U (m s**-1)":0.26,\n    "Wind V (m s**-1)":-0.22,\n    "Wind Speed (m\\/s)":0.34,\n    "Wind Direction (\\u00b0)":310.3\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":16.92,\n    "Total Precipitation (mm\\/month)":64.13,\n    "Wind U (m s**-1)":1.12,\n    "Wind V (m s**-1)":-0.42,\n    "Wind Speed (m\\/s)":1.2,\n    "Wind Direction (\\u00b0)":290.55\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":19.69,\n    "Total Precipitation (mm\\/month)":52.02,\n    "Wind U (m s**-1)":1.48,\n    "Wind V (m s**-1)":-0.12,\n    "Wind Speed (m\\/s)":1.48,\n    "Wind Direction (\\u00b0)":274.6\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.3,\n    "Total Precipitation (mm\\/month)":46.67,\n    "Wind U (m s**-1)":1.01,\n    "Wind V (m s**-1)":-0.41,\n    "Wind Speed (m\\/s)":1.08,\n    "Wind Direction (\\u00b0)":292.12\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.32,\n    "Total Precipitation (mm\\/month)":31.07,\n    "Wind U (m s**-1)":0.54,\n    "Wind V (m s**-1)":0.19,\n    "Wind Speed (m\\/s)":0.57,\n    "Wind Direction (\\u00b0)":250.21\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":10.92,\n    "Total Precipitation (mm\\/month)":31.91,\n    "Wind U (m s**-1)":0.28,\n    "Wind V (m s**-1)":0.48,\n    "Wind Speed (m\\/s)":0.56,\n    "Wind Direction (\\u00b0)":209.96\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.0,\n    "Total Precipitation (mm\\/month)":45.19,\n    "Wind U (m s**-1)":0.54,\n    "Wind V (m s**-1)":1.33,\n    "Wind Speed (m\\/s)":1.43,\n    "Wind Direction (\\u00b0)":202.27\n  }\n]', 'simulation2': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.57,\n    "Total Precipitation (mm\\/month)":65.41,\n    "Wind U (m s**-1)":1.66,\n    "Wind V (m s**-1)":1.05,\n    "Wind Speed (m\\/s)":1.96,\n    "Wind Direction (\\u00b0)":237.74\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":0.83,\n    "Total Precipitation (mm\\/month)":65.67,\n    "Wind U (m s**-1)":1.53,\n    "Wind V (m s**-1)":1.27,\n    "Wind Speed (m\\/s)":1.99,\n    "Wind Direction (\\u00b0)":230.22\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.66,\n    "Total Precipitation (mm\\/month)":39.15,\n    "Wind U (m s**-1)":1.5,\n    "Wind V (m s**-1)":1.09,\n    "Wind Speed (m\\/s)":1.85,\n    "Wind Direction (\\u00b0)":234.02\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.92,\n    "Total Precipitation (mm\\/month)":51.82,\n    "Wind U (m s**-1)":0.73,\n    "Wind V (m s**-1)":0.61,\n    "Wind Speed (m\\/s)":0.95,\n    "Wind Direction (\\u00b0)":230.11\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":9.47,\n    "Total Precipitation (mm\\/month)":46.28,\n    "Wind U (m s**-1)":0.09,\n    "Wind V (m s**-1)":-0.21,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":336.71\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":13.61,\n    "Total Precipitation (mm\\/month)":56.85,\n    "Wind U (m s**-1)":0.06,\n    "Wind V (m s**-1)":-0.36,\n    "Wind Speed (m\\/s)":0.37,\n    "Wind Direction (\\u00b0)":350.32\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":17.58,\n    "Total Precipitation (mm\\/month)":69.71,\n    "Wind U (m s**-1)":0.87,\n    "Wind V (m s**-1)":-0.48,\n    "Wind Speed (m\\/s)":0.99,\n    "Wind Direction (\\u00b0)":298.93\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":20.16,\n    "Total Precipitation (mm\\/month)":50.62,\n    "Wind U (m s**-1)":1.14,\n    "Wind V (m s**-1)":-0.27,\n    "Wind Speed (m\\/s)":1.17,\n    "Wind Direction (\\u00b0)":283.42\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.07,\n    "Total Precipitation (mm\\/month)":55.34,\n    "Wind U (m s**-1)":1.09,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":1.1,\n    "Wind Direction (\\u00b0)":277.86\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.9,\n    "Total Precipitation (mm\\/month)":29.56,\n    "Wind U (m s**-1)":0.57,\n    "Wind V (m s**-1)":0.18,\n    "Wind Speed (m\\/s)":0.6,\n    "Wind Direction (\\u00b0)":252.85\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.33,\n    "Total Precipitation (mm\\/month)":67.89,\n    "Wind U (m s**-1)":1.25,\n    "Wind V (m s**-1)":0.97,\n    "Wind Speed (m\\/s)":1.59,\n    "Wind Direction (\\u00b0)":232.2\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":4.66,\n    "Total Precipitation (mm\\/month)":56.22,\n    "Wind U (m s**-1)":0.48,\n    "Wind V (m s**-1)":0.59,\n    "Wind Speed (m\\/s)":0.76,\n    "Wind Direction (\\u00b0)":219.37\n  }\n]', 'simulation3': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.75,\n    "Total Precipitation (mm\\/month)":63.78,\n    "Wind U (m s**-1)":1.26,\n    "Wind V (m s**-1)":0.99,\n    "Wind Speed (m\\/s)":1.6,\n    "Wind Direction (\\u00b0)":231.88\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":3.35,\n    "Total Precipitation (mm\\/month)":68.94,\n    "Wind U (m s**-1)":2.18,\n    "Wind V (m s**-1)":1.63,\n    "Wind Speed (m\\/s)":2.72,\n    "Wind Direction (\\u00b0)":233.24\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.96,\n    "Total Precipitation (mm\\/month)":65.51,\n    "Wind U (m s**-1)":2.25,\n    "Wind V (m s**-1)":0.78,\n    "Wind Speed (m\\/s)":2.38,\n    "Wind Direction (\\u00b0)":250.93\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.49,\n    "Total Precipitation (mm\\/month)":43.15,\n    "Wind U (m s**-1)":1.62,\n    "Wind V (m s**-1)":0.47,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":253.9\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":10.04,\n    "Total Precipitation (mm\\/month)":36.43,\n    "Wind U (m s**-1)":0.16,\n    "Wind V (m s**-1)":-0.19,\n    "Wind Speed (m\\/s)":0.25,\n    "Wind Direction (\\u00b0)":320.22\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":14.12,\n    "Total Precipitation (mm\\/month)":78.03,\n    "Wind U (m s**-1)":0.03,\n    "Wind V (m s**-1)":-0.61,\n    "Wind Speed (m\\/s)":0.62,\n    "Wind Direction (\\u00b0)":357.53\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":18.46,\n    "Total Precipitation (mm\\/month)":73.02,\n    "Wind U (m s**-1)":0.95,\n    "Wind V (m s**-1)":-0.28,\n    "Wind Speed (m\\/s)":0.99,\n    "Wind Direction (\\u00b0)":286.67\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":20.96,\n    "Total Precipitation (mm\\/month)":52.22,\n    "Wind U (m s**-1)":1.1,\n    "Wind V (m s**-1)":-0.37,\n    "Wind Speed (m\\/s)":1.16,\n    "Wind Direction (\\u00b0)":288.77\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":21.41,\n    "Total Precipitation (mm\\/month)":25.7,\n    "Wind U (m s**-1)":0.96,\n    "Wind V (m s**-1)":-0.34,\n    "Wind Speed (m\\/s)":1.02,\n    "Wind Direction (\\u00b0)":289.59\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.65,\n    "Total Precipitation (mm\\/month)":45.41,\n    "Wind U (m s**-1)":0.78,\n    "Wind V (m s**-1)":0.12,\n    "Wind Speed (m\\/s)":0.79,\n    "Wind Direction (\\u00b0)":261.07\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.78,\n    "Total Precipitation (mm\\/month)":31.58,\n    "Wind U (m s**-1)":0.41,\n    "Wind V (m s**-1)":0.39,\n    "Wind Speed (m\\/s)":0.57,\n    "Wind Direction (\\u00b0)":226.62\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.08,\n    "Total Precipitation (mm\\/month)":52.9,\n    "Wind U (m s**-1)":1.35,\n    "Wind V (m s**-1)":1.16,\n    "Wind Speed (m\\/s)":1.77,\n    "Wind Direction (\\u00b0)":229.34\n  }\n]', 'simulation5': '[\n  {\n    "Temperature (\\u00b0C)":0.47,\n    "Total Precipitation (mm\\/month)":10.96,\n    "Wind U (m s**-1)":0.16,\n    "Wind V (m s**-1)":0.01,\n    "Wind Speed (m\\/s)":0.13,\n    "Wind Direction (\\u00b0)":2.41\n  },\n  {\n    "Temperature (\\u00b0C)":0.01,\n    "Total Precipitation (mm\\/month)":10.77,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":0.22,\n    "Wind Speed (m\\/s)":0.3,\n    "Wind Direction (\\u00b0)":-1.38\n  },\n  {\n    "Temperature (\\u00b0C)":2.95,\n    "Total Precipitation (mm\\/month)":3.98,\n    "Wind U (m s**-1)":1.29,\n    "Wind V (m s**-1)":0.79,\n    "Wind Speed (m\\/s)":1.48,\n    "Wind Direction (\\u00b0)":18.79\n  },\n  {\n    "Temperature (\\u00b0C)":2.76,\n    "Total Precipitation (mm\\/month)":4.31,\n    "Wind U (m s**-1)":-0.32,\n    "Wind V (m s**-1)":0.59,\n    "Wind Speed (m\\/s)":-0.1,\n    "Wind Direction (\\u00b0)":-38.81\n  },\n  {\n    "Temperature (\\u00b0C)":1.52,\n    "Total Precipitation (mm\\/month)":2.68,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":0.13,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":28.49\n  },\n  {\n    "Temperature (\\u00b0C)":-0.56,\n    "Total Precipitation (mm\\/month)":-4.95,\n    "Wind U (m s**-1)":-0.2,\n    "Wind V (m s**-1)":-0.14,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":40.02\n  },\n  {\n    "Temperature (\\u00b0C)":0.66,\n    "Total Precipitation (mm\\/month)":5.58,\n    "Wind U (m s**-1)":-0.25,\n    "Wind V (m s**-1)":-0.06,\n    "Wind Speed (m\\/s)":-0.21,\n    "Wind Direction (\\u00b0)":8.38\n  },\n  {\n    "Temperature (\\u00b0C)":0.47,\n    "Total Precipitation (mm\\/month)":-1.4,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":-0.31,\n    "Wind Direction (\\u00b0)":8.82\n  },\n  {\n    "Temperature (\\u00b0C)":-0.23,\n    "Total Precipitation (mm\\/month)":8.67,\n    "Wind U (m s**-1)":0.08,\n    "Wind V (m s**-1)":0.26,\n    "Wind Speed (m\\/s)":0.02,\n    "Wind Direction (\\u00b0)":-14.26\n  },\n  {\n    "Temperature (\\u00b0C)":0.58,\n    "Total Precipitation (mm\\/month)":-1.51,\n    "Wind U (m s**-1)":0.03,\n    "Wind V (m s**-1)":-0.01,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":2.64\n  },\n  {\n    "Temperature (\\u00b0C)":0.41,\n    "Total Precipitation (mm\\/month)":35.98,\n    "Wind U (m s**-1)":0.97,\n    "Wind V (m s**-1)":0.49,\n    "Wind Speed (m\\/s)":1.03,\n    "Wind Direction (\\u00b0)":22.24\n  },\n  {\n    "Temperature (\\u00b0C)":-2.34,\n    "Total Precipitation (mm\\/month)":11.03,\n    "Wind U (m s**-1)":-0.06,\n    "Wind V (m s**-1)":-0.74,\n    "Wind Speed (m\\/s)":-0.67,\n    "Wind Direction (\\u00b0)":17.1\n  }\n]', 'simulation6': '[\n  {\n    "Temperature (\\u00b0C)":0.65,\n    "Total Precipitation (mm\\/month)":9.33,\n    "Wind U (m s**-1)":-0.24,\n    "Wind V (m s**-1)":-0.05,\n    "Wind Speed (m\\/s)":-0.23,\n    "Wind Direction (\\u00b0)":-3.45\n  },\n  {\n    "Temperature (\\u00b0C)":2.53,\n    "Total Precipitation (mm\\/month)":14.04,\n    "Wind U (m s**-1)":0.86,\n    "Wind V (m s**-1)":0.58,\n    "Wind Speed (m\\/s)":1.03,\n    "Wind Direction (\\u00b0)":1.64\n  },\n  {\n    "Temperature (\\u00b0C)":3.25,\n    "Total Precipitation (mm\\/month)":30.34,\n    "Wind U (m s**-1)":2.04,\n    "Wind V (m s**-1)":0.48,\n    "Wind Speed (m\\/s)":2.01,\n    "Wind Direction (\\u00b0)":35.7\n  },\n  {\n    "Temperature (\\u00b0C)":2.33,\n    "Total Precipitation (mm\\/month)":-4.36,\n    "Wind U (m s**-1)":0.57,\n    "Wind V (m s**-1)":0.45,\n    "Wind Speed (m\\/s)":0.64,\n    "Wind Direction (\\u00b0)":-15.02\n  },\n  {\n    "Temperature (\\u00b0C)":2.09,\n    "Total Precipitation (mm\\/month)":-7.17,\n    "Wind U (m s**-1)":-0.27,\n    "Wind V (m s**-1)":0.15,\n    "Wind Speed (m\\/s)":-0.29,\n    "Wind Direction (\\u00b0)":12.0\n  },\n  {\n    "Temperature (\\u00b0C)":-0.05,\n    "Total Precipitation (mm\\/month)":16.23,\n    "Wind U (m s**-1)":-0.23,\n    "Wind V (m s**-1)":-0.39,\n    "Wind Speed (m\\/s)":0.28,\n    "Wind Direction (\\u00b0)":47.23\n  },\n  {\n    "Temperature (\\u00b0C)":1.54,\n    "Total Precipitation (mm\\/month)":8.89,\n    "Wind U (m s**-1)":-0.17,\n    "Wind V (m s**-1)":0.14,\n    "Wind Speed (m\\/s)":-0.21,\n    "Wind Direction (\\u00b0)":-3.88\n  },\n  {\n    "Temperature (\\u00b0C)":1.27,\n    "Total Precipitation (mm\\/month)":0.2,\n    "Wind U (m s**-1)":-0.38,\n    "Wind V (m s**-1)":-0.25,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":14.17\n  },\n  {\n    "Temperature (\\u00b0C)":1.11,\n    "Total Precipitation (mm\\/month)":-20.97,\n    "Wind U (m s**-1)":-0.05,\n    "Wind V (m s**-1)":0.07,\n    "Wind Speed (m\\/s)":-0.06,\n    "Wind Direction (\\u00b0)":-2.53\n  },\n  {\n    "Temperature (\\u00b0C)":0.33,\n    "Total Precipitation (mm\\/month)":14.34,\n    "Wind U (m s**-1)":0.24,\n    "Wind V (m s**-1)":-0.07,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":10.86\n  },\n  {\n    "Temperature (\\u00b0C)":0.86,\n    "Total Precipitation (mm\\/month)":-0.33,\n    "Wind U (m s**-1)":0.13,\n    "Wind V (m s**-1)":-0.09,\n    "Wind Speed (m\\/s)":0.01,\n    "Wind Direction (\\u00b0)":16.66\n  },\n  {\n    "Temperature (\\u00b0C)":0.08,\n    "Total Precipitation (mm\\/month)":7.71,\n    "Wind U (m s**-1)":0.81,\n    "Wind V (m s**-1)":-0.17,\n    "Wind Speed (m\\/s)":0.34,\n    "Wind Direction (\\u00b0)":27.07\n  }\n]'}, 'content_message': '\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2020-2029. The nextGEMS pre-final simulations for years 2030x..This simulation serves as a historical reference to extract its values from other simulations. :\n {simulation1}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2030-2039. The nextGEMS pre-final simulations for years 2030x. :\n {simulation2}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2040-2049. The nextGEMS pre-final simulations for years 2040x. :\n {simulation3}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2030-2039 compared to the simulation for 2020-2029. :\n {simulation5}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2040-2049 compared to the simulation for 2020-2029. :\n {simulation6}'}
2025-02-26 16:20:39 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:39 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:39 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:40 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:20:40 - root - INFO - Rendered RAG response: The content provided does not contain specific information regarding the cultivation of corn (maize) in Berlin, Germany. However, it discusses the global effects of climatic and environmental stresses on maize yield. These effects include yield constraint due to higher temperatures enhancing ozone production and uptake by plants, which exacerbates yield loss and quality damage. This information is presented at a global scale and is not region-specific to Berlin.
2025-02-26 16:20:40 - climsight_engine - INFO - ipcc_rag_agent_response: The content provided does not contain specific information regarding the cultivation of corn (maize) in Berlin, Germany. However, it discusses the global effects of climatic and environmental stresses on maize yield. These effects include yield constraint due to higher temperatures enhancing ozone production and uptake by plants, which exacerbates yield loss and quality damage. This information is presented at a global scale and is not region-specific to Berlin.
2025-02-26 16:20:40 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:41 - climsight_engine - INFO - zero_agent_response: {'input_params': {'elevation': '36.0', 'soil': 'Cambisols', 'biodiv': 'Pristurus', 'distance_to_coastline': '143240.003744458', 'nat_hazards':        year          disastertype
13415  2002                 storm
13428  2006                 storm
13434  2010                 storm
33496  2003  extreme temperature 
33517  2006  extreme temperature 
33528  2009  extreme temperature 
33536  2009  extreme temperature 
33552  2012  extreme temperature 
33569  2012  extreme temperature , 'population':     Time  TotalPopulationAsOf1July  PopulationDensity  PopulationGrowthRate
0   1980              77786.703000         223.165900             -0.145000
1   1990              78072.678100         223.986330              0.237400
2   2000              80995.587100         232.372000              0.241300
3   2010              81294.847800         233.230560             -0.021600
4   2020              82291.120600         236.088820              0.245600
5   2030              83132.605000         238.502990             -0.082500
6   2040              81965.316200         235.154110             -0.195300
7   2050              80013.692600         229.555010             -0.288400
8   2060              77399.456100         222.054900             -0.362600
9   2070              74806.608900         214.616160             -0.299400
10  2080              72785.056300         208.816440             -0.266400
11  2090              70893.830000         203.390610             -0.247800
12  2100              69481.298222         199.338133             -0.171333
13  2101                       NaN                NaN                   NaN}, 'content_message': 'Elevation above sea level: {elevation} \nCurrent soil type: {soil} \nOccuring species: {biodiv} \nDistance to the closest coastline: {distance_to_coastline} \nNatural hazards: {nat_hazards} \nPopulation in Germany data: {population} \n'}
2025-02-26 16:20:41 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:20:41 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-26 16:20:44 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:20:53 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:20:53 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 16:20:53 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-26 16:20:55 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:20:55 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-26 16:20:55 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-26 16:20:56 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:20:59 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:21:00 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:21:06 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-26 16:21:06 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-26 16:21:06 - climsight_engine - INFO - combine_agent in work
2025-02-26 16:21:06 - climsight_engine - INFO - smart_agent_response: {'output': 'The temperature for growing corn at Berlin is expected to range from 2.1°C in January to 20.3°C in September for the years 2020-2029, with future projections showing a slight increase, reaching up to 21.41°C in September for 2040-2049. According to the ECOCROP database, the optimal temperature for corn is between 18.0°C and 33.0°C. The current and future temperatures in Berlin are within the optimal range during the growing season, indicating favorable conditions for corn growth.\n\nThe precipitation for Berlin is expected to range from 31.07 mm in October to 64.13 mm in July for the years 2020-2029, with future projections showing variability, reaching up to 78.03 mm in June for 2040-2049. According to the ECOCROP database, the optimal rainfall for corn is between 600 mm and 1200 mm annually. The monthly precipitation in Berlin suggests that while some months may be drier, the overall annual precipitation is likely to meet the requirements for corn, though irrigation may be necessary during drier months.'}
2025-02-26 16:21:06 - climsight_engine - INFO - Wikipedia_tool_reponse: • Temperature:
  - Quantitative: Requires warm days above 10 °C (50 °F) for flowering.
  - Qualitative: Maize is cold-intolerant and must be planted in the spring in temperate zones.

• Wind:
  - Qualitative: Maize pollen is dispersed by wind. Maize is prone to being uprooted by severe winds due to its shallow roots.

• Soil Type:
  - Qualitative: Maize is intolerant of nutrient-deficient soils and depends on adequate soil moisture.
2025-02-26 16:21:06 - climsight_engine - INFO - Ecocrop_search_response: Data from ECOCROP database for corn:
Scientific Name: Zea mays ssp. mays
Authority: L.
Family: Liliopsida:Commelinidae:Cyperales:Gramineae
Common names: corn
Life form: grass
Habitat: erect
Life span: annual
Physiology: single stem
Category: cereals & pseudocereals, forage/pasture, vegetables, materials, environmental
Plant attributes: grown on large scale
optimal minimum temperature (°C): 18.0
optimal maximum temperature (°C): 33.0
absolute minimum temperature (°C): 10.0
absolute maximum temperature (°C): 47.0
optimal minimum rainfall (mm): 600.0
optimal maximum rainfall (mm): 1200.0
absolute minimum rainfall (mm): 400.0
absolute maximum rainfall (mm): 1800.0
optimal minimum soil PH: 5.0
optimal maximum soil PH: 7.0
absolute minimum soil PH: 4.5
absolute maximum soil PH: 8.5
absolute minimum latitude: 40.0
absolute maximum latitude: 48.0
absolute maximum altitude (meter above sea level): 4000.0
optimal minimum light intensity: very bright
optimal maximum light intensity: very bright
absolute minimum light intensity: very bright
absolute maximum light intensity: clear skies
Optimal soil depth: medium (50-150 cm)
Absolute soil depth: shallow (20-50 cm)
Optimal soil texture: medium, organic
Absolute soil texture: heavy, medium, light
Optimal soil fertility: high
Absolute soil fertility: low
Optimal soil salinity: low (<4 dS/m)
Absolute soil salinity: medium (4-10 dS/m)
Optimal soil drainage: well (dry spells)
Absolute soil drainage: well (dry spells), excessive (dry/moderately dry)
Photoperiod: short day (<12 hours), neutral day (12-14 hours)
Climate zone: tropical wet & dry (Aw), steppe or semiarid (Bs), subtropical humid (Cf), subtropical dry summer (Cs), subtropical dry winter (Cw), temperate oceanic (Do), temperate continental (Dc), temperate with humid winters (Df), temperate with dry winters (Dw)
Abiotic susceptibility: hail
Introduction risk: mono culture may cause erosion problems, can become a weed
Minimum crop cycle (days): 65.0
Maximum crop cycle (days): 365.0

2025-02-26 16:21:25 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-26 16:21:25 - climsight_engine - INFO - Final_answer: Growing corn (Zea mays) in Berlin, specifically in the vicinity of Berlin Hauptbahnhof, requires careful consideration of local climate conditions, soil characteristics, and urban constraints. The area is characterized by Cambisols, which are generally suitable for agriculture due to their loamy texture that offers good fertility and drainage. However, corn, which thrives in nutrient-rich soils, may require soil amendments to optimize fertility.

The climatic data indicates that Berlin experiences temperatures ranging from 2.1°C in January to 20.3°C in September for the period 2020-2029, with future projections suggesting temperatures as high as 21.41°C in September for 2040-2049. Corn requires a growing season with temperatures above 10°C, ideally between 18°C and 33°C, to thrive. Berlin's summer temperatures fall within this optimal range, making it conducive for corn cultivation during these months.

Precipitation levels in Berlin range from 31.07 mm in October to around 64.13 mm in July, with future projections indicating variability but reaching up to 78.03 mm in June for 2040-2049. Corn requires between 600 mm and 1200 mm of rainfall annually. While some months may be drier, supplemental irrigation might be necessary to meet the corn's water needs, especially during critical growth phases.

The wind conditions, with average speeds ranging from 0.34 m/s in June to 1.83 m/s in January, are generally moderate. However, given that corn is susceptible to wind damage due to its shallow root system, it would be prudent to consider windbreaks or planting strategies that mitigate potential wind damage.

Corn in urban Berlin could face challenges due to the setting's limitations, such as space constraints and possible shade from buildings, affecting the light intensity necessary for optimal growth. The ECOCROP database suggests that corn requires very bright conditions, which might be compromised in a dense urban area like the vicinity of a railway station.

Moreover, historical data on natural hazards in Berlin, such as storms and extreme temperatures, suggests potential risks that could impact corn yields. For instance, extreme temperatures recorded in years like 2003 and 2006 could stress the crops, necessitating adaptive measures such as selecting heat-resistant corn varieties or adjusting planting schedules.

In summary, while Berlin's climate and soil conditions can support corn cultivation, especially during the warmer months, urban challenges like limited space, potential shading, and the need for irrigation should be addressed. Implementing strategies to optimize light exposure and soil fertility, alongside protection against wind and extreme weather, would be vital for successful corn production in this urban environment.
2025-02-26 16:21:25 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-26 16:21:25 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-26 16:21:25 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 12:59:34 - __main__ - INFO - reading config from: config.yml
2025-02-27 12:59:35 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:00:23 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:00:23 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:00:25 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:00:30 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:00:30 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-27 13:00:30 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 13:00:30 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-27 13:00:30 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-27 13:00:30 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 13:00:30 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-27 13:00:30 - root - INFO - Is the point on land? Yes.
2025-02-27 13:00:31 - root - ERROR - Unexpected error in request with osmnx ot OSM: No matching features. Check query location, tags, and log.
2025-02-27 13:00:31 - climsight_engine - INFO - start agent_request
2025-02-27 13:00:33 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:00:33 - climsight_engine - INFO - IPCC RAG agent in work.
2025-02-27 13:00:33 - climsight_engine - INFO - General RAG agent in work.
2025-02-27 13:00:33 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 13:00:34 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 13:00:34 - rag - INFO - Chunks returned from RAG: {'context': 'deficits\nand high temperature curtailed both planting area and yields with compounding negative\nimpacts on final production, particularly for smallholders and more vulnerable households in\nthe Dry Corridor. During the second part of the season, tropical storms and unexpected heavy\nrain events disrupted the normal growth of crops in certain areas near the Central America\nPacific coast. In Haiti, irregular seasonal rainfall, including periods of high-intensity precipitation,\ncontributed to decrease the production of primary crops In 2023, record maize production in\nBrazil compensated for below-average harvests due to prolonged dry spells elsewhere in South\nAmerica, especially in Argentina, where drought conditions were expected to result in a 15%\ndecrease in cereal production compared with the five-year average. The return of El Niño in\n2023 led to adverse consequences through the entire crop cycle of maize in Central America\nand northern parts of South America, where water deficits and high temperature curtailed both\nplanting area and yields with compounding negative impacts on final production, particularly\nfor smallholders and more vulnerable households in the Dry Corridor. During the second part\nof the season, tropical storms and unexpected heavy rain events disrupted the normal growth\nof crops in certain areas near the Central America Pacific coast. In Haiti, irregular seasonal\nrainfall, including periods of high-intensity precipitation, contributed to decrease the production\nof primary crops.113\nOceania is expected to experience the sharpest annual reduction rate in cereal production\nworldwide, with a 31.1% decline in 2023 compared with 2022, although this largely reflects a\nreversion to near-average conditions after exceptionally high production in 2022, with 2023\nonly slightly below five-year averages.114\nIn September, Storm Daniel brought heavy rainfall to coastal and north-eastern Libya, flooding\nnearly 3 000 ha of cropland, particularly in the Al Marj and\n\nThe prolonged flooding made it difficult for people to access basic needs such\nas food, clean water and health care, and contributed to the near collapse of local livelihoods.\nBetween April and July 2023, 7.8 million people, almost two thirds of the total population of\nSouth Sudan, were expected to experience severe acute food insecurity.110\nAfghanistan experienced a substantial reduction in snowmelt and rainfall, resulting in another\npoor crop season. This led to widespread acute food insecurity, particularly in the north and\nnorth-eastern regions. Between May and October 2023, 15.3 million Afghans were estimated\nto face severe acute food insecurity.111 In Yemen, 53% of the population were already classified\nas in a crisis level of acute food insecurity or worse between October and December 2022.\nHigh food and fuel prices, together with floods from March to September 2023, and protracted\nconflict, further aggravated food insecurity.\nIn Indonesia, a meteorological drought linked to El Niño and the positive phase of the Indian\nOcean Dipole (IOD) (see Short-term climate drivers) occurred during the dry season, affecting 23\n450 ha of paddy cultivation and causing 6 964 ha of crop failure as of August 2023. A decrease\nof 645 000 t of rice production was predicted by October 2023,112 and crop planting in late 2023\nwas delayed.\nIn 2023, record maize production in Brazil compensated for below-average harvests due to\nprolonged dry spells elsewhere in South America, especially in Argentina, where drought\nconditions were expected to result in a 15% decrease in cereal production compared with the\nfive-year average. The return of El Niño in 2023 led to adverse consequences through the entire\ncrop cycle of maize in Central America and northern parts of South America, where water deficits\nand high temperature curtailed both planting area and yields with compounding negative\nimpacts on final production, particularly for smallholders and more vulnerable households in\nthe Dry\n\nchange, fertilizer use or irrigation. Cobenefits for biodiversity, ecosystem services and livelihoods,\nas well as co-delivery on other international and national\ncommitments on biodiversity, land degradation and people,\nhave also propelled the use of conventional CDR approaches.\nHowever, the risks and benefits of CDR depend on the\nmethod used and its implementation and management\n(e.g. reforestation with native species versus afforestation\nof non-forest biomes with non-native monocultures).\nCompetition for land is a pressing issue due to numerous\nglobal demands, including for food production, resource\nextraction, infrastructure development, biodiversity and\necosystem services conservation and climate change\nmitigation. Environmental changes, such as climate change,\nmay exacerbate land-use competition, due to complex\nfeedback processes between human and biophysical\ncomponents in the land system (Haberl et al. 2014). Cropland\nand urban expansion therefore also compete with landbased CDR options. Modelling efforts show that cropland\nexpansion to fulfil future food demand is the primary cause\nof such competition, with more severe impacts seen in the\ntropics due to their greater land-based mitigation potential\n(Zheng et al. 2022). Such findings highlight that careful\nspatial planning is essential for sustainable climate policies.\nVarious land-based CDR options have the potential to\nenhance biodiversity. An assessment of the biodiversity\nimpacts of 20 land-based mitigation options showed that\nmost options benefit biodiversity. However, a quarter of the\nassessed options, including bioenergy with carbon capture\nand storage, decreased mean species abundance, while\nafforestation and forest management either positively or\nnegatively affected biodiversity depending on the local\nimplementation method and forest conservation schemes\nadopted (Nunez, Verboom and Alkemade 2020). Recent\nstudies explore how ambitious objectives and multiple\ntargets of biodiversity and climate\n\nof Madagascar, southern Malawi, Mozambique and Zimbabwe. Flooding associated\nwith the cyclone submerged extensive agricultural areas and inflicted severe damage on\ncrops, which has exacerbated a slow economic recovery.106\n900\n\n18\n796.9\n743.7\n\n793.4\n14\n12\n\n701.4\n656.6\n12.1%\n\n597.8\n\n%\n\n10\n\n563.9\n\n588.9\n\n8\n\n8.6%\n\n7.7% 7.9%\n\n783.1 800\n738.9\n735.1\n700\n674.6 690.6\n600\n\n612.8\n9.5%\n8.9%\n7.9%\n\n10.1%\n9.8% 500\n9.3%\n9.2%\n\n8.4% 8.5% 8.7% 400\n\n6\n\n300\n\n4\n\n200\n\n2\n\n100\n0\n\n0\n2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022*\n\nPrevalence of undernourishment\n(percentage, left axis)\n\nNumber of undernourished\n(millions, right axis)\n\nMillions\n\n16\n\nFigure 25. Global prevalence of\nundernourishment (as a percentage) and\nnumber of undernourished (in millions) since\n2005\nSource: The entire series has been updated\nto reflect new information released since\nthe publication of The State of Food\nSecurity and Nutrition in the World\n2023: Urbanization, Agrifood Systems\nTransformation and Healthy Diets Across\nthe Rural–Urban Continuum.107\n\n\x0cFood security\n\nGlobally, annual economic losses from climate- and weather-related disasters have increased\nsince the 2000s.108 Between 2007 and 2022, 88 post-disaster needs assessment surveys\nconducted in 60 countries worldwide showed that over 65% of losses caused by droughts occur\nin the agriculture sector, including crop and livestock production losses. For floods, storms and\ncyclones, about 20% of losses are in agriculture.109\nIn early 2023, South Sudan continued to experience exceptional flooding, with water levels\nremaining high even during the dry season. Despite relatively dry conditions locally, flooding\npersisted owing to high flows from further upstream in the Nile basin and very slow drainage\nfrom earlier floods. The prolonged flooding made it difficult for people to access basic needs such\nas food, clean water and health care, and contributed to the near collapse of local livelihoods.\nBetween April and July', 'location': 'Address: locality: Am Gollenberg, village: Stölln, municipality: Gollenberg, town: Rhinow, county: Havelland, state: Brandenburg, ISO3166-2-lvl4: DE-BB, postcode: 14728, country: Germany, country_code: de', 'question': 'grow corn and potatoes '}
2025-02-27 13:00:34 - rag - INFO - Chunks returned from RAG: {'context': 'on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n 9 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n10 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n11\n\n12 Figure AI.21: Climatic and environmental stresses on global production of maize.\n13 The global effects of five climatic and environmental stresses on maize yield. The combined effect of each\n14 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n15 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n16 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n17 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n18\n\n19 Figure AI.22: Projected changes in global maize production.\n20 For maize production time series are shown as relative changes to the 1983-2013 reference period under\n21 SSP126 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop\n22 model combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model\n23 response (and a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the\n24 historical variability (1983-2013; ensemble median) and open circles highlight the "time of climate impact\n25 emergence" (TCIE), the year in which the smoothed median response exceeds the historical envelope. For\n26 context, the TCIE calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop\n\ntransboundary governance and ecosystem-\n55   based management, livelihood diversification, capacity development and improved knowledge-sharing will\n\n\n     Do Not Cite, Quote or Distribute                      TS-64                                         Total pages: 96\n     FINAL DRAFT                                    Technical Summary             IPCC WGII Sixth Assessment Report\n\n 1   reduce conflict and promote the fair distribution of sustainably-harvested wild products and revenues\n 2   (medium confidence). {5.8.4, 5.14.3, CCP5.4.2, CCB MOVING PLATE}\n 3\n 4   TS.D.5.5 Adaptation options that promote intensification of production have been widely adopted in\n 5   agriculture for climate change adaptation, but with potential negative effects (high confidence).\n 6   Agricultural intensification addresses short-term food security and livelihood goals but has trade-offs in\n 7   equity, biodiversity, and ecosystem services (high confidence). Irrigation is widely used and effective for\n 8   yield stability, but with several negative outcomes, including water demand (high confidence), groundwater\n 9   depletion (high confidence); alteration of local to regional climates (high confidence); increasing soil salinity\n10   (medium confidence) widening inequalities and loss of rural smallholder livelihoods with weak governance\n11   (medium confidence). Conventional breeding assisted by genomics introduces traits that adapt crops to\n12   climate change (high confidence). Genetic improvements through modern biotechnology have the potential\n13   to increase climate resilience in food production systems (high confidence), but with biophysical ceilings,\n14   and technical, agroecosystem, socio-economic and political variables strongly influence and limit uptake of\n15   climate-resilient crops, particularly for smallholders (medium confidence).{4.6.2, Box 4.3, 4.7.1, 5.4.4,\n16   5.12.5, 5.13.4, 5.14.1, 10.2.2, 12.5.4, 13.5.1, 13.5.2, 13.5.14, 14.5.4, 15.3.4, 17.5.1}\n17\n18\n\nis indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop models for current growing regions (>10 ha). Hatching indicates areas\n29 where less than 70% of the climate-crop model combinations agree on the sign of impact. Regional\n30 production time series (e) are similar to (a), but stratified for the four major KoeppenGeiger climate zones\n31 (temperature limited, temperate/humid, subtropical, and tropical). The percentage of the total global\n32 production contributed by each zone is indicated in the top right corner of the inlets. All data are shown for\n33 the default [CO2] {Jägermeyr et al. 2021; 5.4.3.2}\n\n34\n\n35 Figure AI.23: Projected changes in global wheat production.\n36 Production time series are shown as relative changes to the 1983-2013 reference period under SSP126\n37 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop model\n38 combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model response (and\n39 a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the historical variability\n40 (1983-2013; ensemble median) and open circles highlight the "time of climate impact emergence" (TCIE),\n41 the year in which the smoothed median response exceeds the historical envelope. For context, the TCIE\n42 calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on GC6 (>2099 if no\n43 TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under SSP585 across\n44 climate and crop models for current growing regions (>10 ha). Hatching indicates areas where less than 70%\n45 of the climate-crop model combinations agree on the sign of impact. Regional production time series (e) are\n46 similar to (a), but stratified for the four major KoeppenGeiger climate zones (temperature limited,\n47\n\n5.3; 5.5.3; 5.4.1; Figure FAQ 5.1; Figure 9.22; 15.3.4; 15.3.3}\n\n44\n\n45 Figure AI.18: Climatic and environmental stresses on global production of wheat.\n46 The global effects of five climatic and environmental stresses on wheat yield. The combined effect of each\n47 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n48 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n49 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n50 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n51 wheat was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n52\n\n53 Figure AI.19: Climatic and environmental stresses on global production of soybean.\n54 The global effects of five climatic and environmental stresses on soybean yield. The combined effect of each\n55 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n56 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n\nDo Not Cite, Quote or Distribute  AI-63    Total pages: 74\nFINAL DRAFT                       Annex I  IPCC WGII Sixth Assessment Report\n\n 1 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n 2 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n 3 soybean was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n 4\n\n 5 Figure AI.20: Climatic and environmental stresses on global production of rice.\n 6 The global effects of five climatic and environmental stresses on rice yield. The combined effect of each\n 7 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but', 'location': 'Address: locality: Am Gollenberg, village: Stölln, municipality: Gollenberg, town: Rhinow, county: Havelland, state: Brandenburg, ISO3166-2-lvl4: DE-BB, postcode: 14728, country: Germany, country_code: de', 'question': 'grow corn and potatoes '}
2025-02-27 13:00:35 - climsight_engine - INFO - Data agent in work.
2025-02-27 13:00:35 - climsight_engine - INFO - data_agent_response: {'input_params': {'simulation1': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.22,\n    "Total Precipitation (mm\\/month)":50.44,\n    "Wind U (m s**-1)":1.76,\n    "Wind V (m s**-1)":1.21,\n    "Wind Speed (m\\/s)":2.14,\n    "Wind Direction (\\u00b0)":235.53\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":1.02,\n    "Total Precipitation (mm\\/month)":55.9,\n    "Wind U (m s**-1)":1.53,\n    "Wind V (m s**-1)":1.15,\n    "Wind Speed (m\\/s)":1.91,\n    "Wind Direction (\\u00b0)":233.11\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":-0.14,\n    "Total Precipitation (mm\\/month)":39.04,\n    "Wind U (m s**-1)":0.28,\n    "Wind V (m s**-1)":0.38,\n    "Wind Speed (m\\/s)":0.47,\n    "Wind Direction (\\u00b0)":217.08\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":3.16,\n    "Total Precipitation (mm\\/month)":46.82,\n    "Wind U (m s**-1)":1.23,\n    "Wind V (m s**-1)":0.0,\n    "Wind Speed (m\\/s)":1.23,\n    "Wind Direction (\\u00b0)":270.11\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":7.56,\n    "Total Precipitation (mm\\/month)":46.76,\n    "Wind U (m s**-1)":0.51,\n    "Wind V (m s**-1)":-0.38,\n    "Wind Speed (m\\/s)":0.64,\n    "Wind Direction (\\u00b0)":306.25\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":13.46,\n    "Total Precipitation (mm\\/month)":59.33,\n    "Wind U (m s**-1)":0.38,\n    "Wind V (m s**-1)":-0.25,\n    "Wind Speed (m\\/s)":0.45,\n    "Wind Direction (\\u00b0)":303.74\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":16.14,\n    "Total Precipitation (mm\\/month)":61.73,\n    "Wind U (m s**-1)":1.26,\n    "Wind V (m s**-1)":-0.52,\n    "Wind Speed (m\\/s)":1.36,\n    "Wind Direction (\\u00b0)":292.31\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":18.73,\n    "Total Precipitation (mm\\/month)":59.16,\n    "Wind U (m s**-1)":1.7,\n    "Wind V (m s**-1)":-0.16,\n    "Wind Speed (m\\/s)":1.71,\n    "Wind Direction (\\u00b0)":275.43\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":19.41,\n    "Total Precipitation (mm\\/month)":46.31,\n    "Wind U (m s**-1)":1.19,\n    "Wind V (m s**-1)":-0.47,\n    "Wind Speed (m\\/s)":1.28,\n    "Wind Direction (\\u00b0)":291.68\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":16.69,\n    "Total Precipitation (mm\\/month)":30.66,\n    "Wind U (m s**-1)":0.64,\n    "Wind V (m s**-1)":0.21,\n    "Wind Speed (m\\/s)":0.68,\n    "Wind Direction (\\u00b0)":251.45\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":10.5,\n    "Total Precipitation (mm\\/month)":36.2,\n    "Wind U (m s**-1)":0.39,\n    "Wind V (m s**-1)":0.56,\n    "Wind Speed (m\\/s)":0.68,\n    "Wind Direction (\\u00b0)":214.62\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":6.87,\n    "Total Precipitation (mm\\/month)":45.58,\n    "Wind U (m s**-1)":0.72,\n    "Wind V (m s**-1)":1.54,\n    "Wind Speed (m\\/s)":1.7,\n    "Wind Direction (\\u00b0)":205.0\n  }\n]', 'simulation2': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.65,\n    "Total Precipitation (mm\\/month)":64.82,\n    "Wind U (m s**-1)":1.93,\n    "Wind V (m s**-1)":1.16,\n    "Wind Speed (m\\/s)":2.26,\n    "Wind Direction (\\u00b0)":238.93\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":1.05,\n    "Total Precipitation (mm\\/month)":65.65,\n    "Wind U (m s**-1)":1.76,\n    "Wind V (m s**-1)":1.47,\n    "Wind Speed (m\\/s)":2.29,\n    "Wind Direction (\\u00b0)":230.2\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.75,\n    "Total Precipitation (mm\\/month)":39.35,\n    "Wind U (m s**-1)":1.76,\n    "Wind V (m s**-1)":1.25,\n    "Wind Speed (m\\/s)":2.16,\n    "Wind Direction (\\u00b0)":234.64\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.81,\n    "Total Precipitation (mm\\/month)":52.39,\n    "Wind U (m s**-1)":0.89,\n    "Wind V (m s**-1)":0.75,\n    "Wind Speed (m\\/s)":1.17,\n    "Wind Direction (\\u00b0)":230.06\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":9.08,\n    "Total Precipitation (mm\\/month)":43.62,\n    "Wind U (m s**-1)":0.1,\n    "Wind V (m s**-1)":-0.19,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":331.48\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":12.86,\n    "Total Precipitation (mm\\/month)":64.99,\n    "Wind U (m s**-1)":0.13,\n    "Wind V (m s**-1)":-0.46,\n    "Wind Speed (m\\/s)":0.48,\n    "Wind Direction (\\u00b0)":344.14\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":16.74,\n    "Total Precipitation (mm\\/month)":75.21,\n    "Wind U (m s**-1)":1.06,\n    "Wind V (m s**-1)":-0.52,\n    "Wind Speed (m\\/s)":1.17,\n    "Wind Direction (\\u00b0)":296.01\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":19.25,\n    "Total Precipitation (mm\\/month)":53.19,\n    "Wind U (m s**-1)":1.33,\n    "Wind V (m s**-1)":-0.27,\n    "Wind Speed (m\\/s)":1.36,\n    "Wind Direction (\\u00b0)":281.52\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":19.32,\n    "Total Precipitation (mm\\/month)":53.83,\n    "Wind U (m s**-1)":1.3,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":1.31,\n    "Wind Direction (\\u00b0)":276.56\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.33,\n    "Total Precipitation (mm\\/month)":36.88,\n    "Wind U (m s**-1)":0.73,\n    "Wind V (m s**-1)":0.22,\n    "Wind Speed (m\\/s)":0.76,\n    "Wind Direction (\\u00b0)":253.12\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.09,\n    "Total Precipitation (mm\\/month)":64.98,\n    "Wind U (m s**-1)":1.51,\n    "Wind V (m s**-1)":1.07,\n    "Wind Speed (m\\/s)":1.85,\n    "Wind Direction (\\u00b0)":234.64\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":4.67,\n    "Total Precipitation (mm\\/month)":57.6,\n    "Wind U (m s**-1)":0.56,\n    "Wind V (m s**-1)":0.67,\n    "Wind Speed (m\\/s)":0.87,\n    "Wind Direction (\\u00b0)":219.94\n  }\n]', 'simulation3': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.89,\n    "Total Precipitation (mm\\/month)":63.1,\n    "Wind U (m s**-1)":1.47,\n    "Wind V (m s**-1)":1.15,\n    "Wind Speed (m\\/s)":1.86,\n    "Wind Direction (\\u00b0)":232.05\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":3.49,\n    "Total Precipitation (mm\\/month)":68.82,\n    "Wind U (m s**-1)":2.57,\n    "Wind V (m s**-1)":1.89,\n    "Wind Speed (m\\/s)":3.19,\n    "Wind Direction (\\u00b0)":233.66\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":3.09,\n    "Total Precipitation (mm\\/month)":63.76,\n    "Wind U (m s**-1)":2.64,\n    "Wind V (m s**-1)":0.88,\n    "Wind Speed (m\\/s)":2.78,\n    "Wind Direction (\\u00b0)":251.56\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.43,\n    "Total Precipitation (mm\\/month)":44.73,\n    "Wind U (m s**-1)":1.93,\n    "Wind V (m s**-1)":0.56,\n    "Wind Speed (m\\/s)":2.0,\n    "Wind Direction (\\u00b0)":253.84\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":9.61,\n    "Total Precipitation (mm\\/month)":39.15,\n    "Wind U (m s**-1)":0.25,\n    "Wind V (m s**-1)":-0.21,\n    "Wind Speed (m\\/s)":0.32,\n    "Wind Direction (\\u00b0)":309.65\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":13.46,\n    "Total Precipitation (mm\\/month)":77.75,\n    "Wind U (m s**-1)":0.07,\n    "Wind V (m s**-1)":-0.67,\n    "Wind Speed (m\\/s)":0.68,\n    "Wind Direction (\\u00b0)":354.42\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":17.59,\n    "Total Precipitation (mm\\/month)":71.71,\n    "Wind U (m s**-1)":1.05,\n    "Wind V (m s**-1)":-0.25,\n    "Wind Speed (m\\/s)":1.08,\n    "Wind Direction (\\u00b0)":283.64\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":19.93,\n    "Total Precipitation (mm\\/month)":54.01,\n    "Wind U (m s**-1)":1.24,\n    "Wind V (m s**-1)":-0.4,\n    "Wind Speed (m\\/s)":1.3,\n    "Wind Direction (\\u00b0)":287.97\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.3,\n    "Total Precipitation (mm\\/month)":27.02,\n    "Wind U (m s**-1)":1.16,\n    "Wind V (m s**-1)":-0.37,\n    "Wind Speed (m\\/s)":1.21,\n    "Wind Direction (\\u00b0)":287.82\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":16.96,\n    "Total Precipitation (mm\\/month)":51.07,\n    "Wind U (m s**-1)":0.96,\n    "Wind V (m s**-1)":0.15,\n    "Wind Speed (m\\/s)":0.97,\n    "Wind Direction (\\u00b0)":261.08\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.42,\n    "Total Precipitation (mm\\/month)":33.48,\n    "Wind U (m s**-1)":0.56,\n    "Wind V (m s**-1)":0.46,\n    "Wind Speed (m\\/s)":0.72,\n    "Wind Direction (\\u00b0)":230.99\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.12,\n    "Total Precipitation (mm\\/month)":54.4,\n    "Wind U (m s**-1)":1.65,\n    "Wind V (m s**-1)":1.32,\n    "Wind Speed (m\\/s)":2.11,\n    "Wind Direction (\\u00b0)":231.44\n  }\n]', 'simulation5': '[\n  {\n    "Temperature (\\u00b0C)":0.43,\n    "Total Precipitation (mm\\/month)":14.38,\n    "Wind U (m s**-1)":0.17,\n    "Wind V (m s**-1)":-0.05,\n    "Wind Speed (m\\/s)":0.12,\n    "Wind Direction (\\u00b0)":3.4\n  },\n  {\n    "Temperature (\\u00b0C)":0.03,\n    "Total Precipitation (mm\\/month)":9.75,\n    "Wind U (m s**-1)":0.23,\n    "Wind V (m s**-1)":0.32,\n    "Wind Speed (m\\/s)":0.38,\n    "Wind Direction (\\u00b0)":-2.91\n  },\n  {\n    "Temperature (\\u00b0C)":2.89,\n    "Total Precipitation (mm\\/month)":0.31,\n    "Wind U (m s**-1)":1.48,\n    "Wind V (m s**-1)":0.87,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":17.56\n  },\n  {\n    "Temperature (\\u00b0C)":2.65,\n    "Total Precipitation (mm\\/month)":5.57,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":0.75,\n    "Wind Speed (m\\/s)":-0.06,\n    "Wind Direction (\\u00b0)":-40.05\n  },\n  {\n    "Temperature (\\u00b0C)":1.52,\n    "Total Precipitation (mm\\/month)":-3.14,\n    "Wind U (m s**-1)":-0.41,\n    "Wind V (m s**-1)":0.19,\n    "Wind Speed (m\\/s)":-0.42,\n    "Wind Direction (\\u00b0)":25.23\n  },\n  {\n    "Temperature (\\u00b0C)":-0.6,\n    "Total Precipitation (mm\\/month)":5.66,\n    "Wind U (m s**-1)":-0.25,\n    "Wind V (m s**-1)":-0.21,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":40.4\n  },\n  {\n    "Temperature (\\u00b0C)":0.6,\n    "Total Precipitation (mm\\/month)":13.48,\n    "Wind U (m s**-1)":-0.2,\n    "Wind V (m s**-1)":0.0,\n    "Wind Speed (m\\/s)":-0.19,\n    "Wind Direction (\\u00b0)":3.7\n  },\n  {\n    "Temperature (\\u00b0C)":0.52,\n    "Total Precipitation (mm\\/month)":-5.97,\n    "Wind U (m s**-1)":-0.37,\n    "Wind V (m s**-1)":-0.11,\n    "Wind Speed (m\\/s)":-0.35,\n    "Wind Direction (\\u00b0)":6.09\n  },\n  {\n    "Temperature (\\u00b0C)":-0.09,\n    "Total Precipitation (mm\\/month)":7.52,\n    "Wind U (m s**-1)":0.11,\n    "Wind V (m s**-1)":0.32,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":-15.12\n  },\n  {\n    "Temperature (\\u00b0C)":0.64,\n    "Total Precipitation (mm\\/month)":6.22,\n    "Wind U (m s**-1)":0.09,\n    "Wind V (m s**-1)":0.01,\n    "Wind Speed (m\\/s)":0.08,\n    "Wind Direction (\\u00b0)":1.67\n  },\n  {\n    "Temperature (\\u00b0C)":0.59,\n    "Total Precipitation (mm\\/month)":28.78,\n    "Wind U (m s**-1)":1.12,\n    "Wind V (m s**-1)":0.51,\n    "Wind Speed (m\\/s)":1.17,\n    "Wind Direction (\\u00b0)":20.02\n  },\n  {\n    "Temperature (\\u00b0C)":-2.2,\n    "Total Precipitation (mm\\/month)":12.02,\n    "Wind U (m s**-1)":-0.16,\n    "Wind V (m s**-1)":-0.87,\n    "Wind Speed (m\\/s)":-0.83,\n    "Wind Direction (\\u00b0)":14.94\n  }\n]', 'simulation6': '[\n  {\n    "Temperature (\\u00b0C)":0.67,\n    "Total Precipitation (mm\\/month)":12.66,\n    "Wind U (m s**-1)":-0.29,\n    "Wind V (m s**-1)":-0.06,\n    "Wind Speed (m\\/s)":-0.28,\n    "Wind Direction (\\u00b0)":-3.48\n  },\n  {\n    "Temperature (\\u00b0C)":2.47,\n    "Total Precipitation (mm\\/month)":12.92,\n    "Wind U (m s**-1)":1.04,\n    "Wind V (m s**-1)":0.74,\n    "Wind Speed (m\\/s)":1.28,\n    "Wind Direction (\\u00b0)":0.55\n  },\n  {\n    "Temperature (\\u00b0C)":3.23,\n    "Total Precipitation (mm\\/month)":24.72,\n    "Wind U (m s**-1)":2.36,\n    "Wind V (m s**-1)":0.5,\n    "Wind Speed (m\\/s)":2.31,\n    "Wind Direction (\\u00b0)":34.48\n  },\n  {\n    "Temperature (\\u00b0C)":2.27,\n    "Total Precipitation (mm\\/month)":-2.09,\n    "Wind U (m s**-1)":0.7,\n    "Wind V (m s**-1)":0.56,\n    "Wind Speed (m\\/s)":0.77,\n    "Wind Direction (\\u00b0)":-16.27\n  },\n  {\n    "Temperature (\\u00b0C)":2.05,\n    "Total Precipitation (mm\\/month)":-7.61,\n    "Wind U (m s**-1)":-0.26,\n    "Wind V (m s**-1)":0.17,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":3.4\n  },\n  {\n    "Temperature (\\u00b0C)":0.0,\n    "Total Precipitation (mm\\/month)":18.42,\n    "Wind U (m s**-1)":-0.31,\n    "Wind V (m s**-1)":-0.42,\n    "Wind Speed (m\\/s)":0.23,\n    "Wind Direction (\\u00b0)":50.68\n  },\n  {\n    "Temperature (\\u00b0C)":1.45,\n    "Total Precipitation (mm\\/month)":9.98,\n    "Wind U (m s**-1)":-0.21,\n    "Wind V (m s**-1)":0.27,\n    "Wind Speed (m\\/s)":-0.28,\n    "Wind Direction (\\u00b0)":-8.67\n  },\n  {\n    "Temperature (\\u00b0C)":1.2,\n    "Total Precipitation (mm\\/month)":-5.15,\n    "Wind U (m s**-1)":-0.46,\n    "Wind V (m s**-1)":-0.24,\n    "Wind Speed (m\\/s)":-0.41,\n    "Wind Direction (\\u00b0)":12.54\n  },\n  {\n    "Temperature (\\u00b0C)":0.89,\n    "Total Precipitation (mm\\/month)":-19.29,\n    "Wind U (m s**-1)":-0.03,\n    "Wind V (m s**-1)":0.1,\n    "Wind Speed (m\\/s)":-0.07,\n    "Wind Direction (\\u00b0)":-3.86\n  },\n  {\n    "Temperature (\\u00b0C)":0.27,\n    "Total Precipitation (mm\\/month)":20.41,\n    "Wind U (m s**-1)":0.32,\n    "Wind V (m s**-1)":-0.06,\n    "Wind Speed (m\\/s)":0.29,\n    "Wind Direction (\\u00b0)":9.63\n  },\n  {\n    "Temperature (\\u00b0C)":0.92,\n    "Total Precipitation (mm\\/month)":-2.72,\n    "Wind U (m s**-1)":0.17,\n    "Wind V (m s**-1)":-0.1,\n    "Wind Speed (m\\/s)":0.04,\n    "Wind Direction (\\u00b0)":16.37\n  },\n  {\n    "Temperature (\\u00b0C)":0.25,\n    "Total Precipitation (mm\\/month)":8.82,\n    "Wind U (m s**-1)":0.93,\n    "Wind V (m s**-1)":-0.22,\n    "Wind Speed (m\\/s)":0.41,\n    "Wind Direction (\\u00b0)":26.44\n  }\n]'}, 'content_message': '\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2020-2029. The nextGEMS pre-final simulations for years 2030x..This simulation serves as a historical reference to extract its values from other simulations. :\n {simulation1}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2030-2039. The nextGEMS pre-final simulations for years 2030x. :\n {simulation2}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2040-2049. The nextGEMS pre-final simulations for years 2040x. :\n {simulation3}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2030-2039 compared to the simulation for 2020-2029. :\n {simulation5}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2040-2049 compared to the simulation for 2020-2029. :\n {simulation6}'}
2025-02-27 13:00:35 - climsight_engine - INFO - zero_agent_response: {'input_params': {'elevation': '38.0', 'current_land_use': 'farmland', 'soil': 'Luvisols', 'biodiv': 'Cyperus, Mesalina, Pristurus, Chlorophthalmus, Neoharriotta', 'distance_to_coastline': '143860.42026059193', 'nat_hazards': Empty DataFrame
Columns: [year, disastertype]
Index: [], 'population':     Time  TotalPopulationAsOf1July  PopulationDensity  PopulationGrowthRate
0   1980              77786.703000         223.165900             -0.145000
1   1990              78072.678100         223.986330              0.237400
2   2000              80995.587100         232.372000              0.241300
3   2010              81294.847800         233.230560             -0.021600
4   2020              82291.120600         236.088820              0.245600
5   2030              83132.605000         238.502990             -0.082500
6   2040              81965.316200         235.154110             -0.195300
7   2050              80013.692600         229.555010             -0.288400
8   2060              77399.456100         222.054900             -0.362600
9   2070              74806.608900         214.616160             -0.299400
10  2080              72785.056300         208.816440             -0.266400
11  2090              70893.830000         203.390610             -0.247800
12  2100              69481.298222         199.338133             -0.171333
13  2101                       NaN                NaN                   NaN}, 'content_message': 'Elevation above sea level: {elevation} \nCurrent landuse: {current_land_use} \nCurrent soil type: {soil} \nOccuring species: {biodiv} \nDistance to the closest coastline: {distance_to_coastline} \nNatural hazards: {nat_hazards} \nPopulation in Germany data: {population} \n'}
2025-02-27 13:00:38 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:00:38 - root - INFO - Rendered RAG response: Information specific to growing corn and potatoes in the exact location provided (Am Gollenberg, Stölln, Gollenberg, Rhinow, Havelland, Brandenburg, Germany) is not available in the context. 

General Information: Corn and potatoes are sensitive to climatic and environmental stresses, and their yield can be constrained by factors such as temperature, ozone levels, and water availability, as indicated by the Yield Constraint Score (YCS). Higher temperatures can exacerbate these stresses by increasing ozone uptake by plants, leading to yield loss and quality damage.
2025-02-27 13:00:38 - climsight_engine - INFO - ipcc_rag_agent_response: Information specific to growing corn and potatoes in the exact location provided (Am Gollenberg, Stölln, Gollenberg, Rhinow, Havelland, Brandenburg, Germany) is not available in the context. 

General Information: Corn and potatoes are sensitive to climatic and environmental stresses, and their yield can be constrained by factors such as temperature, ozone levels, and water availability, as indicated by the Yield Constraint Score (YCS). Higher temperatures can exacerbate these stresses by increasing ozone uptake by plants, leading to yield loss and quality damage.
2025-02-27 13:00:39 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:00:39 - root - INFO - Rendered RAG response: For the location specified, Stölln in Brandenburg, Germany, there is no specific information available from the provided context. However, based on general agricultural practices in Germany, corn and potatoes are commonly grown crops in the region. Please note that this is a general statement and not directly from the provided context.
2025-02-27 13:00:39 - climsight_engine - INFO - general_rag_agent_response: For the location specified, Stölln in Brandenburg, Germany, there is no specific information available from the provided context. However, based on general agricultural practices in Germany, corn and potatoes are commonly grown crops in the region. Please note that this is a general statement and not directly from the provided context.
2025-02-27 13:00:39 - climsight_engine - INFO - combine_agent in work
2025-02-27 13:00:39 - climsight_engine - INFO - Wikipedia_tool_reponse: 
2025-02-27 13:00:39 - climsight_engine - INFO - Ecocrop_search_response: 
2025-02-27 13:01:03 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:01:03 - climsight_engine - INFO - Final_answer: The decision to grow corn and potatoes in Stölln, Gollenberg, located in the Havelland region of Brandenburg, Germany, requires careful consideration of the local climate conditions and soil characteristics. The selected location is primarily farmland with luvisols soil, a type that generally supports agriculture well due to its fertility and capacity to retain water and nutrients. However, various climatic factors influenced by climate change need to be taken into account for successful cultivation.

### Climatic Conditions

Based on climatological data from atmospheric model simulations for the period 2020-2049, temperatures and precipitation levels are expected to increase progressively. During the 2020s, monthly average temperatures range from 1.02°C in February to 19.41°C in September. Over the next two decades, these figures are projected to increase. For instance, by the 2040s, temperatures could rise to approximately 2.89°C in January and 20.3°C in September.

Precipitation patterns show seasonal variability, with a general increasing trend. During the summer months, when corn and potatoes require ample water, total monthly precipitation is predicted to range from 46.76 mm in May to 77.75 mm in June by the 2040s. The potential for increased precipitation could be beneficial for these crops, which require substantial amounts of water, especially during their growing phases.

### Impact on Corn and Potatoes

**Corn:**
- **Temperature:** Corn thrives in a range with average temperatures between 18 and 24°C during its growing season. The projected rise to 19.93°C in August and 20.3°C in September by the 2040s aligns with these requirements, supporting optimal growth conditions.
- **Water:** Sufficient rainfall is crucial, particularly in the early stages of corn germination and during tasseling. The anticipated increase in rainfall during June and July should support these needs.

**Potatoes:**
- **Temperature:** Potatoes are more sensitive to high temperatures, preferring cooler conditions. The rise in temperature, especially spanning from May (9.61°C) to September (20.3°C), may pose challenges, requiring strategies like selecting heat-tolerant varieties or adjusting planting schedules.
- **Water:** Steady and ample precipitation during the potato growing season, particularly from May to July, is beneficial. Increased precipitation in this period aligns well with these requirements.

### Recommendations

- **Adaptation of Planting Schedules:** Adjust planting and harvesting times to align with the warming trend and precipitation patterns. Early planting might help mitigate heat stress on potatoes.
- **Irrigation Planning:** While increased rainfall seems promising, supplementary irrigation systems should be considered to address any potential fluctuations and ensure plants are not stressed during critical growth phases.
- **Soil Management:** Maintaining the luvisols' health through appropriate soil management practices can reduce the risk of erosion, promote better water retention, and sustain its fertility.

### Conclusion

Growing corn and potatoes in Stölln can be feasible under future climate scenarios given the local climate and soil conditions. However, adopting adaptive agricultural practices will be vital to maximizing crop yield and quality amidst changing environmental conditions. Implementing modern irrigation systems, selecting suitable crop varieties, and adjusting planting schedules are proactive steps that can enhance resilience to climate-induced stresses.
2025-02-27 13:03:37 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:03:38 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:32:19 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:32:20 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:32:41 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:32:43 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:32:43 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-27 13:32:44 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 13:32:44 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-27 13:32:44 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-27 13:32:44 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 13:32:44 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-27 13:32:44 - root - INFO - Is the point on land? Yes.
2025-02-27 13:32:44 - climsight_engine - INFO - start agent_request
2025-02-27 13:32:44 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 401 Unauthorized"
2025-02-27 13:33:50 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:33:50 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:34:03 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:34:03 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-27 13:34:03 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 13:34:03 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-27 13:34:03 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-27 13:34:03 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 13:34:03 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-27 13:34:03 - root - INFO - Is the point on land? Yes.
2025-02-27 13:34:03 - climsight_engine - INFO - start agent_request
2025-02-27 13:34:04 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 401 Unauthorized"
2025-02-27 13:34:38 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:34:38 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-27 13:34:38 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-27 13:34:38 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-27 13:34:38 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-27 13:34:38 - climsight_engine - INFO - start agent_request
2025-02-27 13:34:39 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:34:39 - climsight_engine - INFO - IPCC RAG agent in work.
2025-02-27 13:34:39 - climsight_engine - INFO - General RAG agent in work.
2025-02-27 13:34:40 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 13:34:40 - rag - INFO - Chunks returned from RAG: {'context': 'transboundary governance and ecosystem-\n55   based management, livelihood diversification, capacity development and improved knowledge-sharing will\n\n\n     Do Not Cite, Quote or Distribute                      TS-64                                         Total pages: 96\n     FINAL DRAFT                                    Technical Summary             IPCC WGII Sixth Assessment Report\n\n 1   reduce conflict and promote the fair distribution of sustainably-harvested wild products and revenues\n 2   (medium confidence). {5.8.4, 5.14.3, CCP5.4.2, CCB MOVING PLATE}\n 3\n 4   TS.D.5.5 Adaptation options that promote intensification of production have been widely adopted in\n 5   agriculture for climate change adaptation, but with potential negative effects (high confidence).\n 6   Agricultural intensification addresses short-term food security and livelihood goals but has trade-offs in\n 7   equity, biodiversity, and ecosystem services (high confidence). Irrigation is widely used and effective for\n 8   yield stability, but with several negative outcomes, including water demand (high confidence), groundwater\n 9   depletion (high confidence); alteration of local to regional climates (high confidence); increasing soil salinity\n10   (medium confidence) widening inequalities and loss of rural smallholder livelihoods with weak governance\n11   (medium confidence). Conventional breeding assisted by genomics introduces traits that adapt crops to\n12   climate change (high confidence). Genetic improvements through modern biotechnology have the potential\n13   to increase climate resilience in food production systems (high confidence), but with biophysical ceilings,\n14   and technical, agroecosystem, socio-economic and political variables strongly influence and limit uptake of\n15   climate-resilient crops, particularly for smallholders (medium confidence).{4.6.2, Box 4.3, 4.7.1, 5.4.4,\n16   5.12.5, 5.13.4, 5.14.1, 10.2.2, 12.5.4, 13.5.1, 13.5.2, 13.5.14, 14.5.4, 15.3.4, 17.5.1}\n17\n18\n\nis indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop models for current growing regions (>10 ha). Hatching indicates areas\n29 where less than 70% of the climate-crop model combinations agree on the sign of impact. Regional\n30 production time series (e) are similar to (a), but stratified for the four major KoeppenGeiger climate zones\n31 (temperature limited, temperate/humid, subtropical, and tropical). The percentage of the total global\n32 production contributed by each zone is indicated in the top right corner of the inlets. All data are shown for\n33 the default [CO2] {Jägermeyr et al. 2021; 5.4.3.2}\n\n34\n\n35 Figure AI.23: Projected changes in global wheat production.\n36 Production time series are shown as relative changes to the 1983-2013 reference period under SSP126\n37 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop model\n38 combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model response (and\n39 a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the historical variability\n40 (1983-2013; ensemble median) and open circles highlight the "time of climate impact emergence" (TCIE),\n41 the year in which the smoothed median response exceeds the historical envelope. For context, the TCIE\n42 calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on GC6 (>2099 if no\n43 TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under SSP585 across\n44 climate and crop models for current growing regions (>10 ha). Hatching indicates areas where less than 70%\n45 of the climate-crop model combinations agree on the sign of impact. Regional production time series (e) are\n46 similar to (a), but stratified for the four major KoeppenGeiger climate zones (temperature limited,\n47\n\non yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n 9 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n10 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n11\n\n12 Figure AI.21: Climatic and environmental stresses on global production of maize.\n13 The global effects of five climatic and environmental stresses on maize yield. The combined effect of each\n14 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n15 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n16 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n17 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n18\n\n19 Figure AI.22: Projected changes in global maize production.\n20 For maize production time series are shown as relative changes to the 1983-2013 reference period under\n21 SSP126 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop\n22 model combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model\n23 response (and a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the\n24 historical variability (1983-2013; ensemble median) and open circles highlight the "time of climate impact\n25 emergence" (TCIE), the year in which the smoothed median response exceeds the historical envelope. For\n26 context, the TCIE calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop\n\n5.3; 5.5.3; 5.4.1; Figure FAQ 5.1; Figure 9.22; 15.3.4; 15.3.3}\n\n44\n\n45 Figure AI.18: Climatic and environmental stresses on global production of wheat.\n46 The global effects of five climatic and environmental stresses on wheat yield. The combined effect of each\n47 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n48 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n49 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n50 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n51 wheat was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n52\n\n53 Figure AI.19: Climatic and environmental stresses on global production of soybean.\n54 The global effects of five climatic and environmental stresses on soybean yield. The combined effect of each\n55 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n56 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n\nDo Not Cite, Quote or Distribute  AI-63    Total pages: 74\nFINAL DRAFT                       Annex I  IPCC WGII Sixth Assessment Report\n\n 1 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n 2 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n 3 soybean was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n 4\n\n 5 Figure AI.20: Climatic and environmental stresses on global production of rice.\n 6 The global effects of five climatic and environmental stresses on rice yield. The combined effect of each\n 7 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but', 'location': 'Address: railway: Berlin Hauptbahnhof (Tief), road: Europaplatz, quarter: Europacity, suburb: Moabit, borough: Mitte, city: Berlin, ISO3166-2-lvl4: DE-BE, postcode: 10557, country: Germany, country_code: de', 'question': 'grow tomatoes '}
2025-02-27 13:34:40 - root - ERROR - list index out of range. Continue with: current_land_use = None
Traceback (most recent call last):
  File "/Users/ikuznets/work/projects/climsight/code/climsight/src/climsight/climsight_engine.py", line 669, in zero_rag_agent
    current_land_use = land_use_data["elements"][0]["tags"]["landuse"]
                       ~~~~~~~~~~~~~~~~~~~~~~~~~^^^
IndexError: list index out of range
2025-02-27 13:34:40 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 13:34:40 - rag - INFO - Chunks returned from RAG: {'context': 'infections in humans. In\nparticular, eggs and egg products play a predominant role.\nPork and pork products represent another important\nsource [27, 28]. Increasingly, Salmonella infections are also\nreported to be associated with the consumption of foods\nof non-animal origin [27, 29]. Among these, raw leafy vegetables, onion and stem vegetables, tomatoes, and melons\nwere the most commonly affected products. Contamination of these products with Salmonella can occur both before harvest (through faecal matter, irrigation water, dust,\ninsects, etc.) and after harvest (through harvesting equipment, transport containers, insects, dust, rinsing water, ice,\ntransport vehicles, processing equipment) [30].\nClimate influence on Salmonella infections\nIn Europe, most salmonellosis cases are reported during\nthe summer months [28]. The incidence of Salmonella is\noften lower in northern countries than in countries located\nin warmer climates.\nJournal of Health Monitoring 2023 8(S3)\n\nFOCUS\n\nImpact of climate change on foodborne infections and intoxications\n\nAmbient temperature can influence the development of\nSalmonella at different stages of the food chain: e. g. bacterial contamination during raw food production, transport,\nand improper storage [31]. The optimal temperature for\nSalmonella growth is between 35°C and 37°C; below 15°C,\ngrowth of Salmonella is greatly reduced. Consequently,\nSalmonella multiply faster at higher temperatures. The significant correlation between outdoor temperatures and\noutbreaks caused by Salmonella has been known for some\ntime. Studies have reported the increase of salmonellosis,\nas well as other bacterial enteric diseases, with increasing\ntemperatures [32]. According to Zhang et al. [31], an increase of 8.8% in the number of weekly cases can be expected for a 1°C increase in the mean weekly maximum\ntemperature. With a 1°C increase in the mean weekly minimum temperature, a 5.8% increase in the weekly number\nof cases can be expected.\nTemperature can\n\nfreezing, or other food preparation processes.\nIn the interests of preventive consumer health protection, maximum levels have been set within the EU for five\ndifferent toxin groups in live bivalve molluscs. Furthermore,\nproducts containing compounds of the ciguatoxin group\nthat can cause mild to severe poisoning (ciguatera) in humans [67] may not be marketed within the EU [68, 69].\n5. Conclusion and recommendations\nClimate change affects various habitats, which can be altered by weather events such as prolonged droughts, temperature increases, and heavy rains. These changes also\naffect the foods derived from these habitats and\nmicro-\xadorganisms or toxins that may be associated with\nthese foods. For example, foods may be more heavily contaminated with pathogens or contain germs that were not\npreviously present in a region. But climate-related events\nalso affect food indirectly. For example, ever-increasing water scarcity means more frequent use of treated wastewa-\n\nHome back\n\n86\n\nforward\n\n\x0cJournal of Health Monitoring\n\nter for food irrigation. Because such wastewater may still\ncontain parasites, bacteria, and viruses in disease-causing\nconcentrations, plant parts that are commonly consumed\nraw should not come into contact with treated wastewater,\nor should continue to be irrigated with potable water [23–\n25]. Progressive climate change in Germany is expected to\nlead to an increase of the infections and intoxications discussed here, making them a growing public health concern.\nOur main recommendations for minimising the health\nrisk from foodborne infections and intoxications lie in the\narea of kitchen hygiene, which should always be applied\nwhen preparing food. This includes thorough hand washing and the use of fresh kitchen utensils after handling raw\nmeat and fish, as well as avoidance of cross-contamination,\ni. e. direct or indirect pathogen transmission from one food\nto another. Wearing gloves can prevent the entry of\npathogens via unnoticed skin lesions. Food\n\nanimals, such as reptiles or insects, which\ncan act as vectors and transmit Salmonella to warm-blood-\n\nThe regulation on minimum requirements for water reuse is limited to agricultural irrigation. In addition to uniform minimum\nrequirements for water quality and monitoring, risk management and provisions for data transparency are the main elements of the regulation [22]. In this context, the German Federal Institute for Risk Assessment (BfR) published opinions on\npossible risks associated with the reuse of treated wastewater\nfor irrigation of edible crops [23–25]. Even wastewater that has\nalready been treated may still contain parasites, bacteria and\nviruses in pathogenic concentrations. Therefore, particular care\nshould be taken to ensure that plant parts that are usually consumed raw do not come into direct contact with irrigation water\nor, if this cannot be safely avoided, should continue to be irrigated with potable water. Persons belonging to risk groups are advised against consumption of such raw foods.\n\nHome back\n\n80\n\nforward\n\n\x0cJournal of Health Monitoring\n\ned animals or humans. Although most salmonellae do not\nusually cause symptoms in animals, they can cause mild\nto severe health problems in humans. Human salmonellosis is often accompanied by fever, nausea, vomiting, abdominal pain, and headache, but signs of illness may be\ncompletely absent.\nIn Germany, salmonellosis is the second most frequently reported bacterial foodborne infection in humans after\ncampylobacteriosis; Salmonella (S.) enterica is an important cause of foodborne outbreaks. Among these, S. Enteritidis and S. Typhimurium are the most common serovars,\ntogether accounting for approximately 75% of all reported\nsalmonellosis cases [26]. Poultry is presumed the most\nimportant source of Salmonella infections in humans. In\nparticular, eggs and egg products play a predominant role.\nPork and pork products represent another important\nsource [27, 28]. Increasingly, Salmonella infections are\n\ndeficits\nand high temperature curtailed both planting area and yields with compounding negative\nimpacts on final production, particularly for smallholders and more vulnerable households in\nthe Dry Corridor. During the second part of the season, tropical storms and unexpected heavy\nrain events disrupted the normal growth of crops in certain areas near the Central America\nPacific coast. In Haiti, irregular seasonal rainfall, including periods of high-intensity precipitation,\ncontributed to decrease the production of primary crops In 2023, record maize production in\nBrazil compensated for below-average harvests due to prolonged dry spells elsewhere in South\nAmerica, especially in Argentina, where drought conditions were expected to result in a 15%\ndecrease in cereal production compared with the five-year average. The return of El Niño in\n2023 led to adverse consequences through the entire crop cycle of maize in Central America\nand northern parts of South America, where water deficits and high temperature curtailed both\nplanting area and yields with compounding negative impacts on final production, particularly\nfor smallholders and more vulnerable households in the Dry Corridor. During the second part\nof the season, tropical storms and unexpected heavy rain events disrupted the normal growth\nof crops in certain areas near the Central America Pacific coast. In Haiti, irregular seasonal\nrainfall, including periods of high-intensity precipitation, contributed to decrease the production\nof primary crops.113\nOceania is expected to experience the sharpest annual reduction rate in cereal production\nworldwide, with a 31.1% decline in 2023 compared with 2022, although this largely reflects a\nreversion to near-average conditions after exceptionally high production in 2022, with 2023\nonly slightly below five-year averages.114\nIn September, Storm Daniel brought heavy rainfall to coastal and north-eastern Libya, flooding\nnearly 3 000 ha of cropland, particularly in the Al Marj and', 'location': 'Address: railway: Berlin Hauptbahnhof (Tief), road: Europaplatz, quarter: Europacity, suburb: Moabit, borough: Mitte, city: Berlin, ISO3166-2-lvl4: DE-BE, postcode: 10557, country: Germany, country_code: de', 'question': 'grow tomatoes '}
2025-02-27 13:34:41 - climsight_engine - INFO - Data agent in work.
2025-02-27 13:34:41 - climsight_engine - INFO - data_agent_response: {'input_params': {'simulation1': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.1,\n    "Total Precipitation (mm\\/month)":54.45,\n    "Wind U (m s**-1)":1.5,\n    "Wind V (m s**-1)":1.04,\n    "Wind Speed (m\\/s)":1.83,\n    "Wind Direction (\\u00b0)":235.33\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":0.82,\n    "Total Precipitation (mm\\/month)":54.9,\n    "Wind U (m s**-1)":1.32,\n    "Wind V (m s**-1)":1.05,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":231.6\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":-0.29,\n    "Total Precipitation (mm\\/month)":35.17,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":0.3,\n    "Wind Speed (m\\/s)":0.37,\n    "Wind Direction (\\u00b0)":215.23\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":3.16,\n    "Total Precipitation (mm\\/month)":47.51,\n    "Wind U (m s**-1)":1.05,\n    "Wind V (m s**-1)":0.02,\n    "Wind Speed (m\\/s)":1.05,\n    "Wind Direction (\\u00b0)":268.92\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":7.95,\n    "Total Precipitation (mm\\/month)":43.6,\n    "Wind U (m s**-1)":0.43,\n    "Wind V (m s**-1)":-0.34,\n    "Wind Speed (m\\/s)":0.54,\n    "Wind Direction (\\u00b0)":308.22\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":14.17,\n    "Total Precipitation (mm\\/month)":61.8,\n    "Wind U (m s**-1)":0.26,\n    "Wind V (m s**-1)":-0.22,\n    "Wind Speed (m\\/s)":0.34,\n    "Wind Direction (\\u00b0)":310.3\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":16.92,\n    "Total Precipitation (mm\\/month)":64.13,\n    "Wind U (m s**-1)":1.12,\n    "Wind V (m s**-1)":-0.42,\n    "Wind Speed (m\\/s)":1.2,\n    "Wind Direction (\\u00b0)":290.55\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":19.69,\n    "Total Precipitation (mm\\/month)":52.02,\n    "Wind U (m s**-1)":1.48,\n    "Wind V (m s**-1)":-0.12,\n    "Wind Speed (m\\/s)":1.48,\n    "Wind Direction (\\u00b0)":274.6\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.3,\n    "Total Precipitation (mm\\/month)":46.67,\n    "Wind U (m s**-1)":1.01,\n    "Wind V (m s**-1)":-0.41,\n    "Wind Speed (m\\/s)":1.08,\n    "Wind Direction (\\u00b0)":292.12\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.32,\n    "Total Precipitation (mm\\/month)":31.07,\n    "Wind U (m s**-1)":0.54,\n    "Wind V (m s**-1)":0.19,\n    "Wind Speed (m\\/s)":0.57,\n    "Wind Direction (\\u00b0)":250.21\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":10.92,\n    "Total Precipitation (mm\\/month)":31.91,\n    "Wind U (m s**-1)":0.28,\n    "Wind V (m s**-1)":0.48,\n    "Wind Speed (m\\/s)":0.56,\n    "Wind Direction (\\u00b0)":209.96\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.0,\n    "Total Precipitation (mm\\/month)":45.19,\n    "Wind U (m s**-1)":0.54,\n    "Wind V (m s**-1)":1.33,\n    "Wind Speed (m\\/s)":1.43,\n    "Wind Direction (\\u00b0)":202.27\n  }\n]', 'simulation2': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.57,\n    "Total Precipitation (mm\\/month)":65.41,\n    "Wind U (m s**-1)":1.66,\n    "Wind V (m s**-1)":1.05,\n    "Wind Speed (m\\/s)":1.96,\n    "Wind Direction (\\u00b0)":237.74\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":0.83,\n    "Total Precipitation (mm\\/month)":65.67,\n    "Wind U (m s**-1)":1.53,\n    "Wind V (m s**-1)":1.27,\n    "Wind Speed (m\\/s)":1.99,\n    "Wind Direction (\\u00b0)":230.22\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.66,\n    "Total Precipitation (mm\\/month)":39.15,\n    "Wind U (m s**-1)":1.5,\n    "Wind V (m s**-1)":1.09,\n    "Wind Speed (m\\/s)":1.85,\n    "Wind Direction (\\u00b0)":234.02\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.92,\n    "Total Precipitation (mm\\/month)":51.82,\n    "Wind U (m s**-1)":0.73,\n    "Wind V (m s**-1)":0.61,\n    "Wind Speed (m\\/s)":0.95,\n    "Wind Direction (\\u00b0)":230.11\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":9.47,\n    "Total Precipitation (mm\\/month)":46.28,\n    "Wind U (m s**-1)":0.09,\n    "Wind V (m s**-1)":-0.21,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":336.71\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":13.61,\n    "Total Precipitation (mm\\/month)":56.85,\n    "Wind U (m s**-1)":0.06,\n    "Wind V (m s**-1)":-0.36,\n    "Wind Speed (m\\/s)":0.37,\n    "Wind Direction (\\u00b0)":350.32\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":17.58,\n    "Total Precipitation (mm\\/month)":69.71,\n    "Wind U (m s**-1)":0.87,\n    "Wind V (m s**-1)":-0.48,\n    "Wind Speed (m\\/s)":0.99,\n    "Wind Direction (\\u00b0)":298.93\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":20.16,\n    "Total Precipitation (mm\\/month)":50.62,\n    "Wind U (m s**-1)":1.14,\n    "Wind V (m s**-1)":-0.27,\n    "Wind Speed (m\\/s)":1.17,\n    "Wind Direction (\\u00b0)":283.42\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.07,\n    "Total Precipitation (mm\\/month)":55.34,\n    "Wind U (m s**-1)":1.09,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":1.1,\n    "Wind Direction (\\u00b0)":277.86\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.9,\n    "Total Precipitation (mm\\/month)":29.56,\n    "Wind U (m s**-1)":0.57,\n    "Wind V (m s**-1)":0.18,\n    "Wind Speed (m\\/s)":0.6,\n    "Wind Direction (\\u00b0)":252.85\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.33,\n    "Total Precipitation (mm\\/month)":67.89,\n    "Wind U (m s**-1)":1.25,\n    "Wind V (m s**-1)":0.97,\n    "Wind Speed (m\\/s)":1.59,\n    "Wind Direction (\\u00b0)":232.2\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":4.66,\n    "Total Precipitation (mm\\/month)":56.22,\n    "Wind U (m s**-1)":0.48,\n    "Wind V (m s**-1)":0.59,\n    "Wind Speed (m\\/s)":0.76,\n    "Wind Direction (\\u00b0)":219.37\n  }\n]', 'simulation3': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.75,\n    "Total Precipitation (mm\\/month)":63.78,\n    "Wind U (m s**-1)":1.26,\n    "Wind V (m s**-1)":0.99,\n    "Wind Speed (m\\/s)":1.6,\n    "Wind Direction (\\u00b0)":231.88\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":3.35,\n    "Total Precipitation (mm\\/month)":68.94,\n    "Wind U (m s**-1)":2.18,\n    "Wind V (m s**-1)":1.63,\n    "Wind Speed (m\\/s)":2.72,\n    "Wind Direction (\\u00b0)":233.24\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.96,\n    "Total Precipitation (mm\\/month)":65.51,\n    "Wind U (m s**-1)":2.25,\n    "Wind V (m s**-1)":0.78,\n    "Wind Speed (m\\/s)":2.38,\n    "Wind Direction (\\u00b0)":250.93\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.49,\n    "Total Precipitation (mm\\/month)":43.15,\n    "Wind U (m s**-1)":1.62,\n    "Wind V (m s**-1)":0.47,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":253.9\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":10.04,\n    "Total Precipitation (mm\\/month)":36.43,\n    "Wind U (m s**-1)":0.16,\n    "Wind V (m s**-1)":-0.19,\n    "Wind Speed (m\\/s)":0.25,\n    "Wind Direction (\\u00b0)":320.22\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":14.12,\n    "Total Precipitation (mm\\/month)":78.03,\n    "Wind U (m s**-1)":0.03,\n    "Wind V (m s**-1)":-0.61,\n    "Wind Speed (m\\/s)":0.62,\n    "Wind Direction (\\u00b0)":357.53\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":18.46,\n    "Total Precipitation (mm\\/month)":73.02,\n    "Wind U (m s**-1)":0.95,\n    "Wind V (m s**-1)":-0.28,\n    "Wind Speed (m\\/s)":0.99,\n    "Wind Direction (\\u00b0)":286.67\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":20.96,\n    "Total Precipitation (mm\\/month)":52.22,\n    "Wind U (m s**-1)":1.1,\n    "Wind V (m s**-1)":-0.37,\n    "Wind Speed (m\\/s)":1.16,\n    "Wind Direction (\\u00b0)":288.77\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":21.41,\n    "Total Precipitation (mm\\/month)":25.7,\n    "Wind U (m s**-1)":0.96,\n    "Wind V (m s**-1)":-0.34,\n    "Wind Speed (m\\/s)":1.02,\n    "Wind Direction (\\u00b0)":289.59\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.65,\n    "Total Precipitation (mm\\/month)":45.41,\n    "Wind U (m s**-1)":0.78,\n    "Wind V (m s**-1)":0.12,\n    "Wind Speed (m\\/s)":0.79,\n    "Wind Direction (\\u00b0)":261.07\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.78,\n    "Total Precipitation (mm\\/month)":31.58,\n    "Wind U (m s**-1)":0.41,\n    "Wind V (m s**-1)":0.39,\n    "Wind Speed (m\\/s)":0.57,\n    "Wind Direction (\\u00b0)":226.62\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.08,\n    "Total Precipitation (mm\\/month)":52.9,\n    "Wind U (m s**-1)":1.35,\n    "Wind V (m s**-1)":1.16,\n    "Wind Speed (m\\/s)":1.77,\n    "Wind Direction (\\u00b0)":229.34\n  }\n]', 'simulation5': '[\n  {\n    "Temperature (\\u00b0C)":0.47,\n    "Total Precipitation (mm\\/month)":10.96,\n    "Wind U (m s**-1)":0.16,\n    "Wind V (m s**-1)":0.01,\n    "Wind Speed (m\\/s)":0.13,\n    "Wind Direction (\\u00b0)":2.41\n  },\n  {\n    "Temperature (\\u00b0C)":0.01,\n    "Total Precipitation (mm\\/month)":10.77,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":0.22,\n    "Wind Speed (m\\/s)":0.3,\n    "Wind Direction (\\u00b0)":-1.38\n  },\n  {\n    "Temperature (\\u00b0C)":2.95,\n    "Total Precipitation (mm\\/month)":3.98,\n    "Wind U (m s**-1)":1.29,\n    "Wind V (m s**-1)":0.79,\n    "Wind Speed (m\\/s)":1.48,\n    "Wind Direction (\\u00b0)":18.79\n  },\n  {\n    "Temperature (\\u00b0C)":2.76,\n    "Total Precipitation (mm\\/month)":4.31,\n    "Wind U (m s**-1)":-0.32,\n    "Wind V (m s**-1)":0.59,\n    "Wind Speed (m\\/s)":-0.1,\n    "Wind Direction (\\u00b0)":-38.81\n  },\n  {\n    "Temperature (\\u00b0C)":1.52,\n    "Total Precipitation (mm\\/month)":2.68,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":0.13,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":28.49\n  },\n  {\n    "Temperature (\\u00b0C)":-0.56,\n    "Total Precipitation (mm\\/month)":-4.95,\n    "Wind U (m s**-1)":-0.2,\n    "Wind V (m s**-1)":-0.14,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":40.02\n  },\n  {\n    "Temperature (\\u00b0C)":0.66,\n    "Total Precipitation (mm\\/month)":5.58,\n    "Wind U (m s**-1)":-0.25,\n    "Wind V (m s**-1)":-0.06,\n    "Wind Speed (m\\/s)":-0.21,\n    "Wind Direction (\\u00b0)":8.38\n  },\n  {\n    "Temperature (\\u00b0C)":0.47,\n    "Total Precipitation (mm\\/month)":-1.4,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":-0.31,\n    "Wind Direction (\\u00b0)":8.82\n  },\n  {\n    "Temperature (\\u00b0C)":-0.23,\n    "Total Precipitation (mm\\/month)":8.67,\n    "Wind U (m s**-1)":0.08,\n    "Wind V (m s**-1)":0.26,\n    "Wind Speed (m\\/s)":0.02,\n    "Wind Direction (\\u00b0)":-14.26\n  },\n  {\n    "Temperature (\\u00b0C)":0.58,\n    "Total Precipitation (mm\\/month)":-1.51,\n    "Wind U (m s**-1)":0.03,\n    "Wind V (m s**-1)":-0.01,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":2.64\n  },\n  {\n    "Temperature (\\u00b0C)":0.41,\n    "Total Precipitation (mm\\/month)":35.98,\n    "Wind U (m s**-1)":0.97,\n    "Wind V (m s**-1)":0.49,\n    "Wind Speed (m\\/s)":1.03,\n    "Wind Direction (\\u00b0)":22.24\n  },\n  {\n    "Temperature (\\u00b0C)":-2.34,\n    "Total Precipitation (mm\\/month)":11.03,\n    "Wind U (m s**-1)":-0.06,\n    "Wind V (m s**-1)":-0.74,\n    "Wind Speed (m\\/s)":-0.67,\n    "Wind Direction (\\u00b0)":17.1\n  }\n]', 'simulation6': '[\n  {\n    "Temperature (\\u00b0C)":0.65,\n    "Total Precipitation (mm\\/month)":9.33,\n    "Wind U (m s**-1)":-0.24,\n    "Wind V (m s**-1)":-0.05,\n    "Wind Speed (m\\/s)":-0.23,\n    "Wind Direction (\\u00b0)":-3.45\n  },\n  {\n    "Temperature (\\u00b0C)":2.53,\n    "Total Precipitation (mm\\/month)":14.04,\n    "Wind U (m s**-1)":0.86,\n    "Wind V (m s**-1)":0.58,\n    "Wind Speed (m\\/s)":1.03,\n    "Wind Direction (\\u00b0)":1.64\n  },\n  {\n    "Temperature (\\u00b0C)":3.25,\n    "Total Precipitation (mm\\/month)":30.34,\n    "Wind U (m s**-1)":2.04,\n    "Wind V (m s**-1)":0.48,\n    "Wind Speed (m\\/s)":2.01,\n    "Wind Direction (\\u00b0)":35.7\n  },\n  {\n    "Temperature (\\u00b0C)":2.33,\n    "Total Precipitation (mm\\/month)":-4.36,\n    "Wind U (m s**-1)":0.57,\n    "Wind V (m s**-1)":0.45,\n    "Wind Speed (m\\/s)":0.64,\n    "Wind Direction (\\u00b0)":-15.02\n  },\n  {\n    "Temperature (\\u00b0C)":2.09,\n    "Total Precipitation (mm\\/month)":-7.17,\n    "Wind U (m s**-1)":-0.27,\n    "Wind V (m s**-1)":0.15,\n    "Wind Speed (m\\/s)":-0.29,\n    "Wind Direction (\\u00b0)":12.0\n  },\n  {\n    "Temperature (\\u00b0C)":-0.05,\n    "Total Precipitation (mm\\/month)":16.23,\n    "Wind U (m s**-1)":-0.23,\n    "Wind V (m s**-1)":-0.39,\n    "Wind Speed (m\\/s)":0.28,\n    "Wind Direction (\\u00b0)":47.23\n  },\n  {\n    "Temperature (\\u00b0C)":1.54,\n    "Total Precipitation (mm\\/month)":8.89,\n    "Wind U (m s**-1)":-0.17,\n    "Wind V (m s**-1)":0.14,\n    "Wind Speed (m\\/s)":-0.21,\n    "Wind Direction (\\u00b0)":-3.88\n  },\n  {\n    "Temperature (\\u00b0C)":1.27,\n    "Total Precipitation (mm\\/month)":0.2,\n    "Wind U (m s**-1)":-0.38,\n    "Wind V (m s**-1)":-0.25,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":14.17\n  },\n  {\n    "Temperature (\\u00b0C)":1.11,\n    "Total Precipitation (mm\\/month)":-20.97,\n    "Wind U (m s**-1)":-0.05,\n    "Wind V (m s**-1)":0.07,\n    "Wind Speed (m\\/s)":-0.06,\n    "Wind Direction (\\u00b0)":-2.53\n  },\n  {\n    "Temperature (\\u00b0C)":0.33,\n    "Total Precipitation (mm\\/month)":14.34,\n    "Wind U (m s**-1)":0.24,\n    "Wind V (m s**-1)":-0.07,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":10.86\n  },\n  {\n    "Temperature (\\u00b0C)":0.86,\n    "Total Precipitation (mm\\/month)":-0.33,\n    "Wind U (m s**-1)":0.13,\n    "Wind V (m s**-1)":-0.09,\n    "Wind Speed (m\\/s)":0.01,\n    "Wind Direction (\\u00b0)":16.66\n  },\n  {\n    "Temperature (\\u00b0C)":0.08,\n    "Total Precipitation (mm\\/month)":7.71,\n    "Wind U (m s**-1)":0.81,\n    "Wind V (m s**-1)":-0.17,\n    "Wind Speed (m\\/s)":0.34,\n    "Wind Direction (\\u00b0)":27.07\n  }\n]'}, 'content_message': '\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2020-2029. The nextGEMS pre-final simulations for years 2030x..This simulation serves as a historical reference to extract its values from other simulations. :\n {simulation1}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2030-2039. The nextGEMS pre-final simulations for years 2030x. :\n {simulation2}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2040-2049. The nextGEMS pre-final simulations for years 2040x. :\n {simulation3}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2030-2039 compared to the simulation for 2020-2029. :\n {simulation5}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2040-2049 compared to the simulation for 2020-2029. :\n {simulation6}'}
2025-02-27 13:34:42 - climsight_engine - INFO - zero_agent_response: {'input_params': {'elevation': '36.0', 'soil': 'Cambisols', 'biodiv': 'Pristurus', 'distance_to_coastline': '143240.00374445863', 'nat_hazards':        year          disastertype
13415  2002                 storm
13428  2006                 storm
13434  2010                 storm
33496  2003  extreme temperature 
33517  2006  extreme temperature 
33528  2009  extreme temperature 
33536  2009  extreme temperature 
33552  2012  extreme temperature 
33569  2012  extreme temperature , 'population':     Time  TotalPopulationAsOf1July  PopulationDensity  PopulationGrowthRate
0   1980              77786.703000         223.165900             -0.145000
1   1990              78072.678100         223.986330              0.237400
2   2000              80995.587100         232.372000              0.241300
3   2010              81294.847800         233.230560             -0.021600
4   2020              82291.120600         236.088820              0.245600
5   2030              83132.605000         238.502990             -0.082500
6   2040              81965.316200         235.154110             -0.195300
7   2050              80013.692600         229.555010             -0.288400
8   2060              77399.456100         222.054900             -0.362600
9   2070              74806.608900         214.616160             -0.299400
10  2080              72785.056300         208.816440             -0.266400
11  2090              70893.830000         203.390610             -0.247800
12  2100              69481.298222         199.338133             -0.171333
13  2101                       NaN                NaN                   NaN}, 'content_message': 'Elevation above sea level: {elevation} \nCurrent soil type: {soil} \nOccuring species: {biodiv} \nDistance to the closest coastline: {distance_to_coastline} \nNatural hazards: {nat_hazards} \nPopulation in Germany data: {population} \n'}
2025-02-27 13:34:43 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:34:43 - root - INFO - Rendered RAG response: Information specific to growing tomatoes in Berlin, Germany, is not directly provided in the provided content. However, tomatoes generally require a warm climate, plenty of sunlight, and well-drained soil with good fertility. In a city like Berlin, which experiences a temperate seasonal climate, growing tomatoes might require greenhouse cultivation or starting them indoors to extend their growing season. This information is a general guideline and not specific to Berlin.
2025-02-27 13:34:43 - climsight_engine - INFO - ipcc_rag_agent_response: Information specific to growing tomatoes in Berlin, Germany, is not directly provided in the provided content. However, tomatoes generally require a warm climate, plenty of sunlight, and well-drained soil with good fertility. In a city like Berlin, which experiences a temperate seasonal climate, growing tomatoes might require greenhouse cultivation or starting them indoors to extend their growing season. This information is a general guideline and not specific to Berlin.
2025-02-27 13:34:44 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:34:44 - root - INFO - Rendered RAG response: In Germany, contamination of tomatoes with Salmonella can occur both before harvest (through faecal matter, irrigation water, dust, insects, etc.) and after harvest (through harvesting equipment, transport containers, insects, dust, rinsing water, ice, transport vehicles, processing equipment). Climate change, leading to higher temperatures, can increase the risk of Salmonella growth, given that warmer temperatures favor the development of Salmonella. It is essential to ensure proper hygiene and care in agricultural practices and storage to minimize contamination risks.
2025-02-27 13:34:44 - climsight_engine - INFO - general_rag_agent_response: In Germany, contamination of tomatoes with Salmonella can occur both before harvest (through faecal matter, irrigation water, dust, insects, etc.) and after harvest (through harvesting equipment, transport containers, insects, dust, rinsing water, ice, transport vehicles, processing equipment). Climate change, leading to higher temperatures, can increase the risk of Salmonella growth, given that warmer temperatures favor the development of Salmonella. It is essential to ensure proper hygiene and care in agricultural practices and storage to minimize contamination risks.
2025-02-27 13:34:44 - climsight_engine - INFO - combine_agent in work
2025-02-27 13:34:44 - climsight_engine - INFO - Wikipedia_tool_reponse: 
2025-02-27 13:34:44 - climsight_engine - INFO - Ecocrop_search_response: 
2025-02-27 13:35:04 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:35:04 - climsight_engine - INFO - Final_answer: Growing tomatoes in Berlin, Germany, particularly in the urban setting near Berlin Hauptbahnhof, involves navigating the challenges and opportunities presented by its temperate climate and urban environment. Here's a comprehensive look into the factors influencing tomato cultivation in this location:

### Climate Considerations

1. **Temperature:** Tomatoes thrive in warm conditions, typically requiring daytime temperatures between 20°C to 30°C. The climate data for Berlin shows peak monthly temperatures from June to September, with averages reaching up to around 20.3°C in September (2020-2029). Future climate projections (2040-2049) suggest a slight increase, with September averaging 21.41°C. This warming trend could extend the useful growing season slightly, but marginally increased extreme temperatures (expected as part of climate change) could affect plant stress and productivity if not managed properly.

2. **Precipitation:** Adequate water supply is crucial for tomato growth. Berlin's average monthly precipitation during the key growing months (June-August) ranges from 52.02 mm to 78.03 mm in future predictions. While this provides a consistent water source, ensuring good drainage and possibly supplementary irrigation during dry spells is advisable to prevent waterlogging and manage inconsistent rainfall patterns.

3. **Wind and Light:** Average wind speeds suggest relatively calm conditions which are favorable, though higher winds can lead to plant damage when they exceed thresholds. Tomatoes need ample sunlight, which is usually sufficient in Berlin during the growing season, but urban shading from nearby infrastructure can affect exposure; hence site choice or strategic placement of plants or planters is important.

### Soil and Location

- **Soil Type:** The presence of Cambisols, moderately fertile soil, supports tomato growth when enriched properly with organic matter and nutrients. Urban soil conditions may vary due to contamination or previous uses, requiring soil testing and conditioning with appropriate amendments.
  
- **Urban Constraints:** The railway platform location adds complexity due to potential space limitations, and urban constraints such as pollution from trains could affect the air quality. Utilizing raised beds or containers with clean, imported soil can mitigate soil quality issues, and employing vertical gardening techniques can maximize space efficiency.

### Environmental and Health Factors

- **Contamination Risks:** As warmer temperatures can facilitate the growth of pathogens like Salmonella, maintaining strict hygiene from soil preparation to harvesting is essential to minimize contamination risks. This includes using clean water sources for irrigation and maintaining plant cleanliness.

### Recommendations

In conclusion, while the urban and climatic backdrop of Berlin presents both challenges and opportunities for growing tomatoes, careful planning and execution of cultivation strategies can yield fruitful results:

- **Greenhouses or Polytunnels:** These could mitigate weather extremes and provide controlled conditions, including optimal temperatures and humidity levels, especially essential in extending the growing season at both ends.
  
- **Vertical Gardening:** To cope with urban space restrictions, vertical gardens or trellising systems can optimize growing spaces and help manage air flow and sunlight.

- **Management Practices:** Regular pruning, mulching to retain soil moisture, and intelligent watering systems that respond to real-time weather conditions can help in maintaining a healthy balance conducive to tomato growth.

By adhering to these tailored strategies, tomato cultivation at this location can be both productive and sustainable, leveraging Berlin's evolving climate profile and urban resources efficiently.
2025-02-27 13:35:34 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:35:34 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-27 13:35:34 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-27 13:35:34 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-27 13:35:34 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-27 13:35:34 - climsight_engine - INFO - start agent_request
2025-02-27 13:35:36 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:35:39 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:35:39 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-27 13:35:39 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-27 13:35:39 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-27 13:35:39 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-27 13:35:39 - climsight_engine - INFO - start agent_request
2025-02-27 13:35:41 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:41 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:48 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:35:48 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-27 13:35:48 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-27 13:35:48 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-27 13:35:48 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-27 13:35:48 - climsight_engine - INFO - start agent_request
2025-02-27 13:35:49 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:35:49 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:07 - __main__ - INFO - reading config from: config.yml
2025-02-27 13:36:07 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-27 13:36:07 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-27 13:36:07 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-27 13:36:07 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-27 13:36:07 - climsight_engine - INFO - start agent_request
2025-02-27 13:36:08 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:36:08 - climsight_engine - INFO - IPCC RAG agent in work.
2025-02-27 13:36:08 - climsight_engine - INFO - General RAG agent in work.
2025-02-27 13:36:08 - root - ERROR - list index out of range. Continue with: current_land_use = None
Traceback (most recent call last):
  File "/Users/ikuznets/work/projects/climsight/code/climsight/src/climsight/climsight_engine.py", line 669, in zero_rag_agent
    current_land_use = land_use_data["elements"][0]["tags"]["landuse"]
                       ~~~~~~~~~~~~~~~~~~~~~~~~~^^^
IndexError: list index out of range
2025-02-27 13:36:08 - climsight_engine - INFO - zero_agent_response: {'input_params': {'elevation': '36.0', 'soil': 'Cambisols', 'biodiv': 'Pristurus', 'distance_to_coastline': '143240.00374445863', 'nat_hazards':        year          disastertype
13415  2002                 storm
13428  2006                 storm
13434  2010                 storm
33496  2003  extreme temperature 
33517  2006  extreme temperature 
33528  2009  extreme temperature 
33536  2009  extreme temperature 
33552  2012  extreme temperature 
33569  2012  extreme temperature , 'population':     Time  TotalPopulationAsOf1July  PopulationDensity  PopulationGrowthRate
0   1980              77786.703000         223.165900             -0.145000
1   1990              78072.678100         223.986330              0.237400
2   2000              80995.587100         232.372000              0.241300
3   2010              81294.847800         233.230560             -0.021600
4   2020              82291.120600         236.088820              0.245600
5   2030              83132.605000         238.502990             -0.082500
6   2040              81965.316200         235.154110             -0.195300
7   2050              80013.692600         229.555010             -0.288400
8   2060              77399.456100         222.054900             -0.362600
9   2070              74806.608900         214.616160             -0.299400
10  2080              72785.056300         208.816440             -0.266400
11  2090              70893.830000         203.390610             -0.247800
12  2100              69481.298222         199.338133             -0.171333
13  2101                       NaN                NaN                   NaN}, 'content_message': 'Elevation above sea level: {elevation} \nCurrent soil type: {soil} \nOccuring species: {biodiv} \nDistance to the closest coastline: {distance_to_coastline} \nNatural hazards: {nat_hazards} \nPopulation in Germany data: {population} \n'}
2025-02-27 13:36:08 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 13:36:09 - rag - INFO - Chunks returned from RAG: {'context': "2018), per capita catch (kg/person/year) (FAO, 2018b), percent\n38 reliance on fish for micronutrients, and percent consumption per household (Golden et al., 2016). Z-scores of\n39 each metric were averaged for each country to create a composite index describing `current dependence on\n40 freshwater fish' for each country with darker blue colours indicating higher dependence. (b\xadc) Projected\n41 concentrations (numbers) of vulnerable freshwater fishery species averaged within freshwater ecoregions\n42 under >2°C global warming (b) and >4°C global warming (c) estimated from recent past (1961\xad1992) to the\n43 end of the 21st century (2071 to 2100) (Nyboer et al., 2019). Numbers of vulnerable fish species translate to\n44 an average of 55\xad68% vulnerable at >2°C and 77\xad97% vulnerable at <4°C global warming. Darker reds\n45 indicate higher concentrations of vulnerable fish species. (d\xade) Countries (in green) that have an overlap\n46 between high dependence on freshwater fish and high concentrations of fishery species that are vulnerable to\n47 climate change under two warming scenarios. Inland fisheries (panels f\xadj) comparing countries' current\n48 percent dependence on marine foods for nutrition compared with projected change in maximum catch\n49 potential (MCP) from marine fisheries. (f) The percentage of animal sources foods consumed that originate\n50 from a marine environment. Countries with higher dependence are indicated by darker shades of blue\n51 (Golden et al., 2016). (g\xadh) Projected percent change in maximum catch potential (MCP) of marine fisheries\n52 under 1.6°C global warming (g) and >4°C global warming (h) from recent past (1986\xad2005) to end of 21st\n53 century (2081-2100) in countries' Exclusive Economic Zones (EEZs) (Cheung William et al., 2016). Darker\n54 red indicates greater percent reduction [negative values]. (i\xadj) Countries (in green) that have overlap between\n55 high nutritional dependence and high reduction in MCP under two warming scenarios. {Figure 9.25,\n\nGlobal\n38 Warming Levels (GLWs), and the relatively high uncertainties associated with future irrigation trends for the\n39 second half of the century (see e.g. Viviroli et al., 2020), assessment of risks associated with GLWs greater\n40 than 2.0°C GWL was not conducted. {Figure CCP5.6}\n\n41\n\n42 Figure AI.39: The effect of regional sea level rise on extreme sea level events at coastal locations.\n43 (a) Schematic illustration of extreme sea level events and their average recurrence in the recent past (1986\xad\n44 2005) and the future. As a consequence of mean sea level rise, local sea levels that historically occurred once\n45 per century (historical centennial events, HCEs) are projected to recur more frequently in the future. (b) The\n46 year in which HCEs are expected to recur once per year on average under RCP8.5 and RCP2.6, at the 439\n47 individual coastal locations where the observational record is sufficient. The absence of a circle indicates an\n48 inability to perform an assessment due to a lack of data but does not indicate absence of exposure and risk.\n49 The darker the circle, the earlier this transition is expected. The likely range is ±10 years for locations where\n50 this transition is expected before 2100. White circles (33% of locations under RCP2.6 and 10% under\n51 RCP8.5) indicate that HCEs are not expected to recur once per year before 2100. (c) An indication at which\n52 locations this transition of HCEs to annual events is projected to occur more than 10 years later under\n53 RCP2.6 compared to RCP8.5. As the scenarios lead to small differences by 2050 in many locations results\n54 are not shown here for RCP4.5 but they are available in Chapter 4. {4.2.3, Figure 4.10, Figure 4.12}\n\n55\n\n56 Figure AI.40: Relative trends in projected regional shoreline change.\n\nDo Not Cite, Quote or Distribute  AI-67    Total pages: 74\nFINAL DRAFT                       Annex I  IPCC WGII Sixth Assessment Report\n\n 1 Advance/retreat relative to 2010. Frequency distributions of\n\nTittensor et al., 2018; Tittensor et al., 2021), forced with standardised outputs from two CMIP6 Earth\n56 System Models. {3.4.3; Fig. 3.21}\n\nDo Not Cite, Quote or Distribute  AI-62    Total pages: 74\nFINAL DRAFT                       Annex I  IPCC WGII Sixth Assessment Report\n\n 1\n\n 2 Figure AI.12: Projected change in marine zooplankton biomass.\n 3 Simulated global biomass changes of zooplankton. In the multi-model mean (solid lines) and very likely\n 4 range (envelope) over 2000\xad2100 relative to 1995\xad2014, for SSP1-2.6 and SSP5-8.5. Spatial patterns of\n 5 simulated change by 2090\xad2099 are calculated relative to 1995\xad2014 for SSP1-2.6 and SSP5-8.5.\n 6 Confidence intervals can be affected by the number of models available for the Coupled Model\n 7 Intercomparison Project 6 (CMIP6) scenarios and for different variables.The ensemble projections of global\n 8 changes in zooplankton biomasses updated based on Kwiatkowski et al. (2019) include, under SSP1-2.6 and\n 9 SSP5-8.5, respectively, a total of nine and 10 CMIP6 Earth System Models (ESMs). {3.4.3.4., Figure 3.21}\n\n10\n\n11 Figure AI.13: Spatial patterns of simulated change in total phytoplankton biomass.\n12 Simulated global biomass changes of surface phytoplankton. In the multi-model mean (solid lines) and very\n13 likely range (envelope) over 2000\xad2100 relative to 1995\xad2014, for SSP1-2.6 and SSP5-8.5. Spatial patterns\n14 of simulated change by 2090\xad2099 are calculated relative to 1995\xad2014 for SSP1-2.6 and SSP5-8.5.\n15 Confidence intervals can be affected by the number of models available for the Coupled Model\n16 Intercomparison Project 6 (CMIP6) scenarios and for different variables. The ensemble projections of global\n17 changes in phytoplankton biomasses updated based on Kwiatkowski et al. (2019) include, under SSP1-2.6\n18 and SSP5-8.5, respectively, a total of nine and 10 CMIP6 Earth System Models (ESMs). {3.4.3.4., Figure\n19 3.21}\n\n20\n\n21 Figure AI.14: Spatial patterns of simulated change in total benthic animal\n\nbecome costlier under climate change\n11   (medium confidence). {2.4.2.7.3, 2.5.1.4, 2.5.2.7, 3.5.5, 4.2.4, 4.2.5, 4.3.1, 5.4.1, 5.4.3, 5.5.2, 5.9.4, 5.12,\n12   11.3.1, 13.5.1, 14.5.4, 14.5.6, CCB ILLNESS, CCB MOVING PLATE, CCB COVID}\n13\n14   TS.C.2.3 The ability of natural ecosystems to provide carbon storage and sequestration is increasingly\n15   impacted by heat, wildfire, droughts, loss and degradation of vegetation from land use, and other\n16   impacts (high confidence). Limiting the global temperature increase to 1.5°C, compared to 2.0°C, could\n17   reduce projected permafrost CO2 losses by 2100 by 24.2 GtC (low confidence). Temperature rise of 4ºC by\n18   2100 is projected to increase global burned area 50-70% and fire frequency by ~30%, potentially\n19   releasing 11-200 GtC from the Arctic alone (medium confidence). Changes in plankton community\n20   structure and productivity are projected to reduce carbon sequestration at depth (low to medium confidence).\n21   {2.5.2, 2.5.3, 2.5.4, Figure 2.11, Table 2.5, 3.4.2, 3.4.3, 3.4.2, 4.2.4, 13.3.1, 13.4.1, Box 14.7, Box 3.4}\n22\n23   TS.C.2.4 Climate change impacts on marine ecosystems are projected to lead to profound changes and\n24   irreversible losses in many regions, with negative consequences for human ways of life, economy and\n25   cultural identity (medium confidence). For example, by 2100, 18.8% ± 19.0% to 38.9% ± 9.4% of the\n26   ocean will very likely undergo a change of more than 20 days (advances and delays) in the start of the\n27   phytoplankton growth period under SSP1-2.6 and SSP5-8.5, respectively (low confidence). This altered\n28   timing increases the risk of temporal mismatches between plankton blooms and fish spawning seasons\n29   (medium to high confidence) and increases the risk of fish recruitment failure for species with restricted\n30   spawning locations, especially in mid-to-high latitudes of the northern hemisphere (low confidence) but\n31   provide short-term opportunities to countries", 'location': 'Address: railway: Berlin Hauptbahnhof (Tief), road: Europaplatz, quarter: Europacity, suburb: Moabit, borough: Mitte, city: Berlin, ISO3166-2-lvl4: DE-BE, postcode: 10557, country: Germany, country_code: de', 'question': 'provide temperature and salinity for surface water'}
2025-02-27 13:36:09 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 13:36:09 - rag - INFO - Chunks returned from RAG: {'context': 'https://doi.org/10.1175/JCLI-D-16-0396.1.\nGood, S.A.; Martin, M.J.; Rayner, N.A. EN4: Quality Controlled Ocean Temperature and\nSalinity Profiles and Monthly Objective Analyses with Uncertainty Estimates.\nJournal of Geophysical Research: Oceans 2013, 118 (12), 6704–6716. https://doi.\norg/10.1002/2013JC009067.\nHong, L.; Xu, F.; Zhou, W. et al. Development of a Global Gridded Argo Data Set with Barnes\nSuccessive Corrections. Journal of Geophysical Research: Oceans 2017, 122 (2), 866–889.\nhttps://doi.org/10.1002/2016JC012285.\nHosoda, S.; Ohira, T.; Nakamura, T. A Monthly Mean Dataset of Global Oceanic Temperature\nand Salinity Derived from Argo Float Observations. JAMSTEC Report of\nResearch and Development, 2008, 8, 47–59. https://www.jstage.jst.go.jp/article/\njamstecr/8/0/8_0_47/_article.\nKuusela M.; Stein, M.L. Locally Stationary Spatio-temporal Interpolation of Argo Profiling Float\nData. Proceedings of the Royal Society A 2018, 474, 20180400. http://dx.doi.org/10.1098/\nrspa.2018.0400.\nRoemmich, D.; Gilson, J. The 2004–2008 Mean and Annual Cycle of Temperature, Salinity, and Steric\nHeight in the Global Ocean from the Argo Program. Progress in Oceanography 2009, 82\n(2), 81–100. https://doi.org/10.1016/j.pocean.2009.03.004.\nRoemmich, D.; Church, J.; Gilson, J. et al. Unabated Planetary Warming and its Ocean Structure\nSince 2006. Nature Climate Change 2015, 5, 240–245. https://doi.org/10.1038/\nnclimate2513.\n\nIN ADDITION, DATA USED UP TO 2020:\nChurch, J.A.; White, N.J.; Konikow, L.F. et al. Revisiting the Earth’s Sea-level and Energy\nBudgets from 1961 to 2008. Geophysical Research Letters 2011, 38 (18). https://doi.\norg/10.1029/2011GL048794.\nDomingues, C.M.; Church, J.A.; White, N.J. et al. Improved Estimates of Upper-ocean Warming\nand Multi-decadal Sea-level Rise. Nature 2008, 453, 1090–1093. https://doi.org/10.1038/\nnature07080.\nLi, Y.; Church, J.A.; McDougall, T.J. et al. Sensitivity of Observationally Based Estimates of Ocean\nHeat Content and Thermal Expansion to\n\nbased on satellite altimetry. The black line is the\nbest estimate, and the grey shaded area indicates uncertainty.\nRed and blue annotations indicate the average rate of sea-level\nrise during three decades of the record as indicated.\nSource: AVISO altimetry\n\n\x0cOcean\n\n7\n\nJanuary–February–March average\n\nApril–May–June average\n\n60°N\n\n60°N\n\n30°N\n\n30°N\n\n0°\n\n0°\n\n30°S\n\n30°S\n60°S\n\n60°S\n60°E\n\n180°\n\n60°W\n\n0°\n\n60°E\n\nJuly–August–September average\n\n180°\n\n60°W\n\n0°\n\nOctober–November–December average\n60°N\n\n60°N\n30°N\n\n30°N\n\n0°\n\n0°\n\n30°S\n\n30°S\n60°S\n\n60°S\n60°E\n\n180°\n\n–0.3\n\n60°W\n\n–0.2\n\n0°\n\n60°E\n\n–0.1\n\n0.0\n\n0.1\n\n180°\n\n0.2\n\n60°W\n\n0°\n\n0.3 (m)\n\nFigure 7. Three-month averages of altimetry-based sea-level anomalies (relative to the 1993–2012 average, which is the product\nclimatology) for (top left) January–March 2023, (top right) April–June 2023, (bottom left) July–September 2023 and (bottom right)\nOctober–December 2023\nSource: Data downloaded from the Copernicus Marine Service\n\nthe early stages of the 2023 El Niño (see Short-term climate drivers) led to an increase in sea\nlevels relative to the long-term mean in the most eastern part of the tropical Pacific between\nApril and June. Between July and September, the El Niño signature was clearly visible, with\nabove-average sea levels from the mid-tropical Pacific to the coasts of Central and South\nAmerica. Above-average sea levels were also observed in the tropical and North-East Atlantic,\nassociated with the anomalous warming in these areas during the northern hemisphere\nsummer. From October to the end of the year, the El Niño pattern continued to develop.\nThe shift to the positive phase of the Indian Ocean Dipole (IOD) led to higher-than-average\nsea levels in the western Indian Ocean and lower-than-average sea levels in the East (see\nShort-term climate drivers).\n\n\x0cOcean\n\n8\n\nMARINE HEATWAVES AND COLD SPELLS\nAs with heatwaves and cold spells on land, marine heatwaves and cold spells are prolonged\nperiods of extreme high or low temperatures in\n\n2008, 453, 1090–1093. https://doi.org/10.1038/\nnature07080.\nLi, Y.; Church, J.A.; McDougall, T.J. et al. Sensitivity of Observationally Based Estimates of Ocean\nHeat Content and Thermal Expansion to Vertical Interpolation Schemes. Geophysical\nResearch Letters 2022, 49 (24), e2022GL101079. https://doi.org/10.1029/2022GL101079.\nWijffels, S.; Roemmich, D.; Monselesan, D. et al. Ocean Temperatures Chronicle the Ongoing\nWarming of Earth. Nature Climate Change 2016, 6, 116–118. https://doi.org/10.1038/\nnclimate2924.\n\n36\n\n\x0cData sets and methods\n\nSEA LEVEL\nCopernicus Climate Change Service (C3S), 2018: Sea Level Daily Gridded Data from Satellite\nObservations for the Global Ocean from 1993 to Present. C3S Climate Data Store (CDS),\nhttps://doi.org/10.24381/cds.4c328c78.\nGMSL from CNES/+, https://www.aviso.altimetry.fr/en/data/products/ocean-indicators-products/\nmean-sea-level/data-acces.html#c12195.\n\nMARINE HEATWAVE AND MARINE COLD SPELL\nMarine heatwaves are categorized as moderate when the sea-surface temperature (SST) is\nabove the ninetieth percentile of the climatological distribution for five days or longer. The\nsubsequent categories are defined in respect of the difference between the SST and the\nclimatological distribution average: “strong”, “severe” or “extreme”, if that difference is,\nrespectively, more than two, three or four times the difference between the ninetieth percentile\nand the climatological distribution average.136\nMarine cold spell categories are analogous but counting days below the tenth percentile, except\nfor the “ice” category. This category is given to any marine cold spell when the threshold\nfor the occurrence on any given day of the event is below –1.7 °C.137 These are therefore\nconsidered to be conditions related to sea ice, and not extreme temperature fluctuations.\nThe baseline used for marine heatwaves and cold spells is 1982–2011, which is shifted by one\nyear from the standard normal period of 1981–2010 because the first full year of the\n\n(J/m2)\n\n0\n–0.25\n–0.50\n–0.75\n–1.00\n–1.25\n\n0\n\n6\n19\n\n5\n\n6\n19\n\n0\n\n7\n19\n\n5\n\n7\n19\n\n0\n\n8\n19\n\n5\n\n8\n19\n\n0\n\n9\n19\n\nYear\n\n5\n\n9\n19\n\n20\n\n00\n\n20\n\n05\n\n10\n20\n\n15\n20\n\n20\n\n20\n\nFigure 4. Global ocean heat\ncontent anomalies relative to the\n2005–2021 average for the\n0–2 000 m depth layer 1960–2023\n(orange). Ensemble mean time\nseries and ensemble standard\ndeviation (2-standard deviations,\nshaded) updated from Schuckmann\net al., 2023 (red); Cheng et al., 2017\n(green); Minière et al., 2023 (light\nblue); and Ishii et al., 2017 (dark\nblue).\nSource: Mercator Ocean\ninternational.\n\n\x0cOcean\n\n6\n\nAlthough ocean heat content has increased strongly through the entire water column, the\nrate of warming has not been the same everywhere. 23 The strongest warming in the upper\n2 000 m occurred in the Southern Ocean (60° S–35° S), North Atlantic (20° N–50° N) and South\nAtlantic (60° S–0° S) (see Figure 5). The Southern Ocean domain is the largest reservoir of\nheat, accounting for about 32% of the global ocean heat content increase in the upper 2 000 m\nsince 1958. 24 The Atlantic Ocean accounts for approximately 31% of the global 0–2 000 m\nocean heat content increase, and the Pacific Ocean for about 26%.\nW m–2\n2.0\n\n60°N\n\n1.5\n\nFigure 5. Observed upper 2000 m OHC\ntrend from 1958 to 2023.\nSource: Data updated from Cheng et al. 25\n\n1.0\n\n30°N\n\n0.5\n\nSome relatively small regions\nare cooling, including the\n–0.5\nsubpolar Nor th Atlantic\n30°S\n–1.0\nOcean, extending from near\n–1.5\nthe surface to a depth of over\n60°S\n800 m (also the only area to\n–2.0\nshow centennial cooling at\n60°E 120°E\n180° 120°W 60°W 0°\nthe surface). The contrasting\npattern of cooling (50° N–70°\nN) and warming (20° N–50° N) in the North Atlantic has been associated with a slowing of\nthe Atlantic Meridional Overturning Circulation and local interactions between the air and\nsea. 26 Other cooling regions include the North-West Pacific, the South-West Pacific and the\nSouth-West Indian Ocean.\n0\n\n0°\n\nSEA LEVEL\nIn 2023, global mean sea level reached a', 'location': 'Address: railway: Berlin Hauptbahnhof (Tief), road: Europaplatz, quarter: Europacity, suburb: Moabit, borough: Mitte, city: Berlin, ISO3166-2-lvl4: DE-BE, postcode: 10557, country: Germany, country_code: de', 'question': 'provide temperature and salinity for surface water'}
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2025-02-27 13:36:10 - climsight_engine - INFO - data_agent_response: {'input_params': {'simulation1': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.1,\n    "Total Precipitation (mm\\/month)":54.45,\n    "Wind U (m s**-1)":1.5,\n    "Wind V (m s**-1)":1.04,\n    "Wind Speed (m\\/s)":1.83,\n    "Wind Direction (\\u00b0)":235.33\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":0.82,\n    "Total Precipitation (mm\\/month)":54.9,\n    "Wind U (m s**-1)":1.32,\n    "Wind V (m s**-1)":1.05,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":231.6\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":-0.29,\n    "Total Precipitation (mm\\/month)":35.17,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":0.3,\n    "Wind Speed (m\\/s)":0.37,\n    "Wind Direction (\\u00b0)":215.23\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":3.16,\n    "Total Precipitation (mm\\/month)":47.51,\n    "Wind U (m s**-1)":1.05,\n    "Wind V (m s**-1)":0.02,\n    "Wind Speed (m\\/s)":1.05,\n    "Wind Direction (\\u00b0)":268.92\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":7.95,\n    "Total Precipitation (mm\\/month)":43.6,\n    "Wind U (m s**-1)":0.43,\n    "Wind V (m s**-1)":-0.34,\n    "Wind Speed (m\\/s)":0.54,\n    "Wind Direction (\\u00b0)":308.22\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":14.17,\n    "Total Precipitation (mm\\/month)":61.8,\n    "Wind U (m s**-1)":0.26,\n    "Wind V (m s**-1)":-0.22,\n    "Wind Speed (m\\/s)":0.34,\n    "Wind Direction (\\u00b0)":310.3\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":16.92,\n    "Total Precipitation (mm\\/month)":64.13,\n    "Wind U (m s**-1)":1.12,\n    "Wind V (m s**-1)":-0.42,\n    "Wind Speed (m\\/s)":1.2,\n    "Wind Direction (\\u00b0)":290.55\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":19.69,\n    "Total Precipitation (mm\\/month)":52.02,\n    "Wind U (m s**-1)":1.48,\n    "Wind V (m s**-1)":-0.12,\n    "Wind Speed (m\\/s)":1.48,\n    "Wind Direction (\\u00b0)":274.6\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.3,\n    "Total Precipitation (mm\\/month)":46.67,\n    "Wind U (m s**-1)":1.01,\n    "Wind V (m s**-1)":-0.41,\n    "Wind Speed (m\\/s)":1.08,\n    "Wind Direction (\\u00b0)":292.12\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.32,\n    "Total Precipitation (mm\\/month)":31.07,\n    "Wind U (m s**-1)":0.54,\n    "Wind V (m s**-1)":0.19,\n    "Wind Speed (m\\/s)":0.57,\n    "Wind Direction (\\u00b0)":250.21\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":10.92,\n    "Total Precipitation (mm\\/month)":31.91,\n    "Wind U (m s**-1)":0.28,\n    "Wind V (m s**-1)":0.48,\n    "Wind Speed (m\\/s)":0.56,\n    "Wind Direction (\\u00b0)":209.96\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.0,\n    "Total Precipitation (mm\\/month)":45.19,\n    "Wind U (m s**-1)":0.54,\n    "Wind V (m s**-1)":1.33,\n    "Wind Speed (m\\/s)":1.43,\n    "Wind Direction (\\u00b0)":202.27\n  }\n]', 'simulation2': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.57,\n    "Total Precipitation (mm\\/month)":65.41,\n    "Wind U (m s**-1)":1.66,\n    "Wind V (m s**-1)":1.05,\n    "Wind Speed (m\\/s)":1.96,\n    "Wind Direction (\\u00b0)":237.74\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":0.83,\n    "Total Precipitation (mm\\/month)":65.67,\n    "Wind U (m s**-1)":1.53,\n    "Wind V (m s**-1)":1.27,\n    "Wind Speed (m\\/s)":1.99,\n    "Wind Direction (\\u00b0)":230.22\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.66,\n    "Total Precipitation (mm\\/month)":39.15,\n    "Wind U (m s**-1)":1.5,\n    "Wind V (m s**-1)":1.09,\n    "Wind Speed (m\\/s)":1.85,\n    "Wind Direction (\\u00b0)":234.02\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.92,\n    "Total Precipitation (mm\\/month)":51.82,\n    "Wind U (m s**-1)":0.73,\n    "Wind V (m s**-1)":0.61,\n    "Wind Speed (m\\/s)":0.95,\n    "Wind Direction (\\u00b0)":230.11\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":9.47,\n    "Total Precipitation (mm\\/month)":46.28,\n    "Wind U (m s**-1)":0.09,\n    "Wind V (m s**-1)":-0.21,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":336.71\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":13.61,\n    "Total Precipitation (mm\\/month)":56.85,\n    "Wind U (m s**-1)":0.06,\n    "Wind V (m s**-1)":-0.36,\n    "Wind Speed (m\\/s)":0.37,\n    "Wind Direction (\\u00b0)":350.32\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":17.58,\n    "Total Precipitation (mm\\/month)":69.71,\n    "Wind U (m s**-1)":0.87,\n    "Wind V (m s**-1)":-0.48,\n    "Wind Speed (m\\/s)":0.99,\n    "Wind Direction (\\u00b0)":298.93\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":20.16,\n    "Total Precipitation (mm\\/month)":50.62,\n    "Wind U (m s**-1)":1.14,\n    "Wind V (m s**-1)":-0.27,\n    "Wind Speed (m\\/s)":1.17,\n    "Wind Direction (\\u00b0)":283.42\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.07,\n    "Total Precipitation (mm\\/month)":55.34,\n    "Wind U (m s**-1)":1.09,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":1.1,\n    "Wind Direction (\\u00b0)":277.86\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.9,\n    "Total Precipitation (mm\\/month)":29.56,\n    "Wind U (m s**-1)":0.57,\n    "Wind V (m s**-1)":0.18,\n    "Wind Speed (m\\/s)":0.6,\n    "Wind Direction (\\u00b0)":252.85\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.33,\n    "Total Precipitation (mm\\/month)":67.89,\n    "Wind U (m s**-1)":1.25,\n    "Wind V (m s**-1)":0.97,\n    "Wind Speed (m\\/s)":1.59,\n    "Wind Direction (\\u00b0)":232.2\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":4.66,\n    "Total Precipitation (mm\\/month)":56.22,\n    "Wind U (m s**-1)":0.48,\n    "Wind V (m s**-1)":0.59,\n    "Wind Speed (m\\/s)":0.76,\n    "Wind Direction (\\u00b0)":219.37\n  }\n]', 'simulation3': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.75,\n    "Total Precipitation (mm\\/month)":63.78,\n    "Wind U (m s**-1)":1.26,\n    "Wind V (m s**-1)":0.99,\n    "Wind Speed (m\\/s)":1.6,\n    "Wind Direction (\\u00b0)":231.88\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":3.35,\n    "Total Precipitation (mm\\/month)":68.94,\n    "Wind U (m s**-1)":2.18,\n    "Wind V (m s**-1)":1.63,\n    "Wind Speed (m\\/s)":2.72,\n    "Wind Direction (\\u00b0)":233.24\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.96,\n    "Total Precipitation (mm\\/month)":65.51,\n    "Wind U (m s**-1)":2.25,\n    "Wind V (m s**-1)":0.78,\n    "Wind Speed (m\\/s)":2.38,\n    "Wind Direction (\\u00b0)":250.93\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.49,\n    "Total Precipitation (mm\\/month)":43.15,\n    "Wind U (m s**-1)":1.62,\n    "Wind V (m s**-1)":0.47,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":253.9\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":10.04,\n    "Total Precipitation (mm\\/month)":36.43,\n    "Wind U (m s**-1)":0.16,\n    "Wind V (m s**-1)":-0.19,\n    "Wind Speed (m\\/s)":0.25,\n    "Wind Direction (\\u00b0)":320.22\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":14.12,\n    "Total Precipitation (mm\\/month)":78.03,\n    "Wind U (m s**-1)":0.03,\n    "Wind V (m s**-1)":-0.61,\n    "Wind Speed (m\\/s)":0.62,\n    "Wind Direction (\\u00b0)":357.53\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":18.46,\n    "Total Precipitation (mm\\/month)":73.02,\n    "Wind U (m s**-1)":0.95,\n    "Wind V (m s**-1)":-0.28,\n    "Wind Speed (m\\/s)":0.99,\n    "Wind Direction (\\u00b0)":286.67\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":20.96,\n    "Total Precipitation (mm\\/month)":52.22,\n    "Wind U (m s**-1)":1.1,\n    "Wind V (m s**-1)":-0.37,\n    "Wind Speed (m\\/s)":1.16,\n    "Wind Direction (\\u00b0)":288.77\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":21.41,\n    "Total Precipitation (mm\\/month)":25.7,\n    "Wind U (m s**-1)":0.96,\n    "Wind V (m s**-1)":-0.34,\n    "Wind Speed (m\\/s)":1.02,\n    "Wind Direction (\\u00b0)":289.59\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.65,\n    "Total Precipitation (mm\\/month)":45.41,\n    "Wind U (m s**-1)":0.78,\n    "Wind V (m s**-1)":0.12,\n    "Wind Speed (m\\/s)":0.79,\n    "Wind Direction (\\u00b0)":261.07\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.78,\n    "Total Precipitation (mm\\/month)":31.58,\n    "Wind U (m s**-1)":0.41,\n    "Wind V (m s**-1)":0.39,\n    "Wind Speed (m\\/s)":0.57,\n    "Wind Direction (\\u00b0)":226.62\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.08,\n    "Total Precipitation (mm\\/month)":52.9,\n    "Wind U (m s**-1)":1.35,\n    "Wind V (m s**-1)":1.16,\n    "Wind Speed (m\\/s)":1.77,\n    "Wind Direction (\\u00b0)":229.34\n  }\n]', 'simulation5': '[\n  {\n    "Temperature (\\u00b0C)":0.47,\n    "Total Precipitation (mm\\/month)":10.96,\n    "Wind U (m s**-1)":0.16,\n    "Wind V (m s**-1)":0.01,\n    "Wind Speed (m\\/s)":0.13,\n    "Wind Direction (\\u00b0)":2.41\n  },\n  {\n    "Temperature (\\u00b0C)":0.01,\n    "Total Precipitation (mm\\/month)":10.77,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":0.22,\n    "Wind Speed (m\\/s)":0.3,\n    "Wind Direction (\\u00b0)":-1.38\n  },\n  {\n    "Temperature (\\u00b0C)":2.95,\n    "Total Precipitation (mm\\/month)":3.98,\n    "Wind U (m s**-1)":1.29,\n    "Wind V (m s**-1)":0.79,\n    "Wind Speed (m\\/s)":1.48,\n    "Wind Direction (\\u00b0)":18.79\n  },\n  {\n    "Temperature (\\u00b0C)":2.76,\n    "Total Precipitation (mm\\/month)":4.31,\n    "Wind U (m s**-1)":-0.32,\n    "Wind V (m s**-1)":0.59,\n    "Wind Speed (m\\/s)":-0.1,\n    "Wind Direction (\\u00b0)":-38.81\n  },\n  {\n    "Temperature (\\u00b0C)":1.52,\n    "Total Precipitation (mm\\/month)":2.68,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":0.13,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":28.49\n  },\n  {\n    "Temperature (\\u00b0C)":-0.56,\n    "Total Precipitation (mm\\/month)":-4.95,\n    "Wind U (m s**-1)":-0.2,\n    "Wind V (m s**-1)":-0.14,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":40.02\n  },\n  {\n    "Temperature (\\u00b0C)":0.66,\n    "Total Precipitation (mm\\/month)":5.58,\n    "Wind U (m s**-1)":-0.25,\n    "Wind V (m s**-1)":-0.06,\n    "Wind Speed (m\\/s)":-0.21,\n    "Wind Direction (\\u00b0)":8.38\n  },\n  {\n    "Temperature (\\u00b0C)":0.47,\n    "Total Precipitation (mm\\/month)":-1.4,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":-0.31,\n    "Wind Direction (\\u00b0)":8.82\n  },\n  {\n    "Temperature (\\u00b0C)":-0.23,\n    "Total Precipitation (mm\\/month)":8.67,\n    "Wind U (m s**-1)":0.08,\n    "Wind V (m s**-1)":0.26,\n    "Wind Speed (m\\/s)":0.02,\n    "Wind Direction (\\u00b0)":-14.26\n  },\n  {\n    "Temperature (\\u00b0C)":0.58,\n    "Total Precipitation (mm\\/month)":-1.51,\n    "Wind U (m s**-1)":0.03,\n    "Wind V (m s**-1)":-0.01,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":2.64\n  },\n  {\n    "Temperature (\\u00b0C)":0.41,\n    "Total Precipitation (mm\\/month)":35.98,\n    "Wind U (m s**-1)":0.97,\n    "Wind V (m s**-1)":0.49,\n    "Wind Speed (m\\/s)":1.03,\n    "Wind Direction (\\u00b0)":22.24\n  },\n  {\n    "Temperature (\\u00b0C)":-2.34,\n    "Total Precipitation (mm\\/month)":11.03,\n    "Wind U (m s**-1)":-0.06,\n    "Wind V (m s**-1)":-0.74,\n    "Wind Speed (m\\/s)":-0.67,\n    "Wind Direction (\\u00b0)":17.1\n  }\n]', 'simulation6': '[\n  {\n    "Temperature (\\u00b0C)":0.65,\n    "Total Precipitation (mm\\/month)":9.33,\n    "Wind U (m s**-1)":-0.24,\n    "Wind V (m s**-1)":-0.05,\n    "Wind Speed (m\\/s)":-0.23,\n    "Wind Direction (\\u00b0)":-3.45\n  },\n  {\n    "Temperature (\\u00b0C)":2.53,\n    "Total Precipitation (mm\\/month)":14.04,\n    "Wind U (m s**-1)":0.86,\n    "Wind V (m s**-1)":0.58,\n    "Wind Speed (m\\/s)":1.03,\n    "Wind Direction (\\u00b0)":1.64\n  },\n  {\n    "Temperature (\\u00b0C)":3.25,\n    "Total Precipitation (mm\\/month)":30.34,\n    "Wind U (m s**-1)":2.04,\n    "Wind V (m s**-1)":0.48,\n    "Wind Speed (m\\/s)":2.01,\n    "Wind Direction (\\u00b0)":35.7\n  },\n  {\n    "Temperature (\\u00b0C)":2.33,\n    "Total Precipitation (mm\\/month)":-4.36,\n    "Wind U (m s**-1)":0.57,\n    "Wind V (m s**-1)":0.45,\n    "Wind Speed (m\\/s)":0.64,\n    "Wind Direction (\\u00b0)":-15.02\n  },\n  {\n    "Temperature (\\u00b0C)":2.09,\n    "Total Precipitation (mm\\/month)":-7.17,\n    "Wind U (m s**-1)":-0.27,\n    "Wind V (m s**-1)":0.15,\n    "Wind Speed (m\\/s)":-0.29,\n    "Wind Direction (\\u00b0)":12.0\n  },\n  {\n    "Temperature (\\u00b0C)":-0.05,\n    "Total Precipitation (mm\\/month)":16.23,\n    "Wind U (m s**-1)":-0.23,\n    "Wind V (m s**-1)":-0.39,\n    "Wind Speed (m\\/s)":0.28,\n    "Wind Direction (\\u00b0)":47.23\n  },\n  {\n    "Temperature (\\u00b0C)":1.54,\n    "Total Precipitation (mm\\/month)":8.89,\n    "Wind U (m s**-1)":-0.17,\n    "Wind V (m s**-1)":0.14,\n    "Wind Speed (m\\/s)":-0.21,\n    "Wind Direction (\\u00b0)":-3.88\n  },\n  {\n    "Temperature (\\u00b0C)":1.27,\n    "Total Precipitation (mm\\/month)":0.2,\n    "Wind U (m s**-1)":-0.38,\n    "Wind V (m s**-1)":-0.25,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":14.17\n  },\n  {\n    "Temperature (\\u00b0C)":1.11,\n    "Total Precipitation (mm\\/month)":-20.97,\n    "Wind U (m s**-1)":-0.05,\n    "Wind V (m s**-1)":0.07,\n    "Wind Speed (m\\/s)":-0.06,\n    "Wind Direction (\\u00b0)":-2.53\n  },\n  {\n    "Temperature (\\u00b0C)":0.33,\n    "Total Precipitation (mm\\/month)":14.34,\n    "Wind U (m s**-1)":0.24,\n    "Wind V (m s**-1)":-0.07,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":10.86\n  },\n  {\n    "Temperature (\\u00b0C)":0.86,\n    "Total Precipitation (mm\\/month)":-0.33,\n    "Wind U (m s**-1)":0.13,\n    "Wind V (m s**-1)":-0.09,\n    "Wind Speed (m\\/s)":0.01,\n    "Wind Direction (\\u00b0)":16.66\n  },\n  {\n    "Temperature (\\u00b0C)":0.08,\n    "Total Precipitation (mm\\/month)":7.71,\n    "Wind U (m s**-1)":0.81,\n    "Wind V (m s**-1)":-0.17,\n    "Wind Speed (m\\/s)":0.34,\n    "Wind Direction (\\u00b0)":27.07\n  }\n]'}, 'content_message': '\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2020-2029. The nextGEMS pre-final simulations for years 2030x..This simulation serves as a historical reference to extract its values from other simulations. :\n {simulation1}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2030-2039. The nextGEMS pre-final simulations for years 2030x. :\n {simulation2}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2040-2049. The nextGEMS pre-final simulations for years 2040x. :\n {simulation3}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2030-2039 compared to the simulation for 2020-2029. :\n {simulation5}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2040-2049 compared to the simulation for 2020-2029. :\n {simulation6}'}
2025-02-27 13:36:11 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:36:11 - root - INFO - Rendered RAG response: Based on the provided context, there is no specific data on temperature and salinity for surface waters in Berlin, Germany. Generally, temperature and salinity datasets are more commonly available for oceanic regions, especially those derived from sources like Argo float observations and altimetry-based analyses, which are not directly applicable to a landlocked city like Berlin.
2025-02-27 13:36:11 - climsight_engine - INFO - general_rag_agent_response: Based on the provided context, there is no specific data on temperature and salinity for surface waters in Berlin, Germany. Generally, temperature and salinity datasets are more commonly available for oceanic regions, especially those derived from sources like Argo float observations and altimetry-based analyses, which are not directly applicable to a landlocked city like Berlin.
2025-02-27 13:36:13 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:36:24 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:36:24 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 13:36:25 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 13:36:26 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:36:30 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:36:31 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:36:41 - climsight_engine - INFO - combine_agent in work
2025-02-27 13:36:41 - climsight_engine - INFO - smart_agent_response: {'output': 'The monthly mean temperature for surface water at Berlin is as follows: for 2020-2029, January is 2.1°C, February 0.82°C, March -0.29°C, April 3.16°C, May 7.95°C, June 14.17°C, July 16.92°C, August 19.69°C, September 20.3°C, October 17.32°C, November 10.92°C, and December 7.0°C. For 2030-2039, January is 2.57°C, February 0.83°C, March 2.66°C, April 5.92°C, May 9.47°C, June 13.61°C, July 17.58°C, August 20.16°C, September 20.07°C, October 17.9°C, November 11.33°C, and December 4.66°C. For 2040-2049, January is 2.75°C, February 3.35°C, March 2.96°C, April 5.49°C, May 10.04°C, June 14.12°C, July 18.46°C, August 20.96°C, September 21.41°C, October 17.65°C, November 11.78°C, and December 7.08°C. According to the Wikipedia article, the electrical conductivity of seawater at 15°C is 42.9 mS/cm, and the specific conductivity of river or lake water with a salinity of around 70 mg/L at 25°C is between 80 and 130 μS/cm. \n\nThe temperature data shows a gradual increase over the decades, with notable warming in the summer months. This warming trend could affect the salinity measurements, as solution volume varies with temperature, impacting the chemical properties of dissolved forms in water.'}
2025-02-27 13:36:41 - climsight_engine - INFO - Wikipedia_tool_reponse: • Temperature:
  - Quantitative: The electrical conductivity of seawater at a temperature of 15 °C is 42.9 mS/cm. The specific conductivity of river or lake water with a salinity of around 70 mg/L at 25 °C is between 80 and 130 μS/cm. Conductivity changes by about 2% per degree Celsius.
  - Qualitative: The chemical properties of some dissolved forms in water depend on temperature. Solution volume varies with temperature, affecting salinity measurements.

• Precipitation:
  - Quantitative: Precipitation typically has a total dissolved solids (TDS) of 20 mg/kg or less.
  - Qualitative: No additional qualitative information provided.

• Wind:
  - No information available.

• Elevation Above Sea Level:
  - No information available.

• Population:
  - No information available.

• Natural Hazards:
  - No information available.

• Soil Type:
  - Qualitative: Salinity influences the kinds of plants that will grow on land fed by water. A plant adapted to saline conditions is called a halophyte. Halophytes tolerant to residual sodium carbonate salinity are called glasswort or saltwort or barilla plants.
2025-02-27 13:36:41 - climsight_engine - INFO - Ecocrop_search_response: 
2025-02-27 13:36:55 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 13:36:55 - climsight_engine - INFO - Final_answer: Given the request for temperature and salinity data at the specified location in Berlin, the situation presents certain challenges since Berlin is a landlocked city with no direct access to bodies of water where salinity measurements are typically taken, such as oceans or seas. The information provided indicates Berlin's geographical characteristics, climate parameters, and other relevant data but does not include specific surface water salinity measurements, a typical constraint for inland cities.

For temperature, the retrieved climate simulations provide a comprehensive overview of expected changes. The data captured for the years 2020-2029, 2030-2039, and 2040-2049 show a gradual increase in monthly mean temperatures. For instance, temperatures in July rise from 16.92°C in 2020-2029 to 17.58°C in 2030-2039, and further to 18.46°C in 2040-2049. This indicates a warming trend across the decades, which can have implications for surface water temperatures within the region. Warming trends can influence ecological systems, affecting local species and water quality.

Regarding salinity, the absence of direct data for Berlin is consistent with the reality that salinity is seldom measured far from marine or estuarine environments. Nonetheless, it's important to understand how changes in temperature and precipitation can indirectly impact salinity levels in any nearby freshwater systems (rivers, lakes). Higher temperatures, as indicated, generally result in increased evaporation rates, potentially concentrating salts in basin or river systems if they are present. Similarly, changes in total precipitation will influence the water balance, potentially diluting or concentrating salinity locally in terrestrial water bodies.

Conductivity, closely related to salinity in water bodies, is affected by these factors. The conductivity of natural water can vary with temperature, further complicated by the increment of 2% per degree Celsius, as described in the contextual notes about water properties. This dynamic relationship between temperature, precipitation, and conductivity forms part of the interaction that can affect ecosystems in Berlin's numerous lakes and the Spree River. Nonetheless, specific conductivity for typical freshwater with a salinity around 70 mg/L at moderate temperatures highlights that such waters remain much less conductive than seawater.

While climate change continues influencing the broader climatic patterns, ensuring the continued health of the region's water systems may involve urban planning and sustainable management practices. Berlin could engage in regular monitoring of its freshwater resources, assessing potential changes in chemical composition that could affect local biodiversity and water management practices, especially under predicted climatic warming scenarios.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 13:36:55 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 14:17:49 - __main__ - INFO - reading config from: config.yml
2025-02-27 14:17:49 - __main__ - INFO - reading config from: config.yml
2025-02-27 14:17:56 - __main__ - INFO - reading config from: config.yml
2025-02-27 14:17:56 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-27 14:17:56 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 14:17:56 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-27 14:17:56 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-27 14:17:56 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 14:17:56 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-27 14:17:57 - root - INFO - Is the point on land? Yes.
2025-02-27 14:17:57 - climsight_engine - INFO - start agent_request
2025-02-27 14:17:59 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 14:17:59 - climsight_engine - INFO - IPCC RAG agent in work.
2025-02-27 14:17:59 - climsight_engine - INFO - General RAG agent in work.
2025-02-27 14:17:59 - root - ERROR - list index out of range. Continue with: current_land_use = None
Traceback (most recent call last):
  File "/Users/ikuznets/work/projects/climsight/code/climsight/src/climsight/climsight_engine.py", line 669, in zero_rag_agent
    current_land_use = land_use_data["elements"][0]["tags"]["landuse"]
                       ~~~~~~~~~~~~~~~~~~~~~~~~~^^^
IndexError: list index out of range
2025-02-27 14:17:59 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 14:17:59 - rag - INFO - Chunks returned from RAG: {'context': 'on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n 9 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n10 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n11\n\n12 Figure AI.21: Climatic and environmental stresses on global production of maize.\n13 The global effects of five climatic and environmental stresses on maize yield. The combined effect of each\n14 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n15 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n16 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n17 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n18\n\n19 Figure AI.22: Projected changes in global maize production.\n20 For maize production time series are shown as relative changes to the 1983-2013 reference period under\n21 SSP126 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop\n22 model combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model\n23 response (and a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the\n24 historical variability (1983-2013; ensemble median) and open circles highlight the "time of climate impact\n25 emergence" (TCIE), the year in which the smoothed median response exceeds the historical envelope. For\n26 context, the TCIE calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop\n\nis indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop models for current growing regions (>10 ha). Hatching indicates areas\n29 where less than 70% of the climate-crop model combinations agree on the sign of impact. Regional\n30 production time series (e) are similar to (a), but stratified for the four major KoeppenGeiger climate zones\n31 (temperature limited, temperate/humid, subtropical, and tropical). The percentage of the total global\n32 production contributed by each zone is indicated in the top right corner of the inlets. All data are shown for\n33 the default [CO2] {Jägermeyr et al. 2021; 5.4.3.2}\n\n34\n\n35 Figure AI.23: Projected changes in global wheat production.\n36 Production time series are shown as relative changes to the 1983-2013 reference period under SSP126\n37 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop model\n38 combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model response (and\n39 a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the historical variability\n40 (1983-2013; ensemble median) and open circles highlight the "time of climate impact emergence" (TCIE),\n41 the year in which the smoothed median response exceeds the historical envelope. For context, the TCIE\n42 calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on GC6 (>2099 if no\n43 TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under SSP585 across\n44 climate and crop models for current growing regions (>10 ha). Hatching indicates areas where less than 70%\n45 of the climate-crop model combinations agree on the sign of impact. Regional production time series (e) are\n46 similar to (a), but stratified for the four major KoeppenGeiger climate zones (temperature limited,\n47\n\n5.3; 5.5.3; 5.4.1; Figure FAQ 5.1; Figure 9.22; 15.3.4; 15.3.3}\n\n44\n\n45 Figure AI.18: Climatic and environmental stresses on global production of wheat.\n46 The global effects of five climatic and environmental stresses on wheat yield. The combined effect of each\n47 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n48 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n49 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n50 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n51 wheat was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n52\n\n53 Figure AI.19: Climatic and environmental stresses on global production of soybean.\n54 The global effects of five climatic and environmental stresses on soybean yield. The combined effect of each\n55 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n56 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n\nDo Not Cite, Quote or Distribute  AI-63    Total pages: 74\nFINAL DRAFT                       Annex I  IPCC WGII Sixth Assessment Report\n\n 1 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n 2 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n 3 soybean was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n 4\n\n 5 Figure AI.20: Climatic and environmental stresses on global production of rice.\n 6 The global effects of five climatic and environmental stresses on rice yield. The combined effect of each\n 7 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but\n\nof plant functional types over the 1700\xad2020. Shifts in plant functional\n31 types are indicative of shift in biome function and structure {Box 2.1, Figure Box 2.1.1}\n\n32\n\n33 Figure AI.08: Projected responses of rangeland plants to CO2 fertilization.\n34 Regional percent changes in land cover and soil carbon from ensemble simulation results and plant responses\n35 to CO2 fertilisation. Regions as defined by the United Nations Statistics Division. (Boone et al., 2018)\n36 {5.5.3; Figure 5.11}\n\n37\n\n38 Figure AI.09: People living in land area of high conservation importance:\n39 {CCP1.2.1.3, Figures CCP1.1, CCP1.2}\n\n40\n\n41 Figure AI.10: Present & projected habitat losses of climatically suitable area in terrestrial biodiversity\n42 hotspots.\n43 Projected loss for present-day (around 1°C warming) and at global warming levels of 1.5°C, 2°C and 3°C.\n44 Maps (right hand column) show the regional distribution of losses in five categories of loss (Very low loss\n45 0\xad20%, Low loss 20\xad40%, Medium loss 40\xad60%, High loss 60\xad80%, Very high loss 80\xad100%). The\n46 clusters of circles (middle column) show losses in the five categories of loss in each of the 143 hotspot areas\n47 of high importance for terrestrial biodiversity conservation with circles scaled by area size. {CCP1, Figure\n48 CCP1.6; Table CCP1.1}\n\n49\n\n50 Figure AI.11: Projected change in marine animal biomass.\n51 Simulated global biomass changes of animals. Spatial patterns of simulated change by 2090\xad2099 are\n52 calculated relative to 1995\xad2014 for SSP1-2.6 and SSP5-8.5. The ensemble projections of global changes in\n53 total animal biomass updated based on Tittensor et al. (2021) include 6\xad9 published global fisheries and\n54 marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-\n55 MIP, Tittensor et al., 2018; Tittensor et al., 2021), forced with standardised outputs from two CMIP6 Earth\n56 System Models. {3.4.3; Fig. 3.21}\n\nDo Not Cite, Quote or Distribute  AI-62    Total pages:', 'location': 'Address: railway: Berlin Hauptbahnhof (Tief), road: Europaplatz, quarter: Europacity, suburb: Moabit, borough: Mitte, city: Berlin, ISO3166-2-lvl4: DE-BE, postcode: 10557, country: Germany, country_code: de', 'question': 'Grow grass'}
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2025-02-27 14:18:00 - rag - INFO - Chunks returned from RAG: {'context': 'S.P., Kerr, S. et al. (2022). Land management can\ncontribute to net zero. Science 376(6598), 1163-1165. https://doi.org/10.1126/science.abo0613.\nDigdaya, I.A., Sullivan, I., Lin, M., Han, L., Cheng, W.-H., Atwater, H.A. et al. (2020). A direct coupled electrochemical\nsystem for capture and conversion of CO 2 from oceanwater. Nature Communications 11(1), 4412. https://\ndoi.org/10.1038/s41467-020-18232-y.\nDuncanson, L., Liang, M., Leitold, V., Armston, J., Krishna Moorthy S.M., Dubayah, R. et al. (2023). The effectiveness\nof global protected areas for climate change mitigation. Nature Communications 14(1), 2908. https://doi.\norg/10.1038/s41467-023-38073-9.\n\n75\n\n\x0cEmissions Gap Report 2023: Broken Record\n\nE\n\nF\n\nG\n\n76\n\nElias, M., Dees, J., Cabiyo, B., Saksa, P. and Sanchez, D.L. (2023). Financial analysis of innovative wood products\nand carbon finance to support forest restoration in California. Forest Products Journal 73(1), 31-42. https://\ndoi.org/10.13073/FPJ-D-22-00049.\nErans, M., Sanz-Pérez, E.S., Hanak, D.P., Clulow, Z., Reiner, D.M. and Mutch, G.A. (2022). Direct air capture: Process\ntechnology, techno-economic and socio-political challenges. Energy & Environmental Science 15(4), 13601405. https://doi.org/10.1039/D1EE03523A.\nFasihi, M., Efimova, O. and Breyer, C. (2019). Techno-economic assessment of CO 2 direct air capture plants.\nJournal of Cleaner Production 224, 957-980. https://doi.org/10.1016/j.jclepro.2019.03.086.\nFleming, A., Stitzlein, C., Jakku, E. and Fielke, S. (2019). Missed opportunity? Framing actions around co-benefits\nfor carbon mitigation in Australian agriculture. Land Use Policy 85, 230-238. https://doi.org/10.1016/j.\nlandusepol.2019.03.050.\nForster, J., Vaughan, N.E., Gough, C., Lorenzoni, I. and Chilvers, J. (2020). Mapping feasibilities of greenhouse gas\nremoval: Key issues, gaps and opening up assessments. Global Environmental Change 63, 102073. https://\ndoi.org/10.1016/j.gloenvcha.2020.102073.\nFridahl, M., Bellamy, R., Hansson, A. and\n\nchange, fertilizer use or irrigation. Cobenefits for biodiversity, ecosystem services and livelihoods,\nas well as co-delivery on other international and national\ncommitments on biodiversity, land degradation and people,\nhave also propelled the use of conventional CDR approaches.\nHowever, the risks and benefits of CDR depend on the\nmethod used and its implementation and management\n(e.g. reforestation with native species versus afforestation\nof non-forest biomes with non-native monocultures).\nCompetition for land is a pressing issue due to numerous\nglobal demands, including for food production, resource\nextraction, infrastructure development, biodiversity and\necosystem services conservation and climate change\nmitigation. Environmental changes, such as climate change,\nmay exacerbate land-use competition, due to complex\nfeedback processes between human and biophysical\ncomponents in the land system (Haberl et al. 2014). Cropland\nand urban expansion therefore also compete with landbased CDR options. Modelling efforts show that cropland\nexpansion to fulfil future food demand is the primary cause\nof such competition, with more severe impacts seen in the\ntropics due to their greater land-based mitigation potential\n(Zheng et al. 2022). Such findings highlight that careful\nspatial planning is essential for sustainable climate policies.\nVarious land-based CDR options have the potential to\nenhance biodiversity. An assessment of the biodiversity\nimpacts of 20 land-based mitigation options showed that\nmost options benefit biodiversity. However, a quarter of the\nassessed options, including bioenergy with carbon capture\nand storage, decreased mean species abundance, while\nafforestation and forest management either positively or\nnegatively affected biodiversity depending on the local\nimplementation method and forest conservation schemes\nadopted (Nunez, Verboom and Alkemade 2020). Recent\nstudies explore how ambitious objectives and multiple\ntargets of biodiversity and climate\n\nfor extreme\nweather events, which should take into account different\nvulnerabilities of population groups, especially at the local\ncommunity level [4].\nEconomic development needs to change, prioritising\nhealth-promoting urban development, the use of more efficient and renewable energy sources, and a sustainable and\nmore just food system. Ecological and social determinants\n\nof health need to be addressed together to reduce poverty,\nincrease health equity, and enable all people to live within\nplanetary boundaries [83].\nFriel [84] proposes the concept of ‘Planetary Health\nEquity’. It contains the following elements:\n(1) Embedding policy norms of social equity, environmental\nsustainability, and well-being\n(2) Application of these policy norms and implementation\nin cross-sectoral policies\n(3) Implementation of a guiding national strategy on\nclimate, equity, and health\n(4) Resetting the governance of planetary health equity to\nensure that there are no vested interests and that there\nis civil society participation\nInternationally, the need for community-based, localised\napproaches to climate adaptation is emphasised. Climate\njustice principles can be integrated into public health strategies and interventions for climate adaptation at the community level to increase the resilience of marginalised populations to climate change impacts and other stressors\n[16, 17]. Interactions between exposures, biological sensitivity, adaptive capacity, and the social determinants of\nhealth should be considered location-based from an intersectionality perspective to better understand differences\nin the health effects of climate change impacts as well as\nclimate adaptation measures and to develop differentiated\ninterventions in a participatory manner with population\ngroups [16, 17, 74].\nUrban greening is an essential component of municipal\nclimate adaptation strategies and sustainable, climate-just\n\nhome back\n\n21\n\nforward\n\n\x0cJournal of Health Monitoring\n\nurban development. Urban greening not\n\nChange 2022: Mitigation\nof Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental\nPanel on Climate Change. Shukla, P.R., Skea, J., Reisinger, A., Slade, R., Fradera, R., Pathak, M. et al. (eds.).\nCambridge and New York: Cambridge University Press. 1261-1263. https://www.ipcc.ch/report/ar6/wg3/.\nGidden, M.J., Brutschin, E., Ganti, G., Unlu, G., Zakeri, B., Fricko, O. et al. (2023). Fairness and feasibility in deep\nmitigation pathways with novel carbon dioxide removal considering institutional capacity to mitigate.\nEnvironmental Research Letters 18(7), 074006. https://doi.org/10.1088/1748-9326/acd8d5.\n\n\x0cEmissions Gap Report 2023: Broken Record\n\nH\n\nI\n\nJ\n\nK\n\nL\n\nGidden, M.J., Gasser, T., Grassi, G., Forsell, N., Janssens, I., Lamb, W.F. et al. (2023). Aligning IPCC scenarios to\nnational land emissions inventories shifts global mitigation benchmarks. https://d197for5662m48.cloudfront.\nnet/documents/publicationstatus/125532/preprint_pdf/86adfe1dcbe096720eb2f2156377e00a.pdf.\nGosnell, H., Gill, N. and Voyer, M. (2019). Transformational adaptation on the farm: Processes of change and\npersistence in transitions to ‘climate-smart’ regenerative agriculture. Global Environmental Change 59,\n101965. https://doi.org/10.1016/j.gloenvcha.2019.101965.\nGrant, N., Hawkes, A., Mittal, S. and Gambhir, A. (2021). The policy implications of an uncertain carbon dioxide\nremoval potential. Joule 5(10), 2593-2605. https://doi.org/10.1016/j.joule.2021.09.004.\nGrassi, G., Schwingshackl, C., Gasser, T., Houghton, R.A., Sitch, S., Canadell, J.G. et al. (2023). Harmonising the\nland-use flux estimates of global models and national inventories for 2000–2020. Earth System Science\nData 15(3), 1093-1114. https://doi.org/10.5194/essd-15-1093-2023.\nGrubler, A. and Wilson, C. (eds.) (2013). Energy Technology Innovation: Learning from Historical Successes and\nFailures. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139150880.\nHaberl,', 'location': 'Address: railway: Berlin Hauptbahnhof (Tief), road: Europaplatz, quarter: Europacity, suburb: Moabit, borough: Mitte, city: Berlin, ISO3166-2-lvl4: DE-BE, postcode: 10557, country: Germany, country_code: de', 'question': 'Grow grass'}
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2025-02-27 14:18:00 - root - INFO - Rendered RAG response: None
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2025-02-27 14:18:01 - climsight_engine - INFO - data_agent_response: {'input_params': {'simulation1': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.1,\n    "Total Precipitation (mm\\/month)":54.45,\n    "Wind U (m s**-1)":1.5,\n    "Wind V (m s**-1)":1.04,\n    "Wind Speed (m\\/s)":1.83,\n    "Wind Direction (\\u00b0)":235.33\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":0.82,\n    "Total Precipitation (mm\\/month)":54.9,\n    "Wind U (m s**-1)":1.32,\n    "Wind V (m s**-1)":1.05,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":231.6\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":-0.29,\n    "Total Precipitation (mm\\/month)":35.17,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":0.3,\n    "Wind Speed (m\\/s)":0.37,\n    "Wind Direction (\\u00b0)":215.23\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":3.16,\n    "Total Precipitation (mm\\/month)":47.51,\n    "Wind U (m s**-1)":1.05,\n    "Wind V (m s**-1)":0.02,\n    "Wind Speed (m\\/s)":1.05,\n    "Wind Direction (\\u00b0)":268.92\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":7.95,\n    "Total Precipitation (mm\\/month)":43.6,\n    "Wind U (m s**-1)":0.43,\n    "Wind V (m s**-1)":-0.34,\n    "Wind Speed (m\\/s)":0.54,\n    "Wind Direction (\\u00b0)":308.22\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":14.17,\n    "Total Precipitation (mm\\/month)":61.8,\n    "Wind U (m s**-1)":0.26,\n    "Wind V (m s**-1)":-0.22,\n    "Wind Speed (m\\/s)":0.34,\n    "Wind Direction (\\u00b0)":310.3\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":16.92,\n    "Total Precipitation (mm\\/month)":64.13,\n    "Wind U (m s**-1)":1.12,\n    "Wind V (m s**-1)":-0.42,\n    "Wind Speed (m\\/s)":1.2,\n    "Wind Direction (\\u00b0)":290.55\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":19.69,\n    "Total Precipitation (mm\\/month)":52.02,\n    "Wind U (m s**-1)":1.48,\n    "Wind V (m s**-1)":-0.12,\n    "Wind Speed (m\\/s)":1.48,\n    "Wind Direction (\\u00b0)":274.6\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.3,\n    "Total Precipitation (mm\\/month)":46.67,\n    "Wind U (m s**-1)":1.01,\n    "Wind V (m s**-1)":-0.41,\n    "Wind Speed (m\\/s)":1.08,\n    "Wind Direction (\\u00b0)":292.12\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.32,\n    "Total Precipitation (mm\\/month)":31.07,\n    "Wind U (m s**-1)":0.54,\n    "Wind V (m s**-1)":0.19,\n    "Wind Speed (m\\/s)":0.57,\n    "Wind Direction (\\u00b0)":250.21\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":10.92,\n    "Total Precipitation (mm\\/month)":31.91,\n    "Wind U (m s**-1)":0.28,\n    "Wind V (m s**-1)":0.48,\n    "Wind Speed (m\\/s)":0.56,\n    "Wind Direction (\\u00b0)":209.96\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.0,\n    "Total Precipitation (mm\\/month)":45.19,\n    "Wind U (m s**-1)":0.54,\n    "Wind V (m s**-1)":1.33,\n    "Wind Speed (m\\/s)":1.43,\n    "Wind Direction (\\u00b0)":202.27\n  }\n]', 'simulation2': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.57,\n    "Total Precipitation (mm\\/month)":65.41,\n    "Wind U (m s**-1)":1.66,\n    "Wind V (m s**-1)":1.05,\n    "Wind Speed (m\\/s)":1.96,\n    "Wind Direction (\\u00b0)":237.74\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":0.83,\n    "Total Precipitation (mm\\/month)":65.67,\n    "Wind U (m s**-1)":1.53,\n    "Wind V (m s**-1)":1.27,\n    "Wind Speed (m\\/s)":1.99,\n    "Wind Direction (\\u00b0)":230.22\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.66,\n    "Total Precipitation (mm\\/month)":39.15,\n    "Wind U (m s**-1)":1.5,\n    "Wind V (m s**-1)":1.09,\n    "Wind Speed (m\\/s)":1.85,\n    "Wind Direction (\\u00b0)":234.02\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.92,\n    "Total Precipitation (mm\\/month)":51.82,\n    "Wind U (m s**-1)":0.73,\n    "Wind V (m s**-1)":0.61,\n    "Wind Speed (m\\/s)":0.95,\n    "Wind Direction (\\u00b0)":230.11\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":9.47,\n    "Total Precipitation (mm\\/month)":46.28,\n    "Wind U (m s**-1)":0.09,\n    "Wind V (m s**-1)":-0.21,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":336.71\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":13.61,\n    "Total Precipitation (mm\\/month)":56.85,\n    "Wind U (m s**-1)":0.06,\n    "Wind V (m s**-1)":-0.36,\n    "Wind Speed (m\\/s)":0.37,\n    "Wind Direction (\\u00b0)":350.32\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":17.58,\n    "Total Precipitation (mm\\/month)":69.71,\n    "Wind U (m s**-1)":0.87,\n    "Wind V (m s**-1)":-0.48,\n    "Wind Speed (m\\/s)":0.99,\n    "Wind Direction (\\u00b0)":298.93\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":20.16,\n    "Total Precipitation (mm\\/month)":50.62,\n    "Wind U (m s**-1)":1.14,\n    "Wind V (m s**-1)":-0.27,\n    "Wind Speed (m\\/s)":1.17,\n    "Wind Direction (\\u00b0)":283.42\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.07,\n    "Total Precipitation (mm\\/month)":55.34,\n    "Wind U (m s**-1)":1.09,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":1.1,\n    "Wind Direction (\\u00b0)":277.86\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.9,\n    "Total Precipitation (mm\\/month)":29.56,\n    "Wind U (m s**-1)":0.57,\n    "Wind V (m s**-1)":0.18,\n    "Wind Speed (m\\/s)":0.6,\n    "Wind Direction (\\u00b0)":252.85\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.33,\n    "Total Precipitation (mm\\/month)":67.89,\n    "Wind U (m s**-1)":1.25,\n    "Wind V (m s**-1)":0.97,\n    "Wind Speed (m\\/s)":1.59,\n    "Wind Direction (\\u00b0)":232.2\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":4.66,\n    "Total Precipitation (mm\\/month)":56.22,\n    "Wind U (m s**-1)":0.48,\n    "Wind V (m s**-1)":0.59,\n    "Wind Speed (m\\/s)":0.76,\n    "Wind Direction (\\u00b0)":219.37\n  }\n]', 'simulation3': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.75,\n    "Total Precipitation (mm\\/month)":63.78,\n    "Wind U (m s**-1)":1.26,\n    "Wind V (m s**-1)":0.99,\n    "Wind Speed (m\\/s)":1.6,\n    "Wind Direction (\\u00b0)":231.88\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":3.35,\n    "Total Precipitation (mm\\/month)":68.94,\n    "Wind U (m s**-1)":2.18,\n    "Wind V (m s**-1)":1.63,\n    "Wind Speed (m\\/s)":2.72,\n    "Wind Direction (\\u00b0)":233.24\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.96,\n    "Total Precipitation (mm\\/month)":65.51,\n    "Wind U (m s**-1)":2.25,\n    "Wind V (m s**-1)":0.78,\n    "Wind Speed (m\\/s)":2.38,\n    "Wind Direction (\\u00b0)":250.93\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.49,\n    "Total Precipitation (mm\\/month)":43.15,\n    "Wind U (m s**-1)":1.62,\n    "Wind V (m s**-1)":0.47,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":253.9\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":10.04,\n    "Total Precipitation (mm\\/month)":36.43,\n    "Wind U (m s**-1)":0.16,\n    "Wind V (m s**-1)":-0.19,\n    "Wind Speed (m\\/s)":0.25,\n    "Wind Direction (\\u00b0)":320.22\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":14.12,\n    "Total Precipitation (mm\\/month)":78.03,\n    "Wind U (m s**-1)":0.03,\n    "Wind V (m s**-1)":-0.61,\n    "Wind Speed (m\\/s)":0.62,\n    "Wind Direction (\\u00b0)":357.53\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":18.46,\n    "Total Precipitation (mm\\/month)":73.02,\n    "Wind U (m s**-1)":0.95,\n    "Wind V (m s**-1)":-0.28,\n    "Wind Speed (m\\/s)":0.99,\n    "Wind Direction (\\u00b0)":286.67\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":20.96,\n    "Total Precipitation (mm\\/month)":52.22,\n    "Wind U (m s**-1)":1.1,\n    "Wind V (m s**-1)":-0.37,\n    "Wind Speed (m\\/s)":1.16,\n    "Wind Direction (\\u00b0)":288.77\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":21.41,\n    "Total Precipitation (mm\\/month)":25.7,\n    "Wind U (m s**-1)":0.96,\n    "Wind V (m s**-1)":-0.34,\n    "Wind Speed (m\\/s)":1.02,\n    "Wind Direction (\\u00b0)":289.59\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.65,\n    "Total Precipitation (mm\\/month)":45.41,\n    "Wind U (m s**-1)":0.78,\n    "Wind V (m s**-1)":0.12,\n    "Wind Speed (m\\/s)":0.79,\n    "Wind Direction (\\u00b0)":261.07\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.78,\n    "Total Precipitation (mm\\/month)":31.58,\n    "Wind U (m s**-1)":0.41,\n    "Wind V (m s**-1)":0.39,\n    "Wind Speed (m\\/s)":0.57,\n    "Wind Direction (\\u00b0)":226.62\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.08,\n    "Total Precipitation (mm\\/month)":52.9,\n    "Wind U (m s**-1)":1.35,\n    "Wind V (m s**-1)":1.16,\n    "Wind Speed (m\\/s)":1.77,\n    "Wind Direction (\\u00b0)":229.34\n  }\n]', 'simulation5': '[\n  {\n    "Temperature (\\u00b0C)":0.47,\n    "Total Precipitation (mm\\/month)":10.96,\n    "Wind U (m s**-1)":0.16,\n    "Wind V (m s**-1)":0.01,\n    "Wind Speed (m\\/s)":0.13,\n    "Wind Direction (\\u00b0)":2.41\n  },\n  {\n    "Temperature (\\u00b0C)":0.01,\n    "Total Precipitation (mm\\/month)":10.77,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":0.22,\n    "Wind Speed (m\\/s)":0.3,\n    "Wind Direction (\\u00b0)":-1.38\n  },\n  {\n    "Temperature (\\u00b0C)":2.95,\n    "Total Precipitation (mm\\/month)":3.98,\n    "Wind U (m s**-1)":1.29,\n    "Wind V (m s**-1)":0.79,\n    "Wind Speed (m\\/s)":1.48,\n    "Wind Direction (\\u00b0)":18.79\n  },\n  {\n    "Temperature (\\u00b0C)":2.76,\n    "Total Precipitation (mm\\/month)":4.31,\n    "Wind U (m s**-1)":-0.32,\n    "Wind V (m s**-1)":0.59,\n    "Wind Speed (m\\/s)":-0.1,\n    "Wind Direction (\\u00b0)":-38.81\n  },\n  {\n    "Temperature (\\u00b0C)":1.52,\n    "Total Precipitation (mm\\/month)":2.68,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":0.13,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":28.49\n  },\n  {\n    "Temperature (\\u00b0C)":-0.56,\n    "Total Precipitation (mm\\/month)":-4.95,\n    "Wind U (m s**-1)":-0.2,\n    "Wind V (m s**-1)":-0.14,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":40.02\n  },\n  {\n    "Temperature (\\u00b0C)":0.66,\n    "Total Precipitation (mm\\/month)":5.58,\n    "Wind U (m s**-1)":-0.25,\n    "Wind V (m s**-1)":-0.06,\n    "Wind Speed (m\\/s)":-0.21,\n    "Wind Direction (\\u00b0)":8.38\n  },\n  {\n    "Temperature (\\u00b0C)":0.47,\n    "Total Precipitation (mm\\/month)":-1.4,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":-0.31,\n    "Wind Direction (\\u00b0)":8.82\n  },\n  {\n    "Temperature (\\u00b0C)":-0.23,\n    "Total Precipitation (mm\\/month)":8.67,\n    "Wind U (m s**-1)":0.08,\n    "Wind V (m s**-1)":0.26,\n    "Wind Speed (m\\/s)":0.02,\n    "Wind Direction (\\u00b0)":-14.26\n  },\n  {\n    "Temperature (\\u00b0C)":0.58,\n    "Total Precipitation (mm\\/month)":-1.51,\n    "Wind U (m s**-1)":0.03,\n    "Wind V (m s**-1)":-0.01,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":2.64\n  },\n  {\n    "Temperature (\\u00b0C)":0.41,\n    "Total Precipitation (mm\\/month)":35.98,\n    "Wind U (m s**-1)":0.97,\n    "Wind V (m s**-1)":0.49,\n    "Wind Speed (m\\/s)":1.03,\n    "Wind Direction (\\u00b0)":22.24\n  },\n  {\n    "Temperature (\\u00b0C)":-2.34,\n    "Total Precipitation (mm\\/month)":11.03,\n    "Wind U (m s**-1)":-0.06,\n    "Wind V (m s**-1)":-0.74,\n    "Wind Speed (m\\/s)":-0.67,\n    "Wind Direction (\\u00b0)":17.1\n  }\n]', 'simulation6': '[\n  {\n    "Temperature (\\u00b0C)":0.65,\n    "Total Precipitation (mm\\/month)":9.33,\n    "Wind U (m s**-1)":-0.24,\n    "Wind V (m s**-1)":-0.05,\n    "Wind Speed (m\\/s)":-0.23,\n    "Wind Direction (\\u00b0)":-3.45\n  },\n  {\n    "Temperature (\\u00b0C)":2.53,\n    "Total Precipitation (mm\\/month)":14.04,\n    "Wind U (m s**-1)":0.86,\n    "Wind V (m s**-1)":0.58,\n    "Wind Speed (m\\/s)":1.03,\n    "Wind Direction (\\u00b0)":1.64\n  },\n  {\n    "Temperature (\\u00b0C)":3.25,\n    "Total Precipitation (mm\\/month)":30.34,\n    "Wind U (m s**-1)":2.04,\n    "Wind V (m s**-1)":0.48,\n    "Wind Speed (m\\/s)":2.01,\n    "Wind Direction (\\u00b0)":35.7\n  },\n  {\n    "Temperature (\\u00b0C)":2.33,\n    "Total Precipitation (mm\\/month)":-4.36,\n    "Wind U (m s**-1)":0.57,\n    "Wind V (m s**-1)":0.45,\n    "Wind Speed (m\\/s)":0.64,\n    "Wind Direction (\\u00b0)":-15.02\n  },\n  {\n    "Temperature (\\u00b0C)":2.09,\n    "Total Precipitation (mm\\/month)":-7.17,\n    "Wind U (m s**-1)":-0.27,\n    "Wind V (m s**-1)":0.15,\n    "Wind Speed (m\\/s)":-0.29,\n    "Wind Direction (\\u00b0)":12.0\n  },\n  {\n    "Temperature (\\u00b0C)":-0.05,\n    "Total Precipitation (mm\\/month)":16.23,\n    "Wind U (m s**-1)":-0.23,\n    "Wind V (m s**-1)":-0.39,\n    "Wind Speed (m\\/s)":0.28,\n    "Wind Direction (\\u00b0)":47.23\n  },\n  {\n    "Temperature (\\u00b0C)":1.54,\n    "Total Precipitation (mm\\/month)":8.89,\n    "Wind U (m s**-1)":-0.17,\n    "Wind V (m s**-1)":0.14,\n    "Wind Speed (m\\/s)":-0.21,\n    "Wind Direction (\\u00b0)":-3.88\n  },\n  {\n    "Temperature (\\u00b0C)":1.27,\n    "Total Precipitation (mm\\/month)":0.2,\n    "Wind U (m s**-1)":-0.38,\n    "Wind V (m s**-1)":-0.25,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":14.17\n  },\n  {\n    "Temperature (\\u00b0C)":1.11,\n    "Total Precipitation (mm\\/month)":-20.97,\n    "Wind U (m s**-1)":-0.05,\n    "Wind V (m s**-1)":0.07,\n    "Wind Speed (m\\/s)":-0.06,\n    "Wind Direction (\\u00b0)":-2.53\n  },\n  {\n    "Temperature (\\u00b0C)":0.33,\n    "Total Precipitation (mm\\/month)":14.34,\n    "Wind U (m s**-1)":0.24,\n    "Wind V (m s**-1)":-0.07,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":10.86\n  },\n  {\n    "Temperature (\\u00b0C)":0.86,\n    "Total Precipitation (mm\\/month)":-0.33,\n    "Wind U (m s**-1)":0.13,\n    "Wind V (m s**-1)":-0.09,\n    "Wind Speed (m\\/s)":0.01,\n    "Wind Direction (\\u00b0)":16.66\n  },\n  {\n    "Temperature (\\u00b0C)":0.08,\n    "Total Precipitation (mm\\/month)":7.71,\n    "Wind U (m s**-1)":0.81,\n    "Wind V (m s**-1)":-0.17,\n    "Wind Speed (m\\/s)":0.34,\n    "Wind Direction (\\u00b0)":27.07\n  }\n]'}, 'content_message': '\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2020-2029. The nextGEMS pre-final simulations for years 2030x..This simulation serves as a historical reference to extract its values from other simulations. :\n {simulation1}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2030-2039. The nextGEMS pre-final simulations for years 2030x. :\n {simulation2}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2040-2049. The nextGEMS pre-final simulations for years 2040x. :\n {simulation3}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2030-2039 compared to the simulation for 2020-2029. :\n {simulation5}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2040-2049 compared to the simulation for 2020-2029. :\n {simulation6}'}
2025-02-27 14:18:01 - climsight_engine - INFO - zero_agent_response: {'input_params': {'elevation': '36.0', 'soil': 'Cambisols', 'biodiv': 'Pristurus', 'distance_to_coastline': '143240.00374445863', 'nat_hazards':        year          disastertype
13415  2002                 storm
13428  2006                 storm
13434  2010                 storm
33496  2003  extreme temperature 
33517  2006  extreme temperature 
33528  2009  extreme temperature 
33536  2009  extreme temperature 
33552  2012  extreme temperature 
33569  2012  extreme temperature , 'population':     Time  TotalPopulationAsOf1July  PopulationDensity  PopulationGrowthRate
0   1980              77786.703000         223.165900             -0.145000
1   1990              78072.678100         223.986330              0.237400
2   2000              80995.587100         232.372000              0.241300
3   2010              81294.847800         233.230560             -0.021600
4   2020              82291.120600         236.088820              0.245600
5   2030              83132.605000         238.502990             -0.082500
6   2040              81965.316200         235.154110             -0.195300
7   2050              80013.692600         229.555010             -0.288400
8   2060              77399.456100         222.054900             -0.362600
9   2070              74806.608900         214.616160             -0.299400
10  2080              72785.056300         208.816440             -0.266400
11  2090              70893.830000         203.390610             -0.247800
12  2100              69481.298222         199.338133             -0.171333
13  2101                       NaN                NaN                   NaN}, 'content_message': 'Elevation above sea level: {elevation} \nCurrent soil type: {soil} \nOccuring species: {biodiv} \nDistance to the closest coastline: {distance_to_coastline} \nNatural hazards: {nat_hazards} \nPopulation in Germany data: {population} \n'}
2025-02-27 14:18:02 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 14:18:02 - root - INFO - Rendered RAG response: The provided content does not specifically reference the growing of grass in Berlin, Germany. If focusing on general land management, grass can be an important aspect of ecosystems for carbon capture and biodiversity, as it may interact with various carbon dioxide removal methods discussed in the context, such as reforestation and land-use planning. However, this is a general perspective and not location-specific to Berlin.
2025-02-27 14:18:02 - climsight_engine - INFO - general_rag_agent_response: The provided content does not specifically reference the growing of grass in Berlin, Germany. If focusing on general land management, grass can be an important aspect of ecosystems for carbon capture and biodiversity, as it may interact with various carbon dioxide removal methods discussed in the context, such as reforestation and land-use planning. However, this is a general perspective and not location-specific to Berlin.
2025-02-27 14:18:02 - climsight_engine - INFO - combine_agent in work
2025-02-27 14:18:02 - climsight_engine - INFO - Wikipedia_tool_reponse: 
2025-02-27 14:18:02 - climsight_engine - INFO - Ecocrop_search_response: 
2025-02-27 14:18:21 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 14:18:21 - climsight_engine - INFO - Final_answer: Growing grass in Berlin, especially in an urban setting such as near the Hauptbahnhof (Central Train Station) in Moabit, requires consideration of several specific factors related to climate, soil type, and urban conditions.

The current climate and future projections for Berlin offer useful insights into the feasibility of grass cultivation. The climate simulations reveal that Berlin experiences a range of temperatures from about 2.1°C in January to 20.3°C in September. Precipitation varies throughout the year, with the wettest months being June and July, receiving around 61.8 and 64.13 mm of rain per month, respectively. However, over the next few decades, temperatures in Berlin are expected to rise, with January temperatures increasing by about 0.65°C by the 2040s. Precipitation patterns are also expected to shift, with some months possibly experiencing increased rainfall, particularly in winter months such as February.

From a soil perspective, the region is characterized by Cambisols, a type of soil generally conducive to grass growth due to its ability to retain nutrients and adequate drainage. Additionally, Berlin is urbanized with frequent anthropogenic influences that may affect soil composition and compaction, which could influence grass growth.

Wind speed averages around 1.96 m/s in January and moderates to about 0.95 to 1.17 m/s during late spring to autumn. These conditions are favorable for grass because they are generally low enough to avoid the risk of desiccation from wind but sufficient to assist with pollination processes.

Natural hazards such as extreme temperatures and storms have historically affected Berlin, but with proper selection of grass varieties suited to resilience against minor droughts and temperature fluctuations, these should not pose critical issues. Given the increasingly warm summers, drought-resistant and heat-tolerant grass species might be ideal, such as fescues or certain types of bluegrass, which thrive in fluctuating conditions and can recover well from heat stress.

In summary, while growing grass in the proposed location in Berlin is feasible, attention should be paid to selecting appropriate grass species that can withstand increasing temperatures and urban-induced microclimatic changes. It will also be important to monitor watering practices, especially during peak summer months, to ensure adequate moisture given the potential for increased evaporation amid rising temperatures.
2025-02-27 18:45:23 - __main__ - INFO - reading config from: config.yml
2025-02-27 18:45:23 - __main__ - INFO - reading config from: config.yml
2025-02-27 18:45:31 - __main__ - INFO - reading config from: config.yml
2025-02-27 18:45:31 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-27 18:45:31 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 18:45:31 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-27 18:45:31 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-27 18:45:31 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 18:45:31 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-27 18:45:31 - root - INFO - Is the point on land? Yes.
2025-02-27 18:45:32 - climsight_engine - INFO - start agent_request
2025-02-27 18:45:32 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:32 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:32 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:32 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:32 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:32 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:32 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:32 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:33 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 18:45:33 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:33 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:33 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:33 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:33 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:33 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:33 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:33 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:33 - climsight_engine - INFO - IPCC RAG agent in work.
2025-02-27 18:45:33 - climsight_engine - INFO - General RAG agent in work.
2025-02-27 18:45:34 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 18:45:34 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 18:45:34 - rag - INFO - Chunks returned from RAG: {'context': 'on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n 9 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n10 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n11\n\n12 Figure AI.21: Climatic and environmental stresses on global production of maize.\n13 The global effects of five climatic and environmental stresses on maize yield. The combined effect of each\n14 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n15 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n16 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n17 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n18\n\n19 Figure AI.22: Projected changes in global maize production.\n20 For maize production time series are shown as relative changes to the 1983-2013 reference period under\n21 SSP126 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop\n22 model combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model\n23 response (and a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the\n24 historical variability (1983-2013; ensemble median) and open circles highlight the "time of climate impact\n25 emergence" (TCIE), the year in which the smoothed median response exceeds the historical envelope. For\n26 context, the TCIE calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop\n\n5.3; 5.5.3; 5.4.1; Figure FAQ 5.1; Figure 9.22; 15.3.4; 15.3.3}\n\n44\n\n45 Figure AI.18: Climatic and environmental stresses on global production of wheat.\n46 The global effects of five climatic and environmental stresses on wheat yield. The combined effect of each\n47 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n48 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n49 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n50 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n51 wheat was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n52\n\n53 Figure AI.19: Climatic and environmental stresses on global production of soybean.\n54 The global effects of five climatic and environmental stresses on soybean yield. The combined effect of each\n55 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n56 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n\nDo Not Cite, Quote or Distribute  AI-63    Total pages: 74\nFINAL DRAFT                       Annex I  IPCC WGII Sixth Assessment Report\n\n 1 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n 2 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n 3 soybean was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n 4\n\n 5 Figure AI.20: Climatic and environmental stresses on global production of rice.\n 6 The global effects of five climatic and environmental stresses on rice yield. The combined effect of each\n 7 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but\n\nis indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop models for current growing regions (>10 ha). Hatching indicates areas\n29 where less than 70% of the climate-crop model combinations agree on the sign of impact. Regional\n30 production time series (e) are similar to (a), but stratified for the four major KoeppenGeiger climate zones\n31 (temperature limited, temperate/humid, subtropical, and tropical). The percentage of the total global\n32 production contributed by each zone is indicated in the top right corner of the inlets. All data are shown for\n33 the default [CO2] {Jägermeyr et al. 2021; 5.4.3.2}\n\n34\n\n35 Figure AI.23: Projected changes in global wheat production.\n36 Production time series are shown as relative changes to the 1983-2013 reference period under SSP126\n37 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop model\n38 combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model response (and\n39 a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the historical variability\n40 (1983-2013; ensemble median) and open circles highlight the "time of climate impact emergence" (TCIE),\n41 the year in which the smoothed median response exceeds the historical envelope. For context, the TCIE\n42 calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on GC6 (>2099 if no\n43 TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under SSP585 across\n44 climate and crop models for current growing regions (>10 ha). Hatching indicates areas where less than 70%\n45 of the climate-crop model combinations agree on the sign of impact. Regional production time series (e) are\n46 similar to (a), but stratified for the four major KoeppenGeiger climate zones (temperature limited,\n47\n\ntransboundary governance and ecosystem-\n55   based management, livelihood diversification, capacity development and improved knowledge-sharing will\n\n\n     Do Not Cite, Quote or Distribute                      TS-64                                         Total pages: 96\n     FINAL DRAFT                                    Technical Summary             IPCC WGII Sixth Assessment Report\n\n 1   reduce conflict and promote the fair distribution of sustainably-harvested wild products and revenues\n 2   (medium confidence). {5.8.4, 5.14.3, CCP5.4.2, CCB MOVING PLATE}\n 3\n 4   TS.D.5.5 Adaptation options that promote intensification of production have been widely adopted in\n 5   agriculture for climate change adaptation, but with potential negative effects (high confidence).\n 6   Agricultural intensification addresses short-term food security and livelihood goals but has trade-offs in\n 7   equity, biodiversity, and ecosystem services (high confidence). Irrigation is widely used and effective for\n 8   yield stability, but with several negative outcomes, including water demand (high confidence), groundwater\n 9   depletion (high confidence); alteration of local to regional climates (high confidence); increasing soil salinity\n10   (medium confidence) widening inequalities and loss of rural smallholder livelihoods with weak governance\n11   (medium confidence). Conventional breeding assisted by genomics introduces traits that adapt crops to\n12   climate change (high confidence). Genetic improvements through modern biotechnology have the potential\n13   to increase climate resilience in food production systems (high confidence), but with biophysical ceilings,\n14   and technical, agroecosystem, socio-economic and political variables strongly influence and limit uptake of\n15   climate-resilient crops, particularly for smallholders (medium confidence).{4.6.2, Box 4.3, 4.7.1, 5.4.4,\n16   5.12.5, 5.13.4, 5.14.1, 10.2.2, 12.5.4, 13.5.1, 13.5.2, 13.5.14, 14.5.4, 15.3.4, 17.5.1}\n17\n18', 'location': 'Address: railway: Berlin Hauptbahnhof (Tief), road: Europaplatz, quarter: Europacity, suburb: Moabit, borough: Mitte, city: Berlin, ISO3166-2-lvl4: DE-BE, postcode: 10557, country: Germany, country_code: de', 'question': 'grow maice'}
2025-02-27 18:45:35 - rag - INFO - Chunks returned from RAG: {'context': '60(22), 8196-8208. https://doi.org/10.1021/acs.iecr.0c04839.\nLecocq, F., Winkler, H., Daka, J.P., Fu, S., Gerber, J.S., Kartha, S. et al. (2022). Mitigation and development pathways\nin the near- to mid-term. In Climate Change 2022: Mitigation of Climate Change. Contribution of Working\nGroup III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Shukla, P.R.,\nSkea, J., Reisinger, A., Slade, R., Fradera, R., Pathak, M. et al. (eds.). Cambridge and New York: Cambridge\nUniversity Press. Chapter 4. 409-502. https://doi.org/10.1017/9781009157926.006.\nLenzi, D., Lamb, W.F., Hilaire, J., Kowarsch, M. and Minx, J.C. (2018). Don’t deploy negative emissions technologies\nwithout ethical analysis. Nature 561, 303-305. https://doi.org/10.1038/d41586-018-06695-5.\n\n77\n\n\x0cEmissions Gap Report 2023: Broken Record\n\nM\n\nN\n\nO\nP\n\nR\n\n78\n\nMacDougall, A.H., Frölicher, T.L., Jones, C.D., Rogelj, J., Matthews, H.D., Zickfeld, K., Arora, V.K. et al. (2020).\nIs there warming in the pipeline? A multi-model analysis of the zero emissions commitment from CO 2 .\nBiogeosciences 17(11), 2987-3016. https://doi.org/10.5194/bg-17-2987-2020.\nMace, M.J., Fyson, C.L., Schaeffer, M. and Hare, W.L. (2021). Large-scale carbon dioxide removal to meet the\n1.5°C limit: Key governance gaps, challenges and priority responses. Global Policy 12(S1), 67-81. https://\ndoi.org/10.1111/1758-5899.12921.\nMadhu, K., Pauliuk, S., Dhathri, S. and Creutzig, F. (2021). Understanding environmental trade-offs and resource\ndemand of direct air capture technologies through comparative life-cycle assessment. Nature Energy 6(11),\n1035-1044. https://doi.org/10.1038/s41560-021-00922-6.\nMarkusson, N., McLaren, D. and Tyfield, D. (2018). Towards a cultural political economy of mitigation deterrence\nby negative emissions technologies (NETs). Global Sustainability 1, e10. https://doi.org/10.1017/sus.2018.10.\nMcElwee, P. (2023). Advocating afforestation, betting on BECCS: Land-based negative emissions\n\nmelt conditions through 2023: (left) Cumulative melt days for Greenland in 2023. White areas indicate no melting occurred.\n(right) Percentage of the ice sheet experiencing melting conditions each day.\nSource: Images and analysis courtesy of Thomas Mote, University of Georgia, and the National Snow and Ice Data Center (NSIDC).\n\nBased on estimates from an ensemble of regional climate models, the Greenland ice sheet\ncontinued to lose mass in the hydrological year 2022/2023 (from 1 September 2022 to 31 August\n2023).43 Annual snow accumulation over Greenland still exceeds surface melt in most years,\ngiving a positive surface mass balance. For 2022–2023 this was estimated at +317 Gt, below\nthe long-term average but well above the extreme melt years of 2011–2012 and 2018–2019.\nCombined with the basal mass balance (−27 Gt) and the marine mass balance (−504 Gt), the\nestimated 2022–2023 ice sheet total mass balance was about −217 Gt.\nThe summer 2023 melt season was relatively intense, punctuated by major heatwaves in\nJuly and August.44 Satellite melt extent data indicate that the ice sheet had the third highest\ncumulative melt-day area45 on record (1978–2023), after the extreme melt season of 2012\nand 2010 (see Figure 14). It was the warmest summer on record (1987–present) at Summit\nStation, 46 3.4 °C warmer than the 1991–2020 average and 1.0 °C warmer than the previous\nrecord.47 Summit Station experienced melting conditions for the fifth year on record (2012,\n2019, 2021, 2022 and 2023); ice core records indicate that significant melting conditions last\nhappened in the late nineteenth century.48\nThe Ice Sheet Mass Balance Inter-comparison Exercise has documented the acceleration\nin combined mass loss from the Greenland and Antarctic ice sheets over the period of the\nsatellite record, 1992–2020.49 The average total mass balance of the Greenland and Antarctic\nice sheets over this period were −169 Gt yr-1 and −92 Gt yr-1, 50 respectively, and −261 Gt yr-1\ncombined. Combining\n\nequivalent. This is nominally the largest loss of ice\non record (1950–2023), driven by extremely negative mass balance in both western North America\nand Europe.\nGlaciers in western North America and the European Alps experienced an extreme melt season. In\nSwitzerland, glaciers have lost about 10% of their remaining volume in the past two years.\n\nGlaciers are formed from snow that has compacted to form ice, which then deforms and flows\ndownhill. Glaciers comprise two zones: an accumulation zone, where accumulation of mass\nfrom snowfall exceeds ice loss, and an ablation zone, where ice loss (ablation) from melting\nand other mechanisms exceeds accumulation. Where glaciers end in a lake or the ocean, ice\nloss can occur through melting where the ice meets the water, and via calving, when chunks\nof the glacier break off.\nGlacier mass balance – the amount of mass gained or lost by the glacier – is commonly\nexpressed as the annual thickness change averaged over the glacier area, in turn expressed\nin metres of water equivalent (m w.e.).51 Melt rates are strongly affected by glacier albedo,\nthe fraction of sunlight that is reflected by the glacier surface. Exposed glacier ice is darker\nand therefore has a lower albedo than the seasonal snowpack; it is also sensitive to darkening\nfrom mineral dust, black carbon, algal activity and fallout from forest fires. Reduced snow\ncover, long melt seasons and wildfire activity all serve to concentrate darker material on the\nglacier surface, decreasing its albedo and thereby increasing melt.\n\n\x0cCryosphere\n\n15\n\nPreliminary data from a set of reference glaciers monitored by the World Glacier Monitoring\nService (WGMS) indicate a global annual mass balance for the hydrological year 2022/2023\nof –1.2 m w.e., which is slightly more negative than 2021/2022 for the set of about 60 WGMS\nreference glaciers. Based on the available glacier data, this is nominally a record low mass\nbalance (1950–2023, see Figure 16). Record loss was driven by\n\nCommittee (2022) may assist countries in this\nregard. Most importantly, the inclusion of information on\nthe outcomes associated with implemented actions is\nnecessary for understanding adaptation effectiveness,\nscale and adequacy. These lessons are also relevant when\ncountries prepare their first biennial transparency reports\nthat are due by the end of 2024, even though adaptation is\na voluntary component and LDCs and SIDS have discretion\nover whether to publish a biennial transparency report.\nFinally, it is worth noting that 75 per cent of developing\ncountries that submitted a stand-alone adaptation\ncommunication indicated that they received technical\nand/or financial support for adaptation reporting from\nvarious sources, most frequently from bilateral finance\nproviders through the NAP Global Network and in fewer\ncases through national adaptation plan readiness funds\nfrom GCF. This finding underscores the importance of\nsupport in enabling developing countries, especially\nthose with the lowest resources and capacities, to report\non adaptation.\n\n\x0cChapter 3 – Global progress on adaptation implementation\n\nCase study: Ecosystem-based adaptation – Rice farming in Cambodia and Madagascar\nShifting rainfall patterns, unpredictable temperatures\nand extreme events are putting rice production and rainfed agriculture under immense pressure. More resilience\nto erratic rainfall, drought, higher temperatures and\nother climate hazards is needed urgently.\nIn Cambodia and Madagascar, two countries which are\nheavily reliant on rice cultivation for food security and\neconomic stability, recent advances have shown the\npotential of ecosystem‑based adaptation approaches\nfor climate‑resilient food systems.\nTaking a holistic strategy to implementing integrated\nadaptation measures, which combines climate risk\n\nassessments with the agricultural, environmental,\neconomic and institutional factors of climate\nresilience, in addition to community engagement\nand value restoration of agroecosystems,', 'location': 'Address: railway: Berlin Hauptbahnhof (Tief), road: Europaplatz, quarter: Europacity, suburb: Moabit, borough: Mitte, city: Berlin, ISO3166-2-lvl4: DE-BE, postcode: 10557, country: Germany, country_code: de', 'question': 'grow maice'}
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2025-02-27 18:45:36 - climsight_engine - INFO - data_agent_response: {'input_params': {'simulation1': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.1,\n    "Total Precipitation (mm\\/month)":54.45,\n    "Wind U (m s**-1)":1.5,\n    "Wind V (m s**-1)":1.04,\n    "Wind Speed (m\\/s)":1.83,\n    "Wind Direction (\\u00b0)":235.33\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":0.82,\n    "Total Precipitation (mm\\/month)":54.9,\n    "Wind U (m s**-1)":1.32,\n    "Wind V (m s**-1)":1.05,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":231.6\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":-0.29,\n    "Total Precipitation (mm\\/month)":35.17,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":0.3,\n    "Wind Speed (m\\/s)":0.37,\n    "Wind Direction (\\u00b0)":215.23\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":3.16,\n    "Total Precipitation (mm\\/month)":47.51,\n    "Wind U (m s**-1)":1.05,\n    "Wind V (m s**-1)":0.02,\n    "Wind Speed (m\\/s)":1.05,\n    "Wind Direction (\\u00b0)":268.92\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":7.95,\n    "Total Precipitation (mm\\/month)":43.6,\n    "Wind U (m s**-1)":0.43,\n    "Wind V (m s**-1)":-0.34,\n    "Wind Speed (m\\/s)":0.54,\n    "Wind Direction (\\u00b0)":308.22\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":14.17,\n    "Total Precipitation (mm\\/month)":61.8,\n    "Wind U (m s**-1)":0.26,\n    "Wind V (m s**-1)":-0.22,\n    "Wind Speed (m\\/s)":0.34,\n    "Wind Direction (\\u00b0)":310.3\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":16.92,\n    "Total Precipitation (mm\\/month)":64.13,\n    "Wind U (m s**-1)":1.12,\n    "Wind V (m s**-1)":-0.42,\n    "Wind Speed (m\\/s)":1.2,\n    "Wind Direction (\\u00b0)":290.55\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":19.69,\n    "Total Precipitation (mm\\/month)":52.02,\n    "Wind U (m s**-1)":1.48,\n    "Wind V (m s**-1)":-0.12,\n    "Wind Speed (m\\/s)":1.48,\n    "Wind Direction (\\u00b0)":274.6\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.3,\n    "Total Precipitation (mm\\/month)":46.67,\n    "Wind U (m s**-1)":1.01,\n    "Wind V (m s**-1)":-0.41,\n    "Wind Speed (m\\/s)":1.08,\n    "Wind Direction (\\u00b0)":292.12\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.32,\n    "Total Precipitation (mm\\/month)":31.07,\n    "Wind U (m s**-1)":0.54,\n    "Wind V (m s**-1)":0.19,\n    "Wind Speed (m\\/s)":0.57,\n    "Wind Direction (\\u00b0)":250.21\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":10.92,\n    "Total Precipitation (mm\\/month)":31.91,\n    "Wind U (m s**-1)":0.28,\n    "Wind V (m s**-1)":0.48,\n    "Wind Speed (m\\/s)":0.56,\n    "Wind Direction (\\u00b0)":209.96\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.0,\n    "Total Precipitation (mm\\/month)":45.19,\n    "Wind U (m s**-1)":0.54,\n    "Wind V (m s**-1)":1.33,\n    "Wind Speed (m\\/s)":1.43,\n    "Wind Direction (\\u00b0)":202.27\n  }\n]', 'simulation2': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.57,\n    "Total Precipitation (mm\\/month)":65.41,\n    "Wind U (m s**-1)":1.66,\n    "Wind V (m s**-1)":1.05,\n    "Wind Speed (m\\/s)":1.96,\n    "Wind Direction (\\u00b0)":237.74\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":0.83,\n    "Total Precipitation (mm\\/month)":65.67,\n    "Wind U (m s**-1)":1.53,\n    "Wind V (m s**-1)":1.27,\n    "Wind Speed (m\\/s)":1.99,\n    "Wind Direction (\\u00b0)":230.22\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.66,\n    "Total Precipitation (mm\\/month)":39.15,\n    "Wind U (m s**-1)":1.5,\n    "Wind V (m s**-1)":1.09,\n    "Wind Speed (m\\/s)":1.85,\n    "Wind Direction (\\u00b0)":234.02\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.92,\n    "Total Precipitation (mm\\/month)":51.82,\n    "Wind U (m s**-1)":0.73,\n    "Wind V (m s**-1)":0.61,\n    "Wind Speed (m\\/s)":0.95,\n    "Wind Direction (\\u00b0)":230.11\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":9.47,\n    "Total Precipitation (mm\\/month)":46.28,\n    "Wind U (m s**-1)":0.09,\n    "Wind V (m s**-1)":-0.21,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":336.71\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":13.61,\n    "Total Precipitation (mm\\/month)":56.85,\n    "Wind U (m s**-1)":0.06,\n    "Wind V (m s**-1)":-0.36,\n    "Wind Speed (m\\/s)":0.37,\n    "Wind Direction (\\u00b0)":350.32\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":17.58,\n    "Total Precipitation (mm\\/month)":69.71,\n    "Wind U (m s**-1)":0.87,\n    "Wind V (m s**-1)":-0.48,\n    "Wind Speed (m\\/s)":0.99,\n    "Wind Direction (\\u00b0)":298.93\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":20.16,\n    "Total Precipitation (mm\\/month)":50.62,\n    "Wind U (m s**-1)":1.14,\n    "Wind V (m s**-1)":-0.27,\n    "Wind Speed (m\\/s)":1.17,\n    "Wind Direction (\\u00b0)":283.42\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.07,\n    "Total Precipitation (mm\\/month)":55.34,\n    "Wind U (m s**-1)":1.09,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":1.1,\n    "Wind Direction (\\u00b0)":277.86\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.9,\n    "Total Precipitation (mm\\/month)":29.56,\n    "Wind U (m s**-1)":0.57,\n    "Wind V (m s**-1)":0.18,\n    "Wind Speed (m\\/s)":0.6,\n    "Wind Direction (\\u00b0)":252.85\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.33,\n    "Total Precipitation (mm\\/month)":67.89,\n    "Wind U (m s**-1)":1.25,\n    "Wind V (m s**-1)":0.97,\n    "Wind Speed (m\\/s)":1.59,\n    "Wind Direction (\\u00b0)":232.2\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":4.66,\n    "Total Precipitation (mm\\/month)":56.22,\n    "Wind U (m s**-1)":0.48,\n    "Wind V (m s**-1)":0.59,\n    "Wind Speed (m\\/s)":0.76,\n    "Wind Direction (\\u00b0)":219.37\n  }\n]', 'simulation3': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.75,\n    "Total Precipitation (mm\\/month)":63.78,\n    "Wind U (m s**-1)":1.26,\n    "Wind V (m s**-1)":0.99,\n    "Wind Speed (m\\/s)":1.6,\n    "Wind Direction (\\u00b0)":231.88\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":3.35,\n    "Total Precipitation (mm\\/month)":68.94,\n    "Wind U (m s**-1)":2.18,\n    "Wind V (m s**-1)":1.63,\n    "Wind Speed (m\\/s)":2.72,\n    "Wind Direction (\\u00b0)":233.24\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.96,\n    "Total Precipitation (mm\\/month)":65.51,\n    "Wind U (m s**-1)":2.25,\n    "Wind V (m s**-1)":0.78,\n    "Wind Speed (m\\/s)":2.38,\n    "Wind Direction (\\u00b0)":250.93\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.49,\n    "Total Precipitation (mm\\/month)":43.15,\n    "Wind U (m s**-1)":1.62,\n    "Wind V (m s**-1)":0.47,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":253.9\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":10.04,\n    "Total Precipitation (mm\\/month)":36.43,\n    "Wind U (m s**-1)":0.16,\n    "Wind V (m s**-1)":-0.19,\n    "Wind Speed (m\\/s)":0.25,\n    "Wind Direction (\\u00b0)":320.22\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":14.12,\n    "Total Precipitation (mm\\/month)":78.03,\n    "Wind U (m s**-1)":0.03,\n    "Wind V (m s**-1)":-0.61,\n    "Wind Speed (m\\/s)":0.62,\n    "Wind Direction (\\u00b0)":357.53\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":18.46,\n    "Total Precipitation (mm\\/month)":73.02,\n    "Wind U (m s**-1)":0.95,\n    "Wind V (m s**-1)":-0.28,\n    "Wind Speed (m\\/s)":0.99,\n    "Wind Direction (\\u00b0)":286.67\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":20.96,\n    "Total Precipitation (mm\\/month)":52.22,\n    "Wind U (m s**-1)":1.1,\n    "Wind V (m s**-1)":-0.37,\n    "Wind Speed (m\\/s)":1.16,\n    "Wind Direction (\\u00b0)":288.77\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":21.41,\n    "Total Precipitation (mm\\/month)":25.7,\n    "Wind U (m s**-1)":0.96,\n    "Wind V (m s**-1)":-0.34,\n    "Wind Speed (m\\/s)":1.02,\n    "Wind Direction (\\u00b0)":289.59\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.65,\n    "Total Precipitation (mm\\/month)":45.41,\n    "Wind U (m s**-1)":0.78,\n    "Wind V (m s**-1)":0.12,\n    "Wind Speed (m\\/s)":0.79,\n    "Wind Direction (\\u00b0)":261.07\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.78,\n    "Total Precipitation (mm\\/month)":31.58,\n    "Wind U (m s**-1)":0.41,\n    "Wind V (m s**-1)":0.39,\n    "Wind Speed (m\\/s)":0.57,\n    "Wind Direction (\\u00b0)":226.62\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.08,\n    "Total Precipitation (mm\\/month)":52.9,\n    "Wind U (m s**-1)":1.35,\n    "Wind V (m s**-1)":1.16,\n    "Wind Speed (m\\/s)":1.77,\n    "Wind Direction (\\u00b0)":229.34\n  }\n]', 'simulation5': '[\n  {\n    "Temperature (\\u00b0C)":0.47,\n    "Total Precipitation (mm\\/month)":10.96,\n    "Wind U (m s**-1)":0.16,\n    "Wind V (m s**-1)":0.01,\n    "Wind Speed (m\\/s)":0.13,\n    "Wind Direction (\\u00b0)":2.41\n  },\n  {\n    "Temperature (\\u00b0C)":0.01,\n    "Total Precipitation (mm\\/month)":10.77,\n    "Wind U (m s**-1)":0.21,\n    "Wind V (m s**-1)":0.22,\n    "Wind Speed (m\\/s)":0.3,\n    "Wind Direction (\\u00b0)":-1.38\n  },\n  {\n    "Temperature (\\u00b0C)":2.95,\n    "Total Precipitation (mm\\/month)":3.98,\n    "Wind U (m s**-1)":1.29,\n    "Wind V (m s**-1)":0.79,\n    "Wind Speed (m\\/s)":1.48,\n    "Wind Direction (\\u00b0)":18.79\n  },\n  {\n    "Temperature (\\u00b0C)":2.76,\n    "Total Precipitation (mm\\/month)":4.31,\n    "Wind U (m s**-1)":-0.32,\n    "Wind V (m s**-1)":0.59,\n    "Wind Speed (m\\/s)":-0.1,\n    "Wind Direction (\\u00b0)":-38.81\n  },\n  {\n    "Temperature (\\u00b0C)":1.52,\n    "Total Precipitation (mm\\/month)":2.68,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":0.13,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":28.49\n  },\n  {\n    "Temperature (\\u00b0C)":-0.56,\n    "Total Precipitation (mm\\/month)":-4.95,\n    "Wind U (m s**-1)":-0.2,\n    "Wind V (m s**-1)":-0.14,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":40.02\n  },\n  {\n    "Temperature (\\u00b0C)":0.66,\n    "Total Precipitation (mm\\/month)":5.58,\n    "Wind U (m s**-1)":-0.25,\n    "Wind V (m s**-1)":-0.06,\n    "Wind Speed (m\\/s)":-0.21,\n    "Wind Direction (\\u00b0)":8.38\n  },\n  {\n    "Temperature (\\u00b0C)":0.47,\n    "Total Precipitation (mm\\/month)":-1.4,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":-0.31,\n    "Wind Direction (\\u00b0)":8.82\n  },\n  {\n    "Temperature (\\u00b0C)":-0.23,\n    "Total Precipitation (mm\\/month)":8.67,\n    "Wind U (m s**-1)":0.08,\n    "Wind V (m s**-1)":0.26,\n    "Wind Speed (m\\/s)":0.02,\n    "Wind Direction (\\u00b0)":-14.26\n  },\n  {\n    "Temperature (\\u00b0C)":0.58,\n    "Total Precipitation (mm\\/month)":-1.51,\n    "Wind U (m s**-1)":0.03,\n    "Wind V (m s**-1)":-0.01,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":2.64\n  },\n  {\n    "Temperature (\\u00b0C)":0.41,\n    "Total Precipitation (mm\\/month)":35.98,\n    "Wind U (m s**-1)":0.97,\n    "Wind V (m s**-1)":0.49,\n    "Wind Speed (m\\/s)":1.03,\n    "Wind Direction (\\u00b0)":22.24\n  },\n  {\n    "Temperature (\\u00b0C)":-2.34,\n    "Total Precipitation (mm\\/month)":11.03,\n    "Wind U (m s**-1)":-0.06,\n    "Wind V (m s**-1)":-0.74,\n    "Wind Speed (m\\/s)":-0.67,\n    "Wind Direction (\\u00b0)":17.1\n  }\n]', 'simulation6': '[\n  {\n    "Temperature (\\u00b0C)":0.65,\n    "Total Precipitation (mm\\/month)":9.33,\n    "Wind U (m s**-1)":-0.24,\n    "Wind V (m s**-1)":-0.05,\n    "Wind Speed (m\\/s)":-0.23,\n    "Wind Direction (\\u00b0)":-3.45\n  },\n  {\n    "Temperature (\\u00b0C)":2.53,\n    "Total Precipitation (mm\\/month)":14.04,\n    "Wind U (m s**-1)":0.86,\n    "Wind V (m s**-1)":0.58,\n    "Wind Speed (m\\/s)":1.03,\n    "Wind Direction (\\u00b0)":1.64\n  },\n  {\n    "Temperature (\\u00b0C)":3.25,\n    "Total Precipitation (mm\\/month)":30.34,\n    "Wind U (m s**-1)":2.04,\n    "Wind V (m s**-1)":0.48,\n    "Wind Speed (m\\/s)":2.01,\n    "Wind Direction (\\u00b0)":35.7\n  },\n  {\n    "Temperature (\\u00b0C)":2.33,\n    "Total Precipitation (mm\\/month)":-4.36,\n    "Wind U (m s**-1)":0.57,\n    "Wind V (m s**-1)":0.45,\n    "Wind Speed (m\\/s)":0.64,\n    "Wind Direction (\\u00b0)":-15.02\n  },\n  {\n    "Temperature (\\u00b0C)":2.09,\n    "Total Precipitation (mm\\/month)":-7.17,\n    "Wind U (m s**-1)":-0.27,\n    "Wind V (m s**-1)":0.15,\n    "Wind Speed (m\\/s)":-0.29,\n    "Wind Direction (\\u00b0)":12.0\n  },\n  {\n    "Temperature (\\u00b0C)":-0.05,\n    "Total Precipitation (mm\\/month)":16.23,\n    "Wind U (m s**-1)":-0.23,\n    "Wind V (m s**-1)":-0.39,\n    "Wind Speed (m\\/s)":0.28,\n    "Wind Direction (\\u00b0)":47.23\n  },\n  {\n    "Temperature (\\u00b0C)":1.54,\n    "Total Precipitation (mm\\/month)":8.89,\n    "Wind U (m s**-1)":-0.17,\n    "Wind V (m s**-1)":0.14,\n    "Wind Speed (m\\/s)":-0.21,\n    "Wind Direction (\\u00b0)":-3.88\n  },\n  {\n    "Temperature (\\u00b0C)":1.27,\n    "Total Precipitation (mm\\/month)":0.2,\n    "Wind U (m s**-1)":-0.38,\n    "Wind V (m s**-1)":-0.25,\n    "Wind Speed (m\\/s)":-0.32,\n    "Wind Direction (\\u00b0)":14.17\n  },\n  {\n    "Temperature (\\u00b0C)":1.11,\n    "Total Precipitation (mm\\/month)":-20.97,\n    "Wind U (m s**-1)":-0.05,\n    "Wind V (m s**-1)":0.07,\n    "Wind Speed (m\\/s)":-0.06,\n    "Wind Direction (\\u00b0)":-2.53\n  },\n  {\n    "Temperature (\\u00b0C)":0.33,\n    "Total Precipitation (mm\\/month)":14.34,\n    "Wind U (m s**-1)":0.24,\n    "Wind V (m s**-1)":-0.07,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":10.86\n  },\n  {\n    "Temperature (\\u00b0C)":0.86,\n    "Total Precipitation (mm\\/month)":-0.33,\n    "Wind U (m s**-1)":0.13,\n    "Wind V (m s**-1)":-0.09,\n    "Wind Speed (m\\/s)":0.01,\n    "Wind Direction (\\u00b0)":16.66\n  },\n  {\n    "Temperature (\\u00b0C)":0.08,\n    "Total Precipitation (mm\\/month)":7.71,\n    "Wind U (m s**-1)":0.81,\n    "Wind V (m s**-1)":-0.17,\n    "Wind Speed (m\\/s)":0.34,\n    "Wind Direction (\\u00b0)":27.07\n  }\n]'}, 'content_message': '\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2020-2029. The nextGEMS pre-final simulations for years 2030x..This simulation serves as a historical reference to extract its values from other simulations. :\n {simulation1}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2030-2039. The nextGEMS pre-final simulations for years 2030x. :\n {simulation2}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2040-2049. The nextGEMS pre-final simulations for years 2040x. :\n {simulation3}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2030-2039 compared to the simulation for 2020-2029. :\n {simulation5}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2040-2049 compared to the simulation for 2020-2029. :\n {simulation6}'}
2025-02-27 18:45:36 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 18:45:37 - root - ERROR - land_use_data = None: HTTPConnectionPool(host='overpass-api.de', port=80): Read timed out. (read timeout=3)
Traceback (most recent call last):
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/urllib3/connectionpool.py", line 534, in _make_request
    response = conn.getresponse()
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/urllib3/connection.py", line 516, in getresponse
    httplib_response = super().getresponse()
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/http/client.py", line 1375, in getresponse
    response.begin()
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/http/client.py", line 318, in begin
    version, status, reason = self._read_status()
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/http/client.py", line 279, in _read_status
    line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/socket.py", line 717, in readinto
    return self._sock.recv_into(b)
TimeoutError: timed out

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/requests/adapters.py", line 667, in send
    resp = conn.urlopen(
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/urllib3/connectionpool.py", line 841, in urlopen
    retries = retries.increment(
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/urllib3/util/retry.py", line 474, in increment
    raise reraise(type(error), error, _stacktrace)
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/urllib3/util/util.py", line 39, in reraise
    raise value
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/urllib3/connectionpool.py", line 787, in urlopen
    response = self._make_request(
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/urllib3/connectionpool.py", line 536, in _make_request
    self._raise_timeout(err=e, url=url, timeout_value=read_timeout)
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/urllib3/connectionpool.py", line 367, in _raise_timeout
    raise ReadTimeoutError(
urllib3.exceptions.ReadTimeoutError: HTTPConnectionPool(host='overpass-api.de', port=80): Read timed out. (read timeout=3)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/ikuznets/work/projects/climsight/code/climsight/src/climsight/climsight_engine.py", line 662, in zero_rag_agent
    land_use_data = fetch_land_use(lon, lat)
  File "/Users/ikuznets/work/projects/climsight/code/climsight/src/climsight/geo_functions.py", line 352, in fetch_land_use
    response = requests.get(overpass_url, params={"data": overpass_query}, timeout=3)
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/requests/api.py", line 73, in get
    return request("get", url, params=params, **kwargs)
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/requests/api.py", line 59, in request
    return session.request(method=method, url=url, **kwargs)
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/requests/sessions.py", line 589, in request
    resp = self.send(prep, **send_kwargs)
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/requests/sessions.py", line 703, in send
    r = adapter.send(request, **kwargs)
  File "/Users/ikuznets/miniforge3/envs/climsight/lib/python3.10/site-packages/requests/adapters.py", line 713, in send
    raise ReadTimeout(e, request=request)
requests.exceptions.ReadTimeout: HTTPConnectionPool(host='overpass-api.de', port=80): Read timed out. (read timeout=3)
2025-02-27 18:45:37 - root - ERROR - 'NoneType' object is not subscriptable. Continue with: current_land_use = None
Traceback (most recent call last):
  File "/Users/ikuznets/work/projects/climsight/code/climsight/src/climsight/climsight_engine.py", line 669, in zero_rag_agent
    current_land_use = land_use_data["elements"][0]["tags"]["landuse"]
TypeError: 'NoneType' object is not subscriptable
2025-02-27 18:45:37 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 18:45:37 - root - INFO - Rendered RAG response: The provided content does not specifically address the cultivation of maize in Germany or Berlin. General recommendations for growing maize include ensuring optimal soil conditions, sufficient sunlight, adequate water supply, and managing pests and diseases. If more specific data or practices regarding maize cultivation in Germany are required, external resources or localized agricultural expertise would need to be consulted.
2025-02-27 18:45:37 - climsight_engine - INFO - general_rag_agent_response: The provided content does not specifically address the cultivation of maize in Germany or Berlin. General recommendations for growing maize include ensuring optimal soil conditions, sufficient sunlight, adequate water supply, and managing pests and diseases. If more specific data or practices regarding maize cultivation in Germany are required, external resources or localized agricultural expertise would need to be consulted.
2025-02-27 18:45:39 - climsight_engine - INFO - zero_agent_response: {'input_params': {'elevation': '36.0', 'soil': 'Cambisols', 'biodiv': 'Pristurus', 'distance_to_coastline': '143240.00374445863', 'nat_hazards':        year          disastertype
13415  2002                 storm
13428  2006                 storm
13434  2010                 storm
33496  2003  extreme temperature 
33517  2006  extreme temperature 
33528  2009  extreme temperature 
33536  2009  extreme temperature 
33552  2012  extreme temperature 
33569  2012  extreme temperature , 'population':     Time  TotalPopulationAsOf1July  PopulationDensity  PopulationGrowthRate
0   1980              77786.703000         223.165900             -0.145000
1   1990              78072.678100         223.986330              0.237400
2   2000              80995.587100         232.372000              0.241300
3   2010              81294.847800         233.230560             -0.021600
4   2020              82291.120600         236.088820              0.245600
5   2030              83132.605000         238.502990             -0.082500
6   2040              81965.316200         235.154110             -0.195300
7   2050              80013.692600         229.555010             -0.288400
8   2060              77399.456100         222.054900             -0.362600
9   2070              74806.608900         214.616160             -0.299400
10  2080              72785.056300         208.816440             -0.266400
11  2090              70893.830000         203.390610             -0.247800
12  2100              69481.298222         199.338133             -0.171333
13  2101                       NaN                NaN                   NaN}, 'content_message': 'Elevation above sea level: {elevation} \nCurrent soil type: {soil} \nOccuring species: {biodiv} \nDistance to the closest coastline: {distance_to_coastline} \nNatural hazards: {nat_hazards} \nPopulation in Germany data: {population} \n'}
2025-02-27 18:45:39 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-27 18:45:41 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 18:45:49 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 18:45:49 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 18:45:49 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 18:45:51 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 18:45:51 - chromadb.telemetry.product.posthog - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.
2025-02-27 18:45:52 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 18:45:53 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 18:45:54 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 18:45:55 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 18:46:02 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-27 18:46:02 - climsight_engine - INFO - combine_agent in work
2025-02-27 18:46:02 - climsight_engine - INFO - smart_agent_response: {'output': 'The temperature for maize growth at Berlin is expected to range from 2.1°C in January to 20.3°C in September for the years 2020-2029, with future projections showing a slight increase, reaching up to 21.41°C in September for 2040-2049. According to the ECOCROP database, maize requires a minimum temperature of 10°C and an optimal range of 18°C to 33°C. The current and future temperatures in Berlin are generally suitable for maize growth, especially during the warmer months, although early spring and late autumn may be too cold.\n\nThe precipitation for maize growth at Berlin is expected to range from 31.07 mm in October to 64.13 mm in July for the years 2020-2029, with future projections showing variability, reaching up to 78.03 mm in June for 2040-2049. According to the ECOCROP database, maize requires an optimal rainfall range of 600 mm to 1200 mm annually. The monthly precipitation in Berlin is generally within the suitable range for maize, but the variability and potential decrease in some months may require irrigation to ensure optimal growth.'}
2025-02-27 18:46:02 - climsight_engine - INFO - Wikipedia_tool_reponse: • Temperature:
  - Quantitative: Requires warm days above 10 °C (50 °F) for flowering.
  - Qualitative: Maize is cold-intolerant and must be planted in the spring in temperate zones.

• Wind:
  - Qualitative: Maize pollen is dispersed by wind. Maize is prone to being uprooted by severe winds due to its shallow roots.

• Soil Type:
  - Qualitative: Maize is intolerant of nutrient-deficient soils and depends on adequate soil moisture.
2025-02-27 18:46:02 - climsight_engine - INFO - Ecocrop_search_response: Data from ECOCROP database for maize:
Scientific Name: Zea mays
Authority: L.
Family: Liliopsida:Commelinidae:Cyperales:Gramineae
Synonyms: Zea mays L. ssp. mays, Zea curagua Molina, Zea indentata Sturtev., Zea indurata Sturtev., Zea japonica Van Houtte, Zea saccharata Sturtev.
Common names: maize, corn, Mais, maiz, milho, yumi, khao phoat, bekolo, sila, sila nivava lagi, tomorokpshi, makai, makki, koane, fiso, sana, keto (Simbo/Roviana), kon, mielie, mahindi, ekidid (Karamojong), maidis stigmata, mbemba, poone, upfu, hupfu, mbila
optimal minimum temperature (°C): 18.0
optimal maximum temperature (°C): 33.0
absolute minimum temperature (°C): 10.0
absolute maximum temperature (°C): 47.0
optimal minimum rainfall (mm): 600.0
optimal maximum rainfall (mm): 1200.0
absolute minimum rainfall (mm): 400.0
absolute maximum rainfall (mm): 1800.0
optimal minimum soil PH: 5.0
optimal maximum soil PH: 7.0
absolute minimum soil PH: 4.5
absolute maximum soil PH: 8.5
absolute minimum latitude: 40.0
absolute maximum latitude: 48.0
absolute maximum altitude (meter above sea level): 4000.0
optimal minimum light intensity: very bright
optimal maximum light intensity: very bright
absolute minimum light intensity: clear skies
absolute maximum light intensity: very bright
Optimal soil depth: medium (50-150 cm)
Absolute soil depth: shallow (20-50 cm)
Optimal soil fertility: high
Absolute soil fertility: low
Optimal soil salinity: low (<4 dS/m)
Absolute soil salinity: medium (4-10 dS/m)
Minimum crop cycle (days): 65.0
Maximum crop cycle (days): 365.0

2025-02-27 18:46:23 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 18:46:23 - climsight_engine - INFO - Final_answer: Growing maize in the location specified, which is in central Berlin near the railway station, poses several challenges and opportunities. Given the urban setting, direct cultivation may not be feasible unless there's access to a community garden, urban farm, or an area designated for agricultural use.

**Climate and Temperature Suitability:**
The climate data for Berlin indicates that average monthly temperatures rise to suitable levels for maize, particularly as summer approaches, reaching up to 20.3°C in September under current conditions (2020-2029). The future climate projections for 2040-2049 suggest temperatures in summer could increase up to 21.41°C, which aligns well with the optimal minimum temperature for maize of 18°C. However, the early spring and late autumn months may still be too cool, since temperatures in March, for instance, only average around 2.96°C. It's critical to avoid planting maize when the temperatures are under 10°C, the minimum threshold for maize germination and growth.

**Precipitation and Water Requirements:**
The current and projected monthly precipitation in Berlin ranges mostly from 31.07 mm in October to 78.03 mm in June. While this range falls within the potential annual rainfall required for maize, which is between 600 mm to 1200 mm, periods of lesser precipitation might still require supplementary irrigation. Future projections show variability, indicating that adaptive irrigation strategies might be necessary during drier months to support maize cultivation adequately.

**Soil and Site Conditions:**
The urban location on Cambisols soil means that soil fertility might be a concern, particularly in an underground platform context at the Berlin Hauptbahnhof. Maize typically requires fertile, well-drained soils with adequate moisture. Urban soils can often be compacted and nutrient-poor, which may necessitate the addition of organic matter and fertilizers to enhance soil fertility. The optimal soil pH for maize is between 5.0 and 7.0, which should be tested and adjusted if necessary before planting.

**Adaptation to Natural Hazards:**
Considering Berlin has experienced extreme temperature events and storms in the past, these natural hazards could potentially affect maize viability. Maize can be vulnerable to high winds due to its shallow roots, so protective measures should be considered if maize is grown in areas prone to storm damage.

**Environmental and Legal Considerations:**
Since this is an urban site, ensure compliance with any city regulations regarding urban agriculture. Furthermore, local initiatives or community gardens might offer collective resources in terms of land and knowledge, providing a more sustainable and practical avenue for maize cultivation within the city.

In conclusion, while there are environmental and logistical challenges to growing maize in central Berlin, especially near a railway platform, the increasing temperatures and reasonable precipitation during summer months provide a supportive environment if urban agricultural practices and necessary adaptations (such as the use of raised beds and irrigation systems) are implemented. Strategic planning and collaboration with local urban farming initiatives could make this endeavor feasible.
2025-02-27 18:46:23 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:23 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:23 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:23 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:23 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:23 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:23 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:23 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:23 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:23 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:23 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:23 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:24 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:24 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:24 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:24 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:24 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:24 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:24 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:24 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:24 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:24 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:24 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 18:46:24 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:10:06 - __main__ - INFO - reading config from: config.yml
2025-02-27 20:10:06 - __main__ - INFO - reading config from: config.yml
2025-02-27 20:10:14 - __main__ - INFO - reading config from: config.yml
2025-02-27 20:10:14 - __main__ - INFO - reading config from: config.yml
2025-02-27 20:10:16 - __main__ - INFO - reading config from: config.yml
2025-02-27 20:10:17 - __main__ - INFO - reading config from: config.yml
2025-02-27 20:10:17 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading IPCC RAG database...
2025-02-27 20:10:17 - rag - INFO - RAG database loaded with 14214 documents.
2025-02-27 20:10:17 - streamlit_interface - INFO - RAG is activated and skipllmcall is False. Loading general RAG database...
2025-02-27 20:10:17 - rag - INFO - RAG database loaded with 17913 documents.
2025-02-27 20:10:17 - root - INFO - Is the point on land? Yes.
2025-02-27 20:10:20 - climsight_engine - INFO - start agent_request
2025-02-27 20:10:20 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:20 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:20 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:20 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:20 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:20 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:20 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:20 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:22 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 20:10:22 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:22 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:22 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:22 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:22 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:22 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:22 - climsight_engine - INFO - IPCC RAG agent in work.
2025-02-27 20:10:22 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:22 - climsight_engine - INFO - General RAG agent in work.
2025-02-27 20:10:22 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:22 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 20:10:22 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 20:10:22 - rag - INFO - Chunks returned from RAG: {'context': 'deficits\nand high temperature curtailed both planting area and yields with compounding negative\nimpacts on final production, particularly for smallholders and more vulnerable households in\nthe Dry Corridor. During the second part of the season, tropical storms and unexpected heavy\nrain events disrupted the normal growth of crops in certain areas near the Central America\nPacific coast. In Haiti, irregular seasonal rainfall, including periods of high-intensity precipitation,\ncontributed to decrease the production of primary crops In 2023, record maize production in\nBrazil compensated for below-average harvests due to prolonged dry spells elsewhere in South\nAmerica, especially in Argentina, where drought conditions were expected to result in a 15%\ndecrease in cereal production compared with the five-year average. The return of El Niño in\n2023 led to adverse consequences through the entire crop cycle of maize in Central America\nand northern parts of South America, where water deficits and high temperature curtailed both\nplanting area and yields with compounding negative impacts on final production, particularly\nfor smallholders and more vulnerable households in the Dry Corridor. During the second part\nof the season, tropical storms and unexpected heavy rain events disrupted the normal growth\nof crops in certain areas near the Central America Pacific coast. In Haiti, irregular seasonal\nrainfall, including periods of high-intensity precipitation, contributed to decrease the production\nof primary crops.113\nOceania is expected to experience the sharpest annual reduction rate in cereal production\nworldwide, with a 31.1% decline in 2023 compared with 2022, although this largely reflects a\nreversion to near-average conditions after exceptionally high production in 2022, with 2023\nonly slightly below five-year averages.114\nIn September, Storm Daniel brought heavy rainfall to coastal and north-eastern Libya, flooding\nnearly 3 000 ha of cropland, particularly in the Al Marj and\n\n60(22), 8196-8208. https://doi.org/10.1021/acs.iecr.0c04839.\nLecocq, F., Winkler, H., Daka, J.P., Fu, S., Gerber, J.S., Kartha, S. et al. (2022). Mitigation and development pathways\nin the near- to mid-term. In Climate Change 2022: Mitigation of Climate Change. Contribution of Working\nGroup III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Shukla, P.R.,\nSkea, J., Reisinger, A., Slade, R., Fradera, R., Pathak, M. et al. (eds.). Cambridge and New York: Cambridge\nUniversity Press. Chapter 4. 409-502. https://doi.org/10.1017/9781009157926.006.\nLenzi, D., Lamb, W.F., Hilaire, J., Kowarsch, M. and Minx, J.C. (2018). Don’t deploy negative emissions technologies\nwithout ethical analysis. Nature 561, 303-305. https://doi.org/10.1038/d41586-018-06695-5.\n\n77\n\n\x0cEmissions Gap Report 2023: Broken Record\n\nM\n\nN\n\nO\nP\n\nR\n\n78\n\nMacDougall, A.H., Frölicher, T.L., Jones, C.D., Rogelj, J., Matthews, H.D., Zickfeld, K., Arora, V.K. et al. (2020).\nIs there warming in the pipeline? A multi-model analysis of the zero emissions commitment from CO 2 .\nBiogeosciences 17(11), 2987-3016. https://doi.org/10.5194/bg-17-2987-2020.\nMace, M.J., Fyson, C.L., Schaeffer, M. and Hare, W.L. (2021). Large-scale carbon dioxide removal to meet the\n1.5°C limit: Key governance gaps, challenges and priority responses. Global Policy 12(S1), 67-81. https://\ndoi.org/10.1111/1758-5899.12921.\nMadhu, K., Pauliuk, S., Dhathri, S. and Creutzig, F. (2021). Understanding environmental trade-offs and resource\ndemand of direct air capture technologies through comparative life-cycle assessment. Nature Energy 6(11),\n1035-1044. https://doi.org/10.1038/s41560-021-00922-6.\nMarkusson, N., McLaren, D. and Tyfield, D. (2018). Towards a cultural political economy of mitigation deterrence\nby negative emissions technologies (NETs). Global Sustainability 1, e10. https://doi.org/10.1017/sus.2018.10.\nMcElwee, P. (2023). Advocating afforestation, betting on BECCS: Land-based negative emissions\n\nincreased to record levels in\n2022\nGlobal GHG emissions reached a record high of 57.4 GtCO 2 e\nin 2022, growing by 1.2 per cent (0.6 GtCO 2 e) from the\nprevious year (figure 2.1 and table 2.1). This rate is slightly\nabove the average rate in the decade preceding the\nCOVID-19 pandemic (2010–2019), when GHG emissions\ngrowth averaged 0.9 per cent per year, but was slower\nthan the emissions growth of the 1990s (1.2 per cent per\nyear) and 2000s (2.2 per cent per year). Atmospheric CO 2\nconcentrations grew to 417.9 ± 0.2 parts per million in 2022\n(World Meteorological Organization 2023), and will continue\nto grow until annual emissions are reduced sufficiently to\nbe balanced by removals. In contrast, as shown in chapter 4\nof this report, global GHG emissions must decline to levels\nbetween 33 and 41 GtCO 2 e by 2030 (chapter 4 and table 4.2)\nto get on a least-cost pathway to meeting the temperature\ngoal of the Paris Agreement.\n\n4\n\nFossil CO 2 emissions account for approximately two\nthirds of current GHG emissions using 100-year global\nwarming potentials. According to multiple datasets, fossil\nCO 2 emissions grew between 0.8–1.5 per cent in 2022 and\nwere the main contributor to the overall increase in GHG\nemissions (Friedlingstein et al. 2022; Energy Institute 2023;\nInternational Energy Agency 2023; Liu et al. 2023). CH 4 , N 2 O\nand F-gas emissions account for about one quarter of current\nGHG emissions. Although their absolute contribution to the\noverall increase in 2022 was lower since they represent a\nmuch smaller share of the total, emissions of these gases\nare increasing rapidly: in 2022, F-gas emissions grew by\n5.5 per cent, followed by CH 4 at 1.8 per cent and N 2 O at\n0.9 per cent.\nGlobal net LULUCF CO 2 emissions – using the global\nbookkeeping approach – remained steady in 2022, but\nare based on an early projection of land-use activity with\nrelatively high uncertainties (Friedlingstein et al. 2022).\nUpdated estimates indicate that net LULUCF CO 2 emissions\nslowly\n\nthat receive\nremittances compared with those who do not receive\nremittances but argue that government action to\nincrease climate change literacy could change this.\nMore research is required on the extent to which\ngovernments can encourage remittances to support\nadaptation and on climate justice concerns regarding\nsuch government action and the fact that recipients\nof remittances would use their money to adapt to a\nproblem they might not have contributed to.\n5.\n\nIncrease financing for SMEs. SMEs hold\nconsiderable potential in unlocking climate\nadaptation solutions and engaging the private sector\n(see also GCA and CPI 2021). Since SMEs constitute\nthe bulk of the economy for many developing (and\ndeveloped) countries, financing mechanisms\nshould be tailored to meet their particular needs\nand stimulate their potential to offer adaptationrelevant products and services. Initiatives at the\nglobal level, such as the G20’s Global Partnership\nfor Financial Inclusion, can help mobilize and scale\nadaptation finance for SMEs. Support through the\nG20 can be enhanced, for example, by working\nwith the regional development banks in developing\ncountries to channel funds through well-established\nmechanisms. Regional initiatives such as those in\nLatin America (Botero, Brinks and Gonzalez-Ocantos\neds. 2022), Asia (Papadavid 2021), or Africa (African\nDevelopment Bank 2019) are salient examples.\nMoreover, financial de-risking mechanisms can be\nadapted to include the needs of SMEs, such as in\nfinancing small-scale energy projects. Although\nfinancial de-risking is occurring in various parts of\nthe world, smaller countries with limited financial\nmarkets do not have adequate access to financial\nde-risking instruments (World Bank 2016). Targeted\ninvestments in SMEs can also enable them to\naddress priority areas identified in countries’ NDCs,\nwith evidence showing that some SMEs already\ninvest in adaptation in, for example, tourism (Rasul\net al. 2020; Hess 2020) and agriculture (Gannon et', 'location': 'Address: house_number: 16, road: Dreetzer Straße, hamlet: Blumenaue, village: Dreetz, municipality: Neustadt (Dosse), county: Ostprignitz-Ruppin, state: Brandenburg, ISO3166-2-lvl4: DE-BB, postcode: 16845, country: Germany, country_code: de', 'question': 'Grow maice '}
2025-02-27 20:10:22 - rag - INFO - Chunks returned from RAG: {'context': 'on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n 9 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n10 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n11\n\n12 Figure AI.21: Climatic and environmental stresses on global production of maize.\n13 The global effects of five climatic and environmental stresses on maize yield. The combined effect of each\n14 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n15 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n16 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n17 All data are presented for the 1 × 1° (latitude and longitude) grid squares. {5.4.1; Fig. 5.5}\n\n18\n\n19 Figure AI.22: Projected changes in global maize production.\n20 For maize production time series are shown as relative changes to the 1983-2013 reference period under\n21 SSP126 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop\n22 model combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model\n23 response (and a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the\n24 historical variability (1983-2013; ensemble median) and open circles highlight the "time of climate impact\n25 emergence" (TCIE), the year in which the smoothed median response exceeds the historical envelope. For\n26 context, the TCIE calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop\n\n5.3; 5.5.3; 5.4.1; Figure FAQ 5.1; Figure 9.22; 15.3.4; 15.3.3}\n\n44\n\n45 Figure AI.18: Climatic and environmental stresses on global production of wheat.\n46 The global effects of five climatic and environmental stresses on wheat yield. The combined effect of each\n47 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n48 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n49 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n50 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n51 wheat was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n52\n\n53 Figure AI.19: Climatic and environmental stresses on global production of soybean.\n54 The global effects of five climatic and environmental stresses on soybean yield. The combined effect of each\n55 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n56 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but also ozone\n\nDo Not Cite, Quote or Distribute  AI-63    Total pages: 74\nFINAL DRAFT                       Annex I  IPCC WGII Sixth Assessment Report\n\n 1 uptake by plants thus exacerbating yield loss and quality damage. Data are available at Sharps et al., (2020).\n 2 All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of\n 3 soybean was >500 tonnes (0.0005 Tg). {5.4.1; Fig. 5.5}\n\n 4\n\n 5 Figure AI.20: Climatic and environmental stresses on global production of rice.\n 6 The global effects of five climatic and environmental stresses on rice yield. The combined effect of each\n 7 stress on yield is presented as a Yield Constraint Score (YCS) on a five-category scale from low stress to\n 8 high stress (Mills et al., 2018). Higher temperatures enhance not only ozone production but\n\nis indicated in lighter shades above the TCIE based on\n27 GC6 (>2099 if no TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under\n28 SSP585 across climate and crop models for current growing regions (>10 ha). Hatching indicates areas\n29 where less than 70% of the climate-crop model combinations agree on the sign of impact. Regional\n30 production time series (e) are similar to (a), but stratified for the four major KoeppenGeiger climate zones\n31 (temperature limited, temperate/humid, subtropical, and tropical). The percentage of the total global\n32 production contributed by each zone is indicated in the top right corner of the inlets. All data are shown for\n33 the default [CO2] {Jägermeyr et al. 2021; 5.4.3.2}\n\n34\n\n35 Figure AI.23: Projected changes in global wheat production.\n36 Production time series are shown as relative changes to the 1983-2013 reference period under SSP126\n37 (green) and SSP585 (yellow). Shaded ranges illustrate the interquartile range of all climate and crop model\n38 combinations (5 GCMs x 8 GGCMs). The solid line shows the median climate and crop model response (and\n39 a 30yr moving average). Horizontal dashed lines mark the 5th and 95th percentile of the historical variability\n40 (1983-2013; ensemble median) and open circles highlight the "time of climate impact emergence" (TCIE),\n41 the year in which the smoothed median response exceeds the historical envelope. For context, the TCIE\n42 calculated from GC5 5 simulations is indicated in lighter shades above the TCIE based on GC6 (>2099 if no\n43 TCIE occurs by 2099). The maps (c, d) show median yield changes (2069-2099) under SSP585 across\n44 climate and crop models for current growing regions (>10 ha). Hatching indicates areas where less than 70%\n45 of the climate-crop model combinations agree on the sign of impact. Regional production time series (e) are\n46 similar to (a), but stratified for the four major KoeppenGeiger climate zones (temperature limited,\n47\n\ntransboundary governance and ecosystem-\n55   based management, livelihood diversification, capacity development and improved knowledge-sharing will\n\n\n     Do Not Cite, Quote or Distribute                      TS-64                                         Total pages: 96\n     FINAL DRAFT                                    Technical Summary             IPCC WGII Sixth Assessment Report\n\n 1   reduce conflict and promote the fair distribution of sustainably-harvested wild products and revenues\n 2   (medium confidence). {5.8.4, 5.14.3, CCP5.4.2, CCB MOVING PLATE}\n 3\n 4   TS.D.5.5 Adaptation options that promote intensification of production have been widely adopted in\n 5   agriculture for climate change adaptation, but with potential negative effects (high confidence).\n 6   Agricultural intensification addresses short-term food security and livelihood goals but has trade-offs in\n 7   equity, biodiversity, and ecosystem services (high confidence). Irrigation is widely used and effective for\n 8   yield stability, but with several negative outcomes, including water demand (high confidence), groundwater\n 9   depletion (high confidence); alteration of local to regional climates (high confidence); increasing soil salinity\n10   (medium confidence) widening inequalities and loss of rural smallholder livelihoods with weak governance\n11   (medium confidence). Conventional breeding assisted by genomics introduces traits that adapt crops to\n12   climate change (high confidence). Genetic improvements through modern biotechnology have the potential\n13   to increase climate resilience in food production systems (high confidence), but with biophysical ceilings,\n14   and technical, agroecosystem, socio-economic and political variables strongly influence and limit uptake of\n15   climate-resilient crops, particularly for smallholders (medium confidence).{4.6.2, Box 4.3, 4.7.1, 5.4.4,\n16   5.12.5, 5.13.4, 5.14.1, 10.2.2, 12.5.4, 13.5.1, 13.5.2, 13.5.14, 14.5.4, 15.3.4, 17.5.1}\n17\n18', 'location': 'Address: house_number: 16, road: Dreetzer Straße, hamlet: Blumenaue, village: Dreetz, municipality: Neustadt (Dosse), county: Ostprignitz-Ruppin, state: Brandenburg, ISO3166-2-lvl4: DE-BB, postcode: 16845, country: Germany, country_code: de', 'question': 'Grow maice '}
2025-02-27 20:10:23 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 20:10:23 - root - INFO - Rendered RAG response: None
2025-02-27 20:10:23 - climsight_engine - INFO - general_rag_agent_response: None
2025-02-27 20:10:24 - climsight_engine - INFO - Data agent in work.
2025-02-27 20:10:24 - climsight_engine - INFO - data_agent_response: {'input_params': {'simulation1': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.21,\n    "Total Precipitation (mm\\/month)":50.43,\n    "Wind U (m s**-1)":1.77,\n    "Wind V (m s**-1)":1.21,\n    "Wind Speed (m\\/s)":2.14,\n    "Wind Direction (\\u00b0)":235.64\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":1.0,\n    "Total Precipitation (mm\\/month)":55.92,\n    "Wind U (m s**-1)":1.53,\n    "Wind V (m s**-1)":1.15,\n    "Wind Speed (m\\/s)":1.92,\n    "Wind Direction (\\u00b0)":233.16\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":-0.15,\n    "Total Precipitation (mm\\/month)":39.06,\n    "Wind U (m s**-1)":0.28,\n    "Wind V (m s**-1)":0.38,\n    "Wind Speed (m\\/s)":0.47,\n    "Wind Direction (\\u00b0)":216.72\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":3.14,\n    "Total Precipitation (mm\\/month)":46.94,\n    "Wind U (m s**-1)":1.23,\n    "Wind V (m s**-1)":-0.01,\n    "Wind Speed (m\\/s)":1.23,\n    "Wind Direction (\\u00b0)":270.24\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":7.54,\n    "Total Precipitation (mm\\/month)":46.75,\n    "Wind U (m s**-1)":0.51,\n    "Wind V (m s**-1)":-0.38,\n    "Wind Speed (m\\/s)":0.63,\n    "Wind Direction (\\u00b0)":306.51\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":13.44,\n    "Total Precipitation (mm\\/month)":58.82,\n    "Wind U (m s**-1)":0.37,\n    "Wind V (m s**-1)":-0.25,\n    "Wind Speed (m\\/s)":0.45,\n    "Wind Direction (\\u00b0)":304.12\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":16.12,\n    "Total Precipitation (mm\\/month)":61.48,\n    "Wind U (m s**-1)":1.26,\n    "Wind V (m s**-1)":-0.52,\n    "Wind Speed (m\\/s)":1.36,\n    "Wind Direction (\\u00b0)":292.3\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":18.72,\n    "Total Precipitation (mm\\/month)":59.34,\n    "Wind U (m s**-1)":1.71,\n    "Wind V (m s**-1)":-0.16,\n    "Wind Speed (m\\/s)":1.72,\n    "Wind Direction (\\u00b0)":275.35\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":19.4,\n    "Total Precipitation (mm\\/month)":46.22,\n    "Wind U (m s**-1)":1.2,\n    "Wind V (m s**-1)":-0.47,\n    "Wind Speed (m\\/s)":1.29,\n    "Wind Direction (\\u00b0)":291.56\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":16.68,\n    "Total Precipitation (mm\\/month)":30.64,\n    "Wind U (m s**-1)":0.64,\n    "Wind V (m s**-1)":0.21,\n    "Wind Speed (m\\/s)":0.68,\n    "Wind Direction (\\u00b0)":251.52\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":10.49,\n    "Total Precipitation (mm\\/month)":36.38,\n    "Wind U (m s**-1)":0.39,\n    "Wind V (m s**-1)":0.56,\n    "Wind Speed (m\\/s)":0.68,\n    "Wind Direction (\\u00b0)":214.59\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":6.87,\n    "Total Precipitation (mm\\/month)":45.55,\n    "Wind U (m s**-1)":0.72,\n    "Wind V (m s**-1)":1.54,\n    "Wind Speed (m\\/s)":1.7,\n    "Wind Direction (\\u00b0)":204.98\n  }\n]', 'simulation2': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.65,\n    "Total Precipitation (mm\\/month)":64.85,\n    "Wind U (m s**-1)":1.94,\n    "Wind V (m s**-1)":1.17,\n    "Wind Speed (m\\/s)":2.26,\n    "Wind Direction (\\u00b0)":238.96\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":1.04,\n    "Total Precipitation (mm\\/month)":65.93,\n    "Wind U (m s**-1)":1.76,\n    "Wind V (m s**-1)":1.47,\n    "Wind Speed (m\\/s)":2.29,\n    "Wind Direction (\\u00b0)":230.22\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":2.74,\n    "Total Precipitation (mm\\/month)":39.51,\n    "Wind U (m s**-1)":1.77,\n    "Wind V (m s**-1)":1.25,\n    "Wind Speed (m\\/s)":2.16,\n    "Wind Direction (\\u00b0)":234.71\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.79,\n    "Total Precipitation (mm\\/month)":52.4,\n    "Wind U (m s**-1)":0.89,\n    "Wind V (m s**-1)":0.75,\n    "Wind Speed (m\\/s)":1.17,\n    "Wind Direction (\\u00b0)":230.07\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":9.06,\n    "Total Precipitation (mm\\/month)":43.54,\n    "Wind U (m s**-1)":0.1,\n    "Wind V (m s**-1)":-0.2,\n    "Wind Speed (m\\/s)":0.22,\n    "Wind Direction (\\u00b0)":333.18\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":12.84,\n    "Total Precipitation (mm\\/month)":64.72,\n    "Wind U (m s**-1)":0.13,\n    "Wind V (m s**-1)":-0.46,\n    "Wind Speed (m\\/s)":0.48,\n    "Wind Direction (\\u00b0)":344.73\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":16.72,\n    "Total Precipitation (mm\\/month)":75.38,\n    "Wind U (m s**-1)":1.06,\n    "Wind V (m s**-1)":-0.52,\n    "Wind Speed (m\\/s)":1.17,\n    "Wind Direction (\\u00b0)":296.01\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":19.23,\n    "Total Precipitation (mm\\/month)":53.29,\n    "Wind U (m s**-1)":1.34,\n    "Wind V (m s**-1)":-0.27,\n    "Wind Speed (m\\/s)":1.37,\n    "Wind Direction (\\u00b0)":281.39\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":19.31,\n    "Total Precipitation (mm\\/month)":53.97,\n    "Wind U (m s**-1)":1.3,\n    "Wind V (m s**-1)":-0.15,\n    "Wind Speed (m\\/s)":1.31,\n    "Wind Direction (\\u00b0)":276.5\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":17.32,\n    "Total Precipitation (mm\\/month)":36.56,\n    "Wind U (m s**-1)":0.73,\n    "Wind V (m s**-1)":0.22,\n    "Wind Speed (m\\/s)":0.76,\n    "Wind Direction (\\u00b0)":253.13\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.08,\n    "Total Precipitation (mm\\/month)":64.94,\n    "Wind U (m s**-1)":1.51,\n    "Wind V (m s**-1)":1.07,\n    "Wind Speed (m\\/s)":1.86,\n    "Wind Direction (\\u00b0)":234.68\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":4.66,\n    "Total Precipitation (mm\\/month)":57.69,\n    "Wind U (m s**-1)":0.56,\n    "Wind V (m s**-1)":0.67,\n    "Wind Speed (m\\/s)":0.87,\n    "Wind Direction (\\u00b0)":219.8\n  }\n]', 'simulation3': '[\n  {\n    "Month":"January",\n    "Temperature (\\u00b0C)":2.88,\n    "Total Precipitation (mm\\/month)":63.16,\n    "Wind U (m s**-1)":1.47,\n    "Wind V (m s**-1)":1.15,\n    "Wind Speed (m\\/s)":1.87,\n    "Wind Direction (\\u00b0)":232.11\n  },\n  {\n    "Month":"February",\n    "Temperature (\\u00b0C)":3.48,\n    "Total Precipitation (mm\\/month)":69.05,\n    "Wind U (m s**-1)":2.58,\n    "Wind V (m s**-1)":1.89,\n    "Wind Speed (m\\/s)":3.2,\n    "Wind Direction (\\u00b0)":233.73\n  },\n  {\n    "Month":"March",\n    "Temperature (\\u00b0C)":3.08,\n    "Total Precipitation (mm\\/month)":63.65,\n    "Wind U (m s**-1)":2.65,\n    "Wind V (m s**-1)":0.88,\n    "Wind Speed (m\\/s)":2.8,\n    "Wind Direction (\\u00b0)":251.6\n  },\n  {\n    "Month":"April",\n    "Temperature (\\u00b0C)":5.41,\n    "Total Precipitation (mm\\/month)":44.83,\n    "Wind U (m s**-1)":1.93,\n    "Wind V (m s**-1)":0.56,\n    "Wind Speed (m\\/s)":2.01,\n    "Wind Direction (\\u00b0)":253.9\n  },\n  {\n    "Month":"May",\n    "Temperature (\\u00b0C)":9.59,\n    "Total Precipitation (mm\\/month)":39.17,\n    "Wind U (m s**-1)":0.25,\n    "Wind V (m s**-1)":-0.21,\n    "Wind Speed (m\\/s)":0.32,\n    "Wind Direction (\\u00b0)":310.38\n  },\n  {\n    "Month":"June",\n    "Temperature (\\u00b0C)":13.44,\n    "Total Precipitation (mm\\/month)":77.4,\n    "Wind U (m s**-1)":0.06,\n    "Wind V (m s**-1)":-0.68,\n    "Wind Speed (m\\/s)":0.68,\n    "Wind Direction (\\u00b0)":355.08\n  },\n  {\n    "Month":"July",\n    "Temperature (\\u00b0C)":17.56,\n    "Total Precipitation (mm\\/month)":71.77,\n    "Wind U (m s**-1)":1.05,\n    "Wind V (m s**-1)":-0.25,\n    "Wind Speed (m\\/s)":1.08,\n    "Wind Direction (\\u00b0)":283.57\n  },\n  {\n    "Month":"August",\n    "Temperature (\\u00b0C)":19.92,\n    "Total Precipitation (mm\\/month)":54.13,\n    "Wind U (m s**-1)":1.24,\n    "Wind V (m s**-1)":-0.4,\n    "Wind Speed (m\\/s)":1.31,\n    "Wind Direction (\\u00b0)":287.83\n  },\n  {\n    "Month":"September",\n    "Temperature (\\u00b0C)":20.29,\n    "Total Precipitation (mm\\/month)":26.87,\n    "Wind U (m s**-1)":1.16,\n    "Wind V (m s**-1)":-0.37,\n    "Wind Speed (m\\/s)":1.22,\n    "Wind Direction (\\u00b0)":287.65\n  },\n  {\n    "Month":"October",\n    "Temperature (\\u00b0C)":16.95,\n    "Total Precipitation (mm\\/month)":51.17,\n    "Wind U (m s**-1)":0.96,\n    "Wind V (m s**-1)":0.15,\n    "Wind Speed (m\\/s)":0.97,\n    "Wind Direction (\\u00b0)":261.1\n  },\n  {\n    "Month":"November",\n    "Temperature (\\u00b0C)":11.41,\n    "Total Precipitation (mm\\/month)":33.34,\n    "Wind U (m s**-1)":0.56,\n    "Wind V (m s**-1)":0.46,\n    "Wind Speed (m\\/s)":0.73,\n    "Wind Direction (\\u00b0)":231.1\n  },\n  {\n    "Month":"December",\n    "Temperature (\\u00b0C)":7.11,\n    "Total Precipitation (mm\\/month)":54.7,\n    "Wind U (m s**-1)":1.66,\n    "Wind V (m s**-1)":1.32,\n    "Wind Speed (m\\/s)":2.12,\n    "Wind Direction (\\u00b0)":231.51\n  }\n]', 'simulation5': '[\n  {\n    "Temperature (\\u00b0C)":0.44,\n    "Total Precipitation (mm\\/month)":14.42,\n    "Wind U (m s**-1)":0.17,\n    "Wind V (m s**-1)":-0.04,\n    "Wind Speed (m\\/s)":0.12,\n    "Wind Direction (\\u00b0)":3.32\n  },\n  {\n    "Temperature (\\u00b0C)":0.04,\n    "Total Precipitation (mm\\/month)":10.01,\n    "Wind U (m s**-1)":0.23,\n    "Wind V (m s**-1)":0.32,\n    "Wind Speed (m\\/s)":0.37,\n    "Wind Direction (\\u00b0)":-2.94\n  },\n  {\n    "Temperature (\\u00b0C)":2.89,\n    "Total Precipitation (mm\\/month)":0.45,\n    "Wind U (m s**-1)":1.49,\n    "Wind V (m s**-1)":0.87,\n    "Wind Speed (m\\/s)":1.69,\n    "Wind Direction (\\u00b0)":17.99\n  },\n  {\n    "Temperature (\\u00b0C)":2.65,\n    "Total Precipitation (mm\\/month)":5.46,\n    "Wind U (m s**-1)":-0.34,\n    "Wind V (m s**-1)":0.76,\n    "Wind Speed (m\\/s)":-0.06,\n    "Wind Direction (\\u00b0)":-40.17\n  },\n  {\n    "Temperature (\\u00b0C)":1.52,\n    "Total Precipitation (mm\\/month)":-3.21,\n    "Wind U (m s**-1)":-0.41,\n    "Wind V (m s**-1)":0.18,\n    "Wind Speed (m\\/s)":-0.41,\n    "Wind Direction (\\u00b0)":26.67\n  },\n  {\n    "Temperature (\\u00b0C)":-0.6,\n    "Total Precipitation (mm\\/month)":5.9,\n    "Wind U (m s**-1)":-0.24,\n    "Wind V (m s**-1)":-0.21,\n    "Wind Speed (m\\/s)":0.03,\n    "Wind Direction (\\u00b0)":40.61\n  },\n  {\n    "Temperature (\\u00b0C)":0.6,\n    "Total Precipitation (mm\\/month)":13.9,\n    "Wind U (m s**-1)":-0.2,\n    "Wind V (m s**-1)":0.0,\n    "Wind Speed (m\\/s)":-0.19,\n    "Wind Direction (\\u00b0)":3.71\n  },\n  {\n    "Temperature (\\u00b0C)":0.51,\n    "Total Precipitation (mm\\/month)":-6.05,\n    "Wind U (m s**-1)":-0.37,\n    "Wind V (m s**-1)":-0.11,\n    "Wind Speed (m\\/s)":-0.35,\n    "Wind Direction (\\u00b0)":6.04\n  },\n  {\n    "Temperature (\\u00b0C)":-0.09,\n    "Total Precipitation (mm\\/month)":7.75,\n    "Wind U (m s**-1)":0.1,\n    "Wind V (m s**-1)":0.32,\n    "Wind Speed (m\\/s)":0.02,\n    "Wind Direction (\\u00b0)":-15.06\n  },\n  {\n    "Temperature (\\u00b0C)":0.64,\n    "Total Precipitation (mm\\/month)":5.92,\n    "Wind U (m s**-1)":0.09,\n    "Wind V (m s**-1)":0.01,\n    "Wind Speed (m\\/s)":0.08,\n    "Wind Direction (\\u00b0)":1.61\n  },\n  {\n    "Temperature (\\u00b0C)":0.59,\n    "Total Precipitation (mm\\/month)":28.56,\n    "Wind U (m s**-1)":1.12,\n    "Wind V (m s**-1)":0.51,\n    "Wind Speed (m\\/s)":1.18,\n    "Wind Direction (\\u00b0)":20.09\n  },\n  {\n    "Temperature (\\u00b0C)":-2.21,\n    "Total Precipitation (mm\\/month)":12.14,\n    "Wind U (m s**-1)":-0.16,\n    "Wind V (m s**-1)":-0.87,\n    "Wind Speed (m\\/s)":-0.83,\n    "Wind Direction (\\u00b0)":14.82\n  }\n]', 'simulation6': '[\n  {\n    "Temperature (\\u00b0C)":0.67,\n    "Total Precipitation (mm\\/month)":12.73,\n    "Wind U (m s**-1)":-0.3,\n    "Wind V (m s**-1)":-0.06,\n    "Wind Speed (m\\/s)":-0.27,\n    "Wind Direction (\\u00b0)":-3.53\n  },\n  {\n    "Temperature (\\u00b0C)":2.48,\n    "Total Precipitation (mm\\/month)":13.13,\n    "Wind U (m s**-1)":1.05,\n    "Wind V (m s**-1)":0.74,\n    "Wind Speed (m\\/s)":1.28,\n    "Wind Direction (\\u00b0)":0.57\n  },\n  {\n    "Temperature (\\u00b0C)":3.23,\n    "Total Precipitation (mm\\/month)":24.59,\n    "Wind U (m s**-1)":2.37,\n    "Wind V (m s**-1)":0.5,\n    "Wind Speed (m\\/s)":2.33,\n    "Wind Direction (\\u00b0)":34.88\n  },\n  {\n    "Temperature (\\u00b0C)":2.27,\n    "Total Precipitation (mm\\/month)":-2.11,\n    "Wind U (m s**-1)":0.7,\n    "Wind V (m s**-1)":0.57,\n    "Wind Speed (m\\/s)":0.78,\n    "Wind Direction (\\u00b0)":-16.34\n  },\n  {\n    "Temperature (\\u00b0C)":2.05,\n    "Total Precipitation (mm\\/month)":-7.58,\n    "Wind U (m s**-1)":-0.26,\n    "Wind V (m s**-1)":0.17,\n    "Wind Speed (m\\/s)":-0.31,\n    "Wind Direction (\\u00b0)":3.87\n  },\n  {\n    "Temperature (\\u00b0C)":0.0,\n    "Total Precipitation (mm\\/month)":18.58,\n    "Wind U (m s**-1)":-0.31,\n    "Wind V (m s**-1)":-0.43,\n    "Wind Speed (m\\/s)":0.23,\n    "Wind Direction (\\u00b0)":50.96\n  },\n  {\n    "Temperature (\\u00b0C)":1.44,\n    "Total Precipitation (mm\\/month)":10.29,\n    "Wind U (m s**-1)":-0.21,\n    "Wind V (m s**-1)":0.27,\n    "Wind Speed (m\\/s)":-0.28,\n    "Wind Direction (\\u00b0)":-8.73\n  },\n  {\n    "Temperature (\\u00b0C)":1.2,\n    "Total Precipitation (mm\\/month)":-5.21,\n    "Wind U (m s**-1)":-0.47,\n    "Wind V (m s**-1)":-0.24,\n    "Wind Speed (m\\/s)":-0.41,\n    "Wind Direction (\\u00b0)":12.48\n  },\n  {\n    "Temperature (\\u00b0C)":0.89,\n    "Total Precipitation (mm\\/month)":-19.35,\n    "Wind U (m s**-1)":-0.04,\n    "Wind V (m s**-1)":0.1,\n    "Wind Speed (m\\/s)":-0.07,\n    "Wind Direction (\\u00b0)":-3.91\n  },\n  {\n    "Temperature (\\u00b0C)":0.27,\n    "Total Precipitation (mm\\/month)":20.53,\n    "Wind U (m s**-1)":0.32,\n    "Wind V (m s**-1)":-0.06,\n    "Wind Speed (m\\/s)":0.29,\n    "Wind Direction (\\u00b0)":9.58\n  },\n  {\n    "Temperature (\\u00b0C)":0.92,\n    "Total Precipitation (mm\\/month)":-3.04,\n    "Wind U (m s**-1)":0.17,\n    "Wind V (m s**-1)":-0.1,\n    "Wind Speed (m\\/s)":0.05,\n    "Wind Direction (\\u00b0)":16.51\n  },\n  {\n    "Temperature (\\u00b0C)":0.24,\n    "Total Precipitation (mm\\/month)":9.15,\n    "Wind U (m s**-1)":0.94,\n    "Wind V (m s**-1)":-0.22,\n    "Wind Speed (m\\/s)":0.42,\n    "Wind Direction (\\u00b0)":26.53\n  }\n]'}, 'content_message': '\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2020-2029. The nextGEMS pre-final simulations for years 2030x..This simulation serves as a historical reference to extract its values from other simulations. :\n {simulation1}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2030-2039. The nextGEMS pre-final simulations for years 2030x. :\n {simulation2}\n\n Climate parameters are obtained from atmospheric model simulations. The climatology is based on the average years: 2040-2049. The nextGEMS pre-final simulations for years 2040x. :\n {simulation3}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2030-2039 compared to the simulation for 2020-2029. :\n {simulation5}\n\n Difference between simulations (changes in climatological parameters (Δ)) for the years 2040-2049 compared to the simulation for 2020-2029. :\n {simulation6}'}
2025-02-27 20:10:24 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'numpy.bool_'>)
2025-02-27 20:10:27 - climsight_engine - INFO - zero_agent_response: {'input_params': {'elevation': '23.0', 'current_land_use': 'farmland', 'soil': 'Cambisols', 'biodiv': 'Cyperus, Mesalina, Pristurus, Chlorophthalmus, Neoharriotta', 'distance_to_coastline': '142471.7586611003', 'nat_hazards': Empty DataFrame
Columns: [year, disastertype]
Index: [], 'population':     Time  TotalPopulationAsOf1July  PopulationDensity  PopulationGrowthRate
0   1980              77786.703000         223.165900             -0.145000
1   1990              78072.678100         223.986330              0.237400
2   2000              80995.587100         232.372000              0.241300
3   2010              81294.847800         233.230560             -0.021600
4   2020              82291.120600         236.088820              0.245600
5   2030              83132.605000         238.502990             -0.082500
6   2040              81965.316200         235.154110             -0.195300
7   2050              80013.692600         229.555010             -0.288400
8   2060              77399.456100         222.054900             -0.362600
9   2070              74806.608900         214.616160             -0.299400
10  2080              72785.056300         208.816440             -0.266400
11  2090              70893.830000         203.390610             -0.247800
12  2100              69481.298222         199.338133             -0.171333
13  2101                       NaN                NaN                   NaN}, 'content_message': 'Elevation above sea level: {elevation} \nCurrent landuse: {current_land_use} \nCurrent soil type: {soil} \nOccuring species: {biodiv} \nDistance to the closest coastline: {distance_to_coastline} \nNatural hazards: {nat_hazards} \nPopulation in Germany data: {population} \n'}
2025-02-27 20:10:27 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 20:10:27 - root - INFO - Rendered RAG response: The provided reports do not contain specific information about maize production in the specific location of Dreetzer Straße, Blumenaue, Dreetz, Brandenburg, Germany. However, I can provide general information from the context:

- The yield of maize globally is affected by climatic and environmental stresses, which are measured using a Yield Constraint Score (YCS) on a scale from low to high stress.
- Higher temperatures can increase ozone production and its uptake by plants, leading to potential yield loss and quality damage for maize.
- These assessments are based on data available for general grid squares and not specifically tailored to the region in question.

For specific maize cultivation and yield information in the referenced location, consulting local agricultural resources or databases would be necessary.
2025-02-27 20:10:27 - climsight_engine - INFO - ipcc_rag_agent_response: The provided reports do not contain specific information about maize production in the specific location of Dreetzer Straße, Blumenaue, Dreetz, Brandenburg, Germany. However, I can provide general information from the context:

- The yield of maize globally is affected by climatic and environmental stresses, which are measured using a Yield Constraint Score (YCS) on a scale from low to high stress.
- Higher temperatures can increase ozone production and its uptake by plants, leading to potential yield loss and quality damage for maize.
- These assessments are based on data available for general grid squares and not specifically tailored to the region in question.

For specific maize cultivation and yield information in the referenced location, consulting local agricultural resources or databases would be necessary.
2025-02-27 20:10:27 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-27 20:10:30 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 20:10:38 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 20:10:39 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 20:10:40 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 20:10:41 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
2025-02-27 20:10:41 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 20:10:43 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 20:10:44 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 20:10:48 - langsmith._internal._serde - ERROR - Failed to use model_dump to serialize <class 'climsight_classes.AgentState'> to JSON: PydanticSerializationError(Unable to serialize unknown type: <class 'pandas.core.frame.DataFrame'>)
2025-02-27 20:10:48 - climsight_engine - INFO - combine_agent in work
2025-02-27 20:10:48 - climsight_engine - INFO - smart_agent_response: {'output': "The temperature for maize at Dreetz, Germany is expected to range from 2.21°C in January to 19.4°C in September for the years 2020-2029, with future projections showing a slight increase. According to the ECOCROP database, maize requires temperatures between 18.0°C and 33.0°C for optimal growth. The current and future temperatures during the growing season (May to September) are within the optimal range, indicating favorable conditions for maize cultivation.\n\nThe precipitation for maize at Dreetz, Germany is expected to range from 30.64 mm in October to 61.48 mm in July for the years 2020-2029, with future projections showing variability. The ECOCROP database suggests that maize requires 600 mm to 1200 mm of rainfall annually. The monthly precipitation during the growing season (May to September) is generally lower than the optimal range, which may necessitate irrigation to meet maize's water requirements."}
2025-02-27 20:10:48 - climsight_engine - INFO - Wikipedia_tool_reponse: • Temperature:
  - Quantitative: Requires warm days above 10 °C (50 °F) for flowering.
  - Qualitative: Maize is cold-intolerant and must be planted in the spring in temperate zones.

• Wind:
  - Qualitative: Maize pollen is dispersed by wind. Maize is prone to being uprooted by severe winds due to its shallow roots.

• Soil Type:
  - Qualitative: Maize is intolerant of nutrient-deficient soils and depends on adequate soil moisture.
2025-02-27 20:10:48 - climsight_engine - INFO - Ecocrop_search_response: Data from ECOCROP database for maize:
Scientific Name: Zea mays
Authority: L.
Family: Liliopsida:Commelinidae:Cyperales:Gramineae
Synonyms: Zea mays L. ssp. mays, Zea curagua Molina, Zea indentata Sturtev., Zea indurata Sturtev., Zea japonica Van Houtte, Zea saccharata Sturtev.
Common names: maize, corn, Mais, maiz, milho, yumi, khao phoat, bekolo, sila, sila nivava lagi, tomorokpshi, makai, makki, koane, fiso, sana, keto (Simbo/Roviana), kon, mielie, mahindi, ekidid (Karamojong), maidis stigmata, mbemba, poone, upfu, hupfu, mbila
optimal minimum temperature (°C): 18.0
optimal maximum temperature (°C): 33.0
absolute minimum temperature (°C): 10.0
absolute maximum temperature (°C): 47.0
optimal minimum rainfall (mm): 600.0
optimal maximum rainfall (mm): 1200.0
absolute minimum rainfall (mm): 400.0
absolute maximum rainfall (mm): 1800.0
optimal minimum soil PH: 5.0
optimal maximum soil PH: 7.0
absolute minimum soil PH: 4.5
absolute maximum soil PH: 8.5
absolute minimum latitude: 40.0
absolute maximum latitude: 48.0
absolute maximum altitude (meter above sea level): 4000.0
optimal minimum light intensity: very bright
optimal maximum light intensity: very bright
absolute minimum light intensity: clear skies
absolute maximum light intensity: very bright
Optimal soil depth: medium (50-150 cm)
Absolute soil depth: shallow (20-50 cm)
Optimal soil fertility: high
Absolute soil fertility: low
Optimal soil salinity: low (<4 dS/m)
Absolute soil salinity: medium (4-10 dS/m)
Minimum crop cycle (days): 65.0
Maximum crop cycle (days): 365.0

2025-02-27 20:11:13 - httpx - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2025-02-27 20:11:13 - climsight_engine - INFO - Final_answer: The potential to grow maize at the specified location, Dreetz in Brandenburg, Germany, can be evaluated in light of the current and projected climate data. The primary factors to consider include temperature, precipitation, soil conditions, and the presence of natural hazards, which, according to the data, are not currently observed in this area.

Temperature is a crucial factor for maize cultivation. According to the data, the monthly average temperatures in Dreetz during the growing season (May to September) from 2020-2029 will range from 7.54°C in May to 19.4°C in September. For the following decades, these temperatures are projected to rise slightly, reaching up to 20.29°C in September by 2040-2049. These temperatures align well with the ECOCROP database's optimal range for maize, which is 18.0°C to 33.0°C during the growth period. This suggests that temperature conditions in Dreetz are largely favorable for maize growth.

Precipitation data indicates variability across the years. From 2020-2029, monthly precipitation ranges from 30.64 mm in October to 61.48 mm in July. While maize requires 600 mm to 1200 mm of rainfall annually for optimal growth, the available monthly data during the critical period suggests precipitation might be on the lower end, which could necessitate irrigation to reach optimal water levels, especially in drier months.

The soil type at the location, Cambisols, is typically associated with good fertility and drainage, though maize requires nutrient-rich soil with adequate moisture retention. Ensuring soil fertilization could mitigate potential deficiencies. Cambisols should support maize growth provided they are managed well with appropriate fertilization to maintain soil fertility.

Looking into the climatological data and projections:

- **Temperature**: Increases in temperature could enhance the growing environment for maize, but excessive temperatures, especially peaks beyond 33.0°C, should be monitored to avoid potential heat stress during sensitive phases like flowering.

- **Precipitation**: While the precipitation appears sufficient during the peak summer months, irrigation strategies should be put in place to ensure water availability meets the crop's demands across its cycles, specifically during early growth stages if deficits in rainfall occur.

- **Wind**: The maize plant relies on wind for pollination, but high winds could affect plant stability due to shallow root systems. No significant wind-related data issues are noted here, but reinforcement measures may be necessary depending on future wind pattern changes.

Overall, Dreetz presents a suitable environment for maize cultivation, although considerations around irrigation, soil management, and potential temperature spikes need foresight and planning. As climate patterns shift, regular updates on weather forecasts and adaptive agricultural techniques will be essential for sustained and successful maize production.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
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2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
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2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
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2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
2025-02-27 20:11:13 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.