EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple indicators"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q02  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple indicators > ℹ If you want to know more > Key resources
---
The CDS catalogue entries for the data used were:
* Gridded dataset underpinning the Copernicus Interactive Climate Atlas: [multi-origin-c3s-atlas](https://doi.org/10.24381/cds.h35hb680)
  * [](./derived_multi-origin-c3s-atlas_consistency_q01)
  * **Consistency between the C3S Atlas dataset and its origins: Multiple indicators**
  * [](./derived_multi-origin-c3s-atlas_consistency_q03)
* CMIP6 climate projections: [projections-cmip6](https://doi.org/10.24381/cds.c866074c)
  * [Quality assessments for CMIP6](../Climate_Projections/CMIP6/CMIP6.md)

Code libraries used:
* [earthkit](https://github.com/ecmwf/earthkit)
  * [earthkit-data](https://github.com/ecmwf/earthkit-data)
  * [earthkit-plots](https://github.com/ecmwf/earthkit-plots)
* [User-tools for the C3S Atlas](https://github.com/ecmwf-projects/c3s-atlas)
* [xclim](https://xclim.readthedocs.io/en/stable/) climate indicator tools

More about the Copernicus Interactive Climate Atlas and its IPCC predecessor:
* [Copernicus Interactive Climate Atlas application](https://atlas.climate.copernicus.eu/)
* [Gridded data underpinning the Copernicus Interactive Climate Atlas: Description of the datasets and variables](https://confluence.ecmwf.int/display/CKB/Gridded+data+underpinning+the+Copernicus+Interactive+Climate+Atlas%3A+Description+of+the+datasets+and+variables)
* [The Copernicus Interactive Climate Atlas: a tool to explore regional climate change](https://doi.org/10.21957/ah52ufc369)
* [Copernicus Interactive Climate Atlas: a new tool to visualise climate variability and change](https://www.ecmwf.int/en/newsletter/179/news/copernicus-interactive-climate-atlas-new-tool-visualise-climate-variability)
* [Implementation of FAIR principles in the IPCC: the WGI AR6 Atlas repository](https://doi.org/10.1038/s41597-022-01739-y)
* [Climate Change 2021 – The Physical Science Basis: Atlas](https://doi.org/10.1017/9781009157896.021)


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple indicators"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q02  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple indicators > ℹ If you want to know more > References
---
[[Avila+15](https://doi.org/10.1016/j.wace.2015.06.003)] F. B. Avila et al., ‘Systematic investigation of gridding-related scaling effects on annual statistics of daily temperature and precipitation maxima: A case study for south-east Australia’, Weather and Climate Extremes, vol. 9, pp. 6–16, Aug. 2015, doi: 10.1016/j.wace.2015.06.003.

[[C3S Atlas dataset](https://doi.org/10.24381/cds.h35hb680)] Copernicus Climate Change Service, ‘Gridded dataset underpinning the Copernicus Interactive Climate Atlas’. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), Jun. 17, 2024. doi: 10.24381/cds.h35hb680.

[[CMIP6 dataset](https://doi.org/10.24381/cds.c866074c)] Copernicus Climate Change Service, ‘CMIP6 climate projections’. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), Mar. 23, 2021. doi: 10.24381/cds.c866074c.

[[Gutiérrez+24](https://doi.org/10.21957/ah52ufc369)] J. M. Gutiérrez et al., ‘The Copernicus Interactive Climate Atlas: a tool to explore regional climate change’, ECMWF Newsletter, vol. 181, pp. 38–45, Oct. 2024, doi: 10.21957/ah52ufc369.


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets
---
Production date: 2025-10-14.

Dataset version: 2.0.

Produced by: Olivier Burggraaff, Nicole Reynolds (National Physical Laboratory).


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Quality assessment question
---
* **Are the climate indicators in the dataset underpinning the Copernicus Interactive Climate Atlas consistent with their origin datasets?**
* **Can the dataset underpinning the Copernicus Interactive Climate Atlas be reproduced from its origin datasets?**

The [_Copernicus Interactive Climate Atlas_](https://atlas.climate.copernicus.eu/atlas), or _C3S Atlas_ for short, is a C3S web application providing an easy-to-access tool for exploring climate projections, reanalyses, and observational data [[Gutiérrez+24](https://doi.org/10.21957/ah52ufc369)].
Version 2.0 of the application allows the user to interact with 12 datasets:

| Type               | Dataset       |
|--------------------|---------------|
| Climate Projection | CMIP6         |
| Climate Projection | CMIP5         |
| Climate Projection | CORDEX-CORE   |
| Climate Projection | CORDEX-EUR-11 |
| Reanalysis         | ERA5          |
| Reanalysis         | ERA5-Land     |
| Reanalysis         | ORAS5         |
| Reanalysis         | CERRA         |
| Observations       | E-OBS         |
| Observations       | BERKEARTH     |
| Observations       | CPC           |
| Observations       | SST-CCI       |

These datasets are provided through an intermediary dataset, the [_Gridded dataset underpinning the Copernicus Interactive Climate Atlas_](https://doi.org/10.24381/cds.h35hb680) or _C3S Atlas dataset_ for short [[C3S Atlas dataset](https://doi.org/10.24381/cds.h35hb680)].
Compared to their origins, the versions of the climate datasets within the C3S Atlas dataset have been processed following the workflow in Figure {numref}`{number} <multi-origin-c3s-atlas_consistency_q03_workflow-fig>`.

attachment:c3s_atlas_dataset_workflow.png
---
height: 360px
name: multi-origin-c3s-atlas_consistency_q03_workflow-fig
---
Schematic representation of the workflow for the production of the C3S Atlas dataset from its origin datasets, from the [User-tools for the C3S Atlas](https://ecmwf-projects.github.io/c3s-atlas/chapter01.html).
```

Because a wide range of users interact with climate data through the C3S Atlas application, it is crucial that the underpinning dataset represent its origins correctly.
In other words, the C3S Atlas dataset must be consistent with and reproducible from its origins.
Here, we assess this consistency and reproducibility by comparing climate indicators retrieved from the C3S Atlas dataset with their equivalents calculated from the origin dataset, mirroring the workflow from Figure {numref}`{number} <multi-origin-c3s-atlas_consistency_q03_workflow-fig>`.
While a full analysis and reproduction of every record within the C3S Atlas dataset is outside the scope of quality assessment
(and would require high-performance computing infrastructure),
a case study with a narrower scope probes these quality attributes of the dataset
and can be a jumping-off point for further analysis by the reader.

This notebook is part of a series:
| Notebook | Contents |
|---|---|
| [](./derived_multi-origin-c3s-atlas_consistency_q01) | Comparison between C3S Atlas dataset and one origin dataset (CMIP6) for one indicator (`tx35`), including detailed setup. |
| [](./derived_multi-origin-c3s-atlas_consistency_q02) | Comparison between C3S Atlas dataset and one origin dataset (CMIP6) for multiple indicators. |
| **Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets** | Comparison between C3S Atlas dataset and multiple origin datasets for one indicator. |


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Quality assessment statement
---
These are the key outcomes of this assessment

* Climate indicators (here 3 monthly indicators) provided by the C3S Atlas dataset are highly consistent with values calculated from its origin datasets (here 10 climate projection, observation, and reanalysis datasets).

* There are some differences in coverage between the C3S Atlas dataset and its origins due to differences in the version used, e.g. E-OBS temperature indicators at the edges of Europe, but these do not affect the vast majority of use cases for the C3S Atlas.

* Differences between the C3S Atlas dataset and a manual reproduction are rare and generally negligible (median absolute difference of 0 for `tx35` and `r01`, ≤0.0003 °C for SST). Where differences occur, they can be explained by dataset versions or details of the workflow implementation.

* The C3S Atlas is traceable and reproducible, and can be confidently used to view, analyse, and download climate data.

* For specific use cases, like scientific papers, it is recommended to manually process the origin dataset instead of using the C3S Atlas. This is not necessary for other use cases, such as climate risk assessments and climate reports, in which case the C3S Atlas can be used as is.
```


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Methodology
---
This quality assessment tests the consistency between climate indicators retrieved from the [_Gridded dataset underpinning the Copernicus Interactive Climate Atlas_](https://doi.org/10.24381/cds.h35hb680) [[C3S Atlas dataset](https://doi.org/10.24381/cds.h35hb680)] and their equivalents calculated from the origin datasets,
as well as the reproducibility of said dataset.

This notebook probes the consistency between the C3S Atlas dataset and multiple origin datasets at the same time.
Due to differences in scope (e.g. atmosphere / land / sea), not every indicator is available in every origin dataset or its C3S Atlas derivative.
Furthermore, some origin datasets are historical while others are future projections.
For this reason, we will examine the following indicators in the following origin datasets:

**Monthly count of days with maximum near-surface (2-metre) air temperature above 35 °C (`tx35`)**
| Type               | Dataset      |
|--------------------|--------------|
| Climate Projection | CMIP6        |
| Climate Projection | CMIP5        |
| Climate Projection | CORDEX-EUR-11|
| Reanalysis         | ERA5         |
| Reanalysis         | ERA5-Land    |
| Observations       | E-OBS        |
| Observations       | BERKEARTH    |

Note that CORDEX-CORE has been left out of this assessment because its mosaicking workflow is out of scope.
CERRA has been left out because the C3S User-tools package is currently not fully compatible with this dataset.

**Monthly mean temperature of sea water near the surface (`sst`)**
| Type               | Dataset      |
|--------------------|--------------|
| Reanalysis         | ORAS5        |
| Observations       | SST-CCI      |

**Monthly count of days with daily accumulated precipitation of liquid water equivalent from all phases above 1 mm (`r01`)**
| Type               | Dataset      |
|--------------------|--------------|
| Observations       | CPC          |

The analysis and results are organised in the following steps, which are detailed in the sections below:

**[](section-codesetup)**
 * Install User-tools for the C3S Atlas.
 * Import all required libraries.
 * Define indicators.
 * Define helper functions.

**[](section-indicators)**
 * Download data from the origin datasets.
 * Homogenise data.
 * Calculate indicators.
 * Regrid the origin data to the C3S Atlas grid.
 * Download corresponding data from the C3S Atlas dataset.

**[](section-results)**
 * Consistency: Compare the C3S Atlas and reproduced datasets on native grids.
 * Reproducibility: Compare the C3S Atlas and reproduced datasets on the C3S Atlas grid.


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 1. Code setup
---
This notebook uses [earthkit](https://github.com/ecmwf/earthkit) for
downloading ([earthkit-data](https://github.com/ecmwf/earthkit-data))
and visualising ([earthkit-plots](https://github.com/ecmwf/earthkit-plots)) data.
Because earthkit is in active development, some functionality may change after this notebook is published.
If any part of the code stops functioning, please raise an issue on our GitHub repository so it can be fixed.
```


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 1. Code setup > Install the User-tools for the C3S Atlas
---
This notebook uses the [User-tools for the C3S Atlas](https://github.com/ecmwf-projects/c3s-atlas), which can be installed from GitHub using `pip`.
For convenience, the following cell can do this from within the notebook.
Further details and alternative options for installing this library are available in its [documentation](https://github.com/ecmwf-projects/c3s-atlas?tab=readme-ov-file#requirements).


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 1. Code setup > Import required libraries
---
In this section, we import all the relevant packages needed for running the notebook.

Input / Output
General data handling
Data pre-processing
Climate indicators
Visualisation
Visualisation in Jupyter book -- automatically ignored otherwise


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 1. Code setup > Define indicators
---
This section defines functions and variables for calculating and using the climate indicators.

The following cell includes a helper function (in the form of a decorator) to correctly propagate NaNs, which is important for datasets using land/sea masks:

Calculate indicator as normal
If propagation is not desired (e.g. you know there are no NaNs), simply return the result
This preserves integer data types
Infer input (var-iable), output (ind-icator) keys
Resample data to frequency of indicator, generate mask
Apply mask and return result

The following cell contains functions for calculating the indicators described in the introduction:

The following cell defines [earthkit-plots styles](https://earthkit-plots.readthedocs.io/en/latest/examples/examples/introduction/05-styles.html) for the indicators.
These styles define the colour maps and colour bar ranges for each quantity.

Styles for indicators
tx35 indicator
sst indicator
r01 indicator
Individual styles
Set up like this so they can still be edited individually
Apply general settings


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 1. Code setup > Helper functions
---
This section defines some functions and variables used in the following analysis, allowing code cells in later sections to be shorter and ensuring consistency.

##### Data downloading & (pre-)processing

The following functions help with downloading data from datasets with limits, by generating multiple CDS requests with similar parameters:

Split requests into smaller parts
SST-CCI: Generate monthly batches of requests
Generate requests

The following functions aid in sub-selecting data, e.g. selecting one model from the ensemble included in the C3S Atlas dataset or selecting data for a specific time frame:

Select (multiple) years in a dataset
C3S Atlas dataset: Individual model selection
Ensure the model ID is provided in the right format
Find the corresponding model ID in the list of models
This cannot use .sel because the coordinate is not indexed
Find the corresponding data and return those
C3S Atlas dataset: Common pre-processing

The following functions handle [data chunking in dask](https://docs.xarray.dev/en/latest/user-guide/dask.html) for computational efficiency:

Rechunking of data to speed up dask calculations
Find coordinates (usually lat/lon, sometimes x/y)
Assign new chunk sizes

The following functions handle the homogenisation of origin data to a consistent format using the [User-tools for the C3S Atlas](https://ecmwf-projects.github.io/c3s-atlas/intro.html):

Homogenisation of origin dataset

The following functions handle regridding data based on ESMF as implemented in the [User-tools for the C3S Atlas](https://ecmwf-projects.github.io/c3s-atlas/intro.html).
This step is explained in more detail in the relevant section below.

Regridding from native grid to C3S Atlas grid

##### Statistics
The following functions calculate the difference (absolute / relative) between datasets, handling metadata etc.:

Difference between datasets
Select and calculate
Replace 0/0 with 0

The following functions calculate and display metrics for the difference between two datasets, e.g. mean and median deviation:

Calculate differences
Calculate relative difference if desired
Calculate aggregate statistics
(n.b. in dask, this only queues them up, doesn't actually compute)
Calculate correlation coefficients
Perform the queued-up calculations
Combine statistics into one dataframe
Setup: origins
Calculate statistics per dataset, combine result, return

##### Visualisation
The following cells contain functions for plotting results, starting with some base helper functions (e.g. displaying in Jupyter Notebook or Jupyter Book style, adding textboxes with consistent formatting, etc.):

Visualisation: Helper functions, general
Get the plt.Axes for each ekp.Subplot
Set up location
Add the text

The following functions are also base helper functions, but specific to geospatial plots:

Visualisation: Helper functions for geospatial plots
Create subplots

The following cell contains functions for geospatial comparisons between datasets on their native grids or on a common grid (the latter also showing the per-pixel difference):

Setup: origins
Create figure
Plot individual datasets
Decorate: Text
Colour bar at the bottom
Titles on top
Decorate figure
Visualisation: Plot indicators geospatially
Setup: origins
Create figure
Plot individual datasets
Plot difference
Decorate: Text
Colour bar at the bottom
Titles on top
Decorate figure

The following cell contains functions for histogram comparisons between datasets on their native grids or on a common grid (the latter also showing the per-pixel difference):

Visualisation: Plot data in histograms
Setup: origins
Create figure
Plot histograms of data
Loop over rows / origins
Flatten data
Create histogram
Identify panel
Titles on top
Visualisation: Plot data + difference in histograms
Setup: origins
Create figure
Setup x/y share -- cannot be done in plt.subplots because of difference panel not sharing these
_sharexy(axs[:, -1])  # Leave out for now -- ALlow differences between origin datasets
Plot histograms of data
Loop over rows / origins
Loop over columns / data
Flatten data
Create histogram
Plot difference
Identify panel
Titles on top

(section-indicators)=


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators
---
In the previous two notebooks in this assessment,
the origin data were downloaded,
pre-processed,
used to calculate the relevant indicator(s),
and regridded;
after which the [Atlas dataset](https://doi.org/10.24381/cds.h35hb680) was downloaded.
This notebook follows the same structure but for each origin in turn,
for clarity and to preserve memory when loading multiple datasets at the same time.
As such,
the individual steps are described in less detail,
because this information is available in the previous notebooks.

If you are only interested in specific origin datasets,
or want to limit your bandwidth or memory usage,
you can choose to only run specific subsections below.

This assessment examines the dataset in one year (2080),
for one ensemble member
and
for one climate scenario
– which is not how climate projection data are normally used.
Good practice in climate science is to look at multi-year statistics and trends,
across multiple ensemble members.
However, since the purpose of this assessment is to assess the consistency and reproducibility of the post-processing performed to produce the C3S Atlas dataset,
it is valid to use a subset of the data here.
The specific subset used can be easily tweaked by changing the `EXPERIMENT`, `MODEL`, and `YEARS` variables in the relevant sections.

This notebook uses [earthkit-data](https://github.com/ecmwf/earthkit-data) to download files from the CDS.
If you intend to run this notebook multiple times, it is highly recommended that you [enable caching](https://earthkit-data.readthedocs.io/en/latest/concepts/caching.html) to prevent having to download the same files multiple times.
If you prefer not to use earthkit, the following requests can also be used with the [cdsapi module](https://cds.climate.copernicus.eu/how-to-api#linux-use-client-step).
In either case (earthkit-data or cdsapi), it is required to set up a CDS account and API key as explained [on the CDS website](https://cds.climate.copernicus.eu/how-to-api).

This notebook uses [xESMF](https://github.com/pangeo-data/xESMF) for regridding data.
xESMF is most easily installed using mamba/conda as explained in its documentation.
Users who cannot or do not wish to use mamba/conda can manually compile and install [ESMF](https://earthsystemmodeling.org/docs/release/latest/ESMF_usrdoc/node10.html) on their machines.
In future, this notebook will use [earthkit-regrid](https://github.com/ecmwf/earthkit-regrid) instead, once it reaches suitable maturity.
```

Note that the C3S Atlas workflow calculates indicators first, then regrids.
For operations that involve averaging, like smoothing and regridding, the order of operations can affect the result, especially in areas with steep gradients [[Avila+15](https://doi.org/10.1016/j.wace.2015.06.003)].
Examples of such areas for a temperature index are coastlines and mountain ranges.
In the case of C3S Atlas, this order of operations was a conscious choice to preserve the "raw" signals,
e.g. preventing extreme temperatures from being smoothed out.
However, it can affect the indicator values and therefore must be considered when using the C3S Atlas application or dataset,
particularly when intercomparing it with another dataset.


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators > General setup
---
Throughout this section,
we combine the downloaded datasets into dictionaries for easy access.
They cannot be combined into a single xarray object because of differing grids.
Each dataset is added in its own subsection,
meaning any datasets not downloaded will automatically be skipped in the analysis.

Constants to ensure consistency between origins
Change these to customise the analysis
All days and months

Setup: C3S Atlas request
Templates for C3S Atlas requests


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators > CMIP6
---
Setup: Customisable options for this dataset

Setup: Origin request
Download data, pre-process, calculate indicator, regrid

```text
Unknown file type, no reader available. path=/home/ob2/cds_data/cds-67885792c67c919c6adef8f9452ca11327458aa2fc79e661bd6835af4bbb1791.d/provenance.png magic=b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\n\xd3\x00\x00\x03"\x08\x02\x00\x00\x00\x99\xec9+\x00\x00\x00\x06bKGD\x00\xff\x00\xff\x00\xff\xa0\xbd\xa7\x93\x00\x00 \x00IDATx\x9c\xec\xddw' content_type=None
2025-10-13 16:44:59,142 — Homogenization-fixers — INFO — Dataset has already the correct names for its coordinates
2025-10-13 16:44:59,155 — Homogenization-fixers — INFO — Fixing calendar for <xarray.Dataset> Size: 81MB
Dimensions:    (time: 365, bnds: 2, lat: 192, lon: 288)
Coordinates:
  * time       (time) object 3kB 2080-01-01 12:00:00 ... 2080-12-31 12:00:00
  * lat        (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
  * lon        (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
    height     float64 8B ...
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) object 6kB dask.array<chunksize=(1, 2), meta=np.ndarray>
    lat_bnds   (lat, bnds) float64 3kB dask.array<chunksize=(192, 2), meta=np.ndarray>
    lon_bnds   (lon, bnds) float64 5kB dask.array<chunksize=(288, 2), meta=np.ndarray>
    tasmax     (time, lat, lon) float32 81MB dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
Attributes: (12/48)
    Conventions:            CF-1.7 CMIP-6.2
    activity_id:            ScenarioMIP
    branch_method:          standard
    branch_time_in_child:   60225.0
    branch_time_in_parent:  60225.0
    comment:                none
    ...                     ...
    title:                  CMCC-ESM2 output prepared for CMIP6
    variable_id:            tasmax
    variant_label:          r1i1p1f1
    license:                CMIP6 model data produced by CMCC is licensed und...
    cmor_version:           3.6.0
    tracking_id:            hdl:21.14100/ba2e335b-8bac-45ec-abbe-f1f16299d2d4
2025-10-13 16:44:59,332 — UNITS_TRANSFORM — INFO — The dataset tasmax units are not in the correct magnitude. A conversion from K to Celsius will be performed.
2025-10-13 16:44:59,405 — Homogenization-fixers — INFO — The dataset is in daily or monthly resolution, we don't need to resample it from hourly frequency
```

Setup: C3S Atlas request
Download data, pre-process

Add to collection


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators > CMIP5
---
Setup: Customisable options for this dataset

Setup: Origin request
Note: CMIP5 "period" cannot be [YEAR_FUTURE,]
You may have to change the following line if you want to change
YEAR_FUTURE, CMIP5_MODEL, or CMIP5_EXPERIMENT
Download data, pre-process, calculate indicator, regrid

```text
2025-10-13 16:45:09,478 — Homogenization-fixers — INFO — Dataset has already the correct names for its coordinates
2025-10-13 16:45:09,481 — Homogenization-fixers — INFO — Fixing calendar for <xarray.Dataset> Size: 41MB
Dimensions:    (time: 366, bnds: 2, lat: 145, lon: 192)
Coordinates:
  * time       (time) datetime64[ns] 3kB 2080-01-01T12:00:00 ... 2080-12-31T1...
  * lat        (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0
  * lon        (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1
    height     float64 8B ...
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) datetime64[ns] 6kB dask.array<chunksize=(366, 2), meta=np.ndarray>
    lat_bnds   (lat, bnds) float64 2kB dask.array<chunksize=(145, 2), meta=np.ndarray>
    lon_bnds   (lon, bnds) float64 3kB dask.array<chunksize=(192, 2), meta=np.ndarray>
    tasmax     (time, lat, lon) float32 41MB dask.array<chunksize=(366, 145, 192), meta=np.ndarray>
Attributes: (12/28)
    institution:            CSIRO (Commonwealth Scientific and Industrial Res...
    institute_id:           CSIRO-BOM
    experiment_id:          rcp85
    source:                 ACCESS1-0 2011. Atmosphere: AGCM v1.0 (N96 grid-p...
    model_id:               ACCESS1-0
    forcing:                GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4...
    ...                     ...
    table_id:               Table day (01 February 2012) b6353e9919862612c81d...
    title:                  ACCESS1-0 model output prepared for CMIP5 RCP8.5
    parent_experiment:      historical
    modeling_realm:         atmos
    realization:            1
    cmor_version:           2.8.0
2025-10-13 16:45:09,492 — UNITS_TRANSFORM — INFO — The dataset tasmax units are not in the correct magnitude. A conversion from K to Celsius will be performed.
2025-10-13 16:45:09,543 — Homogenization-fixers — INFO — The dataset is in daily or monthly resolution, we don't need to resample it from hourly frequency
```

Setup: C3S Atlas request
Download data, pre-process

Add to collection


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators > CORDEX-EUR-11
---
Setup: Customisable options for this dataset
Note that the model nomenclature is different for the origin dataset (GCM, RCM separate) vs C3S Atlas (combined)
If you want to change the model, be sure to edit all of the relevant lines below

Setup: Origin request
Note: CORDEX "start_year" and "end_year" cannot be YEAR_FUTURE
and cannot be determined generically for the various model members
You may have to change the following line if you want to change
YEAR_FUTURE, CORDEX_EUR_11_MODEL, or CORDEX_EUR_11_EXPERIMENT
Download data, pre-process, calculate indicator, regrid

```text
2025-10-13 16:45:13,404 — Homogenization-fixers — INFO — Fixing coordinates names: {'rlon': 'x', 'rlat': 'y'}
2025-10-13 16:45:13,407 — Homogenization-fixers — INFO — Fixing calendar for <xarray.Dataset> Size: 263MB
Dimensions:                     (time: 366, bnds: 2, y: 412, x: 424, vertices: 4)
Coordinates:
  * time                        (time) datetime64[ns] 3kB 2080-01-01T12:00:00...
  * y                           (y) float64 3kB -23.38 -23.27 ... 21.72 21.84
  * x                           (x) float64 3kB -28.38 -28.27 ... 18.04 18.16
    lat                         (y, x) float32 699kB dask.array<chunksize=(412, 424), meta=np.ndarray>
    lon                         (y, x) float32 699kB dask.array<chunksize=(412, 424), meta=np.ndarray>
    height                      float64 8B ...
Dimensions without coordinates: bnds, vertices
Data variables:
    time_bnds                   (time, bnds) datetime64[ns] 6kB dask.array<chunksize=(1, 2), meta=np.ndarray>
    rotated_latitude_longitude  int32 4B ...
    lat_vertices                (y, x, vertices) float32 3MB dask.array<chunksize=(412, 424, 4), meta=np.ndarray>
    lon_vertices                (y, x, vertices) float32 3MB dask.array<chunksize=(412, 424, 4), meta=np.ndarray>
    tasmax                      (time, y, x) float32 256MB dask.array<chunksize=(1, 412, 424), meta=np.ndarray>
Attributes: (12/35)
    institution:                    Helmholtz-Zentrum Geesthacht, Climate Ser...
    institute_id:                   GERICS
    experiment_id:                  rcp85
    source:                         GERICS-REMO2015
    model_id:                       GERICS-REMO2015
    forcing:                        N/A
    ...                             ...
    parent_experiment:              N/A
    modeling_realm:                 atmos
    realization:                    1
    cmor_version:                   2.9.1
    tracking_id:                    hdl:21.14103/c3a4e035-9dcd-429f-a86d-8d53...
    c3s_disclaimer:                 This data has been produced in the contex...
2025-10-13 16:45:13,425 — UNITS_TRANSFORM — INFO — The dataset tasmax units are not in the correct magnitude. A conversion from K to Celsius will be performed.
2025-10-13 16:45:13,436 — Homogenization-fixers — INFO — The dataset is in daily or monthly resolution, we don't need to resample it from hourly frequency
```

Setup: C3S Atlas request
Download data, pre-process

Add to collection


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators > ERA5
---
Note that ERA5 data for a year are >30 GB in size.
These data may take up to several hours to download
and
require sufficient storage to download and cache.

Setup: Origin request
Download data, pre-process, calculate indicator

```text
2025-10-13 16:45:32,294 — Homogenization-fixers — INFO — Fixing coordinates names: {'longitude': 'lon', 'latitude': 'lat'}
2025-10-13 16:45:32,298 — Homogenization-fixers — INFO — Fixing calendar for <xarray.Dataset> Size: 2GB
Dimensions:  (time: 365, lat: 721, lon: 1440)
Coordinates:
    number   int64 8B ...
  * lat      (lat) float64 6kB 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0
  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8
  * time     (time) datetime64[ns] 3kB 2010-01-01 2010-01-02 ... 2010-12-31
Data variables:
    tasmax   (time, lat, lon) float32 2GB dask.array<chunksize=(1, 52, 103), meta=np.ndarray>
Attributes:
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 2025-10-07T20:34 GRIB to CDM+CF via cfgrib-0.9.1...
2025-10-13 16:45:32,381 — UNITS_TRANSFORM — INFO — The dataset tasmax units are not in the correct magnitude. A conversion from K to Celsius will be performed.
2025-10-13 16:45:34,651 — Homogenization-fixers — INFO — The dataset is in daily or monthly resolution, we don't need to resample it from hourly frequency
```

Setup: C3S Atlas request
Download data, pre-process

Add to collection


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators > ERA5-Land
---
Note that ERA5-Land data for a year are >200 GB in size.
These data may take up to several hours to download
and
require sufficient storage to download and cache.

Setup: Origin request
ERA5-Land doesn't allow us to download a single year in one go, it has to be split into multiple requests
Download data, pre-process, calculate indicator

```text
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:00<00:00, 247.68it/s]
2025-10-13 16:45:38,419 — Homogenization-fixers — INFO — Fixing coordinates names: {'longitude': 'lon', 'latitude': 'lat'}
2025-10-13 16:45:38,422 — Homogenization-fixers — INFO — Fixing calendar for <xarray.Dataset> Size: 9GB
Dimensions:  (time: 365, lat: 1801, lon: 3600)
Coordinates:
    number   int64 8B 0
  * lat      (lat) float64 14kB 90.0 89.9 89.8 89.7 ... -89.7 -89.8 -89.9 -90.0
  * lon      (lon) float64 29kB 0.0 0.1 0.2 0.3 0.4 ... 359.6 359.7 359.8 359.9
  * time     (time) datetime64[ns] 3kB 2010-01-01 2010-01-02 ... 2010-12-31
Data variables:
    tasmax   (time, lat, lon) float32 9GB dask.array<chunksize=(1, 164, 328), meta=np.ndarray>
Attributes:
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 2025-10-08T09:44 GRIB to CDM+CF via cfgrib-0.9.1...
2025-10-13 16:45:38,476 — UNITS_TRANSFORM — INFO — The dataset tasmax units are not in the correct magnitude. A conversion from K to Celsius will be performed.
2025-10-13 16:45:40,453 — Homogenization-fixers — INFO — The dataset is in daily or monthly resolution, we don't need to resample it from hourly frequency
```

Setup: C3S Atlas request
Download data, pre-process

Regridding for ERA5-Land
The coordinates for C3S Atlas/ERA5-Land are irregular, likely due to floating-point errors.
To mitigate this, we round the coords to one digit.

Add to collection


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators > E-OBS
---
Note that E-OBS data for the full period are >30 GB in size.
These data may take up to several hours to download
and
require sufficient storage to download and cache.

Setup: Origin request
It is not possible to provide specific years to "period",
but a function that picks the shortest period covering all
YEAR_HISTORICAL would be useful to reduce download size
Download data, pre-process, calculate indicator, regrid

```text
2025-10-13 16:45:43,266 — Homogenization-fixers — INFO — Fixing coordinates names: {'longitude': 'lon', 'latitude': 'lat'}
2025-10-13 16:45:43,269 — Homogenization-fixers — INFO — Fixing calendar for <xarray.Dataset> Size: 479MB
Dimensions:  (time: 365, lat: 465, lon: 705)
Coordinates:
  * lat      (lat) float64 4kB 25.05 25.15 25.25 25.35 ... 71.25 71.35 71.45
  * lon      (lon) float64 6kB -24.95 -24.85 -24.75 -24.65 ... 45.25 45.35 45.45
  * time     (time) datetime64[ns] 3kB 2010-01-01 2010-01-02 ... 2010-12-31
Data variables:
    tasmax   (time, lat, lon) float32 479MB dask.array<chunksize=(1, 465, 705), meta=np.ndarray>
Attributes:
    E-OBS_version:  30.0e
    Conventions:    CF-1.4
    References:     http://surfobs.climate.copernicus.eu/dataaccess/access_eo...
    history:        Fri Aug 30 11:43:24 2024: ncks --no-abc -d time,0,27209 /...
    NCO:            netCDF Operators version 5.1.8 (Homepage = http://nco.sf....
2025-10-13 16:45:43,279 — UNITS_TRANSFORM — INFO — The dataset tasmax units are already in the correct magnitude
2025-10-13 16:45:43,422 — Homogenization-fixers — INFO — The dataset is in daily or monthly resolution, we don't need to resample it from hourly frequency
```

Setup: C3S Atlas request
Download data, pre-process

Add to collection


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators > BERKEARTH
---
Setup: Origin request
Download data, pre-process, calculate indicator

```text
2025-10-13 16:45:55,010 — Homogenization-fixers — INFO — Fixing coordinates names: {'longitude': 'lon', 'latitude': 'lat'}
2025-10-13 16:45:55,015 — Homogenization-fixers — INFO — Fixing calendar for <xarray.Dataset> Size: 95MB
Dimensions:  (time: 365, lat: 180, lon: 360)
Coordinates:
  * time     (time) datetime64[ns] 3kB 2010-01-01 2010-01-02 ... 2010-12-31
  * lon      (lon) float32 1kB -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5
  * lat      (lat) float32 720B -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
Data variables:
    tasmax   (time, lat, lon) float32 95MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
Attributes: (12/21)
    CDI:                        Climate Data Interface version 1.9.10 (https:...
    institution:                Berkeley Earth Surface Temperature Project
    Conventions:                Berkeley Earth Internal Convention (based on ...
    title:                      Gridded Berkeley Earth Surface Temperature An...
    source_history:             14-Sep-2020 16:15:39
    comment:                    This file contains surface temperature anomal...
    ...                         ...
    geospatial_lon_resolution:  1.0
    climexp_url:                https://climexp.knmi.nl/select.cgi?berkeley_t...
    history:                    Fri Jun 18 07:44:32 2021: cdo -O -splityear /...
    time_coverage_start:        1880-01-01 00:00:00
    time_coverage_end:          2019-12-31 00:00:00
    CDO:                        Climate Data Operators version 1.9.10 (https:...
2025-10-13 16:45:55,027 — UNITS_TRANSFORM — INFO — The dataset tasmax units are already in the correct magnitude
2025-10-13 16:45:55,109 — Homogenization-fixers — INFO — The dataset is in daily or monthly resolution, we don't need to resample it from hourly frequency
```

Setup: C3S Atlas request
Download data, pre-process

Add to collection


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators > ORAS5
---
Setup: Origin request
Download data, pre-process, calculate indicator, regrid

```text
2025-10-14 00:08:31,387 — Homogenization-fixers — INFO — Fixing coordinates names: {'nav_lon': 'lon', 'nav_lat': 'lat'}
2025-10-14 00:08:31,416 — UNITS_TRANSFORM — INFO — The dataset sst units are already in the correct magnitude
2025-10-14 00:08:31,451 — Homogenization-fixers — INFO — The dataset is in daily or monthly resolution, we don't need to resample it from hourly frequency
```

Setup: C3S Atlas request
Download data, pre-process

Add to collection


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators > SST-CCI
---
Note that SST-CCI data for a year are >150 GB in size.
These data may take up to several hours to download
and
require sufficient storage to download and cache.

Setup: Origin request
SST-CCI doesn't allow us to download a single year in one go, it has to be split into multiple requests
Download data, pre-process, calculate indicator

```text
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36/36 [00:00<00:00, 165.43it/s]
2025-11-03 16:20:40,236 — Homogenization-fixers — INFO — Dataset has already the correct names for its coordinates
2025-11-03 16:20:40,240 — Homogenization-fixers — INFO — Fixing calendar for <xarray.Dataset> Size: 151GB
Dimensions:                   (time: 365, lat: 3600, lon: 7200, bnds: 2)
Coordinates:
  * lat                       (lat) float32 14kB -89.97 -89.93 ... 89.93 89.97
  * lon                       (lon) float32 29kB -180.0 -179.9 ... 179.9 180.0
  * time                      (time) datetime64[ns] 3kB 2010-01-01T12:00:00 ....
Dimensions without coordinates: bnds
Data variables:
    sst                       (time, lat, lon) float32 38GB dask.array<chunksize=(1, 1200, 2400), meta=np.ndarray>
    mask                      (time, lat, lon) float32 38GB dask.array<chunksize=(1, 1800, 3600), meta=np.ndarray>
    sea_ice_fraction          (time, lat, lon) float32 38GB dask.array<chunksize=(1, 1800, 3600), meta=np.ndarray>
    analysed_sst_uncertainty  (time, lat, lon) float32 38GB dask.array<chunksize=(1, 1200, 2400), meta=np.ndarray>
    time_bnds                 (time, bnds) datetime64[ns] 6kB dask.array<chunksize=(1, 2), meta=np.ndarray>
    lat_bnds                  (time, lat, bnds) float32 11MB dask.array<chunksize=(1, 3600, 2), meta=np.ndarray>
    lon_bnds                  (time, lon, bnds) float32 21MB dask.array<chunksize=(1, 7200, 2), meta=np.ndarray>
Attributes: (12/58)
    Conventions:                     CF-1.5, Unidata Observation Dataset v1.0
    title:                           ESA SST CCI OSTIA L4 product
    summary:                         OSTIA L4 product from the ESA SST CCI pr...
    references:                      http://www.esa-sst-cci.org
    institution:                     ESACCI
    history:                         Created using OSTIA reanalysis system v3.0
    ...                              ...
    source:                          ATSR<1,2>-ESACCI-L3U-v2.0, AATSR-ESACCI-...
    platform:                        ERS-<1,2>, Envisat, NOAA-<07,09,11,12,14...
    creator_name:                    ESA SST CCI
    product_specification_version:   SST_CCI-PSD-UKMO-201-Issue-2
    id:                              OSTIA-ESACCI-L4-GLOB-v2.1
    product_version:                 2.1
2025-11-03 16:20:40,294 — UNITS_TRANSFORM — INFO — The dataset sst units are not in the correct magnitude. A conversion from Kelvin to Celsius will be performed.
2025-11-03 16:20:41,695 — Homogenization-fixers — INFO — The dataset is in daily or monthly resolution, we don't need to resample it from hourly frequency
```

Setup: C3S Atlas request
Download data, pre-process
The coordinates for C3S Atlas/SST-CCI are irregular, likely due to floating-point errors.
To mitigate this, we force it to use the same coordinates as the SST-CCI origin.
Please note that this is NOT generally good practice, and is only done here for the sake of the assessment.

Add to collection


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators > CPC
---
Setup: Origin request
Download data, pre-process, calculate indicator

```text
2025-10-13 16:23:32,652 — Homogenization-fixers — INFO — Dataset has already the correct names for its coordinates
2025-10-13 16:23:32,661 — Homogenization-fixers — INFO — Fixing calendar for <xarray.Dataset> Size: 757MB
Dimensions:  (time: 365, lat: 360, lon: 720)
Coordinates:
  * time     (time) datetime64[ns] 3kB 2010-01-01T12:00:00 ... 2010-12-31T12:...
  * lon      (lon) float64 6kB 0.25 0.75 1.25 1.75 ... 358.2 358.8 359.2 359.8
  * lat      (lat) float64 3kB -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75
Data variables:
    pr       (time, lat, lon) float64 757MB dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
Attributes: (12/19)
    CDI:                        Climate Data Interface version 1.9.10 (https:...
    institution:                NOAA/NCEP/CPC (converted to netcdf at KNMI)
    Conventions:                CF-1.4
    NCO:                        netCDF Operators version 4.9.0 (Homepage = ht...
    title:                      CPC unified (gauge-based) precipitation
    source_url:                 ftp://ftp.cpc.ncep.noaa.gov/precip/CPC_UNI_PR...
    ...                         ...
    geospatial_lon_resolution:  0.5
    climexp_url:                https://climexp.knmi.nl/select.cgi?prcp_cpc_d...
    history:                    Fri Jun 18 07:52:31 2021: cdo -O -splityear /...
    time_coverage_start:        1979-01-01 12:00:00
    time_coverage_end:          2021-12-31 12:00:00
    CDO:                        Climate Data Operators version 1.9.10 (https:...
2025-10-13 16:23:32,818 — UNITS_TRANSFORM — INFO — The dataset pr units are not in the correct magnitude. A conversion from mm day**-1 to mm will be performed.
2025-10-13 16:23:32,972 — Homogenization-fixers — INFO — The dataset is in daily or monthly resolution, we don't need to resample it from hourly frequency
```

Setup: C3S Atlas request
Download data, pre-process

Add to collection


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 2. Calculate and retrieve indicators > Cleanup
---
Lastly,
we manually clear out some memory-intensive objects that are no longer necessary.

We also [re-chunk](https://docs.xarray.dev/en/latest/user-guide/dask.html) the datasets to be more computationally efficient:

(section-results)=


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 3. Results
---
This section contains the comparison between the indicator values retrieved from the C3S Atlas dataset vs those reproduced from the origin datasets.

The datasets are first compared on their native grids.
This means a point-by-point comparison is not possible
(because the points are not equivalent),
but the distributions can be compared geospatially and overall.
This qualitative comparison probes the consistency quality attribute:
Are the climate indicators in the dataset underpinning the Copernicus Interactive Climate Atlas consistent with their origin datasets?

Second, the C3S Atlas dataset is compared to the indicators derived from the origin dataset and regridded to the C3S Atlas grid.
This makes a quantitative point-by-point comparison possible.
This second comparison probes how well the dataset underpinning the Copernicus Interactive Climate Atlas can be reproduced from its origin datasets,
based on the workflow (Figure {numref}`{number} <multi-origin-c3s-atlas_consistency_q03_workflow-fig>`).

For the geospatial comparison,
we display the values of the indicators for one month,
across one region and globally.
As an example, we display the results across
Europe
in
June,
which should provide significant spatial variation.
This region can easily be modified in the following code cell using the [domains provided by earthkit-plots](https://earthkit-plots.readthedocs.io/en/latest/examples/examples/introduction/07-domains.html).
Some examples are provided in the cell (commented out using `#`).

Setup: Choose a month to display
Setup: Pick domain using earthkit-plots
domain = "Mediterranean"
Other examples -- uncomment where desired
domain = ekp.geo.domains.union(["Portugal", "Spain"], name="Iberia")
domain = "Italy"
domain = "South America"


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 3. Results > Consistency: Comparison on native grids
---
As in the
[previous](./derived_multi-origin-c3s-atlas_consistency_q01)
[notebooks](./derived_multi-origin-c3s-atlas_consistency_q02),
it is clear from the geospatial comparisons
(Figures {numref}`{number} <derived_multi-origin-c3s-atlas_consistency_q03_fig-geo-tx35future>`–{numref}`{number} <derived_multi-origin-c3s-atlas_consistency_q03_fig-geo-r01>`)
that the C3S Atlas dataset closely resembles a manual reproduction from its origin datasets.
The general distribution of indicator values is the same for
all comparisons.
The
E-OBS `tx35` (Figure {numref}`{number} <derived_multi-origin-c3s-atlas_consistency_q03_fig-geo-tx35historical>`)
comparison shows clear differences in terms of coverage,
which is explained by differences in versions used
(26.0e in the production of the C3S Atlas dataset, 30.0e here).
Additionally,
the CMIP5 comparison
(Figure {numref}`{number} <derived_multi-origin-c3s-atlas_consistency_q03_fig-geo-tx35future>`)
shows clear differences here due to the fact that the C3S Atlas version of CMIP5 is regridded to a coarser resolution
to ensure consistency between the different model members of the ensemble.

This pattern is also visible in the overall distributions
(Figures {numref}`{number} <derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-tx35future-native>`–{numref}`{number} <derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-r01-native>`),
which are again very similar for almost all comparisons.

The overall conclusion from this comparison is the same as in the
previous
notebooks.
The C3S Atlas dataset and its origins are highly consistent,
but small differences exist due to the difference in grid
and differences in the origin dataset version used and workflow.
Large differences were observed
only
in the availability or masking of data in specific areas,
such as
on the edges of the E-OBS dataset.
Users of the C3S Atlas dataset
– and thus users of the C3S Atlas application –
should be aware that the indicator values retrieved for a specific location may differ slightly from a manual analysis of the origin dataset.

Set the plotting dates based on user inputs

::::{tab-set}
:::{tab-item} Projected `tx35`
:sync: tx35future
 derived_multi-origin-c3s-atlas_consistency_q03_fig-geo-tx35future
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-geo-tx35future"

Comparison between C3S Atlas dataset and reproduction for
projected
`tx35` in one month,
across Europe,
on the native grid of each dataset.
```
:::
:::{tab-item} Historical `tx35`
:sync: tx35historical
 derived_multi-origin-c3s-atlas_consistency_q03_fig-geo-tx35historical
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-geo-tx35historical"

Comparison between C3S Atlas dataset and reproduction for
historical
`tx35` in one month,
across Europe,
on the native grid of each dataset.
```
:::
:::{tab-item} `sst`
:sync: sst
 derived_multi-origin-c3s-atlas_consistency_q03_fig-geo-sst
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-geo-sst"

Comparison between C3S Atlas dataset and reproduction for
historical
`sst` in one month,
across Europe,
on the native grid of each dataset.
```
:::
:::{tab-item} `r01`
:sync: r01
 derived_multi-origin-c3s-atlas_consistency_q03_fig-geo-r01
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-geo-r01"

Comparison between C3S Atlas dataset and reproduction for
historical
`r01` in one month,
across Europe,
on the native grid of each dataset.
```
:::
::::


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 3. Results > Consistency: Comparison on native grids
---
_q03_fig-geo-r01"

Comparison between C3S Atlas dataset and reproduction for
historical
`r01` in one month,
across Europe,
on the native grid of each dataset.
```
:::
::::

::::{tab-set}
:::{tab-item} Projected `tx35`
:sync: tx35future
 derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-tx35future-native
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-tx35future-native"

Comparison between overall distributions of
projected
`tx35`
values in the C3S Atlas dataset and its reproduction,
across all spatial and temporal dimensions,
on the native grid of each dataset.
```
:::
:::{tab-item} Historical `tx35`
:sync: tx35historical
 derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-tx35historical-native
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-tx35historical-native"

Comparison between overall distributions of
historical
`tx35`
values in the C3S Atlas dataset and its reproduction,
across all spatial and temporal dimensions,
on the native grid of each dataset.
```
:::
:::{tab-item} `sst`
:sync: sst
 derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-sst-native
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-sst-native"

Comparison between overall distributions of
historical
`sst`
values in the C3S Atlas dataset and its reproduction,
across all spatial and temporal dimensions,
on the native grid of each dataset.
```
:::
:::{tab-item} `r01`
:sync: r01
 derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-r01-native
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-r01-native"

Comparison between overall distributions of
historical
`r01`
values in the C3S Atlas dataset and its reproduction,
across all spatial and temporal dimensions,
on the native grid of each dataset.
```
:::
::::


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 3. Results > Reproducibility: Comparison on C3S Atlas grid
---
After regridding to the C3S Atlas grid,
the indicator values reproduced from the origin dataset
can be compared point-by-point to the values retrieved from the C3S Atlas dataset.
We first examine some metrics that describe the difference Δ between corresponding pixels:

As in the
[previous](./derived_multi-origin-c3s-atlas_consistency_q01)
[notebooks](./derived_multi-origin-c3s-atlas_consistency_q02),
it is clear that
the C3S Atlas dataset and its manual reproduction
are very similar.
The median difference, median absolute difference, and median absolute percentage difference
are all close to 0
and the vast majority of pixels show a near-zero difference
(defined here as |Δ| ≥ ε with ε = 10{sup}`–5` to avoid floating-point errors)
in all comparisons.
E-OBS shows the most significant differences,
likely explained by differences in the version used
as in the previous section.

These observations are confirmed by
the overall distributions
(Figures {numref}`{number} <derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-tx35future-c3s-atlas>`–{numref}`{number} <derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-r01-c3s-atlas>`)
and
the geospatial distributions
(Figures {numref}`{number} <derived_multi-origin-c3s-atlas_consistency_q03_fig-geocomp-tx35future>`–{numref}`{number} <derived_multi-origin-c3s-atlas_consistency_q03_fig-geocomp-r01>`).
Notably,
while the difference between `tx35` in the C3S Atlas dataset and E-OBS is typically small,
with a median of 0 and with ≤4% of pixels showing non-zero differences,
it has a long tail of large differences
(Figure {numref}`{number} <derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-tx35historical-c3s-atlas>`).
These pixels are concentrated at the periphery of the domain
(Figures {numref}`{number} <derived_multi-origin-c3s-atlas_consistency_q03_fig-geocomp-tx35historical>`).
This is likely the result of a difference in
the underlying dataset version
(as before).

We can extend the conclusion from the
[previous](./derived_multi-origin-c3s-atlas_consistency_q01)
[notebooks](./derived_multi-origin-c3s-atlas_consistency_q02),
namely that the C3S Atlas dataset can be considered practically reproducible.
Some indicators in the C3S Atlas dataset
are completely identical to
their origin datasets,
e.g. `r01` in the CPC comparison;
others
show non-zero differences,
e.g. some of the pixels in the CMIP6 and E-OBS `tx35` comparisons.
The causes of these differences are unclear,
but they are generally small and rare enough to be negligible for most users using the C3S Atlas dataset,
especially since they will typically use it through the application.
For further analysis,
it is generally best to manually process the origin dataset,
if only to remove the amount of steps that may affect the result.

::::{tab-set}
:::{tab-item} Projected `tx35`
:sync: tx35future
 derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-tx35future-c3s-atlas
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-tx35future-c3s-atlas"

Comparison between overall distributions of
projected
`tx35`
values in the C3S Atlas dataset and its reproduction
on the C3S Atlas grid,
across all spatial and temporal dimensions,
including the per-pixel difference.

```
:::
:::{tab-item} Historical `tx35`
:sync: tx35historical
 derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-tx35historical-c3s-atlas
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-tx35historical-c3s-atlas"


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > Analysis and results > 3. Results > Reproducibility: Comparison on C3S Atlas grid
---
historical-c3s-atlas
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-tx35historical-c3s-atlas"

Comparison between overall distributions of
historical
`tx35`
values in the C3S Atlas dataset and its reproduction
on the C3S Atlas grid,
across all spatial and temporal dimensions,
including the per-pixel difference.
```
:::
:::{tab-item} `sst`
:sync: sst
 derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-sst-c3s-atlas
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-sst-c3s-atlas"

Comparison between overall distributions of
historical
`sst`
values in the C3S Atlas dataset and its reproduction
on the C3S Atlas grid,
across all spatial and temporal dimensions,
including the per-pixel difference.
```
:::
:::{tab-item} `r01`
:sync: r01
 derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-r01-c3s-atlas
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-hist-r01-c3s-atlas"

Comparison between overall distributions of
historical
`r01`
values in the C3S Atlas dataset and its reproduction
on the C3S Atlas grid,
across all spatial and temporal dimensions,
including the per-pixel difference.
```
:::
::::

::::{tab-set}
:::{tab-item} Projected `tx35`
:sync: tx35future
 derived_multi-origin-c3s-atlas_consistency_q03_fig-geocomp-tx35future
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-geocomp-tx35future"

Comparison between C3S Atlas dataset and reproduction for
projected
`tx35` in one month,
across Europe,
on the C3S Atlas dataset grid,
including the per-pixel difference.
```
:::
:::{tab-item} Historical `tx35`
:sync: tx35historical
 derived_multi-origin-c3s-atlas_consistency_q03_fig-geocomp-tx35historical
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-geocomp-tx35historical"

Comparison between C3S Atlas dataset and reproduction for
historical
`tx35` in one month,
across Europe,
on the C3S Atlas dataset grid,
including the per-pixel difference.
```
:::
:::{tab-item} `sst`
:sync: sst
 derived_multi-origin-c3s-atlas_consistency_q03_fig-geocomp-sst
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-geocomp-sst"

Comparison between C3S Atlas dataset and reproduction for
historical
`sst` in one month,
across Europe,
on the C3S Atlas dataset grid,
including the per-pixel difference.
```
:::
:::{tab-item} `r01`
:sync: r01
 derived_multi-origin-c3s-atlas_consistency_q03_fig-geocomp-r01
:figwidth: 700px
:name: "derived_multi-origin-c3s-atlas_consistency_q03_fig-geocomp-r01"

Comparison between C3S Atlas dataset and reproduction for
historical
`r01` in one month,
across Europe,
on the C3S Atlas dataset grid,
including the per-pixel difference.
```
:::
::::


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > ℹ If you want to know more > Key resources
---
The CDS catalogue entries for the data used were:
* Gridded dataset underpinning the Copernicus Interactive Climate Atlas: [multi-origin-c3s-atlas](https://doi.org/10.24381/cds.h35hb680)
  * [](./derived_multi-origin-c3s-atlas_consistency_q01)
  * [](./derived_multi-origin-c3s-atlas_consistency_q02)
  * **Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets**
* CMIP6 climate projections: [projections-cmip6](https://doi.org/10.24381/cds.c866074c)
  * [Quality assessments for CMIP6](../Climate_Projections/CMIP6/CMIP6.md)
* CMIP5 daily data on single levels: [projections-cmip5-daily-single-levels](https://doi.org/10.24381/cds.d3513dbf)
* CORDEX regional climate model data on single levels: [projections-cordex-domains-single-levels](https://doi.org/10.24381/cds.bc91edc3)
  * [Quality assessments for CORDEX](../Climate_Projections/CORDEX/CORDEX.md)
* ERA5 hourly data on single levels from 1940 to present: [reanalysis-era5-single-levels](https://doi.org/10.24381/cds.adbb2d47)
  * [Quality assessments for Reanalyses](../Reanalyses/reanalysis.md)
* ERA5-Land hourly data from 1950 to present: [reanalysis-era5-land](https://doi.org/10.24381/cds.e2161bac)
  * [Quality assessments for Reanalyses](../Reanalyses/reanalysis.md)
* E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations: [insitu-gridded-observations-europe](https://doi.org/10.24381/cds.151d3ec6)
  * [Quality assessments for Insitu Observations](../In_Situ/insitu.md)
* Temperature and precipitation gridded data for global and regional domains derived from in-situ and satellite observations (BERKEARTH, CPC) [insitu-gridded-observations-global-and-regional](https://doi.org/10.24381/cds.11dedf0c)
  * [Quality assessments for Insitu Observations](../In_Situ/insitu.md)
* ORAS5 global ocean reanalysis monthly data from 1958 to present: [reanalysis-oras5](https://doi.org/10.24381/cds.67e8eeb7)
  * [Quality assessments for Reanalyses](../Reanalyses/reanalysis.md)
* Sea surface temperature daily data from 1981 to present derived from satellite observations (SST-CCI): [satellite-sea-surface-temperature](https://doi.org/10.24381/cds.cf608234)
  * [Quality assessments for Satellite Observations: Ocean](../Satellite_ECVs/Ocean/Ocean.md)

Code libraries used:
* [earthkit](https://github.com/ecmwf/earthkit)
  * [earthkit-data](https://github.com/ecmwf/earthkit-data)
  * [earthkit-plots](https://github.com/ecmwf/earthkit-plots)
* [User-tools for the C3S Atlas](https://github.com/ecmwf-projects/c3s-atlas)
* [xclim](https://xclim.readthedocs.io/en/stable/) climate indicator tools


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > ℹ If you want to know more > Key resources
---
](https://github.com/ecmwf-projects/c3s-atlas)
* [xclim](https://xclim.readthedocs.io/en/stable/) climate indicator tools

More about the Copernicus Interactive Climate Atlas and its IPCC predecessor:
* [Copernicus Interactive Climate Atlas application](https://atlas.climate.copernicus.eu/)
* [Gridded data underpinning the Copernicus Interactive Climate Atlas: Description of the datasets and variables](https://confluence.ecmwf.int/display/CKB/Gridded+data+underpinning+the+Copernicus+Interactive+Climate+Atlas%3A+Description+of+the+datasets+and+variables)
* [The Copernicus Interactive Climate Atlas: a tool to explore regional climate change](https://doi.org/10.21957/ah52ufc369)
* [Copernicus Interactive Climate Atlas: a new tool to visualise climate variability and change](https://www.ecmwf.int/en/newsletter/179/news/copernicus-interactive-climate-atlas-new-tool-visualise-climate-variability)
* [Implementation of FAIR principles in the IPCC: the WGI AR6 Atlas repository](https://doi.org/10.1038/s41597-022-01739-y)
* [Climate Change 2021 – The Physical Science Basis: Atlas](https://doi.org/10.1017/9781009157896.021)


EQC Quality Assessment: "Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets"
Dataset: multi-origin-c3s-atlas [CDS]
Aspect: consistency_q03  |  Category: Derived_Datasets
Section: Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets > ℹ If you want to know more > References
---
[[Avila+15](https://doi.org/10.1016/j.wace.2015.06.003)] F. B. Avila et al., ‘Systematic investigation of gridding-related scaling effects on annual statistics of daily temperature and precipitation maxima: A case study for south-east Australia’, Weather and Climate Extremes, vol. 9, pp. 6–16, Aug. 2015, doi: 10.1016/j.wace.2015.06.003.

[[C3S Atlas dataset](https://doi.org/10.24381/cds.h35hb680)] Copernicus Climate Change Service, ‘Gridded dataset underpinning the Copernicus Interactive Climate Atlas’. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), Jun. 17, 2024. doi: 10.24381/cds.h35hb680.

[[CMIP6 dataset](https://doi.org/10.24381/cds.c866074c)] Copernicus Climate Change Service, ‘CMIP6 climate projections’. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), Mar. 23, 2021. doi: 10.24381/cds.c866074c.

[[Gutiérrez+24](https://doi.org/10.21957/ah52ufc369)] J. M. Gutiérrez et al., ‘The Copernicus Interactive Climate Atlas: a tool to explore regional climate change’, ECMWF Newsletter, vol. 181, pp. 38–45, Oct. 2024, doi: 10.21957/ah52ufc369.


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect
---
Production date: 17-04-2025

Produced by : UVSQ/LSCE-IPSL (Hélène Brogniez) ; CNRS/LMD-IPSL (Giulio Mandorli, Claudia Stubenrauch)


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Quality assessment question:
---
- __Can satellite measurements reproduce the known relationship between clear sky greenhouse effect and total column water vapour (TCWV)?__

The water vapour is the most significant greenhouse gases, contributing to about half of the planet's global greenhouse effect ([[1]](https://doi.org/10.1029/2010JD014287), [[2]](https://doi.org/10.1175/1520-0477%281997%29078<0197:EAGMEB>2.0.CO;2)). As a result, it plays a important role in shaping the Earth's radiation budget.

The warming induced by a forcing, such as an increase in CO$_2$ concentrations, leads to higher levels of water vapor through the Clausius-Clapeyron relationship [[3]](https://doi.org/10.1175/1520-0469%281967%29024<0241:TEOTAW>2.0.CO;2). The increasing of water vapor, in turn, intensifies the greenhouse effect because of its strong radiative properties in the thermal infrared : this is the well-known positive water vapor feedback that amplifies by almost a factor of two the initial warming [[4]](https://doi.org/10.1146/annurev.energy.25.1.441). This positive feedback mechanism underscores the critical importance of monitoring and comprehending the water vapour concentrations to accurately assess and predict its impacts on the Earth's climate system.

In this analysis, we study the contribution of water vapor to the greenhouse effect and aim to determine the relationship between the two variables. We quantify the amount of water vapor in the atmosphere using its vertially integrated value, known as Total Column Water Vapor (TCWV), which is obtained from satellite observations. The data used in this study is __Monthly and 6-hourly total column water vapour over ocean from 1988 to 2020 derived from satellite observations__ [[described here]](https://cds.climate.copernicus.eu/datasets/satellite-total-column-water-vapour-ocean), available on the Climate Data Store of the Copernicus Climate Change Service.


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Quality assessment statement
---
These are the key outcomes of this assessment

- The clear sky greenhouse effect is approximately a linear function of the total column water vapour [[5]](https://doi.org/10.1073/pnas.1809868115).

- This analysis is performed with one month of data. Over this month, the relationship between the greenhouse effect, estimated for clear sky situations, and the total column water vapour is consistent with the literature, the value of the slope being in agreement with [[6]](https://doi.org/full/10.1029/2000JD000040), [[7]](https://doi.org/10.1002/2013JD020184) and [[8]](https://doi.org/10.1029/2019JD031017).

- An estimate of the clear sky water vapour feedback is computed by assuming that the relative humidity remains constant with warming, as in [[4]](https://doi.org/10.1146/annurev.energy.25.1.441). The humidity feedback obtained is $2.5 \pm 0.2$ $W/m^{2}/ K$ which is in agreement with the IPCC report on this topic (Chapter 7, [[9]](https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07.pdf))

- Despite assumptions and the use of only one month, the general agreement with the literature highlights the value of this water vapor data record for studies of the greenhouse effect of water vapor. The combination with other data records (cloud amount, sea surface temperature, and outgoing longwave radiation) of the CDS shows also the relevance of these data records for such climate studies.
```


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Methodology
---
The relationship between clear sky geenhouse effect and total column of water vapour is examined using the available datasets from the Climate Data Record (CDR) for sea surface temperature, clouds, and outgoing longwave radiation, detailed below.

These datasets are daily and the analysis focuses on the __tropical ocean__ over February 2007.

(Method-section-1)=


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Methodology > The Greenhouse effect
---
The greenhouse effect (G, in W/m$^2$) is defined by the difference, in terms of longwave thermal radiation, between the radiation emitted to space at the top of the atmosphere, which is the OLR (in W/m$^2$), and the radiation emitted upward by the surface noted $F_{surf}$:

$$
\text{G} = \text{F}_{surf} - \text{OLR}
$$

Where $\text{F}_{surf} = \epsilon_{surf} \ \sigma \ T_{surf} ^4 $

$T_{surf}$ is the surface temperature (in K), $\epsilon_{surf}$ is the surface emissivity (unitless) and $\sigma$ is the Stefan-Boltzmann constant ($\sigma = 5.67 \ 10^{-8}$ W/m$^2$/K$^4$).

With this definition, G has always positive values, the atmosphere trapping a part of the upward longwave radiation emitted by the surface, this trapping being done by greenhouse gases, clouds and, for a smaller part, aerosols. In the absence of clouds and aerosols, the clear sky greenhouse effect $\text{G}_{CS}$ is dominated by water vapour which accounts for 50 to 60% of the total greenhouse effect ([[1]](https://doi.org/10.1029/2010JD014287), [[2]](https://doi.org/10.1175/1520-0477%281997%29078<0197:EAGMEB>2.0.CO;2)).

attachment:ae4b0f92-acfa-4a45-81db-501203233bd9.png
---
height: 300px
---
Contributions of the individual absorbers to the clear sky greenhouse effect. Numbers taken from [[5]](https://doi.org/10.1073/pnas.1809868115).
```

The estimation of $\text{G}_{CS}$ is performed by selecting only clear sky regions identified at the daily scale using the cloud fraction cover.

The computation of $\text{F}_{surf}$ assumes that the sea water emits thermal radiation very similar to blackbody emission, therefore $\epsilon_{surf}=1$. Considering a blackbody emission rather the true emissivity of sea water yield a small difference of less than 1\% as discussed by [[5]](https://doi.org/10.1073/pnas.1809868115).

The obtained $\text{G}_{CS}$ is then compared with the corresponding TCWV, and their relationship is visualized as a function of TCWV.
Lastly, this derived relationship is utilized to estimate the water vapour feedback, $f_{wv}$.

Several methods are used to compute the greenhouse effect and the water vapor feedbacks [[6]](https://doi.org/full/10.1029/2000JD000040):
- a partial radiative perturbation method, comparing a control climate and a perturbed climate
- a radiative kernel technique, comparing point-by-point perturbed profiles of water vapor and their radiative effect
- a linear regression between the clear sky longwave top-of-the-atmosphere flux to the global mean.

Here we use the latter method to assess the relevance of CDS data for estimating the water vapor feedback

(method-section-2)=


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Methodology > The water vapour feedback
---
The fundamental Clausius-Clapeyron equation gives the saturation vapor pressure $e_{sat}(T)$ (in hPa) at a given temperature $T$:

$$
e_{sat}(T) = 6.11 \times \exp{ \left[ -\frac{L_v}{R_v} \times \left(\frac{1}{T} - \frac{1}{273.15}\right) \right]}
$$

with $L_v = 2.5 \ 10^6$ (J/kg) is the latent heat for vaporisation and $R_v = 461.51$ (J/kg/K) is the specific gas constant for water vapor. $e_{sat}(T)$ defines the capacity of the atmosphere to hold water vapor before it condensates, for a given air temperature $T$.

According to this equation, it can be shown that the sensitivity of $e_{sat}(T)$ to a temperature increase is given by:

$$
\frac{ d (de_{sat}/ e_{sat})}{dT} = \ \frac{L_v}{R_v \ T^2} \ = \ \alpha(T)
$$

with $\alpha(T)$ standing for the Clausius-Clapeyron scaling, which depends strongly on $T$.

Most water vapor resides in the lower troposphere and in the tropics because there temperatures are warmesest. For air temperature typical of the tropical lower atmosphere, ranging between 270 and 310K, $\alpha(T)$ has values between 6.5 to 7.5\% for a 1-K increase of air temperature (see [[8]](https://doi.org/10.1029/2019JD031017)).

As stated in numerous studies addressing the perturbation of the hydrological cycle to surface warming (see [[3]](https://doi.org/10.1175/1520-0469%281967%29024<0241:TEOTAW>2.0.CO;2), [[7]](https://doi.org/10.1002/2013JD020184), [[9]](https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07.pdf) and [[10]](https://doi.org/abs/10.1029/1998JD900007)) tropospheric relative humidity tends to be maintained fixed as climate warms. The small changes observed in the tropospheric relative humidity do not affect significantly the column integrated water vapor [[11]](https://doi.org/10.1175/JCLI3990.1).

Formally, the global climate feedback $f$ (in W/m$^2$/K) represents the sensitivity of the global energy balance, which considers longwave and shorwtave fluxes, to a perturbation of the surface temperature.

Here we consider the clear sky longwave feedback and the water vapor contribution to this feedback, and this feeback parameter $f$ can be broken down into several components, among them the water vapor.

For the present analysis, we look at the part of the clear sky greenhouse effect $\text{G}_{CSwv}$ due to water vapor. Then, and using the Clausius-Clapeyron scaling, the water vapor feedback $f_{wv}$ can be written as:

$$
\begin{align}
f_{wv} & = \frac{d \ \text{G}_{CS wv}}{d \ T_{surf}} \\
       & = \frac{d \ \text{G}_{CS wv}}{d \ TCWV} \frac{d \ TCWV wv}{d \ T_{surf}}
\end{align}
$$

Where $d \ TCWV / d \ T_{surf}$ represents the water vapor response to the temperature variations, while $d \ \text{G}_{CS w} / d \ TCWV$ is the net change in upwelling thermal radiation caused by the associated changes in the water vapor content.

The TCWV is the integral of the water vapor amount q (in kg/kg) over the atmospheric column. Therefore, $TCWV \sim q$.
Moreover, with the approximation of a fixed atmospheric relative humidity with surface warming, then $ d q / \overline{q} = d e_{sat}/ \overline{e_{sat}}$, the notation $\overline{X}$ denoting a spatial average of the parameter $X$.

Combining all the parameters together, assuming constant RH and a 1-K surface warming, it comes that the water vapor feeback can be computed using TCWV according to:


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Methodology > The water vapour feedback
---
denoting a spatial average of the parameter $X$.

Combining all the parameters together, assuming constant RH and a 1-K surface warming, it comes that the water vapor feeback can be computed using TCWV according to:

$$
f_{wv} \ = \ \frac{d \ \text{G}_{CS wv}}{d \ TCWV} \ \overline{TCWV} \ \alpha(T)
$$


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Methodology > Method
---
The analysis comprises the following steps:

__1. [](data-section-1)__
 - Import the relevant packages.
 - Define the parameters of the analysis and set the dataset requests

__2. [](data-section-2)__
 - Download the variables of interest: Columnar water vapor is obtained from __Monthly and 6-hourly total column water vapour over ocean from 1988 to 2020 derived from satellite observations__ [[TCWV]](https://cds.climate.copernicus.eu/datasets/satellite-total-column-water-vapour-ocean), Clear sky is obtained from __Cloud properties global gridded monthly and daily data from 1979 to present derived from satellite observations__ [[clouds]](https://cds.climate.copernicus.eu/datasets/satellite-cloud-properties), Surface Temperature is obtained from __Sea surface temperature daily data from 1981 to present derived from satellite observations__ [[SST]](https://cds.climate.copernicus.eu/datasets/satellite-sea-surface-temperature), Outgoing Longwave Radiation is obtained from __Earth's radiation budget from 1979 to present derived from satellite observations__ [[OLR]](https://cds.climate.copernicus.eu/datasets/satellite-earth-radiation-budget).
 -  The datasets are colocated in space and time over the Tropics.
 -  Gridboxes with cloudy coverage greater than 10% are rejected. Moreover, only the well retrieved TCWV and SST are considered. The amount of radiation emitted by the surface is computed assuming blackbody behavior of the ocean ($\epsilon_{ocean}=1$).
 -  The clear sky greenhouse effect G$_\text{CS}$ is obtained by subtracting outgoing lonwave radiation and surface emission longwave emission.

__3. [](Analysis-section-1)__
 - Maps of TCWV, CFC, SST and OLR are described as well as of G$_\text{CS}$.

__4. [](Plot-and-results)__
 - The clear sky greenhouse effect is plotted as a function of the TCWV. The slope of the linear regression is computed and used to estimate the water vapor feedback $f_{wv}$. Final results are compared with proper references.


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Analysis and results > Choose the data to use and setup code > Define parameters
---
This use case is developed for one month of data (July 2007) and for the tropical belt, restricted to 30$^\circ$S-30$^\circ$N.


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Analysis and results > Choose the data to use and setup code > Set the data request
---
Four datasets are requested :
- Total Column Water Vapor (TCWV)
- Cloud Fraction Cover (CFC)
- Sea Surface Temperature (SST)
- Outgoing Longwave Radiation (OLR)

(data-section-2)=


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Analysis and results > Download the datasets and perform space-time aggregation
---
The datasets are now downloaded.

```text
100%|██████████| 1/1 [00:00<00:00,  3.61it/s]
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/earthkit/data/readers/netcdf/fieldlist.py:318: FutureWarning: In a future version, xarray will not decode timedelta values based on the presence of a timedelta-like units attribute by default. Instead it will rely on the presence of a timedelta64 dtype attribute, which is now xarray's default way of encoding timedelta64 values. To continue decoding timedeltas based on the presence of a timedelta-like units attribute, users will need to explicitly opt-in by passing True or CFTimedeltaCoder(decode_via_units=True) to decode_timedelta. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  return xr.open_dataset(self.path_or_url)
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/earthkit/data/readers/netcdf/fieldlist.py:202: FutureWarning: In a future version, xarray will not decode timedelta values based on the presence of a timedelta-like units attribute by default. Instead it will rely on the presence of a timedelta64 dtype attribute, which is now xarray's default way of encoding timedelta64 values. To continue decoding timedeltas based on the presence of a timedelta-like units attribute, users will need to explicitly opt-in by passing True or CFTimedeltaCoder(decode_via_units=True) to decode_timedelta. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  return xr.open_mfdataset(
100%|██████████| 1/1 [00:00<00:00,  3.92it/s]
100%|██████████| 1/1 [00:00<00:00, 14.78it/s]
100%|██████████| 1/1 [00:00<00:00,  1.62it/s]
```


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Analysis and results > Download the datasets and perform space-time aggregation > Temporal and spatial aggregation
---
All data records are provided at different spatial and temporal resolutions :
 - Cloud Fraction Cover : 0.25$^\circ$ / 1-day
 - Sea Surface Temperature : 0.05$^\circ$ / 1-day
 - Outgoing Longwave Radiation : 0.25$^\circ$ / 1-day
 - Total Column Water Vapor : 0.5$^\circ$ / 6-hourly

This it is a cross-variable analysis, the study requires to process all the datasets at the same horizontal and temporal resolutions.

Note that for the TCWV data record, a reversal of the latitudes is necessary as the data is stored North-to-South, while for the others data records the storage is South-to-North.

The common spatial aggregation is 0.5$^\circ$ and the data records are processed at the daily and monthly scales.

coarsen datasets to 0.5° grid / 1-day


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Analysis and results > Download the datasets and perform space-time aggregation > Computation of the greenhouse effect
---
The variables (daylies and monthlies) are stored into data frames for a better handling.

Then the clear sky scenes are selected using the CFC. A threshold of 10\% of cloud cover within each grid cell is a good compromise between the definition of the clear sky greenhouse effect (mostly clear skies, regardless of cloud altitude) and the number of scenes that are retained for the representativity of the analysis. The grid cells are also filtered from erroneous estimates.

The clear sky greenhouse effect (G$_{CS}$) and the water vapor feedback paramter ($f_{wv}$) are then computed using the methods described above.

Merge datasets
Compute clear sky greenhouse effect and add to the merged dataset
Selection of clear sky regions (at most 10\% of cloud fraction within each grid cell) and quality of retrieved SST and TCVW (SST > 0°K and TCWV > 0 mm)
Compute montly mean from daily & masked data

```text
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/xarray/coding/times.py:650: RuntimeWarning: invalid value encountered in cast
  flat_num = flat_num.astype(np.int64)
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/xarray/coding/times.py:650: RuntimeWarning: invalid value encountered in cast
  flat_num = flat_num.astype(np.int64)
/data/common/miniforge3/envs/wp5/lib/python3.11/site-packages/xarray/coding/times.py:650: RuntimeWarning: invalid value encountered in cast
  flat_num = flat_num.astype(np.int64)
```

Convert the 3D dataset into a dataframe for the analysis
daily dataset
monthly dataset

(Analysis-section-1)=


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Analysis and results > Overview of the geophysical variables of interest > Total Column of Water Vapour and Cloud Fraction Cover
---
The Total Column of Water Vapour (TCWV) is obtained from the HOAPS product, provided by EUMETSAT, at 0.5° resolution. The TCWV is retrieved over ice-free ocean surfaces using the microwave spectrum as measured by the instruments SSM/I and SSMIS using the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) algorithm [[12]](https://essd.copernicus.org/articles/2/215/2010/essd-2-215-2010.html).

The Cloud Fraction Cover (CFC) is obtained from the CLARA-A3 product, provided by EUMETSAT at 0.25° resolution. The CFC is obtained over all surfaces using AVHRR measurements [[13]](https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003).

The TCWV dataset in this assessment is considered at 0.5° over tropical oceans at a 6-hourly resolution.

The maps below show the TCWV (averaged over the 6-hourly time steps of the day) and the CFC for July 1st 2007.

From these two maps, we can see that the highest values of TCWV are associated to cloudy situations and more specifically to storms. Some missing values are visible within areas of high TCWV, and those are where precipitation occurs in the cloudy areas for which no TCWV is retrieved [[12]](https://essd.copernicus.org/articles/2/215/2010/essd-2-215-2010.html).


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Analysis and results > Overview of the geophysical variables of interest > Sea Surface Temperature and the Outgoing Longwave Radiation
---
The daily estimates of Sea Surface Temperature (SST) are based on observations from multiple satellite sensors, and it is produced by the European Space Agency (ESA) SST Climate Change Initiative (CCI) project [[14]](https://doi.org/10.1038/s41597-019-0236-x).
This Climate Data Record cover the entire globe with a resolution of 0.05°.

The Outgoing Longwave Radiation (OLR) is obtained from the CLARA-A3 product, provided by EUMETSAT at 0.25° resolution. The OLR is obtained over all surfaces using AVHRR measurements [[13]](https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003).

The maps below show the SST and the OLR for July 1st 2007.

The patterns of SST are similar to the patterns of TCWV, and this is mostly due to the Clausius-Clapeyron relationship recalled in [](method-section-2) : as surface temperature is higher, there is more evaporation and thus more water vapor in the atmosphere.

The OLR is a combination of several parameters that emit longwave radiation to space : the surface, the atmospheric gases and the clouds.
- Minima of OLR correspond to high-altitude and optically thick clouds that can be also found from the CFC map above (although no cloud altitude is provided). The minima are thus linked to the major convective areas of the tropics: Central America, the Sahelian Africa band, India and the Indonesian Warm Pool.
- Maxima of OLR are located over cloud-free regions, and is a combination of the surface (SST map) and the cloud-free atmosphere above (TCWV map). The maxima are found on both sides (North and South) of the convective areas.


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Analysis and results > Overview of the geophysical variables of interest > The monthly Clear Sky Greenhouse Effect
---
The maps below show the clear sky greenhouse effect G$_\text{CS}$ for July 2007 and the corresponding map of column water vapor TCWV.

As expected, the largest values of G$_\text{CS}$ are obtained where the TCWV is highest: as the amount of water vapour in the atmospheric column is high, there is a high trapping of upwelling thermal radiation.

(Plot-and-results)=