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List every source id or model in the ESGF database.
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Here is a list of every source or model within the ESGF database:
1. 4AOP-v1-5: Line-By-Line Radiative Transfer Model v1.5, Laboratoire Meteorologie Dynamique, GEISA spectroscopic database
2. ACCESS-CM2: Australian Community Climate and Earth System Simulator Climate Model Version 2
3. ACCESS-ESM1-5: Australian Community Climate and Earth System Simulator Earth System Model Version 1.5
4. ACCESS-OM2: Australian Community Climate and Earth System Simulator Ocean Model Version 2
5. ACCESS-OM2-025: Australian Community Climate and Earth System Simulator Ocean Model Version 2 quarter degree
6. ARTS-2-3: ARTS 2.3 (Current development version of the Atmospheric Radiative Transfer Simulator)
7. AWI-CM-1-1-HR: AWI-CM 1.1 HR
8. AWI-CM-1-1-LR: AWI-CM 1.1 LR
9. AWI-CM-1-1-MR: AWI-CM 1.1 MR
10. AWI-ESM-1-1-LR: AWI-ESM 1.1 LR
11. AWI-ESM-1-REcoM: AWI-ESM 1 REcoM
12. BCC-CSM2-HR: BCC-CSM 2 HR
13. BCC-CSM2-MR: BCC-CSM 2 MR
14. BCC-ESM1: BCC-ESM 1
15. CAM-MPAS-HR: CAM MPAS (Community Atmosphere Model - Model for Prediction Across Scales)
16. CAM-MPAS-LR: CAM MPAS (Community Atmosphere Model - Model for Prediction Across Scales)
17. CAMS-CSM1-0: CAMS-CSM 1.0
18. CAS-ESM2-0: CAS-ESM 2.0 (Chinese Academy of Sciences Earth System Model version 2.0)
19. CESM1-1-CAM5-CMIP5
20. CESM1-CAM5-SE-HR: CESM 1.3 CAM5 spectral element configuration with CMIP5 forcings, hi res
21. CESM1-CAM5-SE-LR: CESM 1.3 CAM5 spectral element configuration with CMIP5 forcings, lo res
22. CESM1-WACCM-SC: Community Earth System Model 1, with the Whole Atmosphere Community Climate Model and Specified Chemistry
23. CESM2
24. CESM2-FV2
25. CESM2-WACCM
26. CESM2-WACCM-FV2
27. CIESM: Community Integrated Earth System Model
28. CMCC-CM2-HR4
29. CMCC-CM2-SR5
30. CMCC-CM2-VHR4
31. CMCC-ESM2
32. CNRM-CM6-1
33. CNRM-CM6-1-HR
34. CNRM-ESM2-1
35. CanESM5
36. CanESM5-1: CanESM5.1
37. CanESM5-CanOE
38. E3SM-1-0: E3SM 1.0 (Energy Exascale Earth System Model)
39. E3SM-1-1: E3SM 1.1 (Energy Exascale Earth System Model)
40. E3SM-1-1-ECA: E3SM 1.1 (Energy Exascale Earth System Model) with an experimental land BGC ECA configuration
41. E3SM-2-0: E3SM 2.0 (Energy Exascale Earth System Model)
42. E3SM-2-0-NARRM: E3SM 2.0 NARRM (Energy Exascale Earth System Model version 2.0 North American Regionally Refined Model)
43. E3SM-2-1: E3SM 2.1 (Energy Exascale Earth System Model)
44. EC-Earth3: EC Earth 3.3
45. EC-Earth3-AerChem
46. EC-Earth3-CC
47. EC-Earth3-ESM-1
48. EC-Earth3-HR
49. EC-Earth3-LR
50. EC-Earth3-Veg
51. EC-Earth3-Veg-LR
52. EC-Earth3P: EC-Earth 3.2 in PRIMAVERA
53. EC-Earth3P-HR: EC-Earth3P-HR in PRIMAVERA
54. EC-Earth3P-VHR: EC-Earth3P-VHR in PRIMAVERA
55. ECMWF-IFS-HR: ECMWF-IFS-HR (25 km atmosphere and 25 km ocean)
56. ECMWF-IFS-LR: ECMWF-IFS-LR (50 km atmosphere and 100 km ocean)
57. ECMWF-IFS-MR: ECMWF-IFS-MR (50 km atmosphere and 25 km ocean)
58. FGOALS-f3-H
59. FGOALS-f3-L
60. FGOALS-g3
61. FIO-ESM-2-0: FIO-ESM 2.0
62. GFDL-AM4
63. GFDL-CM4
64. GFDL-CM4C192
65. GFDL-ESM2M
66. GFDL-ESM4
67. GFDL-GRTCODE: GFDL GPU radiative transfer code with two stream solver (March 2019)
68. GFDL-OM4p5B
69. GFDL-RFM-DISORT: GFDL Reference Forward Model Line-by-Line with DISORT solver (March 2019)
70. GISS-E2-1-G: GISS-E2.1G
71. GISS-E2-1-G-CC
72. GISS-E2-1-H: GISS-E2.1H
73. GISS-E2-2-G
74. GISS-E2-2-H: GISS-E2.2H
75. GISS-E3-G
76. HadGEM3-GC31-HH: HadGEM3-GC3.1-N512ORCA12
77. HadGEM3-GC31-HM: HadGEM3-GC3.1-N512ORCA025
78. HadGEM3-GC31-LL: HadGEM3-GC3.1-N96ORCA1
79. HadGEM3-GC31-LM: HadGEM3-GC3.1-N96ORCA025
80. HadGEM3-GC31-MH: HadGEM3-GC3.1-N216ORCA12
81. HadGEM3-GC31-MM: HadGEM3-GC3.1-N216ORCA025
82. HiRAM-SIT-HR: HiRAM Coupling 1-D SIT (25 km atmosphere and 25 km ocean)
83. HiRAM-SIT-LR: HiRAM Coupling 1-D SIT (50 km atmosphere and 25 km ocean)
84. ICON-ESM-LR
85. IITM-ESM
86. INM-CM4-8
87. INM-CM5-0
88. INM-CM5-H
89. IPSL-CM5A2-INCA
90. IPSL-CM6A-ATM-HR
91. IPSL-CM6A-ATM-ICO-HR
92. IPSL-CM6A-ATM-ICO-LR
93. IPSL-CM6A-ATM-ICO-MR
94. IPSL-CM6A-ATM-ICO-VHR
95. IPSL-CM6A-ATM-LR-REPROBUS
96. IPSL-CM6A-LR
97. IPSL-CM6A-LR-INCA
98. IPSL-CM6A-MR1
99. KACE-1-0-G: KACE1.0-GLOMAP
100. KIOST-ESM: KIOST Earth System Model v2
101. LBLRTM-12-8: Line-By-Line Radiative Transfer Model v12.8, aer_v_3.6, MT_CKD_3.2
102. MCM-UA-1-0: Manabe Climate Model v1.0 - University of Arizona
103. MIROC-ES2H
104. MIROC-ES2H-NB: MIROC-ES2H with No BiogenicCycle
105. MIROC-ES2L
106. MIROC6
107. MPI-ESM-1-2-HAM: MPI-ESM1.2-HAM
108. MPI-ESM1-2-HR: MPI-ESM1.2-HR
109. MPI-ESM1-2-LR: MPI-ESM1.2-LR
110. MPI-ESM1-2-XR: MPI-ESM1.2-XR
111. MRI-AGCM3-2-H
112. MRI-AGCM3-2-S
113. MRI-ESM2-0: MRI-ESM2.0
114. NESM3: NUIST ESM v3
115. NICAM16-7S: NICAM.16 gl07-L38 with NSW6
116. NICAM16-8S: NICAM.16 gl08-L38 with NSW6
117. NICAM16-9S: NICAM.16 gl09-L38 with NSW6
118. NorCPM1: Norwegian Climate Prediction Model version 1
119. NorESM1-F: NorESM1-F (a fast version of NorESM that is designed for paleo and multi-ensemble simulations)
120. NorESM2-LM: NorESM2-LM (low atmosphere-medium ocean resolution, GHG concentration driven)
121. NorESM2-MM: NorESM2-MM (medium atmosphere-medium ocean resolution, GHG concentration driven)
122. PCMDI-test-1-0: PCMDI-test 1.0 (This entry is free text for users to contribute verbose information)
123. RRTMG-LW-4-91: RRTM for GCMs v4.91, longwave
124. RRTMG-SW-4-02: RRTM for GCMs v4.02, shortwave
125. RTE-RRTMGP-181204: Radiative Transfer for Energetics using RRTM for GCM applications - Parallel (2018-12-04 full-resolution)
126. SAM0-UNICON: SAM0-UNICON (SNU Atmosphere Model version 0 with Unified Convection Scheme)
127. TaiESM1: Taiwan Earth System Model 1.0
128. TaiESM1-TIMCOM: Taiwan Earth System Model 1.0 using TIMCOM ocean model
129. TaiESM1-TIMCOM2: Taiwan Earth System Model 1.0 using TIMCOM ocean model 2.0
130. UKESM1-0-LL: UKESM1.0-N96ORCA1
131. UKESM1-1-LL: UKESM1.1-N96ORCA1
132. UKESM1-ice-LL: UKESM1.ice-N96ORCA1
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Can you show me every source ID within ESGF?
|
Sure! Here is a list of every source ID within the ESGF database:
1. 4AOP-v1-5: Line-By-Line Radiative Transfer Model v1.5, Laboratoire Meteorologie Dynamique, GEISA spectroscopic database
2. ACCESS-CM2: Australian Community Climate and Earth System Simulator Climate Model Version 2
3. ACCESS-ESM1-5: Australian Community Climate and Earth System Simulator Earth System Model Version 1.5
4. ACCESS-OM2: Australian Community Climate and Earth System Simulator Ocean Model Version 2
5. ACCESS-OM2-025: Australian Community Climate and Earth System Simulator Ocean Model Version 2 quarter degree
6. ARTS-2-3: ARTS 2.3 (Current development version of the Atmospheric Radiative Transfer Simulator)
7. AWI-CM-1-1-HR: AWI-CM 1.1 HR
8. AWI-CM-1-1-LR: AWI-CM 1.1 LR
9. AWI-CM-1-1-MR: AWI-CM 1.1 MR
10. AWI-ESM-1-1-LR: AWI-ESM 1.1 LR
11. AWI-ESM-1-REcoM: AWI-ESM 1 REcoM
12. BCC-CSM2-HR: BCC-CSM 2 HR
13. BCC-CSM2-MR: BCC-CSM 2 MR
14. BCC-ESM1: BCC-ESM 1
15. CAM-MPAS-HR: CAM MPAS (Community Atmosphere Model - Model for Prediction Across Scales)
16. CAM-MPAS-LR: CAM MPAS (Community Atmosphere Model - Model for Prediction Across Scales)
17. CAMS-CSM1-0: CAMS-CSM 1.0
18. CAS-ESM2-0: CAS-ESM 2.0 (Chinese Academy of Sciences Earth System Model version 2.0)
19. CESM1-1-CAM5-CMIP5
20. CESM1-CAM5-SE-HR: CESM 1.3 CAM5 spectral element configuration with CMIP5 forcings, hi res
21. CESM1-CAM5-SE-LR: CESM 1.3 CAM5 spectral element configuration with CMIP5 forcings, lo res
22. CESM1-WACCM-SC: Community Earth System Model 1, with the Whole Atmosphere Community Climate Model and Specified Chemistry
23. CESM2
24. CESM2-FV2
25. CESM2-WACCM
26. CESM2-WACCM-FV2
27. CIESM: Community Integrated Earth System Model
28. CMCC-CM2-HR4
29. CMCC-CM2-SR5
30. CMCC-CM2-VHR4
31. CMCC-ESM2
32. CNRM-CM6-1
33. CNRM-CM6-1-HR
34. CNRM-ESM2-1
35. CanESM5
36. CanESM5-1: CanESM5.1
37. CanESM5-CanOE
38. E3SM-1-0: E3SM 1.0 (Energy Exascale Earth System Model)
39. E3SM-1-1: E3SM 1.1 (Energy Exascale Earth System Model)
40. E3SM-1-1-ECA: E3SM 1.1 (Energy Exascale Earth System Model) with an experimental land BGC ECA configuration
41. E3SM-2-0: E3SM 2.0 (Energy Exascale Earth System Model)
42. E3SM-2-0-NARRM: E3SM 2.0 NARRM (Energy Exascale Earth System Model version 2.0 North American Regionally Refined Model)
43. E3SM-2-1: E3SM 2.1 (Energy Exascale Earth System Model)
44. EC-Earth3: EC Earth 3.3
45. EC-Earth3-AerChem
46. EC-Earth3-CC
47. EC-Earth3-ESM-1
48. EC-Earth3-HR
49. EC-Earth3-LR
50. EC-Earth3-Veg
51. EC-Earth3-Veg-LR
52. EC-Earth3P: EC-Earth 3.2 in PRIMAVERA
53. EC-Earth3P-HR: EC-Earth3P-HR in PRIMAVERA
54. EC-Earth3P-VHR: EC-Earth3P-VHR in PRIMAVERA
55. ECMWF-IFS-HR: ECMWF-IFS-HR (25 km atmosphere and 25 km ocean)
56. ECMWF-IFS-LR: ECMWF-IFS-LR (50 km atmosphere and 100 km ocean)
57. ECMWF-IFS-MR: ECMWF-IFS-MR (50 km atmosphere and 25 km ocean)
58. FGOALS-f3-H
59. FGOALS-f3-L
60. FGOALS-g3
61. FIO-ESM-2-0: FIO-ESM 2.0
62. GFDL-AM4
63. GFDL-CM4
64. GFDL-CM4C192
65. GFDL-ESM2M
66. GFDL-ESM4
67. GFDL-GRTCODE: GFDL GPU radiative transfer code with two stream solver (March 2019)
68. GFDL-OM4p5B
69. GFDL-RFM-DISORT: GFDL Reference Forward Model Line-by-Line with DISORT solver (March 2019)
70. GISS-E2-1-G: GISS-E2.1G
71. GISS-E2-1-G-CC
72. GISS-E2-1-H: GISS-E2.1H
73. GISS-E2-2-G
74. GISS-E2-2-H: GISS-E2.2H
75. GISS-E3-G
76. HadGEM3-GC31-HH: HadGEM3-GC3.1-N512ORCA12
77. HadGEM3-GC31-HM: HadGEM3-GC3.1-N512ORCA025
78. HadGEM3-GC31-LL: HadGEM3-GC3.1-N96ORCA1
79. HadGEM3-GC31-LM: HadGEM3-GC3.1-N96ORCA025
80. HadGEM3-GC31-MH: HadGEM3-GC3.1-N216ORCA12
81. HadGEM3-GC31-MM: HadGEM3-GC3.1-N216ORCA025
82. HiRAM-SIT-HR: HiRAM Coupling 1-D SIT (25 km atmosphere and 25 km ocean)
83. HiRAM-SIT-LR: HiRAM Coupling 1-D SIT (50 km atmosphere and 25 km ocean)
84. ICON-ESM-LR
85. IITM-ESM
86. INM-CM4-8
87. INM-CM5-0
88. INM-CM5-H
89. IPSL-CM5A2-INCA
90. IPSL-CM6A-ATM-HR
91. IPSL-CM6A-ATM-ICO-HR
92. IPSL-CM6A-ATM-ICO-LR
93. IPSL-CM6A-ATM-ICO-MR
94. IPSL-CM6A-ATM-ICO-VHR
95. IPSL-CM6A-ATM-LR-REPROBUS
96. IPSL-CM6A-LR
97. IPSL-CM6A-LR-INCA
98. IPSL-CM6A-MR1
99. KACE-1-0-G: KACE1.0-GLOMAP
100. KIOST-ESM: KIOST Earth System Model v2
101. LBLRTM-12-8: Line-By-Line Radiative Transfer Model v12.8, aer_v_3.6, MT_CKD_3.2
102. MCM-UA-1-0: Manabe Climate Model v1.0 - University of Arizona
103. MIROC-ES2H
104. MIROC-ES2H-NB: MIROC-ES2H with No BiogenicCycle
105. MIROC-ES2L
106. MIROC6
107. MPI-ESM-1-2-HAM: MPI-ESM1.2-HAM
108. MPI-ESM1-2-HR: MPI-ESM1.2-HR
109. MPI-ESM1-2-LR: MPI-ESM1.2-LR
110. MPI-ESM1-2-XR: MPI-ESM1.2-XR
111. MRI-AGCM3-2-H
112. MRI-AGCM3-2-S
113. MRI-ESM2-0: MRI-ESM2.0
114. NESM3: NUIST ESM v3
115. NICAM16-7S: NICAM.16 gl07-L38 with NSW6
116. NICAM16-8S: NICAM.16 gl08-L38 with NSW6
117. NICAM16-9S: NICAM.16 gl09-L38 with NSW6
118. NorCPM1: Norwegian Climate Prediction Model version 1
119. NorESM1-F: NorESM1-F (a fast version of NorESM that is designed for paleo and multi-ensemble simulations)
120. NorESM2-LM: NorESM2-LM (low atmosphere-medium ocean resolution, GHG concentration driven)
121. NorESM2-MM: NorESM2-MM (medium atmosphere-medium ocean resolution, GHG concentration driven)
122. PCMDI-test-1-0: PCMDI-test 1.0 (This entry is free text for users to contribute verbose information)
123. RRTMG-LW-4-91: RRTM for GCMs v4.91, longwave
124. RRTMG-SW-4-02: RRTM for GCMs v4.02, shortwave
125. RTE-RRTMGP-181204: Radiative Transfer for Energetics using RRTM for GCM applications - Parallel (2018-12-04 full-resolution)
126. SAM0-UNICON: SAM0-UNICON (SNU Atmosphere Model version 0 with Unified Convection Scheme)
127. TaiESM1: Taiwan Earth System Model 1.0
128. TaiESM1-TIMCOM: Taiwan Earth System Model 1.0 using TIMCOM ocean model
129. TaiESM1-TIMCOM2: Taiwan Earth System Model 1.0 using TIMCOM ocean model 2.0
130. UKESM1-0-LL: UKESM1.0-N96ORCA1
131. UKESM1-1-LL: UKESM1.1-N96ORCA1
132. UKESM1-ice-LL: UKESM1.ice-N96ORCA1
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Show me all the models in the ESGF database.
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Ok, here is every model that is in the ESGF database:
1. 4AOP-v1-5: Line-By-Line Radiative Transfer Model v1.5, Laboratoire Meteorologie Dynamique, GEISA spectroscopic database
2. ACCESS-CM2: Australian Community Climate and Earth System Simulator Climate Model Version 2
3. ACCESS-ESM1-5: Australian Community Climate and Earth System Simulator Earth System Model Version 1.5
4. ACCESS-OM2: Australian Community Climate and Earth System Simulator Ocean Model Version 2
5. ACCESS-OM2-025: Australian Community Climate and Earth System Simulator Ocean Model Version 2 quarter degree
6. ARTS-2-3: ARTS 2.3 (Current development version of the Atmospheric Radiative Transfer Simulator)
7. AWI-CM-1-1-HR: AWI-CM 1.1 HR
8. AWI-CM-1-1-LR: AWI-CM 1.1 LR
9. AWI-CM-1-1-MR: AWI-CM 1.1 MR
10. AWI-ESM-1-1-LR: AWI-ESM 1.1 LR
11. AWI-ESM-1-REcoM: AWI-ESM 1 REcoM
12. BCC-CSM2-HR: BCC-CSM 2 HR
13. BCC-CSM2-MR: BCC-CSM 2 MR
14. BCC-ESM1: BCC-ESM 1
15. CAM-MPAS-HR: CAM MPAS (Community Atmosphere Model - Model for Prediction Across Scales)
16. CAM-MPAS-LR: CAM MPAS (Community Atmosphere Model - Model for Prediction Across Scales)
17. CAMS-CSM1-0: CAMS-CSM 1.0
18. CAS-ESM2-0: CAS-ESM 2.0 (Chinese Academy of Sciences Earth System Model version 2.0)
19. CESM1-1-CAM5-CMIP5
20. CESM1-CAM5-SE-HR: CESM 1.3 CAM5 spectral element configuration with CMIP5 forcings, hi res
21. CESM1-CAM5-SE-LR: CESM 1.3 CAM5 spectral element configuration with CMIP5 forcings, lo res
22. CESM1-WACCM-SC: Community Earth System Model 1, with the Whole Atmosphere Community Climate Model and Specified Chemistry
23. CESM2
24. CESM2-FV2
25. CESM2-WACCM
26. CESM2-WACCM-FV2
27. CIESM: Community Integrated Earth System Model
28. CMCC-CM2-HR4
29. CMCC-CM2-SR5
30. CMCC-CM2-VHR4
31. CMCC-ESM2
32. CNRM-CM6-1
33. CNRM-CM6-1-HR
34. CNRM-ESM2-1
35. CanESM5
36. CanESM5-1: CanESM5.1
37. CanESM5-CanOE
38. E3SM-1-0: E3SM 1.0 (Energy Exascale Earth System Model)
39. E3SM-1-1: E3SM 1.1 (Energy Exascale Earth System Model)
40. E3SM-1-1-ECA: E3SM 1.1 (Energy Exascale Earth System Model) with an experimental land BGC ECA configuration
41. E3SM-2-0: E3SM 2.0 (Energy Exascale Earth System Model)
42. E3SM-2-0-NARRM: E3SM 2.0 NARRM (Energy Exascale Earth System Model version 2.0 North American Regionally Refined Model)
43. E3SM-2-1: E3SM 2.1 (Energy Exascale Earth System Model)
44. EC-Earth3: EC Earth 3.3
45. EC-Earth3-AerChem
46. EC-Earth3-CC
47. EC-Earth3-ESM-1
48. EC-Earth3-HR
49. EC-Earth3-LR
50. EC-Earth3-Veg
51. EC-Earth3-Veg-LR
52. EC-Earth3P: EC-Earth 3.2 in PRIMAVERA
53. EC-Earth3P-HR: EC-Earth3P-HR in PRIMAVERA
54. EC-Earth3P-VHR: EC-Earth3P-VHR in PRIMAVERA
55. ECMWF-IFS-HR: ECMWF-IFS-HR (25 km atmosphere and 25 km ocean)
56. ECMWF-IFS-LR: ECMWF-IFS-LR (50 km atmosphere and 100 km ocean)
57. ECMWF-IFS-MR: ECMWF-IFS-MR (50 km atmosphere and 25 km ocean)
58. FGOALS-f3-H
59. FGOALS-f3-L
60. FGOALS-g3
61. FIO-ESM-2-0: FIO-ESM 2.0
62. GFDL-AM4
63. GFDL-CM4
64. GFDL-CM4C192
65. GFDL-ESM2M
66. GFDL-ESM4
67. GFDL-GRTCODE: GFDL GPU radiative transfer code with two stream solver (March 2019)
68. GFDL-OM4p5B
69. GFDL-RFM-DISORT: GFDL Reference Forward Model Line-by-Line with DISORT solver (March 2019)
70. GISS-E2-1-G: GISS-E2.1G
71. GISS-E2-1-G-CC
72. GISS-E2-1-H: GISS-E2.1H
73. GISS-E2-2-G
74. GISS-E2-2-H: GISS-E2.2H
75. GISS-E3-G
76. HadGEM3-GC31-HH: HadGEM3-GC3.1-N512ORCA12
77. HadGEM3-GC31-HM: HadGEM3-GC3.1-N512ORCA025
78. HadGEM3-GC31-LL: HadGEM3-GC3.1-N96ORCA1
79. HadGEM3-GC31-LM: HadGEM3-GC3.1-N96ORCA025
80. HadGEM3-GC31-MH: HadGEM3-GC3.1-N216ORCA12
81. HadGEM3-GC31-MM: HadGEM3-GC3.1-N216ORCA025
82. HiRAM-SIT-HR: HiRAM Coupling 1-D SIT (25 km atmosphere and 25 km ocean)
83. HiRAM-SIT-LR: HiRAM Coupling 1-D SIT (50 km atmosphere and 25 km ocean)
84. ICON-ESM-LR
85. IITM-ESM
86. INM-CM4-8
87. INM-CM5-0
88. INM-CM5-H
89. IPSL-CM5A2-INCA
90. IPSL-CM6A-ATM-HR
91. IPSL-CM6A-ATM-ICO-HR
92. IPSL-CM6A-ATM-ICO-LR
93. IPSL-CM6A-ATM-ICO-MR
94. IPSL-CM6A-ATM-ICO-VHR
95. IPSL-CM6A-ATM-LR-REPROBUS
96. IPSL-CM6A-LR
97. IPSL-CM6A-LR-INCA
98. IPSL-CM6A-MR1
99. KACE-1-0-G: KACE1.0-GLOMAP
100. KIOST-ESM: KIOST Earth System Model v2
101. LBLRTM-12-8: Line-By-Line Radiative Transfer Model v12.8, aer_v_3.6, MT_CKD_3.2
102. MCM-UA-1-0: Manabe Climate Model v1.0 - University of Arizona
103. MIROC-ES2H
104. MIROC-ES2H-NB: MIROC-ES2H with No BiogenicCycle
105. MIROC-ES2L
106. MIROC6
107. MPI-ESM-1-2-HAM: MPI-ESM1.2-HAM
108. MPI-ESM1-2-HR: MPI-ESM1.2-HR
109. MPI-ESM1-2-LR: MPI-ESM1.2-LR
110. MPI-ESM1-2-XR: MPI-ESM1.2-XR
111. MRI-AGCM3-2-H
112. MRI-AGCM3-2-S
113. MRI-ESM2-0: MRI-ESM2.0
114. NESM3: NUIST ESM v3
115. NICAM16-7S: NICAM.16 gl07-L38 with NSW6
116. NICAM16-8S: NICAM.16 gl08-L38 with NSW6
117. NICAM16-9S: NICAM.16 gl09-L38 with NSW6
118. NorCPM1: Norwegian Climate Prediction Model version 1
119. NorESM1-F: NorESM1-F (a fast version of NorESM that is designed for paleo and multi-ensemble simulations)
120. NorESM2-LM: NorESM2-LM (low atmosphere-medium ocean resolution, GHG concentration driven)
121. NorESM2-MM: NorESM2-MM (medium atmosphere-medium ocean resolution, GHG concentration driven)
122. PCMDI-test-1-0: PCMDI-test 1.0 (This entry is free text for users to contribute verbose information)
123. RRTMG-LW-4-91: RRTM for GCMs v4.91, longwave
124. RRTMG-SW-4-02: RRTM for GCMs v4.02, shortwave
125. RTE-RRTMGP-181204: Radiative Transfer for Energetics using RRTM for GCM applications - Parallel (2018-12-04 full-resolution)
126. SAM0-UNICON: SAM0-UNICON (SNU Atmosphere Model version 0 with Unified Convection Scheme)
127. TaiESM1: Taiwan Earth System Model 1.0
128. TaiESM1-TIMCOM: Taiwan Earth System Model 1.0 using TIMCOM ocean model
129. TaiESM1-TIMCOM2: Taiwan Earth System Model 1.0 using TIMCOM ocean model 2.0
130. UKESM1-0-LL: UKESM1.0-N96ORCA1
131. UKESM1-1-LL: UKESM1.1-N96ORCA1
132. UKESM1-ice-LL: UKESM1.ice-N96ORCA1
|
|
Show me every source ESGF database.
|
Ok, here is every source that is in the ESGF database:
1. 4AOP-v1-5: Line-By-Line Radiative Transfer Model v1.5, Laboratoire Meteorologie Dynamique, GEISA spectroscopic database
2. ACCESS-CM2: Australian Community Climate and Earth System Simulator Climate Model Version 2
3. ACCESS-ESM1-5: Australian Community Climate and Earth System Simulator Earth System Model Version 1.5
4. ACCESS-OM2: Australian Community Climate and Earth System Simulator Ocean Model Version 2
5. ACCESS-OM2-025: Australian Community Climate and Earth System Simulator Ocean Model Version 2 quarter degree
6. ARTS-2-3: ARTS 2.3 (Current development version of the Atmospheric Radiative Transfer Simulator)
7. AWI-CM-1-1-HR: AWI-CM 1.1 HR
8. AWI-CM-1-1-LR: AWI-CM 1.1 LR
9. AWI-CM-1-1-MR: AWI-CM 1.1 MR
10. AWI-ESM-1-1-LR: AWI-ESM 1.1 LR
11. AWI-ESM-1-REcoM: AWI-ESM 1 REcoM
12. BCC-CSM2-HR: BCC-CSM 2 HR
13. BCC-CSM2-MR: BCC-CSM 2 MR
14. BCC-ESM1: BCC-ESM 1
15. CAM-MPAS-HR: CAM MPAS (Community Atmosphere Model - Model for Prediction Across Scales)
16. CAM-MPAS-LR: CAM MPAS (Community Atmosphere Model - Model for Prediction Across Scales)
17. CAMS-CSM1-0: CAMS-CSM 1.0
18. CAS-ESM2-0: CAS-ESM 2.0 (Chinese Academy of Sciences Earth System Model version 2.0)
19. CESM1-1-CAM5-CMIP5
20. CESM1-CAM5-SE-HR: CESM 1.3 CAM5 spectral element configuration with CMIP5 forcings, hi res
21. CESM1-CAM5-SE-LR: CESM 1.3 CAM5 spectral element configuration with CMIP5 forcings, lo res
22. CESM1-WACCM-SC: Community Earth System Model 1, with the Whole Atmosphere Community Climate Model and Specified Chemistry
23. CESM2
24. CESM2-FV2
25. CESM2-WACCM
26. CESM2-WACCM-FV2
27. CIESM: Community Integrated Earth System Model
28. CMCC-CM2-HR4
29. CMCC-CM2-SR5
30. CMCC-CM2-VHR4
31. CMCC-ESM2
32. CNRM-CM6-1
33. CNRM-CM6-1-HR
34. CNRM-ESM2-1
35. CanESM5
36. CanESM5-1: CanESM5.1
37. CanESM5-CanOE
38. E3SM-1-0: E3SM 1.0 (Energy Exascale Earth System Model)
39. E3SM-1-1: E3SM 1.1 (Energy Exascale Earth System Model)
40. E3SM-1-1-ECA: E3SM 1.1 (Energy Exascale Earth System Model) with an experimental land BGC ECA configuration
41. E3SM-2-0: E3SM 2.0 (Energy Exascale Earth System Model)
42. E3SM-2-0-NARRM: E3SM 2.0 NARRM (Energy Exascale Earth System Model version 2.0 North American Regionally Refined Model)
43. E3SM-2-1: E3SM 2.1 (Energy Exascale Earth System Model)
44. EC-Earth3: EC Earth 3.3
45. EC-Earth3-AerChem
46. EC-Earth3-CC
47. EC-Earth3-ESM-1
48. EC-Earth3-HR
49. EC-Earth3-LR
50. EC-Earth3-Veg
51. EC-Earth3-Veg-LR
52. EC-Earth3P: EC-Earth 3.2 in PRIMAVERA
53. EC-Earth3P-HR: EC-Earth3P-HR in PRIMAVERA
54. EC-Earth3P-VHR: EC-Earth3P-VHR in PRIMAVERA
55. ECMWF-IFS-HR: ECMWF-IFS-HR (25 km atmosphere and 25 km ocean)
56. ECMWF-IFS-LR: ECMWF-IFS-LR (50 km atmosphere and 100 km ocean)
57. ECMWF-IFS-MR: ECMWF-IFS-MR (50 km atmosphere and 25 km ocean)
58. FGOALS-f3-H
59. FGOALS-f3-L
60. FGOALS-g3
61. FIO-ESM-2-0: FIO-ESM 2.0
62. GFDL-AM4
63. GFDL-CM4
64. GFDL-CM4C192
65. GFDL-ESM2M
66. GFDL-ESM4
67. GFDL-GRTCODE: GFDL GPU radiative transfer code with two stream solver (March 2019)
68. GFDL-OM4p5B
69. GFDL-RFM-DISORT: GFDL Reference Forward Model Line-by-Line with DISORT solver (March 2019)
70. GISS-E2-1-G: GISS-E2.1G
71. GISS-E2-1-G-CC
72. GISS-E2-1-H: GISS-E2.1H
73. GISS-E2-2-G
74. GISS-E2-2-H: GISS-E2.2H
75. GISS-E3-G
76. HadGEM3-GC31-HH: HadGEM3-GC3.1-N512ORCA12
77. HadGEM3-GC31-HM: HadGEM3-GC3.1-N512ORCA025
78. HadGEM3-GC31-LL: HadGEM3-GC3.1-N96ORCA1
79. HadGEM3-GC31-LM: HadGEM3-GC3.1-N96ORCA025
80. HadGEM3-GC31-MH: HadGEM3-GC3.1-N216ORCA12
81. HadGEM3-GC31-MM: HadGEM3-GC3.1-N216ORCA025
82. HiRAM-SIT-HR: HiRAM Coupling 1-D SIT (25 km atmosphere and 25 km ocean)
83. HiRAM-SIT-LR: HiRAM Coupling 1-D SIT (50 km atmosphere and 25 km ocean)
84. ICON-ESM-LR
85. IITM-ESM
86. INM-CM4-8
87. INM-CM5-0
88. INM-CM5-H
89. IPSL-CM5A2-INCA
90. IPSL-CM6A-ATM-HR
91. IPSL-CM6A-ATM-ICO-HR
92. IPSL-CM6A-ATM-ICO-LR
93. IPSL-CM6A-ATM-ICO-MR
94. IPSL-CM6A-ATM-ICO-VHR
95. IPSL-CM6A-ATM-LR-REPROBUS
96. IPSL-CM6A-LR
97. IPSL-CM6A-LR-INCA
98. IPSL-CM6A-MR1
99. KACE-1-0-G: KACE1.0-GLOMAP
100. KIOST-ESM: KIOST Earth System Model v2
101. LBLRTM-12-8: Line-By-Line Radiative Transfer Model v12.8, aer_v_3.6, MT_CKD_3.2
102. MCM-UA-1-0: Manabe Climate Model v1.0 - University of Arizona
103. MIROC-ES2H
104. MIROC-ES2H-NB: MIROC-ES2H with No BiogenicCycle
105. MIROC-ES2L
106. MIROC6
107. MPI-ESM-1-2-HAM: MPI-ESM1.2-HAM
108. MPI-ESM1-2-HR: MPI-ESM1.2-HR
109. MPI-ESM1-2-LR: MPI-ESM1.2-LR
110. MPI-ESM1-2-XR: MPI-ESM1.2-XR
111. MRI-AGCM3-2-H
112. MRI-AGCM3-2-S
113. MRI-ESM2-0: MRI-ESM2.0
114. NESM3: NUIST ESM v3
115. NICAM16-7S: NICAM.16 gl07-L38 with NSW6
116. NICAM16-8S: NICAM.16 gl08-L38 with NSW6
117. NICAM16-9S: NICAM.16 gl09-L38 with NSW6
118. NorCPM1: Norwegian Climate Prediction Model version 1
119. NorESM1-F: NorESM1-F (a fast version of NorESM that is designed for paleo and multi-ensemble simulations)
120. NorESM2-LM: NorESM2-LM (low atmosphere-medium ocean resolution, GHG concentration driven)
121. NorESM2-MM: NorESM2-MM (medium atmosphere-medium ocean resolution, GHG concentration driven)
122. PCMDI-test-1-0: PCMDI-test 1.0 (This entry is free text for users to contribute verbose information)
123. RRTMG-LW-4-91: RRTM for GCMs v4.91, longwave
124. RRTMG-SW-4-02: RRTM for GCMs v4.02, shortwave
125. RTE-RRTMGP-181204: Radiative Transfer for Energetics using RRTM for GCM applications - Parallel (2018-12-04 full-resolution)
126. SAM0-UNICON: SAM0-UNICON (SNU Atmosphere Model version 0 with Unified Convection Scheme)
127. TaiESM1: Taiwan Earth System Model 1.0
128. TaiESM1-TIMCOM: Taiwan Earth System Model 1.0 using TIMCOM ocean model
129. TaiESM1-TIMCOM2: Taiwan Earth System Model 1.0 using TIMCOM ocean model 2.0
130. UKESM1-0-LL: UKESM1.0-N96ORCA1
131. UKESM1-1-LL: UKESM1.1-N96ORCA1
132. UKESM1-ice-LL: UKESM1.ice-N96ORCA1
|
|
Name the models that have variable gpp using intake-esgf in python.
|
Right! Here is how we can name the models that have variable gpp using intake-esgf in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
List all models that contain data for variable gpp using intake-esgf in python.
|
Here is how we can list the models that contain data for variable gpp using intake-esgf in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I'm trying to search for models that have data on variable gpp using the intake-esgf library in python. Can you help?
|
Yes! I can help you with that. Here is how we can search for models that have data on variable gpp using the intake-esgf library in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I want python code that lists all the models containing variable gpp.
|
Here is a way we can list all the models containing variable gpp using intake-esgf in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I need help writing intake-esgf code that shows me what models include the variable gpp.
|
I can help you with that! Here is how we can write intake-esgf code that shows you what models include the variable gpp in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Could you provide python code to find models that include variable gpp using intake-esgf?
|
Certainly! Here's python code to find models that include variable gpp using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
How do I list all the models with variable gpp data via intake-esgf in python?
|
To list all the models with variable gpp data via intake-esgf in python, you can use the following code:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Show me a python example that retrieves models containing variable gpp from intake-esgf.
|
Here's a python example that retrieves models containing variable gpp from intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Write a python script using intake-esgf to get all models that have variable gpp.
|
Sure! Here's a python script using intake-esgf to get all models that have variable gpp:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I need a python snippet that searches intake-esgf for models containing variable gpp.
|
No problem! Here is a python snippet that searches intake-esgf for models containing variable gpp:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Which climate models provide data for variable gpp using intake-esgf in Python?
|
Here's how you can check which climate models provide data for variable gpp using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Show me all CMIP6 models that include variable gpp using intake-esgf in Python.
|
Here's Python code to show all CMIP6 models that include variable gpp using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
How can I find models with variable gpp using intake-esgf in Python?
|
You can find models with variable gpp using intake-esgf in Python like this:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I'm looking for models that contain data on gpp. Can you give me Python intake-esgf code?
|
Sure! Here's Python code to list models that contain data on gpp using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Retrieve all models associated with variable gpp using intake-esgf in Python.
|
Here's how to retrieve all models associated with variable gpp using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Can you help me get the model names that include variable gpp using intake-esgf in Python?
|
Of course! Here's how to get the model names that include variable gpp using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Write intake-esgf code in Python to identify models supporting variable gpp.
|
Here's Python code to identify models supporting variable gpp using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Give me an example of Python code that lists models for variable gpp using intake-esgf.
|
Here's an example of Python code that lists models for variable gpp using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
How to check which models have variable gpp data using intake-esgf in Python?
|
You can check which models have variable gpp data using intake-esgf in Python like this:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Find all models that include the variable gpp using intake-esgf in Python.
|
Here's how to find all models that include the variable gpp using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
List models providing variable gpp datasets using intake-esgf in Python.
|
Here's Python code to list models providing variable gpp datasets using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Generate a list of models that feature variable gpp using intake-esgf in Python.
|
Here's how to generate a list of models that feature variable gpp using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
What's the best way to get models with variable gpp in intake-esgf using Python?
|
Here's one way to get models with variable gpp in intake-esgf using Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I only need the model names that have variable gpp using intake-esgf in Python.
|
Here's Python code to get just the model names that have variable gpp using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Display all models containing variable gpp using intake-esgf in Python.
|
Here's code to display all models containing variable gpp using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Which models can I query for variable gpp data using intake-esgf in Python?
|
You can query these models for variable gpp data using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Is there intake-esgf code in Python to show models with variable gpp?
|
Yes! Here's intake-esgf code in Python to show models with variable gpp:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Find the set of all models that have variable gpp using intake-esgf in Python.
|
Here's how to find the set of all models that have variable gpp using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Could you provide a quick Python snippet using intake-esgf to find models for variable gpp?
|
Sure! Here's a quick Python snippet using intake-esgf to find models for variable gpp:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Identify every model with data for variable gpp using intake-esgf in Python.
|
Here's Python code to identify every model with data for variable gpp using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
How do I discover all models that support variable gpp using intake-esgf in Python?
|
Here's Python code to discover all models that support variable gpp using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Search intake-esgf for models including variable gpp using Python.
|
Here's how to search intake-esgf for models including variable gpp using Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Give me the Python intake-esgf command to list models with variable gpp.
|
Here's the Python intake-esgf command to list models with variable gpp:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Which models can I find that hold variable gpp using intake-esgf in Python?
|
You can find these models that hold variable gpp using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I'd like a Python code sample using intake-esgf to extract models containing variable gpp.
|
Here's a Python code sample using intake-esgf to extract models containing variable gpp:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
How can I quickly list models with variable gpp using intake-esgf in Python?
|
Here's how to quickly list models with variable gpp using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Retrieve model names that have variable gpp data using intake-esgf in Python.
|
Here's Python code to retrieve model names that have variable gpp data using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Can you write intake-esgf code in Python for listing models with variable gpp?
|
Sure! Here's intake-esgf code in Python for listing models with variable gpp:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Help me figure out which models include variable gpp using intake-esgf in Python.
|
Here's how to figure out which models include variable gpp using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Show Python intake-esgf code to get all models for variable gpp.
|
Here's Python intake-esgf code to get all models for variable gpp:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["gpp"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Name the models that have variable tas using intake-esgf in python.
|
Right! Here is how we can name the models that have variable tas using intake-esgf in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
List all models that contain data for variable tas using intake-esgf in python.
|
Here is how we can list the models that contain data for variable tas using intake-esgf in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I'm trying to search for models that have data on variable tas using the intake-esgf library in python. Can you help?
|
Yes! I can help you with that. Here is how we can search for models that have data on variable tas using the intake-esgf library in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I want python code that lists all the models containing variable tas.
|
Here is a way we can list all the models containing variable tas using intake-esgf in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I need help writing intake-esgf code that shows me what models include the variable tas.
|
I can help you with that! Here is how we can write intake-esgf code that shows you what models include the variable tas in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Could you provide python code to find models that include variable tas using intake-esgf?
|
Certainly! Here's python code to find models that include variable tas using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
How do I list all the models with variable tas data via intake-esgf in python?
|
To list all the models with variable tas data via intake-esgf in python, you can use the following code:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Show me a python example that retrieves models containing variable tas from intake-esgf.
|
Here's a python example that retrieves models containing variable tas from intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Write a python script using intake-esgf to get all models that have variable tas.
|
Sure! Here's a python script using intake-esgf to get all models that have variable tas:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I need a python snippet that searches intake-esgf for models containing variable tas.
|
No problem! Here is a python snippet that searches intake-esgf for models containing variable tas:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Which climate models provide data for variable tas using intake-esgf in Python?
|
Here's how you can check which climate models provide data for variable tas using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Show me all CMIP6 models that include variable tas using intake-esgf in Python.
|
Here's Python code to show all CMIP6 models that include variable tas using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
How can I find models with variable tas using intake-esgf in Python?
|
You can find models with variable tas using intake-esgf in Python like this:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I'm looking for models that contain data on tas. Can you give me Python intake-esgf code?
|
Sure! Here's Python code to list models that contain data on tas using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Retrieve all models associated with variable tas using intake-esgf in Python.
|
Here's how to retrieve all models associated with variable tas using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Can you help me get the model names that include variable tas using intake-esgf in Python?
|
Of course! Here's how to get the model names that include variable tas using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Write intake-esgf code in Python to identify models supporting variable tas.
|
Here's Python code to identify models supporting variable tas using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Give me an example of Python code that lists models for variable tas using intake-esgf.
|
Here's an example of Python code that lists models for variable tas using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
How to check which models have variable tas data using intake-esgf in Python?
|
You can check which models have variable tas data using intake-esgf in Python like this:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Find all models that include the variable tas using intake-esgf in Python.
|
Here's how to find all models that include the variable tas using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
List models providing variable tas datasets using intake-esgf in Python.
|
Here's Python code to list models providing variable tas datasets using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Generate a list of models that feature variable tas using intake-esgf in Python.
|
Here's how to generate a list of models that feature variable tas using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
What's the best way to get models with variable tas in intake-esgf using Python?
|
Here's one way to get models with variable tas in intake-esgf using Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I only need the model names that have variable tas using intake-esgf in Python.
|
Here's Python code to get just the model names that have variable tas using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Display all models containing variable tas using intake-esgf in Python.
|
Here's code to display all models containing variable tas using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Which models can I query for variable tas data using intake-esgf in Python?
|
You can query these models for variable tas data using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Is there intake-esgf code in Python to show models with variable tas?
|
Yes! Here's intake-esgf code in Python to show models with variable tas:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Find the set of all models that have variable tas using intake-esgf in Python.
|
Here's how to find the set of all models that have variable tas using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Could you provide a quick Python snippet using intake-esgf to find models for variable tas?
|
Sure! Here's a quick Python snippet using intake-esgf to find models for variable tas:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Identify every model with data for variable tas using intake-esgf in Python.
|
Here's Python code to identify every model with data for variable tas using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
How do I discover all models that support variable tas using intake-esgf in Python?
|
Here's Python code to discover all models that support variable tas using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Search intake-esgf for models including variable tas using Python.
|
Here's how to search intake-esgf for models including variable tas using Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Give me the Python intake-esgf command to list models with variable tas.
|
Here's the Python intake-esgf command to list models with variable tas:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Which models can I find that hold variable tas using intake-esgf in Python?
|
You can find these models that hold variable tas using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I'd like a Python code sample using intake-esgf to extract models containing variable tas.
|
Here's a Python code sample using intake-esgf to extract models containing variable tas:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
How can I quickly list models with variable tas using intake-esgf in Python?
|
Here's how to quickly list models with variable tas using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Retrieve model names that have variable tas data using intake-esgf in Python.
|
Here's Python code to retrieve model names that have variable tas data using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Can you write intake-esgf code in Python for listing models with variable tas?
|
Sure! Here's intake-esgf code in Python for listing models with variable tas:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Help me figure out which models include variable tas using intake-esgf in Python.
|
Here's how to figure out which models include variable tas using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Show Python intake-esgf code to get all models for variable tas.
|
Here's Python intake-esgf code to get all models for variable tas:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["tas"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Name the models that have variable pr using intake-esgf in python.
|
Right! Here is how we can name the models that have variable pr using intake-esgf in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
List all models that contain data for variable pr using intake-esgf in python.
|
Here is how we can list the models that contain data for variable pr using intake-esgf in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I'm trying to search for models that have data on variable pr using the intake-esgf library in python. Can you help?
|
Yes! I can help you with that. Here is how we can search for models that have data on variable pr using the intake-esgf library in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I want python code that lists all the models containing variable pr.
|
Here is a way we can list all the models containing variable pr using intake-esgf in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I need help writing intake-esgf code that shows me what models include the variable pr.
|
I can help you with that! Here is how we can write intake-esgf code that shows you what models include the variable pr in python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Could you provide python code to find models that include variable pr using intake-esgf?
|
Certainly! Here's python code to find models that include variable pr using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
How do I list all the models with variable pr data via intake-esgf in python?
|
To list all the models with variable pr data via intake-esgf in python, you can use the following code:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Show me a python example that retrieves models containing variable pr from intake-esgf.
|
Here's a python example that retrieves models containing variable pr from intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Write a python script using intake-esgf to get all models that have variable pr.
|
Sure! Here's a python script using intake-esgf to get all models that have variable pr:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I need a python snippet that searches intake-esgf for models containing variable pr.
|
No problem! Here is a python snippet that searches intake-esgf for models containing variable pr:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Which climate models provide data for variable pr using intake-esgf in Python?
|
Here's how you can check which climate models provide data for variable pr using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Show me all CMIP6 models that include variable pr using intake-esgf in Python.
|
Here's Python code to show all CMIP6 models that include variable pr using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
How can I find models with variable pr using intake-esgf in Python?
|
You can find models with variable pr using intake-esgf in Python like this:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
I'm looking for models that contain data on pr. Can you give me Python intake-esgf code?
|
Sure! Here's Python code to list models that contain data on pr using intake-esgf:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Retrieve all models associated with variable pr using intake-esgf in Python.
|
Here's how to retrieve all models associated with variable pr using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
|
Can you help me get the model names that include variable pr using intake-esgf in Python?
|
Of course! Here's how to get the model names that include variable pr using intake-esgf in Python:
```python
from intake_esgf import ESGFCatalog
# initialize the catalog
cat = ESGFCatalog()
# specify variables to search for
variables = ["pr"]
# search the catalog for models with the specified variable
search_results = cat.search(
variable_id=variables
)
# get a list of unique models
models = sorted(search_results.df["source_id"].unique())
# print out our results
print(f"Models({len(models)} results):")
for i, model in enumerate(models):
print(f"{i+1}. {model}")
```
|
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