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  • Under the World Climate Research Programme (WCRP), the Working Group on Cloupled Modelling (WGCM) established the Coupled Model Intercomparison Project (CMIP) as a standard experimental protocol for studying the output of coupled atmosphere-ocean general circulation models (AOGCMs). CMIP provides a community-based infrastructure in support of climate model diagnosis, validation, intercomparison, documentation and data access. This framework enables a diverse community of scientists to analyze GCMs in a systematic fashion, a process which serves to facilitate model improvement. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) archives much of the CMIP data. Part of the CMIP archive constitutes phase 3 of the Coupled Model Intercomparison Project (CMIP3), a collection of climate model output from simulations of the past, present and future climate. This unprecedented collection of recent model output is officially known as the "WCRP CMIP3 multi-model dataset". It is meant to serve the Intergovernmental Panel on Climate Change (IPCC)'s Working Group 1, which focuses on the physical climate system -- atmosphere, land surface, ocean and sea ice -- and the choice of variables archived reflects this focus. The Intergovernmental Panel on Climate Change (IPCC) was established by the World Meteorological Organization and the United Nations Environmental Program to assess scientific information on climate change. The IPCC publishes reports that summarize the state of the science. The research based on this dataset provided much of the new material underlying the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4).

  • This dataset contains output data from a number of models associated with the IPCC Third Assessment Report. This data was processed at the Climate Research Unit at the University of East Anglia. The data extraction was intended for use by the Climate Impacts Community (and was funded by the UK Department of Environment Food and Rural Affairs, Defra). Data from various modelling centres and models: CCCMA, CSIRO, ECHAM4, GFDL99, HADCM3, NIES99.

  • Data used in Climate Change 2001, the Third Assessment Report (TAR) of the United Nations Intergovernmental Panel on Climate Change (IPCC). Simulations of global climate models were run by various climate modelling groups coordinated by the World Climate Research Programme (WCRP) on behalf of the United Nations Intergovernmental Panel on Climate Change (IPCC). Climatology data calculated from global climate model simulations of experiments representative of Special Report on Emission Scenarios (SRES) scenarios: A1F, A1T, A1a, A2a, A2b, A2c, B1a, B2b. The climatologies are 30-year averages. Climate anomalies are expressed relative to the period 1961-1990. The monthly climatology data covers the period from 1961-2100. The climatologies are of global scope and are provided on latitude-longitude grids.

  • Data used in Climate Change 2001, the Third Assessment Report (TAR) of the United Nations Intergovernmental Panel on Climate Change (IPCC). Simulations of global climate models were run by various climate modelling groups coordinated by the World Climate Research Programme (WCRP) on behalf of the United Nations Intergovernmental Panel on Climate Change (IPCC). Climatology data calculated from global climate model simulations of experiments representative of Special Report on Emission Scenarios (SRES) scenarios: A1F, A1T, A1a, A2a, A2b, A2c, B1a, B2b. The climatologies are 30-year averages. Climate anomalies are expressed relative to the period 1961-1990. The monthly climatology data covers the period from 1961-2100. The climatologies are of global scope and are provided on latitude-longitude grids.

  • Data for Figure 3.2 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.2 shows changes in surface temperature for different paleoclimates. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has three subpanels, the data provided for all panels in subdirectories named panel_a, panel_b, panel_c --------------------------------------------------- List of data provided --------------------------------------------------- For panel (a): - PMIP3 global temperature anomalies over continents and oceans reconstruction sites - PMIP4 CMIP6 global temperature anomalies over continents and oceans reconstruction sites - PMIP4 non-CMIP6 global temperature anomalies over continents and oceans reconstruction sites - Tierney 2020 reconstructions of marine temperature - Cleator 2020 reconstructions of continental temperature For panel (b): - CMIP5 temperature data for paleoclimate periods - CMIP6 temperature data for paleoclimate periods - non-CMIP temperature data for paleoclimate periods - Instrumental observational and observations from reconstructions For panel (c): - Volcanic forcing from TS17, CU12, GRA08 - CMIP6 GMST anomaly with respect to 1850-1900 modelled with TS17 volcanic forcing - CMIP5 GMST anomaly with respect to 1850-1900 modelled with CU12 volcanic forcing - CMIP5 GMST anomaly with respect to 1850-1900 modelled with GRA08 volcanic forcing --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - panel_a/temperature_anomalies_scatter_points.csv relates to the scatter points and their standard deviation for panel (a) - For panel (b) the datasets are stored as following panel_b/temperature_{color}_{marker}_{period}_{model_group}_{additional_info}.csv and relates to the scatter points for panel (b). - For panel (c) the data is stored in panel_c/gmst_changes_paleo_volcanic_forcings.csv and relates to red, green, blue and black lines on the panel as well as grey shadings. Additional information about data provided in relation to figure in files headers. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. CMIP5 is the fifth phase of the Coupled Model Intercomparison Project. PMIP4 is the Paleoclimate Modelling Intercomparison Project phase 4 PMIP3 is the Paleoclimate Modelling Intercomparison Project phase 3 --------------------------------------------------- Temporal Range of Paleoclimate Data --------------------------------------------------- This dataset covers a paleoclimate timespan from 3.3Ma to 6ka (3.3 million years ago to 6 thousand years ago). --------------------------------------------------- Notes on reproducing the figure from the provided data. --------------------------------------------------- For panel (a) the error bar should be plotted as anomalies from columns 2/4 +/- standard deviation. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.

  • Climatology data used in Climate Change 2007, the Fourth Assessment Report (AR4) of the United Nations Intergovernmental Panel on Climate Change (IPCC) calculated from Coupled Model Intercomparison Project Phase 3 (CMIP3) model output. Simulations of global climate models were run by various climate modelling groups coordinated by the World Climate Research Programme (WCRP) on behalf of the IPCC. Climatology data was calculated from global climate model simulations of experiments representative of Special Report on Emission Scenarios (SRES) scenarios: A1b, A2, B1, the commitment scenario experiment (COMMIT), the twentieth century experiment (20C3M), the pre-industrial control (PICTL) and the idealised experiments 1PCTO2X and 1PCTO4X. The AR4 climatologies are 20-year period averages. 30-year period averages have also been calculated for comparison with the IPCC Third Assessment Report (TAR). The monthly climatology data covers the period from 1850-2100. The climatologies are of global scope and are provided on latitude-longitude grids. The period averages are: 20x: 20-year averages [+20-39, +46-65, +80-99, +180-199] as used in chapter 10 of IPCC 2007, 30a: 30-year averages [+01-30, +31-60, +61-90] as used in the observed climatologies, 30b: 30-year averages [+10-39, +40-69, +70-99] for compatibility with the IPCC 3rd Assessment Report (TAR). The climatology period averages listed above are relative to AD2000 for SRES scenarios A1B, A2, B1, and the commitment scenario COMMIT, relative to AD1900 for the twentieth century run (20C3M) and relative to the start of the experiment for the pre-industrial control (PICTL) and the 1PCTO2X and 1PCTO4X runs. Data files are 'tar' files with names of the form [model]_[experiment]_[variable]_{climatology}.tar. The experiments in this dataset include the SRES future scenarios: A1B Globalisation with rapid economic growth, A2 Regionalisation with regionally oriented economic development, B1 Globalisation with global environmental sustainability. Also: COMMIT simulation of climate change we are committed to given the current loading of greenhouse gasses and zero future emissions. PICTL the pre-industrial control. 20C3M the 20th century climate simulaiton. 1PTO2X and 1PTO4X idealised simulaitons where the concentration of atmospheric carbon dioxide (CO2) is increased by 1 percent per year from pre-industrial concentration until reaching 2x and 4x the original concentration. The dataset is comprised of monthly mean surface data for the following atmospheric variables: specific humidity, precipitation flux, air pressure at sea level, surface downwelling shortwave flux in air, air temperature, air temperature daily max, air temperature daily min, eastward wind, northward wind.

  • Data for Cross-Chapter Box 3.1, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Cross-Chapter Box 3.1, Figure 1 shows 15-year trends of surface global warming for 1998-2012 and 2012-2026. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has four panels, with data provided for panels a and b in a subdirectory named panel_ab, and for panels c and d in subdirectories named panel_c and panel_d respectively. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains:  - Observed and modelled global annual mean surface temperature and surface air temperature trends for 1998-2012 - Modelled global annual mean surface air temperature trends for 2012-2026 - Observed annual mean surface temperature trends for 1998-2012 - Composite of modelled annual mean surface air temperature trends for 1998-2012 --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel a: - gmst_trend_1998-2012 in panel_ab/GMST_trend.csv; HadCRUT5 for histogram, ensemble mean of HadCRUT5 and other observations for open triangles at the top, and multimodel ensemble means of CMIP5 and CMIP6 for open diamonds at the top - gsat_trend_1998-2012 in panel_ab/GSAT_trend.csv; CMIP5 and CMIP6 ensembles for histograms, ERA5 for the top filled triangle, and multimodel ensemble means of CMIP5 and CMIP6 for filled diamonds at the top Panel b: - gmst_trend_2012-2026 in panel_ab/GMST_trend.csv; multimodel ensemble means of CMIP5 and CMIP6 for open diamonds at the top - gsat_trend_2012-2026 in panel_ab/GSAT_trend.csv; CMIP5 and CMIP6 ensembles for histograms, and multimodel ensemble means of CMIP5 and CMIP6 for filled diamonds at the top Panel c: - tas in panel_c/TrendPattern_HadCRUT5_mean.nc; shading, with the sig attribute for cross markers Panel d: - tas in panel_d/TrendPattern_composite.nc: shading CMIP5 is the fifth phase of the Coupled Model Intercomparison Project. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. HadCRUT5 is a gridded dataset of global historical near-surface air temperature anomalies since the year 1850. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- Multimodel ensemble means and histograms are calculated after weighting each ensemble member with the inverse of the ensemble size of the same model. The values for panels c and d are stored with the K/year unit but scaled to the K/decade, therefore they need to be multiplied by a factor of 10 in order to be consistent with the plotted values. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.

  • Data for Figure 3.36 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.36 shows observed and simulated life cycle of El Niño and La Niña events. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has four panels. All the data are provided in enso_lifecycle.nc file. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains - Composite time series of the ENSO index for El Niño events - Composite time series of the ENSO index for La Niña events - Mean duration of El Niño events - Mean duration of La Niña events in observations, CMIP5 historical-RCP4.5 and and CMIP6 historical simulations. --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel a: - ts_elnino_obs; black curves . ERSSTv5, dashed lines: dataset = 1 . HadISST, solid lines: dataset = 2 - ts_elnino_cmip5: The ENSO index time series in each ensemble member of CMIP5 models; blue curve and shading - ts_elnino_cmip6: The ENSO index time series in each ensemble member of CMIP6 models; red curve and shading Panel b: - ts_lanina_obs; black curves . ERSSTv5, dashed lines: dataset = 1 . HadISST, solid lines: dataset = 2 - ts_lanina_cmip5: The ENSO index time series in each ensemble member of CMIP5 models; blue curve and shading - ts_lanina_cmip6: The ENSO index time series in each ensemble member of CMIP6 models; red curve and shading Panel c: - duration_elnino_obs; black vertical lines and numbers in the top right box . ERSSTv5, dashed lines: dataset = 1 . HadISST, solid lines: dataset = 2 - duration_elnino_cmip5: El Nino duration in each ensemble member of CMIP5 models; blue box-whisker and number in the top right box - duration_elnino_cmip6; El Nino duration in each ensemble member of CMIP6 models; red dots, red box-whisker and number in the top right box . ACCESS-CM2: ens_cmip6 = 1 - 3 . ACCESS-ESM1-5: ens_cmip6 = 4 - 23 . AWI-CM-1-1-MR: ens_cmip6 = 24 - 28 . AWI-ESM-1-1-LR: ens_cmip6 = 29 . BCC-CSM2-MR: ens_cmip6 = 30 - 32 . BCC-ESM1: ens_cmip6 = 33 - 35 . CAMS-CSM1-0: ens_cmip6 = 36-38 . CanESM5-CanOE: ens_cmip6 = 39 - 41 . CanESM5: ens_cmip6 = 42 - 106 . CESM2-FV2: ens_cmip6 = 107 - 109 . CESM2: ens_cmip6 = 110 - 120 . CESM2-WACCM-FV2: ens_cmip6 = 121 - 123 . CESM2-WACCM: ens_cmip6 = 124 - 126 . CIESM: ens_cmip6 = 127 - 129 . CMCC-CM2-HR4: ens_cmip6 = 130 . CMCC-CM2-SR5: ens_cmip6 = 131 . CMCC-ESM2: ens_cmip6 = 132 . CNRM-CM6-1-HR: ens_cmip6 = 133 . CNRM-CM6-1: ens_cmip6 = 134 - 162 . CNRM-ESM2-1: ens_cmip6 = 163 - 172 . E3SM-1-0: ens_cmip6 = 173 - 177 . E3SM-1-1-ECA: ens_cmip6 = 178 . E3SM-1-1: ens_cmip6 = 179 . EC-Earth3-AerChem: ens_cmip6 = 180, 181 . EC-Earth3-CC: ens_cmip6 = 182 . EC-Earth3: ens_cmip6 = 183 - 204 . EC-Earth3-Veg-LR: ens_cmip6 = 205 - 207 . EC-Earth3-Veg: ens_cmip6 = 208 - 215 . FGOALS-f3-L: ens_cmip6 = 216 - 218 . FGOALS-g3: ens_cmip6 = 219 - 224 . FIO-ESM-2-0: ens_cmip6 = 225 - 227 . GFDL-CM4: ens_cmip6 = 228 . GFDL-ESM4: ens_cmip6 = 229 - 231 . GISS-E2-1-G-CC: ens_cmip6 = 232 . GISS-E2-1-G: ens_cmip6 = 233 - 278 . GISS-E2-1-H: ens_cmip6 = 279 - 302 . HadGEM3-GC31-LL: ens_cmip6 = 303 - 306 . HadGEM3-GC31-MM: ens_cmip6 = 307 - 310 . IITM-ESM: ens_cmip6 = 311 . INM-CM4-8: ens_cmip6 = 312 . INM-CM5-0: ens_cmip6 = 313 - 322 . IPSL-CM5A2-INCA: ens_cmip6 = 323 . IPSL-CM6A-LR: ens_cmip6 = 324 - 355 . KACE-1-0-G: ens_cmip6 = 356-358 . KIOST-ESM: ens_cmip6 = 359 . MCM-UA-1-0: ens_cmip6 = 360, 361 . MIROC6: ens_cmip6 = 362 - 411 . MIROC-ES2L: ens_cmip6 = 412 - 421 . MPI-ESM-1-2-HAM: ens_cmip6 = 422 - 424 . MPI-ESM1-2-HR: ens_cmip6 = 425 - 434 . MPI-ESM1-2-LR: ens_cmip6 = 435 -  444 . MRI-ESM2-0: ens_cmip6 = 445 - 450 . NESM3: ens_cmip6 = 451 - 455 . NorCPM1: ens_cmip6 = 456 - 485 . NorESM2-LM: ens_cmip6 = 486 - 488 . NorESM2-MM: ens_cmip6 = 489 - 490 . SAM0-UNICON: ens_cmip6 = 491 . TaiESM1: ens_cmip6 = 492 . UKESM1-0-LL: ens_cmip6 = 493 - 510 Panel d: - duration_lanina_obs; black vertical lines and numbers in the top right box . ERSSTv5, dashed lines: dataset = 1 . HadISST, solid lines: dataset = 2 - duration_lanina_cmip5; La Nina duration in each ensemble member of CMIP5 models; blue box-whisker and number in the top right box - duration_lanina_cmip6; La Nina duration in each ensemble member of CMIP6 models; red dots, red box-whisker and number in the top right box . ACCESS-CM2: ens_cmip6 = 1 - 3 . ACCESS-ESM1-5: ens_cmip6 = 4 - 23 . AWI-CM-1-1-MR: ens_cmip6 = 24 - 28 . AWI-ESM-1-1-LR: ens_cmip6 = 29 . BCC-CSM2-MR: ens_cmip6 = 30 - 32 . BCC-ESM1: ens_cmip6 = 33 - 35 . CAMS-CSM1-0: ens_cmip6 = 36-38 . CanESM5-CanOE: ens_cmip6 = 39 - 41 . CanESM5: ens_cmip6 = 42 - 106 . CESM2-FV2: ens_cmip6 = 107 - 109 . CESM2: ens_cmip6 = 110 - 120 . CESM2-WACCM-FV2: ens_cmip6 = 121 - 123 . CESM2-WACCM: ens_cmip6 = 124 - 126 . CIESM: ens_cmip6 = 127 - 129 . CMCC-CM2-HR4: ens_cmip6 = 130 . CMCC-CM2-SR5: ens_cmip6 = 131 . CMCC-ESM2: ens_cmip6 = 132 . CNRM-CM6-1-HR: ens_cmip6 = 133 . CNRM-CM6-1: ens_cmip6 = 134 - 162 . CNRM-ESM2-1: ens_cmip6 = 163 - 172 . E3SM-1-0: ens_cmip6 = 173 - 177 . E3SM-1-1-ECA: ens_cmip6 = 178 . E3SM-1-1: ens_cmip6 = 179 . EC-Earth3-AerChem: ens_cmip6 = 180, 181 . EC-Earth3-CC: ens_cmip6 = 182 . EC-Earth3: ens_cmip6 = 183 - 204 . EC-Earth3-Veg-LR: ens_cmip6 = 205 - 207 . EC-Earth3-Veg: ens_cmip6 = 208 - 215 . FGOALS-f3-L: ens_cmip6 = 216 - 218 . FGOALS-g3: ens_cmip6 = 219 - 224 . FIO-ESM-2-0: ens_cmip6 = 225 - 227 . GFDL-CM4: ens_cmip6 = 228 . GFDL-ESM4: ens_cmip6 = 229 - 231 . GISS-E2-1-G-CC: ens_cmip6 = 232 . GISS-E2-1-G: ens_cmip6 = 233 - 278 . GISS-E2-1-H: ens_cmip6 = 279 - 302 . HadGEM3-GC31-LL: ens_cmip6 = 303 - 306 . HadGEM3-GC31-MM: ens_cmip6 = 307 - 310 . IITM-ESM: ens_cmip6 = 311 . INM-CM4-8: ens_cmip6 = 312 . INM-CM5-0: ens_cmip6 = 313 - 322 . IPSL-CM5A2-INCA: ens_cmip6 = 323 . IPSL-CM6A-LR: ens_cmip6 = 324 - 355 . KACE-1-0-G: ens_cmip6 = 356-358 . KIOST-ESM: ens_cmip6 = 359 . MCM-UA-1-0: ens_cmip6 = 360, 361 . MIROC6: ens_cmip6 = 362 - 411 . MIROC-ES2L: ens_cmip6 = 412 - 421 . MPI-ESM-1-2-HAM: ens_cmip6 = 422 - 424 . MPI-ESM1-2-HR: ens_cmip6 = 425 - 434 . MPI-ESM1-2-LR: ens_cmip6 = 435 -  444 . MRI-ESM2-0: ens_cmip6 = 445 - 450 . NESM3: ens_cmip6 = 451 - 455 . NorCPM1: ens_cmip6 = 456 - 485 . NorESM2-LM: ens_cmip6 = 486 - 488 . NorESM2-MM: ens_cmip6 = 489 - 490 . SAM0-UNICON: ens_cmip6 = 491 . TaiESM1: ens_cmip6 = 492 . UKESM1-0-LL: ens_cmip6 = 493 - 510 Acronyms: ENSO - El Niño–Southern Oscillation, CMIP - Coupled Model Intercomparison Project, RCP - Representative Concentration Pathway, ERSST - Extended Reconstructed Sea Surface Temperature, HadISST - Hadley Centre Sea Ice and Sea Surface Temperature, ACCESS- CM2 – Australian Community Climate and Earth System Simulator coupled climate model, ACCESS- ESM – Australian Community Climate and Earth System Simulator Earth system model, AWI - Alfred Wegener Institute, BCC-CSM - Beijing Climate Center Climate System Model, CAMS - Chinese Academy of Meteorological Sciences, CanOE - Canadian Ocean Ecosystem, CESM2 - Community Earth System Model, WACCM - Whole Atmosphere Community Climate Model, CIESM - Community Integrated Earth System Model, CNCC - Centro Euro-Mediterraneo per I Cambiamenti Climatici, CNRM - Centre National de Recherches Météorologiques, E3SM - Energy Exascale Earth System Model, FGOALS - Flexible Global Ocean-Atmosphere-Land System Model, FIO-ESM - First Institute of Oceanography Earth System Model, GFDL - Geophysical Fluid Dynamics Laboratory, GISS - Goddard Institute for Space Studies, IITM - Indian Institute of Tropical Meteorology, INM - Institute for Numerical Mathematics, IPSL - Institut Pierre-Simon Laplace, KIOST-ESM - Korea Institute of Ocean Science & Technology Earth System, MIROC - Model for Interdisciplinary Research on Climate, MPI - Max-Planck-Institut für Meteorologie, NESM - Nanjing University of Information Science and Technology Earth System Model, NorCPM - Norwegian Climate Prediction Model, SAM0-UNICON - Seoul National University Atmosphere Model version 0 with a Unified Convection Scheme (SAM0-UNICON), TaiESM1 - Taiwan Earth System Model version 1, UKESM - The UK Earth System Modelling project. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- Multimodel ensemble means and percentiles are calculated after weighting individual members with the inverse of the ensemble size of the same model. The weight is provided as the weight attribute of ens_cmip5 and ens_cmip6. If X(i) is the array, and w(i) the corresponding weight. - Mean shoud be sum_i(X(i) * w(i)) / sum_i(w(i)) - For percentile values, 1. Sort X and w so that X is in the ascending order 2. Accumulate w until i = j so that accumulated(w)/sum_i(w(i)) equals or exceeds the specified percentile level (e.g. 0.05) 3. Use X(j) or (X(j) + X(j - 1))/2 as the percentile value --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo - Link to the figure on the IPCC AR6 website

  • Data for Figure 3.39 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.39 shows the observed and simulated Pacific Decadal Variability (PDV). --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has six panels. Files are not separated according to the panels. --------------------------------------------------- List of data provided --------------------------------------------------- pdv.obs.nc contains - Observed SST anomalies associated with the PDV pattern - Observed PDV index time series (unfiltered) - Observed PDV index time series (low-pass filtered) - Taylor statistics of the observed PDV patterns - Statistical significance of the observed SST anomalies associated with the PDV pattern pdv.hist.cmip6.nc contains - Simulated SST anomalies associated with the PDV pattern - Simulated PDV index time series (unfiltered) - Simulated PDV index time series (low-pass filtered) - Taylor statistics of the simulated PDV patterns based on CMIP6 historical simulations. pdv.hist.cmip5.nc contains - Simulated SST anomalies associated with the PDV pattern - Simulated PDV index time series (unfiltered) - Simulated PDV index time series (low-pass filtered) - Taylor statistics of the simulated PDV patterns based on CMIP5 historical simulations. pdv.piControl.cmip6.nc contains - Simulated SST anomalies associated with the PDV pattern - Simulated PDV index time series (unfiltered) - Simulated PDV index time series (low-pass filtered) - Taylor statistics of the simulated PDV patterns based on CMIP6 piControl simulations. pdv.piControl.cmip5.nc contains - Simulated SST anomalies associated with the PDV pattern - Simulated PDV index time series (unfiltered) - Simulated PDV index time series (low-pass filtered) - Taylor statistics of the simulated PDV patterns based on CMIP5 piControl simulations. --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel a: - ipo_pattern_obs_ref in pdv.obs.nc: shading - ipo_pattern_obs_signif (dataset = 1) in pdv.obs.nc: cross markers Panel b: - Multimodel ensemble mean of ipo_model_pattern in pdv.hist.cmip6.nc: shading, with their sign agreement for hatching Panel c: - tay_stats (stat = 0, 1) in pdv.obs.nc: black dots - tay_stats (stat = 0, 1) in pdv.hist.cmip6.nc: red crosses, and their multimodel ensemble mean for the red dot - tay_stats (stat = 0, 1) in pdv.hist.cmip5.nc: blue crosses, and their multimodel ensemble mean for the blue dot Panel d: - Lag-1 autocorrelation of tpi in pdv.obs.nc: black horizontal lines in left . ERSSTv5: dataset = 1 . HadISST: dataset = 2 . COBE-SST2: dataset = 3 - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.piControl.cmip5.nc: blue open box-whisker in the left - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.piControl.cmip6.nc: red open box-whisker in the left - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.hist.cmip5.nc: blue filled box-whisker in the left - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.hist.cmip6.nc: red filled box-whisker in the left - Lag-10 autocorrelation of tpi_lp in pdv.obs.nc: black horizontal lines in right . ERSSTv5: dataset = 1 . HadISST: dataset = 2 . COBE-SST2: dataset = 3 - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.piControl.cmip5.nc: blue open box-whisker in the right - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.piControl.cmip6.nc: red open box-whisker in the right - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.hist.cmip5.nc: blue filled box-whisker in the right - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.hist.cmip6.nc: red filled box-whisker in the right Panel e: - Standard deviation of tpi in pdv.obs.nc: black horizontal lines in left . ERSSTv5: dataset = 1 . HadISST: dataset = 2 . COBE-SST2: dataset = 3 - Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.piControl.cmip5.nc: blue open box-whisker in the left - Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.piControl.cmip6.nc: red open box-whisker in the left - Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.hist.cmip5.nc: blue filled box-whisker in the left - Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.hist.cmip6.nc: red filled box-whisker in the left - Standard deviation of tpi_lp in pdv.obs.nc: black horizontal lines in right . ERSSTv5: dataset = 1 . HadISST: dataset = 2 . COBE-SST2: dataset = 3 - Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.piControl.cmip5.nc: blue open box-whisker in the right - Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.piControl.cmip6.nc: red open box-whisker in the right - Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.hist.cmip5.nc: blue filled box-whisker in the right - Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.hist.cmip6.nc: red filled box-whisker in the right Panel f: - tpi_lp in pdv.obs.nc: black curves . ERSSTv5: dataset = 1 . HadISST: dataset = 2 . COBE-SST2: dataset = 3 - tpi_lp in pdv.hist.cmip6.nc: 5th-95th percentiles in red shading, multimodel ensemble mean and its 5-95% confidence interval for red curves - tpi_lp in pdv.hist.cmip5.nc: 5th-95th percentiles in blue shading, multimodel ensemble mean for blue curve CMIP5 is the fifth phase of the Coupled Model Intercomparison Project. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. SST stands for Sea Surface Temperature. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- Multimodel ensemble means and percentiles of historical simulations of CMIP5 and CMIP6 are calculated after weighting individual members with the inverse of the ensemble size of the same model. ensemble_assign in each file provides the model number to which each ensemble member belongs. This weighting does not apply to the sign agreement calculation. piControl simulations from CMIP5 and CMIP6 consist of a single member from each model, so the weighting is not applied. Multimodel ensemble means of the pattern correlation in Taylor statistics in (c) and the autocorrelation of the index in (d) are calculated via Fisher z-transformation and back transformation. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo - Link to the figure on the IPCC AR6 website

  • Data for Figure 3.37 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.37 shows observed and simulated seasonality of ENSO. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has two panels. All the data are provided in enso_seasonality.nc. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains - Climatological standard deviation of the ENSO index - A seasonality metric of the ENSO index in observations, CMIP5 historical-RCP4.5 and CMIP6 historical simulations. --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel a: - stdv_enso_obs; black curves . ERSSTv5, dashed lines: dataset = 1 . HadISST, solid lines: dataset = 2 - stdv_enso_cmip5: Climatological standard deviation of the ENSO index time series in each ensemble member of CMIP5 models blue curve and shading - stdv_enso_cmip6: Climatological standard deviation of the ENSO index time series in each ensemble member of CMIP6 models; red curve and shading . ACCESS-CM2: ens_cmip6 = 1 - 3 . ACCESS-ESM1-5: ens_cmip6 = 4 - 23 . AWI-CM-1-1-MR: ens_cmip6 = 24 - 28 . AWI-ESM-1-1-LR: ens_cmip6 = 29 . BCC-CSM2-MR: ens_cmip6 = 30 - 32 . BCC-ESM1: ens_cmip6 = 33 - 35 . CAMS-CSM1-0: ens_cmip6 = 36-38 . CanESM5-CanOE: ens_cmip6 = 39 - 41 . CanESM5: ens_cmip6 = 42 - 106 . CESM2-FV2: ens_cmip6 = 107 - 109 . CESM2: ens_cmip6 = 110 - 120 . CESM2-WACCM-FV2: ens_cmip6 = 121 - 123 . CESM2-WACCM: ens_cmip6 = 124 - 126 . CIESM: ens_cmip6 = 127 - 129 . CMCC-CM2-HR4: ens_cmip6 = 130 . CMCC-CM2-SR5: ens_cmip6 = 131 . CMCC-ESM2: ens_cmip6 = 132 . CNRM-CM6-1-HR: ens_cmip6 = 133 . CNRM-CM6-1: ens_cmip6 = 134 - 162 . CNRM-ESM2-1: ens_cmip6 = 163 - 172 . E3SM-1-0: ens_cmip6 = 173 - 177 . E3SM-1-1-ECA: ens_cmip6 = 178 . E3SM-1-1: ens_cmip6 = 179 . EC-Earth3-AerChem: ens_cmip6 = 180, 181 . EC-Earth3-CC: ens_cmip6 = 182 . EC-Earth3: ens_cmip6 = 183 - 204 . EC-Earth3-Veg-LR: ens_cmip6 = 205 - 207 . EC-Earth3-Veg: ens_cmip6 = 208 - 215 . FGOALS-f3-L: ens_cmip6 = 216 - 218 . FGOALS-g3: ens_cmip6 = 219 - 224 . FIO-ESM-2-0: ens_cmip6 = 225 - 227 . GFDL-CM4: ens_cmip6 = 228 . GFDL-ESM4: ens_cmip6 = 229 - 231 . GISS-E2-1-G-CC: ens_cmip6 = 232 . GISS-E2-1-G: ens_cmip6 = 233 - 278 . GISS-E2-1-H: ens_cmip6 = 279 - 302 . HadGEM3-GC31-LL: ens_cmip6 = 303 - 306 . HadGEM3-GC31-MM: ens_cmip6 = 307 - 310 . IITM-ESM: ens_cmip6 = 311 . INM-CM4-8: ens_cmip6 = 312 . INM-CM5-0: ens_cmip6 = 313 - 322 . IPSL-CM5A2-INCA: ens_cmip6 = 323 . IPSL-CM6A-LR: ens_cmip6 = 324 - 355 . KACE-1-0-G: ens_cmip6 = 356-358 . KIOST-ESM: ens_cmip6 = 359 . MCM-UA-1-0: ens_cmip6 = 360, 361 . MIROC6: ens_cmip6 = 362 - 411 . MIROC-ES2L: ens_cmip6 = 412 - 421 . MPI-ESM-1-2-HAM: ens_cmip6 = 422 - 424 . MPI-ESM1-2-HR: ens_cmip6 = 425 - 434 . MPI-ESM1-2-LR: ens_cmip6 = 435 -  444 . MRI-ESM2-0: ens_cmip6 = 445 - 450 . NESM3: ens_cmip6 = 451 - 455 . NorCPM1: ens_cmip6 = 456 - 485 . NorESM2-LM: ens_cmip6 = 486 - 488 . NorESM2-MM: ens_cmip6 = 489 - 490 . SAM0-UNICON: ens_cmip6 = 491 . TaiESM1: ens_cmip6 = 492 . UKESM1-0-LL: ens_cmip6 = 493 - 510 Panel b: - seasonality_enso_obs; black vertical lines and numbers in the top right box . ERSSTv5, dashed lines: dataset = 1 . HadISST, solid lines: dataset = 2 - seasonality_enso_cmip5; Seasonality metric in each ensemble member of CMIP5 models; blue box-whisker and number in the top right box - seasonality_enso_cmip6; Seasonality metric in each ensemble member of CMIP6 models; red dots, with their multimodal ensemble mean and percentiles for the red box-whisker and number in the top right box . ACCESS-CM2: ens_cmip6 = 1 - 3 . ACCESS-ESM1-5: ens_cmip6 = 4 - 23 . AWI-CM-1-1-MR: ens_cmip6 = 24 - 28 . AWI-ESM-1-1-LR: ens_cmip6 = 29 . BCC-CSM2-MR: ens_cmip6 = 30 - 32 . BCC-ESM1: ens_cmip6 = 33 - 35 . CAMS-CSM1-0: ens_cmip6 = 36-38 . CanESM5-CanOE: ens_cmip6 = 39 - 41 . CanESM5: ens_cmip6 = 42 - 106 . CESM2-FV2: ens_cmip6 = 107 - 109 . CESM2: ens_cmip6 = 110 - 120 . CESM2-WACCM-FV2: ens_cmip6 = 121 - 123 . CESM2-WACCM: ens_cmip6 = 124 - 126 . CIESM: ens_cmip6 = 127 - 129 . CMCC-CM2-HR4: ens_cmip6 = 130 . CMCC-CM2-SR5: ens_cmip6 = 131 . CMCC-ESM2: ens_cmip6 = 132 . CNRM-CM6-1-HR: ens_cmip6 = 133 . CNRM-CM6-1: ens_cmip6 = 134 - 162 . CNRM-ESM2-1: ens_cmip6 = 163 - 172 . E3SM-1-0: ens_cmip6 = 173 - 177 . E3SM-1-1-ECA: ens_cmip6 = 178 . E3SM-1-1: ens_cmip6 = 179 . EC-Earth3-AerChem: ens_cmip6 = 180, 181 . EC-Earth3-CC: ens_cmip6 = 182 . EC-Earth3: ens_cmip6 = 183 - 204 . EC-Earth3-Veg-LR: ens_cmip6 = 205 - 207 . EC-Earth3-Veg: ens_cmip6 = 208 - 215 . FGOALS-f3-L: ens_cmip6 = 216 - 218 . FGOALS-g3: ens_cmip6 = 219 - 224 . FIO-ESM-2-0: ens_cmip6 = 225 - 227 . GFDL-CM4: ens_cmip6 = 228 . GFDL-ESM4: ens_cmip6 = 229 - 231 . GISS-E2-1-G-CC: ens_cmip6 = 232 . GISS-E2-1-G: ens_cmip6 = 233 - 278 . GISS-E2-1-H: ens_cmip6 = 279 - 302 . HadGEM3-GC31-LL: ens_cmip6 = 303 - 306 . HadGEM3-GC31-MM: ens_cmip6 = 307 - 310 . IITM-ESM: ens_cmip6 = 311 . INM-CM4-8: ens_cmip6 = 312 . INM-CM5-0: ens_cmip6 = 313 - 322 . IPSL-CM5A2-INCA: ens_cmip6 = 323 . IPSL-CM6A-LR: ens_cmip6 = 324 - 355 . KACE-1-0-G: ens_cmip6 = 356-358 . KIOST-ESM: ens_cmip6 = 359 . MCM-UA-1-0: ens_cmip6 = 360, 361 . MIROC6: ens_cmip6 = 362 - 411 . MIROC-ES2L: ens_cmip6 = 412 - 421 . MPI-ESM-1-2-HAM: ens_cmip6 = 422 - 424 . MPI-ESM1-2-HR: ens_cmip6 = 425 - 434 . MPI-ESM1-2-LR: ens_cmip6 = 435 -  444 . MRI-ESM2-0: ens_cmip6 = 445 - 450 . NESM3: ens_cmip6 = 451 - 455 . NorCPM1: ens_cmip6 = 456 - 485 . NorESM2-LM: ens_cmip6 = 486 - 488 . NorESM2-MM: ens_cmip6 = 489 - 490 . SAM0-UNICON: ens_cmip6 = 491 . TaiESM1: ens_cmip6 = 492 . UKESM1-0-LL: ens_cmip6 = 493 - 510 Acronyms - ENSO - El Niño–Southern Oscillation, CMIP - Coupled Model Intercomparison Project, RCP - Representative Concentration Pathway, ERSST - Extended Reconstructed Sea Surface Temperature, HadISST - Hadley Centre Sea Ice and Sea Surface Temperature, ACCESS- CM2 – Australian Community Climate and Earth System Simulator coupled climate model, ACCESS- ESM – Australian Community Climate and Earth System Simulator Earth system model, AWI - Alfred Wegener Institute, BCC-CSM - Beijing Climate Center Climate System Model, CAMS - Chinese Academy of Meteorological Sciences, CanOE - Canadian Ocean Ecosystem, CESM2 - Community Earth System Model, WACCM - Whole Atmosphere Community Climate Model, CIESM - Community Integrated Earth System Model, CNCC - Centro Euro-Mediterraneo per I Cambiamenti Climatici, CNRM - Centre National de Recherches Météorologiques, E3SM - Energy Exascale Earth System Model, FGOALS - Flexible Global Ocean-Atmosphere-Land System Model, FIO-ESM - First Institute of Oceanography Earth System Model, GFDL - Geophysical Fluid Dynamics Laboratory, GISS - Goddard Institute for Space Studies, IITM - Indian Institute of Tropical Meteorology, INM - Institute for Numerical Mathematics, IPSL - Institut Pierre-Simon Laplace, KIOST-ESM - Korea Institute of Ocean Science & Technology Earth System, MIROC - Model for Interdisciplinary Research on Climate, MPI - Max-Planck-Institut für Meteorologie, NESM - Nanjing University of Information Science and Technology Earth System Model, NorCPM - Norwegian Climate Prediction Model, SAM0-UNICON - Seoul National University Atmosphere Model version 0 with a Unified Convection Scheme (SAM0-UNICON), TaiESM1 - Taiwan Earth System Model version 1, UKESM - The UK Earth System Modelling project. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- Multimodel ensemble means and percentiles are calculated after weighting individual members with the inverse of the ensemble size of the same model. The weight is provided as the weight attribute of ens_cmip5 and ens_cmip6. If X(i) is the array, and w(i) the corresponding weight. - Mean shoud be sum_i(X(i) * w(i)) / sum_i(w(i)) - For percentile values, 1. Sort X and w so that X is in the ascending order 2. Accumulate w until i = j so that accumulated(w)/sum_i(w(i)) equals or exceeds the specified percentile level (e.g. 0.05) 3. Use X(j) or (X(j) + X(j - 1))/2 as the percentile value --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo - Link to the figure on the IPCC AR6 website