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 3.38 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.38 shows model evaluation of ENSO teleconnection for 2m-temperature and precipitation in boreal winter (December-January-February). --------------------------------------------------- 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 --------------------------------------------------- Data provided for all panels in one single directory --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains observed global patterns for: - temperature from the Berkeley Earth dataset over land - temperature from ERSSTv5 over ocean - precipitation from GPCC over land (shading, mm day–1) - precipitation from GPCP worldwide (contours, period: 1979-2014) and distributions of regression coefficients in IPCC regions for: - temperature - precipitation --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- maps: - reg_tas_NINO34_BEST_ERSSTv5_1901_2018_DJF.nc (var = 'rc', upper map over land) - reg_sst_NINO34_ERSSTv5_ERSSTv5_1901_2018_DJF.nc (var = 'rc', upper map over ocean) - reg_precip_NINO34_GPCP_ERSST5_1979_2018_DJF.nc (var = 'rc', lower map, contours) - reg_pr_NINO34_GPCC_ERSSTv5_1901_2016_DJF.nc (var = 'rc', lower map, shading) histograms: - tas_enso_regression_pdf_v4_no_cosweight_DJF.nc . upper grey histograms: var = 'region_pdfx_hist' and 'region_pdfy_hist' . MME (black line): var = 'region_ave_hist' . Observations (blue lines): var = 'region_obs' - tas_amip_hist_enso_regression_pdf_v4_no_cosweight_DJF.nc (orange dashed line): var = 'region_ave_amip_hist' => Fields correspond to regions numbers with labels in the plot, namely for temperature: 'EAU/RFE/RAR/NWN/NCA/ENA/NSA/MED/NWS/ESAF' (see variable region_info with attributes making the association between the region index and the acronym/name). - pr_enso_regression_pdf_v4_no_cosweight_DJF.nc . lower grey histograms: var = 'region_pdfx_hist' and 'region_pdfy_hist' . MME (black line): var = 'region_ave_hist' . Observations (blue lines): var = 'region_obs' - pr_amip_hist_enso_regression_pdf_v4_no_cosweight_DJF.nc (orange dahsed line): var = 'region_ave_amip_hist' => Fields correspond to regions numbers with labels in the plot, namely for precipitation: 'EAS/SEA/EAU/WNA/NCA/SES/NSA/ESAF/SEAF/MED' (see variable info_region with attributes making the association between the region index and the acronym/name). ENSO is the El Niño Southern Oscillation. GPCC is the Global Precipitation Climatology Centre. GPCP is the Global Precipitation Climatology Project. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- Data provided in reg_pr_NINO34_GPCC_ERSSTv5_1901_2016_DJF.nc are in mm/month. Values should be divided by 30 for plotting in mm/day. --------------------------------------------------- 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 figure on the IPCC AR6 website

Data for Figure 3.28 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.28 shows long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations. --------------------------------------------------- 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 panels (a), (b), (c), (d), (e), (f), with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d, panel_e and panel_f. --------------------------------------------------- List of data provided --------------------------------------------------- The datasets contains:  - Atlantic and Pacific halosteric sea level trend from CMIP6 models (1950-2014) - Atlantic and Pacific halosteric sea level trend from Durack&Wijffels observations (1950-2019) - Atlantic and Pacific halosteric sea level trend from EN4 observations (1950-2019) - Atlantic and Pacific halosteric sea level trend from Ishii observations (1955-2019) - Atlantic and Pacific thermosteric sea level trend from CMIP6 models (1950-2014) - Atlantic and Pacific thermosteric sea level trend from Durack&Wijffels observations (1950-2019) - Atlantic and Pacific thermosteric sea level trend from EN4 observations (1950-2019) - Atlantic and Pacific thermosteric sea level trend from Ishii observations (1955-2019) - Global halosteric sea level trends from Durack&Wijffels observations (1950-2019) - Global halosteric sea level trends from EN4 observations (1950-2019) - Global halosteric sea level trends from Ishii observations (1955-2019) - Global halosteric sea level trends from CMIP6 multi-model mean (1950-2014) --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - panel_a/halosteric_trends_hist-nat.csv has data for green and black markers. - panel_a/halosteric_trends_historical.csv has data for orange and black markers. - panel_b/thermosteric_trends_hist-nat.csv has data for green and black markers. - panel_b/thermosteric_trends_historical.csv has data for orange and black markers. - panel_c/halosteric_trends_map_DW.nc has data for filled colored contours. - panel_d/halosteric_trends_map_EN4.nc has data for filled colored contours. - panel_e/halosteric_trends_map_Ishii.nc has data for filled colored contours. - panel_f/halosteric_trends_map_cmip6.nc has data for filled colored contours. For panels a and b details about the data provided in relation to the figure in the header of every file. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- The observational data from here (top right panel) is taken from the file: DurackandWijffels_GlobalOceanChanges_19500101-20191231__210122-205355_beta.nc. The field of interest are salinity_mean (shown as black contours) and salinity_change (shown in colourscale). The file was archived as input data for Figure 2.27. The link to this dataset is provided in the Related Documents section of this catalogue record. --------------------------------------------------- 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 input dataset for figure 3.28 - Link to the code for the figure, archived on Zenodo - Link to the figure on the IPCC AR6 website

Data for Figure 3.8 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.8 shows assessed contributions to observed warming, and supporting lines of evidence. --------------------------------------------------- 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. --------------------------------------------------- List of data provided --------------------------------------------------- The dataset contains the drivers of the attributable warming (2010-2019 relative to 1850-1900): - Observed global warming (2010-2019) - Global warming and its drivers reported in the literature sources  (2010-2019) - Global warming and its drivers calculated from CMIP6 models (2010-2019) --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - drivers_observed_warming.csv has data for the shadings and markers in the figure. Additional details of data provided in relation to figure in the file header (BADC-CSV file). --------------------------------------------------- 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.

Input data for Figure 2.16 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 2.16 provides global precipitation minus evaporation trend maps and time series from a variety of data sources --------------------------------------------------- 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: Gulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of 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. 287–422, doi:10.1017/9781009157896.004. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has four panels, with input data provided for all panels in the main directory --------------------------------------------------- List of data provided --------------------------------------------------- The datasets contains: - Global precipitation and evaporation data from ERA5 reanalysis - Time series of global, land-only and ocean-only average annual P–E (mm day–1) from the following reanalysis products: 20CRv3, ERA5, ERA20CM, MERRA, CFSR, ERA20C, JRA55 and MERRA2. --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel a: - Data files: IntermediateData_era5_evap_2.nc and era5_tp_2.nc Panel b: - Data file: GPME2.csv and GPME2.mat Panel c: - Data file: LPME2.csv and LPME2.mat Panel d: - Data file: OPME2.csv and OPME2.mat For panels b to d: I.     Column 2: orange solid line II.    Column 3: cyan solid line III.   Column 4: black solid line IV.   Column 5: grey solid line V.    Column 6: blue solid line VI.   Column 7: dark green solid line VII.  Column 8: brown solid line VIII. Column 9: green solid line 20CRv3 is the NOAA-CIRES-DOE Twentieth Century Reanalysis Version 3. ERA5 is a reanalysis of the global climate from 1950 to present, developed by ECMWF. ERA20CM is a twentieth century atmospheric model ensemble developed by ECMWF. MERRA stands for Modern-Era Retrospective analysis for Research and Applications. CFSR stands for Climate Forecast System Reanalysis. ERA20C is the first atmospheric reanalysis of the 20th century, from 1900-2010, developed by ECMWF. JRA55 stands for Japanese 55-year Reanalysis. MERRA2 stands for Modern-Era Retrospective analysis for Research and Applications, version 2. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- Additional information to correctly reproduce the figure in the corresponding readme files for code archived on Zenodo (see the link to code provided in the Related Documents section of this catalogue record). --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the figure on the IPCC AR6 website - Link to the report component containing the figure (Chapter 2) - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1 - Link to the code for the figure, archived on Zenodo.

Data for Figure 3.40 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.40 shows the observed and simulated Atlantic Multidecadal Variability (AMV). --------------------------------------------------- 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 --------------------------------------------------- amv.obs.nc contains - Observed SST anomalies associated with the AMV pattern - Observed AMV index time series (unfiltered) - Observed AMV index time series (low-pass filtered) - Taylor statistics of the observed AMV patterns amv.hist.cmip6.nc contains - Statistical significance of the observed SST anomalies associated with the AMV pattern - Simulated SST anomalies associated with the AMV pattern - Simulated AMV index time series (unfiltered) - Simulated AMV index time series (low-pass filtered) - Taylor statistics of the simulated AMV patterns based on CMIP6 historical simulations. amv.hist.cmip5.nc contains - Simulated SST anomalies associated with the AMV pattern - Simulated AMV index time series (unfiltered) - Simulated AMV index time series (low-pass filtered) - Taylor statistics of the simulated AMV patterns based on CMIP5 historical simulations. amv.piControl.cmip6.nc contains - Simulated SST anomalies associated with the AMV pattern - Simulated AMV index time series (unfiltered) - Simulated AMV index time series (low-pass filtered) - Taylor statistics of the simulated AMV patterns based on CMIP6 piControl simulations. amv.piControl.cmip5.nc contains - Simulated SST anomalies associated with the AMV pattern - Simulated AMV index time series (unfiltered) - Simulated AMV index time series (low-pass filtered) - Taylor statistics of the simulated AMV patterns based on CMIP5 piControl simulations. --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel a: - amv_pattern_obs_ref in amv.obs.nc: shading - amv_pattern_obs_signif (dataset = 1) in amv.obs.nc: cross markers Panel b: - Multimodel ensemble mean of amv_pattern in amv.hist.cmip6.nc: shading, with their sign agreement for hatching Panel c: - tay_stats (stat = 0, 1) in amv.obs.nc: black dots - tay_stats (stat = 0, 1) in amv.hist.cmip6.nc: red crosses, and their multimodel ensemble mean for the red dot - tay_stats (stat = 0, 1) in amv.hist.cmip5.nc: blue crosses, and their multimodel ensemble mean for the blue dot Panel d: - Lag-1 autocorrelation of amv_timeseries_raw in amv.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 amv_timeseries_raw in amv.piControl.cmip5.nc: blue open box-whisker in the left - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.piControl.cmip6.nc: red open box-whisker in the left - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.hist.cmip5.nc: blue filled box-whisker in the left - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.hist.cmip6.nc: red filled box-whisker in the left - Lag-10 autocorrelation of amv_timeseries in amv.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 amv_timeseries in amv.piControl.cmip5.nc: blue open box-whisker in the right - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.piControl.cmip6.nc: red open box-whisker in the right - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.hist.cmip5.nc: blue filled box-whisker in the right - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.hist.cmip6.nc: red filled box-whisker in the right Panel e: - Standard deviation of amv_timeseries_raw in amv.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 amv_timeseries_raw in amv.piControl.cmip5.nc: blue open box-whisker in the left - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.piControl.cmip6.nc: red open box-whisker in the left - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.hist.cmip5.nc: blue filled box-whisker in the left - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.hist.cmip6.nc: red filled box-whisker in the left - Standard deviation of amv_timeseries in amv.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 amv_timeseries in amv.piControl.cmip5.nc: blue open box-whisker in the right - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.piControl.cmip6.nc: red open box-whisker in the right - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.hist.cmip5.nc: blue filled box-whisker in the right - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.hist.cmip6.nc: red filled box-whisker in the right Panel f: - amv_timeseries in amv.obs.nc: black curves . ERSSTv5: dataset = 1 . HadISST: dataset = 2 . COBE-SST2: dataset = 3 - amv_timeseries in amv.hist.cmip6.nc: 5th-95th percentiles in red shading, multimodel ensemble mean and its 5-95% confidence interval for red curves - amv_timeseries in amv.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 3.36 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