Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.37 (v20220620)

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

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