DOI: 10.5285/f0f622f4e9d14f95949a5cc44451e8bb
title: Chapter 7 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 7.SM.1 (v20220721)

description: Data for Figure 7.SM.1 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 7.SM.1 shows total effective radiative forcing from SSP scenarios with respect to 1750 for 2000-2500, 14 showing best estimate and 5–95% uncertainty range. --------------------------------------------------- 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: Forster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D.J. Lunt, T. Mauritsen, M.D. Palmer, M. Watanabe, M. Wild, and H. Zhang, 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. 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. 923–1054, doi:10.1017/9781009157896.009. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has 13 subpanels, with data provided for all panels in the master GitHub repository linked in the documentation. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains: - Total effective radiative forcing from SSP scenarios with respect to 1750 for 2000-2500, 14 showing best estimate and 5–95% uncertainty range. - Graph (top panel) showing radiative forcing trajectories for (shaded regions): - SSP5-8.5 (brown line) - SSP3-7.0-lownNTCF (red dashed line) - SSP3-7.0 (red line) - SSP3-7.0-lowNTCFCH4 (red dotted line) - SSP4-6.0 (orange line) - SSP2-4.5 (yellow line) - SSP5-3.4-over (early overshoot of purple line) - SSP4-3.4 (light blue line) - SSP1-2.6 (purple line) - SSP1-1.9 (green line) - Radiative forcing component breakdowns (smaller subpanels): - CO2 (carbon dioxide) - CH4 (methane) - N2O (nitrous oxide) - Halogenated gases - O3 (ozone) - Strat H2O (stratospheric water) - Contrails and aviation induced cirrus - Aerosol-radiation interactions - Aerosol-cloud interactions - Light absorbing particles on snow and ice - Land use - Solar Uncertainty ranges are not shown for SSP3-7.0-lowNTCF and SSP3-7.0-NTCFCH4 for visual clarity. Bottom matrix shows the best estimate ERF for each anthropogenic component, and solar (volcanic ERF is zero beyond 2024). SSP stands for Shared Socioeconomic Pathway. SSP119 is the Shared Socioeconomic Pathway which represents the lowest scenario of radiative forcing and development scenarios, consistent with RCP1.9. SSP126 is the Shared Socioeconomic Pathway which represents the lower boundary of radiative forcing and development scenarios, consistent with RCP2.6. SSP245 is the Shared Socioeconomic Pathway which represents the median of radiative forcing and development scenarios, consistent with RCP4.5. SSP370 is the Shared Socioeconomic Pathway which represents the upper-middle range of radiative forcing and development scenarios, consistent with RCP6.0. SSP585 is the Shared Socioeconomic Pathway which represents the upper boundary of radiative forcing and development scenarios, consistent with RCP8.5. NTCF stands for Near-Term Climate Forcer. ERF stands for Effective Radiative Forcing. --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Data provided in relation to Figure 7.SM.1: - Data file: ERF_%_1750-2500.csv' - Data file: ERF_%_1750-2500_pc05.csv - Data file: ERF_%_1750-2500_pc95.csv - Data file: ERF_%_minorGHGs_1750-2500.csv Each % is substituted for one of the following scenarios: SSP119 - best estimate. SSP119 - 5th percentile. SSP119 - 95th percentile. SSP119 minor GHGs - best estimate. SSP126 - best estimate. SSP126 - 5th percentile. SSP126 - 95th percentile. SSP126 minor GHGs - best estimate. SSP245 - best estimate. SSP245 - 5th percentile. SSP245 - 95th percentile. SSP245 minor GHGs - best estimate. SSP334 - best estimate. SSP370 - best estimate. SSP370 - 5th percentile. SSP370 - 95th percentile. SSP370 minor GHGs - best estimate. SSP370 low NTCF - best estimate. SSP370 low NTCF - 5th percentile. SSP370 low NTCF - 95th percentile. SSP370 low NTCF minor GHGs - best estimate. SSP370 low NTCFCH4 - best estimate. SSP370 low NTCFCH4 - 5th percentile. SSP370 low NTCFCH4 - 95th percentile. SSP370 low NTCFCH4 minor GHGs - best estimate. SSP434 - best estimate. SSP434 - 5th percentile. SSP434 - 95th percentile. SSP434 minor GHGs - best estimate. SSP460 - best estimate. SSP460 - 5th percentile. SSP460 - 95th percentile. SSP460 minor GHGs - best estimate. SSP534 over - best estimate. SSP534 over - 5th percentile. SSP534 over - 95th percentile. SSP534 over minor GHGs - best estimate. SSP585 - best estimate. SSP585 - 5th percentile. SSP585 - 95th pecentile. SSP585 minor GHGs - best estimate. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- Data and figures are produced by the Jupyter Notebooks that live inside the notebooks directory. Also listed on the 'master' GitHub page linked in the documentation of this catalogue record are external GitHub repositories and locations within the contributed directory where code for figures have been supplied by other authors. These are provided "as-is" and are not guaranteed to be reproducible within this environment. For external GitHub locations, check out the relevant repository READMEs. Within the processing chain, every notebook is prefixed by a number. To reproduce all results in the chapter, the notebooks should be run in numerical order, because some later things depend on earlier things (historical temperature attribution requires a constrained ensemble of the two layer climate model, which relies on the generation of the radiative forcing time series). This being said, most notebooks should run standalone, as input data is provided where the datasets are small enough (see the 'master;' GitHub page for these). --------------------------------------------------- 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 7) - Link to the Supplementary Material for Chapter 7, which contains details on the input data used in Table 7.SM.1 to 7.SM.7. - Link to the Jupyter notebook for plotting the figure from the Chapter 7 GitHub repository - Link to the code for the figure, archived on Zenodo.