The data is available for download at the AR6 Scenario Explorer hosted by IIASA.

As part of the IPCC's 6th Assessment Report (AR6), authors from Working Group III on Mitigation of Climate Change undertook a comprehensive exercise to collect and assess quantitative, model-based scenarios related to the mitigation of climate change.

Building on previous assessments, such as those undertaken for the 5th Assessment Report (AR5) and the Special Report on Global Warming of 1.5°C (SR15), the calls for AR6 for scenarios have been expanded and includes economy-wide GHG emissions, energy, and sectoral scenarios from global to national scales, thus more broadly supporting the assessment across multiple chapters (see Annex III, Part 2 of the WGIII report for more details).

The compilation and assessment of the scenario ensemble was conducted by authors of the IPCC AR6 report, and the resource is hosted by the International Institute for Applied Systems Analysis (IIASA) as part of a cooperation agreement with Working Group III of the IPCC. The scenario ensemble contains 3,131 quantitative scenarios with data on socio-economic development, greenhouse gas emissions, and sectoral transformations across energy, land use, transportation, buildings and industry. These scenarios derive from 191 unique modelling frameworks, 95+ model families that are either globally comprehensive, national, multi-regional or sectoral.

The criteria for submission included that the scenario is presented in a peer-reviewed journal accepted for publication no later than October 11th, 2021, or published in a report determined by the IPCC WG III Bureau to be eligible grey literature by the same date. The AR6 scenario database is documented in Annex III.2 of the Sixth Assessment Report of Working Group III. For the purpose of the assessment, scenarios have been grouped in various categories relating to, among other things, climate outcomes, overshoot, technology availability and policy assumptions.

For ease of use, the dataset is split into multiple files:

• Scenarios data for the Global region

• Scenarios data for R5 regions

• Scenarios data for R6 regions

• Scenarios data for R10 regions

• Scenarios data for ISO-3 (country) regions

• Global metadata indicators file

• National metadata indicators file

The data is available for download at the AR6 Scenario Explorer hosted by IIASA. The license permits use of the scenario ensemble for scientific research and science communication, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and legal code for more information.

In addition to the data you may find more relevant information and cite one of the relevant chapters of the WG III report.

If working with global or regional (R6, R10) data:

Keywan Riahi, Roberto Schaeffer, et al. Mitigation Pathways Compatible with Long-Term Goals, in "Mitigation of Climate Change".
Intergovernmental Panel on Climate Change, Geneva, 2022.
url: http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/

If working with national data (ISO region data):

Franck Lecocq, Harald Winkler, et al. Mitigation and development pathways in the near- to mid-term, in "Mitigation of Climate Change".
Intergovernmental Panel on Climate Change, Geneva, 2022.
url: http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/

If you find the metadata files particularly useful:

Celine Guivarch, Elmar Kriegler, Joana Portugal Pereira, et al. Annex III: Scenarios and Modelling Methods,

• in "Mitigation of Climate Change".

• Intergovernmental Panel on Climate Change, Geneva, 2022.

• url: http://www.ipcc.ch/report/sixth-assessment-report-working-group-3/

Scenarios data also supports analysis in Chapters 2, 5, 6, 7, 9, 10, 12 and 15

Download and license information

The data is available for download at the AR6 Scenario Explorer.

Details about the be found in the license section of the AR6 Scenario Explorer .

About the data set

To fill in emissions not reported for scenarios in their submission to the database, we use a large set of harmonized AR6 global emissions pathways for inferring pathways based on the relationships between concurrent species development over time observed in the larger set.

Infilling ensures that all relevant anthropogenic emissions are included in each climate run for each scenario. This makes the climate assessment of alternative scenarios more comparable and reduces the risk of a biased climate assessment, because not all climatically active emission species are reported by all IAMs. The infilling methods used are from an open-source Python software package called ‘silicone’ (Lamboll et al. 2020)

This file is the harmonized emissions database that was used as "infiller database" for the IPCC AR6 WGIII report on the Mitigation of Climate Change, using data from the chapter on Mitigation Pathways Compatible with Long-Term Goals (Riahi and Schaeffer et al. 2022) as available in the AR6 Scenarios Database (Byers et al. 2022).

References

Edward Byers, Volker Krey, Elmar Kriegler, Keywan Riahi, Roberto Schaeffer, Jarmo Kikstra, Robin Lamboll, Zebedee Nicholls, Marit Sanstad, Chris Smith, Kaj-Ivar van der Wijst, Franck Lecocq, Joana Portugal-Pereira, Yamina Saheb, Anders Strømann, Harald Winkler, Cornelia Auer, Elina Brutschin, Claire Lepault, Eduardo Müller-Casseres, Matthew Gidden, Daniel Huppmann, Peter Kolp, Giacomo Marangoni, Michaela Werning, Katherine Calvin, Celine Guivarch, Tomoko Hasegawa, Glen Peters, Julia Steinberger, Massimo Tavoni, Detlef von Vuuren, Piers Forster, Jared Lewis, Malte Meinshausen, Joeri Rogelj, Bjorn Samset, Ragnhild Skeie, Alaa Al Khourdajie.
AR6 Scenarios Database hosted by IIASA
International Institute for Applied Systems Analysis, 2022.
doi: 10.5281/zenodo.5886912 | url: data.ene.iiasa.ac.at/ar6/

Keywan Riahi, Roberto Schaeffer, et al.
Mitigation Pathways Compatible with Long-Term Goals, in "Mitigation of Climate Change".
Intergovernmental Panel on Climate Change, Geneva, 2022.
url: Sixth Assessment Report Working Group III

Lamboll, R.D., Nicholls, Z.R., Kikstra, J.S., Meinshausen, M. and
Rogelj, J., 2020. Silicone v1. 0.0: an open-source Python package for
inferring missing emissions data for climate change research. Geoscientific Model Development, 13(11), pp.5259-5275.

The International Panel for Climate Change (IPCC) produces regular Assessment Reports that provide global warming potentials (GWPs) for greenhouse gases (GHG) in the context of multiple time horizons including 20, 100, and 500 years. The GWPs (in kg CO2-equivalent per kg GHG) can be multiplied by kg GHGs emitted for use in estimating CO2-equivalent (CO2e) impacts of GHGs emitted. In the context of life cycle assessment (LCA) , the GWPs can be used as characterization factors in of the life cycle impact assessment. This dataset provides 20- (GWP-20), 100- (GWP-100) and 500-year (GWP-500) GWPs from the 4th (AR4), 6th (AR6) IPCC assessment reports, and 20- (GWP-20) and 100-year (GWP-100) GWPs from the 5th (AR5) report (AR5 provided no 500 yr GWPs). Datasets are provided in simple tables in Excel, in the openLCA JSON-LD format compliant with the U.S. Federal LCA Commons standards, and in Apache parquet format for the most efficient import into applications or scripts using languages like Python and R. The names for GHGs are from the Federal LCA Elementary Flow List (FEDEFL) v1.2, which are names preferred for this GHGs in the USEPA's Substance Registry Service. These datasets were created using the LCIA Formatter v1.1.1 (https://github.com/USEPA/LCIAformatter). The GWP values are provided in these formats for convenient use; the values have not been altered from the values reported in the Assessment Reports. Python code used to produce the data is available in a github gist under the supporting data links along with dataset metadata from the LCIA formatter.

Selected CMIP6 input data assessed by the IPCC Working Group I AR6 is available for 9 regions, 5 experiments, and 12 variables. These data are regional subsets of the assessed CMIP6 input data and part of the Reference Data Archive for AR6 of the IPCC Data Distribution Centre (DDC): https://ipcc.wdc-climate.de.

Input Data for Figure TS.24 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure TS.24 shows projected change in the mean number of days per year with maximum temperature exceeding 35°C for CMIP5 (first column), CMIP6 (second column) and CORDEX (thirth column).

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: Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.

Figure subpanels

The figure has 12 panels with input data provided for all panels.

List of data provided

This dataset contains projected global changes in the mean number of days per year with maximum temperature exceeding 35°C for the multimodel ensemble of CORDEX (third column in the figure), for the SSP5-8.5 and SSP1-2.6 scenarios and for the 2041-2060 and 2081-2100 future periods, relative to the historical 1995–2014 period.

Data provided in relation to figure

• Data file: (panel a) CMIP5_TX35_rcp26_2041-2060_rel.to-1995-2014.nc and CMIP5_TX35_rcp26_2041-2060_rel.to-1995-2014_model_agreement.nc; global changes in the mean number of days per year with maximum temperature exceeding 35°C as projected by the CMIP5 and the RCP2.6 scenario for the future period 2041-2060 relative to the historical 1995-2014 period.
• Data file: (panel b) CMIP5_TX35_rcp26_2081-2100_rel.to-1995-2014.nc and CMIP5_TX35_rcp26_2081-2100_rel.to-1995-2014_model_agreement.nc; global changes in the mean number of days per year with maximum temperature exceeding 35°C as projected by the CMIP5 and the RCP2.6 scenario for the future period 2081-2100 relative to the historical 1995-2014 period.
• Data file: (panel c) CMIP5_TX35_rcp85_2041-2060_rel.to-1995-2014.nc and CMIP5_TX35_rcp85_2041-2060_rel.to-1995-2014_model_agreement.nc; global changes in the mean number of days per year with maximum temperature exceeding 35°C as projected by the CMIP5 and the RCP8.5 scenario for the future period 2041-2060 relative to the historical 1995-2014 period.
• Data file: (panel d) CMIP5_TX35_rcp85_2081-2100_rel.to-1995-2014.nc and CMIP5_TX35_rcp85_2081-2100_rel.to-1995-2014_model_agreement.nc; global changes in the mean number of days per year with maximum temperature exceeding 35°C as projected by the CMIP5 and the RCP8.5 scenario for the future period 2081-2100 relative to the historical 1995-2014 period.
• Data file: (panel e) CMIP6_TX35_ssp126_2041-2060_rel.to-1995-2014.nc and CMIP6_TX35_ssp126_2041-2060_rel.to-1995-2014_model_agreement.nc; global changes in the mean number of days per year with maximum temperature exceeding 35°C as projected by the CMIP6 and the ... For full abstract see: https://catalogue.ceda.ac.uk/uuid/38401030f262490094dc9c8931c851e4.

These data sets contain the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. The dataset groups contain the full set of samples for the global projections (see IPCC AR6 WGI Sea Level Projections global) as well as summary relative sea level projections (see IPCC AR6 WGI Sea Level Projections regional and, without the AR6 estimate of background sea level process rates, see IPCC AR6 WGI Sea Level Projections regional novlm). The confidence output files correspond most directly to the figures and tables in the report. The IPCC AR6 sea level change projection files are provided in a simplified format but represent a more complicated workflow involving combinations of multiple lines of evidence for the various individual contributors to sea level change. It's highly recommended using the data as provided in the confidence output files to remain consistent with the assessment in IPCC AR6 Chapter 9 (see IPCC AR6 WGI Sea Level Projections HowTos for details). Required Acknowledgements and Citation: In order to document the impact of these sea level rise projections, users of the data are obligated to cite chapter 9 of WGI contribution to the IPCC AR6, the FACTS model description paper, and the version of the data set used. When using these data in a publication, please include the information provided in IPCC AR6 WGI Sea Level Projections Acknowledgments. Disclaimer: The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.

Data for Figure TS.22 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure TS.22 shows a synthesis of the geographical distribution of climatic impact-drivers changes and the number of AR6 WGI reference regions where they are projected to change.

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: Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. 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. 33−144, doi:10.1017/9781009157896.002.

Figure subpanels

The figure has two panels with data provided for all panels.

List of data provided

This dataset contains:

• geographical location of regions belonging to one of five groups characterized by a specific combination of changing climatic impact-drivers (CIDs).
• number of AR6 WG1 regions where Climatic Impact Drivers are projected to change if a global warming level of 2°C is reached compared to a climatological reference period included within 1960-2014

Data provided in relation to figure

• 'Figure-F-Panels_IDL.xlsx' - Datafile containing data for both figures in excel sheets

Individual panel data in csv format:

• Panel a: 'Figure-F-Panel_a_IDL.csv' - Description of the clustering used to generate panel a

• Panel b: 'consolidated_data_figure_SPM.9.csv' - Same data used for Figure SPM.9 (count of regions with increasing or decreasing changes in climatic impact-drivers). First row relates to darker purple bars, second row to lighter purple bars, third row refers to lighter brown bars and fourth row to darker brown bars.

Notes on reproducing the figure from the provided data

Link to the related record SPM.9 identical to panel b is provided in the Related Records section under Datasets.

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 (Technical Summary) - Link to the report component of the underlying figures from which this figure was generated (Figure SPM.9) - Link to the SPM.9 catalogue record at CEDA

This repository is linked to the following preprint:

• Prütz, R., Fuss, S., and Rogelj, J.: Imputation of missing IPCC AR6 data on land carbon sequestration, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-68, in review, 2024.

This repository includes:

• An imputation dataset for missing land carbon sequestation data of the AR6 Scenario Database
• Code to test, compare and visualize the performance of regression models to predict missing land removal data
• Code to compare and visualize available AR6 land removal data and existing AR6 data reanalyses

The following two datasets are required to replicate the analysis:

• Byers, E., Krey, V., Kriegler, E., Riahi, K., Schaeffer, R., Kikstra, J., Lamboll, R., Nicholls, Z., Sandstad, M., Smith, C., van der Wijst, K., Al -Khourdajie, A., Lecocq, F., Portugal-Pereira, J., Saheb, Y., Stromman, A., Winkler, H., Auer, C., Brutschin, E., … van Vuuren, D. (2022). AR6 Scenarios Database [Data set]. In Climate Change 2022: Mitigation of Climate Change (1.1). Intergovernmental Panel on Climate Change. https://doi.org/10.5281/zenodo.7197970
• Gidden, M., Gasser, T., Grassi, G., Forsell, N., Janssens, I., Lamb, W. F., Minx, J., Nicholls, Z., Steinhauser, J., & Riahi, K. (2023). Dataset for Gidden et.al. 2023 Updated AR6 Mitigation Benchmarks using National Emissions Inventories (Version v2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10158920

The variable imputation is based on the dataset by Byers et al. (2022). The dataset by Gidden et al. (2023) is used for variable comparison.

Input Data for Figure 7.21 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 7.21 shows emissions metrics for two short-lived greenhouse gases: HFC-32 and methane (CH4; lifetimes of 5.4 and 11.8 years).

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 4 subpanels, with data provided for panels a-d.

List of data provided

This dataset contains:

• Emissions metrics for HFC-32 and methane (CH4): (a) Temperature response to a step change in short-lived greenhouse gas emissions. (b) Temperature response to a pulse CO2 emission. (c) Conventional GTP metrics (pulse vs pulse). (d) Combined GTP metric (step versus pulse).

The temperature response function comes from Supplementary Material 7.SM.5.2. Values for non-CO2 species include the carbon cycle response (Section 7.6.1.3). Results for HFC-32 have been divided by 100 to show on the same scale.

Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

GTP stands for Global Temperature-change Potential.

Data provided in relation to figure

Data provided in relation to Figure 7.21:

• Data file: cgtp.csv

The data in this files is identical to the original data in .npz format. Link to the orginal data in this format used with the code for reproducing the figure is provided in the 'Related Documents' section.

Notes on reproducing the figure from the provided data

Data and figures are produced by the Jupyter Notebooks that live inside the notebooks directory of the Chapter 7 GitHub repository. The link to the input .npz file provided is used in the notebook to output figure 7.21. To reproduce the figure from the input data, you will need to run the notebook from the same directory as the input data and adjust the path to the data in box 3.

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 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 original data in .npz format used in the code - Link to the code for the figure, archived on Zenodo. - Link to the notebook on the Chapter 7 GitHub repository for plotting the figure

Based on submissions to the IPCC AR6 Scenarios Database, this datasets uses the metadata to construct histograms of the submitted scenarios by model family and project, noting the total submissions, vetted scenarios, and climate assessed scenarios. A total of 2304 scenarios were submitted to the global emissions database, of these, 618 did not passing vetting for sufficiently consistency with historical energy and emissions data, and a further 484 did not have sufficient data to perform a climate assessment, leaving a total of 1202 used in the primary assessment of scenarios. The database based on the scenario metadata. The ‘model family’ was determined by removing version numbers from the full model name. The ‘project family’ was obtained using the ‘Scenario family’ variable in metadata, supplemented by manually checking against cited literature. The classification of vetted scenarios was based on the variable ‘Historical vetting’ and the climate assessment on the ‘Climate Category’. This version is based on version 1.0 of the AR6 scenarios database.

Deprecation warning

This is no longer the latest release of the IPCC Assessment Report.

For the latest version please visit https://doi.org/10.5281/zenodo.5886911 and https://data.ece.iiasa.ac.at/ar6 for the AR6 Scenario Explorer where you can download the data set.

As part of the IPCC's Special Report on Global Warming of 1.5°C (SR15), an assessment of quantitative, model-based climate change mitigation pathways was conducted. To support the assessment, the Integrated Assessment Modeling Consortium (IAMC) facilitated a coordinated and systematic community effort by inviting modelling teams to submit their available 1.5°C and related scenarios to a curated database. The compilation and assessment of the scenario ensemble was conducted by authors of the IPCC SR15, and the resource is hosted by the International Institute for Applied Systems Analysis (IIASA) as part of a cooperation agreement with Working Group III of the IPCC. The scenario ensemble contains more than 400 emissions pathways with underlying socio-economic development, energy system transformations and land use change until the end of the century, submitted by over a dozen research teams from around the world. The criteria for submission included that the scenario is presented in a peer-reviewed journal accepted for publication no later than May 15, 2018, or published in a report determined by the IPCC to be eligible grey literature by the same date.

This release extends the original scenario ensemble with additional timeseries data on prices related to agiculture and food supply, which was used in the IPCC's Special Report on Climate Change and Land (SRCCL).

The data is available for download at the IAMC 1.5°C Scenario Explorer hosted by IIASA. The license permits use of the scenario ensemble for scientific research and science communication, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and legal code for more information.

This catalogue entry provides gridded data from global (CMIP5 and CMIP6) and regional (CORDEX) projections for the set of 22 variables and indices included in the IPCC Interactive Atlas, a novel contribution from Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6). These variables and indices are relevant for the climatic impact-drivers used in the regional assessments conducted in AR6 (Chapters 10, 11, 12 and Atlas), related to heat and cold, wet and dry, snow and ice, and wind. This dataset is particularly intended for Climate Data Store (CDS) users who want to develop customised products not directly available from the IPCC Interactive Atlas (e.g. regional information at national or subnational scales). This dataset includes gridded information with monthly/annual temporal resolution for historical experiments and climate projections based on Representative Concentration Pathways (RCP) / Shared Socioeconomic Pathways (SSP) scenarios for CMIP5/6 and CORDEX multi-model ensembles for the 22 variables and indices (computed from daily data). The ensembles are harmonised using regular grids with horizontal resolutions of 2° (CMIP5), 1° (CMIP6), 0.5° (CORDEX), and 0.25° (European CORDEX domain); details on the particular ensembles for each dataset are included in the documentation links. This dataset allows the reproduction, expansion and customisation of the climate change products displayed in the IPCC Interactive Atlas. This includes the global/continental maps of CMIP/CORDEX climate changes (for future periods across scenarios or for global warming levels, e.g. +2°C), and the regionally-aggregated time series, scatter plots, or global warming level plots. Related datasets, also available through the CDS, include the CMIP5/6 global climate projections and the CORDEX regional climate projections. The original CMIP and CORDEX data was produced by the institutions and modelling centres participating in these initiatives, as described in AR6 WGI Annex II, with partial support from different programmes, including support from Copernicus for some of the EURO-CORDEX runs and for data curation and publication of world-wide CORDEX datasets. As a result, the dataset is fully reproducible from the CDS for CORDEX, but not for CMIP (some models and versions are different in the CDS and the Atlas ensembles).
This dataset is distributed as part of the IPCC-DDC Atlas products under a Creative Commons Attribution 4.0 International License (CC-BY 4.0) and Copernicus has supported the standardisation and technical curation.

Data for Figure 9.22 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 9.22 shows simulated versus observed permafrost extent and volume change by warming level.

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: Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. 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. 1211–1362, doi:10.1017/9781009157896.011.

Figure subpanels

The figure has 2 subpanels, with data provided for both panels in one central directory.

List of data provided

This dataset contains:

• (a) Diagnosed Northern Hemisphere permafrost extent (area with perennially frozen ground at 15 m depth, or at the deepest model soil level if this is above 15 m) for 1979–1998, for available CMIP5 and CMIP6 models, from the first ensemble member of the historical coupled run, and for CMIP6 AMIP (atmosphere+land surface, prescribed ocean) and land-hist (land only, prescribed atmospheric forcing) runs.

• (b) Simulated global permafrost volume change between the surface and 3 m depth as a function of the simulated global surface air temperature (GSAT) change, from the first ensemble members of a selection of scenarios, for available CMIP6 models.

Estimates of current permafrost extents based on physical evidence and reanalyses are indicated as black symbols – triangle: Obu et al. (2018); star: Zhang et al. (1999); circle: central value and associated range from Gruber (2012).

Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)

Data provided in relation to figure

Data provided in relation to Figure 9.22

• Data file: pf15m_CMIP5historical_NH_1979-1998.txt
• Data file: pf15m_amip_NH_1979-1998.txt
• Data file: pf15m_historical_NH_1979-1998.txt
• Data file: pf15m_land-hist_NH_1979-1998.txt
• Data file: pfv_ACCESS-CM2_historical_ssp126.nc
• Data file: pfv_ACCESS-CM2_historical_ssp245.nc
• Data file: pfv_ACCESS-CM2_historical_ssp370.nc
• Data file: pfv_ACCESS-CM2_historical_ssp585.nc
• Data file: pfv_ACCESS-ESM1-5_historical_ssp126.nc
• Data file: pfv_ACCESS-ESM1-5_historical_ssp245.nc
• Data file: pfv_ACCESS-ESM1-5_historical_ssp370.nc
• Data file: pfv_ACCESS-ESM1-5_historical_ssp585.nc
• Data file: pfv_BCC-CSM2-MR_historical_ssp126.nc
• Data file: pfv_BCC-CSM2-MR_historical_ssp245.nc
• Data file: pfv_BCC-CSM2-MR_historical_ssp370.nc
• Data file: pfv_BCC-CSM2-MR_historical_ssp585.nc
• Data file: pfv_CAMS-CSM1-0_historical_ssp126.nc
• Data file: pfv_CAMS-CSM1-0_historical_ssp245.nc
• Data file: pfv_CAMS-CSM1-0_historical_ssp370.nc
• Data file: pfv_CAMS-CSM1-0_historical_ssp585.nc
• Data file: pfv_CESM2-WACCM_historical_ssp126.nc
• Data file: pfv_CESM2-WACCM_historical_ssp245.nc
• Data file: pfv_CESM2-WACCM_historical_ssp370.nc
• Data file: pfv_CESM2-WACCM_historical_ssp585.nc
• Data file: pfv_CESM2_historical_ssp126.nc
• Data file: pfv_CESM2_historical_ssp245.nc
• Data file: pfv_CESM2_historical_ssp370.nc
• Data file: pfv_CESM2_historical_ssp585.nc
• Data file: pfv_CNRM-CM6-1-HR_historical_ssp126.nc
• Data file: pfv_CNRM-CM6-1-HR_historical_ssp245.nc
• Data file: pfv_CNRM-CM6-1-HR_historical_ssp370.nc
• Data file: pfv_CNRM-CM6-1-HR_historical_ssp585.nc
• Data file: pfv_CNRM-CM6-1_historical_ssp126.nc
• Data file: pfv_CNRM-CM6-1_historical_ssp245.nc
• Data file: pfv_CNRM-CM6-1_historical_ssp370.nc
• Data file: pfv_CNRM-CM6-1_historical_ssp585.nc
• Data file: pfv_CNRM-ESM2-1_historical_ssp126.nc
• Data file: pfv_CNRM-ESM2-1_historical_ssp245.nc
• Data file: pfv_CNRM-ESM2-1... For full abstract see: https://catalogue.ceda.ac.uk/uuid/503edf9eb68040c4a439fed88b81c8c9.

The record contains information on input dataset usage and citation information from the WCRP Coupled Model Intercomparison Project Phase 6 (CMIP6) for the generation of figure 9.11 of the Sixth Assessment Report of Working Group I of the Intergovernmental Panel on Climate Change (IPCC AR6 WGI). CMIP6 data that were used as input to this figure follow the Data Reference Syntax. Persistent Identifiers (PIDs) for data identification (provenance) are available for individual datasets as Handle IDs. DOIs for data citation are available on the following dataset collections: CMIP6 DOIs contain all datasets provided by an Earth System model to a CMIP6 experiment. The data subsets of CMIP6 dataset collections used in the IPCC AR6 WGI have DOIs at this granularity of a model contribution to an experiment. The AR6 subset DOIs are included because they reside in a long-term preservation archive hosted by the IPCC Data Distribution Centre (DDC).

The uploaded human-readable csv file provides a list of datasets/Handle IDs used for the generation of this figure grouped by the corresponding dataset collections they belong to. The machine-actionable json-ld file describes the record as Complex Citation Object (see Agarwal et al., 2024, https://doi.org/10.5281/zenodo.14106602) in a preliminary standard, using the following vocabulary to specify the different functions of the contained Linked Digital Objects: Credit (input dataset collection to be cited/to which credit is to be assigned), Provenance (dataset contributing information to the generation of the DataProduct), archivedAt (dataset collection with long-term preservation), DigitalDocument (referenced papers containing further information), DataProduct (generated final dataset), ImageObject (visualization of the DataProduct), SoftwareSourceCode (software applied to generate the DataProduct), Chapter (IPCC AR6 WGI chapter in which the DataProduct is used/referenced).

Use and cite this record when referring to the generation of IPCC WGI AR6 figure 9.11.

[ Derived from parent entry - See data hierarchy tab ]

These data sets contain the assessed Global Surface Air Temperature (GSAT) projections and all input data and instructions necessary to reproduce the assessed GSAT projections in the Intergovernmental Panel on Climate Change Sixth Assessment Report (Figure 4.11, IPCC AR6 WGI). The constrained CMIP6 projections are based on the methods from three publications calculating the global mean near surface air temperature relative to the average over the period 1995–2014. They are described in box 4.1. The Effective Radiative Forcing (ERF) time series are reproduced from IPCC WGI chapter 7 and included to facilitate reproduction of the analysis. They are uncoupled to any GSAT change. ERF quantifies the energy gained or lost by the earth system following an imposed perturbation (for instance in green house gases, aerosols or solar irradiance). As such it is a fundamental driver of changes in the earth’s TOA energy budget. ERF is determined by the change in the net downward radiative flux at the top of atmosphere (box 7.1) after the system has adjusted to the perturbation but excluding the radiative response to changes in surface temperature (Figure 7.3, IPCC AR6 WGI). For a detailed description of the ERF time series, please refer to chapter 7 (https://github.com/IPCC-WG1/Chapter-7).

Disclaimer: The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

Required Acknowledgements and Citation: In order to document the impact of assessed Global Surface Air Temperature, users of the data are obligated to cite chapter 4 of WGI contribution to the IPCC AR6.

The record contains information on input dataset usage and citation information from the WCRP Coupled Model Intercomparison Project Phase 6 (CMIP6) for the generation of figure 6.SM.4 of the Sixth Assessment Report of Working Group I of the Intergovernmental Panel on Climate Change (IPCC AR6 WGI). CMIP6 data that were used as input to this figure follow the Data Reference Syntax. Persistent Identifiers (PIDs) for data identification (provenance) are available for individual datasets as Handle IDs. DOIs for data citation are available on the following dataset collections: CMIP6 DOIs contain all datasets provided by an Earth System model to a CMIP6 experiment. The data subsets of CMIP6 dataset collections used in the IPCC AR6 WGI have DOIs at this granularity of a model contribution to an experiment. The AR6 subset DOIs are included because they reside in a long-term preservation archive hosted by the IPCC Data Distribution Centre (DDC).

The uploaded human-readable csv file provides a list of datasets/Handle IDs used for the generation of this figure grouped by the corresponding dataset collections they belong to. The machine-actionable json-ld file describes the record as Complex Citation Object (see Agarwal et al., 2024, https://doi.org/10.5281/zenodo.14106602) in a preliminary standard, using the following vocabulary to specify the different functions of the contained Linked Digital Objects: Credit (input dataset collection to be cited/to which credit is to be assigned), Provenance (dataset contributing information to the generation of the DataProduct), archivedAt (dataset collection with long-term preservation), DigitalDocument (referenced papers containing further information), DataProduct (generated final dataset), ImageObject (visualization of the DataProduct), SoftwareSourceCode (software applied to generate the DataProduct), Chapter (IPCC AR6 WGI chapter in which the DataProduct is used/referenced).

Use and cite this record when referring to the generation of IPCC WGI AR6 figure 6.SM.4.

Regard attached PDF (FACTS_confidence_output_file_readme.pdf) for instructions, how to use offered datasets. Be aware of also attached locations_list.lst file: It specifies, how to cross-reference location IDs with names of the locations (Cite as Kopp, Robert E. (2021). Location List for IPCC AR6 Sea Level Projections (Version 20210809) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6382548).

In addition see the IPCC AR6 Chapter 9 Figures repository, which contains the code and data for all figures from Chapter 9 of the Sixth Assesment Report from the Intergovernmental Panel on Climate Change (IPCC AR6): https://github.com/IPCC-WG1/Chapter-9.

If there is a need to regenerate the sea level rise projections from primary inputs, outputs from the chapter 7 two-layer model emulator have to use to drive all the surface temperature-sensitive parts of the sea level projections and to project thermal expansion (see Github repository https://github.com/chrisroadmap/ar6). The specific notebook that generates this data is https://github.com/chrisroadmap/ar6/blob/main/notebooks/245_chapter9_projections_SSPs.ipynb Used outputs are available via https://www.wdc-climate.de/ui/entry?acronym=IPCC-DDC_AR6_Sup_SLPr_Supp.

The record contains information on input dataset usage and citation information from the WCRP Coupled Model Intercomparison Project Phase 6 (CMIP6) for the generation of figure 11.A.1 of the Sixth Assessment Report of Working Group I of the Intergovernmental Panel on Climate Change (IPCC AR6 WGI). CMIP6 data that were used as input to this figure follow the Data Reference Syntax. Persistent Identifiers (PIDs) for data identification (provenance) are available for individual datasets as Handle IDs. DOIs for data citation are available on the following dataset collections: CMIP6 DOIs contain all datasets provided by an Earth System model to a CMIP6 experiment. The data subsets of CMIP6 dataset collections used in the IPCC AR6 WGI have DOIs at this granularity of a model contribution to an experiment. The AR6 subset DOIs are included because they reside in a long-term preservation archive hosted by the IPCC Data Distribution Centre (DDC).

The uploaded human-readable csv file provides a list of datasets/Handle IDs used for the generation of this figure grouped by the corresponding dataset collections they belong to. The machine-actionable json-ld file describes the record as Complex Citation Object (see Agarwal et al., 2024, https://doi.org/10.5281/zenodo.14106602) in a preliminary standard, using the following vocabulary to specify the different functions of the contained Linked Digital Objects: Credit (input dataset collection to be cited/to which credit is to be assigned), Provenance (dataset contributing information to the generation of the DataProduct), archivedAt (dataset collection with long-term preservation), DigitalDocument (referenced papers containing further information), DataProduct (generated final dataset), ImageObject (visualization of the DataProduct), SoftwareSourceCode (software applied to generate the DataProduct), Chapter (IPCC AR6 WGI chapter in which the DataProduct is used/referenced).

Use and cite this record when referring to the generation of IPCC WGI AR6 figure 11.A.1.

Data for Figure 3.22 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 3.22 shows time series of Northern Hemisphere March-April mean snow cover extent (SCE) from observations, CMIP5 and CMIP6 simulations.

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

There are technically two panels top and bottom (CMIP5 and CMIP6), however, the data is stored in the parent directory.

List of data provided

The data is for the Northern Hemisphere snow cover extent anomalies (SCEA) from models and observations:

• The SCEA observational data from GLDAS-NOAH (1948-2012), Brown-NOAA (1923-2017), Mudryk et al 2020 (1968-2017)
• The SCEA modelled by CMIP5 historical-rcp45 experiment (1923-2017)
• The SCEA modelled by CMIP5 historicalNat experiment (1923-2012)
• The SCEA modelled by CMIP6 historical-ssp245 experiment (1923-2017)
• The SCEA modelled by CMIP6 hist-nat experiment (1923-2017)
• The SCEA modelled by CMIP5 and CMIP6 piControl experiments

Data provided in relation to figure

snow_cover_extent_cmip5_obs.csv is the data for the green and brown lines and shadings in the upper panel and grey lines (1923-2017) snow_cover_extent_cmip6_obs.csv is the data for the green and brown lines and shadings in the lower panel and grey lines (1923-2017) snow_cover_extent_piControl.csv for the blue error bars in the both panels Additional details of data provided in relation to figure in the file header (BADC-CSV file)

CMIP5 is the fifth phase of the Coupled Model Intercomparison Project. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.

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.

[ Derived from parent entry - See data hierarchy tab ]

These data include a subset of the CMIP6 input data assessed by the IPCC AR6 WGI authors for region South Pole (180°W - 180°E, 90°S - 57°S). Included is the monthly mean air temperature in 2m height (tas) data of the experiments historical, ssp126, ssp245, ssp370, and ssp585 for models providing more than 5 out of 12 core variables.

Further details are provided in the additional information "IPCC AR6 Data for Regions".