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22 results for “AR6”
AR6 WG1 Plots and Processing
<p>Repository reproducing plots and processing used in AR6 WG1 made by Zebedee Nicholls, Malte Meinshausen and Jared Lewis.<br> <br> For questions and comments, please contact Zebedee Nicholls (zebedee.nicholls@climate-energy-college.org), Jared Lewis (jared.lewis@climate-resource.com) and Malte Meinshausen (malte.meinshausen@unimelb.edu.au). For full details, please see https://gitlab.com/magicc/ar6-wg1-plots-and-processing.</p>
Reliquary of contacts for: A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations
<p>Photos and metadadata pannels of a "Reliquary of contacts for: A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations" produced to support the "A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations" presentation given at EGU 2024.</p> <p>------</p> <p>With ever growing abilities to process greater volumes of data the abiiity to sustain the citability and tracability of the underluing source data within outputs such as publications is becoming increasingly challenging. With a range of use-cases, work on how to handle complex citations from the perspective of those producing outputs, journals and those handling the knowledge graph and associated services, is exmaning a how to handle these situations in a sustainable and manageable fashion.<br><br>At the European Geophysical Union (EGU) General Assembly in Vienna, 2024, a pragmatic solution using Zenodo to store 'reliquary' objects was presented. The poster presentation demonstrated the use of existing strucutres within a Zenodo object to address the complex citation use-case around figure, the related data and the source datasets related to the IPCC's AR5 figure data. I.e. how to utulise the existing constructs of a Zenodo item and the range of available metadata fields to give an off-the-shelf solution to allow tracability to the specific datasets used (via their Handle identifiers) and citability of the higher level, DOI-ed dataset collections within which the specific Handle-ed datasets were selected from. Additionally, the connectivity between these two levels of PID objects was also captured within the stored files around which the rich metata was captured.<br><br>The concept of a complex citation 'reliquary' as a metadtata rich object, acting as a referencable nexus in the knowledge graph has been put forth as a solution to the complex citation challenge. It borrows the concept from its historical use, denoting a container or shrine, often richly embellished, for sacred relics (e.g. saints bones, artefacts etc). In the same way here we have both the rich metadata 'container' around the specific details (the 'bones in the box', with their preserved connectivity).<br><br>However, the term 'reliquary' is often a hard one to convey, being somewhat of an obscure term (likewise the term 'nexus' may also be one lacking wider recogniton). Thus, to aid the discussions around the presentation by Pascoe et al. (2024) at the EGU 2023 General Assembly, a physical representation of a metadata reliquary object was produced.<br><br>The purpose of this object was two fold:<br><br> - The first was to show how the reliquary container itself is metadata rich, detailing through the use of ORCIDS, RORs and a DOI, references to external items, complemented by further metadata concerning the specifics of the reliquary's own metadata (its title and the credit for the artist that created it). Futher more, the relationship between the reliquary and those referenced parties/objects was also captured. The contents were also used to demonstrate the importance of making the contents useful for onward users (in this case contact details on business cards). <br> - The second, and for the funder of this piece, arguably the most important aspect was a degree of outreach this provided, both to engage the audience of Pascoe et al (2024), and directly to the artist to demonstrate the importance of this work to the international research data management community and overall to aid engagemeng with the funder's work.<br><br>This resource is provided here as a repository of images of the reliquary itself and in context at the EGU 2024 event as a potential resource others may use to aid further discussions around the use of reliquaries with regards to complex citations. The slides provided of the reliquary box labels are also provided with some annotation to further expand on the metadata aspects of their content.</p>
IPCC Working Group 1 (WG1) Sixth Assessment Report (AR6) Annex III Extended Data
<p>Extended data relating to atmospheric abundences and effective radiative forcing from historical and future projections. Data is presented in abridged form in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) Working Group 1 (WG1) Annex 3. </p> <p>In this dataset, data is provided for all years, and includes additional scenarios not included in the published tables.</p> <p><strong>Contents</strong></p> <ul> <li>table A3.1: historical observed greenhouse gas (GHG) abundances. All subtables a-f in the printed report are combined into one CSV file.</li> <li>table A3.2: future projections (2020-2500) of GHG abundances for nine SSP scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP3-7.0-lowNTCF, SSP4-3.4, SSP4-6.0, SSP5-3.4-over, SSP5-8.5). Orignal data is from Meinshausen et al. (2020): https://doi.org/10.5194/gmd-2019-222 </li> <li>table A3.3: historical effective radiative forcing (ERF) for 1750-2019 (unit is W m<sup>-2</sup>) <ul> <li>best estimate</li> <li>5th percentile</li> <li>95th percentile</li> <li>100000 member Monte Carlo ensemble (HDF file)</li> </ul> </li> <li>table A3.4: future projections of ERF from 1750-2500 (including historical to 2014, projections starting from 2015). Unit is W m<sup>-2</sup>. <ul> <li>table A3.4a: SSP1-1.9 (best estimate, 5th and 95th percentile)</li> <li>table A3.4b: SSP1-2.6 (best estimate, 5th and 95th percentile)</li> <li>table A3.4c: SSP2-4.5 (best estimate, 5th and 95th percentile)</li> <li>table A3.4d: SSP3-7.0 (best estimate, 5th and 95th percentile)</li> <li>table A3.4e: SSP5-8.5 (best estimate, 5th and 95th percentile)</li> <li>table A3.4f: breakdown of minor greenhouse gases, and aggregated categories, for the five Tier 1 SSP scenarios in tables A3.4a to A3.4e (best estimate)</li> <li>tables A3.4x: tables A3.4a to A3.4f for Tier 2 SSP scenarios: <ul> <li>SSP3-7.0-lowNTCF</li> <li>SSP3-7.0-lowNTCFCH4</li> <li>SSP4-3.4</li> <li>SSP4-6.0</li> <li>SSP5-3.4-over</li> </ul> </li> </ul> </li> <li>table A3.5: projections of ERF from 1750-2500 from RCP2.6, RCP4.5, RCP6.0 and RCP8.5 using AR6 assessment (best estimate, 5th and 95th percentile, breakdown of minor gases; unit is W m<sup>-2</sup>)</li> </ul> <p><strong>Citation</strong></p> <p>IPCC, 2021: Annex III: Tables of historical and projected well-mixed greenhouse gas mixing ratios and effective radiative forcing of all climate forcers [Dentener F.J., B. Hall, C. Smith (eds.)]. In <em>Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change</em> [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.</p>
IPCC AR6 Relative Sea Level Projection P-Boxes
<p><strong>Description</strong></p> <p>This data set contains detailed elements of the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections for all of the p-boxes described in AR6 WG1 9.6.3 (under ar6-regional-pboxes.zip), as well as a variant excluding the AR6 estimates of background sea level change (under ar6-regional_novlm-pboxes.zip).</p> <p>Most users will not want this dataset, but rather the dataset at https://doi.org/10.5281/zenodo.5914709. 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.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>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, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). 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.</p> <p> </p>
IPCC AR6 Relative Sea Level Projection Distributions
<p><strong>Description</strong></p> <p>This data set contains detailed elements the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projection distributions for all the workflows described in AR6 WG1 9.6.3.2, as well as distributions for the components contributing to relative sea level change.</p> <p>Most users will not want this dataset, but rather the dataset at https://doi.org/10.5281/zenodo.5914709. 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.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>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, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). 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.</p> <p> </p>
IPCC AR6 Relative Sea Level Projections without Background Component
<p><strong>Description</strong></p> <p>This data set contains detailed elements the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WG1 9.6.3.2, as well as p-boxes derived from these distributions.</p> <p>Most users will not want this dataset, but rather the dataset at https://doi.org/10.5281/zenodo.5914709. These data may be of use for users who want to substitute their own estimates of the background term. 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.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>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, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). 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.</p>
IPCC AR6 Sea Level Projections
<p><strong>Description</strong></p> <p>This data set contains the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. It contains the full set of samples for the global projections (under ar6.zip), as well as summary relative sea level projections (under ar6-regional-confidence.zip and, without the AR6 estimate of background sea level process rates, ar6-regional_novlm-confidence.zip). Most users will want to focus on the confidence_output_files, which correspond most directly to the figures and tables in the report. For the global projections, samples from the individual probability distributions described in AR6 WG1 9.6.3 are in the full_sample* directories.</p> <p>Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at <a href="https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool">https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool</a>.</p> <p>See <a href="../communities/ipcc-ar6-sea-level-projections">https://zenodo.org/communities/ipcc-ar6-sea-level-projections</a> for additional related data sets.</p> <p>See <a href="https://github.com/Rutgers-ESSP/IPCC-AR6-Sea-Level-Projections">https://github.com/Rutgers-ESSP/IPCC-AR6-Sea-Level-Projections</a> for a guide to available resources.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>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, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea Level Change Team for developing and hosting the IPCC AR6 Sea Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). 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.</p>
Location List for IPCC AR6 Sea Level Projections
<p>This data set contains the location list file for the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. It can be used to cross-reference location IDs with names of the locations.</p> <p>Column 1 – Location name (string with spaces having been replaced with underscores)<br> Column 2 – Location ID (integer value)<br> Column 3 – Latitude (-90 to 90 degrees)<br> Column 4 – Longitude (-180 to 180 degrees)</p> <p>See <a href="https://zenodo.org/communities/ipcc-ar6-sea-level-projections">https://zenodo.org/communities/ipcc-ar6-sea-level-projections</a> for additional related data sets.</p>
Imputation of missing land carbon sequestration data in the AR6 Scenarios Database
<p>This repository is linked to the following research paper:</p> <ul> <li>Prütz, R., Fuss, S., and Rogelj, J.: Imputation of missing land carbon sequestration data in the AR6 Scenarios Database, Earth Syst. Sci. Data, 2025. <a href="https://doi.org/10.5194/essd-17-221-2025">https://doi.org/10.5194/essd-17-221-2025</a> </li> </ul> <p>This repository includes: </p> <ul> <li>An imputation dataset for missing land carbon sequestation data of the AR6 Scenarios Database for global scenarios and R10 scenario variants</li> <li>Code to test, compare and visualize the performance of regression models to predict missing land removal data</li> <li>Code to compare and visualize available AR6 land removal data and existing AR6 data reanalyses</li> </ul> <p>The following two datasets are required to replicate the analysis:</p> <ul> <li>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. <a href="https://doi.org/10.5281/zenodo.7197970">https://doi.org/10.5281/zenodo.7197970</a></li> <li>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. <a href="https://doi.org/10.5281/zenodo.10158920">https://doi.org/10.5281/zenodo.10158920</a></li> </ul> <p>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. </p>
MAGICC7 SSP carbon cycle output using AR6 tuning
<p>Output from the MAGICC7 carbon cycle model under the SSP scenarios with the configuration used in IPCC AR6. For further details on MAGICC and expectations re use of data and the model, please see https://magicc.org/download/magicc7.</p>
Dataset corresponding to the « Reasons for concern » about climate change impacts from all IPCC reports (TAR to AR6)
<p>This data corresponds to all the "burning embers" diagrams for the "Reasons for Concern" published in IPCC reports (and the related paper Smith et al. 2009 for AR4) until AR6 (thus including TAR, AR4, AR5, SR1.5 and AR6). For TAR to SR1.5, the data is the result of extracting information from the original figures, as presented in the related technical document <a href="https://doi.org/10.5281/zenodo.3992856">10.5281/zenodo.3992856</a>. As also explained in the Supplementary Information of Zommers et al. (2020), the data does not come directly from the IPCC, although it is based on the assessment provided in the IPCC reports listed in the references. For IPCC AR6, the source is the supplementary material of chapter 16. Details regarding specific values provided in the dataset are explained alongside the values in the main file: "RFCs-ALL-2023_05_12.xlsx". </p> <p>The main file includes the parameters needed to produce a diagram that supplements figure 3 from Zommers et al. (2020) with AR6 data and the confidence levels from previous reports when available. The Excel files in RFCs-2023-UsageExamples.zip contain the same data with different parameters, so that uploading these files to the Ember Factory (<a href="https://climrisk.org/emberfactory">https://climrisk.org/emberfactory</a>) produces different figures - including a comparison between AR5 and AR6 (as in IPCC AR6 Synthesis Report, but with AR5 confidence levels included). The resulting diagrams are also provided.</p>
NPJ Climate Action AR6 scenarios database submission histograms by model family and project
<p>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. </p> <p>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’.</p> <p>This version is based on version 1.0 of the AR6 scenarios database.</p>
R10 region mask based on IPCC AR6 WG3 and ISIMIP
<p>region_classification.tsv: Tab-separated value file of ISO3 code, country name and the R10 mapping used.</p> <p>r10masks_fractional.nc: 0.5° grid of fractions of grid cell that is part of one of 10 world regions as defined by IPCC Working Group 3.</p> <p>The "region" variable is a dimension (11, 360, 720) variable. The first axis is the fraction (0-1 scale) of the grid cell at latitude and longitude (defined by second and third axis) falling into each world region.</p> <p>Values of first axis correspond to following regions defined in the country mappings file:</p> <ul> <li>0=South-East Asia and developing Pacific</li> <li>1=Eurasia</li> <li>2=Asia-Pacific</li> <li>3=Africa</li> <li>4=Middle East</li> <li>5=Latin America and Caribbean</li> <li>6=North America</li> <li>7=Eastern Asia</li> <li>8=Southern Asia</li> <li>9=Europe</li> <li>10=World (all land, excluding Antarctica, and the sum of fractions in 0-9).</li> </ul> <p>Oceans and inland lakes are not counted within countries/regions.</p> <p>The starting point for this data is the 0.5° country mask from Perrette (2023).</p> <p>Country mappings in the TSV file follow the prescription of IPCC Working Group 3 Annex II (Al Khourdajie et al. 2022) where possible. Some ambiguities exist with regards to post-colonial, geographically detached, and disputed territories which are not explicitly defined in Annex II. These have been grouped by geographic rather than political region (example: French Guiana is classified as Latin America & Caribbean, which it is most definitely geographically part of, rather than Europe, which it is politically part of).</p> <p> </p> <p>Perette, 2023: ISI-MIP/isipedia-countries (v2.6). GitHub repository. <a href="https://github.com/ISI-MIP/isipedia-countries/releases/tag/v2.6">https://github.com/ISI-MIP/isipedia-countries</a><a href="https://github.com/ISI-MIP/isipedia-countries/releases/tag/v2.6">/releases/tag/v2.6</a></p> <p>Al Khourdajie et al., 2022: Annex II: Definitions, Units and Conventions [Al Khourdajie, A., R. van Diemen, W.F. Lamb, M. Pathak, A. Reisinger, S. de la Rue du Can, J. Skea, R. Slade, S. Some, L. Steg (eds)]. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi: 10.1017/9781009157926.021</p>
Dataset for Gidden et.al. 2023 Updated AR6 Mitigation Benchmarks using National Emissions Inventories
<p>Scenario variables related to LULUCF emissions and removals in IPCC-assessed scenarios from AR6 calculated using OSCAR. Both direct fluxes, corresponding to model-reporting conventions, and indirect fluxes, which constitute an alignment factor to national inventories, are provided. See the original publication for more details (<a href="https://www.nature.com/articles/s41586-023-06724-y">https://www.nature.com/articles/s41586-023-06724-y</a>).</p><h4>Change log from version 1</h4><ul><li><i>AR6 Reanalysis|OSCARv3.2|Emissions|CO2|AFOLU</i> and <i>AR6 Reanalysis|OSCARv3.2|Carbon Removal|Land</i> are removed, users should explicitly calculate if needed but otherwise use direct and indirect fluxes.</li><li><i>AR6 Reanalysis|OSCARv3.2|Carbon Removal</i> is now calculated only using the direct flux component (i.e., <i>AR6 Reanalysis|OSCARv3.2|Carbon Removal|Land|Direct</i>)</li></ul>
Global temperature time series from IPCC AR6
<p>Annual global mean temperature time series used in IPCC Sixth Assessment Report (AR6). Includes both consolidated mulit-dataset mean and individual component data sets. Further documentation is available in AR6 (Working Group I, section 2.3.1). </p>
AR6 WG3 Plots and Processing
<p>Repository reproducing plots and processing used in AR6 WG3 made by Zebedee Nicholls, Malte Meinshausen and Jared Lewis.<br> <br> For questions and comments, please contact Zebedee Nicholls (zebedee.nicholls@climate-energy-college.org), Jared Lewis (jared.lewis@climate-resource.com) and Malte Meinshausen (malte.meinshausen@unimelb.edu.au). For full details, please see https://gitlab.com/magicc/ar6-wg3-plots-and-processing.</p>
IPCC AR6 Sea Level Milestones
<p><strong>Description</strong></p> <p>This data set contains elements of the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains files indicating the likelihood of when sea level milestones are crossed over time. For global mean sea level, it includes probability distributions for all the workflows described in AR6 WG1 9.6.3.2, as well as p-boxes derived from these distributions; for regional projections, with and without vertical land motion, it includes the p-boxes associated corresponding to those shown in the milestone excedance timing figures.</p> <p>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.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>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, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). 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.</p>
IPCC-AR6-SPM material + info on reference regions
<p>### Contents</p> <p>Included material is from sources indicated below.</p> <p>- `spm/` from **[1]**<br> - `reference-regions/` from **[2]** except for `hexagon_grid_locations.csv`<br> - `reference-regions/hexagon_grid_locations.csv` from **[3]**</p> <p>### [1] source of the data in `spm/`</p> <p>```<br> IPCC, 2021: Summary for Policymakers. 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. In Press.<br> ```</p> <p>```<br> Gillett, N.P.; Malinina, E.; Kaufman, D.; Neukom, R. (2021): Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.1 (v20210809). NERC EDS Centre for Environmental Data Analysis, 09 August 2021. doi:10.5285/76cad0b4f6f141ada1c44a4ce9e7d4bd. http://dx.doi.org/10.5285/76cad0b4f6f141ada1c44a4ce9e7d4bd<br> ```</p> <p>### [2] source of the data in csv files</p> <p>```<br> Iturbide, Maialen, Fernández, Jesús, Gutiérrez, José Manuel, Bedia, Joaquín, Cimadevilla, Ezequiel, Díez-Sierra, Javier, Manzanas, Rodrigo, Casanueva, Ana, Baño-Medina, Jorge, Milovac, Josipa, Herrera, Sixto, Cofiño, Antonio S., San Martín, Daniel, García-Díez, Markel, Hauser, Mathias, Huard, David, & Yelekci, Özge. (2021). Repository supporting the implementation of FAIR principles in the IPCC-WGI Atlas (v2.0-final). Zenodo. https://doi.org/10.5281/zenodo.5171760<br> ```</p> <p>### [3] source of the data in csv files</p> <p>```<br> Gael Forget, Lauren Milechin, & Philippe Roy. (2021). JuliaClimate/GlobalOceanNotebooks: new webpage, docker+sysimage+Pluto, add IPCC+other notebooks, cleanup (v0.3.5). Zenodo. https://doi.org/10.5281/zenodo.5537709<br> ```</p>
Relative sea-level projections for Norway based on IPCC AR6
<p>This dataset contains IPCC AR6 relative sea-level projections tailored to Norway and published in the Norwegian Centre for Climate Services report by Simpson et al. (2024).</p> <p>The projections has been produced by taking the IPCC AR6 relative sea-level projections without the background vertical land motion (VLM) component (Kopp, 2021), which have then been combined with the semi-empirical NKG2016LU VLM model (Vestøl et al., 2019). See Simpson et al. (2024) for more details.</p> <p><strong>References</strong></p> <p>Kopp, R. E. (2021). IPCC AR6 Relative Sea Level Projections without Background Component (Version 20210809) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5967269</p> <p>Vestøl, O., Ågren, J., Steffen, H., Kierulf, H., & Tarasov, L. (2019). NKG2016LU: A new land uplift model for Fennoscandia and the Baltic Region. <em>Journal of Geodesy</em>, <em>93</em>(9), 1759–1779. <a href="https://doi.org/10.1007/s00190-019-01280-8">https://doi.org/10.1007/s00190-019-01280-8</a></p> <p>Simpson, M.J.R., Bonaduce, A., Borck, H.S., Breili, K., Breivik, Ø., Ravndal, O.R., Richter, K., 2024. Sea-Level Rise and Extremes in Norway: Observations and Projections Based on IPCC AR6. Norwegian Centre for Climate Services report 1/2024, ISSN 2704-1018, Oslo, Norway.</p>
Analysis of references in the IPCC AR6 WG2 Report of 2022
<p>This repository contains data on 17,419 DOIs cited in the <a href="https://www.ipcc.ch/report/ar6/wg2/">IPCC Working Group 2 contribution to the Sixth Assessment Report</a>, and the code to link them to the dataset built at the Curtin Open Knowledge Initiative (COKI).</p> <p>References were extracted from the report's PDFs (downloaded 2022-03-01) via <a href="https://www.scholarcy.com/">Scholarcy</a> and exported as RIS and BibTeX files. DOI strings were identified from RIS files by pattern matching and saved as CSV file. The list of DOIs for each chapter and cross chapter paper was processed using a custom Python script to generate a pandas DataFrame which was saved as CSV file and uploaded to Google Big Query.</p> <p>We used the main object table of the Academic Observatory, which combines information from Crossref, Unpaywall, Microsoft Academic, Open Citations, the Research Organization Registry and Geonames to enrich the DOIs with bibliographic information, affiliations, and open access status. A custom query was used to join and format the data and the resulting table was visualised in a Google DataStudio dashboard.<br> <br> This version of the repository also includes the set of DOIs from references in the <a href="https://www.ipcc.ch/report/ar6/wg1/">IPCC Working Group 1 contribution to the Sixth Assessment Report</a> as extracted by Alexis-Michel Mugabushaka and shared on Zenodo: <a href="https://doi.org/10.5281/zenodo.5475442">https://doi.org/10.5281/zenodo.5475442</a> (CC-BY)</p> <p>A brief descriptive analysis was provided as a <a href="https://openknowledge.community/tracking-climate-change-openaccess/">blogpost on the COKI website</a>.</p> <p><strong>The repository contains the following content:</strong></p> <p>Data:</p> <ul> <li><strong>data/scholarcy/RIS/</strong> - extracted references as RIS files</li> <li><strong>data/scholarcy/BibTeX/</strong> - extracted references as BibTeX files</li> <li><strong>IPCC_AR6_WGII_dois.csv</strong> - list of DOIs</li> <li><strong>data/10.5281_zenodo.5475442/</strong> - references from IPCC AR6 WG1 report</li> </ul> <p>Processing:</p> <ul> <li><strong>preprocessing.R</strong> - preprocessing steps for identifying and cleaning DOIs</li> <li><strong>process.py</strong> - Python script for transforming data and linking to COKI data through Google Big Query</li> </ul> <p>Outcomes:</p> <ul> <li><a href="https://console.cloud.google.com/bigquery?project=utrecht-university&ws=!1m23!1m3!8m2!1s145441926252!2sd59dfac7972a45f8a2f5ee4ac866c34d!1m4!4m3!1sacademic-observatory!2sobservatory!3sdoi20220226!1m4!4m3!1sutrecht-university!2sipcc_ar6!3sdoi_table!1m3!3m2!1sutrecht-university!2sipcc_ar6!1m4!4m3!1sutrecht-university!2sipcc_ar6!3sipcc_ar6_dois&d=ipcc_ar6&p=utrecht-university&page=table&t=doi_table&pli=1&authuser=1">Dataset on BigQuery</a> - requires a google account for access and bigquery account for querying</li> <li><a href="https://datastudio.google.com/s/vZN2zLr9wS4">Data Studio Dashboard</a> - interactive analysis of the generated data</li> <li><a href="https://www.zotero.org/groups/4614109">Zotero library</a> of references extracted via Scholarcy</li> <li><strong>PDF version of blogpost</strong></li> </ul> <p><strong>Note on licenses:</strong> <br> Data are made available under <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> (with the exception of WG1 reference data, which have been shared under <a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY 4.0</a>)<br> Code is made available under <a href="http://www.apache.org/licenses/">Apache License 2.0</a></p>
ScienceDex guides
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OpenNeuro
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