Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Reversal of the impact chain for actionable climate information

Nature Geoscience volume 18, pages 10–19 (2025)Cite this article

Subjects

Abstract

Escalating impacts of climate change underscore the risks posed by crossing potentially irreversible Earth and socioecological system thresholds and adaptation limits. However, limitations in the provision of actionable climate information may hinder an anticipatory response. Here we suggest a reversal of the traditional impact chain methodology as an end-user focused approach linking specific climate risk thresholds, including at the local level, to emissions pathways. We outline the socioeconomic and value judgement dimensions that can inform the identification of such risk thresholds. The applicability of the approach is highlighted by three examples that estimate the required CO2 emissions constraints to avoid critical levels of health-related heat risks in Berlin, fire weather in Portugal and glacier mass loss in High Mountain Asia. We argue that linking risk threshold exceedance directly to global emissions benchmarks can aid the understanding of the benefits of stringent emissions reductions for societies and local decision-makers.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic of the impact chain from anthropogenic forcing through global climate and CIDs towards socioecological impacts.
Fig. 2
Fig. 3: Increased frequency of hot days with expected important health impacts in Berlin.

Similar content being viewed by others

Data availability

The CMIP6 simulations used in this study are available through the ESGF data portal at https://esgf-node.llnl.gov/projects/cmip6/. The ERA5 reanalysis is available through the Copernicus climate data store at https://cds.climate.copernicus.eu/datasets. Glacier simulations can be downloaded from https://zenodo.org/records/10908278 (ref. 85), https://zenodo.org/records/8286065 (ref. 86) and https://nsidc.org/data/hma2_ggp/versions/1.

Code availability

The code to reproduce the example for heat extremes in Berlin (Fig. 3) and the two other examples in Table 2 can be found at https://zenodo.org/records/13884188 (ref. 87).

References

  1. King, A. D. et al. Emergence of heat extremes attributable to anthropogenic influences. Geophys. Res. Lett. 43, 3438–3443 (2016).

    Google Scholar 

  2. Ranasinghe, R. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) 1767–1926 (Cambridge Univ. Press, 2021); https://doi.org/10.1017/9781009157896.014

  3. Theokritoff, E., Thomas, A., Lissner, T. & Schleussner, C.-F. Interacting adaptation constraints in the Caribbean highlight the importance of sustained adaptation finance. Clim. Risk Manage. 39, 100483 (2023).

    Google Scholar 

  4. Theokritoff, E. & D’haen, S. A. How is science making its way into national climate change adaptation policy? Insights from Burkina Faso. Clim. Dev. 14, 857–865 (2022).

    Google Scholar 

  5. Otto, F. E. L. et al. Challenges to understanding extreme weather changes in lower income countries. Bull. Am. Meteorol. Soc. 101, E1851–E1860 (2020).

    Google Scholar 

  6. Callaghan, M. et al. Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies. Nat. Clim. Change 11, 966–972 (2021).

    Google Scholar 

  7. Goosen, H. et al. Climate adaptation services for the Netherlands: an operational approach to support spatial adaptation planning. Reg. Environ. Change 14, 1035–1048 (2014).

    Google Scholar 

  8. Obsi Gemeda, D., Korecha, D. & Garedew, W. Determinants of climate change adaptation strategies and existing barriers in southwestern parts of Ethiopia. Clim. Serv. 30, 100376 (2023).

    Google Scholar 

  9. Sultan, B. et al. Current needs for climate services in West Africa: results from two stakeholder surveys. Clim. Serv. 18, 100166 (2020).

    Google Scholar 

  10. Weichselgartner, J. & Arheimer, B. Evolving climate services into knowledge–action systems. Weather Clim. Soc. 11, 385–399 (2019).

    Google Scholar 

  11. Kropf, C. M. et al. Uncertainty and sensitivity analysis for probabilistic weather and climate risk modelling: an implementation in CLIMADA v.3.1.0. Geosci. Model Dev. 15, 7177–7201 (2022).

  12. Ruane, A. C. et al. The climatic impact-driver framework for assessment of risk-relevant climate information. Earths Future 10, e2022EF002803 (2022).

    Google Scholar 

  13. Schleussner, C.-F. et al. Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C. Earth Syst. Dyn. 7, 327–351 (2016).

    Google Scholar 

  14. Seneviratne, S. I., Donat, M. G., Pitman, A. J., Knutti, R. & Wilby, R. L. Allowable CO2 emissions based on regional and impact-related climate targets. Nature 529, 477–483 (2016).

    CAS  Google Scholar 

  15. IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2023); https://doi.org/10.1017/9781009157896

  16. IPCC: Summary for Policymakers. In Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).

  17. Matthews, J. B. R. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) 2215–2256 (Cambridge Univ. Press, 2021); https://doi.org/10.1017/9781009157896.022

  18. Tokarska, K. B. et al. Past warming trend constrains future warming in CMIP6 models. Sci. Adv. 6, eaaz9549 (2020).

    CAS  Google Scholar 

  19. Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W. & Zelinka, M. Climate simulations: recognize the ‘hot model’ problem. Nature 605, 26–29 (2022).

    CAS  Google Scholar 

  20. O’Neill, B. et al. in Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O. et al.) 2411–2538 (Cambridge Univ. Press, 2022); https://doi.org/10.1017/9781009325844.025.2412

  21. IPCC: Summary for Policymakers. In Climate Change 2022: Mitigation of Climate Change (eds Shukla, P. R. et al.) (Cambridge Univ. Press, 2022); https://doi.org/10.1017/9781009157926.001

  22. Leitner, M. et al. Monitoring and Evaluation of National Adaptation Policies Throughout the Policy Cycle (Publications Office of the European Union, 2020).

  23. Dilling, L. et al. Is adaptation success a flawed concept? Nat. Clim. Change 9, 572–574 (2019).

    Google Scholar 

  24. Simpson, N. P. et al. Adaptation to compound climate risks: a systematic global stocktake. iScience 26, 105926 (2023).

    Google Scholar 

  25. IPCC: Summary for Policymakers. In Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2023); https://doi.org/10.1017/9781009325844.001

  26. Riahi, K. et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).

    Google Scholar 

  27. IPCC: Technical Summary. In Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).

  28. Harrington, L. J., Schleussner, C.-F. & Otto, F. E. L. Quantifying uncertainty in aggregated climate change risk assessments. Nat. Commun. 12, 7140 (2021).

    CAS  Google Scholar 

  29. Rawshan Ara Begum, R. et al. in Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O. et al.) 121–196 (Cambridge Univ. Press, 2022); https://doi.org/10.1017/9781009325844.003

  30. Thomas, A. et al. Global evidence of constraints and limits to human adaptation. Reg. Environ. Change 21, 85 (2021).

    Google Scholar 

  31. Esperon-Rodriguez, M. et al. Climate change increases global risk to urban forests. Nat. Clim. Change 12, 950–955 (2022).

    Google Scholar 

  32. Lamboll, R., Rogelj, J. & Schleussner, C.-F. A guide to scenarios for the PROVIDE project. Preprint at https://doi.org/10.1002/essoar.10511875.1 (2022).

  33. Pfleiderer, P., Schleussner, C.-F. & Sillmann, J. Limited reversal of regional climate signals in overshoot scenarios. Environ. Res. Clim. 3, 015005 (2024).

    Google Scholar 

  34. Gasser, T. et al. Path-dependent reductions in CO2 emission budgets caused by permafrost carbon release. Nat. Geosci. 11, 830–835 (2018).

    CAS  Google Scholar 

  35. Nauels, A. et al. Attributing long-term sea-level rise to Paris Agreement emission pledges. Proc. Natl Acad. Sci. USA 116, 23487–23492 (2019).

    CAS  Google Scholar 

  36. Mengel, M., Nauels, A., Rogelj, J. & Schleussner, C.-F. Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action. Nat. Commun. 9, 601 (2018).

    Google Scholar 

  37. Haasnoot, M., Kwakkel, J. H., Walker, W. E. & ter Maat, J. Dynamic adaptive policy pathways: a method for crafting robust decisions for a deeply uncertain world. Glob. Environ. Change 23, 485–498 (2013).

    Google Scholar 

  38. Schlumberger, J., Haasnoot, M., Aerts, J. & De Ruiter, M. Proposing DAPP-MR as a disaster risk management pathways framework for complex, dynamic multi-risk. iScience 25, 105219 (2022).

    CAS  Google Scholar 

  39. Battiston, S., Mandel, A., Monasterolo, I., Schütze, F. & Visentin, G. A climate stress-test of the financial system. Nat. Clim. Change 7, 283–288 (2017).

    Google Scholar 

  40. Ebi, K. L. et al. Stress testing the capacity of health systems to manage climate change-related shocks and stresses. Int. J. Environ. Res. Public. Health 15, 2370 (2018).

    Google Scholar 

  41. Linkov, I. et al. Resilience stress testing for critical infrastructure. Int. J. Disaster Risk Reduct. 82, 103323 (2022).

    Google Scholar 

  42. Feygina, I. et al. Localized climate reporting by TV weathercasters enhances public understanding of climate change as a local problem: evidence from a randomized controlled experiment. Bull. Am. Meteorol. Soc. 101, E1092–E1100 (2020).

    Google Scholar 

  43. Moore, F. C. et al. Determinants of emissions pathways in the coupled climate–social system. Nature 603, 103–111 (2022).

    CAS  Google Scholar 

  44. Smith, C. J. et al. FAIR v1.3: a simple emissions-based impulse response and carbon cycle model. Geosci. Model Dev. 11, 2273–2297 (2018).

    CAS  Google Scholar 

  45. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Google Scholar 

  46. Smith, C. et al. Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) Ch. 7 (Cambridge Univ. Press, 2021).

  47. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Google Scholar 

  48. Cannon, A. J., Sobie, S. R. & Murdock, T. Q. Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? J. Clim. 28, 6938–6959 (2015).

    Google Scholar 

  49. Hooyberghs, H., Berckmans, J., Lauwaet, D., Lefebre, F. & De Ridder, K. Climate Variables for Cities in Europe from 2008 to 2017 (CDS, 2019); https://doi.org/10.24381/cds.c6459d3a

  50. Vicedo-Cabrera, A. M. et al. The burden of heat-related mortality attributable to recent human-induced climate change. Nat. Clim. Change 11, 492–500 (2021).

    CAS  Google Scholar 

  51. Palmer, T. E. et al. Performance-based sub-selection of CMIP6 models for impact assessments in Europe. Earth Syst. Dyn. 14, 457–483 (2023).

    Google Scholar 

  52. Wagner, C. E. V. Development and Structure of the Canadian Forest Fire Weather Index System. (Minister of Supply and Services Canada, 1987).

  53. Quilcaille, Y., Batibeniz, F., Ribeiro, A. F. S., Padrón, R. S. & Seneviratne, S. I. Fire weather index data under historical and shared socioeconomic pathway projections in the 6th phase of the Coupled Model Intercomparison Project from 1850 to 2100. Earth Syst. Sci. Data 15, 2153–2177 (2023).

    Google Scholar 

  54. Quilcaille, Y., Gudmundsson, L. & Seneviratne, S. I. Extending MESMER-X: a spatially resolved Earth system model emulator for fire weather and soil moisture. Preprint at EGUsphere https://doi.org/10.5194/egusphere-2023-589 (2023).

  55. Schwaab, J. et al. Spatially resolved emulated annual temperature projections for overshoot pathways. Sci. Data 11, 1262 (2024).

    Google Scholar 

  56. Pfeffer, W. T. et al. The Randolph Glacier Inventory: a globally complete inventory of glaciers. J. Glaciol. 60, 537–552 (2014).

    Google Scholar 

  57. Hugonnet, R. et al. Accelerated global glacier mass loss in the early twenty-first century. Nature 592, 726–731 (2021).

    CAS  Google Scholar 

  58. Farinotti, D. et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nat. Geosci. 12, 168–173 (2019).

    CAS  Google Scholar 

  59. Huss, M. & Hock, R. A new model for global glacier change and sea-level rise. Front. Earth Sci. https://doi.org/10.3389/feart.2015.00054 (2015).

  60. Maussion, F. et al. The Open Global Glacier Model (OGGM) v1.1. Geosci. Model Dev. 12, 909–931 (2019).

    Google Scholar 

  61. Maussion, F. et al. OGGM/oggm: v1.6.1. Zenodo https://doi.org/10.5281/zenodo.8287580 (2023).

  62. Rounce, D. R. et al. Global glacier change in the 21st century: every increase in temperature matters. Science 379, 78–83 (2023).

    CAS  Google Scholar 

  63. Zekollari, H. et al. Twenty-first century global glacier evolution under CMIP6 scenarios and the role of glacier-specific observations. The Cryosphere 18, 5045–5066 (2024).

    Google Scholar 

  64. Chen, D. et al. Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) Ch. 1 (Cambridge Univ. Press, 2021).

  65. Cleveland, W. S. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829–836 (1979).

    Google Scholar 

  66. Bourn, A. AyrtonB/Merit-Order-Effect: v1.0.0. Zenodo https://doi.org/10.5281/ZENODO.4642896 (2021).

  67. State of the Cryosphere 2023: Two Degrees Is Too High (ICCI, 2023); https://iccinet.org/statecryo23/

  68. Schuster, L., Huss, M., Maussion, F., Rounce, D. R. & Tober, B. S. lilianschuster/glacier-model-projections-until2300: v1.0. Zenodo https://doi.org/10.5281/zenodo.10059778 (2023).

  69. Tebaldi, C. et al. Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth Syst. Dyn. 12, 253–293 (2021).

    Google Scholar 

  70. Zickfeld, K. et al. Long-term climate change commitment and reversibility: an EMIC intercomparison. J. Clim. 26, 5782–5809 (2013).

    Google Scholar 

  71. Nicholls, Z. R. J. et al. Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response. Geosci. Model Dev. 13, 5175–5190 (2020).

    CAS  Google Scholar 

  72. Nicholls, Z. et al. Reduced Complexity Model Intercomparison Project Phase 2: synthesizing Earth system knowledge for probabilistic climate projections. Earths Future 9, e2020EF001900 (2021).

    CAS  Google Scholar 

  73. Beusch, L. et al. From emission scenarios to spatially resolved projections with a chain of computationally efficient emulators: coupling of MAGICC (v7.5.1) and MESMER (v0.8.1). Geosci. Model Dev. 15, 2085–2103 (2021).

  74. Nath, S., Lejeune, Q., Beusch, L., Seneviratne, S. I. & Schleussner, C.-F. MESMER-M: an Earth system model emulator for spatially resolved monthly temperature. Earth Syst. Dyn. 13, 851–877 (2022).

    Google Scholar 

  75. Quilcaille, Y., Gudmundsson, L., Beusch, L., Hauser, M. & Seneviratne, S. I. Showcasing MESMER-X: spatially resolved emulation of annual maximum temperatures of Earth system models. Preprint at https://doi.org/10.1002/essoar.10511207.1 (2022).

  76. Liu, G., Peng, S., Huntingford, C. & Xi, Y. A new precipitation emulator (PREMU v1.0) for lower-complexity models. Geosci. Model Dev. 16, 1277–1296 (2023).

    Google Scholar 

  77. Tebaldi, C., Snyder, A. & Dorheim, K. STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations. Earth Syst. Dyn. 13, 1557–1609 (2022).

    Google Scholar 

  78. James, R., Washington, R., Schleussner, C. F., Rogelj, J. & Conway, D. Characterizing half-a-degree difference: a review of methods for identifying regional climate Responses to global warming targets. WIREs Clim. Change 8, e457 (2017).

    Google Scholar 

  79. Terhaar, J., Frölicher, T. L., Aschwanden, M. T., Friedlingstein, P. & Joos, F. Adaptive emission reduction approach to reach any global warming target. Nat. Clim. Change 12, 1136–1142 (2022).

    Google Scholar 

  80. Silvy, Y. et al. AERA-MIP: emission pathways, remaining budgets and carbon cycle dynamics compatible with 1.5 °C and 2 °C global warming stabilization. Preprint at https://doi.org/10.5194/egusphere-2024-488 (2024).

  81. Goodwin, P., Brown, S., Haigh, I. D., Nicholls, R. J. & Matter, J. M. Adjusting mitigation pathways to stabilize climate at 1.5 °C and 2.0 °C rise in global temperatures to year 2300. Earths Future 6, 601–615 (2018).

    Google Scholar 

  82. Zickfeld, K., Eby, M., Matthews, H. D. & Weaver, A. J. Setting cumulative emissions targets to reduce the risk of dangerous climate change. Proc. Natl Acad. Sci. USA 106, 16129–16134 (2009).

    CAS  Google Scholar 

  83. Bossy, T., Gasser, T. & Ciais, P. Pathfinder v1.0.1: a Bayesian-inferred simple carbon–climate model to explore climate change scenarios. Geosci. Model Dev. 15, 8831–8868 (2022).

    CAS  Google Scholar 

  84. Schuster, C., Burkart, K. & Lakes, T. Heat mortality in Berlin—Spatial variability at the neighborhood scale. Urban Clim. 10, 134–147 (2014).

    Google Scholar 

  85. Zekollari, H. et al. GloGEM CMIP6 global glacier projections. Zenodo https://zenodo.org/records/10908278 (2024).

  86. Schuster, L., Schmitt, P., Vlug, A. & Maussion, F. OGG/oggm-standard-projections-csr-files: v1.0. Zenodo https://zenodo.org/records/8286065 (2023).

  87. Pfleiderer, P. peterpeterp/perspective_reversal_of_the_impact_chain: submission 3. Zenodo https://zenodo.org/records/13884188 (2024).

Download references

Acknowledgements

This publication is a result of the PROVIDE project funded by the European Union’s Horizon 2020 research and innovation programmes under grant agreement no. 101003687 (PROVIDE). P.P., T.L.F., C.M.K., R.D.L., Q.L., T.C.L., F.M., J.M., Y.Q., J.R., B.S., L.S., J.S., C.S., E.T. and C.-F.S. acknowledge funding from the European Union’s Horizon 2020 research and innovation programmes under grant agreement no. 101003687 (PROVIDE). Y.Q. acknowledges funding from the European Union’s Horizon 2020 research project no. 101081369 (SPARCCLE). L.S. is recipient of a DOC Fellowship of the Austrian Academy of Sciences at the Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck (no. 25928). J.S. is funded by the German Research Foundation (DFG) under Germany’s Excellence Strategy—EXC 2037:’CLICCS— Climate, Climatic Change, and Society’—project number: 390683824. T.L.F. acknowledges funding from the HEurope TipESM project.

Author information

Authors and Affiliations

  1. Climate Analytics, Berlin, Germany

    Peter Pfleiderer, Quentin Lejeune, Emily Theokritoff & Carl-Friedrich Schleussner

  2. Research Unit for Sustainability and Climate Risks, University of Hamburg, Hamburg, Germany

    Peter Pfleiderer & Jana Sillmann

  3. Institute of Meteorology, Leipzig University, Leipzig, Germany

    Peter Pfleiderer

  4. Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland

    Thomas L. Frölicher

  5. Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

    Thomas L. Frölicher

  6. Institute for Environmental Decisions, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland

    Chahan M. Kropf & Jamie W. McCaughey

  7. Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland

    Chahan M. Kropf

  8. Center for Environmental Policy, Imperial College London, London, UK

    Robin D. Lamboll & Joeri Rogelj

  9. Centre for Ecology, Evolution and Environmental Changes (CE3C) and Global Change and Sustainability Institute (CHANGE), Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal

    Tiago Capela Lourenço

  10. School of Geographical Sciences, University of Bristol, Bristol, UK

    Fabien Maussion

  11. Department of Atmospheric and Cryospheric Sciences (ACINN), Universität Innsbruck, Innsbruck, Austria

    Fabien Maussion & Lilian Schuster

  12. Institute for Atmospheric and Climate Science, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland

    Yann Quilcaille

  13. International Institute for Applied Systems Analysis, Laxenburg, Austria

    Joeri Rogelj & Chris Smith

  14. Grantham Institute for Climate Change and the Environment, Imperial College London, London, UK

    Joeri Rogelj & Emily Theokritoff

  15. CICERO, Oslo, Norway

    Benjamin Sanderson

  16. Met Office Hadley Centre, Exeter, UK

    Chris Smith

  17. University of Leeds, Leeds, UK

    Chris Smith

  18. Geographisches Institut, Humboldt-Universität zu Berlin, Berlin, Germany

    Emily Theokritoff & Carl-Friedrich Schleussner

Authors
  1. Peter Pfleiderer
    View author publications

    Search author on:PubMed Google Scholar

  2. Thomas L. Frölicher
    View author publications

    Search author on:PubMed Google Scholar

  3. Chahan M. Kropf
    View author publications

    Search author on:PubMed Google Scholar

  4. Robin D. Lamboll
    View author publications

    Search author on:PubMed Google Scholar

  5. Quentin Lejeune
    View author publications

    Search author on:PubMed Google Scholar

  6. Tiago Capela Lourenço
    View author publications

    Search author on:PubMed Google Scholar

  7. Fabien Maussion
    View author publications

    Search author on:PubMed Google Scholar

  8. Jamie W. McCaughey
    View author publications

    Search author on:PubMed Google Scholar

  9. Yann Quilcaille
    View author publications

    Search author on:PubMed Google Scholar

  10. Joeri Rogelj
    View author publications

    Search author on:PubMed Google Scholar

  11. Benjamin Sanderson
    View author publications

    Search author on:PubMed Google Scholar

  12. Lilian Schuster
    View author publications

    Search author on:PubMed Google Scholar

  13. Jana Sillmann
    View author publications

    Search author on:PubMed Google Scholar

  14. Chris Smith
    View author publications

    Search author on:PubMed Google Scholar

  15. Emily Theokritoff
    View author publications

    Search author on:PubMed Google Scholar

  16. Carl-Friedrich Schleussner
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Peter Pfleiderer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks Luke Harrington and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pfleiderer, P., Frölicher, T.L., Kropf, C.M. et al. Reversal of the impact chain for actionable climate information. Nat. Geosci. 18, 10–19 (2025). https://doi.org/10.1038/s41561-024-01597-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41561-024-01597-w

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing