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:

Ambiguity of early warning signals for climate tipping points

Nature Climate Change volume 15, pages 479–488 (2025)Cite this article

Subjects

Abstract

There is concern that climate change might lead to abrupt and irreversible changes in parts of the Earth system at so-called tipping points. Theoretical considerations suggest that statistical measures can be used to detect early warning signals (EWSs) for reduced resilience, which could be interpreted as an increased proximity to climate tipping points. Here we discuss limitations of commonly used EWSs and their detection and discuss how alternative explanations can lead to resilience loss in the absence of tipping points. We argue for better testing of the existence of tipping points, beyond the application of EWSs, and propose a method to better quantify the probability of approaching tipping points using EWSs.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: EWSs can occur with and without tipping points.
Fig. 2: Examples for spuriously significant positive trends in EWS metrics produced from noise.
Fig. 3: Spuriously significant positive trends in EWS metrics in a global climate model simulation.
Fig. 4: Bayesian framework for estimating the probability of approaching a tipping point based on observational data via EWSs.

Similar content being viewed by others

Code availability

All code is publicly available via Zenodo at https://doi.org/10.5281/zenodo.14185461 (ref. 108).

References

  1. Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).

    Article  CAS  Google Scholar 

  2. Lenton, T. M. et al. Tipping elements in the Earth’s climate system. Proc. Natl Acad. Sci. USA 105, 1786–1793 (2008).

    Article  CAS  Google Scholar 

  3. Armstrong McKay, D. I. et al. Exceeding 1.5 °C global warming could trigger multiple climate tipping points. Science 377, eabn7950 (2022).

    Article  Google Scholar 

  4. Lenton, T. M. et al. The Global Tipping Points Report 2023 (Univ. Exeter, 2023).

  5. IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).

  6. Scheffer, M. et al. Early warning signals for critical transitions. Nature 461, 53–59 (2009).

    Article  CAS  Google Scholar 

  7. Boers, N. & Rypdal, M. Critical slowing down suggests that the western Greenland Ice Sheet is close to a tipping point. Proc. Natl Acad. Sci. USA 118, e2024192118 (2021).

    Article  CAS  Google Scholar 

  8. Boers, N. Observation-based early-warning signals for a collapse of the Atlantic meridional overturning circulation. Nat. Clim. Change 11, 680–688 (2021).

    Article  Google Scholar 

  9. Boulton, C. A., Lenton, T. M. & Boers, N. Pronounced loss of Amazon rainforest resilience since the early 2000s. Nat. Clim. Change 12, 271–278 (2022).

    Article  Google Scholar 

  10. Ditlevsen, P. & Ditlevsen, S. Warning of a forthcoming collapse of the Atlantic meridional overturing circulation. Nat. Commun. 14, 4254 (2023).

    Article  CAS  Google Scholar 

  11. Boettiger, C. & Hastings, A. Early warning signals and the prosecutor’s fallacy. Proc. R. Soc. B 279, 4734–4739 (2012).

    Article  Google Scholar 

  12. Kéfi, S., Dakos, V., Scheffer, M., Van Nes, E. H. & Rietkerk, M. Early warning signals also precede non-catastrophic transitions. Oikos 122, 641–648 (2013).

    Article  Google Scholar 

  13. Ben-Yami, M., Morr, A., Bathiany, S. & Boers, N. Uncertainties too large to predict tipping times of major Earth system components from historical data. Sci. Adv. 10, eadl4841 (2024).

    Article  Google Scholar 

  14. Van Nes, E. H. et al. What do you mean, ‘tipping point’? Trends Ecol. Evol. 31, 622–632 (2016).

    Google Scholar 

  15. Ashwin, P., Wieczorek, S., Vitolo, R. & Cox, P. Tipping points in open systems: bifurcation, noise-induced and rate-dependent examples in the climate system. Phil. Trans. R. Soc. A 370, 1166–1184 (2012).

    Article  Google Scholar 

  16. Titz, S., Kuhlbrodt, T., Rahmstorf, S. & Freudel, U. On freshwater-dependent bifurcations in box models of the interhemispheric thermohaline circulations. Tellus A 54, 89–98 (2002).

    Article  Google Scholar 

  17. Lux, K., Ashwin, P., Wood, R. & Kuehn, C. Assessing the impact of parametric uncertainty on tipping points of the Atlantic meridional overturing circulation. Environ. Res. Lett. 17, 075002 (2022).

    Article  Google Scholar 

  18. Lohmann, J., Dijkstra, H. A., Jochum, M., Lucarini, V. & Ditlevsen, P. D. Multistability and intermediate tipping of the Atlantic ocean circulation. Sci. Adv. 10, eadi4253 (2024).

    Article  Google Scholar 

  19. Drijfhout, S. et al. Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models. Proc. Natl Acad. Sci. USA 112, E5777–E5786 (2015).

    Article  CAS  Google Scholar 

  20. Bathiany, S., Notz, D., Mauritsen, T., Raedel, G. & Brovkin, V. On the potential for abrupt Arctic winter sea ice loss. J. Clim. 29, 2703–2719 (2016).

    Article  Google Scholar 

  21. Stommel, H. Thermohaline convection with two stable regimes of flow. Tellus 13, 224–230 (1961).

    Article  Google Scholar 

  22. Cessi, P. A. A simple box model of stochastically forced thermohaline flow. J. Phys. Oceanogr. 24, 1911–1920 (1994).

    Article  Google Scholar 

  23. Liu, W., Liu, Z. & Brady, E. C. Why is the AMOC monostable in coupled general circulation models? J. Clim. 27, 2427–2443 (2014).

    Article  Google Scholar 

  24. Jackson, L. C. et al. Understanding AMOC stability: the North Atlantic Hosing Model Intercomparison Project. Geosci. Model Dev. 16, 1975–1995 (2023).

    Article  Google Scholar 

  25. Van Westen, R. M., Kliphuis, M. & Dijkstra, H. A. Physics-based early warning signal shows that AMOC is on tipping course. Sci. Adv. 10, eadk1189 (2024).

    Article  Google Scholar 

  26. Gildor, H. & Tziperman, E. Physical mechanisms behind biogeochemical glacial–interglacial CO2 variations. Geophys. Res. Lett. 28, 2421–2424 (2001).

    Article  CAS  Google Scholar 

  27. Alkhayoun, H. M., Ashwin, P., Jackson, L. C., Quinn, C. & Wood, R. A. Basin bifurcation, oscillatory instability and scale induced thresholds for the Atlantic meridional overturning circulation in a global box model. Proc. R. Soc. A 475, 20190051 (2019).

    Article  Google Scholar 

  28. Rietkerk, M. et al. Evasion of tipping in complex systems through spatial pattern formation. Science 374, eabj0359 (2021).

    Article  CAS  Google Scholar 

  29. Bastiaansen, R., Dijkstra, H. A. & Von der Heydt, A. S. Fragmented tipping in a spatially heterogeneous world. Environ. Res. Let. 17, 045006 (2022).

    Article  Google Scholar 

  30. Laepple, T. et al. Regional but not global temperature variability underestimated by climate models at supradecadal timescales. Nat. Geosci. 16, 958–966 (2024).

    Article  Google Scholar 

  31. Dekker, M. M., von der Heydt, A. S. & Dijkstra, H. A. Cascading transitions in the climate system. Earth Syst. Dyn. 9, 1243–1260 (2018).

    Article  Google Scholar 

  32. Klose, A. K., Karle, V., Winkelmann, R. & Donges, J. F. Emergence of cascading dynamics in interacting tipping elements of ecology and climate. R. Soc. Open Sci. 7, 200599 (2020).

    Article  Google Scholar 

  33. Klose, A. K., Wunderling, N., Winkelmann, R. & Donges, J. F. What do we mean, ‘tipping cascade’? Environ. Res. Let. 16, 125011 (2021).

    Article  CAS  Google Scholar 

  34. Liu, T. et al. Teleconnections among tipping elements in the Earth system. Nat. Clim. Change 13, 67–74 (2023).

    Article  CAS  Google Scholar 

  35. Wunderling, N. et al. Global warming overshoots increase risks of climate tipping cascades in a network model. Nat. Clim. Change 13, 75–82 (2023).

    Article  Google Scholar 

  36. Wunderling, N., Donges, J. F., Kurths, J. & Winkelmann, R. Interacting tipping elements increase risk of climate domino effects under global warming. Earth Syst. Dyn. 12, 601–619 (2021).

    Article  Google Scholar 

  37. Wunderling, N. et al. Climate tipping point interactions and cascades: a review. Earth Syst. Dyn. 15, 41–74 (2024).

    Article  Google Scholar 

  38. Held, H. & Kleinen, T. Detection of climate system bifurcations by degenerate fingerprinting. Geophys. Res. Let. 31, L23207 (2004).

    Article  Google Scholar 

  39. Weinans, E., Ouax, R., Van Nes, E. H. & Van de Leemput, I. A. Evaluating the performance of multivariate indicators of resilience loss. Sci. Rep. 11, 9148 (2021).

    Article  CAS  Google Scholar 

  40. Laitinen, V. & Lahti, L. in Computational Methods in Systems Biology (eds Petre, I. & Păun, A.) 259–274 (Springer, 2022).

  41. Benson, V. et al. Measuring tropical rainforest resilience under non-Gaussian disturbances. Environ. Res. Lett. 19, 024029 (2024).

    Article  Google Scholar 

  42. Morr, A. & Boers, N. Detection of approaching critical transitions in natural systems driven by red noise. Phys. Rev. X 14, 021037 (2024).

    CAS  Google Scholar 

  43. Dakos, V. et al. Tipping point detection and early warnings in climate, ecological, and human systems. Earth Syst. Dyn. 15, 1117–1135 (2024).

    Article  Google Scholar 

  44. Hastings, A. et al. Transient phenomena in ecology. Science 361, eaat6412 (2018).

    Article  Google Scholar 

  45. Sánchez-Pinillos, M., Dakos, V. & Kéfi, S. Ecological dynamic regimes: a key concept for assessing ecological resilience. Biol. Conserv. 289, 110409 (2024).

    Article  Google Scholar 

  46. Dablander, F. et al. Overlapping timescales obscure early warning signals of the second COVID-19 wave. Proc. R. Soc. B 289, 20211809 (2022).

    Article  CAS  Google Scholar 

  47. Bury, T. M., Bauch, C. T. & Anand, M. Detecting and distinguishing tipping points using spectral early warning signals. J. R. Soc. Interface 17, 20200482 (2020).

    Article  CAS  Google Scholar 

  48. Bury, T. M. et al. Deep learning for early warning signals of tipping points. Proc. Natl Acad. Sci. USA 118, e2106140118 (2021).

    Article  CAS  Google Scholar 

  49. Ghil, M. in Encyclopedia of Global Environmental Change Vol. 1 (eds Munn, T. E. et al.) 544–549 (John Wiley & Sons, 2002).

  50. Ditlevsen, P. D. & Johnsen, S. J. Tipping points: early warning and wishful thinking. Geophys. Res. Let. 37, L19703 (2010).

    Article  Google Scholar 

  51. Dijkstra, H. A. & Ghil, M. Low-frequency variability of the large-scale ocean circulation: a dynamical systems approach. Rev. Geophys. 43, RG3002 (2005).

    Article  Google Scholar 

  52. Bascompte, J. & Solé, R. V. Rethinking complexity: modelling spatiotemporal dynamics in ecology. Trends Ecol. Evol. 10, 361–366 (1995).

    Article  CAS  Google Scholar 

  53. Solé, R. & Bascompte, J. Self-Organization in Complex Ecosystems (Princeton Univ. Press, 2006).

  54. Staal, A. et al. Forest-rainfall cascades buffer against drought across the Amazone. Nat. Clim. Change 8, 539–543 (2018).

    Article  Google Scholar 

  55. Meron, E. Nonlinear Physics of Ecosystems (CRC Press, 2015).

    Book  Google Scholar 

  56. Bastiaansen, R. et al. Multistability of model and real dryland ecosystems through spatial self-organization. Proc. Natl Acad. Sci. USA 115, 11256–11261 (2018).

    Article  CAS  Google Scholar 

  57. Dakos, V. et al. Slowing down as an early warning signal for abrupt climate change. Proc. Natl Acad. Sci. USA 105, 14308–14312 (2008).

    Article  CAS  Google Scholar 

  58. Boers, N. Early-warning signals for Dansgaard–Oeschger events in a high-resolution ice core record. Nat. Commun. 9, 2556 (2018).

    Article  Google Scholar 

  59. Michel, S. L. et al. Early warning signal for a tipping point suggested by a millennial Atlantic multidecadal variability reconstruction. Nat. Commun. 13, 5176 (2022).

    Article  CAS  Google Scholar 

  60. Verbesselt, J. et al. Remotely sensed resilience of tropical forests. Nat. Clim. Change 6, 1028–1031 (2016).

    Article  Google Scholar 

  61. Smith, T. et al. Reliability of resilience estimation based on multi-instrument time series. Earth Syst. Dyn. 14, 173–183 (2023).

    Article  Google Scholar 

  62. Rocha, J. C. Ecosystems are showing symptoms of resilience loss. Environ. Res. Let. 17, 065013 (2022).

    Article  Google Scholar 

  63. Dakos, V. et al. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE 7, e41010 (2012).

    Article  CAS  Google Scholar 

  64. Ben-Yami, M., Skiba, V., Bathiany, S. & Boers, N. Uncertainties in critical slowing down indicators of observation-based fingerprints of the Atlantic overturing circulation. Nat. Commun. 14, 8344 (2023).

    Article  CAS  Google Scholar 

  65. Anchukaitis, K. J. & Smerdon, J. E. Progress and uncertainties in global and hemispheric temperature reconstructions of the Common Era. Quat. Sci. Rev. 286, 107537 (2022).

    Article  Google Scholar 

  66. Kennedy, J. J., Rayner, N. A., Atkinson, C. P. & Killick, R. E. An ensemble data set of sea surface temperature change from 1850: the Met Office Hadley Centre HadSST. 4.0. 0.0 data set. J. Geophys. Res. Atmos. 124, 7719–7763 (2019).

    Article  Google Scholar 

  67. King, J. M. et al. A data assimilation approach to last millennium temperature field reconstruction using a limited high-sensitivity proxy network. J. Clim. 34, 7091–7111 (2021).

    Google Scholar 

  68. Huntingford, C., Jones, P. D., Livina, V. N., Lenton, T. M. & Cox, P. M. No increase in global temperature variability despite changing regional patterns. Nature 500, 327–330 (2013).

    Article  CAS  Google Scholar 

  69. Bathiany, S. et al. Statistical indicators of Arctic sea ice stability—prospects and limitations. Cryosphere 10, 1631–1645 (2016).

    Article  Google Scholar 

  70. Lenton, T. M. et al. Observed trends in the magnitude and persistence of monthly temperature variability. Sci. Rep. 7, 5940 (2017).

    Article  Google Scholar 

  71. Rehfeld, K., Münch, T., Ho, S. L. & Laepple, T. Global patterns of declining temperature variability from the Last Glacial Maximum to the Holocene. Nature 554, 356–359 (2018).

    Article  CAS  Google Scholar 

  72. Lovejoy, S. & Varotsos, C. Scaling regimes and linear/nonlinear responses of last millennium climate to volcanic and solar forces. Earth Syst. Dyn. 7, 133–150 (2016).

    Article  Google Scholar 

  73. Livezey, R. E. & Chen, W. Y. Statistical field significance and its determination by Monte Carlo techniques. Mon. Weather Rev. 111, 46–59 (1983).

    Article  Google Scholar 

  74. Dolman, A. M. & Laepple, T. Sedproxy: a forward model for sediment-archived climate proxies. Clim. Past 14, 1851–1868 (2018).

    Article  Google Scholar 

  75. Hirsch, N. et al. Stratigraphic noise and its potential drivers across the plateau of Dronning Maud Land, East Antarctica. Cryosphere 17, 4207–4221 (2023).

    Article  Google Scholar 

  76. Davis, R. E. Predictability of sea surface temperature and sea level pressure anomalies over the North Pacific Ocean. J. Phys. Oceanogr. 6, 249–266 (1976).

    Article  CAS  Google Scholar 

  77. Clements, C. F., Drake, J. M., Griffiths, J. I. & Ozgul, A. Factors influencing the detectability of early warning signals of population collapse. Am. Nat. 186, 50–58 (2015).

    Article  Google Scholar 

  78. Dablander, F., Pichler, A., Cika, A. & Bacilieri, A. Anticipating critical transitions in psychological systems using early warning signals: theoretical and practical considerations. Psychol. Methods 28, 765–790 (2023).

    Article  Google Scholar 

  79. Thompson, W. C. & Schumann, E. L. Interpretation of statistical evidence in criminal trials: the prosecutor’s fallacy and the defense attorney’s fallacy. Law Hum. Behav. 11, 167–187 (1987).

    Article  Google Scholar 

  80. Evett, I. W. Avoiding the transposed conditional. Sci. Justice 2, 127–131 (1995).

    Article  Google Scholar 

  81. Neath, A. A. Statistical inference, statistics education, and the fallacy of the transposed conditional. In Proc. Joint Statistical Meeting 3348–3350 (2010).

  82. Shepherd, T. G. Bringing physical reasoning into statistical practice in climate-change science. Climatic Change 169, 2 (2021).

    Article  Google Scholar 

  83. Boettiger, C. & Hastings, A. Quantifying limits to detection of early warning for critical transitions. J. R. Soc. Interface 9, 2527–2539 (2012).

    Article  Google Scholar 

  84. Brovkin, V. et al. Past abrupt changes, tipping points and cascading impacts in the Earth system. Nat. Geosci. 14, 550–558 (2021).

    Article  CAS  Google Scholar 

  85. NEEM community members. Eemian interglacial reconstructed from a Greenland folded ice core. Nature 493, 489–494 (2013).

    Article  Google Scholar 

  86. Greenland, S. et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur. J. Epidemiol. 31, 337–350 (2016).

    Article  Google Scholar 

  87. Ives, A. R. et al. Statistical inference for trends in spatiotemporal data. Remote Sens. Environ. 266, 112678 (2021).

    Article  Google Scholar 

  88. Watkins, N. W., Pruessner, G., Chapman, S. C., Crosby, N. B. & Jensen, H. J. 25 years of self-organized criticality: concepts and controversies. Space Sci. Rev. 198, 3–44 (2016).

    Article  Google Scholar 

  89. Srokosz, M. A., Holliday, N. P. & Bryden, H. L. Atlantic overturning: new observations and challenges. Phil. Trans. R. Soc. A 381, 20220196 (2023).

    Article  CAS  Google Scholar 

  90. Cranmer, K., Brehmer, J. & Louppe, G. The frontier of simulation-based inference. Proc. Natl Acad. Sci. USA 117, 30055–30062 (2020).

    Article  CAS  Google Scholar 

  91. Shepherd, T. G. et al. Storylines: an alternative approach to representing uncertainty in physical aspects of climate change. Climatic Change 151, 555–571 (2018).

    Article  Google Scholar 

  92. Ritchie, P. D. L. et al. Shifts in national land use and food production in Great Britain after a climate tipping point. Nat. Food 1, 76–83 (2020).

    Article  Google Scholar 

  93. Sahlin, U., Helle, I. & Perepolking, D. ‘This is what we don’t know’: treating epistemic uncertainty in Bayesian networks for risk assessment. Integr. Environ. Assess. Manage. 17, 221–232 (2021).

    Article  Google Scholar 

  94. Lenton, T. M. et al. Climate tipping points—too risky to bet against. Nature 575, 592–595 (2019).

    Article  CAS  Google Scholar 

  95. Morrison, T. H. et al. Radical interventions for climate-impacted systems. Nat. Clim. Change 12, 1100–1106 (2022).

    Article  Google Scholar 

  96. Patterson, A. C., Strang, A. G. & Abbott, K. C. When and where can we expect to see early warning signals in multispecies systems approaching tipping points: insights from theory. Am. Nat. 198, E12 (2021).

    Article  Google Scholar 

  97. Morr, A., Boers, N. & Ashwin, P. Internal noise interference to warnings of tipping points in generic multi-dimensional dynamical systems. SIAM J. Appl. Dyn. Syst. 23, 2793–2806 (2024).

    Article  Google Scholar 

  98. Drake, J. M. & Griffin, B. D. Early warning signals of extinction in deteriorating environments. Nature 467, 456–459 (2010).

    Article  CAS  Google Scholar 

  99. Ricker, W. E. Stock and recruitment. Can. J. Fish. Aquat. Sci. 11, 559–623 (1954).

    Google Scholar 

  100. Rietkerk, M., Ketner, P., Stroosnijder, L. & Prins, H. H. Sahelian rangeland development; a catastrophe? J. Range Manage. 49, 512–519 (1996).

    Article  Google Scholar 

  101. Wissel, C. A. Universal law of the characteristic return time near thresholds. Oecologia 65, 101–107 (1984).

    Article  CAS  Google Scholar 

  102. Boettner, C. & Boers, N. Critical slowing down in dynamical systems driven by non-stationary correlated noise. Phys. Rev. Res. 4, 013230 (2022).

    Article  CAS  Google Scholar 

  103. Heßler, M. & Kamps, O. Bayesian on-line anticipation of critical transitions. N. J. Phys. 24, 063021 (2022).

    Article  Google Scholar 

  104. Clarke, J. J., Huntingford, C., Ritchie, P. D. & Cox, P. M. Seeking more robust early warning signals for climate tipping points: the ratio of spectra method (ROSA). Environ. Res. Let. 18, 035006 (2023).

    Google Scholar 

  105. Smith, T., Traxl, D. & Boers, N. Empirical evidence for recent global shifts in vegetation resilience. Nat. Clim. Change 12, 477–484 (2022).

    Article  Google Scholar 

  106. Huybers, P. & Curry, W. Links between annual, Milankovitch and continuum temperature variability. Nature 441, 329–332 (2006).

    Article  CAS  Google Scholar 

  107. Van Doorn, J. et al. The JASP guidelines for conducting and reporting a Bayesian analysis. Psychon. Bull. Rev. 28, 813–826 (2021).

    Article  Google Scholar 

  108. Skiba, V., Hébert, R. & Weinans, E. Code to produce figures for the manuscript ‘Ambiguity of statistical early warning signals for climate tipping points’. Zenodo https://doi.org/10.5281/zenodo.14185461 (2024).

Download references

Acknowledgements

The research of M.R. is supported by the European Research Council (ERC-Synergy project RESILIENCE, proposal no. 101071417) and by the Dutch Research Council (NWO ‘Resilience in complex systems through adaptive spatial pattern formation’, project no. OCENW.M20.169). This work was conducted as part of the EMBRACER programme, the Earth System Feedback Research Centre, and was financially supported by the SUMMIT programme of the Dutch Research Council (NWO). The research of T.L. is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC-Starting project SPACE, grant agreement no. 716092) and through the Cluster of Excellence, The Ocean Floor—Earth’s Uncharted Interface funded by the German Research Foundation (DFG; EXC 2077, grant no. 390741603). The research of V.S. was supported by German Federal Ministry of Education and Research (BMBF) as Research for Sustainability initiative (FONA); www.fona.de through the Palmod project (FKZ: 01LP2310B). We thank B. Grusdt, A. Dolman, T. Shepherd and P. Zaspel for fruitful discussions. S. Bathiany, A. van der Kaaden and A. Staal are acknowledged for critically reviewing earlier drafts of this paper.

Author information

Author notes

  1. These authors contributed equally: Max Rietkerk, Vanessa Skiba.

Authors and Affiliations

  1. Department of Environmental Sciences, Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, the Netherlands

    Max Rietkerk & Els Weinans

  2. Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Potsdam, Germany

    Vanessa Skiba, Raphaël Hébert & Thomas Laepple

  3. MARUM–Center for Marine Environmental Sciences and Faculty of Geosciences, University of Bremen, Bremen, Germany

    Thomas Laepple

Authors
  1. Max Rietkerk
    View author publications

    Search author on:PubMed Google Scholar

  2. Vanessa Skiba
    View author publications

    Search author on:PubMed Google Scholar

  3. Els Weinans
    View author publications

    Search author on:PubMed Google Scholar

  4. Raphaël Hébert
    View author publications

    Search author on:PubMed Google Scholar

  5. Thomas Laepple
    View author publications

    Search author on:PubMed Google Scholar

Contributions

M.R. and T.L. conceived of the study. M.R. and V.S. wrote the first draft of the paper. T.L. and V.S. provided the drafts of the statistical concepts. M.R., V.S., E.W., R.H. and T.L. reviewed and edited the text. V.S., E.W. and R.H. provided the figures.

Corresponding authors

Correspondence to Max Rietkerk or Vanessa Skiba.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Fabian Dablander, Juan Rocha 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 and 2.

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

Rietkerk, M., Skiba, V., Weinans, E. et al. Ambiguity of early warning signals for climate tipping points. Nat. Clim. Chang. 15, 479–488 (2025). https://doi.org/10.1038/s41558-025-02328-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41558-025-02328-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

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