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The interplay of future emissions and geophysical uncertainties for projections of sea-level rise

Nature Climate Change (2025)Cite this article

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Abstract

Uncertainty in future CO2 emissions and the geophysical response to emissions drives variability in future sea-level rise. However, the relative contributions of emissions and geophysical dynamics (for example, Antarctic Ice Sheet (AIS) tipping points) to future sea-level projections are not well understood. Here we disentangle their relative importance by propagating an ensemble of CO2 emissions trajectories through a calibrated carbon cycle–climate–sea-level model chain. Without negative emissions, the CO2 emissions trajectory, particularly the timing of when emissions are reduced, becomes the primary driver of sea-level variability between 2065 and 2075. Accelerated AIS melting greatly influences the sensitivity of global mean sea-level rise to time-averaged and integrated temperature changes. The most important geophysical uncertainties associated with the risk of exceeding sea-level thresholds are the threshold corresponding to accelerated AIS melting and equilibrium climate sensitivity. Our results highlight the need for both adaptation and rapid decarbonization to manage the risks posed by SLR.

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Fig. 1: Climate and sea-level impacts from emissions ensembles: summaries of simulations.
Fig. 2: Sensitivity of GMSLR to temperature changes.
Fig. 3: Time-varying feature importances for global mean sea-level projections.
Fig. 4: Delayed mitigation reduces the ‘safe operating space’ for avoiding GMSLR thresholds.

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Data availability

All input data used for the analysis are available with the code (in the data folder) except for the SNEASY–BRICK calibration results, which available from Zenodo via https://doi.org/10.5281/zenodo.6626335 (ref. 62) (see the GitHub README for guidance on which file to use). Simulation outputs from this experiment are available from Zenodo via https://doi.org/10.5281/zenodo.10373089 (ref. 63). Outputs for BRICK when forced by the Shared Socioeconomic Pathways are available from Zenodo via https://doi.org/10.5281/zenodo.14346558 (ref. 64).

Code availability

All code used for this analysis is available from Zenodo via https://doi.org/10.5281/zenodo.10391373 (ref. 65). This code is available under a GNU General Public License v.3.0.

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Acknowledgements

The authors thank F. Lehner, R. Gupta, A. Pollack, R. Venturelli, B. Schmidt and J. Lamontagne for valuable inputs and discussions. C.D. and V.S. were partially funded by the College of Agricultural & Life Sciences, Cornell University, and V.S. was partially funded by the US Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics as part of the multiprogramme, collaborative Integrated Coastal Modeling (ICoM) project. T.W. was supported in part by the National Science Foundation under award no. DMS-2213432. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US Department of Energy or the National Science Foundation.

Author information

Author notes

  1. These authors contributed equally: Lisa Rennels, Frank Errickson, Tony Wong.

Authors and Affiliations

  1. Department of Biological & Environmental Engineering, Cornell University, Ithaca, NY, USA

    Chloe Darnell & Vivek Srikrishnan

  2. Doerr School of Sustainability, Stanford University, Stanford, CA, USA

    Lisa Rennels

  3. Independent researcher, Washington, DC, USA

    Frank Errickson

  4. School of Mathematics and Statistics, Rochester Institute of Technology, Rochester, NY, USA

    Tony Wong

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  1. Chloe Darnell
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Contributions

C.D.: conceptualization, methodology, software, formal analysis, writing, visualization. L.R. and F.E.: software, methodology, writing. T.W.: software, methodology, writing, visualization. V.S.: conceptualization, methodology, software, writing, visualization, supervision, funding.

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Correspondence to Vivek Srikrishnan.

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Nature Climate Change thanks Robert Kopp and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Table 1 Shapley indices for individual parameters over time
Full size table

Extended Data Fig. 1 Overview of CO2 emissions ensemble.

Distributions of CO2 emissions scenarios. The CMIP6 SSP-RCP scenarios are provided until 2100 for comparison. a. Probability density of cumulative CO2 emissions from 2022–2100 (GtCO2). b. Cumulative density of cumulative density of CO2 emissions in from 2022–2100 (GtCO2). c. Evolution of CO2 emissions (GtCO2/yr) trajectories by scenario. The median is represented by the solid line and the shaded regions are 90% projection intervals. d. Cumulative density of CO2 emissions in 2100 (GtCO2/yr).

Extended Data Fig. 2 Uncertainty Group Importance for Selected Years.

Group uncertainty importance for the baseline ensemble in 2050, 2060, 2075, and 2100.

Supplementary information

Supplementary Information

Supplementary Figures 1-9, Supplementary Tables 1-2

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Darnell, C., Rennels, L., Errickson, F. et al. The interplay of future emissions and geophysical uncertainties for projections of sea-level rise. Nat. Clim. Chang. (2025). https://doi.org/10.1038/s41558-025-02457-0

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