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# Increased exposure of coastal cities to sea-level rise due to internal climate variability

## Abstract

Adaptation to future sea-level rise is based on projections of continuously improving climate models. These projections are accompanied by inherent uncertainties, including those due to internal climate variability (ICV). The ICV arises from complex and unpredictable interactions within and between climate-system components, rendering its impact irreducible. Although neglecting this uncertainty can lead to an underestimation of future sea-level rise, its estimation and impacts have not been fully explored. Combining the Community Earth System Model version 1 Large Ensemble experiments with power-law statistics, we show that, by 2100, if the ICV uncertainty reaches its upper limit, new sea-level-rise hotspots would appear in Southeast Asian megacities (Chennai, Kolkata, Yangon, Bangkok, Ho Chi Minh City and Manila), in western tropical Pacific Islands and the Western Indian Ocean. The better the ICV uncertainty is taken into account and correctly estimated, the more effective adaptation strategies can be elaborated with confidence and actions to follow.

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## Data availability

All the CESM1-LE datasets are freely available on the Community Earth System Model webpage: http://www.cesm.ucar.edu/projects/community-projects/LENS/data-sets.html. The World Port Index database is provided by the National Geospatial-Intelligence Agency and is freely available at https://msi.nga.mil/Publications/WPI.

## Code availability

All the activities regarding data analysis and results representation were produced by using Matlab software (https://www.mathworks.com), the Matlab package M_Map (www.eoas.ubc.ca/~rich/map.html) and Quantum Geographic Information System (https://www.qgis.org).

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## Acknowledgements

This work was supported by the French Research Agency (Agence Nationale de la Recherche [ANR]) under the Deltas Under Global Impact of Change (DELTA) project (ANR-17-CE03-0001) and by the Centre National d’Etudes Spatiales (CNES) through the project Terre Solide, Océan, Surfaces Continentales et Atmosphère (TOSCA)/GEOMINING. A.H. was supported by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the US Department of Energy’s Office of Biological and Environmental Research (BER) via National Science Foundation IA 1844590. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under contract no. DE-AC02-05CH11231 and resources from the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation and other agencies. We are grateful to C. Deser for helpful and encouraging discussions.

## Author information

Authors

### Contributions

M.B., M.K. and A.H. conceived the study. M.B. performed the analysis and conducted the computations. M.B., M.K. and A.H. discussed the results and wrote the manuscript.

### Corresponding author

Correspondence to M. Becker.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

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

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## Extended data

### Extended Data Fig. 1 CESM1 Large Ensemble.

The CESM1 Large Ensemble data sets.

### Extended Data Fig. 2 The sea level changes simulated by CESM1-LE.

The 40 gray lines are the sea level changes simulated at one grid point by the 40-members of the CESM1-LE historical simulations over 1920–2005. The expected sea level change is measured by Δ, $$\overline {\Delta}$$ and $${\mathrm{ICV}}_{{\mathrm{CESM}}1 - {\mathrm{LE}}}$$ which are computed as described in the text.

### Extended Data Fig. 3 Visualization of the considered quantities Δ and σt.

These quantities are obtained from one member of CESM1-LE historical simulation, at a given grid point, over 1920–2005.

### Extended Data Fig. 4 Comparison of the ICV contributions.

Ratio of ICV contribution to the expected sea level changes between $${{{\mathrm{ICV}}}}_{{{{\mathrm{power}}}} - {{{\mathrm{law}}}}}$$obtained from the power-law statistics (two-sided 95% confidence level) and $${{{\mathrm{ICV}}}}_{{{{\mathrm{CESM}}}}1 - {{{\mathrm{LE}}}}}$$ provided by the spread (2σ) between the $${\Delta}_{{{{{i}}}} = 1 \ldots 40}$$ from the 40-members CESM1-LE under RCP8.5 by 2100 relative 2006.

### Extended Data Fig. 5 Expected externally forced sea level rise by 2100 of major ports world-wide.

Expected externally forced sea level rise (cm, in blue) by 2100 relative to 2006 under a carbon high-emissions global warming scenario (that is RCP8.5). In red: $${{{\mathrm{ICV}}}}_{{{{\mathrm{power}}}} - {{{\mathrm{law}}}}}$$ range (two-sided 95% confidence level) obtained from the power-law statistics. In orange: $${{{\mathrm{ICV}}}}_{{{{\mathrm{CESM}}}}1 - {{{\mathrm{LE}}}}}$$ from the spread (2σ) between 40-members CESM1-LE. The sites are major ports world-wide (the World Port index database https://msi.nga.mil/Publications/WP).

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Becker, M., Karpytchev, M. & Hu, A. Increased exposure of coastal cities to sea-level rise due to internal climate variability. Nat. Clim. Chang. (2023). https://doi.org/10.1038/s41558-023-01603-w

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• DOI: https://doi.org/10.1038/s41558-023-01603-w