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.

  • Article
  • Published:

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

Nature Climate Change volume 13, pages 367–374 (2023)Cite this article

Subjects

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.

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

Access options

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: Scaling exponent α of sterodynamic sea-level fluctuations.
Fig. 2: Geographical distribution of the ICV uncertainty range of sterodynamic sea-level changes by 2005 (relative to 1920).
Fig. 3: Sterodynamic SLR by 2100 under RCP 8.5 (relative to 2006).
Fig. 4: Upper-bound scenario of the expected sterodynamic SLR by 2100 under RCP 8.5 (relative to 2006).

Similar content being viewed by others

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).

References

  1. Becker, M., Karpytchev, M. & Lennartz-Sassinek, S. Long-term sea level trends: natural or anthropogenic? Geophys. Res. Lett. 41, 5571–5580 (2014).

    Article  Google Scholar 

  2. Dangendorf, S. et al. Detecting anthropogenic footprints in sea level rise. Nat. Commun. 6, 7849 (2015).

    Article  CAS  Google Scholar 

  3. Kopp, R. E. et al. Temperature-driven global sea-level variability in the Common Era. Proc. Natl Acad. Sci. USA 113, E1434–E1441 (2016).

    Article  CAS  Google Scholar 

  4. Marcos, M. & Amores, A. Quantifying anthropogenic and natural contributions to thermosteric sea level rise. Geophys. Res. Lett. 41, 2502–2507 (2014).

    Article  Google Scholar 

  5. Meyssignac, B. et al. Tropical Pacific spatial trend patterns in observed sea level: internal variability and/or anthropogenic signature? Climate 8, 787–802 (2012).

    Google Scholar 

  6. Slangen, A. et al. Anthropogenic forcing dominates global mean sea-level rise since 1970. Nat. Clim. Change 6, 701–705 (2016).

    Article  Google Scholar 

  7. Hasselmann, K. Stochastic climate models Part I. Theory. Tellus 28, 473–485 (1976).

    Google Scholar 

  8. Deser, C., Phillips, A., Bourdette, V. & Teng, H. Uncertainty in climate change projections: the role of internal variability. Clim. Dyn. 38, 527–546 (2012).

    Article  Google Scholar 

  9. Ghil, M. in Encyclopedia of Global Environmental Change Vol. 1 (eds MacCracken, M. & Perry, J.) 544–549 (Wiley, 2002).

  10. Deser, C. Certain uncertainty: the role of internal climate variability in projections of regional climate change and risk management. Earths Future 8, e2020EF001854 (2020).

    Article  Google Scholar 

  11. Bordbar, M. H., Martin, T., Latif, M. & Park, W. Effects of long-term variability on projections of twenty-first century dynamic sea level. Nat. Clim. Change 5, 343–347 (2015).

    Article  Google Scholar 

  12. Hu, A. & Bates, S. C. Internal climate variability and projected future regional steric and dynamic sea level rise. Nat. Commun. 9, 1068 (2018).

    Article  Google Scholar 

  13. Hu, A. & Deser, C. Uncertainty in future regional sea level rise due to internal climate variability. Geophys. Res. Lett. 40, 2768–2772 (2013).

    Article  Google Scholar 

  14. Little, C. M., Horton, R. M., Kopp, R. E., Oppenheimer, M. & Yip, S. Uncertainty in twenty-first-century CMIP5 sea level projections. J. Clim. 28, 838–852 (2015).

    Article  Google Scholar 

  15. Meehl, G. A. et al. Initialized Earth system prediction from subseasonal to decadal timescales. Nat. Rev. Earth Environ. 2, 340–357 (2021).

    Article  Google Scholar 

  16. Sérazin, G. et al. Quantifying uncertainties on regional sea level change induced by multidecadal intrinsic oceanic variability. Geophys. Res. Lett. 43, 8151–8159 (2016).

    Article  Google Scholar 

  17. Slangen et al. Projecting twenty-first century regional sea-level changes. Climatic Change 124, 317–332 (2014).

    Article  CAS  Google Scholar 

  18. Fasullo, J. T. & Nerem, R. S. Interannual variability in global mean sea level estimated from the CESM Large and Last Millennium Ensembles. Water 8, 491 (2016).

    Article  Google Scholar 

  19. Hu, A., Meehl, G., Stammer, D., Han, W. & Strand, W. Role of perturbing ocean initial condition in simulated regional sea level change. Water 9, 401 (2017).

    Article  Google Scholar 

  20. Deser, C. et al. Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10, 277–286 (2020).

    Article  Google Scholar 

  21. Kay, J. E. et al. The Community Earth System Model (CESM) Large Ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).

    Article  Google Scholar 

  22. Lyu, K., Zhang, X., Church, J. A., Slangen, A. B. A. & Hu, J. Time of emergence for regional sea-level change. Nat. Clim. Change 4, 1006–1010 (2014).

    Article  Google Scholar 

  23. Yip, S., Ferro, C. A., Stephenson, D. B. & Hawkins, E. A simple, coherent framework for partitioning uncertainty in climate predictions. J. Clim. 24, 4634–4643 (2011).

    Article  Google Scholar 

  24. Carson, M., Köhl, A. & Stammer, D. The impact of regional multidecadal and century-scale internal climate variability on sea level trends in CMIP5 models. J. Clim. 28, 853–861 (2015).

    Article  Google Scholar 

  25. Lyu, K., Zhang, X., Church, J. A. & Hu, J. Quantifying internally generated and externally forced climate signals at regional scales in CMIP5 models. Geophys. Res. Lett. 42, 9394–9403 (2015).

    Article  Google Scholar 

  26. Meyssignac, B. et al. Evaluating model simulations of twentieth-century sea-level rise. Part II: regional sea-level changes. J. Clim. 30, 8565–8593 (2017).

    Article  Google Scholar 

  27. Knutti, R., Furrer, R., Tebaldi, C., Cermak, J. & Meehl, G. A. Challenges in combining projections from multiple climate models. J. Clim. 23, 2739–2758 (2010).

    Article  Google Scholar 

  28. Murphy, J. M. et al. Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430, 768–772 (2004).

    Article  CAS  Google Scholar 

  29. Stainforth, D. A. et al. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433, 403–406 (2005).

    Article  CAS  Google Scholar 

  30. Harrison, C. G. A. Power spectrum of sea level change over fifteen decades of frequency. Geochem. Geophys. Geosyst. https://doi.org/10.1029/2002GC000300 (2002).

  31. Mandelbrot, B. B. & Wallis, J. R. Noah, Joseph, and operational hydrology. Water Resour. Res. 4, 909–918 (1968).

    Article  Google Scholar 

  32. Agnew, D. C. The time-domain behavior of power-law noises. Geophys. Res. Lett. 19, 333–336 (1992).

    Article  Google Scholar 

  33. Monetti, R. A., Havlin, S. & Bunde, A. Long-term persistence in the sea surface temperature fluctuations. Physica A 320, 581–589 (2003).

    Article  Google Scholar 

  34. Barbosa, S.M., Silva, M.E. & Fernandes, M.J. Time Series Analysis of Sea-Level Records: Characterising Long-Term Variability. In Nonlinear Time Series Analysis in the Geosciences. Lecture Notes in Earth Sciences Vol 112 (eds Donner, R.V. & Barbosa, S.M.) 157–173 (Springer, 2008).

  35. Bos, M. S., Williams, S. D. P., Araújo, I. B. & Bastos, L. The effect of temporal correlated noise on the sea level rate and acceleration uncertainty. Geophys. J. Int. 196, 1423–1430 (2013).

    Article  Google Scholar 

  36. Dangendorf, S. et al. Evidence for long-term memory in sea level. Geophys. Res. Lett. 41, 5530–5537 (2014).

    Article  Google Scholar 

  37. Marcos, M. et al. in Integrative Study of the Mean Sea Level and Its Components Vol. 58 (eds Cazenave, A. et al.) 337–356 (Springer, 2017).

  38. Becker, M., Karpytchev, M., Marcos, M., Jevrejeva, S. & Lennartz-Sassinek, S. Do climate models reproduce complexity of observed sea level changes? Geophys. Res. Lett. 43, 5176–5184 (2016).

    Article  Google Scholar 

  39. Peng, C.-K., Havlin, S., Stanley, H. E. & Goldberger, A. L. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5, 82–87 (1995).

    Article  CAS  Google Scholar 

  40. Feder, J. Fractals (Springer, 1988); https://doi.org/10.1007/978-1-4899-2124-6

  41. Talkner, P. & Weber, R. O. Power spectrum and detrended fluctuation analysis: application to daily temperatures. Phys. Rev. E 62, 150 (2000).

    Article  CAS  Google Scholar 

  42. Kantelhardt, J. W., Koscielny-Bunde, E., Rego, H. H., Havlin, S. & Bunde, A. Detecting long-range correlations with detrended fluctuation analysis. Physica A 295, 441–454 (2001).

    Article  Google Scholar 

  43. Gregory, J. M. et al. Concepts and terminology for sea level: mean, variability and change, both local and global. Surv. Geophys. 40, 1251–1289 (2019).

    Article  Google Scholar 

  44. Monselesan, D. P., O’Kane, T. J., Risbey, J. S. & Church, J. Internal climate memory in observations and models. Geophys. Res. Lett. 42, 1232–1242 (2015).

    Article  Google Scholar 

  45. Bloomfield, P. & Nychka, D. Climate spectra and detecting climate change. Climatic Change 21, 275–287 (1992).

    Article  Google Scholar 

  46. Lennartz, S. & Bunde, A. On the estimation of natural and anthropogenic trends in climate records. In Extreme Events and Natural Hazards: The Complexity Perspective (eds A.S. Sharma, A. Bunde, V.P. Dimri & D.N. Baker) 177–189 (2012). https://doi.org/10.1029/2011GM001079

  47. Gulev, S. K. et al. Changing state of the climate system. 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 287–422 (2021).

  48. Lennartz, S. & Bunde, A. Trend evaluation in records with long-term memory: application to global warming. Geophys. Res. Lett. 36, L16706 (2009).

    Article  Google Scholar 

  49. Tamazian, A., Ludescher, J. & Bunde, A. Significance of trends in long-term correlated records. Phys. Rev. E 91, 032806 (2015).

    Article  Google Scholar 

  50. Deser, C., Phillips, A. S., Alexander, M. A. & Smoliak, B. V. Projecting North American climate over the next 50 years: uncertainty due to internal variability. J. Clim. 27, 2271–2296 (2014).

    Article  Google Scholar 

  51. Hinkel, J. et al. Sea-level rise scenarios and coastal risk management. Nat. Clim. Change 5, 188–190 (2015).

    Article  Google Scholar 

  52. Pugh, D. T. Tides, Surges and Mean Sea-Level: A Handbook for Engineers and Scientists (Wiley, 1987).

  53. Hunter, J. Estimating sea-level extremes under conditions of uncertain sea-level rise. Climatic Change 99, 331–350 (2010).

    Article  Google Scholar 

  54. Hunter, J. R., Woodworth, P. L., Wahl, T. & Nicholls, R. J. Using global tide gauge data to validate and improve the representation of extreme sea levels in flood impact studies. Glob. Planet. Change 156, 34–45 (2017).

    Article  Google Scholar 

  55. Hunter, J. A simple technique for estimating an allowance for uncertain sea-level rise. Climatic Change 113, 239–252 (2012).

    Article  Google Scholar 

  56. Slangen et al. The impact of uncertainties in ice sheet dynamics on sea-level allowances at tide gauge locations. J. Mar. Sci. Eng. 5, 21 (2017).

    Article  Google Scholar 

  57. Woodworth, P. L., Hunter, J. R., Marcos, M. & Hughes, C. W. Towards reliable global allowances for sea level rise. Glob. Planet. Change 203, 103522 (2021).

    Article  Google Scholar 

  58. Almar, R. et al. A global analysis of extreme coastal water levels with implications for potential coastal overtopping. Nat. Commun. 12, 3775 (2021).

    Article  CAS  Google Scholar 

  59. Hoornweg, D. & Pope, K. Population predictions for the world’s largest cities in the 21st century. Environ. Urban. 29, 195–216 (2017).

    Article  Google Scholar 

  60. Hurrell, J. W. et al. The Community Earth System Model: a framework for collaborative research. Bull. Am. Meteorol. Soc. 94, 1339–1360 (2013).

    Article  Google Scholar 

Download references

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 and Affiliations

  1. Littoral Environnement et Sociétés (LIENSs), UMR 7266 CNRS, La Rochelle Université, La Rochelle, France

    M. Becker & M. Karpytchev

  2. National Center for Atmospheric Research, Boulder, CO, USA

    A. Hu

Authors
  1. M. Becker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Karpytchev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Peer review

Peer review information

Nature Climate Change thanks Mark Carson 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.

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).

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

Becker, M., Karpytchev, M. & Hu, A. Increased exposure of coastal cities to sea-level rise due to internal climate variability. Nat. Clim. Chang. 13, 367–374 (2023). https://doi.org/10.1038/s41558-023-01603-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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