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:

Progress and future directions in constraining uncertainties in sea-level projections using observations

Nature Climate Change volume 15, pages 1039–1051 (2025)Cite this article

Subjects

Abstract

Coastal planning, mitigation and adaptation efforts rely on credible sea-level projections generated by physical models. However, the large uncertainties in these projections pose a challenge for policymakers. Here we provide an overview of the main sources of uncertainty in model projections of sea-level change on multi-decadal to centennial timescales and we offer perspectives on the use of observations to narrow uncertainties. We propose several directions for future research, including improvements in emerging emulation techniques, more systematic quantification of uncertainty structure within both observations and models, lengthening observational records of processes, and expanding collaborations across physical and social sciences. Advancements in these areas are urgently needed, as the next phase of the Intergovernmental Panel on Climate Change assessment cycle gets underway.

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: Uncertainty in IPCC AR6 projections of component contributions to the 20-year averaged global mean SLC and local relative SLC for two sites.
Fig. 2: Comparison of present-day glacier mass-change estimates from remote-sensing observations and model simulations for four example glacierized regions.
Fig. 3: Probabilistic ice sheet projections, constrained by observations, from previous studies for the Greenland ice sheet and Antarctic ice sheet.
Fig. 4: TWS trends from remote-sensing observations and models.
Fig. 5: Regional SDSL patterns from observations and models.
Fig. 6: GIA and contemporary GRD rates from an observationally constrained model.

Similar content being viewed by others

References

  1. Kulp, S. A. & Strauss, B. H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat. Commun. 10, 4844 (2019).

    Article  CAS  Google Scholar 

  2. Kopp, R. E. et al. Usable science for managing the risks of sea-level rise. Earth’s Future 7, 1235–1269 (2019).

    Article  Google Scholar 

  3. Cash, D. W. et al. Knowledge systems for sustainable development. Proc. Natl Acad. Sci. USA 100, 8086–8091 (2003).

    Article  CAS  Google Scholar 

  4. Fox-Kemper, B. et al. 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 (eds Masson-Delmotte, V. et al.) 1211–1362 (Cambridge Univ. Press, 2021); https://doi.org/10.1017/9781009157896.011

  5. Kopp, R. E. et al. Communicating future sea-level rise uncertainty and ambiguity to assessment users. Nat. Clim. Chang. 13, 648–660 (2023).

    Article  Google Scholar 

  6. Haasnoot, M. et al. Generic adaptation pathways for coastal archetypes under uncertain sea-level rise. Environ. Res. Commun. 1, 071006 (2019).

    Article  Google Scholar 

  7. 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 

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

    Article  Google Scholar 

  9. Pattyn, F., Favier, L., Sun, S. & Durand, G. Progress in numerical modeling of Antarctic ice-sheet dynamics. Curr. Clim. Change Rep. 3, 174–184 (2017).

    Article  Google Scholar 

  10. Goelzer, H., Robinson, A., Seroussi, H. & van de Wal, R. S. W. Recent progress in Greenland ice sheet modelling. Curr. Clim. Change Rep. 3, 291–302 (2017).

    Article  Google Scholar 

  11. Steinberg, J. M., Piecuch, C. G., Hamlington, B. D., Thompson, P. R. & Coats, S. Influence of deep-ocean warming on coastal sea-level decadal trends in the Gulf of Mexico. J. Geophys. Res. 129, e2023JC019681 (2024).

    Article  Google Scholar 

  12. Garner, A. J. et al. Evolution of 21st century sea level rise projections. Earth’s Future 6, 1603–1615 (2018). This article shows how twenty-first century sea-level change projections, and the associated uncertainties, have evolved over time in the research literature.

    Article  Google Scholar 

  13. Oppenheimer, M., O’Neill, B. C. & Webster, M. Negative learning. Clim. Change 89, 155–172 (2008). This article discusses the risk that new technical information, when interpreted wrongly, can cause scientific beliefs to become less rather than more accurate.

    Article  Google Scholar 

  14. Kopp, R. E. et al. Evolving understanding of Antarctic ice-sheet physics and ambiguity in probabilistic sea-level projections. Earth’s Future 5, 1217–1233 (2017).

    Article  Google Scholar 

  15. Hamlington, B. D. et al. Understanding of contemporary regional sea-level change and the implications for the future. Rev. Geophys. 58, e2019RG000672 (2020).

    Article  Google Scholar 

  16. WCRP Global Sea Level Budget Group Global sea-level budget 1993–present. Earth Syst. Sci. Data 10, 1551–1590 (2018).

    Article  Google Scholar 

  17. Frederikse, T. et al. The causes of sea-level rise since 1900. Nature 584, 393–397 (2020).

    Article  CAS  Google Scholar 

  18. Camargo, C. M. L. et al. Regionalizing the sea-level budget with machine learning techniques. Ocean Sci. 19, 17–41 (2023).

    Article  Google Scholar 

  19. Dangendorf, S. et al. Probabilistic reconstruction of sea-level changes and their causes since 1900. Earth Syst. Sci. Data 16, 3471–3494 (2024).

    Article  Google Scholar 

  20. Bouih, M. et al. Regional sea level budget over 2004–2022. Ocean Sci. 21, 1425–1440 (2025).

    Article  Google Scholar 

  21. Hamlington, B. D. et al. The rate of global sea level rise doubled during the past three decades. Commun. Earth Environ. 5, 601 (2024).

    Article  Google Scholar 

  22. Nerem, R. S., Frederikse, T. & Hamlington, B. D. Extrapolating empirical models of satellite-observed global mean sea level to estimate future sea level change. Earth’s Future 10, e2021EF002290 (2022).

    Article  Google Scholar 

  23. Kopp, R. E. et al. The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geosci. Model Dev. 16, 7461–7489 (2023). This article describes the methodology that was used in IPCC AR6 to integrate projections of sea-level components into relative, regional SLC and the associated uncertainty.

    Article  Google Scholar 

  24. Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model Dev. 13, 3571–3605 (2020).

    Article  CAS  Google Scholar 

  25. van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).

    Article  Google Scholar 

  26. IPCC. Climate Change 2022: Mitigation of Climate Change: Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2022); https://doi.org/10.1017/9781009157926

  27. Marzeion, B. et al. Partitioning the uncertainty of ensemble projections of global glacier mass change. Earth’s Future 8, e2019EF001470 (2020). This article provides community glacier mass change estimates used in IPCC AR6 and quantifies how the sources of uncertainty vary over time.

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  29. Berthier, E. et al. Measuring glacier mass changes from space—a review. Rep. Prog. Phys. 86, 036801 (2023).

    Article  CAS  Google Scholar 

  30. Wouters, B., Gardner, A. S. & Moholdt, G. Global glacier mass loss during the GRACE satellite mission (2002–2016). Front. Earth Sci. https://doi.org/10.3389/feart.2019.00096 (2019).

  31. Ciracì, E., Velicogna, I. & Swenson, S. Continuity of the mass loss of the world’s glaciers and ice caps from the GRACE and GRACE follow-on missions. Geophys. Res. Lett. 47, e2019GL086926 (2020).

    Article  Google Scholar 

  32. Jakob, L. & Gourmelen, N. Glacier mass loss between 2010 and 2020 dominated by atmospheric forcing. Geophys. Res. Lett. 50, e2023GL102954 (2023).

    Article  Google Scholar 

  33. Magruder, L. A. et al. Monitoring Earth’s climate variables with satellite laser altimetry. Nat. Rev. Earth Environ. 5, 120–136 (2024).

    Article  Google Scholar 

  34. Braun, M. H. et al. Constraining glacier elevation and mass changes in South America. Nat. Clim. Change 9, 130–136 (2019).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  36. Geyman, E. C., J. J. van Pelt, W., Maloof, A. C., Aas, H. F. & Kohler, J. Historical glacier change on Svalbard predicts doubling of mass loss by 2100. Nature 601, 374–379 (2022).

    Article  CAS  Google Scholar 

  37. Brinkerhoff, D. et al. The demise of the world’s largest piedmont glacier: a probabilistic forecast. Cryosphere 19, 2321–2353 (2025).

    Article  Google Scholar 

  38. Cook, S. J. et al. Committed ice loss in the European Alps until 2050 using a deep-learning-aided 3D ice-flow model with data assimilation. Geophys. Res. Lett. 50, e2023GL105029 (2023).

    Article  Google Scholar 

  39. Millan, R., Mouginot, J., Rabatel, A. & Morlighem, M. Ice velocity and thickness of the world’s glaciers. Nat. Geosci. 15, 124–129 (2022).

    Article  CAS  Google Scholar 

  40. Scher, C., Steiner, N. C. & McDonald, K. C. Mapping seasonal glacier melt across the Hindu Kush Himalaya with time series synthetic aperture radar (SAR). Cryosphere 15, 4465–4482 (2021).

    Article  Google Scholar 

  41. Lorrey, A. M. et al. Southern Alps equilibrium line altitudes: four decades of observations show coherent glacier–climate responses and a rising snowline trend. J. Glaciol. 68, 1127–1140 (2022).

    Article  Google Scholar 

  42. Nowicki, S. et al. Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models. Cryosphere 14, 2331–2368 (2020).

    Article  Google Scholar 

  43. Levermann, A. et al. Projecting Antarctica’s contribution to future sea level rise from basal ice shelf melt using linear response functions of 16 ice sheet models (LARMIP-2). Earth Syst. Dyn. 11, 35–76 (2020).

    Article  Google Scholar 

  44. Edwards, T. L. et al. Projected land ice contributions to twenty-first-century sea level rise. Nature 593, 74–82 (2021).

    Article  CAS  Google Scholar 

  45. Bamber, J. L., Oppenheimer, M., Kopp, R. E., Aspinall, W. P. & Cooke, R. M. Ice sheet contributions to future sea-level rise from structured expert judgment. Proc. Natl Acad. Sci. USA 116, 11195–11200 (2019).

    Article  CAS  Google Scholar 

  46. DeConto, R. M. et al. The Paris Climate Agreement and future sea-level rise from Antarctica. Nature 593, 83–89 (2021).

    Article  CAS  Google Scholar 

  47. Luo, X. & Nowicki, S. Model weighting for ISMIP6-Greenland based on observations and similarity among models. Ann. Glaciol. 66, e14 (2025).

    Article  Google Scholar 

  48. Aschwanden, A. & Brinkerhoff, D. J. Calibrated mass loss predictions for the Greenland ice sheet. Geophys. Res. Lett. 49, e2022GL099058 (2022). This article uses a two-stage calibration of a Greenland ice sheet model ensemble, using multiple types of satellite measurements as constraints.

    Article  Google Scholar 

  49. Applegate, P. J., Kirchner, N., Stone, E. J., Keller, K. & Greve, R. An assessment of key model parametric uncertainties in projections of Greenland ice sheet behavior. Cryosphere 6, 589–606 (2012).

    Article  Google Scholar 

  50. Edwards, T. L. et al. Revisiting Antarctic ice loss due to marine ice-cliff instability. Nature 566, 58–64 (2019). This article uses both paleo reconstructions and contemporary satellite observations to constrain model ensembles of Antarctic mass change, showing that the MICI process is not required in the model to plausibly reproduce the observations within their uncertainties.

    Article  CAS  Google Scholar 

  51. Coulon, V. et al. Disentangling the drivers of future Antarctic ice loss with a historically calibrated ice-sheet model. Cryosphere 18, 653–681 (2024).

    Article  Google Scholar 

  52. Nias, I. J., Nowicki, S., Felikson, D. & Loomis, B. Modeling the Greenland ice sheet’s committed contribution to sea level during the 21st century. J. Geophys. Res. 128, e2022JF006914 (2023).

    Article  Google Scholar 

  53. Ritz, C. et al. Potential sea-level rise from Antarctic ice-sheet instability constrained by observations. Nature 528, 115–118 (2015).

    Article  CAS  Google Scholar 

  54. Seroussi, H. et al. Insights into the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty. Cryosphere 17, 5197–5217 (2023).

    Article  Google Scholar 

  55. Tsai, C.-Y., Forest, C. E. & Pollard, D. Assessing the contribution of internal climate variability to anthropogenic changes in ice sheet volume. Geophys. Res. Lett. 44, 6261–6268 (2017).

    Article  Google Scholar 

  56. Robel, A. A., Seroussi, H. & Roe, G. H. Marine ice sheet instability amplifies and skews uncertainty in projections of future sea-level rise. Proc. Natl Acad. Sci. USA 116, 14887–14892 (2019).

    Article  CAS  Google Scholar 

  57. Otosaka, I. N. et al. Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020. Earth Syst. Sci. Data 15, 1597–1616 (2023).

    Article  Google Scholar 

  58. Felikson, D. et al. Choice of observation type affects Bayesian calibration of Greenland ice sheet model simulations. Cryosphere 17, 4661–4673 (2023).

    Article  Google Scholar 

  59. Zumwald, M. et al. Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles. WIREs Clim. Change 11, e654 (2020).

    Article  Google Scholar 

  60. Dutton, A. et al. Sea-level rise due to polar ice-sheet mass loss during past warm periods. Science 349, aaa4019 (2015).

    Article  CAS  Google Scholar 

  61. Gilford, D. M. et al. Could the last interglacial constrain projections of future Antarctic ice mass loss and sea-level rise? J. Geophys. Res. 125, e2019JF005418 (2020).

    Article  Google Scholar 

  62. Ju, J., Wu, C., Li, J., J.-F. Yeh, P. & Hu, B. X. Global evaluation of model agreement and uncertainty in terrestrial water storage simulations from ISIMIP 2b framework. J. Hydrol. 617, 129137 (2023). This article presents a comprehensive evaluation of 24 TWS models from the ISIMIP2b ensemble against GRACE observations.

    Article  Google Scholar 

  63. Telteu, C.-E. et al. Understanding each other’s models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication. Geosci. Model Dev. 14, 3843–3878 (2021).

    Article  CAS  Google Scholar 

  64. Scanlon, B. R. et al. Global water resources and the role of groundwater in a resilient water future. Nat. Rev. Earth Environ. 4, 87–101 (2023). This article provides a comprehensive review of the resilience of global water resources in the face of increasing climate variability, population growth and water scarcity, with a focus on the role of groundwater.

    Article  Google Scholar 

  65. Chen, J., Famigliett, J. S., Scanlon, B. R. & Rodell, M. Groundwater storage changes: present status from GRACE observations. Surv. Geophys. 37, 397–417 (2016).

    Article  Google Scholar 

  66. Gleeson, T. et al. GMD perspective: the quest to improve the evaluation of groundwater representation in continental- to global-scale models. Geosci. Model Dev. 14, 7545–7571 (2021).

    Article  Google Scholar 

  67. de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H. & Bierkens, M. F. P. A high-resolution global-scale groundwater model. Hydrol. Earth Syst. Sci. 19, 823–837 (2015).

    Article  Google Scholar 

  68. Scanlon, B. R. et al. Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. Proc. Natl Acad. Sci. USA 115, E1080 (2018).

    Article  CAS  Google Scholar 

  69. An, L. et al. Divergent causes of terrestrial water storage decline between drylands and humid regions globally. Geophys. Res. Lett. 48, e2021GL095035 (2021).

    Article  Google Scholar 

  70. Slangen, A. B. A. et al. Projecting twenty-first century regional sea-level changes. Clim. Change 124, 317–332 (2014).

    Article  CAS  Google Scholar 

  71. Lyu, K., Zhang, X. & Church, J. A. Regional dynamic sea level simulated in the CMIP5 and CMIP6 models: mean biases, future projections, and their linkages. J. Clim. 33, 6377–6398 (2020).

    Article  Google Scholar 

  72. Dangendorf, S. et al. Data-driven reconstruction reveals large-scale ocean circulation control on coastal sea level. Nat. Clim. Chang. 11, 514–520 (2021).

    Article  Google Scholar 

  73. Fasullo, J. T. & Nerem, R. S. Altimeter-era emergence of the patterns of forced sea-level rise in climate models and implications for the future. Proc. Natl Acad. Sci. USA 115, 12944–12949 (2018). This article uses climate model ensembles and altimetry data to identify the forced signal of sterodynamic SLC.

    Article  CAS  Google Scholar 

  74. Hamlington, B. D., Fasullo, J. T., Nerem, R. S., Kim, K.-Y. & Landerer, F. W. Uncovering the pattern of forced sea level rise in the satellite altimeter record. Geophys. Res. Lett. 46, 4844–4853 (2019).

    Article  Google Scholar 

  75. 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).

    Article  CAS  Google Scholar 

  76. Lyu, K., Zhang, X. & Church, J. A. Projected ocean warming constrained by the ocean observational record. Nat. Clim. Chang. 11, 834–839 (2021).

    Article  Google Scholar 

  77. Little, C. M., Yeager, S. G., Ponte, R. M., Chang, P. & Kim, W. M. Influence of ocean model horizontal resolution on the representation of global annual-to-multidecadal coastal sea level variability. J. Geophys. Res. 129, e2024JC021679 (2024).

    Article  Google Scholar 

  78. Zhang, X., Church, J. A., Monselesan, D. & McInnes, K. L. Sea level projections for the Australian region in the 21st century. Geophys. Res. Lett. 44, 8481–8491 (2017).

    Article  Google Scholar 

  79. Li, D. et al. The impact of horizontal resolution on projected sea-level rise along US east continental shelf with the community earth system model. J. Adv. Model. Earth Syst. 14, e2021MS002868 (2022).

    Article  Google Scholar 

  80. Calafat, F. M. & Chambers, D. P. Quantifying recent acceleration in sea level unrelated to internal climate variability. Geophys. Res. Lett. 40, 3661–3666 (2013).

    Article  Google Scholar 

  81. Haigh, I. D. et al. Timescales for detecting a significant acceleration in sea level rise. Nat. Commun. 5, 3635 (2014).

    Article  CAS  Google Scholar 

  82. Le Bars, D., Keizer, I. & Drijfhout, S. Constraining local ocean dynamic sea-level projections using observations. Ocean Sci. 21, 1303–1314 (2025).

    Article  Google Scholar 

  83. Hamlington, B. D. et al. Observation-based trajectory of future sea level for the coastal United States tracks near high-end model projections. Commun. Earth Environ. 3, 230 (2022).

    Article  Google Scholar 

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

    Article  Google Scholar 

  85. Tesdal, J.-E. et al. The contribution of sterodynamic changes to the uncertainty of regional sea-level projections over the 21st century. Preprint at ESS Open Archive https://doi.org/10.22541/essoar.174729528.84639181/v1 (2025).

  86. Bonan, D. B. et al. Observational constraints imply limited future Atlantic meridional overturning circulation weakening. Nat. Geosci. 18, 479–487 (2025). This article demonstrates the use of observational estimates of AMOC strength as an emergent constraint on projections.

    Article  CAS  Google Scholar 

  87. Larour, E., Ivins, E. R. & Adhikari, S. Should coastal planners have concern over where land ice is melting? Sci. Adv. 3, e1700537 (2017).

    Article  Google Scholar 

  88. Milne, G. A. & Mitrovica, J. X. Postglacial sea-level change on a rotating Earth. Geophys. J. Int. 133, 1–19 (1998).

    Article  Google Scholar 

  89. Gowan, E. J. et al. A new global ice sheet reconstruction for the past 80 000 years. Nat. Commun. 12, 1199 (2021).

    Article  CAS  Google Scholar 

  90. Nakada, M., Okuno, J., Lambeck, K. & Purcell, A. Viscosity structure of Earth’s mantle inferred from rotational variations due to GIA process and recent melting events. Geophys. J. Int. 202, 976–992 (2015).

    Article  Google Scholar 

  91. Caron, L. et al. GIA model statistics for GRACE hydrology, cryosphere, and ocean science. Geophys. Res. Lett. 45, 2203–2212 (2018). This article uses a formal Bayesian approach to quantifying GIA, using both paleo relative SLC estimates and modern GNSS data.

    Article  Google Scholar 

  92. Lin, Y., Whitehouse, P. L., Valentine, A. P. & Woodroffe, S. A. GEORGIA: a graph neural network based emulator for glacial isostatic adjustment. Geophys. Res. Lett. 50, e2023GL103672 (2023).

    Article  Google Scholar 

  93. Adhikari, S., Ivins, E. R., Frederikse, T., Landerer, F. W. & Caron, L. Sea-level fingerprints emergent from GRACE mission data. Earth Syst. Sci. Data 11, 629–646 (2019).

    Article  Google Scholar 

  94. Mitrovica, J. X. et al. On the robustness of predictions of sea level fingerprints. Geophys. J. Int. 187, 729–742 (2011).

    Article  Google Scholar 

  95. Adhikari, S., Ivins, E. R. & Larour, E. ISSM-SESAW v1.0: mesh-based computation of gravitationally consistent sea-level and geodetic signatures caused by cryosphere and climate driven mass change. Geosci. Model Dev. 9, 1087–1109 (2016).

    Article  Google Scholar 

  96. Al-Attar, D., Syvret, F., Crawford, O., Mitrovica, J. X. & Lloyd, A. J. Reciprocity and sensitivity kernels for sea level fingerprints. Geophys. J. Int. 236, 362–378 (2024).

    Article  Google Scholar 

  97. Gomez, N. et al. The influence of realistic 3D mantle viscosity on Antarctica’s contribution to future global sea levels. Sci. Adv. 10, eadn1470 (2024).

    Article  Google Scholar 

  98. Ivins, E. R., Caron, L. & Adhikari, S. Anthropocene isostatic adjustment on an anelastic mantle. J. Geod. 97, 92 (2023).

    Article  Google Scholar 

  99. Creel, R. C., Miesner, F., Wilkenskjeld, S., Austermann, J. & Overduin, P. P. Glacial isostatic adjustment reduces past and future Arctic subsea permafrost. Nat. Commun. 15, 3232 (2024).

    Article  CAS  Google Scholar 

  100. Adhikari, S. et al. Decadal to centennial timescale mantle viscosity inferred from modern crustal uplift rates in Greenland. Geophys. Res. Lett. 48, e2021GL094040 (2021).

    Article  Google Scholar 

  101. Nield, G. A. et al. Rapid bedrock uplift in the Antarctic Peninsula explained by viscoelastic response to recent ice unloading. Earth Planet. Sci. Lett. 397, 32–41 (2014).

    Article  CAS  Google Scholar 

  102. Lau, H. C. P. Transient rheology in sea level change: implications for Meltwater Pulse 1A. Earth Planet. Sci. Lett. 609, 118106 (2023).

    Article  CAS  Google Scholar 

  103. Simon, K. M., Riva, R. E. M. & Broerse, T. Identifying geographical patterns of transient deformation in the geological sea level record. J. Geophys. Res. 127, e2021JB023693 (2022).

    Article  Google Scholar 

  104. Shirzaei, M. et al. Measuring, modelling and projecting coastal land subsidence. Nat. Rev. Earth Environ. 2, 40–58 (2021).

    Article  Google Scholar 

  105. Buzzanga, B. et al. Localized uplift, widespread subsidence, and implications for sea level rise in the New York City metropolitan area. Sci. Adv. 9, eadi8259 (2023).

    Article  Google Scholar 

  106. Govorcin, M., Bekaert, D. P. S., Hamlington, B. D., Sangha, S. S. & Sweet, W. Variable vertical land motion and its impacts on sea level rise projections. Sci. Adv. 11, eads8163 (2025). This article demonstrates the use of high-resolution satellite-derived VLM rates to adjust projections of relative SLC, arguing for the need to include local VLM information in the global projections.

    Article  Google Scholar 

  107. Hammond, W. C., Blewitt, G., Kreemer, C. & Nerem, R. S. GPS imaging of global vertical land motion for studies of sea level rise. J. Geophys. Res. 126, e2021JB022355 (2021).

    Article  Google Scholar 

  108. Harvey, T. C. et al. Ocean mass, sterodynamic effects, and vertical land motion largely explain US coast relative sea level rise. Commun. Earth Environ. 2, 233 (2021).

    Article  Google Scholar 

  109. Wöppelmann, G. & Marcos, M. Vertical land motion as a key to understanding sea level change and variability. Rev. Geophys. 54, 64–92 (2016).

    Article  Google Scholar 

  110. Wu, P.-C., Wei, M. (Matt) & D’Hondt, S. Subsidence in coastal cities throughout the world observed by InSAR. Geophys. Res. Lett. 49, e2022GL098477 (2022).

    Article  Google Scholar 

  111. Poitevin, C. et al. Vertical land motion and relative sea level changes along the coastline of Brest (France) from combined space-borne geodetic methods. Remote Sens. Environ. 222, 275–285 (2019).

    Article  Google Scholar 

  112. Hamling, I. J., Wright, T. J., Hreinsdóttir, S. & Wallace, L. M. A snapshot of New Zealand’s dynamic deformation field from Envisat InSAR and GNSS observations between 2003 and 2011. Geophys. Res. Lett. 49, e2021GL096465 (2022).

    Article  Google Scholar 

  113. Oelsmann, J. et al. Regional variations in relative sea-level changes influenced by nonlinear vertical land motion. Nat. Geosci. 17, 137–144 (2024).

    Article  CAS  Google Scholar 

  114. Buzzanga, B. et al. Satellite-based vertical land motion for infrastructure monitoring: a prototype roadmap in Greater Houston, Texas. Sci. Rep. 15, 17041 (2025).

    Article  CAS  Google Scholar 

  115. Thiéblemont, R. et al. Assessing current coastal subsidence at continental scale: insights from Europe using the European ground motion service. Earth’s Future 12, e2024EF004523 (2024).

    Article  Google Scholar 

  116. Keogh, M. E. & Törnqvist, T. E. Measuring rates of present-day relative sea-level rise in low-elevation coastal zones: a critical evaluation. Ocean Sci. 15, 61–73 (2019).

    Article  Google Scholar 

  117. Deser, C. et al. Isolating the evolving contributions of anthropogenic aerosols and greenhouse gases: a new CESM1 large ensemble community resource. J. Clim. 33, 7835–7858 (2020).

    Article  Google Scholar 

  118. Rodgers, K. B. et al. Ubiquity of human-induced changes in climate variability. Earth Syst. Dynam. 12, 1393–1411 (2021).

    Article  Google Scholar 

  119. DeConto, R. M. & Pollard, D. Contribution of Antarctica to past and future sea-level rise. Nature 531, 591–597 (2016).

    Article  CAS  Google Scholar 

  120. Willis, J. K., Fournier, S., Marlis, K., Killett, E. & Sanchez, J. NASA-SSH along-track sea surface height from standardized reference missions, version 1. PO.DAAC https://doi.org/10.5067/NSREF-AT0V1 (2005).

Download references

Acknowledgements

D.F. was supported by NASA’s Sea-Level Change Team. D.R.R. was supported by NASA awards 80NSSC20K1296 and 80NSSC24K1530. J.F. was supported by NASA awards 80NSSC21K1191, 80NSSC17K0565 and 80NSSC22K0046. S.A. was supported by NASA’s Sea-Level Change Team (N-SLCT), Earth Surface and Interior (ESI) Focus Area and Modeling Analysis and Prediction (MAP) programme. B.B. was supported by the NASA Sea-Level Change Team and the Strategic Environmental Research and Development Program. S.D. was supported by NASA awards 80NSSC20K1241 and 80NSSC24K1529, by National Science Foundation award ICER-2103754, as part of the Megalopolitan Coastal Transformation Hub (MACH), and also acknowledges David and Jane Flowerree for their endowment. R.E.K. and P.K. were supported by National Science Foundation award ICER-2103754, as part of the MACH, and by Jet Propulsion Laboratory task number 105393.509496.02.08.13.31, as part of the NASA Sea-Level Change Team. R.B.L. was supported by NASA awards 80NSSC20K1296, 80NSSC20K1595, 80NSSC24K1576 and 80NSSC25K7225, and a National Academies of Sciences, Engineering and Medicine award 2000013282. J.T.R. was supported by NASA’s GRACE-FO Science Team. D.B. was supported by NASA award 80NSSC21K0748. B.C. was supported by NASA’s ICESat-2 and Sea-Level Change Teams. M.G. was supported by NASA award 80NSSC21K0321. R.S.N. was supported by NASA award 80NSSC20K1123. S.N. was supported by NASA’s Sea-Level Change Team. J.-E.T. was supported by award NA23OAR4320198 from the National Oceanic and Atmospheric Administration, US Department of Commerce. This is MACH contribution number 80. A portion of this research was conducted at the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA (80NM0018D0004).

Author information

Authors and Affiliations

  1. Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Denis Felikson

  2. Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    David R. Rounce & Matthew Weathers

  3. National Center for Atmospheric Research, National Science Foundation, Boulder, CO, USA

    John Fasullo

  4. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Angelica Rodriguez, Surendra Adhikari, Brett Buzzanga, J. T. Reager, Benjamin Hamlington, Erik R. Ivins & Eric Larour

  5. Joint Institute for Regional Earth System Science and Engineering, University of California Los Angeles, Los Angeles, CA, USA

    Brett Buzzanga

  6. Department of River-Coastal Science and Engineering, Tulane University, New Orleans, LA, USA

    Sönke Dangendorf

  7. Department of Earth and Planetary Sciences and Rutgers Climate and Energy Institute, Rutgers University, Piscataway, NJ, USA

    Robert E. Kopp & Praveen Kumar

  8. Earth Systems Research Center, University of New Hampshire, Durham, NH, USA

    Richard B. Lammers

  9. Department of Computer Science, University of Montana, Missoula, MT, USA

    Doug Brinkerhoff

  10. Department of Earth Sciences, University at Buffalo, Buffalo, NY, USA

    Beata Csatho & Sophie Nowicki

  11. Environmental Science and Policy Management, University of California Berkeley, Berkeley, CA, USA

    Manuela Girotto

  12. Colorado Center for Astrodynamics Research, Ann and H. J. Smead Aerospace Engineering Sciences and Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA

    R. Steven Nerem

  13. Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo, NY, USA

    Sophie Nowicki

  14. NOAA/OAR Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA

    Nicole-Jeanne Schlegel

  15. Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, NJ, USA

    Jan-Erik Tesdal

Authors
  1. Denis Felikson
    View author publications

    Search author on:PubMed Google Scholar

  2. David R. Rounce
    View author publications

    Search author on:PubMed Google Scholar

  3. John Fasullo
    View author publications

    Search author on:PubMed Google Scholar

  4. Angelica Rodriguez
    View author publications

    Search author on:PubMed Google Scholar

  5. Surendra Adhikari
    View author publications

    Search author on:PubMed Google Scholar

  6. Brett Buzzanga
    View author publications

    Search author on:PubMed Google Scholar

  7. Sönke Dangendorf
    View author publications

    Search author on:PubMed Google Scholar

  8. Robert E. Kopp
    View author publications

    Search author on:PubMed Google Scholar

  9. Richard B. Lammers
    View author publications

    Search author on:PubMed Google Scholar

  10. J. T. Reager
    View author publications

    Search author on:PubMed Google Scholar

  11. Doug Brinkerhoff
    View author publications

    Search author on:PubMed Google Scholar

  12. Beata Csatho
    View author publications

    Search author on:PubMed Google Scholar

  13. Manuela Girotto
    View author publications

    Search author on:PubMed Google Scholar

  14. Benjamin Hamlington
    View author publications

    Search author on:PubMed Google Scholar

  15. Erik R. Ivins
    View author publications

    Search author on:PubMed Google Scholar

  16. Praveen Kumar
    View author publications

    Search author on:PubMed Google Scholar

  17. Eric Larour
    View author publications

    Search author on:PubMed Google Scholar

  18. R. Steven Nerem
    View author publications

    Search author on:PubMed Google Scholar

  19. Sophie Nowicki
    View author publications

    Search author on:PubMed Google Scholar

  20. Nicole-Jeanne Schlegel
    View author publications

    Search author on:PubMed Google Scholar

  21. Jan-Erik Tesdal
    View author publications

    Search author on:PubMed Google Scholar

  22. Matthew Weathers
    View author publications

    Search author on:PubMed Google Scholar

Contributions

D.F. and D.R.R. conceived of the overall idea. D.F. led the preparation of the paper. D.F., D.R.R., J.F. and A.R. led discussions among the authors. D.F., D.R.R., J.F., S.A., B.B., S.D., R.E.K., R.B.L. and J.T.R. led the writing of individual subsections. D.F., J.F., S.A., R.E.K., R.B.L. and M.W. created the figures. All authors discussed the contents and contributed to the writing and editing of the paper.

Corresponding author

Correspondence to Denis Felikson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Andra Garner, Dewi Le Bars, Fred Richards and Xuebin Zhang 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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Felikson, D., Rounce, D.R., Fasullo, J. et al. Progress and future directions in constraining uncertainties in sea-level projections using observations. Nat. Clim. Chang. 15, 1039–1051 (2025). https://doi.org/10.1038/s41558-025-02437-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41558-025-02437-4

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