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Climate mode interactions amplify coastal flood risks and their seasonal predictability

Nature Geoscience volume 19, pages 317–324 (2026)Cite this article

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Abstract

Extreme coastal flooding often arises when large-scale climate patterns and local ocean–atmosphere variability combine to magnify water levels beyond what communities can withstand. Understanding and anticipating these interactions is essential for protecting vulnerable coastlines. Here we aim to determine how two major modes of climate variability—the El Niño/Southern Oscillation and the North Atlantic Oscillation—individually and jointly influence extreme coastal water levels worldwide. Using global observational and reanalysis datasets spanning 1958–2023, we analyse their separate effects and diagnose potential nonlinear interactions through statistical and process-based methods. We show that specific, seasonally aligned phases of these two climate modes interact nonlinearly, producing coastal water levels far higher than expected from either mode alone. These combinations enhance storm activity and wave conditions from the eastern seaboard of North America to western Europe and the Mediterranean. We further show that incorporating these nonlinear interactions into a conceptual climate model enables skilful seasonal predictions of coastal flooding hazards several months in advance, demonstrating the feasibility of reliable early-warning systems for coastal risk reduction.

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Fig. 1: Individual and compounded influence of climate modes on coastal risks.
Fig. 2: Coastal risk matrix: severity versus frequency.
Fig. 3: ENSO and NAO atmospheric teleconnections.
Fig. 4: Coastal flood risk seasonal predictions from the XRO.

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Data availability

The SSALTO/DUACS altimeter products were produced and distributed by the Copernicus Marine Environment Monitoring Service (http://marine.copernicus.eu/). Dynamical atmospheric corrections were produced by the Collecte Localisation Satellites Space Oceanography Division using the MOG2D model from Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS) and distributed by AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic data), with support from Centre National d’Etudes Spatiales (CNES) (http://www.aviso.altimetry.fr/). FES2016 tidal data are produced by LEGOS. ERA5 data were produced by the European Centre for Medium-Range Weather Forecasts and are available through Copernicus data centre (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview). Coastal topographical product AW3D30 is also freely available. Coastal DEM is distributed by Climate Central under a non-commercial license. Relevant data are available on request from the authors. The coastal risk dataset110 that supports the findings of this study is openly available in DataSuds repository (IRD, France) at https://dataverse.ird.fr/dataset.xhtml?persistentId=doi:10.23708/OUZPH4. Data reuse is granted under CC-BY license.

Code availability

The XRO model code is publicly available at https://github.com/senclimate/XRO.

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Acknowledgements

F.-F.J., S.Z., M.F.S. and J.B. were supported by the NOAA Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) Program Grant (NA23OAR4310602). R.A. is supported by the French ANR through the project GLOBCOASTS (ANR-22-ASTR-0013 GLOBCOASTS). B.D. acknowledges support from ANID (Concurso de Fortalecimiento al Desarrollo Cientıfico de Centros Regionales 2020-R20F0008-CEAZA, Fondecyt Regular N°1231174). This is IPRC publication 1820 and SOEST contribution 12054.

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Authors and Affiliations

  1. LEGOS, Université de Toulouse III, CNRS/IRD/UPS/CNES, Toulouse, France

    Julien Boucharel & Rafael Almar

  2. Department of Atmospheric Sciences, University of Hawaiʻi at Mānoa, Honolulu, HI, USA

    Julien Boucharel, Fei-Fei Jin & Sen Zhao

  3. International Pacific Research Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA

    Fei-Fei Jin & Malte F. Stuecker

  4. Department of Oceanography, University of Hawaiʻi at Mānoa, Honolulu, HI, USA

    Malte F. Stuecker

  5. CECI, Université de Toulouse III, CERFACS/CNRS, Toulouse, France

    Boris Dewitte

  6. Centro de Estudios Avanzados en Zonas Áridas (CEAZA), Coquimbo, Chile

    Boris Dewitte

  7. Center for Ecology and Sustainable Management of Oceanic Islands (ESMOI), Departamento de Biología Marina, Facultad de Ciencias del Mar, Universidad Católica del Norte, Coquimbo, Chile

    Boris Dewitte

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  4. Sen Zhao
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  5. Malte F. Stuecker
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J.B. and R.A. designed and conceptualized the study. J.B. conducted the analysis and wrote the initial version of the manuscript. S.Z. conducted the XRO coastal risk forecasts. J.B., R.A., F.-F.J., S.Z., M.F.S. and B.D. discussed the results and contributed to writing the manuscript.

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Correspondence to Julien Boucharel, Rafael Almar or Fei-Fei Jin.

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Extended data

Extended Data Fig. 1 Methodology of extraction of coastal flood risk time series.

(a) Schematic of the methodology used to identify coastal flooding risks explained in (b): when the anomalous extreme CWL, calculated as when the monthly aggregated interannual anomalies of the hourly total coastal water level (the sum of SLA composed of the dynamic, steric and surge components, tide and wave-induced water level (Runup)) reach one-third (33%) of the maximum coastal elevation within the first kilometer shown in (c). Panels adapted with permission from: a, ref. 111, HAL; c, ref. 15, Springer Nature Limited.

Extended Data Fig. 2 Coastal flood risk time series with different coastal elevation thresholds.

Observed interannual anomalies (in blue and above one standard deviation in black) of extreme coastal water levels reaching different maximum elevation thresholds: (a) 33%, (b) 50%, (c) 66%, and (d) 75%, averaged along the U.S. West Coast. (e), (f), (g), and (h) show the same for the U.S. East Coast. (i), (j), (k), and (l) show the same for Western Europe. (m), (n), (o), and (p) show the same for the Mediterranean Sea. Notable extreme events, as reported in the press or scientific literature, are highlighted and described to the right of the panels for each region.

Extended Data Fig. 3 Coastal flood risks climatology.

(a) Annual mean and (b) seasonal amplitude (maximum minus minimum of the monthly mean climatology) in (hours/month) of coastal risks calculated as when the monthly aggregated interannual anomalies of the hourly total coastal water level reach a third of the maximum coastal elevation.

Extended Data Fig. 4 Coastal flood risks exceedance probability.

Probability (in %) of the coastal risk (Extreme CWL (ECWL) exceeding the 33% threshold of maximum coastal elevation) anomalies exceeding the 95th percentile during (a), (b) positive phases of the EP (E+) and CP (C+) ENSO modes respectively; (c), (d) positive and negative phases of the NAO and for different combination of climate mode phases: (e) E + /NAO + , (f) C + /NAO + , (g) E + /NAO-, (h) C + /NAO-.

Extended Data Fig. 5 Additive versus multiplicative effects of climate modes phases on coastal flood risks.

Compounding influence of the different phases of ENSO and NAO: Percentage of changes in anomalous extreme CWL conditions during co-occurring phases of climate modes relative to the sum of anomalous extreme CWL conditions during the corresponding individual climate phases (C+ and NAO+ (a), C+ and NAO- (b), E+ and NAO+ (c) and E+ and NAO- (d)). Green, blue, yellow and red dots show the location where the compounded (that is, multiplicative) effects of climate modes (C+ and NAO+ (e), C+ and NAO- (f), E+ and NAO+ (g) and E+ and NAO- (h)) are significant compared to their additive effects at the 50%, 70%, 90% and 95% confidence level respectively based on a Student’s t-test.

Extended Data Fig. 6 Contribution of different components of coastal water level to coastal flood risks.

Contribution (in %) of different components (tide, wave, and SLA) to the monthly mean (left bar) and maximum (right bar) of the total coastal water level over all events when interannual total coastal water level anomalies are greater than one standard deviation averaged over the U.S. west coast (a), U.S. east coast (b), Western Europe (c) and the Mediterranean (d).

Extended Data Fig. 7 Annual Variability of climate mode phases and coastal flood risks.

(a) Monthly standardized variance of climate modes indices (the blue left y-axis corresponds to the EP and CP ENSO modes variance and the orange right y-axis corresponds to the NAO variance). (b) Monthly variance of extreme CWL calculated as when the monthly aggregated interannual anomalies of the hourly total coastal water level reach 33% of the maximum coastal elevation accumulated over different regions (US west coast in blue, US east coast in orange, western Europe in green and the Mediterranean Sea in purple). Monthly occurrence of different co-occurring phases of climate modes (c) E + , (d) C + , (e) NAO + , (f) NAO-, (g) E + /NAO + , (h) C + /NAO + , (i) E-/NAO-, (j) C + /NAO-. (k.) Scatter plots of the EP ENSO index versus the NAO index. The month of occurrence is shown in color when both the EP ENSO and NAO indices are simultaneously greater than one standard deviation, and when the EP ENSO index (E index) is greater while the NAO index is less than one standard deviation. (l.) Same as (k.) but for the CP ENSO (C) and NAO indices. (m.) Time evolution of the E and NAO indices during different EP El Niño events (thin blue and orange lines respectively) and during the composite of these events (thick lines). (n.) Same as (m.) but during CP El Niño events.

Extended Data Fig. 8 Nonlinear relationships between coastal flood risks and coastal water level contributors.

Nonlinear relationships between the extreme coastal water level interannual anomalies (reaching at least one-third of the maximum coastal elevation) and its contributor’s interannual anomalies along the US west coast: wave (a) and SLA (b) monthly mean and tide monthly maximum (c). (d), (e) and (f) are the same as (a), (b) and (c) but along the western European coasts. (g), (h) and (i) are the same as (a), (b) and (c) but along the US east coast. (j), (k) and (l) are the same as (a), (b) and (c) but along the Mediterranean coast. The red lines show the best-fit third-order nonlinear curve.

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Boucharel, J., Almar, R., Jin, FF. et al. Climate mode interactions amplify coastal flood risks and their seasonal predictability. Nat. Geosci. 19, 317–324 (2026). https://doi.org/10.1038/s41561-025-01903-0

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