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Data-driven reconstruction reveals large-scale ocean circulation control on coastal sea level

Nature Climate Change volume 11, pages 514–520 (2021)Cite this article

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Abstract

Understanding historical and projected coastal sea-level change is limited because the impact of large-scale ocean dynamics is not well constrained. Here, we use a global set of tide-gauge records over nine regions to analyse the relationship between coastal sea-level variability and open-ocean steric height, related to density fluctuations. Interannual-to-decadal sea-level variability follows open-ocean steric height variations along many coastlines. We extract their common modes of variability and reconstruct coastal sterodynamic sea level, which is due to ocean density and circulation changes, based on steric height observations. Our reconstruction, tested in Earth system models, explains up to 91% of coastal sea-level variability. Combined with barystatic components related to ocean mass change and vertical land motion, the reconstruction also permits closure of the coastal sea-level budget since 1960. We find ocean circulation has dominated coastal sea-level budgets over the past six decades, reinforcing its importance in near-term predictions and coastal planning.

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Fig. 1: Relative roles of ocean-bottom pressure and steric components for coastal SDSL.
Fig. 2: Origin and reconstruction of coastal SDSL changes.
Fig. 3: Coastal sea-level budget over 1950 to 2012.
Fig. 4: Scaling of local and global SDSL.

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Data availability

The tide-gauge data used in this study is publicly available from the Permanent Service of Mean Sea Level (https://www.psmsl.org/), while the budget components at individual locations are accessible from ref. 4 and/or from the cited literature in Methods. The vertical land motion estimates at each location are available from the corresponding author upon request. The budget components for the nine regions are available as medians with confidence intervals via GitHub and Zenodo79. All CMIP5 and CMIP6 models are available under https://esgf-node.llnl.gov/search/cmip5/ and https://esgf-node.llnl.gov/search/cmip6/, respectively.

Code availability

The code to perform the EOF approach (together with an example dataset) is available from the GitHub/Zenodo webpage of the corresponding author79. Codes for the production of the figures are available from the corresponding author upon request.

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Acknowledgements

S.D. acknowledges the NASA grant no. 80NSSC20K1241, a visiting fellowship from the Stockholm University and the University of Siegen for funding a research stay at Jet Propulsion Laboratory. L.C. is funded by the Swedish Space Agency through the FiNNESS project (Dnr 133/17). We acknowledge C. Piecuch for providing critical comments on an earlier version of the manuscript. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Supplementary Table 2 of this paper) for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

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Authors and Affiliations

  1. Center for Coastal Physical Oceanography, Department of Ocean and Earth Sciences, Old Dominion University, Norfolk, VA, USA

    Sönke Dangendorf, John M. Klinck & Tal Ezer

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

    Thomas Frederikse & Benjamin D. Hamlington

  3. Department of Meteorology and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

    Léon Chafik

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  1. Sönke Dangendorf
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Contributions

S.D. designed and performed the research and wrote the first draft of the paper. T.F. contributed budget and ESM datasets to the study. B.H. helped in the design of the EOF technique. All authors shared ideas and contributed to the writing of the manuscript.

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Correspondence to Sönke Dangendorf.

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Extended data

Extended Data Fig. 1 Performance of ocean reanalysis in simulating SDSL variability and trends.

Shown are linear trends of SDSL as simulated by the a SODAsi.378, b SODA 2.2.480, c ORA S481, d ORA20C82,83, and e GECCO214 reanalysis over the common period from 1960 to 2012. For all reanalysis systems, the model internal global average has been replaced by ref. 24. In b and d, data is only available until 2008 and 2009, respectively. Grey dots show the 89 tide-gauge locations used in this study. f Linear trends in SDSLTG from the virtual stations of the nine coastal regions (grey bars) are compared to trends calculated from SDSL as simulated by the five ocean reanalysis systems (nearest-neighbour series). d, Correlations between detrended SDSLTG and SDSL from the five ocean reanalysis systems. The grey shadings separate the different regions from each other.

Extended Data Fig. 2 Tide-gauge coherence and virtual stations for each region.

a, Cross-Correlation matrix for the 89 tide-gauge records ordered by region. Black boxes mark the locations of the selected tide-gauge records for each region. b, The observed tide-gauge records (corrected for vertical land motion; coloured lines) together with the virtual station for each region (thick black line) that has been built based on gap-filled records (see Methods). The percentage of total data availability in each region is given in brackets.

Extended Data Fig. 3 Origin of coastal SDSL changes.

Spatial correlation patterns between the SDSL residuals at tide gauges (SDSLTG, grey dots) and steric height24 from the open ocean used to assemble Fig. 1a but for each of the nine coastal regions separately. Only significant correlations (P ≤ 0.05) are shown. a, North Sea, b NW Atlantic north of Cape Hatteras, c NW Atlantic south of Cape Hatteras, d NE Pacific, e Hawaii, f Japan Sea, g West Australia, h New Zealand, and i West Pacific.

Extended Data Fig. 4 Reconstruction of coastal SDSL changes.

Extension of Fig. 2b illustrating the reconstruction of SDSLEOF in observations (top) and in validation experiments with ESMs (here illustrated by the CNRM-CM6-1-HR, bottom) for each of the nine coastal regions. a, North Sea, b Northwest Atlantic north of Cape Hatteras, c Northwest Atlantic south of Cape Hatteras, d Northeast Pacific, e Hawaii, f Japan Sea, g West Australia, h New Zealand, and i West Pacific.

Extended Data Fig. 5 Linear trends in steric height and comparison of different observational products.

Shown are the linear trends in steric height calculated over the upper 2000m for different gridded observational products. a, ref. 24 and 67, b ref. 68, c EN469 with ref. 70 corrections, and d EN4 with ref. 71 corrections. The grey dots mark the locations of tide-gauge records used in this study. e, Linear trends for the SDSLEOF reconstructions in each region using the four different data products (grey bars = ref. 24 and67; blue = ref. 68; turquoise = EN469 with ref. 70 corrections; yellow = EN469 with ref. 71 corrections. f, Same as e but showing the correlation between SDSLEOF based on the different products and SDSLTG.

Extended Data Fig. 6 Validation of the vertical land motion (VLM) correction.

Comparison between observed trends (after the removal of barystatic gravitation, rotation and deformation terms) and residual VLM plus Glacial Isostatic Adjustment from the difference between tide gauges and the hybrid reconstruction from ref. 50 as well as the observed trends and residual VLM from Global Navigation Satellite System plus Glacial Isostatic Adjustment and the difference between tide-gauge and satellite altimetry as calculated by ref. 4 (Fred).

Extended Data Fig. 7 Validation of the EOF approach.

a, Shown are the time series of SDSLEOF based on reconstructions using principal components that have been calculated and regressed on the steric height from the open-ocean over the entire 1960 to 2012 period (and as used in the main paper) as well as those based on principal components that have been calculated (and regressed on the steric height from the open-ocean) over the period from 1980–2012. The grey shading marks the corresponding validation period from 1960–1979. b, The corresponding linear trends of SDSLEOF over the common period from 1960–2012. Shadings and error bars represent the 95% confidence intervals.

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Dangendorf, S., Frederikse, T., Chafik, L. et al. Data-driven reconstruction reveals large-scale ocean circulation control on coastal sea level. Nat. Clim. Chang. 11, 514–520 (2021). https://doi.org/10.1038/s41558-021-01046-1

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