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The emerging human influence on the seasonal cycle of sea surface temperature

Nature Climate Change volume 14, pages 364–372 (2024)Cite this article

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Abstract

Gaining insight into anthropogenic influence on seasonality is of scientific, economic and societal importance. Here we show that a human-caused signal in the seasonal cycle of sea surface temperature (SST) has emerged from the noise of natural variability. Geographical patterns of changes in SST seasonal cycle amplitude (SSTAC) reveal two distinctive features: an increase at Northern Hemisphere mid-latitudes related to mixed-layer depth changes and a robust dipole pattern between 40° S and 55° S that is mainly driven by surface wind changes. The model-predicted pattern of SSTAC change is identifiable with high statistical confidence in four observed SST products and in 51 individual model realizations of historical climate evolution. Simulations with individual forcings reveal that GHG increases are the primary driver of changes in SSTAC, with smaller but distinct contributions from anthropogenic aerosol and ozone forcing. The robust human ‘fingerprint’ identified here is likely to have wide-ranging impacts on marine ecosystems.

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Fig. 1: Trends in SSTAC over the period 1950–2014.
Fig. 2: SSTAC fingerprint and S/N ratio analyses.
Fig. 3: S/N ratios and detection times from single-forcing runs and their linear combinations.
Fig. 4: Zonal mean trends over 1950–2014 in monthly mean SST, zonal wind stress and MLD.
Fig. 5: Zonal mean SSTAC changes from FAFMIP perturbation experiments relative to pre-industrial control simulations.
Fig. 6: Changes in annual cycle of SST tendency between 1950–1979 and 1985–2014.

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Data availability

The CMIP6 historical, single-forcing and FAFMIP simulation outputs are available via the Earth System Grid of the Program for Climate Model Diagnosis and Intercomparison (PCMDI): https://esgf-node.llnl.gov/search/cmip6/. HadISST data are available at: https://www.metoffice.gov.uk/hadobs/hadisst. ERSST data are available at: https://www.ncei.noaa.gov/products/extended-reconstructed-sst. COBE data are available at: https://psl.noaa.gov/data/gridded/data.cobe2.html. PCMDI data are available at: https://doi.org/10.22033/ESGF/input4MIPs.16921. ERA5 data are available at: https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5. IAP data are available at: https://climatedataguide.ucar.edu/climate-data/ocean-temperature-analysis-and-heat-content-estimate-institute-atmospheric-physics. The processed data are available via Figshare at https://doi.org/10.6084/m9.figshare.23271569 (ref. 69).

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Acknowledgements

We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which led the design of CMIP6 and coordinated the work, and we also thank individual climate modelling groups (listed in Supplementary Table 1) for their efforts in performing all of the model simulations analysed here. J.-R.S., Y.-O.K. and S.E.W. are supported by US National Science Foundation under grant number OCE-2048336. B.D.S. and Y.-O.K. were supported by the Francis E. Fowler IV Center for Ocean and Climate at Woods Hole Oceanographic Institution (WHOI).

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Authors and Affiliations

  1. Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Jia-Rui Shi, Benjamin D. Santer, Young-Oh Kwon & Susan E. Wijffels

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

    Benjamin D. Santer

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  1. Jia-Rui Shi
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Contributions

J.-R.S. and B.D.S. conceived the study. J.-R.S. conducted the analysis and wrote the first draft. J.-R.S., B.D.S., Y.-O.K. and S.E.W. contributed to interpreting the results, writing and editing the manuscript.

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Correspondence to Jia-Rui Shi or Benjamin D. Santer.

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Extended data

Extended Data Fig. 1 Spatial patterns and zonal mean of the climatology of SST annual cycle amplitude (SSTAC) from four different observational products and from the multi-model mean (MMM) of the HIST simulations.

a Average of four different observed SST datasets. b HIST MMM. c Zonal-mean climatology of the HIST MMM and individual observed SST datasets. d Monthly climatology of SST averaged between 30˚N-45˚N from observations (dashed curves) and the fits of the first harmonic obtained through Fourier analysis (solid curves). Results are calculated over 1950 to 2014.

Extended Data Fig. 2 Leading EOF of SSTAC estimated from the HIST MMM.

a-d Results for four different analysis periods. The explained variances are shown in brackets.

Extended Data Fig. 3 S/N ratios from two selected CMIP6 models.

Results are as in Fig. 2b, but the ‘model only’ S/N ratios here (in grey) are from two models only: CNRM-CM6-1 and MRI-ESM2-0. Individual realizations from each model can have appreciable differences in their S/N behavior.

Extended Data Fig. 4 Scatterplot between the climate sensitivity of the 10 CMIP6 models analyzed here and the final value of the S/N ratio for the 65-year analysis period from 1950 to 2014.

The effective climate sensitivities are based on the results from ref. 40. The correlation between ECS and S/N ratio at 2014 is 0.55.

Extended Data Fig. 5 S/N ratios from the GHG, AER, O3, and NAT single-forcing runs.

Results are based on use of the same HIST fingerprint, which is searched for in the SSTAC changes of each single-forcing run (Method 1). a–d MMM result (the black curve) from GHG (a), AER (b), O3 (c) and NAT (d) single-forcing runs and results from individual realizations (the grey curves). GHG, AER, and NAT results are from 10 models with a total of 51 realizations; only four models with a total of 26 realizations were available for calculating O3 S/N ratios. The horizontal purple line is the 5% significance level. For further details refer to Methods.

Extended Data Fig. 6 Zonal-mean monthly-mean SST trends over 1950 to 2014.

a The ensemble mean of four observed datasets. b-d The MMM of the HIST, GHG, and O3 simulations. In contrast to Fig. 4, the trends are not expressed as departures from annual-mean trends.

Extended Data Fig. 7 Zonal-mean monthly-mean SST trends over 1950 to 2014 in four observed datasets.

a-d Results from four observed datasets: HadISST (a), ERSST (b), COBE (c) and PCMDI (d). The results are expressed as departures from annual-mean trends.

Extended Data Fig. 8 Changes of annual cycle amplitude of SST tendency between 1950–1979 and 1985–2014 due to MLD changes.

a Changes of annual cycle when it is assumed to have a consistent summer MLD change for all 12 months (see Eq. 7). b Changes of annual cycle when MLD is assumed to shoal by 5% at every location and in every month relative to the background monthly value (see Eq. 8).

Extended Data Fig. 9 Zonal-mean monthly-mean trends over 1950 to 2014 in MLD and zonal wind stress.

a-b MLD trends from the IAP product and the MMM of the HIST simulations, respectively. Grey contours highlight the large MLD trends of −6 and −8 m/decade. c-d Zonal wind stress trends from ERA5 and the MMM of the HIST simulations.

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Supplementary Tables 1 and 2 and Figs. 1 and 2.

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Shi, JR., Santer, B.D., Kwon, YO. et al. The emerging human influence on the seasonal cycle of sea surface temperature. Nat. Clim. Chang. 14, 364–372 (2024). https://doi.org/10.1038/s41558-024-01958-8

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