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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).

References

  1. Bindoff, N. L. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 867–952 (IPCC, Cambridge Univ. Press, 2014).

  2. Hegerl, G. C. et al. Detecting greenhouse-gas-induced climate change with an optimal fingerprint method. J. Clim. 9, 2281–2306 (1996).

    Article  Google Scholar 

  3. Santer, B. D. et al. A search for human influences on the thermal structure of the atmosphere. Nature 382, 39–46 (1996).

    Article  CAS  Google Scholar 

  4. Tett, S. F. B., Mitchell, J. F. B., Parker, D. E. & Allen, M. R. Human influence on the atmospheric vertical temperature structure: detection and observations. Science 274, 1170–1173 (1996).

    Article  CAS  Google Scholar 

  5. Stott, P. A. et al. External control of 20th century temperature by natural and anthropogenic forcings. Science 290, 2133–2137 (2000).

    Article  CAS  Google Scholar 

  6. Santer, B. D. et al. Quantifying stochastic uncertainty in detection time of human-caused climate signals. Proc. Natl Acad. Sci. USA 116, 19821–19827 (2019).

    Article  CAS  Google Scholar 

  7. Santer, B. D. et al. Exceptional stratospheric contribution to human fingerprints on atmospheric temperature. Proc. Natl Acad. Sci. USA 120, e2300758120 (2023).

    Article  CAS  Google Scholar 

  8. Stott, P. A., Sutton, R. T. & Smith, D. M. Detection and attribution of Atlantic salinity changes. Geophys. Res. Lett. 35, L21702 (2008).

    Article  Google Scholar 

  9. Santer, B. D. et al. Incorporating model quality information in climate change detection and attribution studies. Proc. Natl Acad. Sci. USA 106, 14778–14783 (2009).

    Article  CAS  Google Scholar 

  10. Terray, L. et al. Near-surface salinity as nature’s rain gauge to detect human influence on the tropical water cycle. J. Clim. 25, 958–977 (2012).

    Article  Google Scholar 

  11. Pierce, D. W., Gleckler, P. J., Barnett, T. P., Santer, B. D. & Durack, P. J. The fingerprint of human-induced changes in the ocean’s salinity and temperature fields. Geophys. Res. Lett. 39, L21704 (2012).

    Article  Google Scholar 

  12. Marvel, K. & Bonfils, C. Identifying external influences on global precipitation. Proc. Natl Acad. Sci. USA 110, 19301–19306 (2013).

    Article  CAS  Google Scholar 

  13. Gillett, N. P., Fyfe, J. C. & Parker, D. E. Attribution of observed sea level pressure trends to greenhouse gas, aerosol, and ozone changes. Geophys. Res. Lett. 40, 2302–2306 (2013).

    Article  CAS  Google Scholar 

  14. Christidis, N. & Stott, P. A. Changes in the geopotential height at 500 hPa under the influence of external climatic forcings. Geophys. Res. Lett. 42, 10798–10806 (2015).

    Article  Google Scholar 

  15. Barnett, T. P. et al. Penetration of human-induced warming into the world’s oceans. Science 309, 284–287 (2005).

    Article  CAS  Google Scholar 

  16. Bilbao, R. A. F., Gregory, J. M., Bouttes, N., Palmer, M. D. & Stott, P. Attribution of ocean temperature change to anthropogenic and natural forcings using the temporal, vertical and geographical structure. Clim. Dynam. 53, 5389–5413 (2019).

    Article  Google Scholar 

  17. Santer, B. D. et al. Human influence on the seasonal cycle of tropospheric temperature. Science 361, eaas8806 (2018).

    Article  Google Scholar 

  18. Min, S.-K., Zhang, X., Zwiers, F. W. & Agnew, T. Human influence on Arctic sea ice detectable from early 1990s onwards. Geophys. Res. Lett. 35, L21701 (2008).

    Article  Google Scholar 

  19. Qian, C. & Zhang, X. Human influences on changes in the temperature seasonality in mid- to high-latitude land areas. J. Clim. 28, 5908–5921 (2015).

    Article  Google Scholar 

  20. Marvel, K. et al. Observed and projected changes to the precipitation annual cycle. J. Clim. 30, 4983–4995 (2017).

    Article  Google Scholar 

  21. Duan, J. et al. Detection of human influences on temperature seasonality from the nineteenth century. Nat. Sustain. 2, 484–490 (2019).

    Article  Google Scholar 

  22. Santer, B. D. et al. Robust anthropogenic signal identified in the seasonal cycle of tropospheric temperature. J. Clim. 35, 6075–6100 (2022).

    Article  Google Scholar 

  23. Dwyer, J. G., Biasutti, M. & Sobel, A. H. The effect of greenhouse gas-induced changes in SST on the annual cycle of zonal mean tropical precipitation. J. Clim. 27, 4544–4565 (2014).

    Article  Google Scholar 

  24. Alexander, M. A. et al. Projected sea surface temperatures over the 21st century: changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elementa 6, 9 (2018).

    Google Scholar 

  25. Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).

    Article  CAS  Google Scholar 

  26. Timmermann, A., Jin, F. F. & Collins, M. Intensification of the annual cycle in the tropical Pacific due to greenhouse warming. Geophys. Res. Lett. 31, L12208 (2004).

    Article  Google Scholar 

  27. Sobel, A. H. & Camargo, S. J. Projected future seasonal changes in tropical summer climate. J. Clim. 24, 473–487 (2011).

    Article  Google Scholar 

  28. Chen, C. & Wang, G. Role of North Pacific mixed layer in the response of SST annual cycle to global warming. J. Clim. 28, 9451–9458 (2015).

    Article  Google Scholar 

  29. Jo, A. R. et al. Future amplification of sea surface temperature seasonality due to enhanced ocean stratification. Geophys. Res. Lett. 49, e2022GL098607 (2022).

    Article  Google Scholar 

  30. Liu, F., Lu, J., Luo, Y., Huang, Y. & Song, F. On the oceanic origin for the enhanced seasonal cycle of SST in the midlatitudes under global warming. J. Clim. 33, 8401–8413 (2020).

    Article  Google Scholar 

  31. Dwyer, J. G., Biasutti, M. & Sobel, A. H. Projected changes in the seasonal cycle of surface temperature. J. Clim. 25, 6359–6374 (2012).

    Article  Google Scholar 

  32. Capotondi, A., Alexander, M. A., Bond, N. A., Curchitser, E. N. & Scott, J. D. Enhanced upper ocean stratification with climate change in the CMIP3 models. J. Geophys. Res. Oceans 117, C04031 (2012).

    Article  Google Scholar 

  33. Li, G. et al. Increasing ocean stratification over the past half-century. Nat. Clim. Change 10, 1116–1123 (2020).

    Article  Google Scholar 

  34. Po-Chedley, S. et al. Natural variability contributes to model–satellite differences in tropical tropospheric warming. Proc. Natl Acad. Sci. USA 118, e2020962118 (2021).

    Article  CAS  Google Scholar 

  35. Po-Chedley, S. et al. Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming. Proc. Natl Acad. Sci. USA 119, e2209431119 (2022).

    Article  CAS  Google Scholar 

  36. Zelinka, M. D. et al. Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett. 47, e2019GL085782 (2020).

    Article  Google Scholar 

  37. Santer, B. D. et al. Identifying human influences on atmospheric temperature. Proc. Natl Acad. Sci. USA 110, 26–33 (2013).

    Article  CAS  Google Scholar 

  38. Solomon, S. et al. Emergence of healing in the Antarctic ozone layer. Science 353, 269–274 (2016).

    Article  CAS  Google Scholar 

  39. Fyfe, J. C., Kharin, V. V., Santer, B. D., Cole, J. N. S. & Gillett, N. P. Significant impact of forcing uncertainty in a large ensemble of climate model simulations. Proc. Natl Acad. Sci. USA 118, e2016549118 (2021).

    Article  CAS  Google Scholar 

  40. Meehl, G. A. et al. Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv. 6, eaba1981 (2020).

    Article  Google Scholar 

  41. 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 

  42. Shi, J.-R., Kwon, Y.-O. & Wijffels, S. E. Two distinct modes of climate responses to the anthropogenic aerosol forcing changes. J. Clim. 35, 3445–3457 (2022).

    Article  Google Scholar 

  43. Shi, J.-R., Kwon, Y. & Wijffels, S. E. Subsurface ocean temperature responses to the anthropogenic aerosol forcing in the North Pacific. Geophys. Res. Lett. 50, e2022GL101035 (2023).

    Article  Google Scholar 

  44. Ribes, A. & Terray, L. Application of regularised optimal fingerprinting to attribution. Part II: application to global near-surface temperature. Clim. Dynam. 41, 2837–2853 (2013).

    Article  Google Scholar 

  45. Marvel, K. et al. Do responses to different anthropogenic forcings add linearly in climate models? Environ. Res. Lett. 10, 104010 (2015).

    Article  Google Scholar 

  46. Maher, N., McGregor, S., England, M. H. & Gupta, A. S. Effects of volcanism on tropical variability. Geophys. Res. Lett. 42, 6024–6033 (2015).

    Article  Google Scholar 

  47. O’Brien, J. P. & Deser, C. Quantifying and understanding forced changes to unforced modes of atmospheric circulation variability over the North Pacific in a coupled model large ensemble. J. Clim. 36, 19–37 (2023).

    Article  Google Scholar 

  48. Deser, C., Simpson, I. R., Phillips, A. S. & McKinnon, K. A. How well do we know ENSO’s climate impacts over North America, and how do we evaluate models accordingly? J. Clim. 31, 4991–5014 (2018).

    Article  Google Scholar 

  49. Fyfe, J. C., Saenko, O. A., Zickfeld, K., Eby, M. & Weaver, A. J. The role of poleward-intensifying winds on Southern Ocean warming. J. Clim. 20, 5391–5400 (2007).

    Article  Google Scholar 

  50. Shi, J.-R., Talley, L. D., Xie, S.-P., Liu, W. & Gille, S. T. Effects of buoyancy and wind forcing on Southern Ocean climate change. J. Clim. 33, 10003–10020 (2020).

    Article  Google Scholar 

  51. Shi, J.-R., Talley, L. D., Xie, S.-P., Peng, Q. & Liu, W. Ocean warming and accelerating Southern Ocean zonal flow. Nat. Clim. Change 11, 1090–1097 (2021).

    Article  Google Scholar 

  52. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  53. Gillett, N. P. et al. The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6. Geosci. Model Dev. 9, 3685–3697 (2016).

    Article  Google Scholar 

  54. Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. Atmos. 108, 4407 (2003).

    Article  Google Scholar 

  55. Huang, B. et al. Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J. Clim. 30, 8179–8205 (2017).

    Article  Google Scholar 

  56. Hirahara, S., Ishii, M. & Fukuda, Y. Centennial-scale sea surface temperature analysis and its uncertainty. J. Clim. 27, 57–75 (2014).

    Article  Google Scholar 

  57. Durack, P. J., Taylor, K. E., Ames, S., Po-Chedley, S. & Mauzey, C. PCMDI AMIP SST and Sea-Ice Boundary Conditions Version 1.1.8 (Earth System Grid Federation, 2022); https://doi.org/10.22033/ESGF/input4MIPs.16921

  58. Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C. & Wang, W. An improved in situ and satellite SST analysis for cimate. J. Clim. 15, 1609–1625 (2002).

    Article  Google Scholar 

  59. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Article  Google Scholar 

  60. Cheng, L. et al. Improved estimates of ocean heat content from 1960 to 2015. Sci. Adv. 3, e1601545 (2017).

    Article  Google Scholar 

  61. Chan, D. & Huybers, P. Correcting observational biases in sea surface temperature observations removes anomalous warmth during World War II. J. Clim. 34, 4585–4602 (2021).

    Article  Google Scholar 

  62. Gregory, J. M. et al. The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) contribution to CMIP6: investigation of sea-level and ocean climate change in response to CO2 forcing. Geosci. Model Dev. 9, 3993–4017 (2016).

    Article  CAS  Google Scholar 

  63. Hasselmann, K. On the Signal-To-Noise Problem in Atmospheric Response Studies (Royal Meteorological Society, 1979).

  64. Van Den Dool, H. M., Saha, S. & Johansson, Å. Empirical orthogonal teleconnections. J. Clim. 13, 1421–1435 (2000).

    Article  Google Scholar 

  65. Irving, D. B., Wijffels, S. & Church, J. A. Anthropogenic aerosols, greenhouse gases, and the uptake, transport, and storage of excess heat in the climate system. Geophys. Res. Lett. 46, 4894–4903 (2019).

    Article  CAS  Google Scholar 

  66. Shi, J.-R., Wijffels, S. E., Kwon, Y. & Xie, S. Interhemispheric contrasts of ocean heat content change reveals distinct fingerprints of anthropogenic climate forcings. Geophys. Res. Lett. 50, e2023GL102741 (2023).

    Article  Google Scholar 

  67. Allen, M. R. & Tett, S. F. B. Checking for model consistency in optimal fingerprinting. Clim. Dynam. 15, 419–434 (1999).

    Article  Google Scholar 

  68. Bonfils, C. J. W. et al. Human influence on joint changes in temperature, rainfall and continental aridity. Nat. Clim. Change 10, 726–731 (2020).

    Article  CAS  Google Scholar 

  69. Shi, J.-R., Santer, B. D., Kwon, Y.-O. & Wijffels, S. E. The emerging human influence on the seasonal cycle of sea surface temperature. Figshare https://doi.org/10.6084/m9.figshare.23271569 (2024).

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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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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.

Supplementary information

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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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