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Marine heatwaves under global warming

Nature volume 560, pages 360–364 (2018)Cite this article

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Abstract

Marine heatwaves (MHWs) are periods of extreme warm sea surface temperature that persist for days to months1 and can extend up to thousands of kilometres2. Some of the recently observed marine heatwaves revealed the high vulnerability of marine ecosystems3,4,5,6,7,8,9,10,11 and fisheries12,13,14 to such extreme climate events. Yet our knowledge about past occurrences15 and the future progression of MHWs is very limited. Here we use satellite observations and a suite of Earth system model simulations to show that MHWs have already become longer-lasting and more frequent, extensive and intense in the past few decades, and that this trend will accelerate under further global warming. Between 1982 and 2016, we detect a doubling in the number of MHW days, and this number is projected to further increase on average by a factor of 16 for global warming of 1.5 degrees Celsius relative to preindustrial levels and by a factor of 23 for global warming of 2.0 degrees Celsius. However, current national policies for the reduction of global carbon emissions are predicted to result in global warming of about 3.5 degrees Celsius by the end of the twenty-first century16, for which models project an average increase in the probability of MHWs by a factor of 41. At this level of warming, MHWs have an average spatial extent that is 21 times bigger than in preindustrial times, last on average 112 days and reach maximum sea surface temperature anomaly intensities of 2.5 degrees Celsius. The largest changes are projected to occur in the western tropical Pacific and Arctic oceans. Today, 87 per cent of MHWs are attributable to human-induced warming, with this ratio increasing to nearly 100 per cent under any global warming scenario exceeding 2 degrees Celsius. Our results suggest that MHWs will become very frequent and extreme under global warming, probably pushing marine organisms and ecosystems to the limits of their resilience and even beyond, which could cause irreversible changes.

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Fig. 1: Simulated changes in MHW characteristics for different levels of global warming.
Fig. 2: Observed and modelled trends in MHW characteristics over the satellite data taking period.
Fig. 3: Regional changes in probability of MHW days for different global warming levels.

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References

  1. Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).

    Article  ADS  Google Scholar 

  2. Scannell, H. A., Pershing, A. J., Alexander, M. A., Thomas, A. C. & Mills, K. E. Frequency of marine heatwaves in the North Atlantic and North Pacific since 1950. Geophys. Res. Lett. 43, 2069–2076 (2016).

  3. Marbà, N. & Duarte, C. M. Mediterranean warming triggers seagrass (Posidonia oceanica) shoot mortality. Glob. Change Biol. 16, 2366–2375 (2010).

    Article  ADS  Google Scholar 

  4. Wernberg, T. et al. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Change 3, 78–82 (2013).

    Article  ADS  Google Scholar 

  5. Di Lorenzo, E. & Mantua, N. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Change 6, 1042–1047 (2016).

    Article  ADS  Google Scholar 

  6. McCabe, R. M. et al. An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys. Res. Lett. 43, 10,366–10,376 (2016).

    Article  Google Scholar 

  7. Cavole, L. et al. Biological impacts of the 2013–2015 warm-water anomaly in the Northeast Pacific: winners, losers, and the future. Oceanography 29, 273–285 (2016).

    Article  Google Scholar 

  8. Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).

    Article  ADS  PubMed  CAS  Google Scholar 

  9. Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).

    Article  ADS  PubMed  CAS  Google Scholar 

  10. Oliver, E. C. J. et al. The unprecedented 2015/16 Tasman Sea marine heatwave. Nat. Commun. 8, 16101 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  11. Garrabou, J. et al. Mass mortality in Northwestern Mediterranean rocky benthic communities: effects of the 2003 heat wave. Glob. Change Biol. 15, 1090–1103 (2009).

    Article  ADS  Google Scholar 

  12. Mills, K. E. et al. Fisheries management in a changing climate: lessons form the 2012 ocean heat wave in the Northwest Atlantic. Oceanography 26, 191–195 (2014).

    Google Scholar 

  13. Caputi, N. et al. Management adaptation of invertebrate fisheries to an extreme marine heat wave event at a global warming hot spot. Ecol. Evol. 6, 3583–3593 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 350, 809–812 (2015).

    Article  ADS  PubMed  CAS  Google Scholar 

  15. Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  16. Climate Action Tracker. Improvement in warming outlook as India and China move ahead, but Paris Agreement gap still looms large (2017); https://climateactiontracker.org/documents/61/CAT_2017-11-15_ImprovementInWarmingOutlook_BriefingPaper.pdf.

  17. Pachauri, R. K. & Meyer, L. A. (eds.) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, Geneva, 2014).

  18. Walsh, J. E. et al. The high latitude marine heat wave of 2016 and its impacts on Alaska. Bull. Am. Meteorol. Soc. 99, 39–43 (2018).

    Article  Google Scholar 

  19. Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nat. Commun. 9, 650 (2018).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  20. Pearce, A. F. & Feng, M. The rise and fall of the ‘marine heat wave’ off Western Australia during the summer of 2010/2011. J. Mar. Syst. 111–112, 139–156 (2013).

    Article  Google Scholar 

  21. Bond, N. A., Cronin, M. F., Freeland, H. & Mantua, N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett. 42, 3414–3420 (2015).

    Article  ADS  Google Scholar 

  22. Banzon, V., Smith, T. M., Liu, C. & Hankins, W. A long-term record of blended satellite and in situ sea surface temperature for climate monitoring, modeling and environmental studies. Earth Syst. Sci. Data 8, 165–176 (2016).

    Article  ADS  Google Scholar 

  23. Morley, J. W., Batt, R. D. & Pinsky, M. L. Marine assemblages respond rapidly to winter climate variability. Glob. Change Biol. 23, 2590–2601 (2017).

    Article  ADS  Google Scholar 

  24. Frölicher, T. L., Rodgers, K. B., Stock, C. A. & Cheung, W. W. L. Sources of uncertainties in 21st century projections of potential ocean ecosystem stressors. Glob. Biogeochem. Cycles 30, 1224–1243 (2016).

    Article  ADS  CAS  Google Scholar 

  25. Carton, J. A., Ding, Y. & Arrigo, K. R. The seasonal cycle of the Arctic Ocean under climate change. Geophys. Res. Lett. 42, 7681–7686 (2015).

    Article  ADS  Google Scholar 

  26. Bindoff, N. L. et al. Detection and Attribution of Climate Change: from Global to Regional. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, Cambridge, 2013).

    Google Scholar 

  27. Comte, L. & Olden, J. D. Climatic vulnerability of the world’s freshwater and marine fishes. Nat. Clim. Change 7, 718–722 (2017).

    Article  ADS  Google Scholar 

  28. Logan, C. A., Dunne, J. P., Eakin, C. M. & Donner, S. D. Incorporating adaptive responses into future projections of coral bleaching. Glob. Change Biol. 20, 125–139 (2014).

    Article  ADS  Google Scholar 

  29. Donelson, J. M., Munday, P. L., McCormick, M. I. & Pitcher, C. R. Rapid transgenerational acclimation of a tropical reef fish to climate change. Nat. Clim. Change 2, 30–32 (2012).

    Article  ADS  Google Scholar 

  30. Stock, C. A. et al. Reconciling fisheries catch and ocean productivity. Proc. Natl Acad. Sci. USA 114, E1441–E1449 (2017).

    Article  PubMed  CAS  Google Scholar 

  31. Reynolds, R. W. et al. Daily high-resolution-blended analyses for sea surface temperature. J. Clim. 20, 5473–5496 (2007).

    Article  ADS  Google Scholar 

  32. Eakin, C. M. et al. Caribbean corals in crisis: record thermal stress, bleaching, and mortality in 2005. PLoS One 5, e13969 (2010).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  33. Stott, P., Stone, D. & Allen, M. Human contribution to the European heatwave of 2003. Nature 432, 610–614 (2004); corrigendum 436, 1200 (2005).

    Article  ADS  PubMed  CAS  Google Scholar 

  34. Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5, 560–564 (2015).

    Article  ADS  Google Scholar 

  35. Jacox, M. G., Alexander, M. A., Mantua, N. & Scott, J. D. Forcing of multiyear extreme ocean temperatures that impacted California Current marine resources in 2016. Bull. Am. Meteorol. Soc. 99, 27–33 (2018).

    Article  Google Scholar 

  36. Newman, M., Wittenberg, A. T., Cheng, L., Compo, G. P. & Smith, C. A. The extreme 2015/16 El Nino, in the context of historical climate variability and change. Bull. Am. Meteorol. Soc. 99, 16–20 (2018).

    Article  Google Scholar 

  37. Allen, M. R. & Ingram, W. J. Constraints on future changes in climate and the hydrologic cycle. Nature 419, 224–232 (2002); corrigendum 489, 590 (2012).

    Article  ADS  PubMed  CAS  Google Scholar 

  38. Angélil, O. et al. Attribution of extreme weather to anthropogenic greenhouse gas emissions: Sensitivity to spatial and temporal scales. Geophys. Res. Lett. 41, 2150–2155 (2014).

    Article  ADS  CAS  Google Scholar 

  39. King, A. D. et al. Emergence of heat extremes attributable to anthropogenic influences. Geophys. Res. Lett. 43, 3438–3443 (2016).

    Article  ADS  Google Scholar 

  40. Frölicher, T. L. & Paynter, D. J. Extending the relationship between global warming and cumulative carbon emissions to multi-millennial timescales. Environ. Res. Lett. 10, 075002 (2015).

    Article  ADS  CAS  Google Scholar 

  41. Fay, A. R. & McKinley, G. A. Global ocean biomes: mean and temporal variability. Earth Syst. Sci. Data 7, 107–128 (2014).

    Article  ADS  Google Scholar 

Download references

Acknowledgements

T.L.F. acknowledges support from the Swiss National Science Foundation under grant PP00P2_170687 and N.G. under grant XEBUS 200020_175787. E.M.F and N.G. acknowledge support from ETH Zürich. This work is part of the Nippon Foundation Nereus Program, a collaborative initiative by the Nippon Foundation and partners, including ETH Zürich. We thank the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP5, and the climate modelling groups (listed in Methods) for producing and making available their model output. We thank T. Stocker, F. Joos and C. Raible for discussions, U. Beyerle for downloading most of the CMIP5 data, and D. Kessler and M. Aschwanden for their initial analyses. The maps in Fig. 3 and Extended Data Figs. 1– 3 were created using the NCAR Command Language (https://www.ncl.ucar.edu).

Reviewer information

Nature thanks R. Asch, E. Oliver and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Authors and Affiliations

  1. Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland

    Thomas L. Frölicher

  2. Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

    Thomas L. Frölicher

  3. Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland

    Erich M. Fischer

  4. Environmental Physics, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, Zürich, Switzerland

    Nicolas Gruber

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Contributions

All authors designed the study. T.L.F. performed the analysis and wrote the manuscript together with N.G., with substantial input from E.M.F.

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Correspondence to Thomas L. Frölicher.

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Extended data figures and tables

Extended Data Fig. 1 Observed and simulated MHW characteristics exceeding the 1982–2016 99th percentile, averaged over the 1982–2016 period.

a, b, Differences between the 99th percentile in SST and the annual mean SST. c, d, Annual mean duration of MHWs. e, f, Maximum annual intensity of MHWs. g, h, Annual cumulative mean intensity of MHWs. Satellite-derived patterns (a, c, e, g) and CMIP5 multi-model mean patterns (b, d, f, h). The black contours in all panels highlight the pattern structures. The spatial correlation between the CMIP5 multi-model mean and the satellite-based estimates is r2 = 0.80 for a and b, r2 = 0.15 for c and d, r2 = 0.43 for e and f, and r2 = 0.18 for g and h.

Extended Data Fig. 2 Spatial extent of observed and simulated MHWs over the satellite data taking period.

a, Histogram of the spatial extent of satellite-observed MHWs above the climatological (1982–2016) 99th percentile for the 1982–1998 (blue) and 1999–2016 (red) period. b, The spatial pattern of the MHW with the largest extent in the satellite data taking period (1982–2016), which occurred on 2 September 2015 in the North Pacific and was associated with the ‘the Blob’21. It had a spatial extent of about twice the area of the United States (that is, 1.85 × 107 km2). Shown are SST anomalies above the 1982–2016 climatological 99th percentile on 2 September 2015. The colour bar shows degrees Celsius. c, Comparison between satellite-based observations (black line) and simulations (red histogram) of the spatial extent of MHWs above the climatological 99th percentile over the 1982–2016 period. The number of MHWs (y axis) is normalized with the total number of MHWs. Deeper red colour indicates a greater number of overlapping models.

Extended Data Fig. 3 Simulated multi-model mean changes in different MHW characteristics exceeding the preindustrial 99th percentile since preindustrial times for different global warming levels.

a–l, Changes in the maximum annual intensity (a, d, g, j), annual mean duration (b, e, h, k) and annual cumulative mean intensity (c, f, i, l) of MHWs for global warming of 1 °C (a–c), 1.5 °C (d–f), 2 °C (g–i) and 3.5 °C (j–l). The black contours highlight the pattern of changes.

Extended Data Fig. 4 Simulated changes in different MHW characteristics exceeding the preindustrial 99th percentile.

The data were obtained from a 10-member ensemble simulation with NCAR-DOE CESM34. a–d, The probability ratio (a), annual mean duration (b), maximum annual intensity (c) and annual cumulative mean intensity (d) of MHWs. The black lines show the individual ensemble members. The red line in a shows the probability ratio versus global warming for a reference period that is defined as the 99th percentile over the 2007–2026 period, obtained using all ten ensemble members. The ensemble members are initialized from different starting points (ocean, sea ice, land and atmosphere) in the preindustrial control simulation. The simulations follow the RCP 8.5 scenario over the 21st century. The time series are smoothed with a 20-year running mean.

Extended Data Fig. 5 Simulated changes in MHW characteristics for different global warming levels and different extreme thresholds.

a, c–e, Global annual mean probability ratio (a; logarithmic scale), duration (c), maximum intensity (d) and cumulative mean intensity (e) of MHWs versus different extreme thresholds for different changes in global mean surface air temperature. b, Changes in the ratio of the mean spatial extent of MHWs between global warming conditions and 1861–1880 conditions. Simulations following only the RCP 8.5 scenario are shown. The shaded areas indicate the maximum range simulated by the CMIP5 models.

Extended Data Fig. 6 Comparison between simulated changes in MHW characteristics exceeding the 99th percentile from a natural-forcing simulation and from a preindustrial control simulation using GFDL ESM2M forced with the RCP 8.5 scenario over the 21st century.

a–d, The probability ratio (a), annual mean duration (b), maximum annual intensity (c) and annual cumulative mean intensity (d) of MHWs. The red line shows the simulated changes exceeding the 99th percentile from a natural-forcing simulation of GFDL ESM2M forced with observed solar and volcanic boundary conditions, but with greenhouse gases and aerosol concentrations set to preindustrial. The black line shows the simulated changes exceeding the preindustrial 99th percentile. The time series are smoothed with a 20-year running mean.

Extended Data Fig. 7 Simulated changes in global SST as a function of global surface air temperature over land.

The light red and blue lines represent individual model projections, whereas the dark red and blue lines represent multi-model averages for the RCP 8.5 (red) and RCP 2.6 (blue) scenarios. The time series are smoothed with a 20-year running mean. The grey dashed 1:1 line is shown for reference.

Extended Data Table 1 Simulated changes in the annual mean spatial extent of MHWs relative to preindustrial times, and simulated annual mean duration, maximum and cumulative mean intensity of MHWs exceeding the preindustrial 99th percentile for different global warming levels
Full size table
Extended Data Table 2 Simulated probability ratio and fraction of attributable risk estimates averaged over the ocean and over land for different global warming levels and for different preindustrial percentile thresholds (99th and 99.99th)
Full size table
Extended Data Table 3 Global climate models used in this study
Full size table

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Frölicher, T.L., Fischer, E.M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018). https://doi.org/10.1038/s41586-018-0383-9

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