Thermal displacement by marine heatwaves

Abstract

Marine heatwaves (MHWs)—discrete but prolonged periods of anomalously warm ocean temperatures—can drastically alter ocean ecosystems, with profound ecological and socioeconomic impacts1,2,3,4,5,6,7,8. Considerable effort has been directed at understanding the patterns, drivers and trends of MHWs globally9,10,11. Typically, MHWs are characterized on the basis of their intensity and persistence at a given location—an approach that is particularly relevant for corals and other sessile organisms that must endure increased temperatures. However, many ecologically and commercially important marine species respond to environmental disruptions by relocating to favourable habitats, and dramatic range shifts of mobile marine species are among the conspicuous impacts of MHWs1,4,12,13. Whereas spatial temperature shifts have been studied extensively in the context of long-term warming trends14,15,16,17,18, they are unaccounted for in existing global MHW analyses. Here we introduce thermal displacement as a metric that characterizes MHWs by the spatial shifts of surface temperature contours, instead of by local temperature anomalies, and use an observation-based global sea surface temperature dataset to calculate thermal displacements for all MHWs from 1982 to 2019. We show that thermal displacements during MHWs vary from tens to thousands of kilometres across the world’s oceans and do not correlate spatially with MHW intensity. Furthermore, short-term thermal displacements during MHWs are of comparable magnitude to century-scale shifts inferred from warming trends18, although their global spatial patterns are very different. These results expand our understanding of MHWs and their potential impacts on marine species, revealing which regions are most susceptible to thermal displacement, and how such shifts may change under projected ocean warming. The findings also highlight the need for marine resource management to account for MHW-driven spatial shifts, which are of comparable scale to those associated with long-term climate change and are already happening.

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Fig. 1: MHWs and their influence on thermal habitat redistribution globally.
Fig. 2: Dependence of thermal displacement on MHW intensity and background SST gradients.
Fig. 3: Thermal displacements for select locations subject to notable MHWs.
Fig. 4: SST and thermal displacement changes under projected 21st-century warming.

Data availability

NOAA High Resolution OISSTv2 data were obtained from NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, at https://www.esrl.noaa.gov/psd/. CMIP5 outputs were obtained from Earth System Grid Federation (https://esgf-node.llnl.gov/projects/cmip5/). The CMIP5 ensemble mean SST fields used in this analysis are available from J.D.S. (james.d.scott@noaa.gov).

Code availability

All analyses were performed using MATLAB. Codes can be accessed at https://github.com/mjacox/Thermal_Displacement.

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Acknowledgements

The idea for this study derived from discussions of the NOAA/NMFS Spatial Indicators working group, led by L. Barnett and E. Ward. We thank L. Barnett, C. Harvey, M. Hunsicker and A. O. Shelton for discussions and for comments on an earlier version of the manuscript. This study was supported by funding from the NOAA Climate Program Office’s Coastal and Ocean Climate Applications programme, the Modeling, Analysis, Predictions, and Projections programme and the NOAA Fisheries Office of Science and Technology (grants NA17OAR4310268 and NA17OAR4310108).

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Authors

Contributions

M.G.J. conceived the study, performed the heatwave analysis and drafted the manuscript. M.A.A. and S.J.B. contributed to the interpretation and presentation of the results. J.D.S. processed the CMIP5 output. All authors revised the manuscript.

Corresponding author

Correspondence to Michael G. Jacox.

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The authors declare no competing interests.

Additional information

Peer review information Nature thanks Mark R. Payne, Laurene Pecuchet, Robert Schlegel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Distributions of MHW intensity and thermal displacement.

a, b, Histograms of MHW intensity (a) and thermal displacement (b) are shown for months with active MHWs from 1982 to 2019, aggregated across all OISST grid cells without ice cover. Vertical lines indicate medians (solid blue lines), 25th and 75th percentiles (dashed blue lines) and means (solid red lines) of each distribution.

Extended Data Fig. 2 Statistics of MHW intensity and thermal displacement.

ah, Median (a, b), 25th–75th percentile range (c, d), minimum (e, f) and maximum (g, h) values of the MHW intensity (a, c, e, g) and thermal displacement (b, d, f, h) calculated across all MHW events from 1982 to 2019.

Extended Data Fig. 3 Spatial variability in thermal displacement is more dependent on spatial SST gradients than on MHW intensity.

a, b, Colours represent the number of 0.25° OISST grid cells that fall into each bin of thermal displacement and MHW intensity (a) or SST gradient (b). The sum of grid cells in all bins is the total number of ice-free OISST grid cells (n ≈ 500,000). Spearman rank correlations are r = −0.27 (a) and r = −0.81 (b).

Extended Data Fig. 4 Temporal variability in thermal displacement is dependent on MHW intensity for much of the global ocean.

Spearman rank correlation coefficients between MHW intensity and thermal displacement are shown for each grid cell. Locations where correlations are insignificant at the 95% significance level are greyed out. Significance calculations assume that each MHW event in a given location is statistically independent.

Extended Data Fig. 5 Thermal displacement methodology.

Steps for calculating thermal displacement are illustrated for a sample location in the Gulf of Alaska (145° W, 50° N). For each ice-free grid cell in the global ocean (n ≈ 500,000), the following steps are taken. a, The 1982–2011 monthly climatological temperature (grey) is calculated from the OISSTv2 data (magenta). b, The monthly climatology is subtracted to obtain monthly anomalies (magenta), which are then linearly detrended (black). c, MHWs (red) are identified as months in which the detrended SST anomaly (black) exceeds a seasonally varying 90th-percentile threshold (dotted black line). For each month with an MHW occurring (August 2019 is highlighted here for example), the detrended SST anomaly (1.3 °C in this case; d) is subtracted from the observed SST (10.3 °C; e) to obtain the ‘normal’ temperature for that month of the year corrected for the warming trend (9.0 °C). e, Thermal displacement is the shortest distance (521 km; white arrow) to SST at or below the ‘normal’ temperature (cyan contour). For the future projections, the same methodology is used after adding the mean projected SST change to the time series in a.

Extended Data Fig. 6 Frequency and duration of MHW events.

ac, For each grid cell, MHW frequency (a), median duration (b) and maximum duration (c), calculated from monthly mean SST anomalies, are shown for 1982–2019.

Extended Data Fig. 7 MHW definitions based on daily versus monthly SST data are consistent.

ai, SST anomaly time series are shown for each of the locations in Fig. 3. Daily data are shown as lines and vertical bars depict monthly data. MHWs defined from the daily data (using a 90th-percentile threshold, a five-day minimum duration and at least two days separating distinct events) are shown as red lines. MHWs defined from the monthly data (using a 90th-percentile threshold and a one-month minimum duration) are shown as purple bars. The SST anomaly thresholds used to define MHWs in each location are shown as red dashed (daily) and purple dotted (monthly) lines, which are often overlapping.

Extended Data Fig. 8 Marine heatwaves and their influence on thermal habitat redistribution globally, calculated with a fixed historical baseline.

a, Median MHW intensity (the SST anomaly associated with an MHW) from 1982 to 2019, calculated at each grid cell from all months with an active MHW. c, Median thermal displacement associated with MHWs. Thermal displacements can be in any direction (see Methods). White regions have seasonal or permanent sea ice cover. b, d, Zonal median values of MHW intensity and thermal displacement, with bands indicating the 25th–75th and 10th–90th percentile ranges. In contrast to Fig. 1, MHWs here were calculated without detrending SST anomalies relative to the 1982–2011 climatology.

Extended Data Fig. 9 Dependence of thermal displacement on MHW intensity and background SST gradients, calculated with a fixed historical baseline.

a, Horizontal SST gradients (colour) and mean SST (contours ranging 2–28 °C at 2 °C intervals), with sample locations indicated by coloured markers. b, Thermal displacement as a function of monthly MHW intensity for all 1982–2019 MHWs in six sample regions, characterized by strong SST gradients (diamonds; Gulf Stream: purple, Antarctic Circumpolar Current: pink), weak SST gradients (squares; Tropical Indian Ocean: yellow), Eastern Tropical Pacific: orange) and coastal upwelling that provides cold refugia (circles; California Current System: green, Humboldt Current System: blue). In contrast to Fig. 2, MHWs here were calculated without detrending SST anomalies relative to the 1982–2011 climatology.

Extended Data Fig. 10 Thermal displacements for select locations subject to notable MHWs, calculated with a fixed historical baseline.

ad, For each region, displacements from select locations (diamonds) are shown for all months with an active MHW from 1982 to 2019 (open circles). Displacements and years of the most intense MHWs are also shown for each location (filled circles). Spatial scales differ between panels; for reference, displacement distances for labelled events are: in a, 1,039 km (Gulf of Alaska, 2014), 895 km and 807 km (US West Coast, 2005 and 2014, respectively); in b, 418 km and 161 km (Northwest Atlantic, 2012 and 2016, respectively); in c, 362 km (Western Australia, 2011), 526 km (Northern Australia 2016) and 251 km (Tasman Sea 2016), and in d, 2,354 km (Eastern Tropical Pacific, 2015), 2,135 km and 1,926 km (South American West Coast, 1997 and 2016, respectively). In contrast to Fig. 3, MHWs here were calculated without detrending SST anomalies relative to the 1982–2011 climatology. Background colour indicates 1982–2019 mean SST.

Extended Data Table 1 Influence of monthly averaging on MHW metrics

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Jacox, M.G., Alexander, M.A., Bograd, S.J. et al. Thermal displacement by marine heatwaves. Nature 584, 82–86 (2020). https://doi.org/10.1038/s41586-020-2534-z

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