Intensification and spatial homogenization of coastal upwelling under climate change

Abstract

The timing and strength of wind-driven coastal upwelling along the eastern margins of major ocean basins regulate the productivity of critical fisheries and marine ecosystems by bringing deep and nutrient-rich waters to the sunlit surface, where photosynthesis can occur1,2,3. How coastal upwelling regimes might change in a warming climate is therefore a question of vital importance4,5. Although enhanced land–ocean differential heating due to greenhouse warming has been proposed to intensify coastal upwelling by strengthening alongshore winds6, analyses of observations and previous climate models have provided little consensus on historical and projected trends in coastal upwelling7,8,9,10,11,12,13. Here we show that there are strong and consistent changes in the timing, intensity and spatial heterogeneity of coastal upwelling in response to future warming in most Eastern Boundary Upwelling Systems (EBUSs). An ensemble of climate models shows that by the end of the twenty-first century the upwelling season will start earlier, end later and become more intense at high but not low latitudes. This projected increase in upwelling intensity and duration at high latitudes will result in a substantial reduction of the existing latitudinal variation in coastal upwelling. These patterns are consistent across three of the four EBUSs (Canary, Benguela and Humboldt, but not California). The lack of upwelling intensification and greater uncertainty associated with the California EBUS may reflect regional controls associated with the atmospheric response to climate change. Given the strong linkages between upwelling and marine ecosystems14,15, the projected changes in the intensity, timing and spatial structure of coastal upwelling may influence the geographical distribution of marine biodiversity.

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Figure 1: Geographic locations of four EBUSs and latitudinal variations in coastal upwelling in each system.
Figure 2: Linear trends in upwelling duration and intensity.
Figure 3: Spatial standard deviations of upwelling duration and intensity.
Figure 4: Regression of summertime upwelling intensity against land–sea temperature difference.

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Acknowledgements

This work was funded by grants from Northeastern University’s Interdisciplinary Research Program and the US National Science Foundation’s Expeditions in Computing program (award no. 1029711). We acknowledge the World Climate Research Program’s Working Group on Coupled Modeling, which is responsible for CMIP5, and we thank the climate modelling groups for producing and making available their model output. For CMIP5 the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

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Contributions

T.C.G., D.W. and A.R.G. designed the study. D.W. and T.C.G. analysed the data. D.W. wrote the initial draft of the manuscript with substantial contributions from T.C.G. All authors discussed and interpreted the results and edited the manuscript.

Corresponding author

Correspondence to Daiwei Wang.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Locations where daily offshore Ekman transport was computed along each EBUS.

Also shown are Aqua MODIS mean ocean chlorophyll a concentrations for 2002–2013 (colour scale) and mean QuikSCAT ocean surface vector winds for 1999–2009 (white arrows) for the CalCS (a), CanCS (b), HCS (c) and BCS (d). The longitudes, latitudes and coast angles of all the locations (open stars) are given in Extended Data Table 1.

Extended Data Figure 2 Linear trends in the timing of the upwelling season.

Multimodel mean (solid lines) and 95% bootstrap confidence intervals (shading) of linear trends in the onset date (a, b) and termination date (c, d) of the upwelling season for 1950–2099 in all four EBUSs. Filled circles represent trends that are robust across climate models (that is, at least 50% of the models show a statistically significant trend and at least 80% of those agree on the sign of the trend). The bootstrap confidence intervals were computed from 999 samples.

Extended Data Figure 3 Linear trends in the upwelling metrics for the individual CMIP5 models.

ad, Generalized least-squares linear trends of upwelling duration for 1950–2099 in the CalCS (a), CanCS (b), HCS (c) and BCS (d). Red and blue respectively indicate positive and negative trend values. Crosses denote trend values that are statistically significant (P value <0.05). The first 22 columns are 22 CMIP5 models; the last column is the multimodel mean. eh, Same as ad but for the onset date of the upwelling season. il, Same as ad but for the termination date of the upwelling season. mp, Same as ad but for upwelling intensity.

Extended Data Figure 4 Spatial standard deviations of the timing of the upwelling season.

Multimodel mean (thick lines) and 95% bootstrap confidence intervals (shading) of the spatial standard deviation of the onset date (a) and termination date (b) of the upwelling season for 1950–2099 in all four EBUSs. The thin straight lines indicate linear trends of the multimodel mean time series. The bootstrap confidence intervals are computed from 999 samples.

Extended Data Figure 5 Trends in the spatial heterogeneity of coastal upwelling for individual CMIP5 models.

ad, Linear trends of the spatial standard deviation of the upwelling duration for 1950–2099 in the CalCS (a), CanCS (b), HCS (c) and BCS (d). Error bars indicate the 95% confidence intervals. The first 22 bars are 22 CMIP5 models; the last bar is the multimodel mean. eh, Same as ad but for the onset date of the upwelling season. il, Same as ad but for the termination date of the upwelling season. mp, Same as ad but for the upwelling intensity.

Extended Data Figure 6 Latitudinal slope coefficients of upwelling metrics.

Multimodel mean (thick lines) and 95% confidence intervals (shading) of the latitudinal slope coefficients of the duration (a; day per degree latitude), onset date (b; day per degree latitude) and termination date (c; day per degree latitude) of the upwelling season, and of the upwelling intensity (d; m2 s−1 per degree latitude) for 1950–2099 in all four EBUSs. The thin straight lines indicate linear trends of the multimodel mean time series.

Extended Data Figure 7 Intra-annual variation in upwelling trends.

Multimodel mean daily offshore Ekman transport for 1950–1999 (blue curve) and 2050–1099 (red curve) averaged over the three highest latitudes in the CalCS (a), CanCS (b), HCS (c) and BCS (d). Positive and negative temporal trends in the daily Ekman transport occur when the red curve is above and, respectively, below the blue curve. These increases and decreases in upwelling transport are also highlighted by the red and, respectively, blue shading between the curves. The onset and termination of the upwelling season correspond to the times of the year when the daily Ekman transport first and, respectively, last reaches zero.

Extended Data Figure 8 Median changes in upwelling metrics for the individual CMIP5 models.

ad, 50-year median change in the upwelling duration for 1950–1999 and 2050–2099 in the CalCS (a), CanCS (b), HCS (c) and BCS (d). Red and blue respectively indicate positive and negative trend values. Crosses denote changes that are statistically significant (P value <0.05) according to the Mann–Whitney U test. The first 22 columns are 22 CMIP5 models; the last column is the multimodel mean. eh, Same as ad but for the onset date of the upwelling season. il, Same as ad but for the termination date of the upwelling season. mp, Same as ad but for the upwelling intensity.

Extended Data Table 1 Latitudes, longitudes and coast angles of the locations representative of the EBUSs
Extended Data Table 2 Correlations between upwelling intensity and land–sea temperature difference for individual CMIP5 models in all four EBUSs

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Wang, D., Gouhier, T., Menge, B. et al. Intensification and spatial homogenization of coastal upwelling under climate change. Nature 518, 390–394 (2015). https://doi.org/10.1038/nature14235

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