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Global changes in oceanic mesoscale currents over the satellite altimetry record


Oceanic mesoscale eddies play a profound role in mixing tracers such as heat, carbon and nutrients, thereby regulating regional and global climate. Yet, it remains unclear how the eddy field has varied over the past few decades. Furthermore, climate model predictions generally do not resolve mesoscale eddies, which could limit their accuracy in simulating future climate change. Here we show a global statistically significant increase of ocean eddy activity using two independent observational datasets of surface mesoscale eddy variability (one estimates surface currents, and the other is derived from sea surface temperature). Maps of mesoscale variability trends show heterogeneous patterns, with eddy-rich regions showing a significant increase in mesoscale variability of 2–5% per decade, while the tropical oceans show a decrease in mesoscale variability. This readjustment of the surface mesoscale ocean circulation has important implications for the exchange of heat and carbon between the ocean and atmosphere.

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Fig. 1: SSH trend, mean surface EKE and surface EKE trend between 1993 and 2020.
Fig. 2: SST trends, mean SST gradient magnitude and SST gradient magnitude trends between 1993 and 2020.
Fig. 3: Regional maps of mesoscale SST gradient magnitude trends and surface EKE trends.
Fig. 4: Time-series and linear trends of area-integrated surface EKE and mesoscale SST gradient magnitudes over various ocean basins.
Fig. 5: Time-series and linear trends of integrated surface EKE and mesoscale SST gradient magnitudes over dynamical regions.

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Data availability

The unprocessed data from the satellite altimetry (produced by Ssalto/Duacs and distributed by EU CMEMS) can be found at The processed data used in this study are publicly available in netCDF format at (ref. 50).

Code availability

All analyses and figures in this manuscript are reproducible via Jupyter notebooks and instructions found in the Github repository EKE_SST_trends51 ( The analyses use the Python package xarrayMannKendall52 (


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We thank R. Holmes for clarifying the equatorial response of El Niño events during 1997–1998 and 2015–2016. The satellite altimetry products were produced by Ssalto/Duacs and distributed by CMEMS ( J.M.-M. was supported by the Consejo Nacional de Ciencia y Tecnología (CONACYT), Mexico. M.H.E. was supported by the Centre for Southern Hemisphere Oceans Research (CSHOR), a joint research centre between Qingdao National Laboratory for Marine Science and Technology (QNLM), Commonwealth Scientific and Industrial Research Organisation (CSIRO), the University of New South Wales (UNSW) and the University of Tasmania (UTAS). A.K.M. was supported by the Australian Research Council DECRA Fellowship no. DE170100184. The analyses were undertaken on the National Computational Infrastructure in Canberra, Australia, which is supported by the Australian Commonwealth Government.

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Authors and Affiliations



J.M.-M., A.McC.H. and M.H.E. conceived the study. J.M.-M. conducted the analyses. All authors contributed to the interpretation of the results and to the writing and revision of the manuscript.

Corresponding author

Correspondence to Josué Martínez-Moreno.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Sophia Moreton and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Regions of statistically significant trends of a, sea surface height; b, surface eddy kinetic energy; c, sea surface temperature; d, sea surface temperature gradient magnitude.

As per figs. 1b, f, 2b, and f in main manuscript, but showing in gray stippling regions that are statistically significant above the 95% confidence level.

Extended Data Fig. 2 Sea surface temperature gradient magnitude trends for periods between 1981-2020 and 1993-2020.

Gray stippling shows regions that are statistically significant above the 95% confidence level.

Extended Data Fig. 3 Sea surface temperature gradient magnitude trend scale analysis.

Large-scale SST gradient magnitudes are computed by filtering the SST field with a 3 kernel filter and a running average of 12 months before computing the gradient magnitudes and their respective trends (see Methods). The small scales correspond to the gradients of the SST minus the large-scale filtered SST field. a, Zonally averaged SST gradient magnitude trends; b, map of SST gradient magnitude trends; c, zonally averaged small-scale SST gradient magnitude trends; d, map of small-scale SST gradient magnitude trends; e, zonally averaged large-scale SST gradient magnitude trends; f, map of large-scale SST gradient magnitude trends. In panels b, d, and f gray stippling shows regions where the trends are statistically significant above the 95% confidence level.

Extended Data Fig. 4 Regional ratio of mesoscale SST gradient magnitude trends and surface EKE trends signs.

a, Kuroshio current; b, Gulf Stream; c, East Australian Current; d, Agulhas retroflection. The ratio was computed by integrating the area weighted sign of the SST gradient magnitude trends and surface EKE trends divided by the total area of the region plotted in the fig. 3. Quadrants I and III of each panel show colocated regions with the same sign in SST gradients and EKE trends, more than 60% of the signs in the a, Kuroshio current, c, East Australian Current, and d, Agulhas retroflection are colocated.

Extended Data Fig. 5 Surface eddy kinetic energy time-series and trends computed from filtered velocities.

Scales larger than typical mesoscale are computed by filtering the surface velocity fields with a 3 kernel filter (\({\overrightarrow{u}}_{ls}\)), and the smaller scales are calculated from the difference of the velocity fields and the filtered velocity field (\({\overrightarrow{u}}_{m}=\overrightarrow{u}-{\overrightarrow{u}}_{ls}\)). Then surface EKE and their respective trends are computed (see Methods). a, EKE time series of scales larger than 3 degrees time series; b, EKE time series of scales smaller than 3 degrees; c, map of large-scale EKE trends; d, map of small-scale EKE trends. Text in panels a and b correspond to trends per decade.

Extended Data Fig. 6 Time-series and trends of surface eddy kinetic energy integrated over boundary currents.

a, Map of boundary current regions defined from climatological mean EKE and time series anomalies (PJ m−1) and trends (PJ m−1 decade−1) for each boundary current: b, Kuroshio Current; c, Agulhas Current; d, East Australian Current and Leeuwin Current; e, Gulf Stream; f, Malvinas Current. g, Linear EKE trends for boundary currents, uncertainties are shown in orange bars and statistically significant trends (above the 95% confidence level) are denoted with solid bars while non-significant trends are translucent.

Extended Data Fig. 7 Comparison of satellite trends using surface EKE and kinetic energy anomaly (KE’) as computed by Hu et al. (2020)15.

a, EKE trend map, b, KE’ trend map, and c, difference between EKE and KE’ trends. The difference between the fields is a consequence of the cross terms due to the Reynolds velocity decomposition. In panel a and b gray stippling shows regions where the trends are statistically significant above the 95% confidence level.

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Martínez-Moreno, J., Hogg, A.M., England, M.H. et al. Global changes in oceanic mesoscale currents over the satellite altimetry record. Nat. Clim. Chang. 11, 397–403 (2021).

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