Phytoplankton biomass exhibits substantial year-to-year changes, and understanding these changes is crucial to fisheries management and projecting future climate. These annual changes result partly from low-frequency climate modes that also lead to variations in sea surface temperature (SST). Here we evaluate the contribution of small scales to annual fluctuations based on a global analysis of satellite observations of sea surface chlorophyll (SChl), an indicator of phytoplankton biomass, and of SST from 1999 to 2018. We disentangle the spatio-temporal scales of variability in the time series and find that besides the prominent seasonal cycle, SChl is dominated by high-frequency fluctuations (<three months) at small spatial scales (<50 km)—which accumulates over annual scales in contrast to SST. The results suggest that observations and models with high spatio-temporal resolutions are necessary to understand annual changes in SChl. The rapid response of SChl to small-scale physical processes, combined with intrinsic ecosystem interactions and air–sea interaction feedbacks, may explain the differences between annual variations in SST and SChl.
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The authors acknowledge the support from ANR-SOBUMS (Agence Nationale de la Recherché, contract number ANR-16-CE01–0014) and Centre national d'études spatiales for this research. M.G.K. is supported by a postdoctoral fellowship from CNRS (Centre National de la Recherche Scientifique). C.J.P. is supported by a National Science Foundation Graduate Research Fellowship under grant DGE-1650112 and a Chateaubriand Fellowship from the Office for Science & Technology of the Embassy of France in the United States.
The authors declare no competing interests.
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SChl data coverage (percentage of time steps with data in each pixel with respect to the total number of time steps), (b) Annual mean and (c) Standard deviation, over the period 1999–2018.
(Left Panels) Decomposition of SChl timeseries at the Seychelles-Chagos Thermocline Ridge station (SCTR) - (a) full Signal Xt, (b) climatological seasonal cycle CSt, (c) nonseasonal variability NSt, and the different components of nonseasonal variability (d) sub-seasonal SSt, (e) delta-seasonal ΔSt and (f) multi-annual MAt. In a), the blue curve shows the un-interpolated raw ESA OC-CCI SChl and the red curve shows the linearly interpolated SChl values. (Right panels) Associated power spectrum of the timeseries shown in the left panel. PSD is power spectral density. The SCTR station is marked on Supplementary Fig. 1c.
(Left panels) SChl time series for the stations marked in Supplementary Fig. 1c. The blue curve shows the un-interpolated raw ESA OC-CCI SChl and the red curve denotes the linearly interpolated SChl on which the decomposition is applied. The power spectrum of the seasonal (middle panels) and non-seasonal (right panels) component for each station is shown. PSD is power spectral density.
Same as Extended Data Fig. 3 but for SST.
Percentage of the sub-seasonal SChl variance explained by sub-seasonal variations with spatial scales >100 km. Regions where sub-seasonal variations explain less than 30% of the total SChl variance is masked.
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Keerthi, M.G., Prend, C.J., Aumont, O. et al. Annual variations in phytoplankton biomass driven by small-scale physical processes. Nat. Geosci. 15, 1027–1033 (2022). https://doi.org/10.1038/s41561-022-01057-3