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• W4313332929 endingPage "113404" @default.
• W4313332929 startingPage "113404" @default.
• W4313332929 abstract "Satellite-derived sea surface temperature (SST) and chlorophyll (Chl) datasets have been invaluable for estimating the oceanic primary production, air-sea heat exchange, and the spatial and seasonal patterns in their variability. However, data gaps, resulting from clouds and other factors, reduce coverage unevenly (to just about 20%) and make it difficult to analyze the temporal variability of Chl and SST on sub-seasonal time scales. Here, we present a MOving Standard deviation Saturation (MOSS) method to enable the analysis of sparse time series (with as little as 10% of the data). We apply the method to identify the dominating (sub-annual) timescales of variability, τd, for SST and Chl in every region. We find that τd values for Chl and SST are not consistent or correlated with each other over large areas, and in general, SST varies on longer timescales than Chl, i.e. τd(SST) >τd(Chl). There is a threefold variability in τd for SST and Chl even within regions that are traditionally considered to be biogeographically homogeneous. The largest τd for Chl is generally found on the equatorial side of the trade wind belts, whereas the smallest τd are found in the tropical Pacific and near coasts, especially where upwelling is common. If the temporal variability in Chl and SST were driven largely by ocean dynamics or advection by the flow, regional patterns of τd for SST and Chl should co-vary. This is seen in coastal upwelling zones, but more broadly, the lack of coherence between τd(Chl) and τd(SST) suggests that biological processes, such as phytoplankton growth and loss, decouple the timescales of Chl variability from those of SST and generate shorter term variability in Chl." @default.
• W4313332929 created "2023-01-06" @default.
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• W4313332929 date "2023-03-01" @default.
• W4313332929 modified "2023-09-26" @default.
• W4313332929 title "Dominant timescales of variability in global satellite chlorophyll and SST revealed with a MOving Standard deviation Saturation (MOSS) approach" @default.
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• W4313332929 doi "https://doi.org/10.1016/j.rse.2022.113404" @default.
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