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Philip Sura
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Prashant D. Sardeshmukh

Abstract

The skewness and kurtosis of daily sea surface temperature (SST) variations are found to be strongly linked at most locations around the globe in a new high-resolution observational dataset, and are analyzed in terms of a simple stochastically forced mixed layer ocean model. The predictions of the analytic theory are in remarkably good agreement with observations, strongly suggesting that a univariate linear model of daily SST variations with a mixture of SST-independent (additive) and SST-dependent (multiplicative) noise forcing is sufficient to account for the skewness–kurtosis link. Such a model of non-Gaussian SST dynamics should be useful in predicting the likelihood of extreme events in climate, as many important weather and climate phenomena, such as hurricanes, ENSO, and the North Atlantic Oscillation (NAO), depend on a detailed knowledge of the underlying local SSTs.

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