A Global View of Non-Gaussian SST Variability

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 analytic theory yields results in remarkable 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 extreme events in climate, as many important weather and climate phenomena, such as hurricanes, ENSO, and the NAO depend on a detailed knowledge of the underlying local SSTs.