Daily to decadal sea surface temperature variability driven by state-dependent stochastic heat fluxes

The classic Frankignoul-Hasselmann null hypothesis for sea surface temperature (SST) variability of an oceanic mixed layer assumes that the surface heat flux can be simply parameterized as noise induced by atmospheric variability plus a linear temperature relaxation occurring at a constant rate. It is suggested here, however, that rapid fluctuations in this rate, as might be expected for example due to gustiness of the sea surface winds, are large enough that they cannot be ignored. Such fluctuations cannot be modeled by noise that is independent of the SST anomaly itself. Rather, they require the inclusion of a multiplicative (that is, state-dependent) noise term, which can be expected to impact both persistence and the relative occurrence of high amplitude anomalies.

As a test of this hypothesis, daily observations at Ocean Weather Station P (and other stations) are examined. Significant skewness and kurtosis of the distributions of SST anomalies is found, which is shown to be consistent with a multiplicative noise model. This model (counterintuitively) implies that the multiplicative noise increases the persistence, predictability, and variance of midlatitude SST anomalies. The effect is strongest on annual and longer time scales and is, therefore, essential to understand and model interannual SST and related climate variability.