Abstract
Sea surface temperature anomalies in the mid-latitude North Pacific Ocean were compared with a first-order autoregression model in which the anomalies are forced by local atmospheric white noise. The results showed that the model can explain the power spectrum of the anomalies for a little over 50% of the investigated regions, mainly in the central regions of the Pacific, but fails, not surprisingly, in regions of strong oceanographic processes.
Highlights
Hasselmann (1976) has proposed a stochastic model of climate variability in which slow changes of climate are explained as the integral response of the climate to continuous random excitation by shorter time-scale disturbances
Sea surfacetemperature anomalies in the mid-latitude North Pacific Ocean were compared with a first-order autoregression model in which the anomalies are forced by local atmospheric white noise
The results showed that the model can explain the power spectrum of the anomalies for a little over 50% of the investigated regions, mainly in the central regions of the Pacific, but fails, not surprisingly, in regions of strong oceanographicprocesses
Summary
Hasselmann (1976) has proposed a stochastic model of climate variability in which slow changes of climate are explained as the integral response of the climate to continuous random excitation by shorter time-scale disturbances. The “weather” components are treated as random (white noise) forcing terms. Frankignoul and Hasselmann (1977) have applied the model to a simplified atmospheric and oceanic system. In this system the climatic components were represented as sea surface temperature (SST) anomalies which were driven by uncorrelated white-noise atmospheric forcing. The SST anomalies have a spectral form, L?(f),given by: where f is frequency and A is proportional to the variance of the white-noise input spectrum. Equation (1) can be recognized as the spectrum of a first-order autoregression process with random noise
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