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Journal Article

Stochastic climate models - 2. Application to sea-surface temperature anomalies and thermocline variability

MPS-Authors

Frankignoul,  Claude
MPI for Meteorology, Max Planck Society;

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Hasselmann,  Klaus
MPI for Meteorology, Max Planck Society;

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Citation

Frankignoul, C., & Hasselmann, K. (1977). Stochastic climate models - 2. Application to sea-surface temperature anomalies and thermocline variability. Tellus, 29, 289-305. doi:10.3402/tellusa.v29i4.11362.


Cite as: https://hdl.handle.net/21.11116/0000-0006-0D95-4
Abstract
The concept of stochastic climate models developed in Part I of this series (Hasselmann, 1976) is applied to the investigation of the low frequency variability of the upper ocean. It is shown that large-scale, long-time sea surface temperature (SST) anomalies may be explained naturally as the response of the oceanic surface layers to short-time-scale atmospheric forcing. The white-noise spectrum of the atmospheric input produces a red response spectrum, with most of the variance concentrated in very long periods. Without stabilizing negative feedback, the oceanic response would be nonstationary, the total SST variance growing indefinitely with time. With negative feedback, the response is asymptotically stationary. These effects are illustrated through numerical experiments with a very simple ocean-atmosphere model. The model reproduces the principal features and orders of magnitude of the observed SST anomalies in mid-latitudes. Independent support of the stochastic forcing model is provided by direct comparisons of observed sensible and latent heat flux spectra with SST anomaly spectra, and also by the structure of the cross correlation functions of atmospheric surface pressure and SST anomaly patterns. The numerical model is further used to simulate anomalies in the near-surface thermocline through Ekman pumping driven by the curl of the wind stress. The results suggest that short-time-scale atmospheric forcing should be regarded as a possible candidate for the origin of large-scale, low-period variability in the seasonal thermocline