Low Frequency North Atlantic SST Variability: Weather Noise Forcing and Coupled Response




Fan, Meizhu

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A method to diagnose the causes of low frequency SST variability is developed, tested and applied in an ideal case and real climate. In the ideal case, a free simulation of the COLA CGCM is taken as synthetic observations. For real climate, we take NCEP reanalysis atmospheric data and Reynolds SST as observations. Both the synthetic and actual observation data show that weather noise is the main component of atmospheric variability at subtropics and high-latitude. Diagnoses of results from the ideal case suggest that most of the synthetic observed SST variability can be reproduced by the weather noise surface fluxes forcing. This includes the “observed” low frequency SST patterns in the North Atlantic and their corresponding time evolution. Among all the noise surface fluxes, heat flux plays a major role. The results from simulations using actual observations also suggest that the observed SST variability is mostly atmospheric weather noise forced. The regional atmospheric noise forcing, especially the heat flux noise forcing, is the major source of the low frequency SST variability in the North Atlantic. The observed SST tripole mode has about a 12 year period and it can be reasonably reproduced by the weather noise forcing in terms of its period, spatial pattern and variance. Based on our diagnosis, it is argued that the SST tripole is mainly forced by local atmospheric heat flux noise. The gyre circulation plays a secondary role: the anomalous gyre circulation advects mean thermal features across the inter-gyre boundary, and the mean gyre advection carries SST anomalies along the inter-gyre boundary. The diagnosis is compared with a delayed oscillator theory. We find that the delayed oscillator theory is not supported and that the SST tripole mode is forced by weather noise heat flux noise. However, the result may be model dependent.



Climate Variability, Climate Feedbacks, Climate Diagnosis, North Atlantic SST Variability, Climate Modeling, Climate Coupled General Circulation Modeling