On the Dynamics and Predictability of the Atlantic Niño

This thesis seeks to broaden our understanding of the Atlantic Niño. The Atlantic Niño is the dominant mode of coupled interannual climate variability in the equatorial Atlantic. Its sea surface temperature (SST) signature is similar to the Pacific Niño, peaking in boreal summer, with a secondary Niño-like phenomenon occurring in boreal winter. Both the summer and winter Niños affect seasonal climate variability locally and in remote regions. To what extent is the Atlantic Niño driven by dynamical processes? Using multiple linear regression, SST variability in the equatorial Atlantic is decomposed into a dynamical part, and a stochastic part. When the Atlantic Niño is active, dynamical SST variability dominates stochastic SST variability, indicating that ocean dynamics are involved in establishing it. The Atlantic Niño is relatively symmetric. Does this symmetry correspond the symmetry of the Atlantic Bjerknes feedback? Decomposing the Bjerknes feedback into three feedback elements, robust regression is used to diagnose the strength of the feedback elements when they act on positive or negative anomalies (composites). In the Pacific, clear asymmetries emerge. In the Atlantic, differences between positive and negative composites are less consistent. Assessing the stationarity of the Bjerknes feedback shows that both the feedback elements and their symmetries vary on decadal time scales. A strong, coupled warm bias in the equatorial Atlantic inhibits realistic simulations of the Atlantic Niño in coupled global climate models of the current generation. A review synthesises our current understanding of the processes that create and maintain the equatorial Atlantic warm bias. Does the warm bias affect the ability of a model to predict the Atlantic Niño? Analysing two hindcasting experiments – one using a biased model, the other employing surface heat flux correction –, shows that bias alleviation enhances the predictability of SST variability in boreal summer.


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