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## Candidate

Yusuf Aydogdu | Applied Mathematics, University of Waterloo

## Title

Dynamics and data assimilation of stochastically forced large scale geophysical flows

## Abstract

The phenomena of El Nino Southern Oscillations (ENSO) is modeled by coupled atmosphere-ocean model together with sea surface temperature (SST) at the equatorial Pacific. ENSO has a significant impact on the global climate. The dynamics is mainly governed by the equatorial Kelvin and Rossby waves, which are fundamental large scale flows and are coupled through the boundaries of the Pacific ocean. The stochastic forcing on the ocean through the atmosphere due to, for example, wind bursts activities is one of the key features that gives rise to the anomalies. This triggers a chaotic (unpredictable) mechanism, which directly influences the strength, dynamics and forecast of ENSO events. In this research, we employ a steady state atmospheric model that is coupled to a nonlinear shallow water ocean system, which is forced by both additive and multiplicative noise. Moreover, these atmosphere-ocean equations are coupled to the nonlinear SST budget, which is essential for ENSO prediction. We utilize a propagator type of Wiener-chaos expansion method for the analysis and numerical simulation of the nonlinear stochastic model. One of the main features of this research is to include appropriate randomness that captures the main structure of ENSO phenomena and develop stochastic parameterization of model components that have different temporal and spatial scales. By combining the parameterized model, real time observations and data assimilation, we aim to achieve accurate estimations by improving the predictability of SST anomalies.