Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach
CALIFORNIA UNIV LOS ANGELES INST OF GEOPHYSICS AND PLANETARY PHYSICS
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The long-term goal of this project is to quantify the extent to which reduced-order models can be used for the description, understanding and prediction of atmospheric, oceanic and sea ice variability on time scales of 1-12 months and beyond. The objectives are to demonstrate the ability of linear and nonlinear, stochastic-dynamic models to capture the dominant and most predictable portion of the climate systems variability. Improve the understanding and prediction of the low-frequency modes LFMs of variability such as the Madden-Julian Oscillation MJO, El Nino-Southern Oscillation ENSO, North Atlantic Oscillation NAO and Pacific-North American PNA pattern. Validate LDMs based on data sets from observations, reanalyses and high-end simulations.
- Physical and Dynamic Oceanography