Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data Driven Approach
- Lead PI: Dr. Michael K. Tippett , Prof. Mark A. Cane , Dake Chen
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Unit Affiliation: International Research Institute for Climate and Society (IRI)
- June 2012 - May 2017
- Inactive
- Global
- Project Type: Research
DESCRIPTION: 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 system's 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.