Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

• Main Authors: Ghil, Michael, Chekroun, Mickael D, Kondrashov, Dmitri, Tippett, Michael K, Robertson, Andrew W, Camargo, Suzana J, Cane, Mark, Chen, Dake, Kaplan, Alexey, Kushnir, Yochanan, Sobel, Adam, Ting, Mingfang, Yuan, Xiaojun
• Other Authors: CALIFORNIA UNIV LOS ANGELES INST OF GEOPHYSICS AND PLANETARY PHYSICS
• Format: Text
• Language: English
• Published: 2013
• Subjects: Meteorology; Physical and Dynamic Oceanography; *CLIMATE; *MODELS; *STOCHASTIC PROCESSES; *VARIATIONS; INDIAN OCEAN; LONG RANGE(TIME); NORTH ATLANTIC OCEAN; OSCILLATION; PACIFIC OCEAN; PATTERNS; PREDICTIONS; EL NINO-SOUTHERN OSCILLATION; LOW FREQUENCY MODELS; MADDEN-JULIAN OSCILLATION; NORTH ATLANTIC OSCILLATION; PACIFIC-NORTH AMERICAN PATTERN; REDUCED ORDER MODELS; Pacific; Indian; Palisades; North Atlantic; North Atlantic oscillation; Sea ice
• Online Access: http://www.dtic.mil/docs/citations/ADA601139; http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA601139

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. Prepared in cooperation with Columbia University, Lamont Campus, Palisades, NY.