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, 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: 2012
• Subjects: Physical and Dynamic Oceanography; *CLIMATE; *OCEANS; *SEASONAL VARIATIONS; *STOCHASTIC PROCESSES; LONG RANGE(TIME); MODELS; PREDICTIONS; EL NINO-SOUTHERN OSCILLATION; LOW DIMENSIONAL MODELS; LOW FREQUENCY MODELS; MADDEN-JULIAN OSCILLATION; NORTH ATLANTIC OSCILLATION; PACIFIC-NORTH AMERICAN PATTERN; STOCHASTIC-DYNAMIC MODELS; SEA SURFACE TEMPERATURE; Pacific; Palisades; North Atlantic; North Atlantic oscillation; Sea ice
• Online Access: http://www.dtic.mil/docs/citations/ADA572180; http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA572180

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