Accession Number : ADA601139
Title : Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach
Descriptive Note : Annual rept.
Corporate Author : CALIFORNIA UNIV LOS ANGELES INST OF GEOPHYSICS AND PLANETARY PHYSICS
Personal Author(s) : 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
Report Date : 30 Sep 2013
Pagination or Media Count : 21
Abstract : 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.
Descriptors : *CLIMATE , *MODELS , *STOCHASTIC PROCESSES , *VARIATIONS , INDIAN OCEAN , LONG RANGE(TIME) , NORTH ATLANTIC OCEAN , OSCILLATION , PACIFIC OCEAN , PATTERNS , PREDICTIONS
Subject Categories : Meteorology
Physical and Dynamic Oceanography
Distribution Statement : APPROVED FOR PUBLIC RELEASE