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

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 a...

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Bibliographic Details
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
Description
Summary: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.

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