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Earth Institute Research Projects

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

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.

SPONSOR:

University of California, Los Angeles

ORIGINATING SPONSOR:

Office of Naval Research

FUNDED AMOUNT:

$4,790,766

RESEARCH TEAM:

Aleksey Kaplan, Adam Sobel, Suzana DeCamargo, Charlene, Mingfang Ting, Naomi Henderson, Andrew Robertson, Yochanan Kushnir, Xiaojun Yuan

COLUMBIA UNIVERSITY COLLABORATORS:

Ocean & Climate Physics (O&CP), Lamont-Doherty Earth Observatory (LDEO)

EXTERNAL COLLABORATORS:

University of California Los Angeles

KEYWORDS

madden-julian oscillation tropical cyclones low-dimensional empirical models north atlantic oscillation reduced-order models climate change low-frequency modes models el nino southern oscillation (enso) predictability pacific-north american pattern stochastic-dynamic models climate variability sea ice climate physics

THEMES

Modeling and Adapting to Future Climate