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

Report Date:

2013-09-30

Pagination or Media Count:

21.0

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 systems 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.

Subject Categories:

  • Meteorology
  • Physical and Dynamic Oceanography

Distribution Statement:

APPROVED FOR PUBLIC RELEASE