Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 30, 2013
- Accession Number
- ADA601139
Entities
People
- Adam Sobel
- Alexey Kaplan
- Andrew W. Robertson
- Dake Chen
- Dmitri Kondrashov
- Mark Cane
- Michael Ghil
- Michael K. Tippett
- Mickael D. Chekroun
- Mingfang Ting
- Suzana J. Camargo
- Xiaojun Yuan
- Yochanan Kushnir
Organizations
- University of California, Los Angeles