Report: Physics Constrained Stochastic Statistical Models for Extended Range Environmental Prediction
Abstract
The PI, Andrew Majda, and Co-PI s, Sam Stechmann (University of Wisconsin), John Harlim (North Carolina State University), Duane Waliser (UCLA/Cal Tech) just received the MURI funding in early August 2012 with a one-day MURI kickoff meeting July 13,2012 so this report is necessarily brief. Nevertheless, there are some exciting initial MURI developments to report on two MURI topics. A) Limits of Predictability in the North Pacific Sector of a Comprehensive Climate Model Majda and new Courant Institute Assistant Professor and MURI collaborator, Dimitri Giannakis, have just submitted a paper to GRL (1). We study limits of interannual to decadal predictability of sea surface temperature (SST) in the North Pacific sector of the Community Climate System Model version 3 (CCSM3). Using a set of low-frequency and intermittent spatiotemporal SST modes acquired through nonlinear Laplacian spectral analysis (a nonlinear data manifold generalization of singular spectrum analysis), we build a hierarchy of regression models with external factors to determine which modes govern the dynamic evolution and predictability of prominent large-scale patterns, namely the Pacific Decadal Oscillation (PDO) and North Pacific Gyre Oscillation (NPGO). Retaining key triple correlations between prognostic variables and external factors, as well as the seasonality of the data, we find that the PDO and NPGO modes of CCSM3 can be described with remarkably high fidelity as an outcome of forcing by the intermittent modes (with phase demodulation by the seasonal cycle) and cubic interactions between the low-frequency modes. Our results differ from the classical picture of ENSO-driven autoregressive models for North Pacific SST variability, providing evidence that intermittent processes, such as variability of the Kuroshio current, limit longrange predictability in this climate model.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 30, 2012
- Accession Number
- ADA590513
Entities
People
- Andrew J. Majda
Organizations
- New York University