UCLA TCD (Theoretical Climate Dynamics) Regression     Statistical Model

Univ. of California at Los Angeles
Los Angeles, California, U.S.

We apply a multiple polynomial regression of sea-surface temperature anomalies (SSTA) to obtain both linear and nonlinear stochastically forced models of ENSO. Our stochastic ENSO model is governed by the set of ordinary differential equations (ODEs) that are derived by applying multiple polynomail regression on a few leading empirical orthogonal functions (EOFs) of observationl data. A multi-layer regression procedure is employed, where the regression residual at a given level is modeled as an ODE of predictor variables at the current, and all preceding levels.

Read about the ENSO multi-level regression system.

View the latest TCD regression forecast.

Contact: Dmitri Kodrashov: dkondras@atmos.ucla.edu

References:
Kravtsov, S., D. Kondrashov, and M. Ghil, 2005: Multilevel Regression Modeling of Nonlinear Processes: Derivation and Applications to Climatic Variability. J. Climate, 18, 4404-4424.
Kondrashov, D., S. Kravtsov, A. W. Robertson, and M. Ghil, 2005: A hierarchy of data-based ENSO models. J. Climate, 18, 4425-4444.