Return to CIG

Search

View All Publications

Go To Publication by Year:

View Publications by Topic:

Adaptation

Agriculture

Air Quality

Aquatic Ecosystems and Fisheries

Background Papers

Climate: Atmospheric Modeling

Climate: Coupled Atmosphere-Ocean Modeling

Climate: Diagnostics

Climate: Global Climate

Climate: Ocean Modeling

Climate: PNW Climate

Climate: Regional Climate Modeling

Coastal Ecosystems

Coastal Environments

Conservation Biology

Data Analysis and Sharing

Energy

Fact Sheets

Forecasts and Applications

Forest Ecosystems

Human Health

Hydrology and Water Resources

Infrastructure

Integrated Assessment

Ocean Acidification

Oceanography

Program Documents

Science Advisory Reports

Societal Dimensions

Special Reports

Theses and Dissertations

View Publications by Author:

Search the Publication Abstracts:


Other CSES Links:

About CSES

CSES Personnel

Data / Links

Publications

Welcome to the publications directory for the Climate Impacts Group and the Climate Dynamics Group. Please contact the web administrator for assistance with any of these publications.


View: Abstract

A linear stochastic dynamical model of ENSO. Part II: Analysis

Thomson, C.J., and D.S. Battisti. 2001. A linear stochastic dynamical model of ENSO. Part II: Analysis. Journal of Climate 14:445-466.

Abstract

In this study the behavior of a linear, intermediate model of ENSO is examined under stochastic forcing. The model was developed in a companion paper (Part I) and is derived from the Zebiak-Cane ENSO model. Four variants of the model are used whose stabilities range from slightly damped to moderately damped. Each model is run as a simulation while being perturbed by noise that is uncorrelated (white) in space and time. The statistics of the model output show the moderately damped models to be more realistic than the slightly damped models.

The moderately damped models have power spectra that are quantitatively quite similar to observations, and a seasonal pattern of variance that is qualitatively similar to observations. All models produce ENSOs that are phase locked to the annual cycle, and all display the ''spring barrier'' characteristic in their autocorrelation patterns, though in the models this ''barrier'' occurs during the summer and is less intense than in the observations (inclusion of nonlinear effects is shown to partially remedy this deficiency). The more realistic models also show a decadal variability in the lagged autocorrelation pattern that is qualitatively similar to observations.

Analysis of the models shows that the greatest part of the variability comes from perturbations that project onto the first singular vector, which then grow rapidly into the ENSO mode. Essentially, the model output represents many instances of the ENSO mode, with random phase and amplitude, stimulated by the noise through the optimal transient growth of the singular vectors.

The limit of predictability for each model is calculated and it is shown that the more realistic (moderately damped) models have worse potential predictability (9-15 months) than the deterministic chaotic models that have been studied widely in the literature. The predictability limits are strongly correlated with the stability of the models' ENSO mode-the more highly damped models having much shorter limits of predictability. A comparison of the two most realistic models shows that even though these models have similar statistics, they have very different predictability limits. The models have a strong seasonal dependence to their predictability limits.

The results of this study (with the companion paper) suggest that the linear, stable dynamical model of ENSO is indeed a plausible hypothesis for the observed ENSO. With very reasonable levels of stochastic forcing, the model produces realistic levels of variance, has a realistic spectrum, and qualitatively reproduces the observed seasonal pattern of variance, the autocorrelation pattern, and the ENSO-like decadal variability.