Data-Driven Reduction and Climate Prediction by Nonlinear Stochastic Energy-Conserving Models
Abstract
Comprehensive dynamical climate models aim at simulating past, present and future climate and, more recently, at predicting it. These models, commonly known as general circulation models or global climate models (GCMs) represent a broad range of time and space scales and use a state vector that has many millions of degrees of freedom. Considerable work, both theoretical and data-based, has shown that much of the observed climate variability can be represented with a substantially smaller number of degrees of freedom. While detailed weather prediction out to a few days requires high numerical resolution, it is fairly clear that the dimension of the phase space in which a major fraction of climate variance can be predicted is likely to be much smaller. Low-dimensional models (LDMs) can simulate and predict that variability provided they are able to account for (i) linear and nonlinear interactions between the resolved high-variance climate components; and (ii) the interactions between the resolved components and the huge number of unresolved ones. Here we will present applications of a particular data-driven LDM approach, namely energy-conserving formulation of empirical model reduction (EMR). As an operational methodology, EMR attempts to construct a low-order nonlinear system of multi-level prognostic equations driven by stochastic forcing, and to estimate both the dynamical operator and the properties of the driving noise directly from observations or from a high-order model's simulation. The multi-level EMR structure for modeling the noise allows one to capture feedback between high- and low-frequency components of the variability, thus parameterizing the "fast" scales — often referred to as the "noise" — in terms of the memory of the "slow" scales, the "signal." EMR already proved to be highly competitive for real-time ENSO prediction among state-of-the art dynamical and statistical models. New opportunities for EMR prediction will be illustrated in the framework of "Past Noise Forecasting", by utilizing on the one hand EMR-estimated history of the driving noise, and on the other hand the phase of low-frequency variability estimated by advanced time series analysis.
- Publication:
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AGU Spring Meeting Abstracts
- Pub Date:
- May 2013
- Bibcode:
- 2013AGUSMIN22A..06K
- Keywords:
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- 1914 INFORMATICS / Data mining;
- 1990 INFORMATICS / Uncertainty;
- 3265 MATHEMATICAL GEOPHYSICS / Stochastic processes;
- 3270 MATHEMATICAL GEOPHYSICS / Time series analysis