Quantifying Uncertainty for Climate Change and Long-Range Forecasting Scenarios with Model Errors. Part I: Gaussian Models

Information theory provides a concise systematic framework for measuring climate consistency and sensitivity for imperfect models. A suite of increasingly complex physically relevant linear Gaussian models with time periodic features mimicking the seasonal cycle is utilized to elucidate central issu...

Full description

Saved in:
Bibliographic Details
Published in:Journal of climate 2012-07, Vol.25 (13), p.4523-4548
Main Authors: Gershgorin, Boris, Majda, Andrew J.
Format: Article
Language:English
Subjects:
Climate change
Climate models
Climate science
Climatology
Dynamical systems
Earth, ocean, space
Entropy
Exact sciences and technology
External geophysics
Forecasting models
Graining
Information theory
Mathematical models
Modeling
Noise
Parametric models
Partial differential equations
Spatial models
Statistical variance
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Information theory provides a concise systematic framework for measuring climate consistency and sensitivity for imperfect models. A suite of increasingly complex physically relevant linear Gaussian models with time periodic features mimicking the seasonal cycle is utilized to elucidate central issues that arise in contemporary climate science. These include the role of model error, the memory of initial conditions, and effects of coarse graining in producing short-, medium-, and long-range forecasts. In particular, this study demonstrates how relative entropy can be used to improve climate consistency of an overdamped imperfect model by inflating stochastic forcing. Moreover, the authors show that, in the considered models, by improving climate consistency, this simultaneously increases the predictive skill of an imperfect model in response to external perturbation, a property of crucial importance in the context of climate change. The three models range in complexity from a scalar time periodic model mimicking seasonal fluctuations in a mean jet to a spatially extended system of turbulent Rossby waves to, finally, the behavior of a turbulent tracer with a mean gradient with the background turbulent field velocity generated by the first two models. This last model mimics the global and regional behavior of turbulent passive tracers under various climate change scenarios. This detailed study provides important guidelines for extending these strategies to more complicated and non-Gaussian physical systems.
ISSN:0894-8755
1520-0442
DOI:10.1175/JCLI-D-11-00454.1