Statistical calibration of climate system properties

Bruno Sansó, Chris Forest

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

The behaviour of modern climate system simulators is controlled by numerous parameters. By matching model outputs with observed data we can perform inference on such parameters. This is a calibration problem that usually requires the ability to evaluate the computer code at any given configuration of the parameters. As the climate system simulator attempts to describe very complex physical phenomena, the task of running the model is very computationally demanding. Thus, a statistical model is required to approximate the model output. In this work, we use output from the Massachusetts Institute of Technology two-dimensional climate model (MIT2DCM), historical records and output from a three-dimensional climate model, to obtain estimates of the climate sensitivity, the effective thermal diffusivity in the deep ocean and the net aerosol forcing that control MIT2DCM. We use a Bayesian approach that allows for the use of scientifically based information on the climate parameters to be used in the calibration process. The model tackles the problem of dealing with multivariate computer model output and incorporates all estimation uncertainties into the posterior distributions of the climate parameters. Additionally we obtain estimates of the correlation structure of the unforced variability of temperature change patterns. These results are critical for understanding uncertainty in future climate change and provide an independent check that the information that is contained in recent climate change is robust to statistical treatment. These results include uncertainties in the estimation of the multivariate covariance matrices.

Original languageEnglish (US)
Pages (from-to)485-503
Number of pages19
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume58
Issue number4
DOIs
StatePublished - Sep 1 2009

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Climate
Calibration
Output
Climate Models
Climate Change
Simulator
Uncertainty Estimation
Uncertainty
Thermal Diffusivity
Model Matching
Multivariate Models
Correlation Structure
Computer Model
Aerosol
Posterior distribution
Bayesian Approach
Ocean
Estimate
Forcing
Statistical Model

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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Statistical calibration of climate system properties. / Sansó, Bruno; Forest, Chris.

In: Journal of the Royal Statistical Society. Series C: Applied Statistics, Vol. 58, No. 4, 01.09.2009, p. 485-503.

Research output: Contribution to journalArticle

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