Inferring climate system properties using a computer model

Bruno Sansó, Chris E. Forest, Daniel Zantedeschi

Research output: Contribution to journalArticle

40 Scopus citations

Abstract

A method is presented to estimate the probability distributions of climate system properties based on a hierarchical Bayesian model. At the base of the model, we use simulations of a climate model in which the outputs depend on the climate system properties and can also be compared with observations. The degree to which the model outputs are consistent with the observations is used to obtain the likelihood for the climate system properties. We dene the climate system properties as those properties of the climate model that control the large-scale response of the climate system to external forcings. In this paper, we use the MIT 2D climate model (MIT2DCM) to provide simulations of ocean, surface and upper atmospheric temperature behavior over zones dened by lati-tude bands. In the MIT2DCM, the climate system properties can be set via three parameters: Climate sensitivity (the equilibrium surface temperature change in response to a doubling of CO2 concentrations), the rate of deep-ocean heat uptake (as set by the di usion of temperature anomalies into the deep-ocean below the climatological mixed layer), and net strength of the anthropogenic aerosol forcings. In this work, we use output from MIT2DCM coupled with historical temperature records to make inference about these climate system properties. Even though the MIT2DCM is far less computationally demanding than a full 3D climate model, the task of running the model for each combination of the climate parameters and processing its output is computationally demanding. Thus, a statistical model is required to approximate the model output. We obtain results that are critical for understanding uncertainty in future climate change and provide an indepen-dent check that the information contained in recent climate change is robust to statistical treatment.

Original languageEnglish (US)
Pages (from-to)1-38
Number of pages38
JournalBayesian Analysis
Volume3
Issue number1
DOIs
StatePublished - Dec 1 2008

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Applied Mathematics

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