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Bayesian estimation of climate sensitivity based on a simple climate model fitted to observations of hemispheric temperatures and global ocean heat content
Author(s) -
Aldrin Magne,
Holden Marit,
Guttorp Peter,
Skeie Ragnhild Bieltvedt,
Myhre Gunnar,
Berntsen Terje Koren
Publication year - 2012
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2140
Subject(s) - climate sensitivity , radiative forcing , climate model , environmental science , climate change , climatology , forcing (mathematics) , transient climate simulation , prior probability , greenhouse gas , sensitivity (control systems) , ocean heat content , global warming , bayesian inference , radiative transfer , climate commitment , bayesian probability , atmospheric sciences , sea surface temperature , mathematics , effects of global warming , physics , statistics , geology , oceanography , electronic engineering , quantum mechanics , engineering
Predictions of climate change are uncertain mainly because of uncertainties in the emissions of greenhouse gases and how sensitive the climate is to changes in the abundance of the atmospheric constituents. The equilibrium climate sensitivity is defined as the temperature increase because of a doubling of the CO 2 concentration in the atmosphere when the climate reaches a new steady state. CO 2 is only one out of the several external factors that affect the global temperature, called radiative forcing mechanisms as a collective term. In this paper, we present a model framework for estimating the climate sensitivity. The core of the model is a simple, deterministic climate model based on elementary physical laws such as energy balance. It models yearly hemispheric surface temperature and global ocean heat content as a function of historical radiative forcing. This deterministic model is combined with an empirical, stochastic model and fitted to observations on global temperature and ocean heat content, conditioned on estimates of historical radiative forcing. We use a Bayesian framework, with informative priors on a subset of the parameters and flat priors on the climate sensitivity and the remaining parameters. The model is estimated by Markov Chain Monte Carlo techniques. Copyright © 2012 John Wiley & Sons, Ltd.