Open AccessA climate sensitivity estimate using Bayesian fusion of instrumental observations and an Earth System model
Author(s) -
Olson Roman,
Sriver Ryan,
Goes Marlos,
Urban Nathan M.,
Matthews H. Damon,
Haran Murali,
Keller Klaus
Publication year - 2012
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2011jd016620
Subject(s) - sensitivity (control systems) , climate model , climate sensitivity , environmental science , probability density function , bayesian probability , markov chain monte carlo , forcing (mathematics) , monte carlo method , meteorology , climatology , climate change , mathematics , statistics , geology , physics , oceanography , electronic engineering , engineering
Current climate model projections are uncertain. This uncertainty is partly driven by the uncertainty in key model parameters such as climate sensitivity ( CS ), vertical ocean diffusivity ( K v ), and strength of anthropogenic sulfate aerosol forcing. These parameters are commonly estimated using ensembles of model runs constrained by observations. Here we obtain a probability density function (pdf) of these parameters using the University of Victoria Earth System Climate Model (UVic ESCM) ‐ an intermediate complexity model with a dynamic three‐dimensional ocean. Specifically, we run an ensemble of UVic ESCM runs varying parameters that affect CS , ocean vertical diffusion, and the effects of anthropogenic sulfate aerosols. We use a statistical emulator that interpolates the UVic ESCM output to parameter settings where the model was not evaluated. We adopt a Bayesian approach to constrain the model output with instrumental surface temperature and ocean heat observations. Our approach accounts for the uncertainties in the properties of model‐data residuals. We use a Markov chain Monte Carlo method to obtain a posterior pdf of these parameters. The mode of the climate sensitivity estimate is 2.8°C, with the corresponding 95% credible interval ranging from 1.8 to 4.9°C. These results are generally consistent with previous studies. The CS pdf is sensitive to the assumptions about the priors, to the effects of anthropogenic sulfate aerosols, and to the background vertical ocean diffusivity. Our method can be used with more complex climate models.