How Well Can Historical Temperature Observations Constrain Climate Sensitivity

R. Olson

Ph.D. Thesis (2013)

Geoscience, Pennsylvania State University


Future climate projections are strongly influenced by climate sensitivity (CS). Many recent studies estimated CS by combining runs of Earth Models of Intermediate Complexity (EMICs) with global mean instrumental observations. Yet, CS estimates remain consistently uncertain. This dissertation addresses four questions: (1) What is the probability distribution function (pdf) of CS implied by global mean surface temperatures and upper ocean warming when a model with full three-dimensional ocean dynamics is used? (2) How sensitive is this pdf to priors? (3) How does the CS estimation uncertainty depend on the true CS of the climate system? and (4) How strongly is the CS uncertainty affected by internal climate variability that is not resolved by the model? These questions are addressed with a 250-member ensemble of UVic ESCM climate model runs varying CS, background vertical mixing in the ocean, and anthropogenic sulfate cooling effects. A Gaussian Process emulator is developed to interpolate UVic ESCM output between the ensemble parameter settings. The emulator is constrained with historical observations of temperature and upper ocean warming using a Bayesian Markov chain Monte Carlo method. The results are combined with prior independent evidence. The method results in the 95

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