Bayesian Errors-in-Variables Estimation of Specific Climate Sensitivity

Estimation of climate sensitivity is fundamental to assessing how global climate will warm as atmospheric (Formula presented.) concentration increases. Geological archives of environmental change provide insights into Earth's past climate, but the incomplete nature of paleoclimate reconstructions and their inherent uncertainties make estimation of climate sensitivity challenging. Thus, quantifying climate sensitivity and assessing how it changed through geological time requires statistical frameworks that can handle data uncertainties in a principled fashion. Here we demonstrate some of the hurdles to estimating climate sensitivity, with a focus on current statistical techniques that may underestimate both climate sensitivity and its associated uncertainty. To solve these issues, we present a Bayesian error-in-variables regression model, which can yield estimates of climate sensitivity without bias. The regression model is flexible and can account for data point uncertainties with a known parametric form. The utility of this approach is demonstrated by estimating specific climate sensitivity with uncertainty for the Eocene.