Abstract
<jats:p>The equilibrium climate sensitivity (ECS) is a key parameter in climate science and other disciplines. This study estimates ECS as a common equilibrium parameter across energy balance models (EBMs). Fitting EBMs to simulated data from 31 climate models under a CO2 quadrupling experiment, we introduce a Bayesian composite likelihood (CL) approach to simultaneously integrate and estimate all the constituent EBMs. Complementing methods of storylines and emergent constraints commonly employed by climate scientists, our econometric alternative provides a data-driven ECS estimator. We find an ECS estimate of 3.65K, characterized by a unimodal and right-skewed posterior distribution that facilitates uncertainty quantification. Our approach also yields a 95% credible interval of (2.5K, 5K), which is narrower and closer to the upper bound of the IPCC’s “very likely” ECS range.</jats:p>