The Effect of Different Climate Sensitivity Priors on Projected Climate: A Probabilistic Analysis

Understanding equilibrium climate sensitivity (ECS, equilibrium warming in response to a doubling of CO₂) uncertainty is fundamental for making reliable climate projections. We leverage the Hector simple climate model in a probabilistic framework to explore how different ECS priors influence uncertainty in long-term (2081–2100) temperature projections. This method demonstrates a computationally efficient probabilistic workflow that explores the effects of parameter priors on climate projections. Excluding process and paleoclimate evidence in ECS priors widens resulting temperature projection uncertainty (a 5%–95% confidence range of 1.12–3.03°C and 1.09–3.33°C, respectively), while synthesizing all lines of evidence narrows temperature projection uncertainty (1.24–2.89°C; 5–95% CI), suggesting a more robust range of future temperature outcomes.