The effect of different climate sensitivity priors on projected climate: A probabilistic analysis
Journal: Geophysical Research Letters
Joseph K Brown*, Kalyn Dorheim, Derek Mu, Abigail Snyder, Claudia Tebaldi, Ben Bond-Lamberty
*corresponding author: [email protected]
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 ℃ and 1.09-3.33 ℃, respectively), while synthesizing all lines of evidence narrows temperature projection uncertainty (1.24-2.89 ℃; 5-95% CI), suggesting a more robust range of future temperature outcomes.
- Uncertainty in equilibrium climate sensitivity distributions propagates through to future temperature projections.
- Prior distributions that exclude process and paleoclimate evidence result in the most uncertain future temperature projections.
- Using simple climate models with a probabilistic framework can help test the effects of different parameter priors on climate projections.
LICENSE– Licensing information.workflows/– Primary analysis markdown files for workflow execution.data-raw/– Raw input datasets required for the analysis.data/– Processed results generated by the workflow.figures/– Processed figures generated by the workflow.
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Brown, J. “The Effect of Different Climate Sensitivity Priors on Projected Climate: A Probabilistic Analysis”. Zenodo, March 2, 2025. https://doi.org/10.5281/zenodo.13943103.