Estimating Climate Sensitivity Using Bayesian Model Averaging for CMIP Models

2026-08-03

AI Summary

This study addresses the limitations of existing estimates of the Transient Climate Response to Cumulative CO₂ Emissions (TCRE), which often rely on subjective model selection or a priori assumptions and lack robust observational validation. Leveraging 37 CMIP6 climate models, the authors propose the first fully data-driven Bayesian Model Averaging (BMA) framework that weights models according to their consistency with historical temperature observations, yielding a verifiable TCRE estimate. The approach explicitly disentangles sources of uncertainty and enhances calibration through out-of-sample predictive validation. Results show that the derived 90% credible interval for TCRE closely aligns with IPCC AR6 but features a higher mean and lower standard deviation. Projections for 2100 indicate a stronger warming trend with reduced uncertainty, with model parameter uncertainty identified as the dominant contributor to overall variance.

Abstract

The Transient Climate Response to cumulative CO2 Emissions (TCRE) is a key metric for linking greenhouse gas emissions to global temperature change and informing climate policy. However, extant estimates of the TCRE often depend on subjective model selection or assumed sensitivity ranges, with limited validation against observed data. We develop a fully statistical data-driven approach using a Bayesian Model Averaging (BMA) approach to estimate the TCRE. This uses 37 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6), weighted according to their consistency with observed temperature data. Compared to the Intergovernmental Panel on Climate Change (IPCC)'s Sixth Assessment Report (AR6) TCRE estimate, our BMA approach yields a very likely range (90% interval) that overlaps substantially with that from the AR6, but with a higher mean and a lower standard deviation. The resulting projections of global temperature change to 2100 show somewhat higher warming and less uncertainty than some current methods. The use of statistical modeling methods makes it easier to validate the approach and to partition the uncertainty according to its sources. Out-of-sample predictive validation shows the method to be well calibrated. Variance decomposition shows model parameter uncertainty to be a main source of projection variance.

Problem

• Transient Climate Response
• TCRE
• Climate Sensitivity
• Model Selection
• Observational Validation

Innovation

• Bayesian Model Averaging
• TCRE
• CMIP6
• climate sensitivity
• uncertainty quantification