Weighting Climate Ensemble Indicators to a Likely Equilibrium Climate Sensitivity Distribution to Improve Representativeness

Using the expected likely distribution of Earth-system equilibrium climate sensitivity (ECS), as well as the effective climate sensitivity for CMIP6 models, we obtain weights for each climate model, constraining the ensemble ECS to its likely distribution. This allows us to de-emphasize models with higher than likely ECS, which is a well-known issue from the CMIP6 generation of climate models. Using these weights, along with a bias-corrected CMIP6 dataset (ESPO-G6-R2), we compute ensemble statistics for a variety of indicators over a southeastern region of Canada. These are then compared to other avenues of resolving the Hot Model Problem, such as removing models which are outside of a selected ECS range.

Advantages and applications to this methodology will be demonstrated. Alongside the poster, an interactive web app will allow attendees to explore the data for themselves.