Causal Weighting for Climate Projections

Accurate climate projections are critical for understanding climate change and to design adaptation and mitigation strategies. Weighting schemes that aggregate a range of climate model projections are widely used to provide more reliable estimates of future climate conditions. Recently, causal discovery has been successfully introduced in the weighting schemes to constrain uncertainties in climate model projections based on the performance and interdependence of climate models. However, the previous methodologies typically (and strongly) only utilize a single metric, the F1 score of performance and similarity between each climate model and observational data,  to compare the different models' causal structures. Here, we introduce alternative and more sophisticated causal weighting schemes inspired by the theory of kernel methods and Gaussian processes to compare causal graphs directly in suitable reproducing kernel Hilbert spaces. In addition, we propose alternative causal weighting schemes that rely on interventions, graph-based distances, and counterfactual evaluations. We will evaluate the causal weighting strategies in various synthetic and CMIP6 model datasets.