Climate model selection via conformal clustering of spatial functional data

Abstract

Climate model selection stands as a critical process in climate science and research. It involves choosing the most appropriate climate models to address specific research questions, simulating climate behaviour, or making projections about future climate conditions. This paper proposes a new approach, using spatial functional data analysis, to asses which of the 18 EURO CORDEX simulation models work better for predicting average temperatures in the Campania region (Italy). The method involves two key steps: first, using functional data analysis to process climate variables and select optimal models by a hierarchical clustering procedure; second, validating the chosen models by proposing a new conformal prediction approach to the anomalies associated to each cluster.

Special issue on modelling complex environmental data

“…The method is illustrated on data of wind speed and temperature. Villani et al (2024) introduce a novel approach to climate model selection, employing spatial functional data analysis to assess EURO CORDEX simulation models for predicting average temperatures in Italy's Campania region. The method involves hierarchical clustering of climate variables followed by validation using a conformal prediction approach, providing a robust framework for selecting and validating models in climate science research.…”