Development of Digital Twin Technologies for Climate Projections

Abstract

Climate projections are increasingly needed for adaptation, climate resilience and related decision making. However, existing projections have systematic biases, are limited in scope, and are not readily available for most potential users. While the ideal of an observational data-driven ‘digital twin’ for climate is initially attractive, there is only a very limited set of climate data available with which to train such a tool. Nonetheless, we are confident that there is a role for ‘digital twin technologies’ in removing biases, increasing computational efficiency, expanding scenarios and data accessibility.