Opinion: Optimizing climate models with process-knowledge, resolution, and AI
Abstract. Accelerating progress in climate modeling is urgent for proactive and effective climate change adaptation. The central challenge lies in accurately representing processes that are small in scale yet are climatically important, such as turbulence and cloud formation. These processes are not explicitly resolvable, necessitating the use of parameterizations. We propose a balanced approach that leverages the strengths of traditional process-based parameterizations and contemporary AI-based data-driven methods to model subgrid-scale processes. This strategy focuses on employing AI to derive data-driven closure functions from both observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. Increasing resolution to resolve a larger fraction of small-scale processes can aid progress toward improved and interpretable climate predictions outside the observed climate distributions, but it must still allow the generation of large ensembles for model calibration and the broad exploration of possible climate outcomes – currently O(10 km) horizontal resolutions are feasible. By synergizing decades of scientific development with advanced AI techniques, this approach aims to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions.
Status: open (until 06 Mar 2024)
Error analysis of climate models from CMIP3 through CMIP6, including AMIP and higher-resolution models https://data.caltech.edu/records/9dzs6-tmg69
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