Preprints
https://doi.org/10.5194/egusphere-2024-20
https://doi.org/10.5194/egusphere-2024-20
24 Jan 2024
 | 24 Jan 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Opinion: Optimizing climate models with process-knowledge, resolution, and AI

Tapio Schneider, L. Ruby Leung, and Robert C. J. Wills

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.

Tapio Schneider, L. Ruby Leung, and Robert C. J. Wills

Status: open (until 06 Mar 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-20', Peter Caldwell, 17 Feb 2024 reply
  • RC2: 'Comment on egusphere-2024-20', Anonymous Referee #2, 21 Feb 2024 reply
Tapio Schneider, L. Ruby Leung, and Robert C. J. Wills

Data sets

Error analysis of climate models from CMIP3 through CMIP6, including AMIP and higher-resolution models Robert C. J. Wills and Tapio Schneider https://data.caltech.edu/records/9dzs6-tmg69

Tapio Schneider, L. Ruby Leung, and Robert C. J. Wills

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Short summary
This paper lays out an approach to achieve substantial progress in climate modeling, balancing increases in resolution with advances in process-based modeling and the use of AI to learn from Earth observations.