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Climate models are hindered by the need to conceptualize and then parameterize complex physical processes that are not explicitly numerically resolved and for which no rigorous theory exists. Machine learning and artificial intelligence methods (ML and AI) offer a promising paradigm that can augment or replace the traditional parameterized approach with models trained on empirical process data. We offer a flexible and efficient plugin, TorchClim, that facilitates the insertion of ML and AI physics surrogates into the climate model to create hybrid models. A reference implementation is presented for the Community Earth System Model (CESM), where moist physics and radiation parameterizations of the Community Atmospheric Model (CAM) are replaced with such a surrogate. We present a set of best-practice principles for doing this with minimal changes to the general circulation model (GCM), exposing the surrogate model as any other parameterization module, and discuss how to accommodate the requirements of physics surrogates such as the need to avoid unphysical values and supply information needed by other GCM components. We show that a deep-neural-network surrogate trained on data from CAM itself can produce a model that reproduces the climate and variability in the original model, although with some biases. The efficiency and flexibility of this approach open up new possibilities for using physics surrogates trained on offline data to improve climate model performance, better understand model physical processes, and flexibly incorporate new processes into climate models. [ABSTRACT FROM AUTHOR]

Climate models are hindered by the need to conceptualize and then parameterize complex physical processes that are not explicitly numerically resolved and for which no rigorous theory exists. Machine learning and artificial intelligence methods (ML/AI) offer a promising paradigm that can augment or replace the traditional parametrized approach with models trained on empirical process data. We offer a flexible and efficient framework, TorchClim, for inserting ML/AI physics surrogates that respect the parallelization of the climate model. A reference implementation of this approach is presented for the Community Earth System Model (CESM), where the authors substitute moist physics and radiative parametrization of the Community Atmospheric Model (CAM) with an ML/AI model. We show that a deep neural network surrogate trained on data from CAM itself can produce a stable model that reproduces the climate and variability of the original model, albeit with some biases. This framework is offered to the research community as an open-source project. The new framework seamlessly integrates into CAM's workflow and code-base and runs with negligible added computational cost, allowing rapid testing of various ML physics surrogates. The efficiency and flexibility of this framework open up new possibilities for using physics surrogates trained on offline data to improve climate model performance and better understand model physical processes. [ABSTRACT FROM AUTHOR]