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arXiv · 1906.06622

Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling

Tom Beucler·Stephan Rasp·Michael Pritchard·Pierre Gentine
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Abstract

Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models. However, neural-network models do not intrinsically conserve energy and mass, which is an obstacle to using them for long-term climate predictions. Here, we propose two methods to enforce linear conservation laws in neural-network emulators of physical models: Constraining (1) the loss function or (2) the architecture of the network itself. Applied to the emulation of explicitly-resolved cloud processes in a prototype multi-scale climate model, we show that architecture constraints can enforce conservation laws to satisfactory numerical precision, while all constraints help the neural-network better generalize to conditions outside of its training set, such as global warming.

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Tom Beucler, Stephan Rasp, Michael Pritchard, Pierre Gentine. 2019-06-15. Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling. https://arxiv.org/abs/1906.06622

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