New Deep Neural Network Could Speed Calculations in Climate Models

A group of researchers from Pacific Northwest National Laboratory created and then embedded a physics-informed deep neural network (DNN)—a sophisticated mathematical model that can learn as it processes data—in a detailed regional model of the Amazon rainforest to demonstrate a new way to speed output and resolve compounding errors.

This work provides a clear proof of concept for successfully implementing machine learning to replace computationally expensive submodules in climate models, thus speeding up complex physics and chemistry calculations in three-dimensional chemical atmospheric transport and climate models.

Using knowledge of multiphase chemistry of secondary organic aerosols, PNNL trained this physics-informed DNN to emulate a particular type of aerosol over the Amazon. They used the Tahoma scientific computer at EMSL, the Environmental Molecular Sciences Laboratory, a Department of Energy (DOE) Office of Science user facility, along with other computational resources, to train the DNN on 7 hours of simulations from the Weather Research and Forecasting Model coupled to chemistry (WRF-Chem). Even with such a limited training, the DNN predictions generalized well over several days of WRF-Chem simulations in both the dry and wet seasons of the Amazon. It could successfully simulate the complex composition, spatial and temporal variations, and chemistry of these secondary organic aerosols. Embedding the DNN into WRF-Chem reduces the computational expense of WRF-Chem by a factor of two. The approach shows substantial promise for application to computationally expensive chemistry solvers in climate models.

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