Building a machine learning surrogate model for wildfire activities within a global Earth system model

Qing Zhu, Fa Li, W. J. Riley, Li Xu, Lei Zhao, Kunxiaojia Yuan, Huayi Wu, Jianya Gong, James T. Randerson

Geoscientific model development · 08 March 2022 · Lawrence Berkeley National Laboratory / NERSC

In one sentence. A DNN surrogate replaces the process-based wildfire model in E3SM, matching observed burned area with R2 0.93 and far lower parameterization cost.

Abstract

Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire number, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model within the Energy Exascale Earth System Model (E3SM). The DNN wildfire model accurately simulates the observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured global dynamics of wildfire burned areas between years 2011 and 2015 (R2 = 0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

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