Dimensionality Reduction and Physics-Informed Recurrent Neural Networks for Climate Land Models

Abstract:

This work focuses on approximating complex land surface models. Such approximations, or surrogates, are necessary for compute-intensive tasks, such as uncertainty quantification or model calibration. The primary model of interest is the land component of the Energy Exascale Earth System Model (E3SM).

Using an ensemble of model training simulations, the development of a model surrogate is cast as a supervised machine learning (ML) problem. For expensive models with a large number of input parameters, the critical challenge is to create high-fidelity spatio-temporal surrogates with as few model training evaluations as possible. We rely on Karhunen-Loeve expansions to account for spatial correlations of model outputs, while the temporal evolution is best approximated with long-short term memory (LSTM) recurrent neural network. Besides, considering the already known interactions between input processes and output quantities of interest (QoIs), we develop a special LSTM architecture with predefined connections between these QoIs. Such physics-informed architecture with reduced spatial dimensionality is shown to outperform, both in accuracy and efficiency, vanilla LSTM implementations and cell-based independent surrogates. We then employ the resulting spatio-temporal surrogate to extract parameter sensitivity indices, as well as to perform model calibration given global observational data on select QoIs.