Machine Learning Predictions of a Multiresolution Climate Model Ensemble

Abstract

AbstractStatistical models of high‐resolution climate models are useful for many purposes, including sensitivity and uncertainty analyses, but building them can be computationally prohibitive. We generated a unique multiresolution perturbed parameter ensemble of a global climate model. We use a novel application of a machine learning technique known as random forests to train a statistical model on the ensemble to make high‐resolution model predictions of two important quantities: global mean top‐of‐atmosphere energy flux and precipitation. The random forests leverage cheaper low‐resolution simulations, greatly reducing the number of high‐resolution simulations required to train the statistical model. We demonstrate that high‐resolution predictions of these quantities can be obtained by training on an ensemble that includes only a small number of high‐resolution simulations. We also find that global annually averaged precipitation is more sensitive to resolution changes than to any of the model parameters considered.