Toward Efficient Calibration of Higher-Resolution Earth System Models

Projections of future climate change to support decision-making require Earth system models (ESMs) running at high spatial resolution, but at present this is computationally prohibitive. A major challenge is the calibration (parameter tuning) during the development of ESMs, which requires running large numbers of simulations to identify optimal values for parameters that are poorly constrained by observations. Here, we train a convolutional neural network (CNN) to emulate perturbed parameter ensembles from two lower-resolution (and thus much less expensive) versions of the same ESM, and a smaller number of higher-resolution simulations. Cross-validated results show that the CNN's skill exceeds that of a climatological baseline for most variables with as few as 5-10 examples of the higher-resolution ESM, and for all variables (including precipitation) with at least 20 examples. This proof-of-concept study demonstrates a machine learning based approach that makes the process of constructing a higher-resolution emulator 20%-40% more computationally efficient, and thus offers the prospect of significantly more efficient calibration of ESMs.