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Global Climate Model (GCM) tuning (calibration) is a tedious andtime-consuming process, with high-dimensional input and output fields. Expertstypically tune by iteratively running climate simulations with hand-pickedvalues of tuning parameters. Many, in both the statistical and climateliterature, have proposed alternative calibration methods, but most areimpractical or difficult to implement. We present a practical, robust andrigorous calibration approach on the atmosphere-only model of the Department ofEnergy's Energy Exascale Earth System Model (E3SM) version 2. Our approach canbe summarized into two main parts: (1) the training of a surrogate thatpredicts E3SM output in a fraction of the time compared to running E3SM, and(2) gradient-based parameter optimization. To train the surrogate, we generatea set of designed ensemble runs that span our input parameter space and usepolynomial chaos expansions on a reduced output space to fit the E3SM output.We use this surrogate in an optimization scheme to identify values of the inputparameters for which our model best matches gridded spatial fields of climateobservations. To validate our choice of parameters, we run E3SMv2 with theoptimal parameter values and compare prediction results to expertly-tunedsimulations across 45 different output fields. This flexible, robust, andautomated approach is straightforward to implement, and we demonstrate that theresulting model output matches present day climate observations as well orbetter than the corresponding output from expert tuned parameter values, whileconsidering high-dimensional output and operating in a fraction of the time.

Autocalibration of the E3SM version 2 atmosphere model using a PCA-based surrogate for spatial fields