Calibration and uncertainty quantification of subgrid scale parameterizations for atmospheric flows

Physical processes such as turbulence and surface-atmosphere interactions are parameterized in the atmospheric boundary layer (ABL) flow models that drive predictions used for decision-making in a wide range of engineering and sustainability applications. Based on the Reynolds Averaged Navier Stokes (RANS) equations, parameterizations of subgrid scale (SGS) processes in weather and climate models are generally calibrated manually by tuning ABL turbulence parameterizations to available high-fidelity data. Manual tuning only targets a limited subset of observational data and parameters. We develop methods for the computationally-efficient calibration and uncertainty quantification (UQ) of model parameters. Uncertainty quantification is performed using the calibrate-emulate-sample approach, which combines stochastic optimization and machine learning emulation to speed up Bayesian learning. The methods are demonstrated in a perfect-model setting through the calibration and UQ of a convective parameterization in an idealized general circulation model (GCM) with a seasonal cycle. Calibration and UQ based on seasonally averaged climate statistics, compared to annually averaged, reduces the calibration error by up to an order of magnitude and narrows the spread of posterior distributions by factors between two and five, depending on the variables used for UQ. The reduction in the size of the parameter posterior distributions leads to a reduction in the uncertainty of climate model predictions. The UQ methodology is extended to perform Bayesian experimental design, where the locations and time periods of data acquisition to maximally reduce parameter uncertainty are identified. Extensions of the methodology to UQ of turbulence modeling in the ABL will be discussed.