Bayesian Optimization With GPU Acceleration for Ocean Models

Abstract Ocean general circulation models (OGCMs) contain numerous parameterizations of subgrid scale processes. The parameter tuning procedure is rarely reported and often done by hand. We present an automated alternative: VerOpt, a Python package for the ocean model Veros, adapts Bayesian optimization to climate model tuning. We use VerOpt to identify a set of model parameter values that minimize mixed layer depth (MLD) bias in Veros. We present the results of three optimization procedures: TWIN, OBS_TKE, and OBS_9D. The goal is to minimize the MLD error relative to a target. In TWIN, the target is MLD simulated using Veros with a known vertical mixing parameterization. VerOpt identifies a set of parameter values that simulate the target MLD with 99% accuracy. In OBS_TKE and OBS_9D, the target is MLD climatology. In the OBS_TKE optimization, only the vertical turbulence closure parameters are calibrated, which is insufficient to reduce MLD biases in Veros. In the OBS_9D experiment, the mesoscale eddy closure and the scaling of the wind stress and salinity forcing masks are tuned in addition to the vertical mixing scheme parameters. MLD biases in Veros are reduced by up to 23% with the largest impact in the Southern Ocean. In the ensemble of the simulations for which MLD biases are minimized, the mesoscale turbulence diffusion coefficients and are halved compared to the control run, and the zonal wind stress is 10% weaker.