Uncertainty Quantification of Ocean Parameterizations: Application to the K‐Profile‐Parameterization for Penetrative Convection.

Parameterizations of unresolved turbulent processes often compromise the fidelity of large‐scale ocean models. In this work, we argue for a Bayesian approach to the refinement and evaluation of turbulence parameterizations. Using an ensemble of large eddy simulations of turbulent penetrative convection in the surface boundary layer, we demonstrate the method by estimating the uncertainty of parameters in the convective limit of the popular "K‐Profile Parameterization." We uncover structural deficiencies and propose an alternative scaling that overcomes them. Plain Language Summary: Climate projections are often compromised by significant uncertainties which stem from the representation of physical processes that cannot be resolved—such as clouds in the atmosphere and turbulent swirls in the ocean—but which have to be parameterized. We propose a methodology for improving parameterizations in which they are tested against, and tuned to, high‐resolution numerical simulations of subdomains that represent them more completely. A Bayesian methodology is used to calibrate the parameterizations against the highly resolved model, to assess their fidelity and identify shortcomings. Most importantly, the approach provides estimates of parameter uncertainty. While the method is illustrated for a particular parameterization of boundary layer mixing, it can be applied to any parameterization. Key Points: A Bayesian methodology can be used to probe turbulence parameterizations and better understand their biases and uncertaintiesParameterization parameter distributions, learned using high‐resolution simulations, can be used as prior distributions for climate studies [ABSTRACT FROM AUTHOR]