Parametrizing the mesoscale enhancement of oceanic surface turbulent fluxes: A physical–statistical approach

International audienceThe mesoscale enhancement of surface turbulent fluxes at the air–sea interface is driven by the mesoscale surface wind-speed variability, especially the gustiness velocity and the mesoscale wind-speed standard variation. This study proposes a parametrization of these two variables. A large dataset based on the operational 2.5-km AROME convection-permitting model is used in a coarse-graining framework, to quantify various quantities that are subgrid at the scale of a 100-km resolution global circulation model grid cell. This provides a learning dataset to help build the parametrization. The analysis of two case studies of intense wind-speed mesoscale variability, combined with a literature review, provides a physically based set of 12 potential predictors, accounting for the convection activity and the large-scale dynamics. The least absolute shrinkage and selection operator then frames a penalized multivariate linear regression approach to identify the most relevant predictors objectively. Five predictors are selected for predicting the gustiness velocity: the updraft mass flux at the lifting condensation level, the density-current spreading velocity, the large-scale horizontal shear and divergence, and the large-scale wind speed. The parametrization of the mesoscale wind-speed standard deviation requires an additional predictor, namely the cold-pool object aggregation index. The proposed parametrization performs significantly better than the previously published parametrizations and is able to capture 80, 99, and 93% of the mesoscale enhancement of the momentum, sensible heat, and latent heat fluxes, respectively. From the perspective of a global circulation model implementation, in which some predictors may be unavailable, simpler versions of the parametrization, that is, involving fewer predictors, are also discussed