Scale-aware space-time stochastic parameterization of subgrid-scale velocity enhancement of sea surface fluxes

Abstract:

We present a statistical scale-aware space-time model for the sub-grid variability of air-sea exchanges driven by surface wind speed. Quantifying the influence of the sub-grid scales on the resolved scales in physics-based models is needed to better represent the entire system. In this work, we evaluate and model the difference between the true turbulent fluxes and those calculated using area-averaged wind speeds. This discrepancy is modelled in space and time, conditioned on the low-resolution fields, with the view of developing a stochastic wind-flux parameterization. A locally stationary space-time Gaussian process is used to model this discrepancy process. Additionally, the Gaussian process is proposed in a scale-aware fashion meaning that the space-time correlation parameters depend on the considered resolution. The scale-aware capability is based on empirical observations from a systematic coarse-graining of a high-resolution model output dataset. It enables to derive a stochastic parameterization of sub-grid variability at any resolution and to characterize statistically the space-time structure of the discrepancy process across scales.