Scale‐Aware Space‐Time Stochastic Parameterization of Subgrid‐Scale Velocity Enhancement of Sea Surface Fluxes

• Published in: Journal of advances in modeling earth systems 2021-04, Vol.13 (4), p.n/a
• Main Authors: Bessac, Julie, Christensen, Hannah M., Endo, Kota, Monahan, Adam H., Weitzel, Nils
• Format: Article
• Language: English
• Subjects: Climate models; Mathematical models; Numerical models; Ocean circulation; Parameterization; Resolution; Sea surface; Statistical models; Stochastic models; Surface fluxes; Variability; Velocity; Wind; Wind variability
• ISSN: 1942-2466; 1942-2466
• DOI: 10.1029/2020MS002367

Stochastic representation of the influence of the subgrid‐scales on the resolved scales in weather and climate models has been shown to improve ensemble spread and resolved variability. We propose a statistical scale‐aware space‐time model to characterize the contribution of mesoscale wind variability to air‐sea exchanges. In an earlier study, we analyzed the difference between “true” fluxes computed from a high resolution simulation and “resolved” fluxes obtained by coarse graining. This discrepancy is modeled in space and time, conditioned on the coarse‐grained wind and precipitation fields, to parameterize the enhancement of fluxes by mesoscale velocity variations. Stochastic parameterization models have traditionally been developed for particular model resolutions without the explicit capability to adapt to model resolution. We present an approach to develop stochastic models that adapt to resolution in a scale‐aware fashion. The scale‐aware parameterization is developed from empirical results for systematically coarse‐grained high‐resolution numerical model output. The statistical model is fit from numerical model output at three different coarsening resolutions. From this scale‐aware parameterization, we derive a stochastic parameterization of flux enhancement by subgrid velocity variations for arbitrary resolutions and characterize the conditional distributions and space‐time structures of the flux enhancement across model resolutions. Plain Language Summary Computer models of weather and climate rely on physics‐based equations, and because of their discretization need to approximate the effects of fine scale phenomena. Different techniques have been used to develop such approximations through physically based or data‐driven methods. In the following, we consider a statistically based approach and propose a statistical model of the enhancement of air‐sea exchanges due to small‐scale wind variability. In particular, we demonstrate that this enhancement is stochastic and exhibits mean and space‐time structures that change with model resolution. Consequently, we build a statistical model that accounts for the resolution‐dependence of the air‐sea exchange enhancement. The proposed approach is shown to realistically represent the effect of small‐scale winds and the change of this effect across resolutions. Key Points Subgrid‐scale enhancement of air‐sea fluxes conditioned on the resolved state shows resolution‐dependent stochastic spatiotemporal structur