Robustness of the Stochastic Parameterization of Subgrid-Scale Wind Variability in Sea Surface Fluxes

Journal Article · Sun Oct 01 00:00:00 EDT 2023 · Monthly Weather Review

DOI:https://doi.org/10.1175/MWR-D-22-0319.1· OSTI ID:1999902

Endo, Kota ^[1]; Monahan, Adam H. ^[1]; Bessac, Julie ^[2]; Christensen, Hannah M. ^[3]; Weitzel, Nils ^[4]

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1. a School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada
2. b Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, Illinois
3. c Department of Physics, University of Oxford, Oxford, United Kingdom
4. d Department of Geosciences, University of Tübingen, Tübingen, Germany, e Institute of Environmental Physics, Heidelberg University, Heidelberg, Germany

Abstract

High-resolution numerical models have been used to develop statistical models of the enhancement of sea surface fluxes resulting from spatial variability of sea surface wind. In particular, studies have shown that flux enhancement is not a deterministic function of the resolved state. Previous studies focused on single geographical areas or used a single high-resolution numerical model. This study extends the development of such statistical models by considering six different high-resolution models, four different geographical regions, and three different 10-day periods, allowing for a systematic investigation of the robustness of both the deterministic and stochastic parts of the data-driven parameterization. Results indicate that the deterministic part, based on regressing the unresolved normalized flux onto resolved-scale normalized flux and precipitation, is broadly robust across different models, regions, and time periods. The statistical features of the stochastic part of the model (spatial and temporal autocorrelation and parameters of a Gaussian process fit to the regression residual) are also found to be robust and not strongly sensitive to the underlying model, modeled geographical region, or time period studied. Best-fit Gaussian process parameters display robust spatial heterogeneity across models, indicating potential for improvements to the statistical model. These results illustrate the potential for the development of a generic, explicitly stochastic parameterization of sea surface flux enhancements dependent on wind variability.

• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: NONE; AC02-06CH11357
• OSTI ID: 1999902
• Journal Information: Monthly Weather Review, Journal Name: Monthly Weather Review Journal Issue: 10 Vol. 151; ISSN 0027-0644
• Publisher: American Meteorological Society
• Country of Publication: United States
• Language: English