Open AccessA Universal Approach for the Non‐Iterative Parametrization of Near‐Surface Turbulent Fluxes in Climate and Weather Prediction Models
Author(s) -
Gryanik V. M.,
Lüpkes C.,
Sidorenko D.,
Grachev A.
Publication year - 2021
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1029/2021ms002590
Subject(s) - parametrization (atmospheric modeling) , stability (learning theory) , mathematics , turbulence , numerical weather prediction , surface (topology) , momentum (technical analysis) , climate model , environmental science , statistical physics , computer science , meteorology , physics , geology , climate change , geometry , oceanography , finance , quantum mechanics , machine learning , economics , radiative transfer
Abstract Weather prediction and climate simulations need reliable parameterizations of turbulent fluxes in the stable surface layer. Especially in these conditions, the uncertainties of such parametrizations are still large. Most of them rely on the Monin‐Obukhov similarity theory (MOST), for which universal stability functions (SFs) represent important ingredients. The SFs are nonlinear, if so, a numerical iteration of the MOST equations is required. Moreover, presently available SFs are significantly different at large stability. To simplify the calculations, a non‐iterative parametrization of fluxes is derived and corresponding bulk transfer coefficients for momentum and heat for a package of five pairs of state‐of‐the‐art SFs are proposed. For the first time, a parametrization of the related transfer coefficients is derived in a universal framework for all package members. The new parametrizations provide a basis for a cheap systematic study of the impact of surface layer turbulent fluxes in weather prediction and climate models.