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Abstract Turbulent mixing in ocean boundary layers is often fully parameterized as a subgrid‐scale process in realistic ocean simulations. However, recent submesoscale modeling studies have advanced to a horizontal grid spacing of (10 m) that is comparable to, or even smaller than, the typical depth of the turbulent surface boundary layer. Meanwhile, efforts toward realistic large‐eddy simulations (LES) nested within regional models require subdomains with similar grid spacings, where turbulent eddies are partially resolved in the mixed layer. The range of intermediate grid spacings, often known as the “gray zone,” presents challenges for model configuration and analysis, including uncertainties regarding the behavior of common turbulence closures outside of their ideal use cases. In this study, we evaluate three common configurations for subgrid turbulence—‐, Smagorinsky, and an implicit no‐closure method—in the gray zone resolutions for the ocean surface mixed layer. Results indicate that, in the gray zone with partially resolved boundary layer turbulence, ‐ can produce accurate mixed layer profiles with little sensitivity to grid spacing. However, it overly damps turbulent motions, significantly reducing small‐scale variability that could otherwise be captured. The Smagorinsky closure and the implicit method, in contrast, exhibit higher sensitivity to grid spacing, initially performing poorly but converging toward baseline solutions at finer grids. Our findings provide guidance for submesoscale and turbulent‐scale modeling, recommending Smagorinsky or implicit methods for nested domains which prioritize resolved turbulence, such as LES. The ‐ closure is suitable for simulations that aim to achieve accurate mean‐state representations rather than explicitly resolving detailed three‐dimensional turbulence.
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On the Sensitivity of Large-Eddy Simulations of the Atmospheric Boundary Layer Coupled with Realistic Large-Scale Dynamics
Abstract We present a new ensemble of 36 numerical experiments aimed at comprehensively gauging the sensitivity of nested large-eddy simulations (LES) driven by large-scale dynamics. Specifically, we explore 36 multiscale configurations of the Weather Research and Forecasting (WRF) Model to simulate the boundary layer flow over the complex topography at the Perdigão field site, with five nested domains discretized at horizontal resolutions ranging from 11.25 km to 30 m. Each ensemble member has a unique combination of the following input factors: (i) large-scale initial and boundary conditions, (ii) subgrid turbulence modeling in thegray zoneof turbulence, (iii) subgrid-scale (SGS) models in LES, and (iv) topography and land-cover datasets. We probe their relative importance for LES calculations of velocity, temperature, and moisture fields. Variance decomposition analysis unravels large sensitivities to topography and land-use datasets and very weak sensitivity to the LES SGS model. Discrepancies within ensemble members can be as large as 2.5 m s−1for the time-averaged near-surface wind speed on the ridge and as large as 10 m s−1without time averaging. At specific time points, a large fraction of this sensitivity can be explained by the different turbulence models in the gray zone domains. We implement a horizontal momentum and moisture budget routine in WRF to further elucidate the mechanisms behind the observed sensitivity, paving the way for an increased understanding of the tangible effects of the gray zone of turbulence problem. Significance StatementSeveral science and engineering applications, including wind turbine siting and operations, weather prediction, and downscaling of climate projections, call for high-resolution numerical simulations of the lowest part of the atmosphere. Recent studies have highlighted that such high-resolution simulations, coupled with large-scale models, are challenging and require several important assumptions. With a new set of numerical experiments, we evaluate and compare the significance of different assumptions and outstanding challenges in multiscale modeling (i.e., coupling large-scale models and high-resolution atmospheric simulations). The ultimate goal of this analysis is to put each individual assumption into the wider perspective of a realistic problem and quantify its relative importance compared to other important modeling choices.
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- Award ID(s):
- 2236504
- PAR ID:
- 10562518
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Monthly Weather Review
- Volume:
- 152
- Issue:
- 4
- ISSN:
- 0027-0644
- Page Range / eLocation ID:
- 1057 to 1075
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1175/MWR-D-23-0101.1
