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1. Swell Modulation on Wind Stress in the Constant Flux Layer

   https://doi.org/10.1029/2020GL089883  

   Chen, Sheng ; Qiao, Fangli ; Zhang, Jun A. ; Ma, Hongyu ; Xue, Yuhuan ; Chen, Siyu ( October 2020 , Geophysical Research Letters)

   The impact of swell on wind stress is investigated through direct three‐layer flux measurements taken by a fixed tower in the marine atmospheric boundary layer. Observations confirm that the assumption of constant momentum flux layer is valid under swell‐dominated conditions around the reference height of 10 m. The swell can modulate the total wind stress to be less than the turbulent stress derived from the first‐order closure method, and the extent of this modulation decreases with height. The critical layer that represents the top of the layer affected by stronger swells is estimated to reach 45‐m altitude, and the depth of this layer decreases as the swells weaken and the wind speed increases. Furthermore, a simple swell correction scheme for the total stress calculation is developed, showing good performance against observations.

    

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2. Machine Learning for Improving Surface-Layer-Flux Estimates

   https://doi.org/10.1007/s10546-022-00727-4  

   McCandless, Tyler ; Gagne, David John ; Kosović, Branko ; Haupt, Sue Ellen ; Yang, Bai ; Becker, Charlie ; Schreck, John ( September 2022 , Boundary-Layer Meteorology)

   Flows in the atmospheric boundary layer are turbulent, characterized by a large Reynolds number, the existence of a roughness sublayer and the absence of a well-defined viscous layer. Exchanges with the surface are therefore dominated by turbulent fluxes. In numerical models for atmospheric flows, turbulent fluxes must be specified at the surface; however, surface fluxes are not known a priori and therefore must be parametrized. Atmospheric flow models, including global circulation, limited area models, and large-eddy simulation, employ Monin–Obukhov similarity theory (MOST) to parametrize surface fluxes. The MOST approach is a semi-empirical formulation that accounts for atmospheric stability effects through universal stability functions. The stability functions are determined based on limited observations using simple regression as a function of the non-dimensional stability parameter representing a ratio of distance from the surface and the Obukhov length scale (Obukhov in Trudy Inst Theor Geofiz AN SSSR 1:95–115, 1946),$$z/L$$$z/L$. However, simple regression cannot capture the relationship between governing parameters and surface-layer structure under the wide range of conditions to which MOST is commonly applied. We therefore develop, train, and test two machine-learning models, an artificial neural network (ANN) and random forest (RF), to estimate surface fluxes of momentum, sensible heat, and moisture based on surface and near-surface observations. To train and test these machine-learning algorithms, we use several years of observations from the Cabauw mast in the Netherlands and from the National Oceanic and Atmospheric Administration’s Field Research Division tower in Idaho. The RF and ANN models outperform MOST. Even when we train the RF and ANN on one set of data and apply them to the second set, they provide more accurate estimates of all of the fluxes compared to MOST. Estimates of sensible heat and moisture fluxes are significantly improved, and model interpretability techniques highlight the logical physical relationships we expect in surface-layer processes.

    

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3. Scalar Flux Profiles in the Unstable Atmospheric Surface Layer Under the Influence of Large Eddies: Implications for Eddy Covariance Flux Measurements and the Non‐Closure Problem

   https://doi.org/10.1029/2023GL106649  

   Liu, Heping ; Liu, Cheng ; Huang, Jianping ; Desai, Ankur R. ; Zhang, Qianyu ; Ghannam, Khaled ; Katul, Gabriel G. ( January 2024 , Geophysical Research Letters)

   How convective boundary‐layer (CBL) processes modify fluxes of sensible (SH) and latent (LH) heat and CO₂(F_c) in the atmospheric surface layer (ASL) remains a recalcitrant problem. Here, large eddy simulations for the CBL show that whileSHin the ASL decreases linearly with height regardless of soil moisture conditions,LHandF_cdecrease linearly with height over wet soils but increase with height over dry soils. This varying flux divergence/convergence is regulated by changes in asymmetric flux transport between top‐down and bottom‐up processes. Such flux divergence and convergence indicate that turbulent fluxes measured in the ASL underestimate and overestimate the “true” surface interfacial fluxes, respectively. While the non‐closure of the surface energy balance persists across all soil moisture states, it improves over drier soils due to overestimatedLH. The non‐closure does not imply thatF_cis always underestimated;F_ccan be overestimated over dry soils despite the non‐closure issue.

    

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4. Airborne Measurements of Scale‐Dependent Latent Heat Flux Impacted by Water Vapor and Vertical Velocity Over Heterogeneous Land Surfaces During the CHEESEHEAD19 Campaign

   https://doi.org/10.1029/2023JD039586  

   Lin, Guo ; Wang, Zhien ; Chu, Yufei ; Ziegler, Conrad L. ; Hu, Xiao‐Ming ; Xue, Ming ; Geerts, Bart ; Paleri, Sreenath ; Desai, Ankur R. ; Yang, Kang ; et al ( February 2024 , Journal of Geophysical Research: Atmospheres)

   The water vapor transport associated with latent heat flux (LE) in the planetary boundary layer (PBL) is critical for the atmospheric hydrological cycle, radiation balance, and cloud formation. The spatiotemporal variability of LE and water vapor mixing ratio (r_v) are poorly understood due to the scale‐dependent and nonlinear atmospheric transport responses to land surface heterogeneity. Here, airborne in situ measurements with the wavelet technique are utilized to investigate scale‐dependent relationships among LE, vertical velocity (w) variance (), andr_vvariance () over a heterogeneous surface during the Chequamegon Heterogeneous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign. Our findings reveal distinct scale distributions of LE, , and at 100 m height, with a majority scale range of 120 m–4 km in LE, 32 m–2 km in , and 200 m–8 km in . The scales are classified into three scale ranges, the turbulent scale (8–200 m), large‐eddy scale (200 m–2 km), and mesoscale (2–8 km) to evaluate scale‐resolved LE contributed by and . The large‐eddy scale in PBL contributes over 70% of the monthly mean total LE with equal parts (50%) of contributions from and . The monthly temporal variations mainly come from the first two major contributing classified scales in LE, , and . These results confirm the dominant role of the large‐eddy scale in the PBL in the vertical moisture transport from the surface to the PBL, while the mesoscale is shown to contribute an additional ∼20%. This analysis complements published scale‐dependent LE variations, which lack detailed scale‐dependent vertical velocity and moisture information.

    

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5. Connection Between Mass Flux Transport and Eddy Diffusivity in Convective Atmospheric Boundary Layers

   https://doi.org/10.1029/2020GL092073  

   Li, Qi ; Cheng, Yu ; Gentine, Pierre ( April 2021 , Geophysical Research Letters)

   Turbulence parameterizations for convective boundary layer in coarse‐scale atmospheric models usually consider a combination of the eddy‐diffusive transport and a non‐local transport, typically in the form of a mass flux term, such as the widely adopted eddy‐diffusivity mass‐flux (EDMF) approach. These two types of turbulent transport are generally considered to be independent of each other. Using results from large‐eddy simulations, here, we show that a Taylor series expansion of the updraft and downdraft mass‐flux transport can be used to approximate the eddy‐diffusivity transport in the atmospheric surface layer and the lower part of the mixed layer, connecting both eddy‐diffusivity and mass‐flux transport theories in convective conditions, which also quantifies departure from the Monin‐Obukhov similarity (MOS) in the surface layer. This study provides a theoretical support for a unified EDMF parameterization applied to both the surface layer and mixed layer and highlights important correction required for surface models relying on MOS.

    

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