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Increased resolution has enabled kilometer‐scale weather and climate models to partially resolve secondary circulations, including horizontal convective rolls (HCRs) and cold pool gust fronts. Although these circulations are ubiquitous in convective boundary layers over land, their impacts on the surface energy balance are largely unknown. Doppler lidar and surface observations were combined with DOE E3SM land model experiments, revealing increased surface winds (5 m/s) and heat fluxes (50 W/m2) in convergent branches of HCRs. Larger wind‐driven flux responses (up to 150 W/m2) were found along gust fronts. Surface energy balance shifts to accommodate wind‐driven fluxes, reducing ground heat conduction and longwave cooling. Our findings from the US Southern Great Plains are broadly relevant to modeling convective boundary layers. In particular, widely used subgrid wind gust parameterizations were found to be physically inconsistent with resolved secondary circulations and could worsen climate prediction biases at kilometer‐scales. Plain Language Summary Earth's surface is heated by solar radiation, and this energy is transferred to the overlying air in the form of sensible and latent heat fluxes. Surface heat fluxes are generated by turbulent motions that are too small to be directly simulated in weather and climate models. Instead, models use mathematical functions, known as parameterizations, to predict surface fluxes from simulated winds and surface‐to‐air differences in moisture and temperature. Observed winds near Earth's surface are known to organize into patterns referred to as secondary circulations, creating frequently observed “cloud streets,” and influencing the soaring patterns of birds. With increasing computational power, weather, and climate models have begun to resolve these circulations in winds simulated at kilometer scales. Although they are widely observed, this study provides new evidence that secondary circulations significantly alter surface heat fluxes and the energy balance of the land surface. It is also shown that current parameterizations of wind‐driven heat fluxes can be made more realistic to improve predictions in weather and climate models that are run at kilometer‐scale spatial resolutions. Key Points Lidar and surface wind measurements provide evidence linking widely observed secondary circulations to surface wind gusts The circulations alter the land surface energy balance and increase surface heat fluxes in convergent branches of circulation updrafts Land model parameterizations are inconsistent with resolved circulations, pointing to needed improvements at kilometer‐scale resolutions

We present a method to infer ocean diapycnal diffusivity based on high‐resolution ocean model predictions of the depth‐dependent viscous dissipation associated with internal wave shear. This method relies on recent advances in modeling and the parameterization of stratified turbulent mixing processes. Especially important in the latter regard is the distinction between irreversible and reversible mixing processes associated with internal wave breaking. Utilizing the Bouffard–Boegman (BB) compilation of data, we derive depth‐dependent profiles of diapycnal diffusivity from viscous dissipation rates obtained from downscaled internal wave fields of the global ocean simulation LLC4320. Our methodology displays some skill in matching observationally‐informed inferences of diapycnal diffusivity and demonstrates that the KPP‐based production of diapycnal diffusivity fails to account for the distinction between reversible and irreversible mixing components. This work provides a framework for further improving the parameterization of mixing processes in large scale climate models through simulations of the background internal wave field.

Considering the influence of the downslope windstorm called “Vento Norte” (VNOR; Portuguese for “North Wind”) in planetary boundary layer turbulent features, a new set of turbulent parameterizations, which are to be used in atmospheric dispersion models, has been derived. Taylor’s statistical diffusion theory, velocity spectra obtained at four levels (3, 6, 14, and 30 m) in a micrometeorological tower, and the energy-containing eddy scales are used to calculate neutral planetary boundary layer turbulent parameters. Vertical profile formulations of the wind velocity variances and Lagrangian decorrelation time scales are proposed, and to validate this new parameterization, it is applied in a Lagrangian Stochastic Particle Dispersion Model to simulate the Prairie Grass concentration experiments. The simulated concentration results were shown to agree with those observed.

Using in situ microstructure observations from 2010 to 2018, this study assesses the applicability of turbulent mixing parameterization schemes in the northwestern South China Sea (NSCS) and improves the MG model proposed by MacKinnon and Gregg in 2003 using machine learning methods. The results show that the estimation error of the MG model is still more than one order of magnitude in the NSCS. Also, the importance of parameters obtained from machine learning indicates that the normalized depth (D) is one of the most relevant parameters to the turbulent kinetic energy dissipation rate ε. Therefore, in this study, D is introduced into the MG model to obtain an improved MG model (IMG). The IMG model has an average correlation (r) between the estimated and observed log10ε of 0.79, which is at least 49% higher than the MG model, and an average root mean square error (RMSE) of 0.25, which is at least 42% lower than that of the MG model. The IMG model accurately estimates the multi-year turbulent mixing observed in the NSCS, including before and after tropical cyclone passages. This provides a new perspective to study the physical principles and spatial and temporal distribution of turbulent mixing.

The Indian Ocean hosts a vigorous basin‐scale overturning that constitutes one of the major deep upwelling branches of the global meridional overturning circulation (MOC). The extent to which the deep Indian Ocean MOC is sustained by breaking internal waves is assessed by quantifying and comparing the energetics of the overturning and those of the regional internal wave field. A range of published inverse estimates of the circulation across 32°S is used to assess the basin average buoyancy fluxes. The turbulent dissipation needed to sustain the MOC ranges between 0.17 ± 0.04 and 1.19 ± 0.17 TW, which is consistent with the estimated 0.35−0.26+1.04 TW dissipated by breaking internal waves, as inferred from observed fine structure. Both estimates of turbulent dissipation are consistent with the total energy input into the regional internal wave field (0.21−0.05+0.08TW) based on published estimates of energy conversion from winds, tides and geostrophic bottom flows. However, a discrepancy arises when comparing the energetics at different density levels. At mid‐ocean density levels (∼1000–3000 m) the dissipation of internal wave energy is found to be significantly smaller (factor 5–10) than the dissipation needed to sustain inverse estimates of the MOC. The uncertainty related to undersampling of internal wave breaking hot spots was analyzed and found to be small, which suggests that mixing processes other than wave breaking due to weak wave‐wave interactions, may be significant in the deep Indian Ocean. Key Points Internal wave dissipation is insufficient to sustain the Indian Ocean MOC

Using data on wind stress, significant height of combined wind waves and swell, potential temperature, salinity and seawater velocity, as well as objectively-analyzed in situ temperature and salinity, we established a global ocean dataset of calculated wind- and tide-induced vertical turbulent mixing coefficients. We then examined energy conservation of ocean vertical mixing from the point of view of ocean wind energy inputs, gravitational potential energy change due to mixing （with and without ar- tificially limiting themixing coefficient）, and K-theory vertical turbulent parameterization schemes regardless of energy inputs. Our research showed that calculating the mixing coefficient with average data and artificial limiting the mixing coefficient can cause a remarkable lack of energy conservation, with energy losses of up to 90% and changes in the energy oscillation period. The data also show that wind can introduce a huge amount of energy into the upper layers of the Southern Ocean, and that tidesdo so in regions around underwater mountains. We argue that it is necessary to take wind and tidal energy inputs into ac- count forlong-term ocean climate numerical simulations. We believe that using this ocean vertical turbulent mixing coefficient climatic dataset is a fast and efficient method to maintain the ocean energy balance in ocean modeling research.

This study examines climate simulations with the National Center for Atmospheric Research Community Atmosphere Model version 3 (NCAR CAM3) using a new air‐sea turbulent flux parameterization scheme. The current air‐sea turbulent flux scheme in CAM3 consists of three basic bulk flux equations that are solved simultaneously by an iterative computational technique. We recently developed a new turbulent flux parameterization scheme where the Obukhov stability length is parameterized directly by using a bulk Richardson number, an aerodynamic roughness length, and a heat roughness length. Its advantages are that it (1) avoids the iterative process and thus increases the computational efficiency, (2) takes account of the difference between z0m and z0h and allows large z0m/z0h, and (3) preserves the accuracy of iteration. An offline test using Tropical Ocean–Global Atmosphere Coupled Ocean‐Atmosphere Response Experiment (TOGA COARE) data shows that the original scheme overestimates the surface fluxes under very weak winds but the new scheme gives better results. Under identical initial and boundary conditions, the original CAM3 and CAM3 coupled with the new turbulent flux scheme are used to simulate the global distribution of air‐sea surface turbulent fluxes, and precipitation. Comparisons of model outputs against the European Remote Sensing Satellites (ERS), the Objectively Analyzed air‐sea Fluxes (OAFlux), and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) show that: (1) the new scheme produces more realistic surface wind stress in the North Pacific and North Atlantic trade wind belts and wintertime extratropical storm track regions; (2) the latent heat flux in the Northern Hemisphere trade wind zones shows modest improvement in the new scheme, and the latent heat flux bias in the western boundary current region of the Gulf Stream is reduced; and (3) the simulated precipitation in the new scheme is closer to observation in the Asian monsoon region.

This study examines the modelled surface turbulent fluxes over sea ice from the bulk algorithms of the Beijing Climate Centre Climate System Model (BCC_CSM), the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the Community Earth System Model (CESM) with data from the fourth Chinese National Arctic Research Expedition (CHINARE 2010) and the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment. Of all the model algorithms, wind stresses are replicated well and have small annual biases (−0.6% in BCC_CSM, 0.2% in CESM and 17% in ECMWF) with observations, annual sensible heat fluxes are consistently underestimated by 83-141%, and annual latent heat fluxes are generally overestimated by 49-73%. Five sets of stability functions for stable stratification are evaluated based on theoretical and observational analyses, and the superior stability functions are employed in a new bulk algorithm proposal, which also features varying roughness lengths. Compared to BCC_CSM, the new algorithm can estimate the friction velocity with significantly reduced bias, 84% smaller in winter and 56% smaller in summer, respectively. For the sensible heat flux, the bias of the new algorithm is 30% smaller in winter and 19% smaller in summer than that of BCC_CSM. Finally, the bias of modelled latent heat fluxes is 27% smaller in summer.

The climate modeling community has been challenged to develop a method for improving the simulation of the Pacific-North America （PNA） teleconnection pattern in climate models. The accuracy of PNA teleconnection simulation is significantly improved by considering mesoscale convection contributions to sea surface fluxes. The variation in the PNA over the past 22 years was simulated by the Grid Atmospheric Model of lAP LASG version 1.0 （GAMIL1.0）, which was guided by observational SST from January 1979 to December 2000. Results show that heating in the tropical central-eastern Pacific is simulated more realistically, and sea surface latent heat flux and precipitation anomalies are more similar to the reanalysis data when mesoscale enhancement is considered during the parameterization scheme of sea surface turbulent fluxes in GAMIL1.0. Realistic heating in the tropical central-eastern Pacific in turn significantly improves the simulation of interannual variation and spatial patterns of PNA.

A new three-dimensional (3D) turbulent kinetic energy (TKE) subgrid mixing scheme is developed using the Advanced Research version of the Weather Research and Forecasting (WRF) Model (WRF-ARW) to address the gray-zone problem in the parameterization of subgrid turbulent mixing. The new scheme combines the horizontal and vertical subgrid turbulent mixing into a single energetically consistent framework, in contrast to the conventionally separate treatment of the vertical and horizontal mixing. The new scheme is self-adaptive to the grid-size change between the large-eddy simulation (LES) and mesoscale limits. A series of dry convective boundary layer (CBL) idealized simulations are carried out to compare the performance of the new scheme and the conventional treatment of subgrid mixing to the WRF-ARW LES dataset. The importance of including the nonlocal component in the vertical buoyancy specification in the newly developed general TKE-based scheme is illustrated in the comparison. The improvements of the new scheme with the conventional treatment of subgrid mixing across the gray-zone model resolutions are demonstrated through the partitioning of the total vertical flux profiles. Results from real-case simulations show the feasibility of using the new scheme in the WRF Model in lieu of the conventional treatment of subgrid mixing.