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The Southern Ocean's eddy response to changing climate remains unclear, with observations suggesting non‐monotonic changes in eddy kinetic energy (EKE) across scales. Here simulations reappear that smaller‐mesoscale EKE is suppressed while larger‐mesoscale EKE increases with strengthened winds. This change was linked to scale‐wise changes in the kinetic energy cycle, where a sensitive balance between the dominant mesoscale energy sinks—inverse KE cascade, and source—baroclinic energization. Such balance induced a strong (weak) mesoscale suppression in the flat (ridge) channel. Mechanistically, this mesoscale suppression is attributed to stronger zonal jets weakening smaller mesoscale eddies and promoting larger‐scale waves. These EKE multiscale changes lead to multiscale changes in meridional and vertical eddy transport, which can be parameterized using a scale‐dependent diffusivity linked to the EKE spectrum. This multiscale eddy response may have significant implications for understanding and modeling the Southern Ocean eddy activity and transport under a changing climate. Plain Language Summary The response of eddies in the Southern Ocean to climate change is not well understood. In this study, we used a channel model that simulates the effects of wind on eddies. We found that smaller eddies have less kinetic energy (KE) when the winds are stronger. On the other hand, larger‐scale eddies have more KE with stronger winds. Similar phenomena are also observed in the observations. By analyzing the eddy's KE budget, the interaction between different scales of eddies and the interaction between the eddies and mean flow are strengthened when the winds get stronger. This leads to a reduction of eddy KE at smaller mesoscale scales and an increase at larger scales. From the observational view, stronger winds weaken smaller eddies and promote larger waves. This change in eddy KE also affects how eddies meridionally transport materials and how eddy diffusivity varies at different scales. Smaller eddies transport materials less when their KE is weakened, while larger eddies become stronger in transporting materials. These findings determine how eddy diffusivity responds to the changed eddy KE at different scales. The multi‐scale response of eddies to wind has important implications for understanding the behavior of Southern Ocean eddies in a changing climate. Key Points Larger eddies got stronger and smaller eddies got weaker as Southern Ocean westerlies strengthened Both flat and ridge channel simulations suggest that these changes may be linked to changes in the inverse energy cascade The corresponding changes in scale‐wise meridional and vertical transport are also non‐monotonic

Quantitative knowledge of the surface energy balance is essential for the prediction of weather and climate. However, a multitude of studies from around the world indicate that the turbulent heat fluxes are generally underestimated using eddy-covariance measurements, and hence, the energy balance is not closed. This energy-balance-closure problem, which has been heavily covered in the literature for more than 25 years, is the topic of the present review, in which we provide an overview of the potential reason for the lack of closure. We demonstrate the effects of the diurnal cycle on the energy balance closure, and address questions with regard to the partitioning of the energy balance residual between the sensible and the latent fluxes, and whether the magnitude of the flux underestimation can be predicted based on other variables typically measured at micrometeorological stations. Remaining open questions are discussed and potential avenues for future research on this topic are laid out. Integrated studies, combining multi-tower experiments and scale-crossing, spatially-resolving lidar and airborne measurements with high-resolution large-eddy simulations, are considered to be of critical importance for enhancing our understanding of the underlying transport processes in the atmospheric boundary layer.

With a focus towards developing multiscale capabilities in numerical weather prediction models, the specific problem of the transition from the mesoscale to the microscale is investigated. For that purpose, idealized one-way nested mesoscale to large-eddy simulation (LES) experiments were carried out using the Weather Research and Forecasting model framework. It is demonstrated that switching from one-dimensional turbulent diffusion in the mesoscale model to three-dimensional LES mixing does not necessarily result in an instantaneous development of turbulence in the LES domain. On the contrary, very large fetches are needed for the natural transition to turbulence to occur. The computational burden imposed by these long fetches necessitates the development of methods to accelerate the generation of turbulence on a nested LES domain forced by a smooth mesoscale inflow. To that end, four new methods based upon finite amplitude perturbations of the potential temperature field along the LES inflow boundaries are developed, and investigated under convective conditions. Each method accelerated the development of turbulence within the LES domain, with two of the methods resulting in a rapid generation of production and inertial range energy content associated to microscales that is consistent with non-nested simulations using periodic boundary conditions. The cell perturbation approach, the simplest and most efficient of the best performing methods, was investigated further under neutral and stable conditions. Successful results were obtained in all the regimes, where satisfactory agreement of mean velocity, variances and turbulent fluxes, as well as velocity and temperature spectra, was achieved with reference non-nested simulations. In contrast, the non-perturbed LES solution exhibited important energy deficits associated to a delayed establishment of fully-developed turbulence. The cell perturbation method has negligible computational cost, significantly accelerates the generation of realistic turbulence, and requires minimal parameter tuning, with the necessary information relatable to mean inflow conditions provided by the mesoscale solution.

The influence of drifting and blowing snow on surface mass and energy exchange is difficult to quantify due to limitations in both measurements and models, but is still potentially very important over large areas with seasonal or perennial snow cover. We present a unique set of measurements that make possible the calculation of turbulent moisture, heat, and momentum fluxes during conditions of drifting and blowing snow. From the data, Monin–Obukhov estimation of bulk fluxes is compared to eddy-covariance-derived fluxes. In addition, large-eddy simulations with sublimating particles are used to more completely understand the vertical profiles of the fluxes. For a storm period at the Syowa S17 station in East Antarctica, the bulk parametrization severely underestimates near-surface heat and moisture fluxes. The large-eddy simulations agree with the eddy-covariance fluxes when the measurements are minimally disturbed by the snow particles. We conclude that overall exchange over snow surfaces is much more intense than current models suggest, which has implications for the total mass balance of the Antarctic ice sheet and the cryosphere.

Mesoscale eddy mixing profoundly modulates the ocean tracer budgets, and is typically parameterized via the isopycnal eddy diffusivity in ocean climate models. However, relatively little is known about the magnitude/structure of isopycnal eddy diffusivity across continental slopes, which hinders the understanding and prediction of shelf‐open ocean exchanges. In this study, we quantify the isopycnal eddy diffusivity in a suite of eddy‐resolving, process‐oriented simulations of mesoscale turbulence over continental slopes under upwelling‐favorable winds, a configuration that commonly arises around the margins of subtropical gyres. Cross‐shore eddy diffusivity is found to be suppressed in the upper open ocean occupied by strong alongshore flows, but enhanced at depths where alongshore flows are weakened, a finding that is consistent with the enhancement of eddy mixing near the steering level. Over continental slopes, eddy diffusivity also strengthens at mid‐depths, but almost vanishes near the seafloor. To theoretically constrain the simulated eddy fluxes, we examine the scaling of eddy diffusivity proposed by Ferrari and Nikurashin (2010, https://doi.org/10.1175/2010JPO4278.1), which accounts for the suppression of eddy mixing induced by the relative propagation of eddies to the mean flow. We show that, apart from the mean‐flow suppression effect, the eddy anisotropy effect induced by steep topography shapes both the horizontal and vertical structures of cross‐shore eddy diffusivity. Finally, we propose prospective closures of the eddy propagation speed and eddy anisotropy effect over continental slopes using the large‐scale flow and bathymetric quantities. This work offers a basis upon which a “slope‐aware” parameterization of mesoscale eddy mixing can be developed. Plain Language Summary The ocean is replete with turbulent eddies that are highly efficient in mixing oceanic tracers. Across continental margins, turbulent eddies drive the exchanges between shelf seas and open ocean, which modulate the coastal water properties and sea level, upon which human habitation depends the most. Understanding the impact of eddies on shelf‐ocean exchanges in a changing climate is therefore critical, and would typically rely on predictive ocean climate models. However, owing to limited computer powers, even state‐of‐the‐art ocean climate models must adopt a grid spacing that is too coarse to resolve turbulent eddies across continental margins. Instead, these models rely on parameterization schemes that infer eddy effects from the large‐scale, explicitly resolved flow properties. This work investigated the utility of a parameterization developed based on open ocean turbulent properties in quantifying eddy tracer fluxes across continental margins, following which further refinements of the parameterization have been made. Our results show that, apart from previously considered ingredients, the tendency of steep topography across continental margins to “squeeze” the eddy shapes must be incorporated into this parameterization. This work provides a basis upon which a parameterization scheme for eddy tracer fluxes across both continental margins and open ocean environments can be devised. Key Points The scaling of isopycnal eddy diffusivity proposed by Ferrari and Nikurashin (2010) is adapted to continental slopes Topographically induced eddy anisotropy effect modulates both the horizontal and vertical structures of isopycnal eddy diffusivity A basis for parameterizing isopycnal eddy mixing across continental slopes is presented

We present a review of existing wind–wave coupling models and parameterizations used for large-eddy simulation of the marine atmospheric boundary layer. The models are classified into two main categories: (i) the wave-phase-averaged, sea surface–roughness models and (ii) the wave-phase-resolved models. Both categories are discussed from their implementation, validity, and computational efficiency viewpoints, with emphasis given on their applicability in offshore wind energy problems. In addition to the various models discussed, a review of laboratory-scale and field-measurement databases is presented thereafter. The majority of the presented data have been gathered over many decades of studying air–sea interaction phenomena, with the most recent ones compiled to reflect an offshore wind energy perspective. Both provide valuable data for model validation. We also discuss the modeling knowledge gaps and computational challenges ahead.

It is frequently observed in field experiments that the eddy-covariance heat fluxes are systematically underestimated compared to the available energy, a phenomenon known as the surface energy balance (SEB) closure problem. A large-eddy simulation (LES) study is presented that investigates the behavior of turbulent organized structures (TOS) and their impact on the SEB closure problem. LES experiments are conducted for the daytime atmospheric boundary layer heated over a flat surface with atmospheric stability parameters -zi/L ranging from 8 to 130. Local imbalance is defined as the deviation of the ‘observed’ heat flux at a grid point from the regionally ‘representative’ heat flux; systematic imbalance is defined as the horizontally averaged local imbalance. A thorough analysis of the correlation between local imbalance and various local variables is conducted. Local imbalance exhibited the most significant correlation with the variance of potential temperature, implying its potential usefulness in parameterizing local imbalance. In characterizing the vertical variation of systematic imbalance, the spatial variances of mean vertical velocity and potential temperature play key roles. Results reveal that TOS induced a correlation between the mean vertical velocity and potential temperature in a horizontal space, resulting in systematic imbalance. The local imbalance is further contributed by the inhomogeneous distribution of the observed heat flux. Our results advance the understanding of the SEB closure problem and pave a possible way for the development of parameterization schemes.

Developing urban land surface models for modeling cities at high resolutions needs to better account for the city‐specific multi‐scale land surface heterogeneities at a reasonable computational cost. We propose using an encoder‐decoder convolutional neural network to develop a computationally efficient model for predicting the mean velocity field directly from urban geometries. The network is trained using the geometry‐resolving large eddy simulation results. Systematic testing on urban structures with increasing deviations from the training geometries shows the prediction error plateaus at 15%, compared to errors sharply increasing up to 35% in the null models. This is explained by the trained model successfully capturing the effects of pressure drag, especially for tall buildings. The prediction error of the aerodynamic drag coefficient is reduced by 32% compared with the default parameterization implemented in mesoscale modeling. This study highlights the potential of combining computational fluid dynamics modeling and machine learning to develop city‐specific parameterizations. Plain Language Summary Predicting the velocity field in the urban area with fine resolution at the meter scale is computationally expensive. Yet a detailed velocity field is necessary for improving the accuracy of urban land surface representation in weather and climate models. We propose using a convolutional neural network to predict the velocity field from the three‐dimensional (3D) building distribution. The similarity between the predicted velocity fields and LES simulations in the testing geometries illustrates the prediction capability of the trained model. We also investigate the aerodynamic drag coefficient, a key parameter for quantifying the land‐atmosphere momentum exchange. The results indicate that the trained model prediction is much closer to values derived from large‐eddy simulation models than those from the default parameterization scheme, showing the promise of using machine learning to improve urban land surface modeling. Key Points Machine learning (ML) can help develop city‐specific parameterization that fully utilizes urban form data It is a first attempt to develop an ML model for high‐Reynolds number urban canopy flow with multiple bluff‐body obstacles Limitation of the geometry to flow field approach is quantified by accessing the extrapolative capability of the trained model