Abstract. This paper presents the Meso-NH model version 5.4. Meso-NH is an atmospheric non hydrostatic research model that is applied to a broad range of resolutions, from synoptic to turbulent scales, and is designed for studies of physics and chemistry. It is a limited-area model employing advanced numerical techniques, including monotonic advection schemes for scalar transport and fourth-order centered or odd-order WENO advection schemes for momentum. The model includes state-of-the-art physics parameterization schemes that are important to represent convective-scale phenomena and turbulent eddies, as well as flows at larger scales. In addition, Meso-NH has been expanded to provide capabilities for a range of Earth system prediction applications such as chemistry and aerosols, electricity and lightning, hydrology, wildland fires, volcanic eruptions, and cyclones with ocean coupling. Here, we present the main innovations to the dynamics and physics of the code since the pioneer paper of Lafore et al. (1998) and provide an overview of recent applications and couplings.

In polar regions, where the boundary layer is often stably stratified, atmospheric models produce large biases depending on the boundary-layer parametrizations and the parametrization of the exchange of energy at the surface. This model intercomparison focuses on the very stable stratification encountered over the Antarctic Plateau in 2009. Here, we analyze results from 10 large-eddy-simulation (LES) codes for different spatial resolutions over 24 consecutive hours, and compare them with observations acquired at the Concordia Research Station during summer. This is a challenging exercise for such simulations since they need to reproduce both the 300-m-deep convective boundary layer and the very thin stable boundary layer characterized by a strong vertical temperature gradient (10 K difference over the lowest 20 m) when the sun is low over the horizon. A large variability in surface fluxes among the different models is highlighted. The LES models correctly reproduce the convective boundary layer in terms of mean profiles and turbulent characteristics but display more spread during stable conditions, which is largely reduced by increasing the horizontal and vertical resolutions in additional simulations focusing only on the stable period. This highlights the fact that very fine resolution is needed to represent such conditions. Complementary sensitivity studies are conducted regarding the roughness length, the subgrid-scale turbulence closure as well as the resolution and domain size. While we find little dependence on the surface-flux parametrization, the results indicate a pronounced sensitivity to both the roughness length and the turbulence closure.

Atmospheric global or regional circulation models used either for numerical weather prediction (NWP) or climate studies encompass a dynamical core and a physical component. The dynamical core computes the spatio-temporal evolution of atmospheric state variables by solving a discrete version of the fluid dynamic equations. The physical component quantifies the impact on the resolved variables of radiative, thermodynamical, and chemical processes, as well as dynamical processes that occur at scales smaller than the computational grid. These processes are handled by a suite of sub-models, most often referred to as parameterizations, which provide source terms in the resolved-scale equations. Parameterizations (e.g., turbulence, convection, radiation, microphysics) are often based on a mixture of physical principles and heuristic description of the involved processes, of their interactions and of their impact on the larger resolved scales. Although it is difficult to trace back the origin of the term "parameterization" in climate modeling, it semantically points to the fact that the sub-models summarize the processes as functions of the model state vector x (typically the value of zonal and meridional wind, temperature, and water phases at each point of the three-dimensional [3D] model grid) that depends on some free parameters. These free parameters arise from the simplification of the complex nature of the subgrid processes (e.g., assuming a bulk thermal plume instead of a population of plumes, stationarity). The atmospheric model can be summarized as      () (,)