Global reanalyses like ERA5 accurately capture atmospheric processes at spatial scales of O(10) km or larger. By downscaling ERA5 with large-eddy simulation (LES), LES can provide details about processes at spatio-temporal scales down to meters and seconds. Here, we present an open-source Python package named the “Large-eddy simulation and Single-column model - Large-Scale Dynamics”, or (LS)2D in short, designed to simplify the downscaling of ERA5 with doubly-periodic LES. A validation with observations, for several sensitivity experiments consisting of month-long LESs over Cabauw (the Netherlands), demonstrates both its usefulness and limitations. The day-to-day variability in the weather is well captured by (LS)2D and LES, but the setup under-performs in conditions with broken or near overcast clouds. As a novel application of this modeling system, we used (LS)2D to study surface solar irradiance variability, as this quantity directly links land-surface processes, turbulent transport, and clouds, to radiation. At a horizontal resolution of 25 m, the setup reproduces satisfactorily the solar irradiance variability down to a timescale of seconds. This demonstrates that the coupled LES-ERA5 setup is a useful tool that can provide details on the physics of turbulence and clouds, but can only improve on its host reanalysis when applied to meteorological suitable conditions.

Numerical simulations of the tropical mesoscales often exhibit a self-reinforcing feedback between cumulus convection and shallow circulations, which leads to the self-aggregation of large cloud structures. We investigate whether this basic feedback can be adequately captured by large-eddy simulations (LESs). To do so, we simulate the non-precipitating, cumulus-topped boundary layer of the canonical ‘BOMEX’; case over a range of numerical settings in two models. Since the energetic convective scales underpinning the self-aggregation are only slightly larger than typical LES grid spacings, aggregation timescales do not converge even at rather high resolutions (less than 100m). Therefore, high resolutions or improved unresolved scales models may be required to faithfully represent certain forms of trade-wind mesoscale cloud patterns and self-aggregating deep convection in large-eddy and cloud-resolving models, and to understand their significance relative to other processes that organise the tropical mesoscales.

Shallow cloud fields over the subtropical ocean exhibit many spatial patterns. The frequency of occurrence of these patterns can change under global warming. Hence, they may influence subtropical marine clouds’ climate feedback. While numerous metrics have been proposed to quantify cloud patterns, a systematic, widely accepted description is still missing. Therefore, this paper suggests one. We compute 21 metrics for 5000 satellite scenes of shallow clouds over the subtropical Atlantic Ocean and translate the resulting dataset to its principal components (PCs). This yields a unimodal, continuous distribution without distinct classes, whose first four PCs explain 82% of all 21 metrics’ variance. The PCs correspond to four interpretable dimensions: Characteristic length, void size, directional alignment and horizontal cloud-top height variance. These dimensions span a space in which an effective pattern description can be given, which may be used to better understand the patterns’ underlying physics and feedback on climate.