An activity designed to characterise patterns of mesoscale (20 to 2,000 km) organisation of shallow clouds in the downstream trades is described. Patterns of mesoscale organisation observed from space were subjectively defined and learned by 12 trained scientists. The ability of individuals to communicate, learn and replicate the classification was evaluated. Nine‐hundred satellite images spanning the area from 48°W to 58°W, 10°N to 20°N for the boreal winter months (December–February) over 10 years (2007/2008 to 2016/2017) were classified. Each scene was independently labelled by six scientists as being dominated by one of six patterns (one of which was “no‐pattern”). Four patterns of mesoscale organisation could be labelled in a reproducible manner, and were labelled Sugar, Gravel, Fish and Flowers. Sugar consists of small, low clouds of low reflectivity, Gravel clouds form along apparent gust fronts, Fish are skeletal networks (often fishbone‐like) of clouds, while Flowers are circular clumped features defined more by their stratiform cloud elements. Both Fish and Flowers are surrounded by large areas of clear air. These four named patterns were identified 40% of the time, with the most common pattern being Gravel. Sugar was identified the least and suggests that unorganised and very shallow convection is unlikely to dominate large areas of the downstream trade winds. Some of the patterns show signs of seasonal and interannual variability, and some degree of scale selectivity. Comparison of typical patterns with radar imagery suggests that even this subjective and qualitative visual inspection of imagery appears to capture several important physical differences between shallow cloud regimes, such as precipitation and radiative effects.

Currently major efforts are underway toward refining the horizontal resolution (or grid spacing) of climate models to about 1 km, using both global and regional climate models (GCMs and RCMs). Several groups have succeeded in conducting kilometer-scale multiweek GCM simulations and decadelong continental-scale RCM simulations. There is the well-founded hope that this increase in resolution represents a quantum jump in climate modeling, as it enables replacing the parameterization of moist convection by an explicit treatment. It is expected that this will improve the simulation of the water cycle and extreme events and reduce uncertainties in climate change projections. While kilometer-scale resolution is commonly employed in limitedarea numerical weather prediction, enabling it on global scales for extended climate simulations requires a concerted effort. In this paper, we exploit an RCM that runs entirely on graphics processing units (GPUs) and show examples that highlight the prospects of this approach. A particular challenge addressed in this paper relates to the growth in output volumes. It is argued that the data avalanche of high-resolution simulations will make it impractical or impossible to store the data. Rather, repeating the simulation and conducting online analysis will become more efficient. A prototype of this methodology is presented. It makes use of a bit-reproducible model version that ensures reproducible simulations across hardware architectures, in conjunction with a data virtualization layer as a common interface for output analyses. An assessment of the potential of these novel approaches will be provided.

Although crucial for the Earth's climate, clouds are poorly represented in current climate models, which operate at too coarse grid resolutions and rely on convection parameterizations. Thanks to advances in high‐performance computing, it is becoming feasible to perform high‐resolution climate simulations with explicitly resolved deep convection. The added value of such convection‐resolving simulations for the representation of precipitation has already been demonstrated in a number of studies, but assessments about clouds are still rare. In the present study, we analyze the representation of clouds in decade‐long convection‐resolving climate simulations (2.2‐km horizontal grid spacing) over a computational domain with 1,536 × 1,536 × 60 grid points covering Europe and compare it against coarser‐resolution convection‐parameterizing simulations (12‐km horizontal spacing). The simulations have been performed with a version of the COSMO model that runs entirely on graphics processing units. The European Centre for Medium‐Range Weather Forecasts Re‐Analysis‐Interim reanalysis‐driven present climate simulations (1999–2008) show that biases in mean summertime cloudiness and top‐of‐the‐atmosphere radiation budget are reduced when convection is resolved instead of parameterized. Especially, the typically underestimated midtropospheric cloud layer is enhanced, thanks to stronger vertical exchange. Future climate simulations (2079–2088) conducted using pseudo global warming experiments for a Representative Concentration Pathway 8.5 scenario show a predominating reduction in low‐level and midlevel cloud cover fraction and an increase in cloud top height, implying positive cloud‐amount and cloud‐height feedbacks. These positive feedbacks are only partly compensated by the negative cloud‐thickness feedback. Although the simulations exhibit substantial differences in terms of clouds in the present climate, the simulated cloud feedbacks are similar between the 2.2‐ and 12‐km models.

We analyse a multi-model ensemble at convection-resolving resolution based on the DYAMOND models, and a resolution ensemble based on the limited area model COSMO over 40 days to study how tropical and subtropical marine low clouds are represented at kilometer-scale resolution. The analysed simulations produce low cloud fields that look in general realistic in comparison to satellite images. The evaluation of the radiative balance, however, reveals substantial inter-model differences and an underestimated low cloud cover in most models. Models that simulate increased low cloud cover are found to have a deeper marine boundary layer (MBL), stronger entrainment, and an enhanced latent heat flux. These findings demonstrate that some of the fundamental relations of the MBL are systematically represented by the model ensemble which implies that the relevant dynamical processes start to become resolved on the model grid at kilometer-scale resolution. A sensitivity experiment with the COSMO model suggests that differences in the strength of turbulent vertical mixing may contribute to the inter-model spread in cloud cover.

No abstract