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.

More than one hundred days were simulated over very large domains with fine (0.156 km to 2.5 km) grid spacing for realistic conditions to test the hypothesis that storm (kilometer) and large-eddy (hectometer) resolving simulations would provide an improved representation of clouds and precipitation in atmospheric simulations. At scales that resolve convective storms (storm-resolving for short) scales, the vertical velocity variance becomes resolved and a better physical basis is achieved for representing clouds and precipitation. Similar to past studies we find an improved representation of precipitation at kilometer scales, as compared to models with parameterised convection. The main precipitation features (location, diurnal cycle and spatial propagation) are well captured already at kilometer scales, and refining resolution to hectometer scales does not substantially change the simulations in these respects. It does, however, lead to a reduction in the precipitation on the timescales considered-most notably over the Tropical ocean. Changes in the distribution of precipitation, with less frequent extremes are also found in simulations incorporating hecto-meter scales. Hectometer scales appear more important for the representation of clouds, and make it possible to capture many important aspects of the cloud field, from the vertical distribution of cloud cover, to the distribution of cloud sizes, to the diel (daily) cycle. Qualitative improvements, particularly in the ability to differentiate cumulus from stratiform clouds, are seen when reducing the grid spacing from kilometer to hectometer scales. At the hectometer scale new challenges arise, but the similarity of observed and simulated scales, and the more direct 1 connection between the circulation and the unconstrained degrees of freedom make these challenges less daunting. This quality, combined with an already improved simulation as compared to more parameterised models, underpins our conviction that the use and further development of storm-resolving models offers exciting opportunities for advancing understanding of climate and climate change.

A description of the daily cycle of oceanic shallow cumulus for undisturbed boreal winter conditions in the North Atlantic trades is presented. Modern investigation tools are used, including storm‐resolving and large‐eddy simulations, runover large domains in realistic configurations, and observations from in situ measurements and satellite‐based retrievals. Models and observations clearly show pronounced diurnal variations in cloudiness, both near cloud base and below the trade inversion. The daily cycle reflects the evolution of two cloud populations: (i) a population of nonprecipitating small cumuli with weak vertical extent, which grows during the day and maximizes around sunset, and (ii) a population o deeper precipitating clouds with a stratiform cloud layer below the trade inversion, which grows during the night and maximizes just before sunrise. Previous studies have reported that cloudiness near cloud base undergoes weak variations on time scales longer than a day. However, here we find that it can vary strongly at the diurnal time scale. This daily cycle could serve as a critical test of the models' representation of the physical processes controlling cloudiness near cloud base, which is thought to be key for the determination of the Earth's climate response to warming.

The representation of tropical precipitation is evaluated across three generations of models participating in the Coupled Model Intercomparison Project (CMIP), phases 3, 5 and 6. Compared to state-of-the-art observations, improvements in tropical precipitation in the CMIP6 models are identified for some metrics, but we find no general improvement in tropical precipitation on different temporal and spatial scales. Our results indicate overall little changes across the CMIP phases for the summer monsoons, the double-ITCZ bias and the diurnal cycle of tropical precipitation. We find a reduced amount of drizzle events in CMIP6, but tropical precipitation occurs still too frequently. Continuous improvements across the CMIP phases are identified for the number of consecutive dry days, the representation of modes of variability, namely the Madden-Julian Oscillation and the El Niño Southern Oscillation, as well as the trends in dry months in the 20th century. The observed positive trend in extreme wet months is, however, not captured by any of the CMIP phases, which simulate negative trends for extremely wet months in the 20th century. The regional biases are larger than a climate-change signal one hopes to use the models to identify. Given the pace of climate change as compared to the pace of model improvements to simulate tropical precipitation, we question the past strategy of the development of the present class of global climate models as the mainstay of the scientific response to climate change. We suggest to explore alternative approaches such as high-resolution storm-resolving models that can offer better prospects to inform us about how tropical precipitation might change with anthropogenic warming.

We evaluate the new icosahedral nonhydrostatic atmospheric (ICON‐A) general circulation model of the Max Planck Institute for Meteorology that is flexible to be run at grid spacings from a few tens of meters to hundreds of kilometers. A simulation with ICON‐A at a low resolution (160 km) is compared to a not‐tuned fourfold higher‐resolution simulation (40 km). Simulations using the last release of the ECHAM climate model (ECHAM6.3) are also presented at two different resolutions. The ICON‐A simulations provide a compelling representation of the climate and its variability. The climate of the low‐resolution ICON‐A is even slightly better than that of ECHAM6.3. Improvements are obtained in aspects that are sensitive to the representation of orography, including the representation of cloud fields over eastern‐boundary currents, the latitudinal distribution of cloud top heights, and the spatial distribution of convection over the Indian Ocean and the Maritime Continent. Precipitation over land is enhanced, in particular at high‐resolution ICON‐A. The response of precipitation to El Niño sea surface temperature variability is close to observations, particularly over the eastern Indian Ocean. Some parameterization changes lead to improvements, for example, with respect to rain intensities and the representation of equatorial waves, but also imply a warmer troposphere, which we suggest leads to an unrealistic poleward mass shift. Many biases familiar to ECHAM6.3 are also evident in ICON‐A, namely, a too zonal SPCZ, an inadequate representation of north hemispheric blocking, and a relatively poor representation of tropical intraseasonal variability.