Purpose of Review Global cloud-resolving models (GCRMs) are a new type of atmospheric model which resolve nonhydrostatic accelerations globally with kilometer-scale resolution. This review explains what distinguishes GCRMs from other types of models, the problems they solve, and the questions their more commonplace use is raising. Recent Findings GCRMs require high-resolution discretization over the sphere but can differ in many other respects. They are beginning to be used as a main stream research tool. The first GCRM intercomparison studies are being coordinated, raising new questions as to how best to exploit their advantages. Summary GCRMs are designed to resolve the multiscale nature of moist convection in the global dynamics context, without using cumulus parameterization. Clouds are simulated with cloud microphysical schemes in ways more comparable to observations. Because they do not suffer from ambiguity arising from cumulus parameterization, as computational resources increase, GCRMs are the promise of a new generation of global weather and climate simulations.

Floating oil, plastics, and marine organisms are continually redistributed by ocean surface currents. Prediction of their resulting distribution on the surface is a fundamental, long-standing, and practically important problem. The dominant paradigm is dispersion within the dynamical context of a nondivergent flow: objects initially close together will on average spread apart but the area of surface patches of material does not change. Although this paradigm is likely valid at mesoscales, larger than 100 km in horizontal scale, recent theoretical studies of submesoscales (less than ∼10 km) predict strong surface convergences and downwelling associated with horizontal density fronts and cyclonic vortices. Here we show that such structures can dramatically concentrate floating material. More than half of an array of ∼200 surface drifters covering ∼20 × 20 km ² converged into a 60 × 60 m region within a week, a factor of more than 10 ⁵ decrease in area, before slowly dispersing. As predicted, the convergence occurred at density fronts and with cyclonic vorticity. A zipperlike structure may play an important role. Cyclonic vorticity and vertical velocity reached 0.001 s ⁻¹ and 0.01 ms ⁻¹ , respectively, which is much larger than usually inferred. This suggests a paradigm in which nearby objects form submesoscale clusters, and these clusters then spread apart. Together, these effects set both the overall extent and the finescale texture of a patch of floating material. Material concentrated at submesoscale convergences can create unique communities of organisms, amplify impacts of toxic material, and create opportunities to more efficiently recover such material.

Global convection-permitting models enable weather prediction from local to planetary scales and are therefore often expected to transform the weather prediction enterprise. This potential, however, depends on the predictability of the atmosphere, which was explored here through identical twin experiments using the Model for Prediction Across Scales. The simulations were produced on a quasi-uniform 4-km mesh, which allowed the illumination of error growth from convective to global scales. During the first two days, errors grew through moist convection and other mesoscale processes, and the character of the error growth resembled the case of [ ] turbulence. Between 2 and 13 days, errors grew with the background baroclinic instability, and the character of the error growth mirrored the case of [ ] turbulence. The existence of an error growth regime with properties similar to [ ] turbulence confirmed the radical idea of E. N. Lorenz that the atmosphere has a finite limit of predictability, no matter how small the initial error. The global-mean predictability limit of the troposphere was estimated here to be around 2–3 weeks, which is in agreement with previous work. However, scale-dependent predictability limits differed between the divergent and rotational wind component and between vertical levels, indicating that atmospheric predictability is a more complex problem than that of homogeneous, isotropic turbulence. The practical value of global cloud-resolving models is discussed in light of the various aspects of atmospheric predictability.

The predictability of the atmosphere has important implications for weather prediction, because it determines what forecast problems are potentially tractable. Even though our general understanding of error growth and predictability has been increasing, relatively little is known about the detailed structure of atmospheric predictability, such as how it varies between climate regions. The present study addresses this issue by exploring error growth and predictability in three latitude zones, using model output from a previous global storm-resolving predictability experiment by Judt published in 2018. It was determined that the tropics have longer predictability than the middle latitudes and polar regions (tropics, >20 days; middle latitudes and polar regions, a little over 2 weeks). Each latitude zone had distinct error growth characteristics, and error growth was broadly consistent with the underlying dynamics of each zone. Evidence suggests that equatorial waves play a role in the comparatively long predictability of the tropics; specifically, equatorial waves seem to be less prone to error growth than middle-latitude baroclinic disturbances. Even though the generality of the findings needs to be assessed in future studies, the overall conclusions agree with previous work in that current numerical weather prediction procedures have not reached the limits of atmospheric predictability, especially in the tropics. One way to exploit tropical predictability is to reduce model error, for example, by using global storm-resolving models instead of conventional models that parameterize convection.

Eyewall replacements in mature tropical cyclones usually cause intensity fluctuations. One reason for eyewall replacements remaining a forecasting challenge is the lack of understanding of how secondary eyewalls form. This study uses high-resolution, full-physics-model forecast fields of Hurricanes Katrina and Rita (2005) to better understand potential vorticity (PV) generation in the rainbands and the role that convectively generated PV played in the formation of a secondary eyewall in Hurricane Rita. Previous studies have focused on dynamic processes in the inner core and/or the effects of certain specified PV distributions. However, the initial development of a concentric PV ring in the rainband region has not been fully addressed. Katrina and Rita were extensively observed by three research aircraft during the Hurricane Rainband and Intensity Change Experiment (RAINEX), which was designed to study the interaction of the rainbands and the inner core. Rita developed a secondary eyewall and went through an eyewall replacement cycle, whereas Katrina maintained a single primary eyewall during the RAINEX observation period before landfall. These distinct features observed in RAINEX provide a unique opportunity to examine the physical and dynamical processes that lead to formation of concentric eyewalls. A triply nested high-resolution model with 1.67-km resolution in the innermost domain, initialized with operational model forecasts in real time during RAINEX, is used in this study. Analyses of wind, vorticity, PV, and vortex Rossby wave (VRW) activity in the inner-core region were conducted using both RAINEX airborne observations and model output. The results show that a higher PV generation rate and accumulation in the rainband region in Rita leads to a secondary PV/vorticity maximum, which eventually became the secondary eyewall. A strong moat area developed between the primary eyewall and the concentric ring of convection in Rita, prohibiting VRW activity. In contrast, VRWs propagated radially outward from the inner core to the rainband region in Katrina. The VRWs were not a contributing factor in the initial formation of the secondary eyewall in Rita since the moat region with near-zero PV gradient did not allow for radial propagation of VRWs. The large accumulation of convectively generated PV in the rainband region was the key factor in the formation of the secondary eyewall in Rita.

Recent progress in computing and model development has initiated the era of global storm-resolving modeling and with it the potential to transform weather and climate prediction. Within the general theme of vetting this new class of models, the present study evaluates nine global-storm resolving models in their ability to simulate tropical cyclones (TCs). Results show that, broadly speaking, the models produce realistic TCs and remove longstanding issues known from global models such as the deficiency to accurately simulate TC intensity. However, TCs are strongly affected by model formulation, and all models suffer from unique biases regarding the number of TCs, intensity, size, and structure. Some models simulated TCs better than others, but no single model was superior in every way. The overall results indicate that global storm-resolving models are able to open a new chapter in TC prediction, but they need to be improved to unleash their full potential.