Global Cloud-Resolving Models

Global cloud-resolving models (GCRMs) are a new category of atmospheric global models designed to solve different flavors of the nonhydrostatic equations through the use of kilometer-scale global meshes. GCRMs make it possible to explicitly simulate deep convection, thereby avoiding the need for cumulus parameterization and allowing for clouds to be resolved by microphysical models responding to grid-scale forcing. GCRMs require high-resolution discretization over the globe, for which a variety of mesh structures have been proposed and employed. The first GCRM was constructed 15 years ago, and in recent years, other groups have also begun adopting this approach, enabling the first intercomparison studies of such models. Because conventional general circulation models (GCMs) suffer from large biases associated with cumulus parameterization, GCRMs are attractive tools for researchers studying global weather and climate. In this review, GCRMs are described, with some emphasis on their historical development and the associated literature documenting their use. The advantages of GCRMs are presented, and currently existing GCRMs are listed and described. Future prospects for GCRMs are also presented in the final section

Ocean convergence and the dispersion of flotsam

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 km2 converged into a 60 × 60 m region within a week, a factor of more than 105 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

Ocean convergence and the dispersion of flotsam

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 km2 converged into a 60 × 60 m region within a week, a factor of more than 105 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

DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains

A review of the experimental protocol and motivation for DYAMOND, the first intercomparison project of global storm-resolving models, is presented. Nine models submitted simulation output for a 40-day (1 August–10 September 2016) intercomparison period. Eight of these employed a tiling of the sphere that was uniformly less than 5 km. By resolving the transient dynamics of convective storms in the tropics, global storm-resolving models remove the need to parameterize tropical deep convection, providing a fundamentally more sound representation of the climate system and a more natural link to commensurately high-resolution data from satellite-borne sensors. The models and some basic characteristics of their output are described in more detail, as is the availability and planned use of this output for future scientific study. Tropically and zonally averaged energy budgets, precipitable water distributions, and precipitation from the model ensemble are evaluated, as is their representation of tropical cyclones and the predictability of column water vapor, the latter being important for tropical weather

Tropical cyclones in global storm-resolving models

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

Predictability of Tropical Cyclone Intensity

The main goal of this work is to quantify the predictability of tropical cyclone (TC) intensity. Motivated by the lack of improvement in TC intensity prediction, a systematic study on the intrinsic predictability of TC intensity is conducted using a set of five high-resolution, cloud-resolving realistic model ensembles. The ensembles are generated with a stochastic kinetic-energy backscatter (SKEBS) method. Error growth is addressed by imposing stochastic perturbations with various spatial scales on the TC and its environment. The SKEBS ensembles feature convective-scale, mesoscale and synopticscale perturbations to better understand the growth of scale-dependent errors and their impact on TC uncertainty and predictability. TC intensity predictability is determined by computing the error magnitude associated with each component of the Fourierdecomposed TC wind fields at forecast times up to 7 days. It is found that forecast errors grow rapidly and saturate within 6-12 h on small scales (~ 30 km) in all five ensembles, independent of perturbation scale. Errors grow relatively slower on scales corresponding to rainbands (200-500 km), limiting the predictability of these features to 1-5 days. The predictability limit of rainbands strongly depends on perturbation scale, indicating that error downscaling is more detrimental than the upscale spread of small-scale errors. In long-lived TCs, the storm-scale circulation (i.e., the mean TC vortex and wavenumber-1 asymmetry) is resistant to upscale error propagation and remains predictable for at least 7 days. Uncertainty of the storm-scale circulation is only significant when the mean vortex is perturbed dircectly, demonstrating that TC intensity uncertainty and predictability is mainly affected by large-scale errors. This suggests that the predictability of the stormscale circulation is predominately controlled by the large-scale environment. A novel TC wind speed climatology based on 15 years of aircraft observations is created to investigate the predictability of various TC wind speed metrics using an information theory approach. Consistent with the results from the error growth approach, we show that the storm-scale wind field is predictable for more than 7 days. However, the wind speed at the radius of maximum wind loses its predictability during a period of rapid intensification (RI), which agrees with the extreme uncertainty of the peak wind during RI in the SKEBS ensembles. The predictability of the wind speed at the radius of maximum winds “recovers” during the maximum intensity phase, demonstrating that TC intensity predictability is intimately related to the phase of TC evolution and distinct physical processes. Environmental and internal mechanisms associated with RI uncertainty were investigated to better understand RI predictability. Both environmental (e.g., vertical wind shear) and internal (e.g., inner-core inertial stability) parameters play a role in the uncertainty of RI timing, indicating that the predictability of RI is a complex problem. The environment, which has longer predictability, controls the general tendency for RI to occur. In contrast, the impact of small-scale processes implies that the exact timing of RI has a short intrinsic predictability limit. The ensemble members are divided into two groups depending on their RI onset time. A comparison of the dynamic and thermodynamic fields shows that the physical mechanisms associated with RI differ significantly between the two groups. In the early RI cases, mid-tropospheric radial inflow is strong, leading to a fast contraction of the radius of maximum wind, increasing inertial stability, and the development of an eyewall. In contrast, the late cases have a well-developed (but broader) eyewall before RI onset, and the radius of maximum wind contracts slowly. The development of an upper-level warm core accompanies RI in the late cases. These results indicate that RI is associated with different physical processes during distinct stages of the tropical cyclone lifecycle

Convectively-Generated Potential Vorticity in Rainbands and Secondary Eyewall Formation in Hurricanes

Concentric eyewall formation and eyewall replacement cycles are intrinsic processes that determine the intensity of a tropical cyclone, as opposed to purely environmental factors such as wind shear or the ocean heat content. Although extensive research has been done in this area, there is not a single widely accepted theory on the formation of secondary eyewall structures. Many previous studies focused on dynamic processes in the inner core of a tropical cyclone that would precede and ultimately lead to the formation of a secondary eyewall. Hurricanes Katrina and Rita in 2005 were frequently sampled by research aircraft which gathered a copious amount of data. During this time, Rita developed a secondary eyewall which eventually replaced the original eyewall. This thesis will investigate the formation of a secondary eyewall with particular emphasis on the rainband region, as observations show that an outer principal rainband transformed into the secondary ring. A high resolution, full physics model (MM5) initialized with global model forecast fields correctly predicted the secondary eyewall formation in Rita. The model output will be used to investigate both Katrina and Rita in terms of their PV generation characteristics since PV and vorticity maxima correlate well with wind maxima that accompany the eyewall and rainbands. Furthermore, dynamical processes such as vortex Rossby wave (VRW) activity in the inner core region will be analyzed. Comparison of the differences in the two storms might shed some light on dynamics that can lead to structure changes. Comparison of the model data with aircraft observation is used to validate the results. Doppler radar derived wind fields will be used to calculate the vertical vorticity. The vorticity field is closely related to PV and thus a manifestation of the PV generation process in the rainband. The investigation has shown that Rita?s principal rainband features higher PV generation rates at radii beyond 80 km. Both the azimuthal component and the projection of asymmetric PV generated by convection onto the azimuthal mean connected with the principal band are hypothesized to be of importance for the formation of the secondary eyewall. VRW were found not to be important for the initial formation of the ring but might enhance convective activity once the outer eyewall contracts

A new aircraft hurricane wind climatology and applications in assessing the predictive skill of tropical cyclone intensity using high‐resolution ensemble forecasts

Hurricane surface wind is a key measure of storm intensity. However, a climatology of hurricane winds is lacking to date, largely because hurricanes are relatively rare events and difficult to observe over the open ocean. Here we present a new hurricane wind climatology based on objective surface wind analyses, which are derived from Stepped Frequency Microwave Radiometer measurements acquired by NOAA WP‐3D and U.S. Air Force WC‐130J hurricane hunter aircraft. The wind data were collected during 72 aircraft reconnaissance missions into 21 western Atlantic hurricanes from 1998 to 2012. This climatology provides an opportunity to validate hurricane intensity forecasts beyond the simplistic maximum wind speed metric and allows evaluating the predictive skill of probabilistic hurricane intensity forecasts using high‐resolution model ensembles. An example of application is presented here using a 1.3 km grid spacing Weather Research and Forecasting model ensemble forecast of Hurricane Earl (2010). Key Points Aircraft observation‐based hurricane wind climatology is created Evaluation of hurricane intensity predictions beyond simplistic point metrics Ensemble forecast loses predictive skill during rapid intensificatio