Years-long global storm-resolving simulations of the present and warmed climates are conducted with 3.25km horizontal grid spacing. We focus on wintertime precipitation in the coastal western United States, a notably water-sensitive region with complex topography. The model generates more realistic orographic precipitation compared with a coarser-resolution model having the same dynamical core. In response to uniform sea surface warming, the increase in extreme precipitation rates together with the better resolved orographic precipitation lead to persistence of snowpack in parts of the coastal western United States. In contrast, snowpack in the coarser-resolution model is largely eliminated in the warmed climate. Whether snowpack persists influences the regional surface energy budget due to the snow-albedo feedback. The results highlight the importance of resolving orographic precipitation and the large-scale circulation response to warming in a consistent framework.

Global storm-resolving model (GSRM) simulations (kilometer-scale horizontal resolution) of the atmosphere can capture the interaction between the scales of deep cumulus convection and the large-scale dynamics and thermodynamic properties of the atmosphere. Here, we assess the vertical structure of tropical temperature change in the GSRM X-SHiELD, developed by the Geophysical Fluid Dynamics Laboratory, perturbed by a uniform sea surface temperature (SST) warming and/or increased CO2 concentration. The simulated response to SST warming shows weakly amplified warming from the surface through the mid-troposphere before increasing to a factor of about 2.5 near the tropopause. This combination of muted warming in the mid-troposphere and amplified warming aloft is within the range of CMIP6 models at individual pressure levels but, taken together, is distinctive behavior. The response to CO2 increase with unchanged SST is an approximately vertically uniform warming, comparable to CMIP6 models, and is linearly additive with the SST-induced warming in X-SHiELD.

We investigate the representation of individual supercells and intriguing tornado-like vortices in a simplified, locally-refined global atmosphere model. The model, featuring grid stretching, can locally enhance the model resolution and economically reach the cloud-resolving scale. Given an unstable sheared environment, the model can simulate supercells realistically, with a near-ground vortex and funnel cloud at the center of a rotating updraft reminiscent of a tornado. An analysis of the vorticity budget suggests that the updraft core of the supercell tilts environmental horizontal vorticity into the tornado-like vortex. The updraft also acts to amplify the vortex through vertical stretching. Results suggest that the simulated vortex is dynamically similar to observed tornadoes and modeling studies at much higher horizontal resolution.

Observations reveal a clear difference in the intensity of deep convection over tropical land and ocean. This observed land-ocean contrast provides a natural benchmark for evaluating the fidelity of global storm-resolving models (GSRMs; global models with horizontal resolution on the order of kilometers), and GSRMs provide a potentially valuable tool for probing unresolved scientific questions about the origin of the observed land-ocean contrast. However, land-ocean differences in convective intensity have received relatively little attention in GSRM research. Here, we show that the strength of the land-ocean contrast simulated by GSRMs is strongly sensitive to details of GSRM implementations, and not clearly governed by any of several hypothesized drivers of the observed land-ocean contrast. We first examine DYAMOND Summer GSRM simulations, and show that only a subset produce a clear land-ocean contrast in the frequency of strong updrafts. We then show that the use of a sub-grid shallow convection scheme can determine whether or not the GSRM X-SHiELD produces a clear land-ocean contrast. Finally, we show that three hypothesized drivers of the observed land-ocean contrast all fail to explain why a land-ocean contrast is present in X-SHiELD simulations with sub-grid shallow convection disabled. These results provide encouraging evidence that GSRMs can mimic the observed land-ocean convective intensity contrast. However, they also show that their ability to do so can be sensitive to uncertain sub-grid parameterizations, and suggest that existing theory may not fully capture drivers of the land-ocean contrast simulated by some GSRMs.

Recent advances have allowed for integration of global storm resolving models (GSRMs) to a timescale of several years. These short simulations are sufficient for studying characteristics and statistics of short- and small-scale phenomena; however, it is questionable what we can learn from these integrations about the large-scale climate response to perturbations. To address this question, we use the response of X-SHiELD (a GSRM) to uniform SST warming and CO$_2$ increase in a two-year integration and compare it to similar CMIP6 experiments. Specifically, we assess the statistical meaning of having two years in one model outside the spread of another model or model ensemble. This is of particular interest because X-SHiELD shows a distinct response of the global mean precipitation to uniform warming, and the northern hemisphere jet shift response to isolated CO$_2$ increase. We use the CMIP6 models to estimate the probability of two years in one model being more than one standard deviation away from another model (ensemble) mean, knowing the mean of two models. For example, if two years in one model are more than one standard deviation away from the other model’s mean, we find that the chances for these models’ means to be within one standard deviation are $\sim 25\%$. We find that for some large-scale metrics, there is an important base-state dependence that, when taken into account, can qualitatively change the interpretation of the results. We note that a year-to-year comparison is physically meaningful due to the use of prescribed sea-surface-temperature simulations.

Though tropical cyclone (TC) models have been routinely evaluated against track and intensity observations, little work has been performed to validate modeled TC wind fields over land. In this paper, we present a simple framework for evaluating simulated low-level inland winds with in-situ observations and existing TC structure theory. The Automated Surface Observing Systems, Florida Coastal Monitoring Program, and best track data are used to generate a theory-driven wind profile that reasonably represents the observed radial distribution of TC wind speeds. We quantitatively and qualitatively evaluated the modeled inland TC wind fields, and described the model performance with a set of simple indicators. The framework was used to examine the performance of a high-resolution two-way nested Geophysical Fluid Dynamics Laboratory model on recent U.S. landfalling TCs. Results demonstrate the capacity of using this framework to assess the modeled TC low-level wind field in the absence of dense inland observations.

The Tropical cyclone (TC) forecast skill of the eight global medium-range forecast models which are participating in the DIMOSIC (DIfferent Models, Same Initial Conditions) project is investigated in this study. Each model was used to generate 10-day forecasts from the same initial conditions provided by the European Centre for Medium-Range Weather Forecasts. There are a total of 123 initial dates spanning in one year from June 2018 to June 2019 with a 3-day interval. The TC track and intensity forecasts are evaluated against the best track dataset. TC-related precipitation and tropical cyclogenesis forecasts are also compared to explore the differences and similarities of TC forecasts across the models. This comparison of TC forecasts allows model developers in different centers to benchmark their model against other models, with the impact of the initial condition quality removed. The verifications reveal that most models show slow-moving and right-of-track biases in their TC track forecasts. Also, a common dry bias in TC-related precipitation indicates a general deficiency in TC intensity and convection in the models which should be related to insufficient model resolution. These findings provide important references for future model developments.

Tropical anvil clouds are an important player in Earth’s climate and climate sensitivity, but simulations of anvil clouds are uncertain. Here we pinpoint one source of uncertainty by demonstrating a marked increase of anvil cloud fraction with resolution in cloud-resolving simulations of radiative-convective equilibrium. This increase in cloud fraction can be traced back to the resolution dependence of horizontal mixing between clear and cloudy air. A mixing timescale is diagnosed for each simulation using the cloud fraction theory of Seeley et al. (2019), and is found to scale linearly with grid spacing, as expected from a simple scaling law. Thus mixing becomes more efficient with increasing resolution, generating more evaporation, decreased precipitation efficiency, greater mass flux, and hence greater detrainment and cloud fraction.The decrease in precipitation efficiency also yields a marked increase in relative humidity with resolution.

We present the System for High-resolution prediction on Earth-to-Local Domains (SHiELD), an atmosphere model coupling the nonhydrostatic FV3 Dynamical Core to a physics suite originally taken from the Global Forecast System. SHiELD is designed to demonstrate new capabilities within its components, explore new model applications, and to answer scientific questions through these new functionalities. A variety of configurations are presented, including short-to-medium-range and subseasonal-to-seasonal (S2S) prediction, global-to-regional convective-scale hurricane and contiguous US precipitation forecasts, and global cloud-resolving modeling. Advances within SHiELD can be seamlessly transitioned into other Unified Forecast System (UFS) or FV3-based models, including operational implementations of the UFS. Continued development of SHiELD has shown improvement upon existing models. The flagship 13-km SHiELD demonstrates steadily improved large-scale prediction skill and precipitation prediction skill. SHiELD and the coarser-resolution S-SHiELD demonstrate a superior diurnal cycle compared to existing climate models; the latter also demonstrates 28 days of useful prediction skill for the Madden-Julian Oscillation. The global-to-regional nested configurations T-SHiELD (tropical Atlantic) and C-SHiELD (contiguous United States) shows significant improvement in hurricane structure from a new tracer advection scheme and promise for medium-range prediction of convective storms, respectively.

Intense convection (updrafts exceeding 10 m∙s-1) plays an essential role in severe weather and Earth’s energy balance. Despite its importance, how the global pattern of intense convection changes in response to warmed climates remains unclear, as simulations from traditional climate models are too coarse to simulate intense convection. Here we take advantage of a kilometer-scale global storm resolving model and conduct year-long simulations of a control run, forced by analyzed sea surface temperature (SST), and one with a 4-K increase in SST for comparison. Comparisons show that the increased SST enhances the frequency of intense convection globally with large spatial and seasonal variations. Increases in the intense convection frequency do not necessarily reflect increases in convective available potential energy (CAPE). Results are also compared with traditional climate model projections. Changes in the spatial pattern of intense convection are associated with changes in planetary circulation.

We describe the model performance and simulation characteristics of a new global coupled climate model configuration, CM4-MG2. Beginning with the Geophysical Fluid Dynamics Laboratory’s fourth-generation physical climate model (CM4.0), we incorporate a two-moment Morrison-Gettelman bulk cloud microphysics scheme with prognostic precipitation (MG2), and a mineral dust and temperature-dependent cloud ice nucleation scheme. We then conduct and analyze a set of fully coupled atmosphere-ocean-land-sea ice simulations, following Coupled Model Intercomparison Project Phase 6 (CMIP6) protocols. CM4-MG2 generally captures CM4.0’s baseline simulation characteristics, but with several improvements, including better marine stratocumulus clouds off the west coasts of Africa and North and South America, a reduced bias toward “double” Intertropical Convergence Zones south of the equator, and a stronger Madden-Julian Oscillation (MJO). Some degraded features are also identified, including excessive Arctic sea ice extent and a stronger-than-observed El Nino-Southern Oscillation (ENSO). Compared to CM4.0, the climate sensitivity is reduced by about 10\% in CM4-MG2.

The 13 km SHiELD (System for High-resolution prediction on Earth-to-Local Domains) global model that is under development at the Geophysical Fluid Dynamics Laboratory (GFDL) and runs in near real time, produced outstanding tropical cyclone track forecasts during the 2021 Atlantic hurricane season, compared to both the upgraded National Weather Service Global Forecast System (GFSv16), the Hurricane Weather Research and Forecasting (HWRF) model and the European Centre for Medium-Range Weather Forecast Integrated Forecasting System (IFS). SHiELD’s average track forecast errors were 10% and 15% less than the GFSv16 and HWRF, respectively, for the 3-5 day forecast lead times. SHiELD’s track forecast skill was comparable to the National Hurricane Center’s official forecast at several forecast lead times, and approached 70% skill relative to the Climatology and Persistence Model (CLIPER) at 3 and 4 days. Similar improvements were found in the western Pacific basin in 2021, with improvements also seen in the eastern Pacific at days 4 and 5. Improved performance was also found in the 2019 Atlantic hurricane season, with neutral performance in 2020, when SHiELD was run retrospectively from the GFSv16 initial conditions. Distribution of the spatial errors and biases showed that in both the 2021 Atlantic hurricane season and the previous two years, the largest track forecast errors from both SHiELD and GFSv16 occurred in the subtropical eastern Atlantic, associated with a distinct northeast bias. Analysis indicated that some of the excessive north bias in the GFSv16 is associated with lower geopotential height fields compared to those in SHiELD.