Leveraging the novel kilometer-scale global storm-resolving model GFDL X-SHiELD, we investigate the impact of a 4 K increase in sea surface temperatures on Northern Hemisphere midlatitude cyclones, over a three-year-long period. X-SHiELD simulations show a poleward shift in midlatitude cyclone tracks under +4 K warming, consistent with CMIP model projections. Thanks to X-SHiELD’s high resolution and explicit deep convection, we provide a detailed analysis of the warm and cold sectors of midlatitude cyclones, which are typically underrepresented in coarser CMIP models. Compositing the 100 most intense midlatitude cyclones in the North Atlantic, we find that the warm sector exhibits wind speed and precipitation increases of up to 15% and 20% per degree of warming, respectively, while changes in the cold sector are less pronounced. This study demonstrates X-SHiELD’s potential to provide a realistic-looking picture into the evolving risks posed by midlatitude cyclones in a warmer world.

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.

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.

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.

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.

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.

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.

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.

This paper documents the third version of the GFDL cloud microphysics scheme (GFDL MP v3) used in the System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model. Compared to the GFDL MP v2, the GFDL MP v3 is entirely reorganized, optimized, and modularized by functions. In addition, the particle size distribution (PSD) of all cloud categories is redefined to mimic the latest observations, and the cloud condensation nuclei (CCNs) are calculated from the MERRA2 aerosol data. The GFDL MP has been redesigned so all processes use the redefined PSD to ensure overall consistency and easily permit introductions of new PSDs and microphysical processes. Analyses gathered from simulations by SHiELD with selected configurations are examined. Compared to the GFDL MP v2, the GFDL MP v3 significantly improves the predictions of geopotential height, air temperature, and specific humidity in the Troposphere, as well as the high, middle and total cloud fractions and the liquid water path. With the more realistic PSD implemented in GFDL MP v3, the predictions of geopotential height in the Troposphere, low and total cloud fractions are further improved. Furthermore, using climatological aerosol data to calculate CCNs leads to even better predictions of geopotential height, air temperature, and specific humidity in the Troposphere, high and middle cloud fractions, as well as the liquid and ice water paths. However, the upgrade of the GFDL MP shows little impact on the precipitation prediction. Degradation due to the scheme upgrade is also addressed and discussed to guide the future GFDL MP development.

We present the characteristics of rotating convective updrafts in the 2021 version of GFDL’s Experimental System for High-resolution prediction on Earth-to-Local Domains (X-SHiELD), a kilometer scale global storm resolving model (GSRM). Rotation is quantified using 2–5 km Updraft Helicity (UH) in a year-long integration forced by analyzed SSTs. Updrafts with UH magnitudes above 50 m2/s2; are common over the mid-latitude continents, especially in the warm seasons where they are associated with severe weather but are also common over most tropical ocean basins. In nearly all areas cyclonically rotating convection dominates, with larger UH values increasingly preferring cyclonic rotation. The ratio of cyclonic to anticyclonic updrafts is largest in the subtropical and mid-latitude oceans and is slightly lower over mid-latitude continents. The ratio of cyclonic to anticyclonic updrafts can be substantively explained by the mean storm-relative helicity (SRH) in convective regions, indicating the importance for environmental controls on the sense of storm rotation, although internal storm dynamics also plays a role in the generation of anticyclonic updrafts.

One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned state-dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine-grid global storm-resolving model (GSRM). Our past work demonstrating this approach was trained with short (40-day) simulations of GFDL’s X-SHiELD GSRM with 3 km global horizontal grid spacing. Here, we extend this approach to span the full annual cycle by training and testing our machine learning (ML) using a new year-long GSRM simulation. Our corrective ML models are trained by learning the state-dependent tendencies of temperature and humidity and surface radiative fluxes needed to nudge a closely related 200~km grid coarse model, FV3GFS, to the GSRM evolution. Coarse-grid simulations adding these learned ML corrections run stably for multiple years. Compared to a no-ML baseline, the time-mean spatial pattern errors with respect to the fine-grid target are reduced by 6-25% for land surface temperature and 9-25% for land surface precipitation. The ML-corrected simulations develop other biases in climate and circulation that differ from, but have comparable amplitude to, the no-ML baseline simulation.