Kai-Yuan Cheng

Using the GFDL EXperimental System for High‐resolution prediction on Earth‐to‐Local Domains (X‐SHiELD), a global storm‐resolving atmosphere model with a slab ocean, we examine extratropical cyclone (ETC) responses under three scenarios: 4 K sea surface temperature (SST) warming, increased CO ₂ concentration, and their combination. Compared to ERA5, the X‐SHiELD control run produces more intense and short‐lived ETCs, as well as a stronger seasonal cycle with fewer summer cyclones. Relative to the control run, SST warming reduces cyclone frequency in both the North Atlantic and North Pacific, especially for short‐lived winter cyclones, while storm‐track displacement is most evident over the North Atlantic. CO ₂ forcing shows similar but weaker effects. While the average cyclone central pressure remains nearly unchanged, SST warming increases the variability of cyclone central pressure and intensifies the strongest ETCs, while higher CO ₂ concentration reduces the frequency of intense ETCs. The mean 925‐hPa wind speeds intensify in the warm sector of a cyclone under both forcings, with the increase in the 95th percentile wind speeds 2–3 times larger than the increase in the mean. Additionally, SST warming enhances precipitation near and north of cyclone centers, while CO ₂ slightly suppresses precipitation. These precipitation changes are more closely associated with changes in moisture availability and moisture convergence than with changes in vertical motion. Additionally, the comparison among different experiments shows that precipitation responses to individual forcings are approximately additive while the wind speed response is more nonlinear.

This paper reports the results of the first quantitative study on the flow fields of freely falling snow aggregates. The motions are investigated by performing numerical simulations of four snow aggregate models consisting of 2, 3, 4, and 5 conformally shaped dendrites of different sizes. The dendrites are attached primarily in a horizontal configuration, with some at small angles, to resemble commonly observed snowflakes. These models are assumed to represent an early stage of aggregate formation, with the largest dimension of the aggregates approximately 2.15–2.3 mm, corresponding to Reynolds numbers in the range Re = 44–54. The flow fields around the falling aggregates are determined by numerically solving the unsteady Navier–Stokes equations using a solver capable of six‐degrees‐of‐freedom (6‐DOF) motion. Distributions of pressure, velocity, and vorticity around the falling aggregates are shown, and empirical formulas for the drag coefficient, terminal velocity, and ventilation coefficient are provided. No simple relationship between the kinematic properties and aggregate dimension exists because of the highly asymmetric shapes and the many possible arrangements of the component dendrites. All aggregates appear to move in a preferred horizontal direction, possibly determined by the specific geometry of each particle. Such fall attitudes have important implications for the collision growth of snowflakes.

Throughout the western US communities rely on atmospheric rivers (ARs) to fill reservoirs, accumulate snowpack, and mitigate drought while remaining wary of their potential to produce widespread flooding, debris flows, and property/infrastructure damage. This makes long‐range AR forecasts, specifically at subseasonal timescales, a valuable tool for both water resource and emergency management personnel. Ensemble forecasts of the system for high‐resolution prediction on Earth‐to‐Local Domains (SHiELD) model, configured with a CONUS nested grid, are evaluated for prediction skill relating to both weekly landfalling AR activity and AR‐related precipitation. Results indicate that SHiELD exhibits skillful prediction of landfalling AR activity and extreme daily AR‐related precipitation through week 2 with some skill even extending into week 3. Inspired by divergent outcomes regarding SHiELD's ability to predict two high‐impact ARs, successful week 2 forecasts of landfalling AR activity in SHiELD are found to be dependent, at least in part, on how favorably the large‐scale environment is preconditioned to the development and/or maintenance of AR activity during the preceding week. Our findings suggest SHiELD can skillfully predict AR activity and its related extreme precipitation at leads of 2–3 weeks, providing stakeholders ample time to monitor areas at risk for widespread flooding while also preparing them for potential impacts. Lastly, SHiELD is shown to under‐predict the frequency of AR activity over the southern half of the western US due to systematic mean state integrated vapor transport biases, a finding that highlights an avenue for further advancement of landfalling AR predictability in SHiELD.

Mesoscale convective systems (MCSs) are critical components of global energy and water cycles and significantly contribute to extreme weather events. However, projecting future MCS behavior remains challenging due to the limitations of regional models and the inadequate representation of MCSs in coarser climate models. In this study, we use GFDL's global storm‐resolving model (GSRM), X‐SHiELD, to explore the response of global MCSs to both increased sea surface temperatures (SST) and elevated CO ₂ levels using three sets of unique two‐year‐long warming simulations. We find that SST warming leads to an increase in MCS occurrence over ocean regions while reducing it over land, whereas elevated CO ₂ results in an overall increase over ocean and land. When SST and CO ₂ increases are combined, their impacts on MCS changes are generally additive. Using stepwise multiple linear regression, we identify the key environmental drivers of these changes across five MCS hotspots, highlighting the regional variability in MCS responses. Furthermore, MCS‐associated precipitation and its contribution to total rainfall are shaped by changes in both MCS frequency and the precipitation intensity within each event. By utilizing the explicit MCS‐resolving capabilities of GSRMs, this study provides critical insights into future changes in MCS characteristics and their implications for global precipitation patterns.

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 aggregated statistics of short-timescale and small spatial-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 sea surface temperature warming and CO₂ increase in two-year integrations and compare it to similar CMIP 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 CO₂ increase. To estimate the probability of X-SHiELD’s and the CMIP models having different means, we take the approach of Bayesian inference. We derive a posterior distribution for the differences in the mean between X-SHiELD and the CMIP models taking into account the X-SHiELD values for the global-mean precipitation response to uniform warming and the response of the norther hemisphere jet latitude to isolated CO₂ increase. We find that the most probable value for the difference between X-SHiELD and the CMIP mean is larger than one standard deviation, representing both internal variability and inter-model spread of the CMIP models. We also find that there is an important base-state dependence for some large-scale metrics that, when taken into account, can qualitatively change the interpretation of the results. We note that a year-to-year comparison is meaningful due to the use of prescribed sea-surface-temperature simulations.

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 putative drivers of the observed land‐ocean contrast (convective available potential energy, boundary layer depth, and microphysics) 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.

Using the novel kilometer‐scale global storm‐resolving model Geophysical Fluid Dynamics Laboratory eXperimental System for High‐resolution prediction on Earth‐to‐Local Domains (X‐SHiELD), we investigate the impact of a 4 K increase in sea surface temperatures on Northern Hemisphere midlatitude cyclones, during the January 2020–January 2022 period. X‐SHiELD simulations reveal a poleward shift in cyclone tracks under warming, consistent with CMIP projections. However, X‐SHiELD's high resolution and explicit deep convection allowed for a detailed analysis of the warm and cold sectors, which are instead typically underrepresented in traditional CMIP models. Instead, compositing the 100 most intense midlatitude cyclones in the North Atlantic, we find that the warm sector exhibits statistically significant increases in wind speed and precipitation of up to 15% locally per degree of warming, while changes in the cold sector are less pronounced. This study demonstrates X‐SHiELD's potential to provide a realistic‐looking perspective into the evolving risks posed by midlatitude cyclones in a warming climate.

As the atmosphere gets warmer, it is expected to hold more water vapor, thereby fueling stronger storms. At the same time, the condensation of this vapor increases the combined load of liquid water and ice aloft, forcing convection to do more work to lift water to the level where it precipitates. This takes away from the generation of kinetic energy, thereby creating a “brake” on atmospheric motions. The evolution of this precipitation “brake” with warming determines the magnitude of future storm intensification, with important societal implications. The new generation of kilometer-scale climate models is capable of projecting this evolution. In this presentation, we show how the NOAA/GFDL X-SHiELD experimental global storm-resolving model can be used to estimate the total mechanical work done by convection and the work done to lift water which is then subsequently dissipated by friction during precipitation. The statistics are computed in year-long simulations of the present climate and of a 4K warmer climate.We find that the ratio of kinetic energy generation vs work spent to lift water is respectively 30% vs 70% of the total mechanical work done by convection on global average, with a relative stability across regions and in the present vs future climate.Moving beyond regional averages, when we organize the space by decreasing values of dissipation, we find that the ratio of work spent to lift water to total mechanical work strongly increases in the most convective percentiles, that is, most of the work done by convection is used to lift water in the extremes, showing that water loading strongly opposes kinetic energy generation. We also find that the total work done by convection, the work spent to lift water and the precipitation-induced dissipation all increase similarly with warming in the most convective percentiles. This suggests that, as the Earth warms, the updrafts tend to “kill” themselves in situ from increased water loading instead of generating a response at larger scale.

We introduce a 6.5‐km version of the Geophysical Fluid Dynamics Laboratory (GFDL)'s System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD). This global model is designed to bridge the gap between global medium‐range weather prediction and global storm‐resolving simulation while remaining practical for real‐time forecast. The 6.5‐km SHiELD represents a significant advancement over GFDL's flagship global forecast system, the 13‐km SHiELD. This global model features a holistically‐developed scale‐aware suite of physical parameterizations, stepping into the formidable convective “gray zone” of resolutions below 10 km. Comparative analyses with the 13‐km SHiELD, conducted over a 3‐year hindcast period, highlight noteworthy improvements across global‐scale, regional‐scale, tropical cyclone (TC), and continental convection predictions. In particular, the 6.5‐km SHiELD excels in predicting considerably finer‐scale convective systems associated with large‐scale frontal systems and extratropical cyclones. The predictions of global temperature, wind, cloud, and precipitation are significantly improved in this global model. Regionally, over the contiguous United States and the Maritime Continent, substantial reductions in prediction biases of precipitation, cloud cover, and wind fields are also found. In the mesoscale realm, the model demonstrates prominent improvements in global TC intensity and continental convective precipitation prediction: biases are relieved, and skill is higher. These findings affirm the superiority of the 6.5‐km SHiELD compared to the current 13‐km SHiELD, which will advance weather prediction by successfully addressing both synoptic weather systems and specific storm‐scale phenomena in the same global model.