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.
Years-long global storm-resolving model (GSRM) simulations of the present-day and warmed climates are conducted with 3.25-km 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, modulated by shifts in the larger-scale circulations. 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 value of consistently representing both local orographic circulations and the large-scale circulation responses to warming, which is provided by a GSRM but not by either conventional climate models or regional downscaling approaches.
The Geophysical Fluid Dynamics Laboratory (GFDL)'s System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model typically uses the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis to initialize its medium-range forecasts. A data assimilation (DA) system has been implemented for the global SHiELD to demonstrate the prediction skills of the model initialized from its own analysis. The DA system leverages the advanced DA techniques used in GFS and assimilates all the observations assimilated in GFS. Compared to the forecasts initialized from GFS analysis, SHiELD forecast skills are significantly improved by using its own analysis. Remarkable improvement is found in the southern hemisphere with positive impact lasting up to 10 days. The DA system is useful in identifying and understanding model errors. The most noticeable model error detected by the DA system originates from the turbulent kinetic energy (TKE)-based moist eddy-diffusivity mass-flux vertical turbulent mixing (TKE-EDMF) scheme. The model error leads to insufficient ensemble spread. Including two versions of the TKE-EDMF scheme in the ensemble helps increase ensemble spread, further improves forecast skills and alleviate the systematic errors in marine stratocumulus regions. Applying interchannel correlated observation errors for Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) also reduces the systematic errors and improves the forecast skill up to day 5. Further investigation of the forecast errors reveals that the ensemble spread is largely affected by the parameterization of eddy diffusivity through its impact on the gradient of the model state. The systematic forecast errors in marine stratocumulus regions are associated with the vertical location of the stratocumulus cloud, which is sensitive to model vertical resolution within the cloud layer. Enhancing eddy diffusion within the cloud or near cloud top elevates cloud top but reduces cloud amount.
Global storm-resolving models (GSRMs) that can explicitly resolve some of deep convection are now being integrated for climate timescales. GSRMs are able to simulate more realistic precipitation distributions relative to traditional Coupled Model Intercomparison Project 6 (CMIP6) models. In this study, we present results from two-year-long integrations of a GSRM developed at Geophysical Fluid Dynamics Laboratory, eXperimental System for High-resolution prediction on Earth-to-Local Domains (X-SHiELD), for the response of precipitation to sea surface temperature warming and an isolated increase in CO2 and compare it to CMIP6 models. At leading order, X-SHiELD's response is within the range of the CMIP6 models. However, a close examination of the precipitation distribution response reveals that X-SHiELD has a different response at lower percentiles and the response of the extreme events are at the lower end of the range of CMIP6 models. A regional decomposition reveals that the difference is most pronounced for midlatitude land, where X-SHiELD shows a lower increase at intermediate percentiles and drying at lower percentiles.
The climate simulation frontier of a global storm-resolving model (GSRM; or k-scale model because of its kilometer-scale horizontal resolution) is deployed for climate change simulations. The climate sensitivity, effective radiative forcing, and relative humidity changes are assessed in multiyear atmospheric GSRM simulations with perturbed sea-surface temperatures and/or carbon dioxide concentrations. Our comparisons to conventional climate model results can build confidence in the existing climate models or highlight important areas for additional research. This GSRM’s climate sensitivity is within the range of conventional climate models, although on the lower end as the result of neutral, rather than amplifying, shortwave feedbacks. Its radiative forcing from carbon dioxide is higher than conventional climate models, and this arises from a bias in climatological clouds and an explicitly simulated high-cloud adjustment. Last, the pattern and magnitude of relative humidity changes, simulated with greater fidelity via explicitly resolving convection, are notably similar to conventional climate models.
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 Geophysical Fluid Dynamics Laboratory's GSRM X-SHiELD, perturbed by a uniform sea surface temperature (SST) warming and/or increased CO2 concentration. The simulated warming from an SST increase is weakly amplified relative to the surface through the mid-troposphere before increasing to a factor of about 2.5 in the upper troposphere. 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 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.
Changes in tropical deep convection with global warming are a leading source of uncertainty for future climate projections. A comparison of the responses of active sensor measurements of cloud ice to interannual variability and next-generation global storm-resolving model (also known as k-scale models) simulations to global warming shows similar changes for events with the highest column-integrated ice. The changes reveal that the ice loading decreases outside the most active convection but increases at a rate of several percent per Kelvin surface warming in the most active convection. Disentangling thermodynamic and vertical velocity changes shows that the ice signal is strongly modulated by structural changes of the vertical wind field towards an intensification of strong convective updrafts with warming, suggesting that changes in ice loading are strongly influenced by changes in convective velocities, as well as a path toward extracting information about convective velocities from observations.
We present the global 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 s−2 are common over the mid-latitude continents, where they are associated with severe weather especially in the warm seasons 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.
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 use a kilometer-scale global storm resolving model (GSRM) 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. Comparisons show that the increased SST enhances the frequency of intense convection globally with large spatial and seasonal variations. Changes in the spatial pattern of intense convection are associated with changes in planetary circulation. Increases in the intense convection frequency do not necessarily reflect increases in convective available potential energy. The GSRM results are also compared with previously published traditional climate model projections.
Hazelton, Andrew T., Kun Gao, Morris A Bender, Levi Cowan, Ghassan J Alaka Jr, Alex Kaltenbaugh, Lew Gramer, Xuejin Zhang, Lucas Harris, Timothy Marchok, Matthew J Morin, Avichal Mehra, Zhan Zhang, Bin Liu, and Frank D Marks, January 2022: Performance of 2020 real-time Atlantic hurricane forecasts from high-resolution global-nested hurricane models: HAFS-globalnest and GFDL T-SHiELD. Weather and Forecasting, 37(1), DOI:10.1175/WAF-D-21-0102.1143-161. Abstract
The global-nested Hurricane Analysis and Forecast System (HAFS-globalnest) is one piece of NOAA’s Unified Forecast System (UFS) application for hurricanes. In this study, results are analyzed from 2020 real-time forecasts by HAFS-globalnest and a similar global-nested model, the Tropical Atlantic version of GFDL’s System for High‐resolution prediction on Earth‐to‐Local Domains (T-SHiELD). HAFS-globalnest produced the highest track forecast skill compared to several operational and experimental models, while T-SHiELD showed promising track skills as well. The intensity forecasts from HAFS-globalnest generally had a positive bias at longer lead times primarily due to the lack of ocean coupling, while T-SHiELD had a much smaller intensity bias particularly at longer forecast lead times. With the introduction of a modified planetary boundary layer scheme and an increased number of vertical levels, particularly in the boundary layer, HAFS forecasts of storm size had a smaller positive bias than occurred in the 2019 version of HAFS-globalnest. Despite track forecasts that were comparable to the operational GFS and HWRF, both HAFS-globalnest and T-SHiELD suffered from a persistent right-of-track bias in several cases at the 4–5-day forecast lead times. The reasons for this bias were related to the strength of the subtropical ridge over the western North Atlantic and are continuing to be investigated and diagnosed. A few key case studies from this very active hurricane season, including Hurricanes Laura and Delta, were examined.
We present the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD), an atmosphere model developed by the Geophysical Fluid Dynamics Laboratory (GFDL) 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 prediction, global‐to‐regional convective‐scale hurricane and contiguous U.S. precipitation forecasts, and global cloud‐resolving modeling. Advances within SHiELD can be seamlessly transitioned into other Unified Forecast System or FV3‐based models, including operational implementations of the Unified Forecast System. 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) show significant improvement in hurricane structure from a new tracer advection scheme and promise for medium‐range prediction of convective storms.
