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.
The Integrated Forecasting System (IFS) developed by the European Centre for Medium-range Weather Forecasts (ECMWF) has been regarded as the best guidance for hurricane track forecasts for years. However, the performance of US models on hurricane forecasts have been catching up. Since 2019, various FV3 (Finite-Volume Cubed-Sphere Dynamical Core)-based models, including National Centers for Environmental Prediction (NCEP) operational Global Forecast System (GFS), newly operational Hurricane Analysis and Forecast System (HAFS), and research-oriented Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD), have consistently demonstrated improved hurricane forecasts in the North Atlantic basin, relative to the previous generation of National Oceanic and Atmospheric Administration (NOAA) operational and research models. This article presents the progress that has been made, and identifies areas for improvement for US model development on hurricane forecasts.
This study compares the characteristics of global mesoscale convective systems (MCSs) simulated in a global storm-resolving model (GSRM) and a high-resolution (∼25-km) general circulation model (GCM), both developed at the Geophysical Fluid Dynamics Laboratory. By comparing with two satellite datasets, we examine the spatial distribution, seasonal/diurnal cycles, and event-based features such as duration, size, intensity, and propagation of MCSs across six global hotspots. MCS-related precipitation features and their contribution to the total precipitation are also analyzed. Our results show that both models effectively capture the observed spatial patterns and seasonal cycles of MCSs, although notable differences exist in absolute values, particularly in the GCM. Both models not only simulate event-based statistics but also show large geographical variations with an overall tendency to produce longer-lasting and larger MCSs. The GSRM performs better in simulating MCS diurnal cycle and MCS intensity. While both models replicate spatial patterns of MCS-related precipitation, they struggle with accurately capturing intensity, and their contributions to total precipitation vary. This comparison highlights strengths and limitations of these two types of models, calling for further process-level investigation of model deficiencies and a detailed evaluation of observations due to dataset discrepancies.
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 CO2 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 CO2 results in an overall increase over ocean and land. When SST and CO2 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.
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.
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.
Chen, Jan-Huey, Adam J Clark, Guoqing Ge, Lucas Harris, Kimberly Hoogewind, Anders Jensen, Hosmay Lopez, Joseph Mouallem, Breanna L Zavadoff, Xuejin Zhang, and Linjiong Zhou, January 2024: 2022-2023 Global-Nest Initiative Activity Summary: Recent Results and Future Plan, Princeton, NJ: NOAA Technical Memorandum OAR GFDL, 2023-001, DOI:10.25923/yx20-3k04 14pp. Abstract
The Global-Nest Initiative takes new technologies developed at Geophysical Fluid Dynamics Laboratory (GFDL) and partners to create convective-scale digital twins of the earth system to better simulate and predict extreme weather events, their impacts, and their role within the broader earth system, and to create actionable information at all time scales. This annual report describes the activities and results of the NOAA Global-Nest Initiative during Fiscal Year 2022-2023.
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 reach cloud-resolving scales with modest computational resources. Given a conditionally 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 Eulerian vertical vorticity budget suggests that the updraft core of the supercell tilts horizontal vorticity into the tornado-like vortex, which is then amplified through vertical stretching by the updraft. Results suggest that the simulated vortex is dynamically similar to observed tornadoes, as well as those simulated in modeling studies at much higher horizontal resolution. Lastly, we discuss the prospects for the study of cross-scale interactions involving supercells.
Mesoscale convective systems (MCSs) are pivotal in global energy/water cycles and typically produce extreme weather events. Despite their importance, our understanding of their future change remains limited, largely due to inadequate representation in current climate models. Here, using a global storm-resolving model that accurately simulates MCSs, we conclude contrasting responses to increased SST in their occurrence, that is, notable decreases over land but increases over ocean. This land-ocean contrast is attributed to the changes in convective available potential energy (CAPE) and convective inhibition (CIN). Over land, notable rises in CIN alongside moderate increases in CAPE effectively suppress (favor) weak to moderate (intense) MCSs, resulting in an overall reduction in MCS occurrences. In contrast, substantial increases in CAPE with minimal changes in CIN over ocean contribute to a significant rise in MCS occurrences. The divergent response in MCS occurrence has profound impacts on both mean and extreme precipitation.
Tropical cyclone (TC) intensity forecasting poses challenges due to complex dynamical processes and data inadequacies during model initialization. This paper describes efforts to improve TC intensity prediction in the Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model by implementing a Vortex Initialization (VI) technique. The GFDL SHiELD model, relying on the Global Forecast System (GFS) analysis for initialization, faces deficiencies in initial TC structure and intensity. The VI method involves adjusting the TC vortex inherited from the GFS analysis and merging it back into the environment at the observed location, enhancing the analyzed representation of storm structure. We made modifications to the VI package implemented in the operational Hurricane Analysis and Forecast System, including handling initial condition data, reducing input domain size, and improving storm intensity enhancement. Experiments using the T-SHiELD configuration demonstrate that using VI significantly improves the representation of initial TC intensity and size, enhancing TC predictions, particularly in storm intensity and outer wind forecasts within the first 48 h.
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.
We present a variable-resolution global chemistry-climate model (AM4VR) developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) for research at the nexus of US climate and air quality extremes. AM4VR has a horizontal resolution of 13 km over the US, allowing it to resolve urban-to-rural chemical regimes, mesoscale convective systems, and land-surface heterogeneity. With the resolution gradually reducing to 100 km over the Indian Ocean, we achieve multi-decadal simulations driven by observed sea surface temperatures at 50% of the computational cost for a 25-km uniform-resolution grid. In contrast with GFDL's AM4.1 contributing to the sixth Coupled Model Intercomparison Project at 100 km resolution, AM4VR features much improved US climate mean patterns and variability. In particular, AM4VR shows improved representation of: precipitation seasonal-to-diurnal cycles and extremes, notably reducing the central US dry-and-warm bias; western US snowpack and summer drought, with implications for wildfires; and the North American monsoon, affecting dust storms. AM4VR exhibits excellent representation of winter precipitation, summer drought, and air pollution meteorology in California with complex terrain, enabling skillful prediction of both extreme summer ozone pollution and winter haze events in the Central Valley. AM4VR also provides vast improvements in the process-level representations of biogenic volatile organic compound emissions, interactive dust emissions from land, and removal of air pollutants by terrestrial ecosystems. We highlight the value of increased model resolution in representing climate–air quality interactions through land-biosphere feedbacks. AM4VR offers a novel opportunity to study global dimensions to US air quality, especially the role of Earth system feedbacks in a changing climate.
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.
Chen, Jan-Huey, Linjiong Zhou, Linus Magnusson, Ron McTaggart-Cowan, and M Köhler, July 2023: Tropical cyclone forecasts in the DIMOSIC project—medium-range forecast models with common initial conditions. Earth and Space Science, 10(7), DOI:10.1029/2023EA002821. Abstract
The tropical cyclone (TC) forecast skill of the eight global medium-range forecast models which are participating in the 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 at 3-day intervals. The TC track and intensity forecasts are evaluated against the best track data set. 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.
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-predicted 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.
High-resolution atmospheric models are powerful tools for hurricane track and intensity predictions. Although using high resolution contributes to better representation of hurricane structure and intensity, its value in the prediction of steering flow and storm tracks is uncertain. Here we present experiments suggesting that biases in the predicted North Atlantic hurricane tracks in a high-resolution (approximately 3 km grid-spacing) model originates from the model's explicit simulation of deep convection. Differing behavior of explicit convection leads to changes in the synoptic-scale pattern and thereby to the steering flow. Our results suggest that optimizing small-scale convection activity, for example, through the model's horizontal advection scheme, can lead to significantly improved hurricane track prediction (∼10% reduction of mean track error) at lead times beyond 72 hr. This work calls attention to the behavior of explicit convection in high-resolution models, and its often overlooked role in affecting larger-scale circulations and hurricane track prediction.
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.
We describe the model performance 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 stratiform 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 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. Compared to CM4.0, the climate sensitivity is reduced by about 10% in CM4-MG2.
Jeevanjee, Nadir, and Linjiong Zhou, March 2022: On the resolution-dependence of anvil cloud fraction and precipitation efficiency in radiative-convective equilibrium. Journal of Advances in Modeling Earth Systems, 14(3), DOI:10.1029/2021MS002759. Abstract
Tropical anvil clouds are an important player in Earth's climate and climate sensitivity, but simulations of anvil clouds are uncertain. Here we identify and investigate 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, Jeevanjee, Langhans, and Romps (2019) (https://doi.org/10.1029/2018GL080747) 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 in middle and lower tropospheric updrafts. This decreases their precipitation efficiency (PE), thereby increasing their overall mass flux, leading to greater detrainment at the anvil level and hence higher anvil cloud fraction. The decrease in PE also yields a marked increase in relative humidity with resolution.
Magnusson, Linus, Duncan Ackerley, Yves Bouteloup, Jan-Huey Chen, James D Doyle, Paul Earnshaw, Y C Kwon, M Köhler, Simon T K Lang, Y-J Lim, Mio Matsueda, Takumi Matsunobu, Ron McTaggart-Cowan, Alex Rienecke, Munehiko Yamaguchi, and Linjiong Zhou, September 2022: Skill of medium-range forecast models using the same initial conditions. Bulletin of the American Meteorological Society, 103(9), DOI:10.1175/BAMS-D-21-0234.1E2050-E2068. Abstract
In the Different Models, Same Initial Conditions (DIMOSIC) project, forecasts from different global medium-range forecast models have been created based on the same initial conditions. The dataset consists of 10-day deterministic forecasts from seven models and includes 122 forecast dates spanning one calendar year. All forecasts are initialized from the same ECMWF operational analyses to minimize the differences due to initialization. The models are run at or near their respective operational resolutions to explore similarities and differences between operational global forecast models. The main aims of this study are 1) to evaluate the forecast skill and how it depends on model formulation, 2) to assess systematic differences and errors at short lead times, 3) to compare multimodel ensemble spread to model uncertainty schemes, and 4) to identify models that generate similar solutions. Our results show that all models in this study are capable of producing high-quality forecasts given a high-quality analysis. But at the same time, we find a large variety in model biases, both in terms of temperature errors and precipitation. We are able to identify models whose forecasts are more similar to each other than they are to those of other systems, due to the use of similar model physics packages. However, in terms of multimodel ensemble spread, our results also demonstrate that forecast sensitivities to different model formulations still are substantial. We therefore believe that the diversity in model design that stems from parallel development efforts at global modeling centers around the world remains valuable for future progress in the numerical weather prediction community.
A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.
We describe the third version of the Geophysical Fluid Dynamics Laboratory cloud microphysics scheme (GFDL MP v3) implemented in the System for High-resolution prediction on Earth-to-Local Domains (SHiELD). Compared to the GFDL MP v2, the GFDL MP v3 is entirely reorganized, optimized, and modularized into functions. The particle size distribution (PSD) of all hydrometeor categories is redefined to better mimic observations, and the cloud droplet number concentration (CDNC) is calculated from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2) aerosol data. In addition, the GFDL MP has been redesigned so all processes use the redefined PSD to ensure overall consistency and easily permit the introduction of new PSDs and microphysical processes. A year's worth of global 13-km, 10-day weather forecasts were performed with the new GFDL MP. Compared to the GFDL MP v2, the GFDL MP v3 significantly improves SHiELD's 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. The predictions are improved even further by the use of reanalysis aerosol data to calculate CDNC, and also by using the more realistic PSD available in GFDL MP v3. However, the upgrade of the GFDL MP shows little impact on the precipitation prediction. Degradations caused by the new scheme are discussed and provide a guide for future GFDL MP development.
Clouds play critical roles in our daily weather and in the global energy and water budgets that regulate the climate of the Earth (Lamb and Verlinde, 2011; Houze, 2014). The formation and evolution of clouds significantly impact precipitation forecasts in numerical weather prediction (Seifert and Beheng, 2005; Morrison and Grabowski, 2008; Baldauf et al., 2011; Bauer et al., 2015). Clouds and their impacts on solar and thermal radiation are among the most challenging aspects of climate prediction (Trenberth et al., 2009; Stephens et al., 2012; Wild et al., 2019). Therefore, the representation of clouds in atmospheric models has to be paid particular attention to. Among all physical processes in a model, cloud microphysics is less well represented but is of critical importance. Because the processes are not readily resolved in time and space, cloud microphysics parameterization is essential from large-eddy to global simulations (Morrison and Grabowski, 2008; Kogan, 2013; Nogherotto et al., 2016).
Zhou, Linjiong, and Lucas Harris, November 2022: Integrated dynamics-physics coupling for weather to climate models: GFDL SHiELD with in-line microphysics. Geophysical Research Letters, 49(21), DOI:10.1029/2022GL100519. Abstract
We propose an integrated dynamics-physics coupling framework for weather and climate-scale models. Each physical parameterization would be advanced on its natural time scale, revise the thermodynamics to include moist effects, and finally integrated into the relevant components of the dynamical core. We show results using a cloud microphysics scheme integrated within the dynamical core of the Geophysical Fluid Dynamics Laboratory System for High-resolution prediction on Earth-to-Local Domains weather model to demonstrate the promise of this concept. We call it the in-line microphysics as it is in-lined within the dynamical core. Statistics gathered from 1 year of weather forecasts show significantly better prediction skills when the model is upgraded to use the in-line microphysics. However, we do find that some biases are degraded with the in-line microphysics. The in-line microphysics also shows larger-amplitude and higher-frequency variations in cloud structures within a tropical cyclone than the traditionally-coupled microphysics. Finally, we discuss the prospects for further development of this integrated dynamics-physics coupling.
Gallo, Burkely T., Jamie K Wolff, Adam J Clark, Israel Jirak, Lindsay R Blank, Brett Roberts, Yunheng Wang, Chunxi Zhang, Ming Xue, Timothy A Supinie, Lucas Harris, Linjiong Zhou, and Curtis Alexander, February 2021: Exploring convection-allowing model evaluation strategies for severe local storms using the Finite-Volume Cubed-Sphere (FV3) Model Core. Weather and Forecasting, 36(1), DOI:10.1175/WAF-D-20-0090.13-19. Abstract
Verification methods for convection-allowing models (CAMs) should consider the finescale spatial and temporal detail provided by CAMs, and including both neighborhood and object-based methods can account for displaced features that may still provide useful information. This work explores both contingency table–based verification techniques and object-based verification techniques as they relate to forecasts of severe convection. Two key fields in severe weather forecasting are investigated: updraft helicity (UH) and simulated composite reflectivity. UH is used to generate severe weather probabilities called surrogate severe fields, which have two tunable parameters: the UH threshold and the smoothing level. Probabilities computed using the UH threshold and smoothing level that give the best area under the receiver operating curve result in very high probabilities, while optimizing the parameters based on the Brier score reliability component results in much lower probabilities. Subjective ratings from participants in the 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (SFE) provide a complementary evaluation source. This work compares the verification methodologies in the context of three CAMs using the Finite-Volume Cubed-Sphere Dynamical Core (FV3), which will be the foundation of the U.S. Unified Forecast System (UFS). Three agencies ran FV3-based CAMs during the five-week 2018 SFE. These FV3-based CAMs are verified alongside a current operational CAM, the High-Resolution Rapid Refresh version 3 (HRRRv3). The HRRR is planned to eventually use the FV3 dynamical core as part of the UFS; as such evaluations relative to current HRRR configurations are imperative to maintaining high forecast quality and informing future implementation decisions.
We investigate the sensitivity of hurricane intensity and structure to the horizontal tracer advection in the Geophysical Fluid Dynamics Laboratory (GFDL) Finite-Volume Cubed-Sphere Dynamical Core (FV3). We compare two schemes, a monotonic scheme and a less diffusive positive-definite scheme. The positive-definite scheme leads to significant improvement in the intensity prediction relative to the monotonic scheme in a suite of 5-day forecasts that mostly consist of rapidly intensifying hurricanes. Notable storm structural differences are present: the radius of maximum wind (RMW) is smaller and eyewall convection occurs farther inside the RMW when the positive-definite scheme is used. Moreover, we find that the horizontal tracer advection scheme affects the eyewall convection location by affecting the moisture distribution in the inner-core region. This study highlights the importance of dynamical core algorithms in hurricane intensity prediction.
A two-moment Morrison-Gettelman bulk cloud microphysics with prognostic precipitation (MG2), together with a mineral dust and temperature-dependent ice nucleation scheme, have been implemented into the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4.0 (AM4.0). We refer to this configuration as AM4-MG2. This paper describes the configuration of AM4-MG2, evaluates its performance, and compares it with AM4.0. It is shown that the global simulations with AM4-MG2 compare favorably with observations and reanalyses. The model skill scores are close to AM4.0. Compared to AM4.0, improvements in AM4-MG2 include (a) better coastal marine stratocumulus and seasonal cycles, (b) more realistic ice fraction, and (c) dominant accretion over autoconversion. Sensitivity tests indicate that nucleation and sedimentation schemes have significant impacts on cloud liquid and ice water fields, but higher horizontal resolution (about 50 km instead of 100 km) does not.
Harris, Lucas, Xi Chen, William M Putman, Linjiong Zhou, and Jan-Huey Chen, June 2021: A Scientific Description of the GFDL Finite-Volume Cubed-Sphere Dynamical Core, Princeton, NJ: NOAA Technical Memorandum OAR GFDL, 2021-001, DOI:10.25923/6nhs-5897 109pp.
Judt, Falko, Daniel Klocke, Rosimar Rios-Berrios, Benoit Vanniere, Florian Ziemen, Ludovic Auger, Joachim Biercamp, Christopher S Bretherton, Xi Chen, Peter Düben, Cathy Hohenegger, Marat Khairoutdinov, Chihiro Kodama, Luis Kornblueh, Shian-Jiann Lin, Masuo Nakano, Philipp Neumann, William M Putman, Niklas Röber, Malcolm J Roberts, Masaki Satoh, Ryosuke Shibuya, Bjorn Stevens, Pier Luigi Vidale, Nils Wedi, and Linjiong Zhou, June 2021: Tropical cyclones in global storm-resolving models. Journal of the Meteorological Society of Japan. Ser. II, 99(3), DOI:10.2151/jmsj.2021-029579-602. Abstract
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 indicate that, broadly speaking, the models produce realistic TCs and remove longstanding issues known from global models such as the deficiency in accurately simulating 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 can open a new chapter in TC prediction, but they need to be improved to unleash their full potential.
This technical note explains updates to the GFDL Finite-Volume Cubed-Sphere Dynamical Core, abbreviated FV3 or FV[superscript 3], and the Split GFDL Microphysics. It does not repeat the contents of earlier documentation, especially publications. A list of publications and prior technical notes describing FV3 is available on the GFDL website.
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.
This document describes the nonhydrostatic solver of the GFDL Finite-Volume Cubed-Sphere Dynamical Core, FV3. The nonhydrostatic solver works identically to the hydrostatic solver except for the need to solve for two new prognostic variables, the vertical velocity and geometric layer depth; and to use the full nonhydrostatic pressure in computing the pressure gradient force. In particular the Lagrangian dynamics described within L04 remains valid and all vertical processes (advection, wave propagation) remain implicit while all horizontal processes are explicit. This document assumes working knowledge of the hydrostatic discretization of FV3 described in LR96, LR97, L97, L04, PL07, and HL13. It is strongly recommended that anyone who wishes to understand the nonhydrostatic FV3 solver read and understand these documents first. Additional relevant material may be found in LPH17 and LH18. All of these documents may be found at www. gfdl.noaa.gov/fv3/fv3-documentation-and-references/.
Motivated by the use of the GFDL microphysics scheme in the FV3GFS, the all-sky radiance assimilation framework has been expanded to include precipitating hydrometeors. Adding precipitating hydrometeors allows the assimilation of precipitation-affected radiance in addition to cloudy radiance. In this upgraded all-sky framework, the five hydrometeors, including cloud liquid water, cloud ice, rain, snow and graupel, are the new control variables, replacing the original cloud water control variable. The Community Radiative Transfer Model (CRTM) was interfaced with the newly added precipitating hydrometeors. Sub-grid cloud variability was considered by using the average cloud overlap scheme. Multiple scattering radiative transfer was activated in the upgraded framework. Radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Advanced Technology Microwave Sounder (ATMS) over ocean were assimilated in all-sky approach. This new constructed all-sky framework shows neutral to positive impact on overall forecast skill. Improvement was found in 500 hPa geopotential height forecast in both Northern and Southern Hemispheres. Temperature forecast was also improved at 850 hPa in the Southern Hemisphere and the Tropics.
We use the fvGFS model developed at the Geophysical Fluid Dynamics Laboratory (GFDL) to demonstrate the potential of the upcoming United States Next Generation Global Prediction System for hurricane prediction. The fvGFS retrospective forecasts initialized with the European Centre for Medium‐Range Weather Forecasts (ECMWF) data showed much‐improved track forecasts for the 2017 Atlantic hurricane season compared to the best performing ECMWF operational model. The fvGFS greatly improved the ECMWF's poor track forecast for Hurricane Maria (2017). For Hurricane Irma (2017), a well‐predicted case by the ECMWF model, the fvGFS produced even lower 5‐day track forecast errors. The fvGFS also showed better intensity prediction than both the United States and the ECMWF operational models, indicating the robustness of its numerical algorithms.
A new global model using the GFDL nonhydrostatic Finite-Volume Cubed-Sphere Dynamical Core (FV3) coupled to physical parameterizations from the National Centers for Environmental Prediction's Global Forecast System (NCEP/GFS) was built at GFDL, named fvGFS. The modern dynamical core, FV3, has been selected for National Oceanic and Atmospheric Administration’s Next Generation Global Prediction System (NGGPS) due to its accuracy, adaptability, and computational efficiency, which brings a great opportunity for the unification of weather and climate prediction systems.
The performance of tropical cyclone (TC) forecasts in the 13-km fvGFS is evaluated globally based on 363 daily cases of 10-day forecasts in 2015. Track and intensity errors of TCs in fvGFS are compared to those in the operational GFS. The fvGFS outperforms the GFS in TC intensity prediction for all basins. For TC track prediction, the fvGFS forecasts are substantially better over the northern Atlantic basin and the northern Pacific Ocean than the GFS forecasts. An updated version of the fvGFS with the GFDL 6-category cloud microphysics scheme is also investigated based on the same 363 cases. With this upgraded microphysics scheme, fvGFS shows much improvement in TC intensity prediction over the operational GFS. Besides track and intensity forecasts, the performance of TC genesis forecast is also compared between the fvGFS and operational GFS. In addition to evaluating the hit/false alarm ratios, a novel method is developed to investigate the lengths of TC genesis lead times in the forecasts. Both versions of fvGFS show higher hit ratios, lower false alarm ratios and longer genesis lead times than those of the GFS model in most of the TC basins.
We present a new global‐to‐regional model, cfvGFS, able to explicitly (without parameterization) represent convection over part of the earth. This model couples the Geophysical Fluid Dynamics Laboratory Finite‐Volume Cubed‐Sphere Dynamical Core (FV3) to the Global Forecast System (GFS) physics and initial conditions, augmented with a six‐category microphysics and a modified planetary boundary layer scheme. We examine the characteristics of cfvGFS on a 3‐km continental United States domain nested within a 13‐km global model. The nested cfvGFS still has good hemispheric skill comparable to or better than the operational GFS, while supercell thunderstorms, squall lines, and derechos are explicitly‐represented over the refined region. In particular, cfvGFS has excellent representations of fine‐scale updraft helicity fields, an important proxy for severe weather forecasting. Precipitation biases are found to be smaller than in uniform‐resolution global models and competitive with operational regional models; the 3‐km domain also improves upon the global models in 2‐m temperature and humidity skill. We discuss further development of cfvGFS and the prospects for a unified global‐to‐regional prediction system.
He, Bian, Qing Bao, X Wang, and Linjiong Zhou, et al., August 2019: CAS FGOALS-f3-L Model Datasets for CMIP6 Historical Atmospheric Model Intercomparison Project Simulation. Advances in Atmospheric Sciences, 36(8), DOI:10.1007/s00376-019-9027-8. Abstract
The outputs of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic, Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project (CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total, there are three ensemble experiments over the period 1979–2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden-Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP.
Understanding the root causes of forecast errors and occasional very poor forecasts is essential but difficult. In this paper we investigate the relative importance of initial conditions and model formulation for medium‐range errors in 500‐hPa geopotential height. The question is addressed by comparing forecasts produced with ECMWF‐IFS and NCEP‐GFS forecasting systems, and with the GFDL‐fvGFS model initialised with ECMWF and NCEP initial conditions. This gives two pairs of configurations that use the same initial conditions but different models, and one pair with the same model but different initial conditions. The first conclusion is that the initial conditions play the major role for differences between the configurations in terms of the average root‐mean‐square error for both northern and southern hemispheres as well as Europe and the contiguous U.S (CONUS), while the model dominates the systematic errors. A similar conclusion is also found by verifying precipitation over low latitudes and the CONUS. The day‐to‐day variations of 500‐hPa geopotential height scores are exemplified by one case of a forecast bust over Europe, where the error is found to be dominated by initial errors. The results are generalised by calculating correlations between errors integrated over Europe, CONUS and a region in southeastern Pacific respectively from the different configurations. For Europe and southeast Pacific, the correlations in the medium‐range are highest between the pairs that use the same initial conditions, while over CONUS it is for the pair with the same model. This suggests different mechanisms behind the day‐to‐day variability of the score for these regions. Over CONUS the link is made to the propagation of troughs over the Rockies, and the result suggests that the large differences in parameterisations of orographic drag between the models plays a role.
Stevens, Bjorn, Masaki Satoh, Ludovic Auger, Joachim Biercamp, Christopher S Bretherton, Xi Chen, Peter Düben, Falko Judt, Marat Khairoutdinov, Daniel Klocke, Chihiro Kodama, Luis Kornblueh, Shian-Jiann Lin, Philipp Neumann, William M Putman, Niklas Röber, Ryosuke Shibuya, Benoit Vanniere, Pier Luigi Vidale, Nils Wedi, and Linjiong Zhou, September 2019: DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains. Progress in Earth and Planetary Science, 6, 61, DOI:10.1186/s40645-019-0304-z. Abstract
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.
Wang, Lei, Qing Bao, W-C Wang, Y Liu, Guoxiong Wu, and Linjiong Zhou, et al., July 2019: LASG Global AGCM with a Two-moment Cloud Microphysics Scheme: Energy Balance and Cloud Radiative Forcing Characteristics. Advances in Atmospheric Sciences, 36(7), DOI:10.1007/s00376-019-8196-9. Abstract
Cloud dominates influence factors of atmospheric radiation, while aerosol-cloud interactions are of vital importance in its spatiotemporal distribution. In this study, a two-moment (mass and number) cloud microphysics scheme, which significantly improved the treatment of the coupled processes of aerosols and clouds, was incorporated into version 1.1 of the IAP/LASG global Finite-volume Atmospheric Model (FAMIL1.1). For illustrative purposes, the characteristics of the energy balance and cloud radiative forcing (CRF) in an AMIP-type simulation with prescribed aerosols were compared with those in observational/reanalysis data. Even within the constraints of the prescribed aerosol mass, the model simulated global mean energy balance at the top of the atmosphere (TOA) and at the Earth’s surface, as well as their seasonal variation, are in good agreement with the observational data. The maximum deviation terms lie in the surface downwelling longwave radiation and surface latent heat flux, which are 3.5 W m-2 (1%) and 3 W m-2 (3.5%), individually. The spatial correlations of the annual TOA net radiation flux and the net CRF between simulation and observation were around 0.97 and 0.90, respectively. A major weakness is that FAMIL1.1 predicts more liquid water content and less ice water content over most oceans. Detailed comparisons are presented for a number of regions, with a focus on the Asian monsoon region (AMR). The results indicate that FAMIL1.1 well reproduces the summer-winter contrast for both the geographical distribution of the longwave CRF and shortwave CRF over the AMR. Finally, the model bias and possible solutions, as well as further works to develop FAMIL1.1 are discussed.
The variable-resolution version of a Finite-Volume Cubed-Sphere Dynamical Core (FV3)-based global model improves the prediction of convective-scale features while maintaining skillful global forecasts.
The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a new variable-resolution global model with the ability to represent convective-scale features that serves as a prototype of the Next Generation Global Prediction System (NGGPS). The goal of this prediction system is to maintain the skill in large-scale features while simultaneously improving the prediction skill of convectively-driven mesoscale phenomena. This paper demonstrates the new capability of this model in convective-scale prediction relative to the current operational Global Forecast System (GFS). This model uses the stretched-grid functionality of the Finite-Volume Cubed-Sphere Dynamical Core (FV3) to refine the global 13-km uniform-resolution model down to 4-km convection-permitting resolution over the Contiguous United States (CONUS), and implements the GFDL single-moment six-category cloud microphysics to improve the representation of moist processes.
Statistics gathered from two years of simulations by the GFS and select configurations of the FV3-based model are carefully examined. The variable-resolution FV3-based model is shown to possess global forecast skill comparable with that of the operational GFS while quantitatively improving skill and better representing the diurnal cycle within the high-resolution area compared to the uniform mesh simulations. Forecasts of the occurrence of extreme precipitation rates over the Southern Great Plains are also shown to improve with the variable-resolution model. Case studies are provided of a squall line and a hurricane to demonstrate the effectiveness of the variable-resolution model to simulate convective-scale phenomena.
Liu, P, Y Zhu, Q Zhang, J Gottschalck, M Zhang, C Melhauser, Wei Li, Hong Guan, Xiaqiong Zhou, Dingchen Hou, M Peña, Guoxiong Wu, Y Liu, and Linjiong Zhou, et al., July 2018: Climatology of tracked persistent maxima of 500-hPa geopotential height. Climate Dynamics, 51(1-2), DOI:10.1007/s00382-017-3950-0. Abstract
Persistent open ridges and blocking highs (maxima) of 500-hPa geopotential height (Z500; PMZ) adjacent in space and time are identified and tracked as one event with a Lagrangian objective approach to derive their climatological statistics with some dynamical reasoning. A PMZ starts with a core that contains a local eddy maximum of Z500 and its neighboring grid points whose eddy values decrease radially to about 20 geopotential meters (GPMs) smaller than the maximum. It connects two consecutive cores that share at least one grid point and are within 10° of longitude of each other using an intensity-weighted location. The PMZ ends at the core without a successor. On each day, the PMZ impacts an area of grid points contiguous to the core and with eddy values decreasing radially to 100 GPMs. The PMZs identified and tracked consist of persistent ridges, omega blockings and blocked anticyclones either connected or as individual events. For example, the PMZ during 2–13 August 2003 corresponds to persistent open ridges that caused the extreme heatwave in Western Europe. Climatological statistics based on the PMZs longer than 3 days generally agree with those of blockings. In the Northern Hemisphere, more PMZs occur in DJF season than in JJA and their duration both exhibit a log-linear distribution. Because more omega-shape blocking highs and open ridges are counted, the PMZs occur more frequently over Northeast Pacific than over Atlantic-Europe during cool seasons. Similar results are obtained using the 200-hPa geopotential height (in place of Z500), indicating the quasi-barotropic nature of the PMZ.
