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.
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.
Watt-Meyer, Oliver, Noah D Brenowitz, Spencer K Clark, Brian Henn, Anna Kwa, Jeremy McGibbon, W Andre Perkins, Lucas Harris, and Christopher S Bretherton, February 2024: Neural network parameterization of subgrid-scale physics from a realistic geography global storm-resolving simulation. Journal of Advances in Modeling Earth Systems, 16(2), DOI:10.1029/2023MS003668. Abstract
Parameterization of subgrid-scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm-resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study uses machine learning to replace the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarse-grid (200 km) global atmosphere model, using training data obtained by spatially coarse-graining a 40-day realistic geography global storm-resolving simulation. The training targets are the three-dimensional fields of effective heating and moistening rates, including the effect of grid-scale motions that are resolved but imperfectly simulated by the coarse model. A neural network is trained to predict the time-dependent heating and moistening rates in each grid column using the coarse-grained temperature, specific humidity, surface turbulent heat fluxes, cosine of solar zenith angle, land-sea mask and surface geopotential of that grid column as inputs. The coefficient of determination R2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes. Online, we achieve stable 35-day simulations, with metrics of skill such as the time-mean pattern of near-surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic. Overall, this study shows potential for the replacement of human-designed parameterizations with data-driven ones in a realistic setting.
Willson, Justin L., Kevin A Reed, Christiane Jablonowski, James Kent, Peter H Lauritzen, Ramachandran Nair, Mark A Taylor, Paul A Ullrich, Colin M Zarzycki, David M Hall, Don Dazlich, Ross Heikes, Celal Konor, David A Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, and Lucas Harris, et al., April 2024: DCMIP2016: The tropical cyclone test case. Geoscientific Model Development, 17(7), DOI:10.5194/gmd-17-2493-20242493-2507. Abstract
This paper describes and analyzes the Reed–Jablonowski (RJ) tropical cyclone (TC) test case used in the 2016 Dynamical Core Model Intercomparison Project (DCMIP2016). This intermediate-complexity test case analyzes the evolution of a weak vortex into a TC in an idealized tropical environment. Reference solutions from nine general circulation models (GCMs) with identical simplified physics parameterization packages that participated in DCMIP2016 are analyzed in this study at 50 km horizontal grid spacing, with five of these models also providing solutions at 25 km grid spacing. Evolution of minimum surface pressure (MSP) and maximum 1 km azimuthally averaged wind speed (MWS), the wind–pressure relationship, radial profiles of wind speed and surface pressure, and wind composites are presented for all participating GCMs at both horizontal grid spacings. While all TCs undergo a similar evolution process, some reach significantly higher intensities than others, ultimately impacting their horizontal and vertical structures. TCs simulated at 25 km grid spacings retain these differences but reach higher intensities and are more compact than their 50 km counterparts. These results indicate that dynamical core choice is an essential factor in GCM development, and future work should be conducted to explore how specific differences within the dynamical core affect TC behavior in GCMs.
Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory’s SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting.
Adams-Selin, Rebecca D., Christina Kalb, Tara Jensen, John Henderson, Timothy A Supinie, Lucas Harris, Yunheng Wang, Burkely T Gallo, and Adam J Clark, February 2023: Just what is “good”? Musings on hail forecast verification through evaluation of FV3-HAILCAST hail forecasts. Weather and Forecasting, 38(2), DOI:10.1175/WAF-D-22-0087.1371-387. Abstract
Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFEs). As part of this evaluation, HWT SFE participants were polled about their definition of a “good” hail forecast. Participants were presented with two different verification methods conducted over three different spatiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verification methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast expected by the end-user interpreting and applying the forecast. The hail forecasts evaluated during this period included an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1- and 24-h periods found continued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 ensemble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to understand the improvement, agreeing with the HWT SFE participants’ recommendations for multiple verification methods.
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.
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.
Dahm, Johann P., Eddie C Davis, Florian Deconinck, Oliver Elbert, Rhea C George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer, May 2023: Pace v0.2: a Python-based performance-portable atmospheric model. Geoscientific Model Development, 16(9), DOI:10.5194/gmd-16-2719-20232719-2736. Abstract
Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore's law driving forward rapid specialization of hardware architectures, building simulation codes on a low-level language with hardware-specific optimizations is a significant risk. As a solution, we present Pace, an implementation of the nonhydrostatic FV3 dynamical core and GFDL cloud microphysics scheme which is entirely Python-based. In order to achieve high performance on a diverse set of hardware architectures, Pace is written using the GT4Py domain-specific language. We demonstrate that with this approach we can achieve portability and performance, while significantly improving the readability and maintainability of the code as compared to the Fortran reference implementation. We show that Pace can run at scale on leadership-class supercomputers and achieve performance speeds 3.5–4 times faster than the Fortran code on GPU-accelerated supercomputers. Furthermore, we demonstrate how a Python-based simulation code facilitates existing or enables entirely new use cases and workflows. Pace demonstrates how a high-level language can insulate us from disruptive changes, provide a more productive development environment, and facilitate the integration with new technologies such as machine learning.
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.
Kwa, Anna, Spencer K Clark, Brian Henn, Noah D Brenowitz, Jeremy McGibbon, Oliver Watt-Meyer, W Andre Perkins, Lucas Harris, and Christopher S Bretherton, May 2023: Machine-Learned Climate Model Corrections From a Global Storm-Resolving Model: Performance Across the Annual Cycle. Journal of Advances in Modeling Earth Systems, 15(5), DOI:10.1029/2022MS003400. Abstract
One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned (ML) state-dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine-grid global storm-resolving model (GSRM). Our past work demonstrating this approach was trained with short (40-day) simulations of GFDL's X-SHiELD GSRM with 3 km global horizontal grid spacing. Here, we extend this approach to span the full annual cycle by training and testing our ML using a new year-long GSRM simulation. Our corrective ML models are trained by learning the state-dependent tendencies of temperature and humidity and surface radiative fluxes needed to nudge a closely related 200 km grid coarse model, FV3GFS, to the GSRM evolution. Coarse-grid simulations adding these learned ML corrections run stably for multiple years. Compared to a no-ML baseline, the time-mean spatial pattern errors with respect to the fine-grid target are reduced by 6%–26% for land surface temperature and 9%–25% for land surface precipitation. The ML-corrected simulations develop other biases in climate and circulation that differ from, but have comparable amplitude to, the no-ML baseline simulation.
The gnomonic cubed-sphere grid has excellent accuracy and uniformity, but the “kink” in the coordinates at the cube edges in the halo region can leave an imprint of the cube in the solution, and requires special edge handling. To reduce grid imprinting, we implement the novel “Duo-Grid” within the Geophysical Fluid Dynamics Laboratory's (GFDL) Finite-Volume Cubed-Sphere Dynamical Core (FV3). The Duo-Grid remaps a cube face's data from neighboring face from kinked to natural locations along great circle lines using 1D piecewise linear interpolation. A 2D interpolation algorithm is used to fill correct data at the eight corners of the cubed-sphere needed for FV3's 2D advection scheme. The Duo-Grid was tested in idealized tests using the 2D shallow water solver and the 3D hydrostatic and non-hydrostatic solvers. We found that error norms are greatly reduced and grid imprinting is practically eliminated when employing the Duo-Grid. These results indicate that FV3's accuracy and robustness have improved.
Ye, Jiacheng, Zhuo Wang, Fanglin Yang, Lucas Harris, Tara Jensen, Douglas E Miller, Christina Kalb, Daniel Adriaansen, and Weiwei Li, June 2023: Evaluation and process-oriented diagnosis of the GEFSv12 reforecasts. Journal of Climate, 36(12), DOI:10.1175/JCLI-D-22-0772.14255-4274. Abstract
Three levels of process-oriented model diagnostics are applied to evaluate the Global Ensemble Forecast System version 12 (GEFSv12) reforecasts. The level-1 diagnostics are focused on model systematic errors, which reveals that precipitation onset over tropical oceans occurs too early in terms of column water vapor accumulation. Since precipitation acts to deplete water vapor, this results in prevailing negative biases of precipitable water in the tropics. It is also associated with overtransport of moisture into the mid- and upper troposphere, leading to a dry bias in the lower troposphere and a wet bias in the mid–upper troposphere. The level-2 diagnostics evaluate some major predictability sources on the extended-range time scale: the Madden–Julian oscillation (MJO) and North American weather regimes. It is found that the GEFSv12 can skillfully forecast the MJO up to 16 days ahead in terms of the Real-time Multivariate MJO indices (bivariate correlation ≥ 0.6) and can reasonably represent the MJO propagation across the Maritime Continent. The weakened and less coherent MJO signals with increasing forecast lead times may be attributed to humidity biases over the Indo-Pacific warm pool region. It is also found that the weather regimes can be skillfully predicted up to 12 days ahead with persistence comparable to the observation. In the level-3 diagnostics, we examined some high-impact weather systems. The GEFSv12 shows reduced mean biases in tropical cyclone genesis distribution and improved performance in capturing tropical cyclone interannual variability, and midlatitude blocking climatology in the GEFSv12 also shows a better agreement with the observations than in the GEFSv10.
Zavadoff, Breanna L., Kun Gao, Hosmay Lopez, Sang-Ki Lee, Dongmin Kim, and Lucas Harris, January 2023: Improved MJO forecasts using the experimental global-nested GFDL SHiELD model. Geophysical Research Letters, 50(6), DOI:10.1029/2022GL101622. Abstract
Sitting at the crossroads of weather and climate, the Madden-Julian Oscillation (MJO) is considered a primary source of subseasonal predictability. Despite its importance, numerical models struggle with MJO prediction as its convection moves through the complex Maritime Continent (MC) environment. Motivated by the ongoing effort to improve MJO prediction, we use the System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model to run two sets of forecasts, one with and one without a nested grid over the MC. By efficiently leveraging high-resolution grid spacing, the nested grid reduces amplitude and phase errors and extends the model's predictive skill by about 10 days. These enhancements are tied to improvements in predicted zonal wind from the Indian Ocean to the Pacific, facilitated by westerly wind bias reduction in the nested grid. Results from this study suggest that minimizing circulation biases over the MC can lead to substantial advancements in skillful MJO prediction.
Bretherton, Christopher S., Brian Henn, Anna Kwa, Noah D Brenowitz, Oliver Watt-Meyer, Jeremy McGibbon, W Andre Perkins, Spencer K Clark, and Lucas Harris, February 2022: Correcting coarse-grid weather and climate models by machine learning from global storm-resolving simulations. Journal of Advances in Modeling Earth Systems, 14(2), DOI:10.1029/2021MS002794. Abstract
Global atmospheric “storm-resolving” models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse-grid global climate models across a range of climates, reducing uncertainties in regional precipitation and temperature trends. Here, machine learning of nudging tendencies as functions of column state is used to correct the physical parameterization tendencies of temperature, humidity, and optionally winds, in a real-geography coarse-grid model (FV3GFS with a 200 km grid) to be closer to those of a 40-day reference simulation using X-SHiELD, a modified version of FV3GFS with a 3 km grid. Both simulations specify the same historical sea-surface temperature fields. This methodology builds on a prior study using a global observational analysis as the reference. The coarse-grid model without machine learning corrections has too few clouds, causing too much daytime heating of land surfaces that creates excessive surface latent heat flux and rainfall. This bias is avoided by learning downwelling radiative flux from the fine-grid model. The best configuration uses learned nudging tendencies for temperature and humidity but not winds. Neural nets slightly outperform random forests. Forecasts of 850 hPa temperature gain 18 hr of skill at 3–7 days leads and time-mean precipitation patterns are improved 30% by applying the ML correction. Adding machine-learned wind tendencies improves 500 hPa height skill for the first five days of forecasts but degrades time-mean upper tropospheric temperature and zonal wind patterns thereafter.
Cheng, Kai-Yuan, Lucas Harris, and Yongqiang Sun, February 2022: Enhancing the accessibility of unified modeling systems: GFDL System for High-resolution prediction on Earth-to-Local Domains (SHiELD) v2021b in a container. Geoscientific Model Development, 15(3), DOI:10.5194/gmd-15-1097-20221097-1105. Abstract
Container technology provides a pathway to facilitate easy access to unified modeling systems and opens opportunities for collaborative model development and interactive learning. In this paper, we present the implementation of software containers for the System for High-resolution prediction on Earth-to-Local Domains (SHiELD), a unified atmospheric model for weather-to-seasonal prediction. The containerized SHiELD is cross-platform and easy to install. Flexibility of the containerized SHiELD is demonstrated as it can be configured as a global, a global–nest, and a regional model. Bitwise reproducibility is achieved on various x86 systems tested in this study. Performance and scalability of the containerized SHiELD are evaluated and discussed.
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.
Clark, Spencer K., Noah D Brenowitz, Brian Henn, Anna Kwa, Jeremy McGibbon, W Andre Perkins, Oliver Watt-Meyer, Christopher S Bretherton, and Lucas Harris, September 2022: Correcting a 200 km resolution climate model in multiple climates by machine learning from 25 km resolution simulations. Journal of Advances in Modeling Earth Systems, 14(9), DOI:10.1029/2022MS003219. Abstract
Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse-resolution global atmosphere model with real geography (a ∼200 km version of NOAA's FV3GFS) evolve more like a fine-resolution model, at the scales resolved by both. This study extends that work for application in multiple climates and multi-year ML-corrected simulations. Here four fine-resolution (∼25 km) 2 year reference simulations are run using FV3GFS with climatological sea surface temperatures perturbed uniformly by −4, 0, +4, and +8 K. A data set of state-dependent corrective tendencies is then derived through nudging the ∼200 km model to the coarsened state of the fine-resolution simulations in each climate. Along with the surface radiative fluxes, the corrective tendencies of temperature and specific humidity are machine-learned as functions of the column state. ML predictions for the fluxes and corrective tendencies are applied in 5.25 years ∼200 km resolution simulations in each climate, and improve the spatial pattern errors of land precipitation by 8%–28% and land surface temperature by 19%–25% across the four climates. The ML has a neutral impact on the pattern error of oceanic precipitation.
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.
Two-way multiple same-level and telescoping grid nesting capabilities are implemented in the Geophysical Fluid Dynamics Laboratory (GFDL)'s Finite-Volume Cubed-Sphere Dynamical Core (FV3). Simulations are performed within GFDL's System for High-resolution modeling for Earth-to-Local Domains (SHiELD) using global and regional multiple nest configurations. Results show that multiple same-level and multi-level telescoping nests were able to capture various weather events in greater details by resolving smaller-scale flow structures. Two-way updates do not introduce numerical errors in their corresponding parent grids where the nests are located. The cases of Hurricane Laura's landfall and an atmospheric river in California were found to be more intense with increased levels of telescoping nesting. All nested grids run concurrently, and adding additional nests with computer cores to a setup does not degrade the computational performance nor increase the simulation run time if the cores are optimally distributed among the grids.
Stephan, Claudia C., Julia Duras, Lucas Harris, Daniel Klocke, William M Putman, Mark A Taylor, Nils Wedi, Nedjeljka Žagar, and Florian Ziemen, April 2022: Atmospheric energy spectra in global kilometre-scale models. Tellus A: Dynamic Meteorology and Oceanography, 74, DOI:10.16993/tellusa.26280-299. Abstract
Eleven 40-day long integrations of five different global models with horizontal resolutions of less than 9 km are compared in terms of their global energy spectra. The method of normal-mode function decomposition is used to distinguish between balanced (Rossby wave; RW) and unbalanced (inertia-gravity wave; IGW) circulation. The simulations produce the expected canonical shape of the spectra, but their spectral slopes at mesoscales, and the zonal scale at which RW and IGW spectra intersect differ significantly. The partitioning of total wave energies into RWs an IGWs is most sensitive to the turbulence closure scheme and this partitioning is what determines the spectral crossing scale in the simulations, which differs by a factor of up to two. It implies that care must be taken when using simple spatial filtering to compare gravity wave phenomena in storm-resolving simulations, even when the model horizontal resolutions are similar. In contrast to the energy partitioning between the RWs and IGWs, changes in turbulence closure schemes do not seem to strongly affect spectral slopes, which only exhibit major differences at mesoscales. Despite their minor contribution to the global (horizontal kinetic plus potential available) energy, small scales are important for driving the global mean circulation. Our results support the conclusions of previous studies that the strength of convection is a relevant factor for explaining discrepancies in the energies at small scales. The models studied here produce the major large-scale features of tropical precipitation patterns. However, particularly at large horizontal wavenumbers, the spectra of upper tropospheric vertical velocity, which is a good indicator for the strength of deep convection, differ by factors of three or more in energy. High vertical kinetic energies at small scales are mostly found in those models that do not use any convective parameterisation.
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.
Landfalling tropical cyclones (LTCs) are the most devastating disaster to affect the U.S., while the demonstration of skillful subseasonal (between 10 days and one season) prediction of LTCs is less promising. Understanding the mechanisms governing the subseasonal variation of TC activity is fundamental to improving its forecast, which is of critical interest to decision-makers and the insurance industry. This work reveals three localized atmospheric circulation modes with significant 10–30 days subseasonal variations: Piedmont Oscillation (PO), Great America Dipole (GAD), and the Subtropical High ridge (SHR) modes. These modes strongly modulate precipitation, TC genesis, intensity, track, and landfall near the U.S. coast. Compared to their strong negative phases, the U.S. East Coast has 19 times more LTCs during the strong positive phases of PO, and the Gulf Coast experiences 4–12 times more frequent LTCs during the positive phases of GAD and SHR. Results from the GFDL SPEAR model show a skillful prediction of 13, 9, and 22 days for these three modes, respectively. Our findings are expected to benefit the prediction of LTCs on weather timescale and also suggest opportunities exist for subseasonal predictions of LTCs and their associated heavy rainfalls.
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.
Harris, Lucas, July 2021: A new semi-Lagrangian finite volume advection scheme combines the best of both worlds. Advances in Atmospheric Sciences, 38, DOI:10.1007/s00376-021-1181-01608-1609.
McGibbon, Jeremy, Noah D Brenowitz, Mark Cheeseman, Spencer K Clark, Johann P S Dahm, Eddie C Davis, Oliver Elbert, Rhea C George, and Lucas Harris, et al., July 2021: fv3gfs-wrapper: A Python wrapper of the FV3GFS atmospheric model. Geoscientific Model Development, 14(7), DOI:10.5194/gmd-14-4401-20214401-4409. Abstract
Simulation software in geophysics is traditionally written in Fortran or C++ due to the stringent performance requirements these codes have to satisfy. As a result, researchers who use high-productivity languages for exploratory work often find these codes hard to understand, hard to modify, and hard to integrate with their analysis tools. fv3gfs-wrapper is an open-source Python-wrapped version of the NOAA (National Oceanic and Atmospheric Administration) FV3GFS (Finite-Volume Cubed-Sphere Global Forecast System) global atmospheric model, which is coded in Fortran. The wrapper provides simple interfaces to progress the Fortran main loop and get or set variables used by the Fortran model. These interfaces enable a wide range of use cases such as modifying the behavior of the model, introducing online analysis code, or saving model variables and reading forcings directly to and from cloud storage. Model performance is identical to the fully compiled Fortran model, unless routines to copy the state in and out of the model are used. This copy overhead is well within an acceptable range of performance and could be avoided with modifications to the Fortran source code. The wrapping approach is outlined and can be applied similarly in other Fortran models to enable more productive scientific workflows.
We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL ‐ the AM4 atmosphere model, MOM6 ocean code, LM4 land model and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0o (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1o to 0.25o. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM 4 models, but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time‐mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM 4 models.
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/.
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.
We demonstrate that two‐way nesting significantly improves the structure of simulated hurricane in an atmospheric general circulation model. Two sets of 30‐day hindcast experiments are conducted, one with the global‐uniform‐resolution (approximately 25‐km nominal horizontal resolution) and the other with a regionally refined two‐way nest (approximately 8 km over the tropical North Atlantic). The increase in the horizontal resolution on the nested grid improves the representation of storm intensity and intensification rate. When normalized by the radius of maximum wind (RMW), composite hurricane structures are generally similar in both simulations and compare well to observations. However, the hurricanes in the globally uniform configuration have much larger RMWs than observed, while those in the two‐way‐nested configuration have more realistic RMWs. We also find that the representation of the RMW has a critical impact on the simulation of inertial stability and boundary‐layer convergence in the inner‐core region. The more realistic inner‐core size (indicated by RMW) and structure are possible reasons for the improved intensification rates in the two‐way‐nested configuration.
We investigate the monthly prediction of North Atlantic hurricane and especially major hurricane activity based on the Geophysical Fluid Dynamics Laboratory High‐Resolution Atmospheric Model (HiRAM). We compare the performance of two versions of HiRAM: a globally‐uniform 25‐km grid and the other with an 8‐km interactive nest over the tropical North Atlantic. Both grid configurations show skills in predicting anomalous monthly hurricane frequency and accumulated cyclone energy (ACE). Particularly the 8‐km nested model shows improved skills in predicting major hurricane frequency and ACE. The skill in anomalous monthly hurricane occurrence prediction arises from the accurate prediction of zonal wind shear anomalies in the Main Development Region, which in turn arises from the SST anomalies persisted from the initialization time. The enhanced resolution on the nested grid permits a better representation of hurricanes and especially intense hurricanes, thereby showing the ability and the potential for prediction of major hurricanes on subseasonal timescales.
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.
We describe GFDL's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the pre‐industrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top‐of‐atmosphere fluxes, surface temperature, and precipitation; reduced double Intertropical Convergence Zone bias; dramatically improved representation of ocean boundary currents; a high quality simulation of climatological Arctic sea ice extent and its recent decline; and excellent simulation of the El Niño‐Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas; an inaccurate Antarctic sea ice simulation; precipitation and wind composites still affected by the equatorial cold tongue bias; muted variability in the Atlantic Meridional Overturning Circulation; strong 100 year quasi‐periodicity in Southern Ocean ventilation; and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.
Potvin, C K., J R Carley, Adam J Clark, L J Wicker, P S Skinner, A E Reinhart, Burkely T Gallo, J S Kain, G Romine, E Aligo, K Brewster, D C Dowell, and Lucas Harris, et al., October 2019: Systematic comparison of convection-allowing models during the 2017 NOAA HWT Spring Forecasting Experiment. Weather and Forecasting, 34(5), DOI:10.1175/WAF-D-19-0056.1. Abstract
The 2016–2018 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFE) featured the Community Leveraged Unified Ensemble (CLUE), a coordinated convection-allowing model (CAM) ensemble framework designed to provide empirical guidance for development of operational CAM systems. The 2017 CLUE included 81 members that all used 3-km horizontal grid spacing over the CONUS, enabling direct comparison of forecasts generated using different dynamical cores, physics schemes, and initialization procedures. This study uses forecasts from several of the 2017 CLUE members and one operational model to evaluate and compare CAM representation and next-day prediction of thunderstorms. The analysis utilizes existing techniques and novel, object-based techniques that distill important information about modeled and observed storms from many cases. The National Severe Storms Laboratory Multi-Radar/Multi-Sensor product suite is used to verify model forecasts and climatologies of observed variables. Unobserved model fields are also examined to further illuminate important inter-model differences in storms and near-storm environments.
No single model performed better than the others in all respects. However, there were many systematic inter-model and inter-core differences in specific forecast metrics and model fields. Some of these differences can be confidently attributed to particular differences in model design. Model intercomparison studies similar to the one presented here are important to better understand the impacts of model and ensemble configurations on storm forecasts and to help optimize future operational CAM systems.
Responses of tropical cyclones (TCs) to CO2 doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~ 200 km, ~ 50 km and ~ 25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~ 25 km model also has a substantial and spatially-ubiquitous increase of Category 3–4–5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model’s transient fully-coupled 2 × CO2 TC activity response is largely recovered by “time-slice” experiments using time-invariant SST perturbations added to each model’s own SST climatology. The TC response to SST forcing depends on each model’s background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~ 25 km model, in response to CO2-induced warming patterns and CO2 doubling. Isolated CO2 doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~ 25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC “seeds”, which increase due to warming (more so in the ~ 25 km model) and decrease due to higher CO2 concentrations, and (2) less efficient development of these“seeds” into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.
Zarzycki, Colin M., Christiane Jablonowski, James Kent, Peter H Lauritzen, Ramachandran Nair, Kevin A Reed, Paul A Ullrich, David M Hall, Don Dazlich, Ross Heikes, Celal Konor, David A Randall, Xi Chen, and Lucas Harris, et al., March 2019: DCMIP2016: the splitting supercell test case. Geoscientific Model Development, 12(3), DOI:10.5194/gmd-12-879-2019. Abstract
This paper describes the splitting supercell idealized test case used in the 2016 Dynamical Core Model Intercomparison Project (DCMIP2016). These storms are useful testbeds for global atmospheric models because the horizontal scale of convective plumes is O(1km), emphasizing non-hydrostatic dynamics. The test case simulates a supercell on a reduced radius sphere with nominal resolutions ranging from 4km to 0.5km and is based on the work of Klemp et al. (2015). Models are initialized with an atmospheric environment conducive to supercell formation and forced with a small thermal perturbation. A simplified Kessler microphysics scheme is coupled to the dynamical core to represent moist processes. Reference solutions for DCMIP2016 models are presented. Storm evolution is broadly similar between models, although differences in final solution exist. These differences are hypothesized to result from different numerical discretizations, physics-dynamics coupling, and numerical diffusion. Intramodel solutions generally converge as models approach 0.5km resolution. These results can be used as a reference for future dynamical core evaluation, particularly with the development of non-hydrostatic global models intended to be used in convective-permitting and convective-allowing regimes.
Zhang, F, M Minamide, R G Nystrom, X Chen, Shian-Jiann Lin, and Lucas Harris, July 2019: Improving Harvey forecasts with next-generation weather satellites. Bulletin of the American Meteorological Society, 100(7), DOI:10.1175/BAMS-D-18-0149.1. Abstract
The experimental forecasts initialized with the ensemble assimilation of the all-sky radiances—from the next-generation satellite (GOES-16)—has demonstrated its potential in more accurate prediction of Hurricane Harvey (2017) before its rapid intensification.
Hurricane Harvey brought catastrophic destruction and historical flooding to the Gulf Coast region in late August 2017. Guided by numerical weather prediction models, operational forecasters at NOAA provided outstanding forecasts of Harvey’s future path and potential for record flooding days in advance. These forecasts were valuable to the public and emergency managers in protecting lives and property. The current study shows the potential for further improving Harvey’s analysis and prediction through advanced ensemble assimilation of high-spatiotemporal all-sky infrared radiances from the newly-launched, next-generation geostationary weather satellite, GOES-16. Although findings from this single-event study should be further evaluated, the results highlight the potential improvement in hurricane prediction that is possible via sustained investment in advanced observing systems, such as those from weather satellites, comprehensive data assimilation methodologies that can more effectively ingest existing and future observations, higher-resolution weather prediction models with more accurate numerics and physics, and high-performance computing facilities that can perform advanced analysis and forecasting in a timely manner.
Zhang, C, Ming Xue, Timothy A Supinie, F Kong, N Snook, K W Thomas, K Brewster, Y Jung, Lucas Harris, and Shian-Jiann Lin, March 2019: How Well Does an FV3-based Model Predict Precipitation at a Convection-Allowing Resolution? Results from CAPS Forecasts for the 2018 NOAA Hazardous Weather Testbed with Different Physics Combinations. Geophysical Research Letters, 46(6), DOI:10.1029/2018GL081702. Abstract
The Geophysical Fluid Dynamics Laboratory (GFDL) Finite‐Volume Cubed‐Sphere (FV3) numerical forecast model was chosen in late 2016 by the National Weather Service (NWS) to serve as the dynamic core of the Next‐Generation Global Prediction System (NGGPS). The operational Global Forecasting System (GFS) physics suite implemented in FV3, however, was not necessarily suitable for convective‐scale prediction. We implemented several advanced physics schemes from the Weather Research and Forecasting (WRF) model within FV3 and ran 10 forecasts with combinations of five planetary boundary layer and two microphysics (MP) schemes, with an ~3.5‐km convection‐allowing grid two‐way nested within am ~13‐km grid spacing global grid during the 2018 Spring Forecasting Experiment at National Oceanic and Atmospheric Administration (NOAA)'s Hazardous Weather Testbed. Objective verification results show that the Thompson MP scheme slightly outperforms the National Severe Storms Laboratory MP scheme in precipitation forecast skill, while no planetary boundary layer scheme clearly stands out. The skill of FV3 is similar to that of the more‐established WRF at a similar resolution. These results establish the viability of the FV3 dynamic core for convective‐scale forecasting as part of the single‐core unification of the NWS modeling suite.
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.
Anber, Usama, Nadir Jeevanjee, Lucas Harris, and Isaac M Held, July 2018: Sensitivity of Radiative‐Convection Equilibrium to Divergence Damping in GFDL‐FV3 Based Cloud‐Resolving Model Simulations. Journal of Advances in Modeling Earth Systems, 10(7), DOI:10.1029/2017MS001225. Abstract
Using a non‐hydrostatic model based on a version of GFDL's FV3 dynamical core at a cloud‐resolving resolution in radiative‐convective equilibrium (RCE) configuration, the sensitivity of the mean RCE climate to the magnitude and scale‐selectivity of the divergence damping is explored. Divergence damping is used to reduce small‐scale noise in more realistic configurations of this model. This sensitivity is tied to the strength (and width) of the convective updrafts, which decreases (increases) with increased damping and acts to organize the convection, dramatically drying out the troposphere and increasing the outgoing longwave radiation.
Increased damping also results in a much‐broadened precipitation probability distribution and larger extreme values, as well as reduction in cloud fraction, which correspondingly decreases the magnitude of shortwave and longwave cloud radiative effects. Solutions exhibit a monotonic dependence on the strength of the damping and asymptotically converge to the inviscid limit. While the potential dependence of RCE simulations on resolution and microphysical assumptions are generally appreciated, these results highlight the potential significance of the choice of sub‐grid numerical diffusion in the dynamical core.
Chen, Xi, Shian-Jiann Lin, and Lucas Harris, September 2018: Towards an unstaggered finite‐volume dynamical core with a fast Riemann solver: 1D linearized analysis of dissipation, dispersion, and noise control. Journal of Advances in Modeling Earth Systems, 10(9), DOI:10.1029/2018MS001361. Abstract
Many computational fluid dynamics codes use Riemann solvers on an unstaggered grid for finite volume methods, but this approach is computationally expensive compared to existing atmospheric dynamical cores equipped with hyper‐diffusion or other similar relatively simple diffusion forms. We present a simplified Low Mach number Approximate Riemann Solver (LMARS), made computationally efficient through assumptions appropriate for atmospheric flows: low Mach number, weak discontinuities, and locally‐uniform sound speed. This work will examine the dissipative and dispersive properties of LMARS using Von Neumann linearized analysis to the one‐dimensional linearized shallow water equations. We extend these analyses to higher‐order methods by numerically solving the Fourier‐transformed equations. It is found that the pros and cons due to grid staggering choices diminish with high‐order schemes.
The linearized analysis is limited to modal, smooth solutions using simple numerical schemes, and cannot analyze solutions with discontinuities. To address this problem, this work presents a new idealized test of a discontinuous wave packet, a single Fourier mode modulated by a discontinuous square‐wave. The experiments include studies of well‐resolved and (near) grid‐scale wave profiles, as well as the representation of discontinuous features and the results are validated against the Von Neumann analysis. We find the higher‐order LMARS produces much less numerical noise than do inviscid unstaggered and especially staggered schemes while retaining accuracy for better‐resolved modes.
Hazelton, Andrew T., Lucas Harris, and Shian-Jiann Lin, April 2018: Evaluation of Tropical Cyclone Structure Forecasts in a High-Resolution Version of the Multiscale GFDL fvGFS Model. Weather and Forecasting, 33(2), DOI:10.1175/WAF-D-17-0140.1. Abstract
A nested version of the FV3 dynamical core with GFS physics (fvGFS) is capable of tropical cyclone (TC) prediction across multiple space and time scales, from subseasonal prediction to high-resolution structure and intensity forecasting. Here, a version of fvGFS with 2 km resolution covering most of the North Atlantic is evaluated for its ability to simulate TC track, intensity, and fine-scale structure. TC structure is evaluated through comparison of forecasts with 3-dimensional Doppler radar from P-3 flights by NOAA’s Hurricane Research Division (HRD), and structural metrics evaluated include the 2-km radius of maximum wind (RMW), slope of the RMW, depth of the TC vortex, and horizontal vortex decay rate.
7 TCs from the 2010-2016 seasons are evaluated, including 10 separate model runs and 38 individual flights. The model had some success in producing rapid intensification (RI) forecasts for Earl, Edouard, and Matthew. fvGFS successfully predicts RMW in the 25-50 km range, but tends to have a small bias at very large radii and a large bias at very small radii. The wind peak also tends to be somewhat too sharp, and the vortex depth occasionally has a high bias, especially for storms that are observed to be shallow. Composite radial wind shows that the boundary layer tends to be too deep, although the outflow structure aloft is relatively consistent with observations. These results highlight the utility of structural evaluation of TC forecasts, and also show the promise of fvGFS for forecasting TCs.
The 2017 Atlantic hurricane season had several high-impact tropical cyclones (TCs), including multiple cases of rapid intensification (RI). A high-resolution nested version of the GFDL fvGFS model (HifvGFS) was used to conduct hindcasts of all Atlantic TCs between August 7 and October 15.
HifvGFS showed promising track forecast performance, with similar error patterns and skill compared to the operational GFS and HWRF models. Some of the larger track forecast errors were associated with the erratic tracks of Jose and Lee. A case study of Maria found that although the track forecasts were generally skillful, a right-of-track bias was noted in some cases associated with initialization and prediction of ridging north of the storm.
The intensity forecasts showed large improvement over the GFS and global fvGFS models, but were somewhat less skillful than HWRF. The largest negative intensity forecast errors were associated with the RI of Irma, Lee, and Maria, while the largest positive errors were found with recurving cases that were generally weakening. The structure forecasts were also compared with observations, and HifvGFS was found to generally have wind radii larger than observations. Detailed examination of the forecasts of Hurricanes Harvey and Maria showed that HifvGFS was able to predict the structural evolution leading to RI in some cases, but was not as skillful with other RI cases. One case study of Maria suggested that inclusion of ocean coupling could significantly reduce the positive bias seen during and after recurvature.
In this two-part paper, a description is provided of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). This version, with roughly 100km horizontal resolution and 33 levels in the vertical, contains an aerosol model that generates aerosol fields from emissions and a “light” chemistry mechanism designed to support the aerosol model but with prescribed ozone. In Part I, the quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode – with prescribed sea surface temperatures (SSTs) and sea ice distribution – is described and compared with previous GFDL models and with the CMIP5 archive of AMIP simulations. The model's Cess sensitivity (response in the top-of-atmosphere radiative flux to uniform warming of SSTs) and effective radiative forcing are also presented. In Part II, the model formulation is described more fully and key sensitivities to aspects of the model formulation are discussed, along with the approach to model tuning.
In Part II of this two-part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part I. Part II provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.
The Tropical Cyclones (TC) that form over the warm waters in the Gulf of Mexico region pose a major threat to the surrounding coastal communities. Skillful sub-seasonal prediction of TC activity is important for early preparedness and reducing the TC damage in this region. In this study, we evaluate the performance of a 25-km resolution Geophysical Fluid Dynamics Laboratory (GFDL) High Resolution Atmospheric Model (HiRAM) in simulating the modulation of the TC activity in the Gulf of Mexico and western Caribbean Sea by the Intraseasonal Oscillation (ISO) based on multi-year retrospective seasonal predictions. We demonstrate that the HiRAM faithfully captures the observed influence of ISO on TC activity over the region of interest, including the formation of tropical storms and (major) hurricanes, as well as the landfalling storms. This is likely because of the realistic representation of the large-scale anomalies associated with boreal summer ISO over Northeast Pacific in HiRAM, especially the enhanced (reduced) moisture throughout the troposphere during the convectively enhanced (suppressed) phase of ISO. The reasonable performance of HiRAM suggests its potential for the subseasonal prediction of regional TC risk.
The modon, a pair of counter-rotating vortices propelling one another along a straight line, is an idealization of some observed large- and small-scale atmospheric and oceanic processes (e.g., twin cyclones), providing a challenging nonlinear test for fluid-dynamics solvers (known as “dynamical cores”). We present an easy-to-setup test of colliding modons suitable for both shallow-water and three-dimensional dynamical cores on the sphere. Two pairs of idealized modons are configured to collide, exchange vortices, and depart in opposite directions, repeating indefinitely in the absence of ambient rotation. This test is applicable to both hydrostatic and nonhydrostatic dynamical cores and particularly challenging for refined grids on the sphere, regardless of solution methodology or vertical coordinate.
We applied this test to three popular dynamical cores, used by three different general circulation models: the spectral element core of the Community Atmosphere Model, the Geophysical Fluid Dynamics Laboratory (GFDL) spectral core, and the GFDL finite-volume cubed-sphere core, FV3. Tests with a locally-refined grid and nonhydrostatic dynamics were also performed with FV3. All cores tested were able to capture the propagation, collision, and exchange of the modons, albeit the rate at which the modon was diffused varied between the three cores and showed a strong dependence on the strength of hyper-diffusion.
Ullrich, Paul A., Christiane Jablonowski, James Kent, Peter H Lauritzen, Ramachandran Nair, Kevin A Reed, Colin M Zarzycki, David M Hall, Don Dazlich, Ross Heikes, Celal Konor, David A Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, and Lucas Harris, et al., December 2017: DCMIP2016: a review of non-hydrostatic dynamical core design and intercomparison of participating models. Geoscientific Model Development, 10(12), DOI:10.5194/gmd-10-4477-2017. Abstract
Atmospheric dynamical cores are a fundamental component of global atmospheric modeling systems and are responsible for capturing the dynamical behavior of the Earth's atmosphere via numerical integration of the Navier–Stokes equations. These systems have existed in one form or another for over half of a century, with the earliest discretizations having now evolved into a complex ecosystem of algorithms and computational strategies. In essence, no two dynamical cores are alike, and their individual successes suggest that no perfect model exists. To better understand modern dynamical cores, this paper aims to provide a comprehensive review of 11 non-hydrostatic dynamical cores, drawn from modeling centers and groups that participated in the 2016 Dynamical Core Model Intercomparison Project (DCMIP) workshop and summer school. This review includes a choice of model grid, variable placement, vertical coordinate, prognostic equations, temporal discretization, and the diffusion, stabilization, filters, and fixers employed by each system.
An analytic Schmidt transformation is used to create locally-refined global model grids capable of efficient climate simulation with grid-cell-widths as small as 10 km in the GFDL HIRAM model. This method of grid stretching produces a grid which varies very gradually into the region of enhanced resolution without changing the topology of the model grid and does not require radical changes to the solver. AMIP integrations were carried out with two grids stretched to 10 km minimum grid-cell width: one centered over east Asia and the western Pacific warm pool, and the other over the continental United States. Robust improvements to orographic precipitation, the diurnal cycle of warm-season continental precipitation, and tropical cyclone maximum intensity were found in the region of enhanced resolution, compared to 25-km uniform-resolution HiRAM. The variations in grid size were not found to create apparent grid artifacts, and in some measures the global-mean climate improved in the stretched-grid simulations. In the enhanced-resolution regions, the number of tropical cyclones was reduced, but the fraction of storms reaching hurricane intensity increased, compared to a uniform-resolution simulation. This behavior was also found in a stretched-grid perpetual-September aquaplanet simulation with 12-km resolution over a part of the tropics. Furthermore, the stretched-grid aquaplanet simulation was also largely free of grid artifacts except for an artificial Walker-type circulation, and simulated an ITCZ in its unrefined region more resembling that of higher-resolution Aquaplanet simulations, implying that the unrefined region may also be improved in stretched-grid simulations. The improvements due to stretching are attributable to improved resolution as these stretched-grid simulations were sparingly tuned.
Scott, R K., Lucas Harris, and Lorenzo M Polvani, January 2016: A test case for the inviscid shallow water equations on the sphere. Quarterly Journal of the Royal Meteorological Society, 142(694), DOI:10.1002/qj.2667. Abstract
A numerically converged solution to the inviscid global shallow water equations on a predefined time interval is documented to provide a convenient benchmark for model validation. The solution is based on the same initial conditions as a previously documented solution for the viscous equations. The solution is computed using two independent numerical schemes, one a pseudospectral scheme based on an expansion in spherical harmonics, the other a finite-volume scheme on a cubed-sphere grid. Flow fields and various integral norms are both documented to facilitate model comparison and validation. Attention is drawn to the utility of the potential vorticity supremum as a convenient and sensitive test of numerical convergence, in which the exact value is known a priori over the entire time interval.
This study aims to assess whether, and the extent to which, an increase in atmospheric resolution in versions of the Geophysical Fluid Dynamics Laboratory (GFDL) High-Resolution Forecast-oriented Low Ocean Resolution Version of CM2.5 (FLOR) with 50 km and HiFLOR with 25 km improves the simulation of the El Niño Southern Oscillation-tropical cyclone (ENSO-TC) connections in the western North Pacific (WNP). HiFLOR simulates better ENSO-TC connections in the WNP including TC track density, genesis and landfall than FLOR in both long-term control experiments and sea surface temperature (SST)- and sea surface salinity (SSS)-restoring historical runs (1971-2012). Restoring experiments are performed with SSS and SST restored to observational estimates of climatological SSS and interannually-varying monthly SST. In the control experiments of HiFLOR, an improved simulation of the Walker circulation arising from more realistic SST and precipitation is largely responsible for its better performance in simulating ENSO-TC connections in the WNP. In the SST-restoring experiments of HiFLOR, more realistic Walker circulation and steering flow during El Niño/La Niña are responsible for the improved simulation of ENSO-TC connections in the WNP. The improved simulation of ENSO-TC connections with HiFLOR arises from a better representation of SST and better responses of environmental large-scale circulation to SST anomalies associated with El Niño/La Niña. A better representation of ENSO-TC connections in HiFLOR can benefit the seasonal forecasting of TC genesis, track and landfall, improve our understanding of the interannual variation of TC activity, and provide better projection of TC activity under climate change.
The Arctic troposphere has warmed faster than the global average over the last several decades. It was suggested that atmospheric northward energy transport (ANET) into the Arctic had contributed to tropospheric warming in the Arctic. Here we calculate ANET based on the NCEP/NCAR reanalysis data from 1979 to 2012. During this period the zonally integrated energy flux into the Arctic has decreased rather than increased in all seasons. However, the trends are statistically insignificant except for the winter and annual mean fluxes. For the winter season, the transient eddy flux of energy increases over Greenland and the Greenland Sea and decreases over west-central Siberia (WCS). Trends in meridional wind variance and vorticity also indicate increasing transient eddy activity over Northern Canada, the Greenland Sea and the Norwegian Sea and decreasing activity over WCS. Inter-winter variations in local vorticity over the WCS are significantly anti-correlated with the Arctic climate.
A new high-resolution Geophysical Fluid Dynamics Laboratory (GFDL) coupled model (HiFLOR) has been developed and used to investigate potential skill in simulation and prediction of tropical cyclone (TC) activity. HiFLOR comprises of high-resolution (~25-km mesh) atmosphere and land components and a more moderate-resolution (~100-km mesh) sea ice and ocean components. HiFLOR was developed from the Forecast Oriented Low Resolution Ocean model (FLOR) by decreasing the horizontal grid spacing of the atmospheric component from 50-km to 25-km, while leaving most of the sub-gridscale physical parameterizations unchanged. Compared with FLOR, HiFLOR yields a more realistic simulation of the structure, global distribution, and seasonal and interannual variations of TCs, and a comparable simulation of storm-induced cold wakes and TC-genesis modulation induced by the Madden Julian Oscillation (MJO). Moreover, HiFLOR is able to simulate and predict extremely intense TCs (categories 4 and 5) and their interannual variations, which represents the first time a global coupled model has been able to simulate such extremely intense TCs in a multi-century simulation, sea surface temperature restoring simulations, and retrospective seasonal predictions.
While tropical cyclone (TC) prediction, in particular TC genesis, remains very challenging, accurate prediction of TCs is critical for timely preparedness and mitigation. Using a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the authors studied the predictability of two destructive landfall TCs, Hurricane Sandy in 2012 and Super Typhoon Haiyan in 2013. Results demonstrate that the geneses of these two TCs are highly predictable with the maximum prediction lead-time reaching 11 days. The “beyond weather time scale” predictability of tropical cyclogenesis is primarily attributed to the model’s skillful prediction of the intraseasonal Madden-Julian Oscillation (MJO) and the westward propagation of easterly waves. Meanwhile, the landfall location and time can be predicted one week ahead for Sandy’s U.S landfall, and two weeks ahead for Haiyan’s landing in the Philippines. The success in predicting Sandy and Haiyan, together with low false alarms, indicates the potential using the GFDL coupled model for operational extended-range predictions of TCs.
A two-way nested-grid version of the Geophysical Fluid Dynamics Laboratory High Resolution Atmosphere Model (HiRAM) has been developed which uses simple methods for providing nested-grid boundary conditions and mass-conserving nested-to-global communication. Nested-grid simulations over the Maritime Continent and over North America were performed, each at two different resolutions: a 110-km mean grid-cell-width refined by a factor of three, and a 50-km mean grid-cell-width refined by a factor of two. Nested grid simulations were compared against uniform-resolution simulations, and against reanalyses, to determine the effect of grid nesting on both the modeled global climate and on the simulation of small-scale features.
Orographically-forced precipitation was robustly found to be simulated with more detail and greater realism in a nested grid simulation compared with when only the coarse grids were simulated alone. Tropical precipitation biases were reduced in the Maritime continent region when a nested grid was introduced. Both results were robust to changes in the nested grid parameterization tunings. In North America, cold-season orographic precipitation was improved by nesting, but precipitation biases in the central and eastern United States were little changed. Improving the resolution through nesting also allowed for more intense rainfall events, greater Kelvin-wave activity, and stronger tropical cyclones. Nested grid boundary artifacts were more pronounced when a one-way, noninteractive nested grid was used.
A nested-grid model is constructed using the Geophysical Fluid Dynamics Laboratory Finite-Volume dynamical core on the cubed sphere. The use of a global grid avoids the need for externally-imposed lateral boundary conditions, and the use of the same governing equations and discretization on the global and regional domains prevents inconsistencies that may arise when these differ between grids. A simple interpolated nested-grid boundary condition is used, and two-way updates use a finite-volume averaging method. Mass conservation is achieved in two-way nesting by simply not updating the mass field.
Despite the simplicity of the nesting methodology, the distortion of the large-scale flow by the nested grid is such that the increase in global error norms is a factor of two or less in shallow-water test cases. The effect of a nested grid in the tropics on the zonal means and eddy statistics of an idealized Held-Suarez climate integration is minor, and artifacts due to the nested grid are comparable to those at the edges of the cubed-sphere grid and decrease with increasing resolution. The baroclinic wave train in a Jablonowski-Williamson test case was preserved in a nested-grid simulation while fine-scale features were represented with greater detail in the nested-grid region. We also found that lee vortices could propagate out of the nested region and onto a coarse grid which by itself could not produce vortices. Finally, we discuss how concurrent integration of the nested and coarse grids can be significantly more efficient than when integrating the two grids sequentially.
Harris, Lucas, Peter H Lauritzen, and R Mittal, February 2011: A flux-form version of the conservative semi-Lagrangian multi-tracer transport scheme (CSLAM) on the cubed sphere grid. Journal of Computational Physics, 230(4), DOI:10.1016/j.jcp.2010.11.001. Abstract
A conservative semi-Lagrangian cell-integrated transport scheme (CSLAM) was recently introduced, which ensures global mass conservation and allows long timesteps, multi-tracer efficiency, and shape preservation through the use of reconstruction filtering. This method is fully two-dimensional so that it may be easily implemented on non-cartesian grids such as the cubed-sphere grid. We present a flux-form implementation, FF-CSLAM, which retains the advantages of CSLAM while also allowing the use of flux-limited monotonicity and positivity preservation and efficient tracer sub-cycling. The methods are equivalent in the absence of flux limiting or reconstruction filtering.
FF-CSLAM was found to be third-order accurate when an appropriately smooth initial mass distribution and flow field (with at least a continuous second derivative) was used. This was true even when using highly deformational flows and when the distribution is advected over the singularities in the cubed sphere, the latter a consequence of the full two-dimensionality of the method. Flux-limited monotonicity preservation, which is only available in a flux-form method, was found to be both less diffusive and more efficient than the monotone reconstruction filtering available to CSLAM. Despite the additional overhead of computing fluxes compared to CSLAM's cell integrations, the non-monotone FF-CSLAM was found to be at most only 40% slower than CSLAM for Courant numbers less than one, with greater overhead for successively larger Courant numbers.
Most mesoscale models can be run with either one-way (parasitic) or two-way (interactive) grid nesting. This paper presents results from a linear 1D shallow-water model to determine whether the choice of nesting method can have a significant impact on the solution. Two-way nesting was found to be generally superior to one-way nesting. The only situation in which one-way nesting performs better than two-way is when very poorly resolved waves strike the nest boundary. A simple filter is proposed for use exclusively on the coarse-grid values within the sponge zone of an otherwise conventional sponge boundary condition (BC). The two-way filtered sponge BC gives better results than any of the other methods considered in these tests. Results for all wavelengths were found to be robust to other changes in the formulation of the sponge boundary, particularly with the width of the sponge layer. The increased reflection for longer-wavelength disturbances in the one-way case is due to a phase difference between the coarse- and nested-grid solutions at the nested-grid boundary that accumulates because of the difference in numerical phase speeds between the grids. Reflections for two-way nesting may be estimated from the difference in numerical group velocities between the coarse and nested grids, which only becomes large for waves that are poorly resolved on the coarse grid.
