The growing demand for renewable energy underscores the importance of accurate dust forecasting in regions with abundant wind and solar resources. However, leading real-time global numerical weather prediction (NWP) models often lack dust modules due to computational constraints. Current “Near-Real-Time” dust forecasting services can only run after the completion of NWP, failing to meet the timeliness requirements for reporting power generation plans to the grids. This work proposes a global dust-weather integrated (iDust) model development paradigm, efficiently incorporating dust modules into the dynamical core. Using about one-eighth additional computing power, iDust extends global 12.5 km resolution NWP with dust prediction capabilities. iDust's forecasting abilities are evaluated against ECMWF CAMS forecast and NASA MERRA2 reanalysis, including verifications over China from March to May 2023 and three extreme dust events. Results show that iDust outperforms its counterparts in dust storm forecasting intensity and timing. Using iDust, global 12.5-km 10-day hourly dust storm forecast simulations initiated at 00UTC can produce results by 06UTC, enabling timely forecasting of severe dust storms with concentrations exceeding 1,000 μg/m3. This novel capability of iDust can meet the urgent forecasting needs of the renewable energy industry for extreme dust conditions, supporting the green energy transition.
Using the novel kilometer-scale global storm-resolving model Geophysical Fluid Dynamics Laboratory eXperimental System for High-resolution prediction on Earth-to-Local Domains (X-SHiELD), we investigate the impact of a 4 K increase in sea surface temperatures on Northern Hemisphere midlatitude cyclones, during the January 2020–January 2022 period. X-SHiELD simulations reveal a poleward shift in cyclone tracks under warming, consistent with CMIP projections. However, X-SHiELD's high resolution and explicit deep convection allowed for a detailed analysis of the warm and cold sectors, which are instead typically underrepresented in traditional CMIP models. Instead, compositing the 100 most intense midlatitude cyclones in the North Atlantic, we find that the warm sector exhibits statistically significant increases in wind speed and precipitation of up to 15% locally per degree of warming, while changes in the cold sector are less pronounced. This study demonstrates X-SHiELD's potential to provide a realistic-looking perspective into the evolving risks posed by midlatitude cyclones in a warming climate.
Santos, Luan F., Joseph Mouallem, and Pedro S Peixoto, February 2025: Analysis of finite-volume transport schemes on cubed-sphere grids and an accurate scheme for divergent winds. Journal of Computational Physics, 522, 113618, doi:10.1016/j.jcp.2024.113618. [ Abstract ]
The cubed-sphere finite-volume dynamical core (FV3), developed by GFDL-NOAA-USA, serves as the dynamical core for many models worldwide. In 2019, it was officially designated as the dynamical core for the new Global Forecast System of the National Weather Service in the USA, replacing the spectral model. The finite-volume approach employed by FV3 to solve horizontal dynamics involves the application of transport finite-volume fluxes for different variables. Hence, the transport scheme plays a key role in the model. Therefore, this work proposes to revisit the details of the transport scheme of FV3 with the aim of adding enhancements. We proposed modifications to the FV3 transport scheme, which notably enhanced accuracy, particularly in the presence of divergent winds, as evidenced by numerical experiments. In contrast to the FV3 scheme's first-order accuracy in the presence of divergent winds, the proposed scheme achieves second-order accuracy. For divergence-free winds, both schemes are second-order, with our scheme being slightly more accurate. Additionally, the proposed scheme exhibits slight computational overhead but is easily implemented in the current code. In summary, the proposed scheme offers significant improvements in accuracy, particularly in the presence of divergent winds, which are present in various atmospheric phenomena, while maintaining computational efficiency.
The Geophysical Fluid Dynamics Laboratory (GFDL)'s System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model typically uses the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis to initialize its medium-range forecasts. A data assimilation (DA) system has been implemented for the global SHiELD to demonstrate the prediction skills of the model initialized from its own analysis. The DA system leverages the advanced DA techniques used in GFS and assimilates all the observations assimilated in GFS. Compared to the forecasts initialized from GFS analysis, SHiELD forecast skills are significantly improved by using its own analysis. Remarkable improvement is found in the southern hemisphere with positive impact lasting up to 10 days. The DA system is useful in identifying and understanding model errors. The most noticeable model error detected by the DA system originates from the turbulent kinetic energy (TKE)-based moist eddy-diffusivity mass-flux vertical turbulent mixing (TKE-EDMF) scheme. The model error leads to insufficient ensemble spread. Including two versions of the TKE-EDMF scheme in the ensemble helps increase ensemble spread, further improves forecast skills and alleviate the systematic errors in marine stratocumulus regions. Applying interchannel correlated observation errors for Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) also reduces the systematic errors and improves the forecast skill up to day 5. Further investigation of the forecast errors reveals that the ensemble spread is largely affected by the parameterization of eddy diffusivity through its impact on the gradient of the model state. The systematic forecast errors in marine stratocumulus regions are associated with the vertical location of the stratocumulus cloud, which is sensitive to model vertical resolution within the cloud layer. Enhancing eddy diffusion within the cloud or near cloud top elevates cloud top but reduces cloud amount.
Barbero, Tyler W., Michael M Bell, Jan-Huey Chen, and Philip J Klotzbach, March 2024: A potential vorticity diagnosis of tropical cyclone track forecast errors. Journal of Advances in Modeling Earth Systems, 16(3), doi:10.1029/2023MS004008. [ Abstract ]
Tropical cyclone (TC) track forecasting provides essential guidance for coastal communities. However, track forecast errors still occur, highlighting the need for continued research into error sources. Piecewise potential vorticity (PV) inversion is used systematically to quantitatively diagnose errors in track forecasts in four models during the 2017 Atlantic hurricane season. The deep layer mean steering flow (DLMSF) provides a sufficient proxy for hurricane movement, and DLMSF errors are correlated with TC track errors. Analysis of track forecasts for Hurricanes Harvey, Irma, and Maria reveals that their track errors are attributed to steering errors caused by misrepresentations of specific pressure systems. Harvey's westward track error in the GFS resulted from zonal wind errors from the Continental High, while Irma's northward track error in the SHiELD gfsIC resulted from meridional wind errors in the Bermuda High and Continental High. Maria's southward track error in the IFS resulted from meridional wind errors in the Bermuda High and a misrepresentation of Jose to Maria's northwest. The mean absolute error of the DLMSF shows that the Bermuda High contributed the most to steering flow errors in the cases examined. Our results show that piecewise PV inversion can identify the sources of biases in TC track forecasts. The correction of these biases may lead to improved track forecasts. Quantitative diagnostics presented here provide useful information for future model development.
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/yx20-3k0414pp. [ Abstract ]
The Global-Nest Initiative takes new technologies developed at Geophysical Fluid Dynamics Laboratory (GFDL) and partners to create convective-scale digital twins of the earth system to better simulate and predict extreme weather events, their impacts, and their role within the broader earth system, and to create actionable information at all time scales. This annual report describes the activities and results of the NOAA Global-Nest Initiative during Fiscal Year 2022-2023.
We investigate the representation of individual supercells and intriguing tornado-like vortices in a simplified, locally refined global atmosphere model. The model, featuring grid stretching, can locally enhance the model resolution and reach cloud-resolving scales with modest computational resources. Given a conditionally unstable sheared environment, the model can simulate supercells realistically, with a near-ground vortex and funnel cloud at the center of a rotating updraft reminiscent of a tornado. An analysis of the Eulerian vertical vorticity budget suggests that the updraft core of the supercell tilts horizontal vorticity into the tornado-like vortex, which is then amplified through vertical stretching by the updraft. Results suggest that the simulated vortex is dynamically similar to observed tornadoes, as well as those simulated in modeling studies at much higher horizontal resolution. Lastly, we discuss the prospects for the study of cross-scale interactions involving supercells.
Mesoscale convective systems (MCSs) are pivotal in global energy/water cycles and typically produce extreme weather events. Despite their importance, our understanding of their future change remains limited, largely due to inadequate representation in current climate models. Here, using a global storm-resolving model that accurately simulates MCSs, we conclude contrasting responses to increased SST in their occurrence, that is, notable decreases over land but increases over ocean. This land-ocean contrast is attributed to the changes in convective available potential energy (CAPE) and convective inhibition (CIN). Over land, notable rises in CIN alongside moderate increases in CAPE effectively suppress (favor) weak to moderate (intense) MCSs, resulting in an overall reduction in MCS occurrences. In contrast, substantial increases in CAPE with minimal changes in CIN over ocean contribute to a significant rise in MCS occurrences. The divergent response in MCS occurrence has profound impacts on both mean and extreme precipitation.
Elsberry, Russell L., Hsiao-Chung Tsai, Wei-Chia Chin, and Timothy Marchok, May 2024: ECMWF ensemble forecasts of six tropical cyclones that formed during a long-lasting Rossby wave breaking event in the western North Pacific. Atmosphere, 15(5), doi:10.3390/atmos15050610. [ Abstract ]
The ECMWF‘s ensemble (ECEPS) predictions are documented for the lifecycles of six tropical cyclones (TCs) that formed during a long-lasting Rossby wave breaking event in the western North Pacific. All six TC tracks started between 20° N and 25° N, and between 136° E and 160° E. All five typhoons recurved north of 30° N, and the three typhoons that did not make landfall had long tracks to 50° N and beyond. The ECEPS weighted mean vector motion track forecasts from pre-formation onward are quite accurate, with track forecast spreads that are primarily related to initial position uncertainties. The ECEPS intensity forecasts have been validated relative to the Joint Typhoon Warning Center (JTWC) Working Best Track (WBT) intensities (when available). The key results for Tokage (11 W) were the ECEPS forecasts of the intensification to a peak intensity of 100 kt, and then a rapid decay as a cold-core cyclone. For Hinnamnor (12 W), the key result was the ECEPS intensity forecasts during the post-extratropical transition period when Hinnamnor was rapidly translating poleward through the Japan Sea. For Muifa (14 W), the key advantage of the ECEPS was that intensity guidance was provided for longer periods than the JTWC 5-day forecast. The most intriguing aspect of the ECEPS forecasts for post-Merbok (15 W) was its prediction of a transition to an intense, warm-core vortex after Merbok had moved beyond 50° N and was headed toward the Aleutian Islands. The most disappointing result was that the ECEPS over-predicted the slow intensification rate of Nanmadol (16 W) until the time-to-typhoon (T2TY), but then failed to predict the large rapid intensification (RI) following the T2TY. The tentative conclusion is that the ECEPS model‘s physics are not capable of predicting the inner-core spin-up rates when a small inner-core vortex is undergoing large RI.
Eusebi, Ryan, Gabriel A Vecchi, Ching-Yao Lai, and Mingjing Tong, January 2024: Realistic tropical cyclone wind and pressure fields can be reconstructed from sparse data using deep learning. Communications Earth and Environment, 5, 8, doi:10.1038/s43247-023-01144-2. [ Abstract ]
Tropical cyclones are responsible for large-scale loss of life and property1,2,3,4, motivating accurate risk assessment and forecasting. These objectives require accurate reconstructions of storms’ wind and pressure fields which assimilate real-time observations5,6,7,8,9, but current methods used for these reconstructions remain computationally expensive and limited10. Here, we show that a physics-informed neural network11,12 can be a promising and computationally efficient algorithm for tropical cyclone data assimilation. Using synthetic training data sparsely sampled from hurricanes simulated in a forecast model, a physics-informed neural network is able to reconstruct full realistic 2- and 3-dimensional wind and pressure fields which capture key features of the cyclone. We also demonstrate how a set of sparse, real-time observations, can be used to accurately reconstruct Hurricane Ida. Our results highlight how recent advances in deep learning can augment data assimilation schemes. The methods are also general and can be applied to other flow problems.
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.
Finger-like km-scale features have been observed along the inner-edge of the eyewall of intense hurricanes. But due to the limited availability of observations, many important aspects of these features remain unknown. In this study, we aim to offer insights on the nature of these phenomena based on a four-day-duration O(100 m) grid spacing simulation that covers the inner-core region of an idealized hurricane. The simulation successfully captured the finger-like features, which closely resemble observed ones. We propose that these features are formed due to the shear instability associated with vertical distribution of the tangential wind in the inner-core region. This proposed mechanism offers insights on several key characteristics of the features of interest, including their emergence time, frequency, radial location and vertical extent. Our study also demonstrates the feasibility of using multi-level nesting for O(100 m) grid spacing hurricane simulations and predictions, aligning with the goals for next generation hurricane models.
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.
McTaggart-Cowan, Ron, David S Nolan, Rabah Aider, Martin Charron, and Jan-Huey Chen, et al., March 2024: Reducing a tropical cyclone weak-intensity bias in a global numerical weather prediction system. Monthly Weather Review, 152(3), doi:10.1175/MWR-D-23-0193.1837-863. [ Abstract ]
The operational Canadian Global Deterministic Prediction System suffers from a weak-intensity bias for simulated tropical cyclones. The presence of this bias is confirmed in progressively simplified experiments using a hierarchical system development technique. Within a semi-idealized, simplified-physics framework, an unexpected insensitivity to the representation of relevant physical processes leads to investigation of the model’s semi-Lagrangian dynamical core. The root cause of the weak-intensity bias is identified as excessive numerical dissipation caused by substantial off-centering in the two time-level time integration scheme used to solve the governing equations. Any (semi)implicit semi-Lagrangian model that employs such off-centering to enhance numerical stability will be afflicted by a misalignment of the pressure gradient force in strong vortices. Although the associated drag is maximized in the tropical cyclone eyewall, the impact on storm intensity can be mitigated through an intercomparison-constrained adjustment of the model’s temporal discretization. The revised configuration is more sensitive to changes in physical parameterizations and simulated tropical cyclone intensities are improved at each step of increasing experimental complexity. Although some rebalancing of the operational system may be required to adapt to the increased effective resolution, significant reduction of the weak-intensity bias will improve the quality of Canadian guidance for global tropical cyclone forecasting.
Menemenlis, Sofia, Gabriel A Vecchi, Kun Gao, James A Smith, and Kai-Yuan Cheng, July 2024: Extreme rainfall risk in Hurricane Ida's extratropical stage: An analysis with convection-permitting ensemble hindcasts. Journal of the Atmospheric Sciences, 81(7), doi:10.1175/JAS-D-23-0160.1. [ Abstract ]
The extratropical stage of Hurricane Ida (2021) brought extreme subdaily rainfall and devastating flooding to parts of eastern Pennsylvania, New Jersey, and New York. We investigate the predictability and character of this event using 31-member ensembles of perturbed initial condition hindcasts with the Tropical Atlantic version of GFDL’s System for High-resolution prediction on Earth-to-Local Domains (T-SHiELD), a ∼13-km global weather forecast model with a ∼3-km nested grid. At lead times of up to 4 days, the ensembles are able to capture the most extreme observed hourly and daily rainfall accumulations but are negatively biased in the spatial extent of heavy precipitation. Large intraensemble differences in the magnitudes and locations of simulated extremes suggest that although impacts were highly localized, risks were widespread. In Ida’s tropical stage, interensemble spread in extreme hourly rainfall is well predicted by large-scale moisture convergence; by contrast, in Ida’s extratropical stage, the most extreme rainfall is governed by mesoscale processes that exhibit chaotic and diverse forms across the ensembles. Our results are relevant to forecasting and communication in advance of extratropical transition and imply that flood preparedness efforts should account for the widespread possibility of severe localized impacts.
The climate simulation frontier of a global storm-resolving model (GSRM; or k-scale model because of its kilometer-scale horizontal resolution) is deployed for climate change simulations. The climate sensitivity, effective radiative forcing, and relative humidity changes are assessed in multiyear atmospheric GSRM simulations with perturbed sea-surface temperatures and/or carbon dioxide concentrations. Our comparisons to conventional climate model results can build confidence in the existing climate models or highlight important areas for additional research. This GSRM’s climate sensitivity is within the range of conventional climate models, although on the lower end as the result of neutral, rather than amplifying, shortwave feedbacks. Its radiative forcing from carbon dioxide is higher than conventional climate models, and this arises from a bias in climatological clouds and an explicitly simulated high-cloud adjustment. Last, the pattern and magnitude of relative humidity changes, simulated with greater fidelity via explicitly resolving convection, are notably similar to conventional climate models.
Global storm-resolving model (GSRM) simulations (kilometer-scale horizontal resolution) of the atmosphere can capture the interaction between the scales of deep cumulus convection and the large-scale dynamics and thermodynamic properties of the atmosphere. Here, we assess the vertical structure of tropical temperature change in Geophysical Fluid Dynamics Laboratory's GSRM X-SHiELD, perturbed by a uniform sea surface temperature (SST) warming and/or increased CO2 concentration. The simulated warming from an SST increase is weakly amplified relative to the surface through the mid-troposphere before increasing to a factor of about 2.5 in the upper troposphere. This combination of muted warming in the mid-troposphere and amplified warming aloft is within the range of CMIP6 models at individual pressure levels but, taken together, is distinctive behavior. The response to CO2 increase with unchanged SST is an approximately vertically uniform warming, comparable to CMIP6 models, and is linearly additive with the SST-induced warming in X-SHiELD.
Mouallem, Joseph, November 2024: In Running SHiELD with GFDL's FMS full coupler infrastructure, Princeton, NJ, NOAA Technical Memorandum OAR GFDL, 2024-002, doi:10.25923/ezfm-az21. [ Abstract ]
This document outlines the technical requirements and upgrades implemented across various code repositories to enable the FV3-based
atmospheric model, SHiELD, to run with the FMS full coupler infrastructure. It provides an overview of the build environment and the simplifed SHiELD coupler. It details the necessary modifcations for transitioning to the FMS full coupler infrastructure and ofers recommendations for future work to ensure the seamless integration of SHiELD into GFDL’s modeling suite.
Wang, Hai, Xiao-Tong Zheng, Wenju Cai, Zi-Wen Han, Shang-Ping Xie, Sarah M Kang, Yu-Fan Geng, Fukai Liu, Chuan-Yang Wang, Yue Wu, Baoqiang Xiang, and Lei Zhou, May 2024: Atmosphere teleconnections from abatement of China aerosol emissions exacerbate Northeast Pacific warm blob events. Proceedings of the National Academy of Sciences, 121(21), doi:10.1073/pnas.2313797121. [ Abstract ]
During 2010 to 2020, Northeast Pacific (NEP) sea surface temperature (SST) experienced the warmest decade ever recorded, manifested in several extreme marine heatwaves, referred to as “warm blob” events, which severely affect marine ecosystems and extreme weather along the west coast of North America. While year-to-year internal climate variability has been suggested as a cause of individual events, the causes of the continuous dramatic NEP SST warming remain elusive. Here, we show that other than the greenhouse gas (GHG) forcing, rapid aerosol abatement in China over the period likely plays an important role. Anomalous tropospheric warming induced by declining aerosols in China generated atmospheric teleconnections from East Asia to the NEP, featuring an intensified and southward-shifted Aleutian Low. The associated atmospheric circulation anomaly weakens the climatological westerlies in the NEP and warms the SST there by suppressing the evaporative cooling. The aerosol-induced mean warming of the NEP SST, along with internal climate variability and the GHG-induced warming, made the warm blob events more frequent and intense during 2010 to 2020. As anthropogenic aerosol emissions continue to decrease, there is likely to be an increase in NEP warm blob events, disproportionately large beyond the direct radiative effects.
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.
Boreal summer intraseasonal oscillation (BSISO) is a primary source of predictability for summertime weather and climate on the subseasonal-to-seasonal (S2S) time scale. Using the GFDL SPEAR S2S prediction system, we evaluate the BSISO prediction skills based on 20-yr (2000–19) hindcast experiments with initializations from May to October. It is revealed that the overall BSISO prediction skill using all hindcasts reaches out to 22 days as measured by BSISO indices before the bivariate anomalous correlation coefficient (ACC) drops below 0.5. Results also show that the northeastward-propagating canonical BSISO (CB) event has a higher prediction skill than the northward dipole BSISO (DB) event (28 vs 23 days). This is attributed to CB’s more periodic nature, resulting in its longer persistence, while DB events are more episodic accompanied by a rapid demise after reaching maximum enhanced convection over the equatorial Indian Ocean. From a forecaster’s perspective, a precursory strong Kelvin wave component in the equatorial western Pacific signifies the subsequent development of a CB event, which is likely more predictable. Investigation of individual CB events shows a large interevent spread in terms of their prediction skills. For CB, the events with weaker and fluctuating amplitude during their lifetime have relatively lower prediction skills likely linked to their weaker convection–circulation coupling. Interestingly, the prediction skills of individual CB events tend to be relatively higher and less scattered during late summer (August–October) than those in early summer (May–July), suggestive of the seasonal modulation on the evolution and predictability of BSISO.
Yang, Jing, Tao Zhu, Frederic Vitart, Bin Wang, Baoqiang Xiang, Qing Bao, and June-Yi Lee, July 2024: Synchronous Eurasian heat extremes tied to boreal summer combined extratropical intraseasonal waves. npj Climate and Atmospheric Science, 7, 169, doi:10.1038/s41612-024-00714-1. [ Abstract ]
Heat extremes frequently hit different regions synchronously during boreal summer over the Eurasian continent. A remarkable coupling is first revealed between Eurasian heat extreme occurrence and individual extratropical intraseasonal oscillation (EISO). Further, the combined EISOs facilitate and largely increase the occurrence probabilities of synchronous Eurasian heat extremes. These dominant combined EISOs together contribute 20–45% to the total heat extreme days over the five Eurasian regions where the climatological heat extremes occur most frequently. A multi-model hindcast further shows that the subseasonal prediction exhibits higher skills for synchronous heat extremes over the combined-EISO hotspot regions when the associated combined EISOs are active, supporting the notion that the monitoring and prediction of EISOs are crucial for heat extremes’ early warning. Skillful prediction of EISOs opens a pathway for heat extremes’ prediction by extending it from the weather to the subseasonal timescales.
Yoon, Donghyuck, Jan-Huey Chen, and Eunkyo Seo, December 2024: Contribution of land-atmosphere coupling in 2022 CONUS compound drought-heatwave events and implications for forecasting. Weather and Climate Extremes, 46, 100722, doi:10.1016/j.wace.2024.100722. [ Abstract ]
Severe compound drought-heatwave events were observed over three regions of the Contiguous United States (CONUS), Northwest (NW), Great Plains (GP), and Northeast (NE) regions, during July and August 2022. In this study, we have found that the developments of these drought-heatwave events were shaped by different land-atmosphere coupling behaviors which are associated with water and energy limitation regimes in these regions. In the NW and GP regions, the surface soil moisture (SM) and evapotranspiration (ET) were coupled through water-limited processes. Heatwaves in these two regions were affected by the decrease of ET and the available SM due to the precipitation deficit. This type of land-atmosphere coupling was especially prominent in the GP. In the NE region, the heatwave governed ET through the increase of potential ET (PET) based on energy-limited coupling, which played a crucial role in the development of drought.
The impacts of the different land-atmosphere coupling behaviors on the predictability of the 13-km Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) were also investigated by checking its 10-day forecasts during the same period. The analysis was particularly focused on the GP and NE regions, where different land-atmosphere coupling behaviors were observed. The model's warm bias in the GP region was associated with the overestimated net radiation, and the bias was further amplified through the water-limited coupling. In the NE region, the PET-related variables, including surface air temperature, influenced the predictability of drought onset by limiting ET through the energy-limited coupling. Based on our findings, this study highlights the crucial role of land-atmosphere coupling behaviors and provides a scientific strategy for enhancing the model predictability of compound drought-heatwaves.
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.
Zhou, Xuan, Lu Wang, Pang-Chi Hsu, Tim Li, and Baoqiang Xiang, October 2024: Understanding the factors controlling MJO prediction skill across events. Journal of Climate, 37(20), doi:10.1175/JCLI-D-23-0635.15323-5336. [ Abstract ]
The prediction skill for individual Madden–Julian oscillation (MJO) events is highly variable, but the key factors behind this remain unclear. Using the latest hindcast results from the subseasonal-to-seasonal (S2S) phase II models, this study attempts to understand the diverse prediction skill for the MJO events with an enhanced convective anomaly over the eastern Indian Ocean (IO) at the forecast start date, by investigating the preference of the prediction skill to the MJO-associated convective anomalies and low-frequency background states (LFBS). Compared to the low-skill MJO events, the high-skill events are characterized by a stronger intraseasonal convection–circulation couplet over the IO before the forecast start date, which could result in a longer zonal propagation range during the forecast period, thereby leading to a higher score for assessing the prediction skill. The difference in intraseasonal fields can further be attributed to the LFBS of IO sea surface temperature (SST) and quasi-biannual oscillation (QBO), with the high-skill (low-skill) events corresponding to a warmer (colder) IO and easterly (westerly) QBO phase. The physical link is that a warm IO could increase the low-level convective instability and thus amplify MJO convection over the IO, whereas an easterly QBO phase could weaken the Maritime Continent barrier effect by weakening the static stability near the tropopause, thus favoring eastward propagation of the MJO. It is also found that the combined effects of IO SST and QBO phases are more effective in influencing MJO prediction skill than individual LFBS.
We introduce a 6.5-km version of the Geophysical Fluid Dynamics Laboratory (GFDL)'s System for High-resolution prediction on Earth-to-Local Domains (SHiELD). This global model is designed to bridge the gap between global medium-range weather prediction and global storm-resolving simulation while remaining practical for real-time forecast. The 6.5-km SHiELD represents a significant advancement over GFDL's flagship global forecast system, the 13-km SHiELD. This global model features a holistically-developed scale-aware suite of physical parameterizations, stepping into the formidable convective “gray zone” of resolutions below 10 km. Comparative analyses with the 13-km SHiELD, conducted over a 3-year hindcast period, highlight noteworthy improvements across global-scale, regional-scale, tropical cyclone (TC), and continental convection predictions. In particular, the 6.5-km SHiELD excels in predicting considerably finer-scale convective systems associated with large-scale frontal systems and extratropical cyclones. The predictions of global temperature, wind, cloud, and precipitation are significantly improved in this global model. Regionally, over the contiguous United States and the Maritime Continent, substantial reductions in prediction biases of precipitation, cloud cover, and wind fields are also found. In the mesoscale realm, the model demonstrates prominent improvements in global TC intensity and continental convective precipitation prediction: biases are relieved, and skill is higher. These findings affirm the superiority of the 6.5-km SHiELD compared to the current 13-km SHiELD, which will advance weather prediction by successfully addressing both synoptic weather systems and specific storm-scale phenomena in the same global model.
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.
Cai, Wenju, Libao Gao, Yiyong Luo, Xichen Li, Xiao-Tong Zheng, Xuebin Zhang, Xuhua Cheng, Fan Jia, Ariaan Purich, Agus Santoso, Yan Du, David M Holland, Jia-Rui Shi, Baoqiang Xiang, and Shang-Ping Xie, May 2023: Southern Ocean warming and its climatic impacts. Science Bulletin, 68(9), doi:10.1016/j.scib.2023.03.049946-960. [ Abstract ]
The Southern Ocean has warmed substantially, and up to early 21st century, Antarctic stratospheric ozone depletion and increasing atmospheric CO2 have conspired to intensify Southern Ocean warming. Despite a projected ozone recovery, fluxes to the Southern Ocean of radiative heat and freshwater from enhanced precipitation and melting sea ice, ice shelves, and ice sheets are expected to increase, as is a Southern Ocean westerly poleward intensification. The warming has far-reaching climatic implications for melt of Antarctic ice shelf and ice sheet, sea level rise, and remote circulations such as the intertropical convergence zone and tropical ocean-atmosphere circulations, which affect extreme weathers, agriculture, and ecosystems. The surface warm and freshwater anomalies are advected northward by the mean circulation and deposited into the ocean interior with a zonal-mean maximum at ∼45°S. The increased momentum and buoyancy fluxes enhance the Southern Ocean circulation and water mass transformation, further increasing the heat uptake. Complex processes that operate but poorly understood include interactive ice shelves and ice sheets, oceanic eddies, tropical-polar interactions, and impact of the Southern Ocean response on the climate change forcing itself; in particular, limited observations and low resolution of climate models hinder rapid progress. Thus, projection of Southern Ocean warming will likely remain uncertain, but recent community effort has laid a solid foundation for substantial progress.
Chen, Jan-Huey, Linjiong Zhou, Linus Magnusson, Ron McTaggart-Cowan, and M Köhler, July 2023: Tropical cyclone forecasts in the DIMOSIC project—medium-range forecast models with common initial conditions. Earth and Space Science, 10(7), doi:10.1029/2023EA002821. [ Abstract ]
The tropical cyclone (TC) forecast skill of the eight global medium-range forecast models which are participating in the DIfferent Models, Same Initial Conditions project is investigated in this study. Each model was used to generate 10-day forecasts from the same initial conditions provided by the European Centre for Medium-Range Weather Forecasts. There are a total of 123 initial dates spanning in one year from June 2018 to June 2019 at 3-day intervals. The TC track and intensity forecasts are evaluated against the best track data set. TC-related precipitation and tropical cyclogenesis forecasts are also compared to explore the differences and similarities of TC forecasts across the models. This comparison of TC forecasts allows model developers in different centers to benchmark their model against other models, with the impact of the initial condition quality removed. The verifications reveal that most models show slow-moving and right-of-track biases in their TC track forecasts. Also, a common dry bias in TC-related precipitation indicates a general deficiency in TC intensity and convection in the models which should be related to insufficient model resolution. These findings provide important references for future model developments.
Though tropical cyclone (TC) models have been routinely evaluated against track and intensity observations, little work has been performed to validate modeled TC wind fields over land. In this paper, we present a simple framework for evaluating simulated low-level inland winds with in-situ observations and existing TC structure theory. The Automated Surface Observing Systems, Florida Coastal Monitoring Program, and best track data are used to generate a theory-predicted wind profile that reasonably represents the observed radial distribution of TC wind speeds. We quantitatively and qualitatively evaluated the modeled inland TC wind fields, and described the model performance with a set of simple indicators. The framework was used to examine the performance of a high-resolution two-way nested Geophysical Fluid Dynamics Laboratory model on recent U.S. landfalling TCs. Results demonstrate the capacity of using this framework to assess the modeled TC low-level wind field in the absence of dense inland observations.
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.
Elsberry, Russell L., Hsiao-Chung Tsai, Corie Capalbo, Wei-Chia Chin, and Timothy Marchok, March 2023: Critical pre-formation decision flowchart to apply tropical cyclone lifecycle predictions in eastern North Pacific. Atmosphere, 14(4), 616, doi:10.3390/atmos14040616. [ Abstract ]
We have previously demonstrated that the ECMWF ensemble (ECEPS) provides early forecasts not only of the Time-to-Tropical Storm (T2TS) and of the Time-to-Hurricane (T2HU), but also of the Time-Ending-Hurricane (TEHU) and Time-Ending-Tropical Storm (TETS) times and positions along those 15-day ECEPS track forecasts, which then provides an opportunity for high-wind warnings along the path during the entire lifecycle of these Hurricanes. The focus in this study is the Decision Flowchart that has been developed to assist the forecasters to select the pre-formation disturbance that is most likely to become the next Tropical Storm with the potential to become a Hurricane. The most crucial decision is to detect and eliminate any disturbance that likely originated from a precursor Caribbean false alarm circulation. Summaries of other steps in the Decision Flowchart “To Watch”, or to reject, other storm options in the twice-daily ECEPS forecasts are provided for Hurricanes Enrique and Felicia, and for strong Tropical Storm Guillermo and weak Tropical Storm Jimena. The first detections in the ECEPS forecasts for the Tropical Storms during the 2021 season averaged 6 days, 18 h in advance with a range of only 2 days, 6 h in advance for TS Terry to 9 days, 18 h in advance for TS Sandra.
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.
Kim, Jiyeong, Sarah M Kang, Shang-Ping Xie, Baoqiang Xiang, Doyeon Kim, Xiao-Tong Zheng, and Hai Wang, June 2023: Large-scale climate response to regionally confined extratropical cooling: effect of ocean dynamics. Climate Dynamics, 60, doi:10.1007/s00382-022-06500-03291-3306. [ Abstract ]
This study investigates the effect of ocean dynamics on the tropical climate response to localized radiative cooling over three northern extratropical land regions using hierarchical model simulations that vary in the degree of ocean coupling. Without ocean dynamics, the tropical climate response is independent of the extratropical forcing location, characterized by a southward tropical precipitation shift with a high degree of zonal symmetry, a reduced zonal sea surface temperature gradient along the equatorial Pacific, and the eastward-shifted Walker circulation. When ocean dynamical adjustments are allowed, the zonal-mean tropical precipitation shift is damped primarily via Eulerian-mean ocean heat transport. The oceanic damping effect is strongest (weakest) for North Asian (American) cooling, associated with the largest (smallest) Eulerian-mean ocean heat transport across the equatorial Pacific. The cross-equatorial ocean heat transport in the Pacific is anchored to the North Pacific subtropical high, the response of which can be inferred from the corresponding slab ocean simulations. Hence, the slab ocean simulations provide useful a priori prediction for oceanic damping efficiency. Ocean dynamics also modulates the spatial pattern of climate response in a distinct manner depending on the zonal distribution of imposed forcing. North Asian forcing induces a pronounced eastern equatorial Pacific cooling extending to the western basin, accompanying the westward shifted Walker circulation. European forcing causes cooling confined to the eastern equatorial Pacific and strengthens the Walker circulation. The tropical precipitation response in these two cases exhibits large zonal variations with a high degree of equatorial symmetry, being essentially uncorrelated with the corresponding slab ocean simulations. By contrast, North American forcing induces a sufficiently strong inter-hemispheric contrast in the tropical Pacific SST response, due to the relatively weak oceanic damping effect, producing a weaker but spatially similar tropical response to that in the slab ocean simulation. This study demonstrates that the effect of ocean dynamics in modulating the tropical climate response depends on the extratropical forcing location. The results are relevant for understanding the distinct climate response induced by aerosols from different continental sites.
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.
Wang, Bin, Weiyi Sun, Chunhan Jin, Xiao Luo, Young-Min Yang, Tim Li, Baoqiang Xiang, Michael J McPhaden, Mark Cane, Fei-Fei Jin, Fei Liu, and Jian Liu, September 2023: Understanding the recent increase in multiyear La Niñas. Nature Climate Change, doi:10.1038/s41558-023-01801-6. [ Abstract ]
Five out of six La Niña events since 1998 have lasted two to three years. Why so many long-lasting multiyear La Niña events have emerged recently and whether they will become more common remains unknown. Here we show that ten multiyear La Niña events over the past century had an accelerated trend, with eight of these occurring after 1970. The two types of multiyear La Niña events over this time period followed either a super El Niño or a central Pacific El Niño. We find that multiyear La Niña events differ from single-year La Niñas by a prominent onset rate, which is rooted in the western Pacific warming-enhanced zonal advective feedback for the central Pacific multiyear La Niña events type and thermocline feedback for the super El Niño multiyear La Niña events type. The results from large ensemble climate simulations support the observed multiyear La Niña events–western Pacific warming link. More multiyear La Niña events will exacerbate adverse socioeconomic impacts if the western Pacific continues to warm relative to the central Pacific.
Wei, Yuntao, Hong-Li Ren, Baoqiang Xiang, Yan Wang, Jie Wu, and Shuguang Wang, April 2023: Diverse MJO Genesis and Predictability. Bulletin of the American Meteorological Society, 104(1), doi:10.1175/BAMS-D-22-0101.1E792-E809. [ Abstract ]
The Madden–Julian oscillation (MJO) is the dominant intraseasonal wave phenomenon influencing extreme weather and climate worldwide. Realistic simulations and accurate predictions of MJO genesis are the cornerstones for successfully monitoring, forecasting, and managing meteorological disasters 3–4 weeks in advance. Nevertheless, the genesis processes and emerging precursor signals of an eastward-propagating MJO event remain largely uncertain. Here, we find that the MJO genesis processes observed in the past four decades exhibit remarkable diversity with different seasonality and can be classified objectively into four types, namely, a novel downstream origin from the westward-propagating intraseasonal oscillation (WPISO; 20.4%), localized breeding from the Indian Ocean suppressed convection (IOSC; 15.4%), an upstream succession of the preceding weakly dispersive (WD; 25.9%), and strongly dispersive (SD; 38.3%) MJO. These four types are associated with different oceanic background states, characterized by central Pacific cooling, southern Maritime Continent warming, eastern Pacific cooling, and central Pacific warming for the WPISO, IOSC, WD, and SD types, respectively. The SD type is also favored during the easterly phase of the stratospheric quasi-biennial oscillation. Diverse convective initiations possibly imply various kinds of propagations of MJO. The subseasonal reforecasts indicate robustly distinct prediction skills for the diverse MJO genesis. A window of opportunity for skillful week 3–4 prediction probably opens with the aid of the WPISO-type MJO precursor, which has increased the predictability of primary MJO onset by 1 week. These findings suggest that the diversified MJO genesis can be skillfully foreseen by monitoring unique precursor signals and can also serve as benchmarks for evaluating contemporary models’ modeling and predicting capabilities.
Xiang, Baoqiang, Shang-Ping Xie, Sarah M Kang, and Ryan J Kramer, June 2023: An emerging Asian aerosol dipole pattern reshapes the Asian summer monsoon and exacerbates northern hemisphere warming. npj Climate and Atmospheric Science, 6, 77, doi:10.1038/s41612-023-00400-8. [ Abstract ]
Since the early 2010s, anthropogenic aerosols have started decreasing in East Asia (EA) while have continued to increase in South Asia (SA). Yet the climate impacts of this Asian aerosol dipole (AAD) pattern remain largely unknown. Using a state-of-the-art climate model, we demonstrate that the climate response is distinctly different between the SA aerosol increases and EA aerosol decreases. The SA aerosol increases lead to ~2.7 times stronger land summer precipitation change within the forced regions than the EA aerosol decreases. Contrastingly, the SA aerosol increases, within the tropical monsoon regime, produce weak and tropically confined responses, while the EA aerosol decreases yield a pronounced northern hemisphere warming aided by extratropical mean westerly and positive air-sea feedbacks over the western North Pacific. By scaling the observed instantaneous shortwave radiative forcing, we reveal that the recent AAD induces a pronounced northern hemisphere extratropical (beyond 30°N) warming (0.024 ± 0.010 °C decade−1), particularly over Europe (0.049 ± 0.009 °C decade−1). These findings highlight the importance of the pattern effect of forcings in driving global climate and have important implications for decadal prediction.
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.
This study investigates how climate sensitivity depends upon the spatial pattern of radiative forcing. Sensitivity experiments using a coupled ocean-atmosphere model were conducted by adding anomalous incoming solar radiation over the entire globe, Northern Hemisphere mid-latitudes, Southern Ocean, and tropics. The varied forcing patterns led to highly divergent climate sensitivities. Specifically, the climate is nearly twice as sensitive to Southern Ocean forcing as tropical forcing. Strong coupling between the surface and free troposphere in the tropics increases the inversion strength, leading to smaller cloud feedback in the tropical forcing experiments. In contrast, the extratropics exhibit weaker coupling, a decrease or near-zero change in the inversion strength, and strong positive cloud feedback. These results contrast with the conventional SST-pattern effect in which tropical surface temperature changes regulate climate sensitivity. They also have important implications for other potentially asymmetric forcings, such as those from geoengineering, volcanic eruptions, and paleoclimatic changes.
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.
Elsberry, Russell L., Hsiao-Chung Tsai, Corie Capalbo, Wei-Chia Chin, and Timothy Marchok, June 2022: Opportunity for tropical cyclone lifecycle predictions from pre-formation to ending stage: Eastern North Pacific 2021 season. Atmosphere, 13(7), doi:10.3390/atmos13071008. [ Abstract ]
Building on previous studies of western North Pacific formation and intensity predictions along the ECMWF ensemble medium-range track forecasts, the first objective of this transition to the eastern North Pacific was to provide earlier forecasts of the Time-to-Formation (T2F) and Time-to-Hurricane (T2H) than are available from the National Hurricane Center Advisories. For the first six hurricanes of the 2021 season, the first detections in the ECMWF ensemble were 8 days to 12 days in advance of the T2F times and 9 days to 13 days in advance of the T2H times. The major advance in this study has been to document that the ECMWF ensemble is also capable of predicting Ending-T2H and Ending-T2F timings and positions along those 15-day ECMWF ensemble track forecasts. This study for the first time documents the opportunity for high wind warnings during the entire lifecycle of the 2021 season hurricanes even days in advance of formation. Validations of the pre-hurricane and Ending-hurricane tracks and timings are provided for the lifecycles of seven hurricanes and the “Almost-Hurricane Guillermo”. Because the technique has been accepted for operational testing at the Joint Typhoon Warning Center, a companion article has been submitted that will describe the flowchart methodology for evaluating the twice-daily ECMWF ensemble forecasts to select the most likely pre-hurricane circulation as early as possible while non-selecting the likely false alarm circulations.
We describe the model performance of a new global coupled climate model configuration, CM4-MG2. Beginning with the Geophysical Fluid Dynamics Laboratory's fourth-generation physical climate model (CM4.0), we incorporate a two-moment Morrison-Gettelman bulk stratiform microphysics scheme with prognostic precipitation (MG2), and a mineral dust and temperature-dependent cloud ice nucleation scheme. We then conduct and analyze a set of fully coupled atmosphere-ocean-land-sea ice simulations, following Coupled Model Intercomparison Project Phase 6 protocols. CM4-MG2 generally captures CM4.0's baseline simulation characteristics, but with several improvements, including better marine stratocumulus clouds off the west coasts of Africa and North and South America, a reduced bias toward “double” Intertropical Convergence Zones south of the equator, and a stronger Madden-Julian Oscillation (MJO). Some degraded features are also identified, including excessive Arctic sea ice extent and a stronger-than-observed El Nino-Southern Oscillation. Compared to CM4.0, the climate sensitivity is reduced by about 10% in CM4-MG2.
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.
Hitchcock, Peter, Amy Butler, Andrew J Charlton-Perez, Chaim I Garfinkel, T N Stockdale, James Anstey, Dann Mitchell, Daniela I V Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, William J Merryfield, Michael Sigmond, Baoqiang Xiang, and Liwei Jia, et al., July 2022: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): a protocol for investigating the role of stratospheric polar vortex disturbances in subseasonal to seasonal forecasts. Geoscientific Model Development, 15(13), doi:10.5194/gmd-15-5073-20225073-5092. [ Abstract ]
Major disruptions of the winter season, high-latitude stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research.
We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortex disturbances for surface predictability in subseasonal to seasonal forecast models. Based on a set of controlled, subseasonal ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models. Fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts.
Jeevanjee, Nadir, and Linjiong Zhou, March 2022: On the resolution-dependence of anvil cloud fraction and precipitation efficiency in radiative-convective equilibrium. Journal of Advances in Modeling Earth Systems, 14(3), doi:10.1029/2021MS002759. [ Abstract ]
Tropical anvil clouds are an important player in Earth's climate and climate sensitivity, but simulations of anvil clouds are uncertain. Here we identify and investigate one source of uncertainty by demonstrating a marked increase of anvil cloud fraction with resolution in cloud-resolving simulations of radiative-convective equilibrium. This increase in cloud fraction can be traced back to the resolution dependence of horizontal mixing between clear and cloudy air. A mixing timescale is diagnosed for each simulation using the cloud fraction theory of Seeley, Jeevanjee, Langhans, and Romps (2019) (https://doi.org/10.1029/2018GL080747) and is found to scale linearly with grid spacing, as expected from a simple scaling law. Thus mixing becomes more efficient with increasing resolution, generating more evaporation in middle and lower tropospheric updrafts. This decreases their precipitation efficiency (PE), thereby increasing their overall mass flux, leading to greater detrainment at the anvil level and hence higher anvil cloud fraction. The decrease in PE also yields a marked increase in relative humidity with resolution.
In this paper, U.S. landfalling tropical cyclone (TC) activity is projected for the late twenty-first century using a two-step dynamical downscaling framework. A regional atmospheric model, is run for 27 seasons, to generate tropical storm cases. Each storm case is -resimulated (up to 15 days) using the higher-resolution Geophysical Fluid Dynamics Laboratory hurricane model. Thirteen CMIP3 or CMIP5 climate change scenarios are explored. Robustness of projections is assessed using statistical significance tests and comparing changes across models. The proportion of TCs making U.S. landfall increases for the warming scenarios, due, in part, to an increases in the percentage of TC genesis near the U.S. coast and a change in climatological steering flows favoring more U.S. landfall events. The increases in U.S. landfall proportion leads to an increase in U.S. landfalling category 4–5 hurricane frequency, averaging about + 400% across the models; 10 of 13 models/ensembles project an increase (which is statistically significant in three of 13 models). We have only tentative confidence in this latter increase, which occurs despite a robust decrease in Atlantic basin category 1–5 hurricane frequency, no robust change in Atlantic basin category 4–5 and U.S. landfalling category 1–5 hurricane frequency, and no robust change in U.S. landfalling hurricane intensities. Rainfall rates, averaged within a 100-km radius of the storms, are projected to increase by about 18% for U.S. landfalling TCs. Important caveats to the study include low correlation (skill) for interannual variability of modeled vs. observed U.S. TC landfall frequency and model bias of excessive TC genesis near and east of the U.S. east coast in present-day simulations.
Lee, Jiheun, Sarah M Kang, Hanjun Kim, and Baoqiang Xiang, January 2022: Disentangling the effect of regional SST bias on the double-ITCZ problem. Climate Dynamics, 58, doi:10.1007/s00382-021-06107-x3441-3453. [ Abstract ]
This study investigates the causes of the double intertropical convergence zone (ITCZ) bias, characterized by too northward northern Pacific ITCZ, too dry equatorial Pacific, and too zonally elongated southern Pacific rainband. While the biases within one fully coupled model GFDL CM2.1 are examined, the large-scale bias patterns are broadly common to CMIP5/6 models. We disentangle the individual contribution of regional sea surface temperature (SST) biases to the double-ITCZ bias pattern using a series of slab ocean model experiments. A previously suggested Southern Ocean warm bias effect in displacing the zonal-mean ITCZ southward is manifested in the northern Pacific ITCZ while having little contribution to the zonally elongated wet bias south of the equatorial Pacific. The excessive southern Pacific precipitation is instead induced by the warm bias along the west coast of South America. The Southern Ocean bias effect on the zonal-mean ITCZ position is diminished by the neighboring midlatitude bias of opposite sign in GFDL CM2.1. As a result, the northern extratropical cold bias turns out to be most responsible for a southward-displaced zonal-mean ITCZ. However, this southward ITCZ displacement results from the northern Pacific branch, so ironically fixing the extratropical biases only deteriorates the northern Pacific precipitation bias. Thus, we emphasize that the zonal-mean diagnostics poorly represent the spatial pattern of the tropical Pacific response. Examination of longitude-latitude structure indicates that the overall tropical precipitation bias is mostly locally driven from the tropical SST bias. While our model experiments are idealized with no ocean dynamics, the results shed light on where preferential foci should be applied in model development to improve particular features of tropical precipitation bias.
Magnusson, Linus, Duncan Ackerley, Yves Bouteloup, Jan-Huey Chen, James D Doyle, Paul Earnshaw, Y C Kwon, M Köhler, Simon T K Lang, Y-J Lim, Mio Matsueda, Takumi Matsunobu, Ron McTaggart-Cowan, Alex Rienecke, Munehiko Yamaguchi, and Linjiong Zhou, September 2022: Skill of medium-range forecast models using the same initial conditions. Bulletin of the American Meteorological Society, 103(9), doi:10.1175/BAMS-D-21-0234.1E2050-E2068. [ Abstract ]
In the Different Models, Same Initial Conditions (DIMOSIC) project, forecasts from different global medium-range forecast models have been created based on the same initial conditions. The dataset consists of 10-day deterministic forecasts from seven models and includes 122 forecast dates spanning one calendar year. All forecasts are initialized from the same ECMWF operational analyses to minimize the differences due to initialization. The models are run at or near their respective operational resolutions to explore similarities and differences between operational global forecast models. The main aims of this study are 1) to evaluate the forecast skill and how it depends on model formulation, 2) to assess systematic differences and errors at short lead times, 3) to compare multimodel ensemble spread to model uncertainty schemes, and 4) to identify models that generate similar solutions. Our results show that all models in this study are capable of producing high-quality forecasts given a high-quality analysis. But at the same time, we find a large variety in model biases, both in terms of temperature errors and precipitation. We are able to identify models whose forecasts are more similar to each other than they are to those of other systems, due to the use of similar model physics packages. However, in terms of multimodel ensemble spread, our results also demonstrate that forecast sensitivities to different model formulations still are substantial. We therefore believe that the diversity in model design that stems from parallel development efforts at global modeling centers around the world remains valuable for future progress in the numerical weather prediction community.
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.
Zhang, Wei, Ben P Kirtman, Baoqiang Xiang, Leo Siqueira, Johnna Infanti, and Natalie Perlin, January 2022: Decadal variability of Southeast US rainfall in an eddying global coupled model. Geophysical Research Letters, 49(1), doi:10.1029/2021GL096709. [ Abstract ]
Ocean variability is a dominant source of remote rainfall predictability, but in many cases the physical mechanisms driving this predictability are not fully understood. This study examines how ocean mesoscales (i.e., the Gulf Stream SST front) affect decadal Southeast US (SEUS) rainfall, arguing that the local imprint of large-scale teleconnections is sensitive to resolved mesoscale features. Based on global coupled model experiments with eddying and eddy-parameterizing ocean, we find that a resolved Gulf Stream improves localized rainfall and remote circulation response in the SEUS. The eddying model generally improves the air-sea interactions in the Gulf Stream and the North Atlantic Subtropical High that modulate SEUS rainfall over decadal timescales. The eddy-parameterizing simulation fails to capture the sharp SST gradient associated with the Gulf Stream and overestimates the role of tropical Pacific SST anomalies in the SEUS rainfall.
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.
Chen, Xi, January 2021: The LMARS based shallow-water dynamical core on generic gnomonic cubed-sphere geometry. Journal of Advances in Modeling Earth Systems, 13(1), doi:10.1029/2020MS002280. [ Abstract ]
The rapidly increasing computing powers allow global atmospheric simulations with aggressively high resolutions, challenging traditional model design principles. This study presents a Low Mach number Approximate Riemann Solver (LMARS) based unstaggered finite-volume model for solving the shallow-water equations on arbitrary gnomonic cubed-sphere grids. Using a novel reference line-based grid-generation process, it unifies the representation of arbitrary gnomonic cubed-sphere grid projections and permits high-efficiency 1D reconstruction in the halo regions. The numerical discretization also extends a widely used pressure gradient algorithm with the LMARS viscous term, thus improves the model's stability for various numerical applications. The solver demonstrates a broad range of organic diffusion control without any explicit filters, validated by a comprehensive set of test cases. Lastly, a newly introduced splash on the sphere test verifies the solver's desirable dispersion properties and consistent performance among different grid types. This study paves a solid foundation for a new generation of global circulation models with kilometer horizontal scales.
Elsberry, Russell L., Hsiao-Chung Tsai, Wei-Chia Chin, and Timothy Marchok, June 2021: Predicting rapid intensification events following tropical cyclone formation in the western North Pacific based on ECMWF ensemble warm core evolutions. Atmosphere, 12(7), doi:10.3390/atmos12070847. [ Abstract ]
When the environmental conditions over the western North Pacific are favorable for tropical cyclone formation, a rapid intensification event will frequently follow formation. In this extension of our combined three-stage 7-day Weighted Analog Intensity Pacific prediction technique, the European Centre for Medium-range Weather Prediction ensemble predictions of the warm core magnitudes of pre-tropical cyclone circulations are utilized to define the Time-to-Formation (35 knots) and to estimate the Likely Storm Category. If that category is a Typhoon, the bifurcation version of our technique is modified to better predict the peak intensity by selecting only Cluster 1 analog storms with the largest peak intensities that are most likely to have under-gone rapid intensification. A second modification to improve the peak intensity magnitude and timing was to fit a cubic spline curve through the weighted-mean peak intensities of the Cluster 1 analogs. The performance of this modified technique has been evaluated for a sequence of western North Pacific tropical cyclones during 2019 in terms of: (i) Detection time in advance of formation; (ii) Accuracy of Time-to-Formation; (iii) Intensification stage prediction; and (iv) Peak intensity magnitude/timing. This modified technique would provide earlier guidance as to the threat of a Typhoon along the 15-day ensemble storm track forecast, which would be a benefit for risk management officials.
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-5897109pp.
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.
Hazelton, Andrew T., Zhan Zhang, Bin Liu, Jili Dong, Ghassan Alaka, Weiguo Wang, Timothy Marchok, Avichal Mehra, Sundararaman Gopalakrishnan, Xuejin Zhang, Morris A Bender, Vijay Tallapragada, and Frank D Marks, April 2021: 2019 Atlantic hurricane forecasts from the global-nested Hurricane Analysis and Forecast System: Composite statistics and key events. Weather and Forecasting, 36(2), doi:10.1175/WAF-D-20-0044.1519-538. [ Abstract ]
NOAA’s Hurricane Analysis and Forecast System (HAFS) is an evolving FV3-based hurricane modeling system that is expected to replace the operational hurricane models at the National Weather Service. Supported by the Hurricane Forecast Improvement Program (HFIP), global-nested and regional versions of HAFS were run in real time in 2019 to create the first baseline for the HAFS advancement. In this study, forecasts from the global-nested configuration of HAFS (HAFS-globalnest) are evaluated and compared with other operational and experimental models. The forecasts by HAFS-globalnest covered the period from July through October during the 2019 hurricane season. Tropical cyclone (TC) track, intensity, and structure forecast verifications are examined. HAFS-globalnest showed track skill superior to several operational hurricane models and comparable intensity and structure skill, although the skill in predicting rapid intensification was slightly inferior to the operational model skill. HAFS-globalnest correctly predicted that Hurricane Dorian would slow and turn north in the Bahamas and also correctly predicted structural features in other TCs such as a sting jet in Hurricane Humberto during extratropical transition. Humberto was also a case where HAFS-globalnest had better track forecasts than a regional version of HAFS (HAFS-SAR) due to a better representation of the large-scale flow. These examples and others are examined through comparisons with airborne tail Doppler radar from the NOAA WP-3D to provide a more detailed evaluation of TC structure prediction. The results from this real-time experiment motivate several future model improvements, and highlight the promise of HAFS-globalnest for improved TC prediction.
Judt, Falko, Daniel Klocke, Rosimar Rios-Berrios, Benoit Vanniere, Florian Ziemen, Ludovic Auger, Joachim Biercamp, Christopher S Bretherton, Xi Chen, Peter Düben, Cathy Hohenegger, Marat Khairoutdinov, Chihiro Kodama, Luis Kornblueh, Shian-Jiann Lin, Masuo Nakano, Philipp Neumann, William M Putman, Niklas Röber, Malcolm J Roberts, Masaki Satoh, Ryosuke Shibuya, Bjorn Stevens, Pier Luigi Vidale, Nils Wedi, and Linjiong Zhou, June 2021: Tropical cyclones in global storm-resolving models. Journal of the Meteorological Society of Japan. Ser. II, 99(3), doi:10.2151/jmsj.2021-029579-602. [ Abstract ]
Recent progress in computing and model development has initiated the era of global storm-resolving modeling, and with it the potential to transform weather and climate prediction. Within the general theme of vetting this new class of models, the present study evaluates nine global-storm resolving models in their ability to simulate tropical cyclones (TCs). Results indicate that, broadly speaking, the models produce realistic TCs and remove longstanding issues known from global models such as the deficiency in accurately simulating TC intensity. However, TCs are strongly affected by model formulation, and all models suffer from unique biases regarding the number of TCs, intensity, size, and structure. Some models simulated TCs better than others, but no single model was superior in every way. The overall results indicate that global storm-resolving models can open a new chapter in TC prediction, but they need to be improved to unleash their full potential.
Kang, Sarah M., Shang-Ping Xie, Clara Deser, and Baoqiang Xiang, December 2021: Zonal mean and shift modes of historical climate response to evolving aerosol distribution. Science Bulletin, 66(23), doi:10.1016/j.scib.2021.07.0132405-2411. [ Abstract ]
Anthropogenic aerosols are effective radiative forcing agents that perturb the Earth’s climate. Major emission sources shifted from the western to eastern hemisphere around the 1980s. An ensemble of single-forcing simulations with an Earth System Model reveals two stages of aerosol-induced climate change in response to the global aerosol increase for 1940–1980 and the zonal shift of aerosol forcing for 1980–2020, respectively. Here, using idealized experiments with hierarchical models, we show that the aerosol increase and shift modes of aerosol-forced climate change are dynamically distinct, governed by the inter-hemispheric energy transport and basin-wide ocean–atmosphere interactions, respectively. The aerosol increase mode dominates in the motionless slab ocean model but is damped by ocean dynamics. Free of zonal-mean energy perturbation, characterized by an anomalous North Atlantic warming and North Pacific cooling, the zonal shift mode is amplified by interactive ocean dynamics through Bjerknes feedback. Both modes contribute to a La Niña-like pattern over the equatorial Pacific. We suggest that a global perspective that accommodates the evolving geographical distribution of aerosol emissions is vital for understanding the aerosol-forced historical climate change.
Marchok, Timothy, September 2021: Important factors in the tracking of tropical cyclones in operational models. Journal of Applied Meteorology and Climatology, 60(9), doi:10.1175/JAMC-D-20-0175.11265-1284. [ Abstract ]
Multiple configurations of the Geophysical Fluid Dynamics Laboratory vortex tracker are tested to determine a setup that produces the best representation of a model forecast tropical cyclone center fix for the purpose of providing track guidance with the highest degree of accuracy and availability. Details of the tracking algorithms are provided, including descriptions of both the Barnes analysis used for center fixing most variables and a separate scheme used for center fixing wind circulation. The tracker is tested by running multiple configurations on all storms from the 2015–17 hurricane seasons in the Atlantic and eastern Pacific basins using forecasts from two operational National Weather Service models, the Global Forecast System (GFS) and the Hurricane Weather Research and Forecasting Model (HWRF). A configuration that tracks only 850-mb geopotential height has the smallest forecast track errors of any configuration based on an individual parameter. However, a configuration composed of the mean of 11 parameters outperforms any of the configurations that are based on individual parameters. Configurations composed of subsets of the 11 parameters and including both mass and momentum variables provide results comparable to or better than the full 11-parameter configuration. In particular, a subset configuration with thickness variables excluded generally outperforms the 11-parameter mean, while one composed of variables from only the 850-mb and near-surface layers performs nearly as well as the 11-parameter mean. Tracker configurations composed of multiple variables are more reliable in providing guidance through the end of a forecast period than are tracker configurations based on individual parameters.
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.
Elsberry, Russell L., Hsiao-Chung Tsai, Wei-Chia Chin, and Timothy Marchok, September 2020: Advanced global model ensemble forecasts of tropical cyclone formation, and intensity predictions along medium-range tracks. Atmosphere, 11(9), doi:10.3390/atmos11091002. [ Abstract ]
Marchok vortex tracker outputs from the European Centre for Medium-Range Weather Forecasts ensemble (ECEPS) and National Centers for Environmental Prediction ensemble (GEFS) are utilized to provide the Time-to-Formation (T2F of 25 kt or 35 kt) timing and positions along the weighted-mean vector motion (WMVM) track forecasts, and our weighted analog intensity Pacific (WAIP) technique provides 7-day intensity forecasts after the T2F. Example T2F(35) forecasts up to 5 days in advance of two typhoons and one non-developer in the western North Pacific are described in detail. An example T2F forecast of pre-Hurricane Kiko in the eastern North Pacific indicated that Hawaii would be under threat by the end of the 15-day ECEPS WMVM track forecast. An example T2F forecast of pre-Hurricane Lorenzo in the eastern Atlantic demonstrates that both the ECEPS and GEFS predict up to 5 days in advance that the precursor African wave will become a Tropical Storm off the west coast and will likely become a hurricane. Validations of the T2F(25) and T2F(35) timing and position errors are provided for all ECEPS and GEFS forecasts of the two typhoons and Hurricanes Kiko and Lorenzo. If the T2F timing errors are small (<1 day), the T2F position errors along the WMVM track forecasts will be small (<300 km). Although the primary focus is on the western North Pacific, the examples from the Atlantic and eastern/central North Pacific indicate the potential for future application in other basins.
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 describe the baseline model configuration and simulation characteristics of the Geophysical Fluid Dynamics Laboratory (GFDL)'s Atmosphere Model version 4.1 (AM4.1), which builds on developments at GFDL over 2013–2018 for coupled carbon‐chemistry‐climate simulation as part of the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL's AM4.0 development effort, which focused on physical and aerosol interactions and which is used as the atmospheric component of CM4.0, AM4.1 focuses on comprehensiveness of Earth system interactions. Key features of this model include doubled horizontal resolution of the atmosphere (~200 to ~100 km) with revised dynamics and physics from GFDL's previous‐generation AM3 atmospheric chemistry‐climate model. AM4.1 features improved representation of atmospheric chemical composition, including aerosol and aerosol precursor emissions, key land‐atmosphere interactions, comprehensive land‐atmosphere‐ocean cycling of dust and iron, and interactive ocean‐atmosphere cycling of reactive nitrogen. AM4.1 provides vast improvements in fidelity over AM3, captures most of AM4.0's baseline simulations characteristics, and notably improves on AM4.0 in the representation of aerosols over the Southern Ocean, India, and China—even with its interactive chemistry representation—and in its manifestation of sudden stratospheric warmings in the coldest months. Distributions of reactive nitrogen and sulfur species, carbon monoxide, and ozone are all substantially improved over AM3. Fidelity concerns include degradation of upper atmosphere equatorial winds and of aerosols in some regions.
Positive precipitation biases over western North America have remained a pervasive problem in the current generation of coupled global climate models. These biases are substantially reduced, however, in a version of the Geophysical Fluid Dynamics Laboratory Forecast-oriented Low Ocean Resolution (FLOR) coupled climate model with systematic sea surface temperature (SST) biases artificially corrected through flux adjustment. This study examines how the SST biases in the Atlantic and Pacific Oceans contribute to the North American precipitation biases. Experiments with the FLOR model in which SST biases are removed in the Atlantic and Pacific are carried out to determine the contribution of SST errors in each basin to precipitation statistics over North America. Tropical and North Pacific SST biases have a strong impact on northern North American precipitation, while tropical Atlantic SST biases have a dominant impact on precipitation biases in southern North America, including the western United States. Most notably, negative SST biases in the tropical Atlantic in boreal winter induce an anomalously strong Aleutian low and a southward bias in the North Pacific storm track. In boreal summer, the negative SST biases induce a strengthened North Atlantic Subtropical High and Great Plains low-level jet. Each of these impacts contributes to positive annual mean precipitation biases over western North America. Both North Pacific and North Atlantic SST biases induce SST biases in remote basins through dynamical pathways, so a complete attribution of the effects of SST biases on precipitation must account for both the local and remote impacts.
Kang, Sarah M., Shang-Ping Xie, Yechul Shin, Hanjun Kim, Yen-Ting Hwang, Malte F Stuecker, Baoqiang Xiang, and Matt Hawcroft, November 2020: Walker circulation response to extratropical radiative forcing. Science Advances, 6(47), doi:10.1126/sciadv.abd3021. [ Abstract ]
Walker circulation variability and associated zonal shifts in the heating of the tropical atmosphere have far-reaching global impacts well into high latitudes. Yet the reversed high latitude–to–Walker circulation teleconnection is not fully understood. Here, we reveal the dynamical pathways of this teleconnection across different components of the climate system using a hierarchy of climate model simulations. In the fully coupled system with ocean circulation adjustments, the Walker circulation strengthens in response to extratropical radiative cooling of either hemisphere, associated with the upwelling of colder subsurface water in the eastern equatorial Pacific. By contrast, in the absence of ocean circulation adjustments, the Walker circulation response is sensitive to the forcing hemisphere, due to the blocking effect of the northward-displaced climatological intertropical convergence zone and shortwave cloud radiative effects. Our study implies that energy biases in the extratropics can cause pronounced changes of tropical climate patterns.
Liu, Qingfu, Xuejin Zhang, and Mingjing Tong, et al., September 2020: Vortex Initialization in the NCEP Operational Hurricane Models. Atmosphere, 11(9), doi:10.3390/atmos11090968. [ Abstract ]
This paper describes the vortex initialization (VI) currently used in NCEP operational hurricane models (HWRF and HMON, and possibly HAFS in the future). The VI corrects the background fields for hurricane models: it consists of vortex relocation, and size and intensity corrections. The VI creates an improved background field for the data assimilation and thereby produces an improved analysis for the operational hurricane forecast. The background field after VI can be used as an initial field (as in the HMON model, without data assimilation) or a background field for data assimilation (as in HWRF model).
Owing to the limited length of observed tropical cyclone data and the effects of multidecadal internal variability, it has been a challenge to detect trends in tropical cyclone activity on a global scale. However, there is a distinct spatial pattern of the trends in tropical cyclone frequency of occurrence on a global scale since 1980, with substantial decreases in the southern Indian Ocean and western North Pacific and increases in the North Atlantic and central Pacific. Here, using a suite of high-resolution dynamical model experiments, we show that the observed spatial pattern of trends is very unlikely to be explained entirely by underlying multidecadal internal variability; rather, external forcing such as greenhouse gases, aerosols, and volcanic eruptions likely played an important role. This study demonstrates that a climatic change in terms of the global spatial distribution of tropical cyclones has already emerged in observations and may in part be attributable to the increase in greenhouse gas emissions.
Qian, Yitian, Pang-Chi Hsu, Hiroyuki Murakami, Baoqiang Xiang, and Lijun You, October 2020: A hybrid dynamical-statistical model for advancing subseasonal tropical cyclone prediction over the western North Pacific. Geophysical Research Letters, 47(20), doi:10.1029/2020GL090095. [ Abstract ]
Tropical cyclone (TC) genesis prediction at the extended-range to subseasonal timescale (a week to several weeks) is a gap between weather and climate predictions. The current dynamical prediction systems and statistical models show limited skills in TC genesis forecasting at the lead time of 1–3 weeks. A hybrid dynamical-statistical model is developed that reveals capability in predicting basin-wide TC frequency in every 10-day period over the western North Pacific at a 25-day forecast lead, which is superior to the statistical and dynamical model-based predictions examined in this study. In this hybrid model, the cyclogenesis counts for different TC clusters are predicted, respectively, using the statistical models in which the large-scale predictors associated with intraseasonal oscillation evolutions are provided by a dynamical model. A probabilistic map of TC tracks at the subseasonal timescale is further predicted by incorporating the climatological probability of track distributions of these TC clusters.
Sun, Yongqiang, F Zhang, Linus Magnusson, R Buizza, Jan-Huey Chen, and Kerry A Emanuel, February 2020: Reply to “Comments on ‘What Is the Predictability Limit of Midlatitude Weather?’”. Journal of the Atmospheric Sciences, 77(2), doi:10.1175/JAS-D-19-0308.1. [ Abstract ]
In their comment, Žagar and Szunyogh raised concerns about a recent study by Zhang et al. that examined the predictability limit of midlatitude weather using two up-to-date global models. Zhang et al. showed that deterministic weather forecast may, at best, be extended by 5 days, assuming we could achieve minimal initial-condition uncertainty (e.g., 10% of current operational value) with a nearly perfect model. Žagar and Szunyogh questioned the methodology and the experiments of Zhang et al. Specifically, Žagar and Szunyogh raised issues regarding the effects of model error on the growth of the forecast uncertainty. They also suggested that estimates of the predictability limit could be obtained using a simple parametric model. This reply clarifies the misunderstandings in Žagar and Szunyogh and demonstrates that experiments conducted by Zhang et al. are reasonable. In our view, the model error concern in Žagar and Szunyogh does not apply to the intrinsic predictability limit, which is the key focus of Zhang et al. and the simple parametric model described in Žagar and Szunyogh does not serve the purpose of Zhang et al.
Motivated by the use of the GFDL microphysics scheme in the FV3GFS, the all-sky radiance assimilation framework has been expanded to include precipitating hydrometeors. Adding precipitating hydrometeors allows the assimilation of precipitation-affected radiance in addition to cloudy radiance. In this upgraded all-sky framework, the five hydrometeors, including cloud liquid water, cloud ice, rain, snow and graupel, are the new control variables, replacing the original cloud water control variable. The Community Radiative Transfer Model (CRTM) was interfaced with the newly added precipitating hydrometeors. Sub-grid cloud variability was considered by using the average cloud overlap scheme. Multiple scattering radiative transfer was activated in the upgraded framework. Radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Advanced Technology Microwave Sounder (ATMS) over ocean were assimilated in all-sky approach. This new constructed all-sky framework shows neutral to positive impact on overall forecast skill. Improvement was found in 500 hPa geopotential height forecast in both Northern and Southern Hemispheres. Temperature forecast was also improved at 850 hPa in the Southern Hemisphere and the Tropics.
Tsai, Hsiao-Chung, Russell L Elsberry, Wei-Chia Chin, and Timothy Marchok, October 2020: Opportunity for early warnings of Typhoon Lekima from two global ensemble model forecasts of formation with 7-day intensities along medium-range tracks. Atmosphere, 11(11), doi:10.3390/atmos11111162. [ Abstract ]
Typhoon Lekima (2019) with its heavy rains and floods is an excellent example of the need to provide the earliest possible warnings of the formation, intensification, and subsequent track before a typhoon makes landfall along a densely populated coast. To demonstrate an opportunity to provide early (10 days in advance) warnings of the threat of Typhoon Lekima, the ensemble models from the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Predictions have been used to provide time-to-formation timing and positions along the weighted-mean vector motion track forecasts. In addition, the seven-day intensity forecasts after the formation using a weighted analog intensity prediction technique are provided. A detailed description of one European Center ensemble forecast is provided to describe the methodology for estimating the formation time and generating the intensity forecasts. Validation summary tables of the formation timing and position errors, and the intensity errors versus the Joint Typhoon Warning Center intensities, are presented. The availability of these ensemble forecasts would have been an opportunity to issue alerts/watches/warnings of Lekima even seven days in advance of when Lekima became a Tropical Storm. These ensemble forecasts also represent an opportunity to extend support on the 5–15 day timescale for the decision-making processes of water resource management and hydrological operations
Subseasonal climate prediction has emerged as a top forecast priority but remains a great challenge. Subseasonal extreme prediction is even more difficult than predicting the time‐mean variability. Here we show that the wintertime cold extremes, measured by the frequency of extreme cold days (ECDs), are skillfully predicted by the European Centre for Medium‐Range Weather Forecasts (ECMWF) model 2‐4 weeks in advance over a large fraction of the Northern Hemisphere land region. The physical basis for such skill in predicting ECDs is primarily rooted in predicting a small subset of leading empirical orthogonal function (EOF) modes of ECDs identified from observations, including two modes in Eurasia (North Atlantic Oscillation and Eurasia Meridional Dipole mode), and three modes in North America (North Pacific Oscillation, Pacific‐North America teleconnection mode and the North America Zonal Dipole mode). It is of interest to note that these two modes in Eurasia are more predictable than the three leading modes in North America mainly due to their longer persistence.
The source of predictability for the leading EOF modes mainly originates from atmospheric internal modes and the land‐atmosphere coupling. All these modes are strongly coupled to dynamically coherent planetary‐scale atmospheric circulations, which not only amplify but also prolong the surface air temperature anomaly, serving as a source of predictability at subseasonal timescales. The Eurasian Meridional Dipole mode is also tied to the lower‐boundary snow anomaly, and the snow‐atmosphere coupling helps sustain this mode and provides a source of predictability.
Zhang, Zhan, and Mingjing Tong, et al., July 2020: The Impact of Stochastic Physics-Based Hybrid GSI/EnKF Data Assimilation on Hurricane Forecasts Using EMC Operational Hurricane Modeling System. Atmosphere, 11(8), 801, doi:10.3390/atmos11080801. [ Abstract ]
The National Oceanic and Atmospheric Administration’s (NOAA) cloud-permitting high-resolution operational Hurricane Weather and Research Forecasting (HWRF) model includes the sophisticated hybrid grid-point statistical interpolation (GSI) and Ensemble Kalman Filter (EnKF) data assimilation (DA) system, which allows assimilating high-resolution aircraft observations in tropical cyclone (TC) inner core regions. In the operational HWRF DA system, the flow-dependent background error covariance matrix is calculated from the HWRF self-cycled 40-member ensemble. This DA system has proved to provide improved initial TC structure and therefore improved TC track and intensity forecasts. However, the uncertainties from the model physics are not taken into account in the FY2017 version of the HWRF DA system. In order to further improve the HWRF DA system, the stochastic physics perturbations are introduced in the HWRF DA, including the cumulus convection scheme, the planetary boundary layer (PBL) scheme, and model surface physics (drag coefficient), for HWRF-based ensembles. This study shows that both TC initial conditions and TC track and intensity forecast skills are improved by adding stochastic model physics in the HWRF self-cycled DA system. It was found that the improvements in the TC initial conditions and forecasts are the results of ensemble spread increases which realistically represent the model background error covariance matrix in HWRF DA. For all 2016 Atlantic storms, the TC track and intensity forecast skills are improved by about ~3% and 6%, respectively, compared to the control experiment. The case study shows that the stochastic physics in HWRF DA is especially helpful for those TCs that have inner-core high-resolution aircraft observations, such as tail Doppler radar (TDR) data.
We document the configuration and emergent simulation features from the Geophysical Fluid Dynamics Laboratory (GFDL) OM4.0 ocean/sea‐ice model. OM4 serves as the ocean/sea‐ice component for the GFDL climate and Earth system models. It is also used for climate science research and is contributing to the Coupled Model Intercomparison Project version 6 Ocean Model Intercomparison Project (CMIP6/OMIP). The ocean component of OM4 uses version 6 of the Modular Ocean Model (MOM6) and the sea‐ice component uses version 2 of the Sea Ice Simulator (SIS2), which have identical horizontal grid layouts (Arakawa C‐grid). We follow the Coordinated Ocean‐sea ice Reference Experiments (CORE) protocol to assess simulation quality across a broad suite of climate relevant features. We present results from two versions differing by horizontal grid spacing and physical parameterizations: OM4p5 has nominal 0.5° spacing and includes mesoscale eddy parameterizations and OM4p25 has nominal 0.25° spacing with no mesoscale eddy parameterization.
MOM6 makes use of a vertical Lagrangian‐remap algorithm that enables general vertical coordinates. We show that use of a hybrid depth‐isopycnal coordinate reduces the mid‐depth ocean warming drift commonly found in pure z* vertical coordinate ocean models. To test the need for the mesoscale eddy parameterization used in OM4p5, we examine the results from a simulation that removes the eddy parameterization. The water mass structure and model drift are physically degraded relative to OM4p5, thus supporting the key role for a mesoscale closure at this resolution.
Successful collaborations played a pivotal role in transitioning the GFDL hurricane research model into a long-standing state-of-the-art operational system that provided critical guidance for over 20 years.
The hurricane project at the NOAA Geophysical Fluid Dynamics Laboratory (GFDL) was established in 1970. By the mid 1970s pioneering research had led to the development of a new hurricane model. As the reputation of the model grew, GFDL was approached in 1986 by the director of the National Meteorological Center about establishing collaboration between the two Federal organizations to transition the model into an operational modeling system. After a multi-year effort by GFDL scientists to develop a system that could support rigorous requirements of operations, and multi-year testing had demonstrated its superior performance compared to existing guidance products, operational implementation was made in 1995. Through collaboration between GFDL and the US Navy, the model was also made operational at Fleet Numerical Meteorology and Oceanography Center in 1996. GFDL scientists continued to support and improve the model during the next two decades by collaborating with other scientists at GFDL, the NCEP Environmental Modeling Center (EMC), the National Hurricane Center, the US Navy, the University of Rhode Island (URI), Old Dominion University, and the NOAA Hurricane Research Division. Scientists at GFDL, URI, and EMC collaborated to transfer key components of the GFDL model to the NWS new Hurricane Weather and Research Forecast (HWRF) model that became operational in 2007. The purpose of the article is to highlight the critical role of these collaborations. It is hoped that the experiences of the authors will serve as an example of how such collaboration can benefit the nation with improved weather guidance products.
We use the fvGFS model developed at the Geophysical Fluid Dynamics Laboratory (GFDL) to demonstrate the potential of the upcoming United States Next Generation Global Prediction System for hurricane prediction. The fvGFS retrospective forecasts initialized with the European Centre for Medium‐Range Weather Forecasts (ECMWF) data showed much‐improved track forecasts for the 2017 Atlantic hurricane season compared to the best performing ECMWF operational model. The fvGFS greatly improved the ECMWF's poor track forecast for Hurricane Maria (2017). For Hurricane Irma (2017), a well‐predicted case by the ECMWF model, the fvGFS produced even lower 5‐day track forecast errors. The fvGFS also showed better intensity prediction than both the United States and the ECMWF operational models, indicating the robustness of its numerical algorithms.
A new global model using the GFDL nonhydrostatic Finite-Volume Cubed-Sphere Dynamical Core (FV3) coupled to physical parameterizations from the National Centers for Environmental Prediction's Global Forecast System (NCEP/GFS) was built at GFDL, named fvGFS. The modern dynamical core, FV3, has been selected for National Oceanic and Atmospheric Administration’s Next Generation Global Prediction System (NGGPS) due to its accuracy, adaptability, and computational efficiency, which brings a great opportunity for the unification of weather and climate prediction systems.
The performance of tropical cyclone (TC) forecasts in the 13-km fvGFS is evaluated globally based on 363 daily cases of 10-day forecasts in 2015. Track and intensity errors of TCs in fvGFS are compared to those in the operational GFS. The fvGFS outperforms the GFS in TC intensity prediction for all basins. For TC track prediction, the fvGFS forecasts are substantially better over the northern Atlantic basin and the northern Pacific Ocean than the GFS forecasts. An updated version of the fvGFS with the GFDL 6-category cloud microphysics scheme is also investigated based on the same 363 cases. With this upgraded microphysics scheme, fvGFS shows much improvement in TC intensity prediction over the operational GFS. Besides track and intensity forecasts, the performance of TC genesis forecast is also compared between the fvGFS and operational GFS. In addition to evaluating the hit/false alarm ratios, a novel method is developed to investigate the lengths of TC genesis lead times in the forecasts. Both versions of fvGFS show higher hit ratios, lower false alarm ratios and longer genesis lead times than those of the GFS model in most of the TC basins.
Ding, L, Tim Li, Baoqiang Xiang, and M S Peng, October 2019: On the Westward Turning of Hurricane Sandy (2012): Effect of Atmospheric Intraseasonal Oscillations. Journal of Climate, 32(20), doi:10.1175/JCLI-D-18-0663.1. [ Abstract ]
Hurricane Sandy (2012) experienced an unusual westward turning and made landfall in New Jersey after its northward movement over the Atlantic. The landfall caused severe casualties and great economic losses. The westward turning took place in the mid-latitude Atlantic where the climatological mean wind is eastward. The cause of this unusual westward track is investigated through both observational analysis and model simulations.
The observational analysis indicates that the hurricane steering flow was primarily controlled by atmospheric intraseasonal oscillation (ISO), which was characterized by a pair of anticyclonic and cyclonic circulation systems. The anticyclone to the north was part of a global wave train forced by convection over the tropical Indian Ocean through Rossby wave energy dispersion, while the cyclone to the south originated from the tropical Atlantic through northward propagation. Hindcast experiments using a global coupled model show that the model is able to predict the observed circulation pattern as well as the westward steering flow six days prior to Sandy’s landfall. Sensitivity experiments with different initial dates confirm the important role of the ISO in establishing the westward steering flow in the mid-latitude Atlantic. Thus the successful numerical model experiments suggest a potential for extended-range dynamical tropical cyclone track predictions.
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.
Ham, S, A-Young Lim, Suchul Kang, H Jeong, Y Jeong, Bin Wang, and Baoqiang Xiang, et al., September 2019: Correction to: A newly developed APCC SCoPS and its prediction of East Asia seasonal climate variability. Climate Dynamics, 53(5-6), doi:10.1007/s00382-019-04894-y. [ Abstract ]
The original article can be found online at https://doi.org/10.1007/s00382-018-4516-5
We present a new global‐to‐regional model, cfvGFS, able to explicitly (without parameterization) represent convection over part of the earth. This model couples the Geophysical Fluid Dynamics Laboratory Finite‐Volume Cubed‐Sphere Dynamical Core (FV3) to the Global Forecast System (GFS) physics and initial conditions, augmented with a six‐category microphysics and a modified planetary boundary layer scheme. We examine the characteristics of cfvGFS on a 3‐km continental United States domain nested within a 13‐km global model. The nested cfvGFS still has good hemispheric skill comparable to or better than the operational GFS, while supercell thunderstorms, squall lines, and derechos are explicitly‐represented over the refined region. In particular, cfvGFS has excellent representations of fine‐scale updraft helicity fields, an important proxy for severe weather forecasting. Precipitation biases are found to be smaller than in uniform‐resolution global models and competitive with operational regional models; the 3‐km domain also improves upon the global models in 2‐m temperature and humidity skill. We discuss further development of cfvGFS and the prospects for a unified global‐to‐regional prediction system.
He, Bian, Qing Bao, X Wang, and Linjiong Zhou, et al., August 2019: CAS FGOALS-f3-L Model Datasets for CMIP6 Historical Atmospheric Model Intercomparison Project Simulation. Advances in Atmospheric Sciences, 36(8), doi:10.1007/s00376-019-9027-8. [ Abstract ]
The outputs of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic, Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project (CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total, there are three ensemble experiments over the period 1979–2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden-Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP.
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.
Heming, J T., F Prates, and Morris A Bender, et al., December 2019: Review of Recent Progress in Tropical Cyclone Track Forecasting and Expression of Uncertainties. Tropical Cyclone Research and Review, 8(4), doi:10.1016/j.tcrr.2020.01.001. [ Abstract ]
The Ninth International Workshop on Tropical Cyclones (IWTC-9) took place in Hawaii, USA in December 2018. This review paper was presented at the Workshop under the Tropical Cyclone Track topic. The forecasting of tropical cyclone (TC) track has seen significant improvements in recent decades both by numerical weather prediction models and by regional warning centres who issue forecasts having made use of these models and other forecasting techniques. Heming and Goerss (2010) gave an overview of forecasting techniques and models available for TC forecasting, including evidence of the improvement in performance over the years. However, the models and techniques used for TC forecasting have continued to develop in the last decade. This presentation gives an updated overview of many of the numerical weather prediction models and other techniques used for TC track prediction. It includes recent performance statistics both by the models and the regional warning centres.
Kang, Sarah M., Matt Hawcroft, and Baoqiang Xiang, et al., December 2019: Extratropical–Tropical Interaction Model Intercomparison Project (Etin-Mip): Protocol and Initial Results. Bulletin of the American Meteorological Society, 100(12), doi:10.1175/BAMS-D-18-0301.1. [ Abstract ]
This article introduces the Extratropical–Tropical Interaction Model Intercomparison Project (ETIN-MIP), where a set of fully coupled model experiments are designed to examine the sources of longstanding tropical precipitation biases in climate models. In particular, we reduce insolation over three targeted latitudinal bands of persistent model biases: the southern extratropics, the southern tropics, and the northern extratropics. To address the effect of regional energy bias corrections on the mean distribution of tropical precipitation, such as the double intertropical convergence zone problem, we evaluate the quasi-equilibrium response of the climate system corresponding to a 50-yr period after the 100 years of prescribed energy perturbation. Initial results show that, despite a large intermodel spread in each perturbation experiment due to differences in ocean heat uptake response and climate feedbacks across models, the southern tropics is most efficient at driving a meridional shift of tropical precipitation. In contrast, the extratropical energy perturbations are effectively damped by anomalous heat uptake over the subpolar oceans, thereby inducing a smaller meridional shift of tropical precipitation compared with the tropical energy perturbations. The ETIN-MIP experiments allow us to investigate the global implications of regional energy bias corrections, providing a route to guide the practice of model development, with implications for understanding dynamical responses to anthropogenic climate change and geoengineering.
The Caribbean low-level jet (CLLJ) is an important component of the atmospheric circulation over the Intra-Americas Sea (IAS) which impacts the weather and climate both locally and remotely. It influences the rainfall variability in the Caribbean, Central America, northern South America, the tropical Pacific and the continental Unites States through the transport of moisture. We make use of high-resolution coupled and uncoupled models from the Geophysical Fluid Dynamics Laboratory (GFDL) to investigate the simulation of the CLLJ and its teleconnections and further compare with low-resolution models. The high-resolution coupled model FLOR shows improvements in the simulation of the CLLJ and its teleconnections with rainfall and SST over the IAS compared to the low-resolution coupled model CM2.1. The CLLJ is better represented in uncoupled models (AM2.1 and AM2.5) forced with observed sea-surface temperatures (SSTs), emphasizing the role of SSTs in the simulation of the CLLJ. Further, we determine the forecast skill for observed rainfall using both high- and low-resolution predictions of rainfall and SSTs for the July–August–September season. We determine the role of statistical correction of model biases, coupling and horizontal resolution on the forecast skill. Statistical correction dramatically improves area-averaged forecast skill. But the analysis of spatial distribution in skill indicates that the improvement in skill after statistical correction is region dependent. Forecast skill is sensitive to coupling in parts of the Caribbean, Central and northern South America, and it is mostly insensitive over North America. Comparison of forecast skill between high and low-resolution coupled models does not show any dramatic difference. However, uncoupled models show improvement in the area-averaged skill in the high-resolution atmospheric model compared to lower resolution model. Understanding and improving the forecast skill over the IAS has important implications for highly vulnerable nations in the region.
Li, Cheng, and Xi Chen, February 2019: Simulating Nonhydrostatic Atmospheres on Planets (SNAP): Formulation, Validation, and Application to the Jovian Atmosphere. Astrophysical Journal Supplement Series, 240(2), doi:10.3847/1538-4365/aafdaa. [ Abstract ]
A new nonhydrostatic and cloud-resolving atmospheric model is developed for studying moist convection and cloud formation in planetary atmospheres. It is built on top of the Athena++ framework, utilizing its static/adaptive mesh-refinement, parallelization, curvilinear geometry, and dynamic task scheduling. We extend the original hydrodynamic solver to vapors, clouds, and precipitation. Microphysics is formulated generically so that it can be applied to both Earth and Jovian planets. We implemented the Low Mach number Approximate Riemann Solver for simulating low-speed atmospheric flows in addition to the usual Roe and Harten–Lax–van Leer-Contact (HLLC) Riemann solvers. Coupled with a fifth-order weighted essentially nonoscillatory subgrid-reconstruction method, the sharpness of critical fields such as clouds is well-preserved, and no extra hyperviscosity or spatial filter is needed to stabilize the model. Unlike many atmospheric models, total energy is used as the prognostic variable of the thermodynamic equation. One significant advantage of using total energy as a prognostic variable is that the entropy production due to irreversible mixing processes can be properly captured. The model is designed to provide a unified framework for exploring planetary atmospheres across various conditions, both terrestrial and Jovian. First, a series of standard numerical tests for Earth's atmosphere is performed to demonstrate the performance and robustness of the new model. Second, simulation of an idealized Jovian atmosphere in radiative-convective equilibrium shows that (1) the temperature gradient is superadiabatic near the water condensation level because of the changing of the mean molecular weight, and (2) the mean profile of ammonia gas shows a depletion in the subcloud layer down to nearly 10 bars. Relevance to the recent Juno observations is discussed.
Lim, A H., J A Jung, S E Nebuda, J M Daniels, W Bresky, Mingjing Tong, and Vijay Tallapragada, February 2019: Tropical Cyclone Forecasts Impact Assessment from the Assimilation of Hourly Visible, Shortwave, and Clear-Air Water Vapor Atmospheric Motion Vectors in HWRF. Weather and Forecasting, 34(1), doi:10.1175/WAF-D-18-0072.1. [ Abstract ]
The assimilation of atmospheric motion vectors (AMVs) provides important wind information to conventional data-lacking oceanic regions, where tropical cyclones spend most of their lifetimes. Three new AMV types, shortwave infrared (SWIR), clear-air water vapor (CAWV), and visible (VIS), are produced hourly by NOAA/NESDIS and are assimilated in operational NWP systems. The new AMV data types are added to the hourly infrared (IR) and cloud-top water vapor (CTWV) AMV data in the 2016 operational version of the HWRF Model. In this study, we update existing quality control (QC) procedures and add new procedures specific to tropical cyclone assimilation. We assess the impact of the three new AMV types on tropical cyclone forecasts by conducting assimilation experiments for 25 Atlantic tropical cyclones from the 2015 and 2016 hurricane seasons. Forecasts are analyzed by considering all tropical cyclones as a group and classifying them into strong/weak storm vortices based on their initial model intensity. Metrics such as track error, intensity error, minimum central pressure error, and storm size are used to assess the data impact from the addition of the three new AMV types. Positive impact is obtained for these metrics, indicating that assimilating SWIR-, CAWV-, and VIS-type AMVs are beneficial for tropical cyclone forecasting. Given the results presented here, the new AMV types were accepted into NOAA/NCEP’s operational HWRF for the 2017 hurricane season.
Magnusson, Linus, Jan-Huey Chen, Shian-Jiann Lin, Linjiong Zhou, and Xi Chen, July 2019: Dependence on initial conditions vs. model formulations for medium‐range forecast error variations. Quarterly Journal of the Royal Meteorological Society, 145(722), doi:10.1002/qj.3545. [ Abstract ]
Understanding the root causes of forecast errors and occasional very poor forecasts is essential but difficult. In this paper we investigate the relative importance of initial conditions and model formulation for medium‐range errors in 500‐hPa geopotential height. The question is addressed by comparing forecasts produced with ECMWF‐IFS and NCEP‐GFS forecasting systems, and with the GFDL‐fvGFS model initialised with ECMWF and NCEP initial conditions. This gives two pairs of configurations that use the same initial conditions but different models, and one pair with the same model but different initial conditions. The first conclusion is that the initial conditions play the major role for differences between the configurations in terms of the average root‐mean‐square error for both northern and southern hemispheres as well as Europe and the contiguous U.S (CONUS), while the model dominates the systematic errors. A similar conclusion is also found by verifying precipitation over low latitudes and the CONUS. The day‐to‐day variations of 500‐hPa geopotential height scores are exemplified by one case of a forecast bust over Europe, where the error is found to be dominated by initial errors. The results are generalised by calculating correlations between errors integrated over Europe, CONUS and a region in southeastern Pacific respectively from the different configurations. For Europe and southeast Pacific, the correlations in the medium‐range are highest between the pairs that use the same initial conditions, while over CONUS it is for the pair with the same model. This suggests different mechanisms behind the day‐to‐day variability of the score for these regions. Over CONUS the link is made to the propagation of troughs over the Rockies, and the result suggests that the large differences in parameterisations of orographic drag between the models plays a role.
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.
Satoh, Masaki, Bjorn Stevens, Falko Judt, Marat Khairoutdinov, and Shian-Jiann Lin, et al., September 2019: Global Cloud-Resolving Models. Current Climate Change Reports, 5(3), doi:10.1007/s40641-019-00131-0. [ Abstract ]
Purpose of Review
Global cloud-resolving models (GCRMs) are a new type of atmospheric model which resolve nonhydrostatic accelerations globally with kilometer-scale resolution. This review explains what distinguishes GCRMs from other types of models, the problems they solve, and the questions their more commonplace use is raising.
Recent Findings
GCRMs require high-resolution discretization over the sphere but can differ in many other respects. They are beginning to be used as a main stream research tool. The first GCRM intercomparison studies are being coordinated, raising new questions as to how best to exploit their advantages.
Summary
GCRMs are designed to resolve the multiscale nature of moist convection in the global dynamics context, without using cumulus parameterization. Clouds are simulated with cloud microphysical schemes in ways more comparable to observations. Because they do not suffer from ambiguity arising from cumulus parameterization, as computational resources increase, GCRMs are the promise of a new generation of global weather and climate simulations.
Surface layer (SL) variables [e.g., 2‐m temperature (T2) and 10‐m wind (U10)] are diagnosed by applying the flux‐profile relationships based on Monin‐Obukhov similarity theory to the lowest model height (LMH). This assumes that the LMH is in the SL, which is approximately the bottom 10% of the boundary layer, but atmospheric general circulation models rarely satisfy this in stable boundary layers (SBLs). To assess errors in the diagnostic variables due to the LMH solely linked to the diagnostic algorithm, offline tests of the flux‐profile relationships are performed with LMH from a few meters to 60 m for three SBL regimes: weakly stable, very stable, and transition stability regimes. The results show that T2 and U10 are underestimated by O(0.1–1 °C) and O(0.1–1 m s−1), respectively, if the LMH is higher than the SL height. The stronger the SL stability is, the larger the temperature biases are. The negative wind biases increase with the surface stress. Based on these findings, we analyze the impacts of the LMH on the climatologies of the diagnostic parameters in the GFDL AM4.0/LM4.0. The results show reduced negative biases in T2 and U10 by lowering the LMH. The decrease of the overall bias over land is mainly due to the sensitivity of the diagnostic method to the LMH in SBLs, as shown in the offline tests. The overall increase in T2 and U10 over the oceans results from the increase in the actual near‐surface temperature and wind rather than from the diagnostic method.
Stevens, Bjorn, Masaki Satoh, Ludovic Auger, Joachim Biercamp, Christopher S Bretherton, Xi Chen, Peter Düben, Falko Judt, Marat Khairoutdinov, Daniel Klocke, Chihiro Kodama, Luis Kornblueh, Shian-Jiann Lin, Philipp Neumann, William M Putman, Niklas Röber, Ryosuke Shibuya, Benoit Vanniere, Pier Luigi Vidale, Nils Wedi, and Linjiong Zhou, September 2019: DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains. Progress in Earth and Planetary Science, 6, 61, doi:10.1186/s40645-019-0304-z. [ Abstract ]
A review of the experimental protocol and motivation for DYAMOND, the first intercomparison project of global storm-resolving models, is presented. Nine models submitted simulation output for a 40-day (1 August–10 September 2016) intercomparison period. Eight of these employed a tiling of the sphere that was uniformly less than 5 km. By resolving the transient dynamics of convective storms in the tropics, global storm-resolving models remove the need to parameterize tropical deep convection, providing a fundamentally more sound representation of the climate system and a more natural link to commensurately high-resolution data from satellite-borne sensors. The models and some basic characteristics of their output are described in more detail, as is the availability and planned use of this output for future scientific study. Tropically and zonally averaged energy budgets, precipitable water distributions, and precipitation from the model ensemble are evaluated, as is their representation of tropical cyclones and the predictability of column water vapor, the latter being important for tropical weather.
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.
Wang, Lei, Qing Bao, W-C Wang, Y Liu, Guoxiong Wu, and Linjiong Zhou, et al., July 2019: LASG Global AGCM with a Two-moment Cloud Microphysics Scheme: Energy Balance and Cloud Radiative Forcing Characteristics. Advances in Atmospheric Sciences, 36(7), doi:10.1007/s00376-019-8196-9. [ Abstract ]
Cloud dominates influence factors of atmospheric radiation, while aerosol-cloud interactions are of vital importance in its spatiotemporal distribution. In this study, a two-moment (mass and number) cloud microphysics scheme, which significantly improved the treatment of the coupled processes of aerosols and clouds, was incorporated into version 1.1 of the IAP/LASG global Finite-volume Atmospheric Model (FAMIL1.1). For illustrative purposes, the characteristics of the energy balance and cloud radiative forcing (CRF) in an AMIP-type simulation with prescribed aerosols were compared with those in observational/reanalysis data. Even within the constraints of the prescribed aerosol mass, the model simulated global mean energy balance at the top of the atmosphere (TOA) and at the Earth’s surface, as well as their seasonal variation, are in good agreement with the observational data. The maximum deviation terms lie in the surface downwelling longwave radiation and surface latent heat flux, which are 3.5 W m-2 (1%) and 3 W m-2 (3.5%), individually. The spatial correlations of the annual TOA net radiation flux and the net CRF between simulation and observation were around 0.97 and 0.90, respectively. A major weakness is that FAMIL1.1 predicts more liquid water content and less ice water content over most oceans. Detailed comparisons are presented for a number of regions, with a focus on the Asian monsoon region (AMR). The results indicate that FAMIL1.1 well reproduces the summer-winter contrast for both the geographical distribution of the longwave CRF and shortwave CRF over the AMR. Finally, the model bias and possible solutions, as well as further works to develop FAMIL1.1 are discussed.
With a GFDL coupled model, the subseasonal prediction of wintertime (December‐February) surface air temperature (SAT) is investigated through the analysis of 11‐year hindcasts. Significant subseasonal week 3‐5 correlation skill exists over a large portion of the global land domain, and the predictability originates primarily from the eight most predictable SAT modes. The first three modes, identified as the El Niño‐Southern Oscillation mode, the North Atlantic Oscillation (NAO) mode, and the Eurasia Meridional Dipole (EMD) mode, can be skillfully predicted more than 5 weeks in advance. The NAO and EMD modes are strongly correlated with the initial stratospheric polar vortex strength, highlighting the role of stratosphere in subseasonal prediction. Interestingly, the Madden‐Julian Oscillation is not essential for the subseasonal land SAT prediction in the Northern Hemisphere extratropics. The spatial correlation skill exhibits considerable intraseasonal and interannual fluctuations, indicative of the importance to identify the time window of opportunity for subseasonal prediction.
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.
Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Center for Medium Range Weather Forecasting (ECMWF, 9-km operational model) and identical-twin experiments of the US next-generation global prediction system (NGGPS, 3-km). Results suggest that predictability limit for mid-latitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.
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.
Zhou, Xiaoye, F Liu, Bin Wang, and Baoqiang Xiang, et al., August 2019: Different responses of East Asian summer rainfall to El Nino decays. Climate Dynamics, 53(3-4), doi:10.1007/s00382-019-04684-6. [ Abstract ]
The East Asian summer monsoon (EASM) shows notable change during the summer after El Nino peak. This delayed response, however, is variable and difficult to predict. Here, we revisit this issue by separating El Nino decays into early transition and late transition. In the summer after an early transition, the central-to-eastern Pacific evolves into a La Nina condition, with positive rainfall anomaly occurring over most parts of eastern China. In contrast, in the summer after a late transition, the central-to-eastern Pacific sea surface temperature (SST)anomaly remains neutral or slightly above normal; correspondingly, the East Asian rainfall anomaly shows a tripolar structure with positive anomaly over the Yangtze-Huaihe River valley and negative anomalies over northern and southern China. These different rainfall responses are mainly related to different locations of the anomalous anticyclone (AAC) over the western North Pacific (WNP): it is centered at (165 degrees E, 25 degrees N) for late-transition El Ninos, but at (135 degrees E, 16 degrees N) for early-transition El Ninos. During the late transition, the AAC-SST feedback, identified by the dipole SST mode consisting of WNP cooling and northern Indian Ocean (NIO) warming, mainly works to support the WNP AAC. During the early transition, the AAC-SST feedback is weak and mainly attributed to NIO warming. The strong easterly anomaly over the western equatorial Pacific, which is tied to the central-to-eastern equatorial Pacific cooling and dipole precipitation pattern from western equatorial Pacific to the Maritime Continent, occurs to support the AAC and pulls it equatorward. These distinct responses exist in the last century, and the CMIP5 models can reproduce these distinct responses well except that the models underestimate the AAC-SST feedback for late-transition El Ninos. The findings in this study help predict the EASM rainfall in post-El Nino years, but the key is the accurate prediction of the timing of decay.
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.
Gao, Kun, and Isaac Ginis, August 2018: On the characteristics of linear-phase roll vortices under a moving hurricane boundary layer. Journal of the Atmospheric Sciences, 75(8), doi:10.1175/JAS-D-17-0363.1. [ Abstract ]
Previous theoretical and numerical studies only focused on the formation of roll vortices (rolls) under a stationary and axisymmetric hurricane. The effect of the asymmetric wind structure induced by the storm movement on the roll characteristics remains unknown. In this study, we present the first attempt to investigate the characteristics of linear-phase rolls under a moving hurricane by embedding a linear two-dimensional (2-D) roll-resolving model into a 3-D hurricane boundary layer model. It is found that the roll horizontal wavelength under the moving hurricane is largely determined by the radial shear layer depth, defined as the thickness of the layer with positive radial wind shear. The horizontal distribution of the roll wavelength resembles the asymmetric pattern of the radial shear layer depth. Interestingly, the roll growth rate is not only affected by the radial wind shear magnitude alluded to in previous studies, but also by the radial shear layer depth. A deeper (shallower) radial shear layer tends to decrease (increase) the roll growth rate. Such an effect is due to the presence of the bottom boundary. The bottom boundary constrains the lower-level roll streamlines and reduces the efficiency of rolls in extracting kinetic energy from the radial shear. This effect is more pronounced under a deeper shear layer, which favors the formation of larger-size rolls. This study improves the understanding of the main factors affecting the structure and growth of rolls, and will provide guidance for interpreting the spatial distribution of rolls under realistic hurricanes in observations and high-resolution simulations.
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.
Motivated by increasing demand in the community for intraseasonal predictions of weather extremes, predictive skill of tropical cyclogenesis is investigated in this study based on a global coupled model system. Limited intraseasonal cyclogenesis prediction skill with a high false alarm rate is found when averaged over about 600 tropical cyclones (TCs) over global oceans from 2003 to 2013, particularly over the North Atlantic (NA). Relatively skillful genesis predictions with more than one-week lead time are only evident for about 10 percent of the total TCs. Further analyses suggest that TCs with relatively higher genesis skill are closely associated with the Madden-Julian Oscillation (MJO) and tropical synoptic waves, with their geneses strongly phase-locked to the convectively active region of the MJO and low-level cyclonic vorticity associated with synoptic-scale waves. Moreover, higher cyclogenesis prediction skill is found for TCs that formed during the enhanced periods of strong MJO episodes than those during weak or suppressed MJO periods. All these results confirm the critical role of the MJO and tropical synoptic waves for intraseasonal prediction of TC activity.
Tropical cyclogenesis prediction skill in this coupled model is found to be closely associated with model predictability of several large-scale dynamical and thermo-dynamical fields. Particularly over the NA, higher predictability of low-level relative vorticity, mid-level humidity, and vertical zonal wind shear are evident along a tropical belt from the West Africa coast to Caribbean Seas, in accord with more predictable cyclogenesis over this region. Over the extratropical NA, large-scale variables exhibit less predictability due to influences of extratropical systems, leading to poor cyclogenesis predictive skill.
Liu, P, Y Zhu, Q Zhang, J Gottschalck, M Zhang, C Melhauser, Wei Li, Hong Guan, Xiaqiong Zhou, Dingchen Hou, M Peña, Guoxiong Wu, Y Liu, and Linjiong Zhou, et al., July 2018: Climatology of tracked persistent maxima of 500-hPa geopotential height. Climate Dynamics, 51(1-2), doi:10.1007/s00382-017-3950-0. [ Abstract ]
Persistent open ridges and blocking highs (maxima) of 500-hPa geopotential height (Z500; PMZ) adjacent in space and time are identified and tracked as one event with a Lagrangian objective approach to derive their climatological statistics with some dynamical reasoning. A PMZ starts with a core that contains a local eddy maximum of Z500 and its neighboring grid points whose eddy values decrease radially to about 20 geopotential meters (GPMs) smaller than the maximum. It connects two consecutive cores that share at least one grid point and are within 10° of longitude of each other using an intensity-weighted location. The PMZ ends at the core without a successor. On each day, the PMZ impacts an area of grid points contiguous to the core and with eddy values decreasing radially to 100 GPMs. The PMZs identified and tracked consist of persistent ridges, omega blockings and blocked anticyclones either connected or as individual events. For example, the PMZ during 2–13 August 2003 corresponds to persistent open ridges that caused the extreme heatwave in Western Europe. Climatological statistics based on the PMZs longer than 3 days generally agree with those of blockings. In the Northern Hemisphere, more PMZs occur in DJF season than in JJA and their duration both exhibit a log-linear distribution. Because more omega-shape blocking highs and open ridges are counted, the PMZs occur more frequently over Northeast Pacific than over Atlantic-Europe during cool seasons. Similar results are obtained using the 200-hPa geopotential height (in place of Z500), indicating the quasi-barotropic nature of the PMZ.
This study describes the performance of two Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric general circulation models (AGCMs) in simulating the climatologies of planetary boundary layer (PBL) parameters, with a particular focus on the diurnal cycles. The two models differ solely in the PBL parameterization: one uses a prescribed K-profile PBL (KPP) scheme with an entrainment parameterization, and the other employs a turbulence kinetic energy (TKE) scheme. The models are evaluated through the comparison to the reanalysis ensemble, which is generated from ERA-20C, ERA-Interim, NCEP-CFSR and NASA-MERRA, and the following systematic biases are identified. The models exhibit wide-spread cold biases in the high latitudes, and the biases are smaller when the KPP scheme is used. The diurnal cycle amplitudes are underestimated in most dry regions, and the model with the TKE scheme simulates larger amplitudes. For the near-surface winds, the models underestimate both the daily means and the diurnal amplitudes. The differences between the models are relatively small compared to the biases.
The role of the PBL schemes in simulating the PBL parameters is investigated through the analysis of vertical profiles. The Sahara, which is suitable for focusing on the role of vertical mixing in dry PBLs, is selected for a detailed analysis. It reveals that compared to the KPP scheme, the heat transport is weaker with the TKE scheme in both convective and stable PBLs due to weaker vertical mixing, resulting in larger diurnal amplitudes. Lack of non-local momentum transport from the nocturnal low-level jets to the surfaces appears to explain the underestimation of the near-surface winds in the models.
Tong, Mingjing, et al., December 2018: Impact of Assimilating Aircraft Reconnaissance Observations on Tropical Cyclone Initialization and Prediction using Operational HWRF and GSI Ensemble-Variational Hybrid Data Assimilation. Monthly Weather Review, 146(12), doi:10.1175/MWR-D-17-0380.1. [ Abstract ]
This study evaluates the impact of assimilating high-resolution inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble-variational system that in this study uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18-24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spin-down of strong storms, which has also been seen in other studies.
There are several reasons for the degradation of intense storms. First, a newly-discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spin-down.
Wang, Bin, Juan Li, Mark Cane, J Liu, P J Webster, and Baoqiang Xiang, et al., April 2018: Towards predicting changes in the land monsoon rainfall a decade in advance. Journal of Climate, 31(7), doi:10.1175/JCLI-D-17-0521.1. [ Abstract ]
Predictions of changes of the land monsoon rainfall (LMR) in the coming decades are of vital importance for successful sustainable economic development. Current dynamic models, though, have shown little skill in the decadal prediction of the Northern Hemisphere (NH) LMR. The physical basis and predictability for such predictions remain largely unexplored. Decadal change of the NHLMR reflects changes in the total NH continental precipitation, tropical general circulation, and regional land monsoon rainfall over northern Africa, India, East Asia, and North America. Using observations from 1901 to 2014 and numerical experiments, we show that the decadal variability of the NHLMR is rooted primarily in (a) the north-south hemispheric thermal contrast in the Atlantic-Indian Ocean sector measured by the North Atlantic-southern Indian Ocean dipole (NAID) sea surface temperature (SST) index, and (b) an east-west thermal contrast in the Pacific measured by an Extended El Nino-Southern Oscillation (XEN) index. Results from a 500-year pre-industrial control experiment demonstrate that the leading mode of decadal NHLMR and the associated NAID and XEN SST anomalies may be largely an internal mode of the Earth’s climate system, although possibly modified by natural and anthropogenic external forcing. A 51-year, independent forward–rolling decadal hindcast was made with a hybrid dynamic-conceptual model and using the NAID index predicted by a multi-climate model ensemble. The results demonstrate that the decadal changes in the NHLMR can be predicted approximately a decade in advance with significant skills, opening a promising way forward for decadal predictions of regional land monsoon rainfall worldwide.
Xiang, Baoqiang, Ming Zhao, and Yi Ming, et al., July 2018: Contrasting Impacts of radiative forcing in the Southern Ocean versus Southern Tropics on ITCZ position and energy transport in one GFDL climate model. Journal of Climate, 31(14), doi:10.1175/JCLI-D-17-0566.1. [ Abstract ]
Most current climate models suffer from pronounced cloud and radiation biases in the Southern Ocean (SO) and in the tropics. Using one GFDL climate model, this study investigates the migration of the Inter-tropical Convergence Zone (ITCZ) with prescribed Top of Atmosphere (TOA) shortwave radiative heating in the SO (50°S-80°S) versus the Southern Tropics (ST, 0-20°S). Results demonstrate that the ITCZ position response to the ST forcing is twice as strong as the SO forcing, which is primarily driven by the contrasting sea surface temperature (SST) gradient over the tropics; however, the mechanism for the formation of the SST pattern remains elusive.
Energy budget analysis reveals that the conventional energetic constraint framework is inadequate in explaining the ITCZ shift in these two perturbed experiments. For both cases, the anomalous Hadley circulation does not contribute to transport the imposed energy from the Southern Hemisphere to the Northern Hemisphere, given a positive mean gross moist stability in the equatorial region. Changes in the cross-equatorial atmospheric energy are primarily transported by atmospheric transient eddies when the anomalous ITCZ shift is most pronounced during December-May.
The partitioning of energy transport between the atmosphere and ocean shows latitudinal dependence: the atmosphere and ocean play an overall equivalent role in transporting the imposed energy for the extratropical SO forcing, while for the ST forcing, the imposed energy is nearly completely transported by the atmosphere. This contrast originates from the different ocean heat uptake and also the different meridional scale of the anomalous ocean circulation.
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 impact of storm size on the forecast of tropical cyclone storm track and intensity is investigated using the 2016 version of the operational GFDL hurricane model. Evaluation was made for 1,529 forecasts in the Atlantic, eastern Pacific, and western North Pacific basins, during the 2014 and 2015 seasons. The track and intensity errors were computed from forecasts in which the 34-knot wind radii obtained from the operational TC-Vitals that are used to initialize TCs in the GFDL model were replaced with wind radii estimates derived using an equally-weighted average of six objective estimates. It was found that modifying the radius of 34-knot winds had a significant positive impact on the intensity forecasts in the 1-2 day lead times. For example, at 48h, the intensity error was reduced 10%, 5% and 4% in the Atlantic, eastern Pacific, and western North Pacific, respectively. The largest improvements in intensity forecasts were for those tropical cyclones undergoing rapid intensification, with a maximum error reduction in the 1-2 day forecast lead time of 14 and 17% in the eastern and western North Pacific, respectively. The large negative intensity biases in the eastern and western North Pacific were also reduced 25% and 75% in the 12 to 72h forecast lead times. Although the overall impact on the average track error was neutral, forecasts of recurving storms were improved and tracks of non-recurving storms degraded. Results also suggest that objective specification of storm size may impact intensity forecasts in other high resolution numerical models, particularly for tropical cyclones entering a rapid intensification phase.
Freychet, N, A Duchez, C-H Wu, C-A Chen, H-H Hsu, J Hirschi, A Forryan, B Sinha, A L New, T Graham, M B Andrews, C Tu, and Shian-Jiann Lin, February 2017: Variability of hydrological extreme events in East Asia and their dynamical control: a comparison between observations and two high-resolution global climate models. Climate Dynamics, 48(3-4), doi:10.1007/s00382-016-3108-5. [ Abstract ]
This work investigates the variability of extreme weather events (drought spells, DS15, and daily heavy rainfall, PR99) over East Asia. It particularly focuses on the large scale atmospheric circulation associated with high levels of the occurrence of these extreme events. Two observational datasets (APHRODITE and PERSIANN) are compared with two high-resolution global climate models (HiRAM and HadGEM3-GC2) and an ensemble of other lower resolution climate models from CMIP5. We first evaluate the performance of the high resolution models. They both exhibit good skill in reproducing extreme events, especially when compared with CMIP5 results. Significant differences exist between the two observational datasets, highlighting the difficulty of having a clear estimate of extreme events. The link between the variability of the extremes and the large scale circulation is investigated, on monthly and interannual timescales, using composite and correlation analyses. Both extreme indices DS15 and PR99 are significantly linked to the low level wind intensity over East Asia, i.e. the monsoon circulation. It is also found that DS15 events are strongly linked to the surface temperature over the Siberian region and to the land-sea pressure contrast, while PR99 events are linked to the sea surface temperature anomalies over the West North Pacific. These results illustrate the importance of the monsoon circulation on extremes over East Asia. The dependencies on of the surface temperature over the continent and the sea surface temperature raise the question as to what extent they could affect the occurrence of extremes over tropical regions in future projections.
Gao, Kun, et al., September 2017: Effect of Boundary Layer Roll Vortices on the Development of an Axisymmetric Tropical Cyclone. Journal of the Atmospheric Sciences, 74(9), doi:10.1175/JAS-D-16-0222.1. [ Abstract ]
In this study, the authors numerically investigate the response of an axisymmetric tropical cyclone (TC) vortex to the vertical fluxes of momentum, heat, and moisture induced by roll vortices (rolls) in the boundary layer. To represent the vertical fluxes induced by rolls, a two-dimensional high-resolution Single-Grid Roll-Resolving Model (SRM) is embedded at multiple horizontal grid points in the mesoscale COAMPS for Tropical Cyclones (COAMPS-TC) model domain. Idealized experiments are conducted with the SRM embedded within 3 times the radius of maximum wind of an axisymmetric TC. The results indicate that the rolls induce changes in the boundary layer wind distribution and cause a moderate (approximately 15%) increase in the TC intensification rate by increasing the boundary layer convergence in the eyewall region and induce more active eyewall convection. The numerical experiments also suggest that the roll-induced tangential momentum flux is most important in contributing to the TC intensification process, and the rolls generated at different radii (within the range considered in this study) all have positive contributions. The results are not qualitatively impacted by the initial TC vortex or the setup of the vertical diffusivity in COAMPS-TC.
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.
This study explores the role of the stratosphere as a source of seasonal predictability of surface climate over Northern Hemisphere extra-tropics both in the observations and climate model predictions. A suite of numerical experiments, including climate simulations and retrospective forecasts, are set up to isolate the role of the stratosphere in seasonal predictive skill of extra-tropical near surface land temperature. We show that most of the lead-0 month spring predictive skill of land temperature over extra-tropics, particularly over northern Eurasia, stems from stratospheric initialization. We further reveal that this predictive skill of extra-tropical land temperature arises from skillful prediction of the Arctic Oscillation (AO). The dynamical connection between the stratosphere and troposphere is also demonstrated by the significant correlation between the stratospheric polar vortex and sea level pressure anomalies, as well as the migration of the stratospheric zonal wind anomalies to the lower troposphere.
Lin, Shian-Jiann, Lucas Harris, Xi Chen, Weiye Yao, and Junyi Chai, November 2017: Colliding Modons: A Nonlinear Test for the Evaluation of Global Dynamical Cores. Journal of Advances in Modeling Earth Systems, 9(7), doi:10.1002/2017MS000965. [ Abstract ]
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.
Sampson, C R., E M Fukada, J A Knaff, B R Strahl, M J Brennan, and Timothy Marchok, June 2017: Tropical cyclone gale wind radii estimates for the western North Pacific. Weather and Forecasting, 32(3), doi:10.1175/WAF-D-16-0196.1. [ Abstract ]
The Joint Typhoon Warning Center’s (JTWC) forecast improvement goals include reducing 34-kt wind radii forecast errors, so accurate real-time estimates and post-season analysis of the 34-kt wind radii are critical to reaching this goal. Accurate real-time 34-kt wind radii estimates are also critical for decisions regarding base preparedness and asset protection, but still represent a significant operational challenge at JTWC for several reasons. These reasons include: a paucity of observations, the timeliness and availability of guidance, a lack of analysis tools, and a perceived shortage of personnel to perform the analysis; however, the number of available objective wind radii estimates is expanding and the topic of estimating 34-kt wind radii warrants revisiting.
In this work we describe an equally-weighted mean of real-time 34-kt wind radii objective estimates that provides real-time, routine operational guidance. This objective method is also used to retrospectively produce a two-year (2014-2015) 34-kt wind radii objective analysis, the results of which compare favorably to the post-season National Hurricane Center data (i.e., the best tracks), and a newly created best track data set for the western North Pacific seasons. This equally-weighted mean, when compared to the individual 34-kt wind radii estimate methods, is shown to have among the lowest mean absolute errors and smallest biases. In an ancillary finding, the western North Pacific basin average 34-kt wind radii calculated from the 2014-2015 seasons are estimated to be 134 n mi, which is larger than the estimates for storms in either the Atlantic (95 n mi) or eastern North Pacific (82 n mi) basins for the same years.
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.
The severity of the double Intertropical Convergence Zone (DI) problem in climate models can be measured by a tropical precipitation asymmetry index (PAI), indicating whether tropical precipitation favors the Northern Hemisphere or the Southern Hemisphere. Examination of 19 Coupled Model Intercomparison Project phase 5 models reveals that the PAI is tightly linked to the tropical sea surface temperature (SST) bias. As one of the factors determining the SST bias, the asymmetry of tropical net surface heat flux in Atmospheric Model Intercomparison Project (AMIP) simulations is identified as a skillful predictor of the PAI change from an AMIP to a coupled simulation, with an intermodel correlation of 0.90. Using tropical top-of-atmosphere (TOA) fluxes, the correlations are lower but still strong. However, the extratropical asymmetries of surface and TOA fluxes in AMIP simulations cannot serve as useful predictors of the PAI change. This study suggests that the largest source of the DI bias is from the tropics and from atmospheric models.
