Hurricane Model Research Articles

What Are the Finger‐Like Clouds in the Hurricane Inner‐Core Region?

AbstractFinger‐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.

Numerical modeling of infrasounds radiation and propagation in hurricanes

Infrasound can be useful for monitoring natural or human-induced energetic events in the atmosphere. For high-energy sources, the infrasonic signals can propagate over large distances around the globe, given the low rate of atmospheric absorption. This study focuses on the numerical simulation of infrasound propagation in realistic large-scale three-dimensional (3D) environments. More precisely, we are interested in the simulation of infrasound, called microbaroms, which are radiated from extreme weather conditions such as hurricanes. The simulation of hurricane-induced microbaroms relies on three main components: i) a hurricane model describing the atmospheric wind and pressure fields, ii) a microbarom source model, and iii) an infrasound propagation model. 3D simulations have been performed on a rectangular mesh. Azimuthal deviation caused by the hurricane and the stratified atmosphere can be seen in 3D. Further studies are needed to improve each model, and the correlation between them. The ultimate goal is to be able to simulate radiated infrasound propagation for any meteorological conditions.

Empirical hurricane fragility assessment of elevated and slab-on-grade residential houses

The widespread hurricane-induced damage to residential houses in the United States leads to severe social and economic impacts. Recent community-wide efforts by researchers within the natural hazards community to collect and curate post-hazard damage data have advanced the potential to assess structural vulnerability during extreme wind events. These efforts have led to data re-use and further analysis for detailed evaluation, allowing for the comparison of the performance of elevated versus slab-on-grade single-family residential houses during hurricanes. The empirically derived fragility functions from these damage assessment data offer a unique opportunity to evaluate the vulnerability of constructed buildings. This paper presents the development of empirical fragility functions for elevated and slab-on-grade residential houses, along with their building envelope components, when subjected to hurricane winds. It assesses the post-hurricane occupiability and the annual probability of failure for both elevated and slab-on-grade houses. Utilizing post-hurricane building performance data collected from 618 elevated and 1188 slab-on-grade single-family residential houses impacted by Hurricanes Irma and Harvey in 2017, we analyzed the relationship of damage to the estimated peak 3-sec gust wind speed at each house. The analysis yields the assignment of each house to one of five damage state categories, from which we developed the fragility functions of buildings and building components in accordance with FEMA's HAZUS Hurricane model. Results showed that the developed empirical fragility functions for building envelope components of elevated houses have a higher probability of failure compared to those installed on slab-on-grade houses. Additionally, the likelihood of elevated houses being unoccupiable after a hurricane is significantly higher than that of slab-on-grade houses in both Florida and Texas. For residential buildings constructed in the state of Florida, this study finds that elevated houses have a 39 %–74 % higher annual probability of failure due to hurricane wind compared to slab-on-grade houses for the different considered damage states.

Lifetime Performance of the Operational Hurricane Weather Research and Forecasting Model (HWRF) for North Atlantic Tropical Cyclones

Abstract The Hurricane Weather Research and Forecasting Model (HWRF) was the flagship hurricane model at NOAA’s National Centers for Environmental Prediction for 16 years and a state-of-the-art tool for tropical cyclone (TC) intensity prediction at the National Weather Service and across the globe. HWRF was a joint development between NOAA research and operations, specifically the Environmental Modeling Center and the Atlantic Oceanographic and Meteorological Laboratory. Significant support also came from the National Hurricane Center, Developmental Testbed Center, University Corporation for Atmospheric Research, universities, cooperative institutes, and the TC community. In the North Atlantic basin, where most improvement efforts focused, HWRF intensity forecast errors decreased by 45%–50% at many lead times between 2007 and 2022. These large improvements resulted from increases in horizontal and vertical resolution, as well as advances in model physics and data assimilation. HWRF intensity forecasts performed particularly well over the Gulf of Mexico in recent years, providing useful guidance for a large number of impactful landfalling hurricanes. Such advances were made possible not only by significant gains in computing but also through substantial investment from the Hurricane Forecast Improvement Program.

Evaluation of the ocean component on different coupled hurricane forecasting models using upper-ocean metrics relevant to air-sea heat fluxes during Hurricane Dorian (2019)

In August 2019 Hurricane Dorian traveled through the Caribbean Sea and Tropical Atlantic before devastating the Bahamas. The operational hurricane forecasting models under-predicted the intensity evolution of Dorian prior to the storm reaching its maximum strength. Research studies have shown that a more realistic upper-ocean characterization in coupled atmosphere-ocean models used to forecast hurricanes has the potential to lead to more accurate hurricane intensity forecasts. In this work, we evaluated four ocean products: the ocean component from one NOAA operational hurricane forecasting model that used ocean initial conditions from climatology, the ocean components from two NOAA experimental models using ocean initial conditions from a data-assimilative operational ocean model, and one US Navy data-assimilative operational ocean model for reference. The upper-ocean metrics used to evaluate the models include mixed layer temperature, mixed layer salinity, ocean heat content and depth-averaged temperature in the top 100 m. The observations used are temperature and salinity profiles from an array of six autonomous underwater gliders deployed in the Caribbean region during the 2019 hurricane season. We found that, even though the four models have good skill in predicting temperature and salinity over the whole observed water column, skill significantly deteriorates for the upper-ocean metrics. In particular, the models failed to capture the barrier layer that was present during the passage of Hurricane Dorian through the glider array. We also found that even small differences in the mixed layer temperature along the storm track on the hurricane models evaluated, led to noticeable differences in the total enthalpy fluxes delivered from the ocean to the atmosphere throughout the storm’s synoptic history. These findings highlight the need to improve the upper-ocean initial conditions and representation in coupled atmosphere-ocean models as part of the larger efforts to improve the various modeling aspects that control the hurricane intensity forecast.

Modeling the potential impact of storm surge and sea level rise on coastal archaeological heritage: A case study from Georgia.

Climate change poses great risks to archaeological heritage, especially in coastal regions. Preparing to mitigate these challenges requires detailed and accurate assessments of how coastal heritage sites will be impacted by sea level rise (SLR) and storm surge, driven by increasingly severe storms in a warmer climate. However, inconsistency between modeled impacts of coastal erosion on archaeological sites and observed effects has thus far hindered our ability to accurately assess the vulnerability of sites. Modeling of coastal impacts has largely focused on medium-to-long term SLR, while observations of damage to sites have almost exclusively focused on the results of individual storm events. There is therefore a great need for desk-based modeling of the potential impacts of individual storm events to equip cultural heritage managers with the information they need to plan for and mitigate the impacts of storm surge in various future sea level scenarios. Here, we apply the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model to estimate the risks that storm surge events pose to archaeological sites along the coast of the US State of Georgia in four different SLR scenarios. Our results, shared with cultural heritage managers in the Georgia Historic Preservation Division to facilitate prioritization, documentation, and mitigation efforts, demonstrate that over 4200 archaeological sites in Georgia alone are at risk of inundation and erosion from hurricanes, more than ten times the number of sites that were previously estimated to be at risk by 2100 accounting for SLR alone. We hope that this work encourages necessary action toward conserving coastal physical cultural heritage in Georgia and beyond.

Component-based estimation of recovery time and time-related expenses after hurricane events

Introduction: Due to hurricane damage, building residents or businesses must be relocated during the recovery time, which leads to time-related expenses (TRE), also known as additional living expenses (ALE) or extra expense coverage (EEC) or business interruption insurance (BIC). TRE are difficult to predict since they depend on the damage and time necessary to repair the building as well as on external factors such as damaged utilities and the availability of labor and materials, among other issues.Methods: In this study, we developed a new TRE hurricane vulnerability model for mid/high-rise buildings. The model combines estimates of repair time (Trepair), delay time (Tdelay), and utilities downtime (Tdown) to predict overall recovery time (Treco).Results: The outputs of the model include 1) TRE vulnerability matrices, which yield probabilities of Trepair, Tdelay, Tdown, Treco, and TRE conditional on either maximum 3-s wind speed or overall building damage ratio; 2) the corresponding vulnerability curves, which yield the mean values as a function wind speed or damage ratio.Discussion: Insurers can use these results to project TRE, and emergency managers and urban planners can use the recovery times to characterize the resilience of coastal communities. This paper summarizes the methodology and illustrates its implementation and results. The selected results of Treco are compared with the recovery times provided using the HAZUS-MH Hurricane Model.

Hindcast Insights from Storm Surge Forecasting of Super Typhoon Saola (2309) in Hong Kong with the Sea, Lake and Overland Surges from Hurricanes Model

Super Typhoon Saola (2309) skirted past south-southeast of Hong Kong within 40 km on the night of 1 September 2023, posing a significant storm surge threat to Hong Kong. Given the close proximity of Saola with a peak intensity of about 210 km/h within 300 km of Hong Kong, a close call of the “super typhoon direct-hit” scenario, this case provides valuable insights from a hindcast review of storm surge forecasts and warning operation using the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model, which is the operational storm surge model adopted by the Hong Kong Observatory (HKO). The performance of the HKO’s PRobabilistic Inundation Map Evaluation System (PRIMES) using both statistical and model ensemble approaches was also reviewed in this paper. Saola was a challenging case for operational forecasting of a compact TC structure with changes in storm size and intensity when it came close to Hong Kong. With major observations of storm structure using weather radar and dense automatic weather station, tide gauge and water level gauge networks, the high sensitivity of storm surge forecasts to the storm size parameter and the distance of closest approach was clearly revealed in the case of Saola. Even with a circularly symmetric TC parametric model like SLOSH, the hindcast review results illustrated that the model outputs were reasonably accurate during the closest approach of Saola given an accurate storm size and distance of closest approach were input, and using a highly computationally efficient storm surge model made it possible for the nowcasting of storm surges to handle compact and intense TC direct-hit cases in operational TC forecasting. Taking a nowcasting approach not only helps provide more reliable storm tide forecasts, but also facilitates the formulation of a better warning strategy when making final-call decisions in emergency response actions, based on the more frequent real-time analysis of TC position, intensity and storm size and the more accurate prediction of these parameters. A nowcasting workflow for storm surge operation was proposed in this paper.

Operational Storm Surge Forecasting at the National Hurricane Center: The Case for Probabilistic Guidance and the Evaluation of Improved Storm Size Forecasts Used to Define the Wind Forcing

Abstract The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.

Tropical cyclone winds and precipitation stimulate cone production in the masting species longleaf pine (Pinus palustris).

Many trees exhibit masting - where reproduction is temporally variable and synchronous over large areas. Several dominant masting species occur in tropical cyclone (TC)-prone regions, but it is unknown whether TCs correlate with mast seeding. We analyzed long-term data (1958-2022) to test the hypothesis that TCs influence cone production in longleaf pine (Pinus palustris). We integrate field observations, weather data, satellite imagery, and hurricane models to test whether TCs influence cone production via: increased precipitation; canopy density reduction; and/or mechanical stress from wind. Cone production was 31% higher 1 yr after hurricanes and 71% higher after 2 yr, before returning to baseline levels. Cyclone-associated precipitation was correlated with increased cone production in wet years and cone production increased after low-intensity winds (≤ 25 m s-1 ) but not with high-intensity winds (> 25 m s-1 ). Tropical cyclones may stimulate cone production via precipitation addition, but high-intensity winds may offset any gains. Our study is the first to support the direct influence of TCs on reproduction, suggesting a previously unknown environmental correlate of masting, which may occur in hurricane-prone forests world-wide.

A Comparison of the Impacts of Inner-Core, In-Vortex, and Environmental Dropsondes on Tropical Cyclone Forecasts during the 2017–20 Hurricane Seasons

Abstract This study conducts the first large-sample comparison of the impact of dropsondes in the tropical cyclone (TC) inner core, vortex, and environment on NWP-model TC forecasts. We analyze six observing-system experiments, focusing on four sensitivity experiments that denied dropsonde observations within annuli corresponding with natural breakpoints in reconnaissance sampling. These are evaluated against two other experiments detailed in a recent parallel study: one that assimilated and another that denied dropsonde observations. Experiments used a basin-scale, multistorm configuration of the Hurricane Weather Research and Forecasting (HWRF) Model and covered active periods of the 2017–20 North Atlantic hurricane seasons. Analysis focused on forecasts initialized with dropsondes that used mesoscale error covariance derived from a cycled HWRF ensemble, as these forecasts were where dropsondes had the greatest benefits in the parallel study. Some results generally support findings of previous research, while others are novel. Most notable was that removing dropsondes anywhere, particularly from the vortex, substantially degraded forecasts of maximum sustained winds. Removing in-vortex dropsondes also degraded outer-wind-radii forecasts in many instances. As such, in-vortex dropsondes contribute to a majority of the overall impacts of the dropsonde observing system. Additionally, track forecasts of weak TCs benefited more from environmental sampling, while track forecasts of strong TCs benefited more from in-vortex sampling. Finally, inner-core-only sampling strategies should be avoided, supporting a change made to the U.S. Air Force Reserve’s sampling strategy in 2018 that added dropsondes outside of the inner core. Significance Statement This study uses a regional hurricane model to conduct the most comprehensive assessment to date of the impact of dropsondes at different distances away from the center of a tropical cyclone (TC) on TC forecasts. The main finding is that in-vortex dropsondes are most important for intensity and outer-wind-radii forecasts. Particularly notable is the impact of dropsondes on TC maximum wind speed forecasts, as reducing sampling anywhere would degrade those forecasts.

2022 real-time Hurricane forecasts from an experimental version of the Hurricane analysis and forecast system (HAFSV0.3S)

During the 2022 hurricane season, real-time forecasts were conducted using an experimental version of the Hurricane Analysis and Forecast System (HAFS). The version of HAFS detailed in this paper (HAFSV0.3S, hereafter HAFS-S) featured the moving nest recently developed at NOAA AOML, and also model physics upgrades: TC-specific modifications to the planetary boundary layer (PBL) scheme and introduction of the Thompson microphysics scheme. The real-time forecasts covered a large dataset of cases across the North Atlantic and eastern North Pacific 2022 hurricane seasons, providing an opportunity to evaluate this version of HAFS ahead of planned operational implementation of a similar version in 2023. The track forecast results show that HAFS-S outperformed the 2022 version of the operational HWRF model in the Atlantic, and was the best of several regional hurricane models in the eastern North Pacific for track. The intensity results were more mixed, with a dropoff in skill at Days 4–5 in the Atlantic but increased skill in the eastern North Pacific. HAFS-S also showed some larger errors than the long-time operational Hurricane Weather Research and Forecasting (HWRF) model in the radius of 34-knot wind, but other radii metrics are improved. Detailed analysis of Hurricane Ian in the Atlantic highlights both the strengths of HAFS and opportunities for further development and improvement.

Improving the Intensity Forecast of Tropical Cyclones in the Hurricane Analysis and Forecast System

Abstract A modification to the mixing length formulation in a planetary boundary layer (PBL) scheme is introduced to improve the intensity forecast of tropical cyclones (TCs) in a basin-scale Hurricane Analysis and Forecast System (HAFS) for the real-time experiment in 2021. The 2020 basin-scale HAFS with the physics suite of the NCEP operational Global Forecast System performs well in terms of the reduced root-mean-square (RMS) errors in track and intensity except for the mean intensity bias, compared with NCEP operational hurricane models. To address the large intensity bias issue, the vertical mixing length near the surface used in the PBL scheme is increased to follow the similarity theory, consistent with that used in the surface layer scheme. Test results show that the RMS error and bias in intensity are further reduced without the degradation of the track forecast. An idealized one-dimensional TC PBL model is used to understand the model response to the modification, indicating that the radial wind is strengthened to dynamically balance the enhanced downward momentum mixing. This is also exhibited in the case study of a three-dimensional HAFS simulation, with the improved vertical distribution of the simulated wind speed in the eyewall area. Given the improvement, the modification has been implemented in one of the configurations of the first version of the operational HAFS at NCEP. Finally, the adjustment of the parameterization of diffusion and mixing in TC simulations is discussed. Significance Statement A modification to the mixing length formulation in a PBL scheme is described, which improves the intensity forecast of tropical cyclones simulated in the Hurricane Analysis and Forecast System (HAFS). Retrospective tests indicate that the modification can reduce the root-mean-square error and bias of the simulated TC intensity by 5%–10% and 50%, respectively. This modification has been implemented in one of the operational configurations of HAFS, version 1, at NCEP, improving the hurricane model guidance.

Research progress of compound flood in coastal cities

Facilitating the development of cities in coastal areas is an economic strategy from a global perspective. Currently it can be seen that large developed cities in many countries are distributed along the coast. However, in recent years, with the influence of climate change, sea level rise, cyclone changes and land subsidence, the frequency of compound storm floods has been increasing. More academics begin work on compound storm flooding. It can be found that the development of the flood model has experienced a process from ordinary static simulation to one-Dimensional-three-Dimensional coupled model. At present, there are mainly hurricane model and hydrodynamic model to study the composite storm flood. Through current storm flood models, it is known that the intensity and frequency of storm floods will increase under current climate change conditions. At the same time, this paper also looks forward to the models, and proposes an improvement plan to increase the application scope of the model.

Evaluation of Experimental High-Resolution Model Forecasts of Tropical Cyclone Precipitation Using Object-Based Metrics

Abstract Tropical cyclone (TC) precipitation poses serious hazards including freshwater flooding. High-resolution hurricane models predict the location and intensity of TC rainfall, which can influence local evacuation and preparedness policies. This study evaluates 0–72-h precipitation forecasts from two experimental models, the Hurricane Analysis and Forecast System (HAFS) model and the basin-scale Hurricane Weather Research and Forecasting (HWRF-B) Model, for 2020 North Atlantic landfalling TCs. We use an object-based method that quantifies the shape and size of the forecast and observed precipitation. Precipitation objects are then compared for light, moderate, and heavy precipitation using spatial metrics (e.g., area, perimeter, elongation). Results show that both models forecast precipitation that is too connected, too close to the TC center, and too enclosed around the TC center. Collectively, these spatial biases suggest that the model forecasts are too intense even though there is a negative intensity bias for both models, indicating there may be an inconsistency between the precipitation configuration and the maximum sustained winds in the model forecasts. The HAFS model struggles with forecasting stratiform versus convective precipitation and with the representation of lighter (stratiform) precipitation during the first 6 h after initialization. No such spinup issues are seen in the HWRF-B forecasts, which instead exhibit systematic biases at all lead times and systematic issues across all rain-rate thresholds. Future work will investigate spinup issues in the HAFS model forecast and how the microphysics parameterization affects the representation of precipitation in both models.

The Development of a Consensus Machine Learning Model for Hurricane Rapid Intensification Forecasts with Hurricane Weather Research and Forecasting (HWRF) Data

Abstract This study focused on developing a consensus machine learning (CML) model for tropical cyclone (TC) intensity-change forecasting, especially for rapid intensification (RI). This CML model was built upon selected classical machine learning models with the input data extracted from a high-resolution hurricane model, the Hurricane Weather Research and Forecasting (HWRF) system. The input data contained 21 or 34 RI-related predictors extracted from the 2018 version of HWRF (H218). This study found that TC inner-core predictors can be critical for improving RI predictions, especially the inner-core relative humidity. Moreover, this study emphasized the importance of performing resampling on an imbalanced input dataset. Edited nearest-neighbor and synthetic minority oversampling techniques improved the probability of detection (POD) by ∼10% for the RI class. This study also showed that the CML model has satisfactory performance on RI predictions compared to the operational models. CML reached 56% POD and 46% false alarm ratio (FAR), while the operational models had only 10%–30% POD but 50%–60% FAR. The CML performance on the non-RI classes was comparable to the operational models. The results indicated that, with proper and sufficient training data and RI-related predictors, CML has the potential to provide reliable probabilistic RI forecasts during a hurricane season.

A Systematic Assessment of the Overall Dropsonde Impact during the 2017–20 Hurricane Seasons Using the Basin-Scale HWRF

Abstract This study comprehensively assesses the overall impact of dropsondes on tropical cyclone (TC) forecasts. We compare two experiments to quantify dropsonde impact: one that assimilated and another that denied dropsonde observations. These experiments used a basin-scale, multistorm configuration of the Hurricane Weather Research and Forecasting Model (HWRF) and covered active North Atlantic basin periods during the 2017–20 hurricane seasons. The importance of a sufficiently large sample size as well as thoroughly understanding the error distribution by stratifying results are highlighted by this work. Overall, dropsondes directly improved forecasts during sampled periods and indirectly impacted forecasts during unsampled periods. Benefits for forecasts of track, intensity, and outer wind radii were more pronounced during sampled periods. The forecast improvements of outer wind radii were most notable given the impact that TC size has on TC-hazards forecasts. Additionally, robustly observing the inner- and near-core region was necessary for hurricane-force wind radii forecasts. Yet, these benefits were heavily dependent on the data assimilation (DA) system quality. More specifically, dropsondes only improved forecasts when the analysis used mesoscale error covariance derived from a cycled HWRF ensemble, suggesting that it is a vital DA component. Further, while forecast improvements were found regardless of initial classification and in steady-state TCs, TCs undergoing an intensity change had diminished benefits. The diminished benefits during intensity change probably reflect continued DA deficiencies. Thus, improving DA system quality and observing system limitations would likely enhance dropsonde impacts. Significance Statement This study uses a regional hurricane model to conduct the most comprehensive assessment of the impact of dropsondes on tropical cyclone (TC) forecasts to date. The main finding is that dropsondes can improve many aspects of TC forecasts if their data are assimilated with sufficiently advanced assimilation techniques. Particularly notable is the impact of dropsondes on TC outer-wind-radii forecasts, since improving those forecasts leads to more effective TC-hazard forecasts.

Hunting Hurricanes

NOAA’s Hurricane Hunters risk their lives each time they fly into the eye of a storm to collect crucial data for forecasting, hurricane modeling, and research.

Increased U.S. coastal hurricane risk under climate change.

Several pathways for how climate change may influence the U.S. coastal hurricane risk have been proposed, but the physical mechanisms and possible connections between various pathways remain unclear. Here, future projections of hurricane activity (1980-2100), downscaled from multiple climate models using a synthetic hurricane model, show an enhanced hurricane frequency for the Gulf and lower East coast regions. The increase in coastal hurricane frequency is driven primarily by changes in steering flow, which can be attributed to the development of an upper-level cyclonic circulation over the western Atlantic. The latter is part of the baroclinic stationary Rossby waves forced mainly by increased diabatic heating in the eastern tropical Pacific, a robust signal across the multimodel ensemble. Last, these heating changes also play a key role in decreasing wind shear near the U.S. coast, further aggravating coastal hurricane risk enhanced by the physically connected steering flow changes.

North Atlantic Tropical Cyclone Outer Size and Structure Remain Unchanged by the Late Twenty-First Century

Abstract There is a lack of consensus on whether North Atlantic tropical cyclone (TC) outer size and structure (i.e., change in outer winds with increasing radius from the TC) will differ by the late twenty-first century. Hence, this work seeks to examine whether North Atlantic TC outer wind field size and structure will change by the late twenty-first century using multiple simulations under CMIP3 SRES A1B and CMIP5 RCP4.5 scenarios. Specifically, our analysis examines data from the GFDL High-Resolution Forecast-Oriented Low Ocean Resolution model (HiFLOR) and two versions of the GFDL hurricane model downscaling climate model output. Our results show that projected North Atlantic TC outer size and structure remain unchanged by the late twenty-first century within nearly all HiFLOR and GFDL hurricane model simulations. Moreover, no significant regional outer size differences exist in the North Atlantic within most HiFLOR and GFDL hurricane model simulations. No changes between the control and late-twenty-first-century simulations exist over the storm life cycle in nearly all simulations. For the simulation that shows significant decreases in TC outer size, the changes are attributed to reductions in storm lifetime and outer size growth rates. The absence of differences in outer size among most simulations is consistent with the process that controls the theoretical upper bound of storm size (i.e., Rhines scaling), which is thermodynamically invariant. However, the lack of complete consensus among simulations for many of these conclusions suggests nontrivial uncertainty in our results.