Low Ocean Resolution Research Articles

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.

Mechanisms of Regional Arctic Sea Ice Predictability in Two Dynamical Seasonal Forecast Systems

Abstract Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict pan-Arctic sea ice extent (SIE) on the seasonal time scale; however, there have been fewer assessments of prediction skill on user-relevant spatial scales. In this work, we evaluate regional Arctic SIE predictions made with the Forecast-Oriented Low Ocean Resolution (FLOR) and Seamless System for Prediction and Earth System Research (SPEAR_MED) dynamical seasonal forecast systems developed at the NOAA/Geophysical Fluid Dynamics Laboratory. Compared to FLOR, we find that the recently developed SPEAR_MED system displays improved skill in predicting regional detrended SIE anomalies, partially owing to improvements in sea ice concentration (SIC) and thickness (SIT) initial conditions. In both systems, winter SIE is skillfully predicted up to 11 months in advance, whereas summer minimum SIE predictions are limited by the Arctic spring predictability barrier, with typical skill horizons of roughly 4 months. We construct a parsimonious set of simple statistical prediction models to investigate the mechanisms of sea ice predictability in these systems. Three distinct predictability regimes are identified: a summer regime dominated by SIE and SIT anomaly persistence; a winter regime dominated by SIE and upper-ocean heat content (uOHC) anomaly persistence; and a combined regime in the Chukchi Sea, characterized by a trade-off between uOHC-based and SIT-based predictability that occurs as the sea ice edge position evolves seasonally. The combination of regional SIE, SIT, and uOHC predictors is able to reproduce the SIE skill of the dynamical models in nearly all regions, suggesting that these statistical predictors provide a stringent skill benchmark for assessing seasonal sea ice prediction systems.

A Comparison of Tropical Cyclone Projections in a High-resolution Global Climate Model and from Downscaling by Statistical and Statistical-deterministic Methods

AbstractIn this study, we investigate the response of tropical cyclones (TCs) to climate change by using the Princeton environment-dependent probabilistic tropical cyclone (PepC) model and a statistical-deterministic method to downscale TCs using environmental conditions obtained from the Geophysical Fluid Dynamics Laboratory (GFDL) High-resolution Forecast-oriented Low Ocean Resolution (HiFLOR) model, under the Representative Concentration Pathway 4.5 (RCP4.5) emissions scenario for the North Atlantic basin. The downscaled TCs for the historical climate (1986-2005) are compared with those in the mid- (2016-35) and late-twenty-first century (2081-2100). The downscaled TCs are also compared with TCs explicitly simulated in HiFLOR. We show that while significantly more storms are detected in HiFLOR towards the end of the twenty-first century, the statistical-deterministic model projects a moderate increase in TC frequency, and PepC projects almost no increase in TC frequency. The changes in storm frequency in all three datasets are not significant in the mid-twenty-first century. All three project that storms will become more intense and the fraction of major hurricanes and Category 5 storms will significantly increase in the future climates. However, HiFLOR projects the largest increase in intensity while PepC projects the least. The results indicate that HiFLOR’s TC projection is more sensitive to climate change effects and statistical models are less sensitive. Nevertheless, in all three datasets, storm intensification and frequency increase lead to relatively small changes in TC threat as measured by the return level of landfall intensity.

A hybrid framework for forecasting monthly reservoir inflow based on machine learning techniques with dynamic climate forecasts, satellite-based data, and climate phenomenon information

In this study, we developed and evaluated a hybrid framework for reservoir inflow forecast. This framework is unprecedented, which integrates new quasi-globally available observation-, satellite-, or model-based datasets using machine learing models to forecast inflow at the local scale. Under this framework, we compared random forests, gradient boosting machine, extreme learning machine, M5-cubist, elastic net, as well as their multi-model ensemble using Bayesian model averaging (BMA), and tested contributions from different input datasets, including retrospective forecast (reforecast) from florecast oriented low ocean resolution (FLOR) dynamic climate model, satellite-based hydrologic products, and climate phenomenon information. The performance was evaluated using Kling–Gupta efficiency (KGE) and correlation coefficient (R) in two headwater reservoirs, Harris reservoir in the humid Alabama–Coosa–Tallapoosa river basin and the Navajo reservoir in the arid Upper Colorado River Basin (UCRB). The results showed that for the Harris reservoir, the BMA combining five models with antecedent inflow and satellite-based hydrologic information as model inputs provided the best performance (KGE = 0.66, R = 0.76). For the Navajo reservoir, the gradient boosting machine model with all variables combined as input showed the best performance (KGE = 0.76, R = 0.83). Satellite-based soil moisture and evaporation consistently showed significant contributions to the inflow forecast. Benefits from climate indices and FLOR reforecast varied by locations, with more benefits coming from climate indices than FLOR potential evaporation reforecast at the Navajo reservoir in UCRB. Given the global coverage of the model inputs, our approach can be potentially applicable to improve reservoir inflow forecasts in different regions of the world.

The Time-Scale-Dependent Response of the Wintertime North Atlantic to Increased Ocean Model Resolution in a Coupled Forecast Model

AbstractThis study uses initialized forecasts and climate integrations to evaluate the wintertime North Atlantic response to an increase of ocean model resolution from ~100 km [low-resolution ocean (LRO)] to ~25 km [high-resolution ocean (HRO)] in the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS). Importantly, the simulated impacts are time-scale dependent such that impacts in subseasonal and seasonal forecasts cannot be extrapolated to climate time scales. In general, mean biases are reduced in HRO relative to LRO configurations and the impact is increased at longer lead times. At subseasonal to seasonal lead times, surface heating anomalies over the Gulf Stream are associated with local increases to the poleward heat flux associated with transient atmospheric eddies. In contrast, surface heating anomalies in climate experiments are balanced by changes to the time-mean surface winds that resemble the steady response under linear dynamics. Some aspects of air–sea interaction exhibit a clear improvement with increased resolution at all lead times. However, it is difficult to identify the impact of increased ocean eddy activity in the variability of the overlying atmosphere. In particular, atmospheric blocking and the intensity of the storm track respond more strongly to mean biases and thus have a larger response at longer lead times. Finally, increased ocean resolution drives improvements to subseasonal predictability over Europe. This increase in skill seems to be a result of improvements to the Madden–Julian oscillation and its associated teleconnections rather than changes to air–sea interaction in the North Atlantic region.

Application of the Cyclone Phase Space to Extratropical Transition in a Global Climate Model

AbstractThe authors analyze the global statistics of tropical cyclones undergoing extratropical transition (ET) in the Forecast‐oriented Low Ocean Resolution version of CM2.5 with Flux Adjustment (FLOR‐FA). The cyclone phase space (CPS) is used to diagnose ET. A simulation of the recent historical climate is analyzed and compared with data from the Japanese 55‐year Reanalysis (JRA‐55), and then a simulation of late 21st century climate under Representative Concentration Pathway 4.5 is compared to the historical simulation. When CPS is applied to the FLOR‐FA output in the historical simulation, the results diverge from those obtained from JRA‐55 by having an unrealistic number of ET cases at low latitudes, due to the presence of strong local maxima in the upper‐level geopotential. These features mislead CPS into detecting a cold core where one is not present. The misdiagnosis is largely corrected by replacing the maxima required by CPS with the 95th percentile values, smoothing the CPS trajectories in time, or both. Other climate models may contain grid‐scale structures akin to those in FLOR‐FA and, when used for CPS analysis, require solutions such as those discussed here. Comparisons of ET in the projected future climate with the historical climate show a number of changes that are robust to the details of the ET diagnosis, though few are statistically significant according to standard tests. Among them are an increase in the ET fraction and a reduction in the mean latitude at which ET occurs in the western North Pacific.

The Impact of Sea Surface Temperature Biases on North American Precipitation in a High-Resolution Climate Model

AbstractPositive 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.

On the Mechanisms of the Active 2018 Tropical Cyclone Season in the North Pacific

AbstractThe 2018 tropical cyclone (TC) season in the North Pacific was very active, with 39 tropical storms including eight typhoons/hurricanes. This activity was successfully predicted up to 5 months in advance by the Geophysical Fluid Dynamics Laboratory Forecast‐Oriented Low Ocean Resolution (FLOR) global coupled model. In this work, a suite of idealized experiments with three dynamical global models (FLOR, Nonhydrostatic Icosahedral Atmospheric Model, and Meteorological Research Institute Atmospheric General Circulation Model) was used to show that the active 2018 TC season was primarily caused by warming in the subtropical Pacific and secondarily by warming in the tropical Pacific. Furthermore, the potential effect of anthropogenic forcing on the active 2018 TC season was investigated using two of the global models (FLOR and Meteorological Research Institute Atmospheric General Circulation Model). The models projected opposite signs for the changes in TC frequency in the North Pacific by an increase in anthropogenic forcing, thereby highlighting the substantial uncertainty and model dependence in the possible impact of anthropogenic forcing on Pacific TC activity.

Seasonal Prediction Potential for Springtime Dustiness in the United States

AbstractMost dust forecast models focus on short, subseasonal lead times, that is, 3 to 6 days, and the skill of seasonal prediction is not clear. In this study we examine the potential of seasonal dust prediction in the United States using an observation‐constrained regression model and key variables predicted by a seasonal prediction model developed at National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory, the Forecast‐Oriented Low Ocean Resolution (FLOR) model. Our method shows skillful predictions of spring dustiness 3 to 6 months in advance. It is found that the regression model explains about 71% of the variances of dust event frequency over the Great Plains and 63% over the southwestern United States in March‐May from 2004 to 2016 using predictors from FLOR initialized on 1 December. Variations in springtime dustiness are dominated by springtime climatic factors rather than wintertime factors. Findings here will help development of a seasonal dust prediction system and hazard prevention.

Impact of Anthropogenic Climate Change on United States Major Hurricane Landfall Frequency

Although anthropogenic climate change has contributed to warmer ocean temperatures that are seemingly more favorable for Atlantic hurricane development, no major hurricanes made landfall in the United States between 2006 and 2016. The U.S., therefore, experienced a major hurricane landfall drought during those years. Using the high-resolution Geophysical Fluid Dynamics Laboratory 25 km grid High-Resolution Forecast-Oriented Low Ocean Resolution (HiFLOR) global climate model, the present study shows that increases in anthropogenic forcing, due to increases in greenhouse gasses, are associated with fewer long-duration major hurricane landfall droughts in the U.S., which implies an increase in major hurricane landfall frequency. We create six different fixed-distance ‘buffers’ that artificially circle the United States coastline in 100 km radial increments and can compensate for the bias in hurricane landfall calculations with six-hourly datasets. Major hurricane landfall frequencies are computed by applying the buffer zones to the six-hourly observed and simulated storm track datasets, which are then compared with the observed recorded major hurricane frequencies. We found that the major hurricane landfall frequencies generated with the 200 km buffer using the six-hourly observed best-track dataset are most correlated with the observed recorded major hurricane landfall frequencies. Using HiFLOR with an implemented buffer system, we found less frequent projections of long-duration major hurricane landfall drought events in controlled scenarios with greater anthropogenic global warming, which is independent on the radius of the coastal buffer. These results indicate an increase in U.S. major hurricane landfall frequencies with an increase in anthropogenic warming, which could pose a substantial threat to coastal communities in the U.S.

Halving warming with idealized solar geoengineering moderates key climate hazards

Solar geoengineering (SG) has the potential to restore average surface temperatures by increasing planetary albedo1–4, but this could reduce precipitation5–7. Thus, although SG might reduce globally aggregated risks, it may increase climate risks for some regions8–10. Here, using the high-resolution forecast-oriented low ocean resolution (HiFLOR) model—which resolves tropical cyclones and has an improved representation of present-day precipitation extremes11,12—alongside 12 models from the Geoengineering Model Intercomparison Project (GeoMIP), we analyse the fraction of locations that see their local climate change exacerbated or moderated by SG. Rather than restoring temperatures, we assume that SG is applied to halve the warming produced by doubling CO2 (half-SG). In HiFLOR, half-SG offsets most of the CO2-induced increase of simulated tropical cyclone intensity. Moreover, neither temperature, water availability, extreme temperature nor extreme precipitation are exacerbated under half-SG when averaged over any Intergovernmental Panel on Climate Change (IPCC) Special Report on Extremes (SREX) region. Indeed, for both extreme precipitation and water availability, less than 0.4% of the ice-free land surface sees exacerbation. Thus, while concerns about the inequality of solar geoengineering impacts are appropriate, the quantitative extent of inequality may be overstated13. High-resolution coupled climate model simulations suggest only 0.4% of the land surface will see exacerbated hydrological risks under solar geoengineering that halves warming, indicating that geoengineering-related risks may be overstated.

Development and Evaluation of a Pan-European Multimodel Seasonal Hydrological Forecasting System

Abstract Hydrological forecasts with a high temporal and spatial resolution are required to provide the level of information needed by end users. So far high-resolution multimodel seasonal hydrological forecasts have been unavailable due to 1) lack of availability of high-resolution meteorological seasonal forecasts, requiring temporal and spatial downscaling; 2) a mismatch between the provided seasonal forecast information and the user needs; and 3) lack of consistency between the hydrological model outputs to generate multimodel seasonal hydrological forecasts. As part of the End-to-End Demonstrator for Improved Decision Making in the Water Sector in Europe (EDgE) project commissioned by the Copernicus Climate Change Service (ECMWF), this study provides a unique dataset of seasonal hydrological forecasts derived from four general circulation models [CanCM4, GFDL Forecast-Oriented Low Ocean Resolution version of CM2.5 (GFDL-FLOR), ECMWF Season Forecast System 4 (ECMWF-S4), and Météo-France LFPW] in combination with four hydrological models [mesoscale hydrologic model (mHM), Noah-MP, PCRaster Global Water Balance (PCR-GLOBWB), and VIC]. The forecasts are provided at daily resolution, 6-month lead time, and 5-km spatial resolution over the historical period from 1993 to 2012. Consistency in hydrological model parameterization ensures an increased consistency in the hydrological forecasts. Results show that skillful discharge forecasts can be made throughout Europe up to 3 months in advance, with predictability up to 6 months for northern Europe resulting from the improved predictability of the spring snowmelt. The new system provides an unprecedented ensemble of seasonal hydrological forecasts with significant skill over Europe to support water management. This study highlights the potential advantages of multimodel based forecasting system in providing skillful hydrological forecasts.

Improved Simulations of Tropical Pacific Annual‐Mean Climate in the GFDL FLOR and HiFLOR Coupled GCMs

AbstractThe National Oceanic and Atmospheric Administration's Geophysical Fluid Dynamics Laboratory has recently developed two global coupled general circulation models, the Forecast‐oriented Low Ocean Resolution (FLOR) model and the High atmospheric resolution Forecast‐oriented Low Ocean Resolution (HiFLOR) model, which are now being utilized for climate research and seasonal predictions. Compared to their predecessor Coupled Model version 2.1 (CM2.1), the new versions have improved ocean/atmosphere physics and numerics and refinement of the atmospheric horizontal grid from 220 km (CM2.1) to 55 km (FLOR) and 26 km (HiFLOR). Both FLOR and HiFLOR demonstrate greatly improved simulations of the tropical Pacific annual‐mean climatology, with FLOR practically eliminating any equatorial cold bias in sea surface temperature. An additional model experiment (Low Ocean Atmosphere Resolution version 1) using FLOR's ocean/atmosphere physics, but with the atmospheric grid coarsened toward that of CM2.1, is used to further isolate the impacts of the refined atmospheric grid versus the improved physics and numerics. The improved ocean/atmosphere formulations are found to produce more realistic tropical Pacific patterns of sea surface temperature and rainfall, surface heat fluxes, ocean mixed layer depths, surface currents, and tropical instability wave activity; enhance the near‐surface equatorial upwelling; and reduce the intercentennial warm drift of the tropical Pacific upper ocean. The atmospheric grid refinement further improves these features and also improves the tropical Pacific surface wind stress, implied Ekman and Sverdrup transports, subsurface temperature and salinity structure, and heat advection in the equatorial upper ocean. The results highlight the importance of nonlocal air‐sea interactions in the tropical Pacific climate system, including the influence of off‐equatorial surface fluxes on the equatorial annual‐mean state. Implications are discussed for improving future simulations, observations, and predictions of tropical Pacific climate.

Understanding the Equatorial Pacific Cold Tongue Time-Mean Heat Budget. Part II: Evaluation of the GFDL-FLOR Coupled GCM

The heat budget of the Pacific equatorial cold tongue (ECT) is explored using the GFDL-FLOR coupled GCM (the forecast-oriented low ocean resolution version of CM2.5) and ocean reanalyses, leveraging the two-layer framework developed in Part I. Despite FLOR’s relatively weak meridional stirring by tropical instability waves (TIWs), the model maintains a reasonable SST and thermocline depth in the ECT via two compensating biases: 1) enhanced monthly-scale vertical advective cooling below the surface mixed layer (SML), due to overly cyclonic off-equatorial wind stress that acts to cool the equatorial source waters; and 2) an excessive SST contrast between the ECT and off-equator areas, which boosts the equatorward heat transport by TIWs. FLOR’s strong advective cooling at the SML base is compensated by strong downward diffusion of heat out of the SML, which then allows FLOR’s ECT to take up a realistic heat flux from the atmosphere. Correcting FLOR’s climatological SST and wind stress biases via flux adjustment (FA) leads to weaker deep advective cooling of the ECT, which then erodes the upper-ocean thermal stratification, enhances vertical mixing, and excessively deepens the thermocline. FA does strengthen FLOR’s meridional shear of the zonal currents in the east Pacific, but this does not amplify either the simulated TIWs or their equatorward heat transport, likely due to FLOR’s coarse zonal ocean resolution. The analysis suggests that to advance coupled simulations of the ECT, improved winds and surface heat fluxes must go hand in hand with improved subseasonal and parameterized ocean processes. Implications for model development and the tropical Pacific observing system are discussed.

Understanding the Equatorial Pacific Cold Tongue Time-Mean Heat Budget. Part I: Diagnostic Framework

The Pacific equatorial cold tongue plays a leading role in Earth’s strongest and most predictable climate signals. To illuminate the processes governing cold tongue temperatures, the upper-ocean heat budget is explored using the GFDL-FLOR coupled GCM (the forecast-oriented low ocean resolution version of CM2.5). Starting from the exact temperature budget for layers of time-varying thickness, the layer temperature tendency terms are studied using hourly-, daily-, and monthly-mean output from a 30-yr simulation driven by present-day radiative forcings. The budget is then applied to 1) a surface mixed layer whose temperature is highly correlated with SST, in which the air–sea heat flux is balanced mainly by downward diffusion of heat across the layer base, and 2) a thicker advective layer that subsumes most of the vertical mixing, in which the air–sea heat flux is balanced mainly by monthly-scale advection. The surface warming from shortwave fluxes and submonthly meridional advection and the subsurface cooling from monthly vertical advection are both shown to be essential to maintain the cold tongue thermal stratification against the destratifying effects of vertical mixing. Although layer undulations strongly mediate the tendency terms on diurnal-to-interannual scales, the 30-yr-mean tendencies are found to be well summarized by analogous budgets developed for stationary but spatially varying layers. The results are used to derive practical simplifications of the exact budget, to support the analyses in Part II of this paper, and to facilitate broader application of heat budget analyses when evaluating and comparing climate simulations.

Towards Dynamical Seasonal Forecast of Extratropical Transition in the North Atlantic

AbstractExtratropical transition can extend the threat of tropical cyclones into the midlatitudes and modify it through expansion of rainfall and wind fields. Despite the scientific and socioeconomic interest, the seasonal forecast of extratropical transition has received little attention. The GFDL High‐Resolution Forecast‐Oriented Low Ocean Resolution (FLOR) model (HiFLOR) shows skill in seasonal forecasts of tropical cyclone frequency as well as major hurricanes. A July‐initialized 12‐member ensemble retrospective seasonal forecast experiment with HiFLOR in the North Atlantic is conducted, representing one of the very first attempts to predict the extratropical transition activity months in advance. HiFLOR exhibits retrospective skill in seasonal forecasts of basin‐wide and regional ET activity relative to best track and reanalysis data. In contrast, the skill of HiFLOR in predictions of non‐ET activity is limited. Future work targeted at improved predictions of non‐ET storms provides a path for enhanced TC activity forecasting.

Lifetime Evolution of Outer Tropical Cyclone Size and Structure as Diagnosed from Reanalysis and Climate Model Data

The present study examines the lifetime evolution of outer tropical cyclone (TC) size and structure in the North Atlantic (NA) and western North Pacific (WNP). The metric for outer TC size is the radius at which the azimuthal-mean 10-m azimuthal wind equals 8 m s−1 ( r8) derived from the NCEP Climate Forecast System Reanalysis (CFSR) and GFDL High-Resolution Forecast-Oriented Low Ocean Resolution model (HiFLOR). Radial profiles of the azimuthal-mean 10-m azimuthal wind are also analyzed to demonstrate that the results are robust across a broad range of wind radii. The analysis shows that most TCs in both basins are characterized by 1) minimum lifetime r8 at genesis, 2) subsequent substantial increases in r8 as the TC wind field expands, 3) peak r8 values occurring near or after the midpoint of the TC lifetime, and 4) nontrivial decreases in r8 and outer winds during the latter part of the TC lifetime. Compared to the NA, WNP TCs are systematically larger up until the end of their lifetime, exhibit r8 growth and decay rates that are larger in magnitude, and are characterized by an earlier onset of lifetime maximum r8 near their lifetime midpoint. In both basins, the TCs exhibiting the largest r8 increases are the longest lived, especially those that traverse the longest distances (i.e., recurving TCs). Finally, analysis of TCs undergoing extratropical transition (ET) shows that NA TCs exhibit negligible changes in r8 during ET, while WNP ET cases either show r8 decreases (CFSR) or negligible changes in r8 (HiFLOR).

Projected Response of Tropical Cyclone Intensity and Intensification in a Global Climate Model

As one of the first global coupled climate models to simulate and predict category 4 and 5 (Saffir–Simpson scale) tropical cyclones (TCs) and their interannual variations, the High-Resolution Forecast-Oriented Low Ocean Resolution (HiFLOR) model at the Geophysical Fluid Dynamics Laboratory (GFDL) represents a novel source of insight on how the entire TC intensification distribution could be transformed because of climate change. In this study, three 70-yr HiFLOR experiments are performed to identify the effects of climate change on TC intensity and intensification. For each of the experiments, sea surface temperature (SST) is nudged to different climatological targets and atmospheric radiative forcing is specified, allowing us to explore the sensitivity of TCs to these conditions. First, a control experiment, which uses prescribed climatological ocean and radiative forcing based on observations during the years 1986–2005, is compared to two observational records and evaluated for its ability to capture the mean TC behavior during these years. The simulated intensification distributions as well as the percentage of TCs that become major hurricanes show similarities with observations. The control experiment is then compared to two twenty-first-century experiments, in which the climatological SSTs from the control experiment are perturbed by multimodel projected SST anomalies and atmospheric radiative forcing from either 2016–35 or 2081–2100 (RCP4.5 scenario). The frequency, intensity, and intensification distribution of TCs all shift to higher values as the twenty-first century progresses. HiFLOR’s unique response to climate change and fidelity in simulating the present climate lays the groundwork for future studies involving models of this type.

Rainfall from tropical cyclones: high-resolution simulations and seasonal forecasts

This study examines the performance of the Geophysical Fluid Dynamics Laboratory Forecast-Oriented Low Ocean Resolution version of CM2.5 (FLOR; ~ 50-km mesh) and high-resolution FLOR (HiFLOR; ~ 25-km mesh) in reproducing the climatology and interannual variability in rainfall associated with tropical cyclones (TCs) in both sea surface temperature (SST)-nudging and seasonal-forecast experiments. Overall, HiFLOR outperforms FLOR in capturing the observed climatology of TC rainfall, particularly in East Asia, North America and Australia. In general, both FLOR and HiFLOR underestimate the observed TC rainfall in the coastal regions along the Bay of Bengal, connected to their failure to accurately simulate the bimodal structure of the TC genesis seasonality. A crucial factor in capturing the climatology of TC rainfall by the models is the simulation of the climatology of spatial TC density. Overall, while HiFLOR leads to a better characterization of the areas affected by TC rainfall, the SST-nudging and seasonal-forecast experiments with both models show limited skill in reproducing the year-to-year variation in TC rainfall. Ensemble-based estimates from these models indicate low potential skill for year-to-year variations in TC rainfall, yet the models show lower skill than this. Therefore, the low skill for interannual TC rainfall in these models reflects both a fundamental limit on predictability/reproducibility of seasonal TC rainfall as well as shortcomings in the models.

Projection of Landfalling–Tropical Cyclone Rainfall in the Eastern United States under Anthropogenic Warming

AbstractLandfalling–tropical cyclone (TC) rainfall is an important element of inland flood hazards in the eastern United States. The projection of landfalling-TC rainfall under anthropogenic warming provides insight into future flood risks. This study examines the frequency of landfalling TCs and associated rainfall using the GFDL Forecast-Oriented Low Ocean Resolution (FLOR) climate model through comparisons with observed TC track and rainfall over the July–November 1979–2005 seasons. The projection of landfalling-TC frequency and rainfall under the representative concentration pathway (RCP) 4.5 scenario for the late twenty-first century is explored, including an assessment of the impacts of extratropical transition (ET). In most regions of the southeastern United States, competition between increased storm rain rate and decreased storm frequency dominates the change of annual TC rainfall, and rainfall from ET and non-ET storms. In the northeastern United States, a prominent feature is the striking increase of ET-storm frequency but with tropical characteristics (i.e., prior to the ET phase), a key element of increased rainfall. The storm-centered rainfall composite analyses show the greatest increase at a radius of a few hundred kilometers from the storm centers. Over both ocean and land, the increase of rainfall within 500 km from the storm center exceeds the Clausius–Clapeyron scaling for TC-phase storms. Similar results are found in the front-left quadrant of ET-phase storms. Future work involving explorations of multiple models (e.g., higher atmospheric resolution version of the FLOR model) for TC-rainfall projection is expected to add more robustness to projection results.