Global Tropical Cyclone Research Articles

Dependence of tropical cyclone seeds and climate sensitivity on tropical cloud response.

Projections of future tropical cyclone frequency are uncertain, ranging from a slight increase to a considerable decrease according to climate models. Estimation of how much the Earth's surface temperature warms in response to greenhouse gas increase, quantified by effective climate sensitivity, is also uncertain. These two uncertainties have historically been studied independently as they concern different scales: One quantifies the extreme weather and the other the mean climate. Here, we show that these two uncertainties are not independent and are both influenced by the response of tropical clouds to warming. Across climate models, we show an anticorrelation between shortwave cloud radiative feedback and changes in the frequency of seed vortices, a prevalent type of tropical cyclone precursors. We further show an anticorrelation between effective climate sensitivity and tropical cyclone frequency changes, suggesting that global tropical cyclone frequency tends to decrease more substantially in models with larger temperature increase.

Fidelity of global tropical cyclone activity in a new reanalysis dataset (CRA40)

AbstractThis study systematically evaluated global tropical cyclone (TC) activity in a new global atmospheric reanalysis dataset named the “40‐year Global Reanalysis” (CRA40) against the best track data. For comparison, four state‐of‐the‐art reanalyses—ERA5, JRA55, CFSR, and MERRA2—were also assessed. The results showed that there is a general underestimation of global TC genesis frequency and intensity in both CRA40 and other reanalyses. A detailed investigation of spatial distribution, seasonality, interannual variation, and long‐term trend for TC genesis frequency, as well as pressure–wind relationship for TC intensity, revealed similarities and differences among these reanalyses datasets. Overall, CRA40 does not exhibit clear advantages over other reanalyses in these aspects, but its biases are also not more pronounced. However, regarding TC translation speed, CRA40 outpeforms other reanalyses, evident by its high level of consistency with the observation in the zonal average pattern, meridional distribution at peak latitudes, and interannual variation, suggesting its reasonable capability in capturing large‐scale atmospheric characteristics. Our findings indicate that the use of CRA40 is appropriate for conducting TC‐related studies, within the scope of its limitations.

Evolution of global tropical cyclone exposure risk

Evolution of global tropical cyclone exposure risk

The atmospheric effect of aerosols on future tropical cyclone frequency and precipitation in the Energy Exascale Earth System Model

This study uses experiments from the Energy Exascale Earth System Model (E3SM) to compare the influence on tropical cyclone (TC) activity of: (i) the atmospheric effect of aerosols under specified sea-surface temperatures (SSTs); and (ii) the net effect of greenhouse gases (GhGs) (including changes in SSTs). The experiments were performed using the CMIP6 Shared Socioeconomic Pathway SSP5-8.5 emissions scenario with GhG-induced SST warming specified and atmospheric aerosol effects simulated but without explicit ocean coupling. Insignificant changes in global TC frequency are found in response to the atmospheric effect of future aerosols and GhGs, as significant regional responses in TC frequency counteract each other. Future GhGs contribute to more frequent TCs in the North Atlantic, and reductions over the Northwestern Pacific and Southern Indian Ocean. The atmospheric effect of future aerosols drives more frequent TCs over the Northwestern Pacific and reductions over the Northeast Pacific and North Atlantic. Along with increases in TC intensity, global TC precipitation (TCP) is projected to increase by 52.8% (14.1%/K) due to the combined effect of future aerosols and GhGs. Although both forcings contribute to TCP increases (14.7–19.3% from reduced aerosols alone and 28.1–33.3% from increased GhGs alone), they lead to different responses in the spatial structure of TCP. TCP increases preferentially in the inner-core due to increased GhGs, whereas TCP decreases in the inner-core and increases in the outer-bands in response to the atmospheric effects of decreased aerosols. These changes are distinct from those caused by aerosol-induced SST changes, which have been considered in other studies.

Decreasing global tropical cyclone frequency in CMIP6 historical simulations.

The impact of anthropogenic global warming on tropical cyclone (TC) frequency remains a challenging issue, partly due to a relatively short period of reliable observational TC records and inconsistencies in climate model simulations. Using TC detection from 20 CMIP6 historical simulations, we show that the majority (75%) of these models show a decrease in global-scale TC frequency from 1850 to 2014. We demonstrated that this result is largely explained by weakened mid-tropospheric upward motion in CMIP6 models over the Pacific and Atlantic main development regions. The reduced upward motion is due to a zonal circulation adjustment and shifts in Intertropical Convergence Zone in response to global warming. In the South Indian Ocean, reduced TC frequency is mainly due to the decreased survival rate of TC seeds because of an increased saturation deficit in a warming climate. Our analysis highlights global warming's potential impact on the historical decrease in global TC frequency.

Anomalously weak intensity of tropical cyclones striking eastern China over the past two millennia

Anthropogenic climate warming is predicted to increase the intensity of global tropical cyclones (TCs) on decadal timescales, known as the ‘temperature-TC intensity’ paradigm. However, no proxy is currently available to directly quantify TC intensity in the northwestern Pacific region over centennial to millennial timescales. Here, we reconstruct the intensity of past TCs inferred from event-beds detected in two sedimentary systems in eastern China spanning approximately 1910 to 645 yr BP using an instrumental-calibrated technique, thereby encompassing a sufficiently wide range of temperatures to test the paradigm in the time domain. Intriguingly, our two intensity indices, based on flooding depth and wind speed, provide the initial quantitative evidence that TC intensity in eastern China has been anomalously weak since around 1485 ± 45 yr BP, with a reduction of approximately 30 ± 8% in intensity, despite no concurrent temperature shift. This reduction appears to have been pre-conditioned by a combined influence of a weaker El Niño-Southern Oscillation, a stronger Atlantic Meridional Overturning Circulation, and an increased level of Saharan dust. We suggest that the magnitudes of these factors may have crossed a tipping point and have not reverted to their pre-shift levels since that time, resulting in their impact on TC intensity exceeding that of temperature by triggering changes in the oceanic and atmospheric state within the tropical Pacific region where TCs originate.

Distinct responses of tropical cyclone activity to spatio-uniform and nonuniform SST warming patterns

Most previous studies have reported a decrease in global tropical cyclone (TC) genesis frequency (TCGF) under anthropogenic warming. However, little attention has been drawn to the influence of sea surface temperature (SST) warming patterns on TCGF changes. Here, we investigate the impacts of three distinct SST warming patterns on global TCGF: uniform SST warming, nonuniform (El Niño-like) SST warming, and a combination of both. Results show that spatio-uniform SST warming has a limited impact on global TCGF, instead redistributing the TC genesis locations. Conversely, nonuniform SST warming significantly suppresses global TCGF. The combined warming produces a similar decrease in TCGF to nonuniform warming albeit with differences in spatial distribution. This indicates the dominant role of nonuniform SST warming in affecting TCGF and highlights the nonlinearity of the process. Further analysis shows that these differences in TCGF primarily stem from the distinct responses of tropical circulations to the three warming patterns.

Ocean internal tides suppress tropical cyclones in the South China Sea

Tropical Cyclones (TCs) are devastating natural disasters. Analyzing four decades of global TC data, here we find that among all global TC-active basins, the South China Sea (SCS) stands out as particularly difficult ocean for TCs to intensify, despite favorable atmosphere and ocean conditions. Over the SCS, TC intensification rate and its probability for a rapid intensification (intensification by ≥ 15.4 m s−1 day−1) are only 1/2 and 1/3, respectively, of those for the rest of the world ocean. Originating from complex interplays between astronomic tides and the SCS topography, gigantic ocean internal tides interact with TC-generated oceanic near-inertial waves and induce a strong ocean cooling effect, suppressing the TC intensification. Inclusion of this interaction between internal tides and TC in operational weather prediction systems is expected to improve forecast of TC intensity in the SCS and in other regions where strong internal tides are present.

Reducing a Tropical Cyclone Weak-Intensity Bias in a Global Numerical Weather Prediction System

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. Significance Statement Global numerical weather prediction systems provide important guidance to forecasters about tropical cyclone development, motion, and intensity. Despite recent improvements in the Canadian operational model’s ability to predict tropical cyclone formation, the system systematically underpredicts the intensity of these storms. In this study, we use a set of increasingly simplified experiments to identify the source of this error, which lies in the numerical time-stepping scheme used to solve the model equations. By decreasing numerical drag on the tropical cyclone circulation, intensity predictions that resemble those of other global modeling systems are achieved. This will improve the quality of Canadian tropical cyclone guidance for forecasters around the world.

Relating Tropical Cyclone Intensification Rate to Precipitation and Convective Features in the Inner Core

Abstract Using Tropical Rainfall Measuring Mission Microwave Imager observations of global tropical cyclones (TCs) from 1998 to 2013, relationships between TC intensification rate and inner-core convective and precipitation parameters are examined by decoupling the dependency of these parameters on TC intensity and that on TC intensification rate. A total of 16 TC intensity change–intensity categories are categorized based on the initial intensity and 24-h future intensity change. The results show that the TC inner-core mean rain rate, convective intensity, and stratiform rain occurrence, and axisymmetric index of convective intensity increase significantly with TC intensification rate for each TC intensity category. The symmetry of rain rate and stratiform rainfall occurrence also increase significantly with TC intensification rate for each intensity category, except from slowly intensifying (SI) to rapidly intensifying (RI) group when the initial intensity is major hurricane. The RI major hurricanes have significantly more asymmetric rainfall distribution and distribution of stratiform rainfall occurrence than those of SI major hurricanes. For TCs with initial intensity in tropical depression, tropical storm, and major hurricane categories, the RI group has a significantly more asymmetric pattern of shallow precipitation/convection occurrence in the inner core than the SI group, while it has a significantly more symmetric pattern of deep convection occurrence than the SI group. The inner-core size, as quantified by the radius of maximum azimuthal mean rainfall decreases with both TC intensification rate and TC intensity.

A dataset of global tropical cyclone wind and surface wave measurements from buoy and satellite platforms

There are now a range of potential data sources for wind and surface wave conditions within tropical cyclones. These sources include: in situ buoy data and remote sensing data from satellite altimeters, scatterometers, and radiometers. In addition, data providing estimates of tropical cyclone tracks and wind field parameters are available from best track archives. The present dataset brings together this information in a single archive, providing the available data for each tropical cyclone from each of the data sources in a single file. The data consists of observations in a total of 2927 global tropical cyclones over the period from 1985 to 2017. Global statistics of the observations are provided, along with data on the geographic distribution of tropical cyclones within the database.

Predicting Short-Term Intensity Change in Tropical Cyclones Using a Convolutional Neural Network

Abstract This study details a two-method, machine learning approach to predict current and short-term intensity change in global tropical cyclones (TCs), “D-MINT” and “D-PRINT.” D-MINT and D-PRINT use infrared imagery and environmental scalar predictors, while D-MINT also employs microwave imagery. Results show that current TC intensity estimates from D-MINT and D-PRINT are more skillful than three established intensity estimation methods routinely used by operational forecasters for North Atlantic and eastern and western North Pacific TCs. Short-term intensity predictions are validated against five operational deterministic guidances at 6-, 12-, 18-, and 24-h lead times. D-MINT and D-PRINT are less skillful than NHC and consensus TC intensity predictions in North Atlantic and eastern North Pacific TCs, but are more skillful than the other guidances for at least half of the lead times. In western North Pacific, north Indian Ocean, and Southern Hemisphere TCs, D-MINT is more skillful than the JTWC and other individual TC intensity forecasts for over half of the lead times. When probabilistically predicting TC rapid intensification (RI), D-MINT is more skillful in North Atlantic and western North Pacific TCs than three operationally used RI guidances, but less skillful for yes–no RI forecasts. In addition, this work demonstrates the importance of microwave imagery, as D-MINT is more skillful than D-PRINT. Since D-MINT and D-PRINT are convolutional neural network models interrogating two-dimensional structures within TC satellite imagery, this study also demonstrates that those features can yield better short-term predictions than existing scalar statistics of satellite imagery in operational models. Finally, a diagnostics tool is revealed to aid the attribution of the D-MINT/D-PRINT intensity predictions. Significance Statement This study develops a method to predict current and short-term forecasts of tropical cyclone (TC) intensity using artificial intelligence. The resultant models use a convolutional neural network (CNN) that can identify two-dimensional features in satellite imagery that are indicative of TC intensity and future intensity change. The performance results indicate that in several TC basins, the CNN approach is generally more skillful than alternative satellite-based estimates of TC intensity as well as operational short-term forecasts of deterministic intensity change and of similar skill to probabilistic rapid intensification forecasts.

Trend of landfalling tropical cyclones in the East China Sea and their storm surge response

In recent years, the influence of global tropical cyclones (TCs) has a tendency to expand towards the polar direction, and the proportion of high-intensity TCs has also increased greatly. The Northwest Pacific is a high incidence area of TCs, the northward shift of landfalling TCs in China and the increase of high-intensity TCs will lead to an increased risk of storm surge in the East China Sea (ECS). However, due to the lack of long-term observations in the ECS, previous studies have focused on the analysis of individual TC, and the response mechanism of storm surge to long-term trends of TCs is not clear. Based on the long-term observations of TCs from 1949 to 2022 and the numerical experiments of storm surge, the trend of landfalling TCs in China and the response characteristics of storm surge in the ECS were studied in this paper. The observations showed that the probability of TCs entering ECS became greater in the latter 37 years compared with the former 37 years. The average intensity of landfalling TCs (maximum wind speed) increased by 36% to 30 m/s, and the average speed of landfalling TCs increased by 100% to 8 m/s. The model results showed that the intensity of the storm surge is closely related to the intensity and translation speed of TCs. Under the condition of the same translation speed of TCs, the storm surge enhanced with the intensity of TCs. For the TCs with high intensity (strong typhoon and super typhoon), the intensity of the storm surge continues to increase as the translation speed of the TCs increases. However, for the TCs with low or medium intensity (severe tropical storms and typhoons), the intensity of the storm surge reaches a peak with the increase of the translation speed of TCs. The intensity of the storm surge is determined by both the wind speed and the duration of strong wind. The TCs with faster translation speed can produce a stronger wind field but shorter duration of strong wind. Therefore, the intensity of the storm surge decreases when the translation speed of TCs exceeds the threshold value.

Applicability Evaluation of the Global Synthetic Tropical Cyclone Hazard Dataset in Coastal China

A tropical cyclone dataset is an important data source for tropical cyclone disaster research, and the evaluation of its applicability is a necessary prerequisite. The Global Synthetic Tropical Cyclone Hazard (GSTCH) dataset is a dataset of global tropical cyclone activity for 10,000 years from 2018, and has become accepted as a major data source for the study of global tropical cyclone hazards. On the basis of the authoritative Tropical Cyclone Best Track (TCBT) dataset proposed by the China Meteorological Administration, this study evaluated the applicability of the GSTCH dataset in relation to two regions: the Northwest Pacific and China’s coastal provinces. For the Northwest Pacific, the results show no significant differences in the means and standard deviations of landfall wind speed, landfall pressure, and annual occurrence number between the two datasets at the 95% confidence level. They also show the cumulative distributions of central minimum pressure and central maximum wind speed along the track passed the Kolmogorov–Smirnov (K-S) test at the 95% confidence level, thereby verifying that the GSTCH dataset is consistent with the TCBT dataset at sea-area scale. For China’s coastal provinces, the results show that the means or standard deviations of tropical cyclone characteristics between the two datasets were not significantly different in provinces other than Guangdong and Hainan, and further analysis revealed that the cumulative distributions of the tropical cyclone characteristics in Guangdong and Hainan provinces passed the K-S test at the 95% confidence level, thereby verifying that the GSTCH dataset is consistent with the TCBT dataset at province scale. The applicability evaluation revealed that no significant differences exist between most of the tropical cyclone characteristics in the TCBT and GSTCH datasets, and that the GSTCH dataset is an available and reliable data source for tropical cyclone hazard studies in China’s coastal areas.

Impacts of tropical cyclones on the global water budget

Tropical cyclones (TCs) require substantial amounts of moisture for their genesis and development, acting as important moisture drivers from the ocean to land and from tropical to subtropical and extratropical regions. Quantifying anomalous moisture transport related to TCs is crucial for understanding long-term TC-induced changes in the global hydrological cycle. Our results highlight that, in terms of the global water budget, TCs enhance moisture transport from evaporative regions and precipitation over sink regions, leading to predominantly anomalous positive surface freshwater flux areas over the tropics and more regionally concentrated negative areas over the Intertropical Convergence Zone. Furthermore, we detected seasonal variability in the impact of TC on the hydrological cycle, which is closely related to the annual and seasonal TC frequency. Our analysis also revealed a global statistically significant drop (~40 mm year−1) in TC-induced surface freshwater fluxes from 1980 to 2018 in response to the increasing sea surface temperature and slightly decrease in global TC frequency and lifetime in the last two decades. These findings have important implications for predicting the impacts of TCs on the hydrological cycle under global warming conditions.

Uncertainties and sensitivities in the quantification of future tropical cyclone risk

Tropical cyclone risks are expected to increase with climate change and socio-economic development and are subject to substantial uncertainties. We thus assess future global tropical cyclone risk drivers and perform a systematic uncertainty and sensitivity analysis. We combine synthetic tropical cyclones downscaled from CMIP6 global climate models for several emission scenarios with economic growth factors derived from the Shared Socioeconomic Pathways and a wide range of vulnerability functions. We highlight non-trivial effects between climate change and socio-economic development that drive future tropical cyclone risk. Furthermore, we show that the choice of climate model affects the output uncertainty most among all varied model input factors. Finally, we discover a positive correlation between climate sensitivity and tropical cyclone risk increase. We assert that quantitative estimates of uncertainty and sensitivity to model parameters greatly enhance the value of climate risk assessments, enabling more robust decision-making and offering a richer context for model improvement.

Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity

AbstractIt has been widely recognized that tropical cyclone (TC) genesis requires favorable large‐scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large‐scale environmental favorability in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relationship between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer‐learning technique to train ensemble of CNNs first on suites of high‐resolution climate model simulations with realistic seasonal TC activities and large‐scale environmental conditions, and then on a state‐of‐the‐art reanalysis from 1950 to 2019. The trained CNNs can well reproduce the historical TC records and yields significant seasonal prediction skills when the large‐scale environmental inputs are provided by operational climate forecasts. Furthermore, by inputting the ensemble CNNs with 20th century reanalysis products and Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we investigated TC variability and its changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our future projections using high‐resolution climate model.

An update on the influence of natural climate variability and anthropogenic climate change on tropical cyclones

A substantial number of studies have been published since the Ninth International Workshop on Tropical Cyclones (IWTC-9) in 2018, improving our understanding of the effect of climate change on tropical cyclones (TCs) and associated hazards and risks. These studies have reinforced the robustness of increases in TC intensity and associated TC hazards and risks due to anthropogenic climate change. New modeling and observational studies suggested the potential influence of anthropogenic climate forcings, including greenhouse gases and aerosols, on global and regional TC activity at the decadal and century time scales. However, there are still substantial uncertainties owing to model uncertainty in simulating historical TC decadal variability in the Atlantic, and the limitations of observed TC records. The projected future change in the global number of TCs has become more uncertain since IWTC-9 due to projected increases in TC frequency by a few climate models. A new paradigm, TC seeds, has been proposed, and there is currently a debate on whether seeds can help explain the physical mechanism behind the projected changes in global TC frequency. New studies also highlighted the importance of large-scale environmental fields on TC activity, such as snow cover and air-sea interactions. Future projections on TC translation speed and medicanes are new additional focus topics in our report. Recommendations and future research are proposed relevant to the remaining scientific questions and assisting policymakers.

Diurnal variations of tropical cyclone outer region size growth

AbstractVarious aspects of tropical cyclones (TCs) fluctuate with the diurnal cycle. TC size is critical to the extent of its damage, but diurnal variations in the growth of the outer region size have largely remained unexplored. This paper examines the diurnal variations in the growth of the radius of gale‐force winds (34 kt; R34) by analyzing best‐track data from 2001 to 2019 for both North Atlantic TCs and global TCs outside of the North Atlantic region. Statistically significant diurnal variations was found for R34 growth, with the maximum 6‐h growth rate occurring at 2100–0300 local solar time (LST) for North Atlantic TCs, and at 0300–0900 LST for global TCs excluding the North Atlantic which experienced rapid intensification (≥30 knots within 24 h). The higher R34 6‐h growth rates during the night were linked to a larger extent of very deep convective clouds with infrared brightness temperatures <208 K during this time.

Global tropical cyclone precipitation scaling with sea surface temperature

Understanding the relationship between tropical cyclone (TC) precipitation and sea surface temperature (SST) is essential for both TC hazard forecasting and projecting how these hazards will change in the future due to climate change. This work untangles how global TC precipitation is impacted by present-day SST variability (known as apparent scaling) and by long-term changes in SST caused by climate change (known as climate scaling). A variety of datasets are used including precipitation and SST observations, realistic climate model simulations, and idealized climate model simulations. The apparent scaling rates depend on precipitation metric; examples shown here have ranges of 6.1 to 9.5% per K versus 5.9 to 9.8% per K for two different metrics. The climate scaling is estimated at about 5% per K, which is slightly less than the atmospheric moisture scaling based on thermodynamic principles of about 7% per K (i.e., the Clausius–Clapeyron scaling). The apparent scaling is greater than the climate scaling, which implies that the relationship between TC precipitation and present-day SST variability should not be used to project the long-term response of TC precipitation to climate change.