Global Tropical Cyclone Research Articles

Impact of Refractivity Profiles from a Proposed GNSS-RO Constellation on Tropical Cyclone Forecasts in a Global Modeling System

Abstract A global observing system simulation experiment (OSSE) was used to assess the potential impact of a proposed Global Navigation Satellite System (GNSS) radio occultation (RO) constellation on tropical cyclone (TC) track, maximum 10-m wind speed (Vmax), and integrated kinetic energy (IKE) forecasts. The OSSE system was based on the 7-km NASA nature run and simulated RO refractivity determined by the spatial distribution of observations from the original planned (i.e., including both equatorial and polar orbits) Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2). Data were assimilated using the NOAA operational weather analysis and forecasting system. Three experiments generated global TC track, Vmax, and IKE forecasts over 6 weeks of the North Atlantic hurricane season in the North Atlantic, east Pacific, and west Pacific basins. Confidence in our results was bolstered because track forecast errors were similar to those of official National Hurricane Center forecasts, and Vmax errors and IKE errors showed similar results. GNSS-RO assimilation did not significantly impact global track forecasts, but did slightly degrade Vmax and IKE forecasts in the first 30–60 h of lead time. Global forecast error statistics show adding or excluding explicit random errors to RO profiles made little difference to forecasts. There was large forecast-to-forecast variability in RO impact. For two cases studied in depth, track and Vmax improvements and degradations were traced backward through the previous 24 h of assimilation cycles. The largest Vmax degradation was traced to particularly good control analyses rather than poor analyses caused by GNSS-RO.

The storm activities in the West Pacific from 1980 to 2018

The storm is an important weather system that significantly affects socioeconomics and human living. This study uses the global tropical cyclone best track data from IBTrACS, along with other meteorological states from ECMWF ERA-5 reanalysis from 1980 to 2018. In the West Pacific, storms are more likely to occur in summer (July, August and September). During the 39-year period, the occurrence of storm activities tends to be less frequent in summer, accompanied by increased sea surface temperature, total precipitation and total column water. It is also found that the storm number exhibited a high correlation with sea surface temperature, surface pressure, total precipitation and total column water in the middle West Pacific as well as the sea of Okhotsk in summer.

Sensitivity of Tropical Cyclone Formation to Resolution‐Dependent and Independent Tracking Schemes in High‐Resolution Climate Model Simulations

AbstractIn the present study, global tropical cyclone (TC) formation characteristics are estimated using two fundamentally different Commonwealth Scientific and Industrial Research Organisation (CSIRO) and Okubo‐Weiss zeta parameter (OWZP) tracking schemes in the reanalysis data and in a high‐resolution climate model with interannually varying sea surface temperatures. Both the schemes have a reasonable global geographical distribution of TC genesis locations with under simulation in the eastern North Atlantic and northwestern Australian regions. The mean annual TC frequency in the model is similar to observations using the CSIRO scheme but higher using the OWZP scheme, whereas the annual frequency in reanalysis using the OWZP scheme is similar to observations but halved using the CSIRO scheme. In the CSIRO scheme, both the resolution‐dependent thresholds and large‐scale climate may play a role for skilful TC formation statistics. In contrast, large‐scale climate leads to changes in OWZP TC detections. This highlights the importance of the large‐scale environment for TC detections in both the tracking schemes. The OWZP scheme can differentiate the monsoon lows from the actual TCs in the north Indian Ocean compared to the CSIRO scheme, which incorrectly detects them as TCs in the monsoon season. The distribution of TC lifetime in the model using the OWZP scheme is similar to observations. Conversely, the CSIRO scheme detected TCs have shorter lifetimes, perhaps due to intrinsic tracking scheme differences. Although the tracking schemes are fundamentally different, the study shows that there exist some similarities between them and for certain TC formation characteristics the OWZP scheme performs better compared to the CSIRO scheme.

Tropical Cyclones and Climate Change Assessment: Part II: Projected Response to Anthropogenic Warming

AbstractModel projections of tropical cyclone (TC) activity response to anthropogenic warming in climate models are assessed. Observations, theory, and models, with increasing robustness, indicate rising global TC risk for some metrics that are projected to impact multiple regions. A 2°C anthropogenic global warming is projected to impact TC activity as follows. 1) The most confident TC-related projection is that sea level rise accompanying the warming will lead to higher storm inundation levels, assuming all other factors are unchanged. 2) For TC precipitation rates, there is at least medium-to-high confidence in an increase globally, with a median projected increase of 14%, or close to the rate of tropical water vapor increase with warming, at constant relative humidity. 3) For TC intensity, 10 of 11 authors had at least medium-to-high confidence that the global average will increase. The median projected increase in lifetime maximum surface wind speeds is about 5% (range: 1%–10%) in available higher-resolution studies. 4) For the global proportion (as opposed to frequency) of TCs that reach very intense (category 4–5) levels, there is at least medium-to-high confidence in an increase, with a median projected change of +13%. Author opinion was more mixed and confidence levels lower for the following projections: 5) a further poleward expansion of the latitude of maximum TC intensity in the western North Pacific; 6) a decrease of global TC frequency, as projected in most studies; 7) an increase in global very intense TC frequency (category 4–5), seen most prominently in higher-resolution models; and 8) a slowdown in TC translation speed.

Modelling global tropical cyclone wind footprints

Abstract. A novel approach to modelling the surface wind field of landfalling tropical cyclones (TCs) is presented. The modelling system simulates the evolution of the low-level wind fields of landfalling TCs, accounting for terrain effects. A two-step process models the gradient-level wind field using a parametric wind field model fitted to TC track data and then brings the winds down to the surface using a numerical boundary layer model. The physical wind response to variable surface drag and terrain height produces substantial local modifications to the smooth wind field provided by the parametric wind profile model. For a set of US historical landfalling TCs the accuracy of the simulated footprints compares favourably with contemporary modelling approaches. The model is applicable from single-event simulation to the generation of global catalogues. One application demonstrated here is the creation of a dataset of 714 global historical TC overland wind footprints. A preliminary analysis of this dataset shows regional variability in the inland wind speed decay rates and evidence of a strong influence of regional orography. This dataset can be used to advance our understanding of overland wind risk in regions of complex terrain and support wind risk assessments in regions of sparse historical data.

Exploring Controls on Tropical Cyclone Count through the Geography of Environmental Favorability

AbstractGlobally, on the order of 100 tropical cyclones (TCs) occur annually, yet the processes that control this number remain unknown. Here we test a simple hypothesis that this number is limited by the geography of thermodynamic environments favorable for TC formation and maintenance. First, climatologies of TC potential intensity and environmental ventilation are created from reanalyses and are used in conjunction with historical TC data to define the spatiotemporal geography of favorable environments. Based on a range of predefined separation distances, the geographic domain of environmental favorability is populated with randomly placed TCs assuming a fixed minimum separation distance to achieve a maximum daily packing density of storms. Inclusion of a fixed storm duration yields an annual “maximum potential genesis” (MPG) rate, which is found to be an order of magnitude larger than the observed rate on Earth. The mean daily packing density captures the seasonal cycle reasonably well for both the Northern and Southern Hemispheres, though it substantially overestimates TC counts outside of each hemisphere’s active seasons. Interannual variability in MPG is relatively small and is poorly correlated with annual storm count globally and across basins, though modest positive correlations are found in the North Atlantic and east Pacific basins. Overall, the spatiotemporal distribution of favorable environmental conditions appears to strongly modulate the seasonal cycle of TCs, which certainly strongly influences the TC climatology, though it does not explicitly constrain the global annual TC count. Our methodology provides the first estimate of an upper bound for annual TC frequency and outlines a framework for assessing how local and large-scale factors may act to limit global TC count below the maximum potential values found here.

Real World and Tropical Cyclone World. Part I: High-Resolution Climate Model Verification

AbstractRecent global climate models with sufficient resolution and physics offer a promising approach for simulating real-world tropical cyclone (TC) statistics and their changing relationship with climate. In the first part of this study, we examine the performance of a high-resolution (~40-km horizontal grid) global climate model, the atmospheric component of the Australian Community Climate and Earth System Simulator (ACCESS) based on the Met Office Unified Model (UM8.5) Global Atmosphere (GA6.0). The atmospheric model is forced with observed sea surface temperature, and 20 years of integrations (1990–2009) are analyzed for evaluating the simulated TC statistics compared with observations. The model reproduces the observed climatology, geographical distribution, and interhemispheric asymmetry of global TC formation rates reasonably well. The annual cycle of regional TC formation rates over most basins is also well captured. However, there are some regional biases in the geographical distribution of TC formation rates. To identify the sources of these biases, a suite of model-simulated large-scale climate conditions that critically modulate TC formation rates are further evaluated, including the assessment of a multivariate genesis potential index. Results indicate that the model TC genesis biases correspond well to the inherent biases in the simulated large-scale climatic states, although the relative effects on TC genesis of some variables differs between basins. This highlights the model’s mean-state dependency in simulating accurate TC formation rates.

A global evaluation of multi‐model ensemble tropical cyclone track probability forecasts

AbstractAt the Met Office, dynamic ensemble forecasts from the Met Office Global and Regional Ensemble Prediction System (MOGREPS‐G), the European Centre for Medium‐Range Weather Forecasts Ensemble (ECMWF ENS) and National Centers for Environmental Prediction Global Ensemble Forecast System (NCEP GEFS) global ensemble forecast models are post‐processed to identify and track tropical cyclones. The ensemble members from each model are also combined into a 108‐member multi‐model ensemble. Track probability forecasts are produced for named tropical cyclones showing the probability of a location being within 120 km of a named tropical cyclone at any point in the next 7 days, and also broken down into each 24‐hour forecast period. This study presents the verification of these named‐storm track probabilities over a two‐year period across all global tropical cyclone basins, and compares the results from basin to basin. The combined multi‐model ensemble is found to increase the skill and value of the track probability forecasts over the best‐performing individual ensemble (ECMWF ENS), for both overall 7‐day track probability forecasts and 24‐hour track probabilities. Basin‐based and storm‐based verification illustrates that the best performing individual ensemble can change from basin to basin and from storm to storm, but that the multi‐model ensemble adds skill in every basin, and is also able to match the best performing individual ensemble in terms of overall probabilistic forecast skill in several high‐profile case‐studies. This study helps to illustrate the potential value and skill to be gained if operational tropical cyclone forecasting can continue to migrate away from a deterministic‐focused forecasting environment to one where the probabilistic situation‐based uncertainty information provided by the dynamic multi‐model ensembles can be incorporated into operational forecasts and warnings.

Are global tropical cyclones moving slower in a warming climate?

The local tropical-cyclone-related rainfall totals largely depend on the rain rate near the center and the translation speed of a tropical cyclone. Understanding how they respond to a changing climate has been a hot topic. A recent astounding study reported a 10% slowdown in global tropical-cyclone translation speed over the past 68 years (1949–2016) and implicitly related this to the weakening of tropical circulation forced by the anthropogenic warming. It thereby suggested that it might result in more local rainfall totals in a warming climate. However, here this study shows that no robust and significant observational and modeling evidences reveal that they are. The data artefacts introduced by the changes in measurement practices, particularly the introduction of satellite capabilities since the 1970s, are likely the main source of heterogeneities leading to such disagreement. The global slowdown of tropical-cyclone translation speed becomes indeterminate and a significant global speedup trend is even found over land if the records in more reliable satellite sensing era period starting from 1970 are examined, where this period is also the most pronounced warming period in the last half-century. The relationship between the slowdown of tropical cyclones and anthropogenic warming is therefore not apparent and the relevant potential increase in local rainfall totals in the future warming climate is suspicious.

Tropical cyclone sensitivities to CO2 doubling: roles of atmospheric resolution, synoptic variability and background climate changes

Responses of tropical cyclones (TCs) to CO2 doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~ 200 km, ~ 50 km and ~ 25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~ 25 km model also has a substantial and spatially-ubiquitous increase of Category 3–4–5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model’s transient fully-coupled 2 × CO2 TC activity response is largely recovered by “time-slice” experiments using time-invariant SST perturbations added to each model’s own SST climatology. The TC response to SST forcing depends on each model’s background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~ 25 km model, in response to CO2-induced warming patterns and CO2 doubling. Isolated CO2 doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~ 25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC “seeds”, which increase due to warming (more so in the ~ 25 km model) and decrease due to higher CO2 concentrations, and (2) less efficient development of these“seeds” into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.

Modulation of bay of bengal tropical cyclone activity by the madden-julian oscillation

The influence of the Madden-Julian oscillation (MJO) on global tropical cyclone (TC) activity has been well documented in many earlier studies. However, no prior studies have focused specifically on the MJO's impacts on TCs in the Bay of Bengal (BoB). Therefore, the present study examines the impact of the MJO on BoB TC activity during the two peak TC periods i.e. April–June (AMJ) and October–December (OND) from 1974 to 2015. The MJO considerably modulates various measures of TC activity in the BoB, including the number of TCs, the number of TC days, accumulated cyclone energy, the power dissipation index, TC genesis location and TC tracks. TC activity is significantly enhanced (suppressed) over the BoB when the convectively active MJO phase is positioned over the eastern Indian Ocean and the Maritime Continent (the Western Hemisphere and Africa). Alterations in the TC genesis locations and their tracks are more pronounced in OND than in AMJ. These changes in TC characteristics are mainly attributed to MJO-driven modulations in large-scale environmental conditions such as deep convection, mid-tropospheric relative humidity, sea surface temperature, sea level pressure, lower- and upper-level winds and vertical wind shear. A comprehensive knowledge of the MJO-TC relationship may be beneficial for short-term predictions of TC activity in the BoB.

Little evidence of reduced global tropical cyclone activity following recent volcanic eruptions

The impact of volcanic aerosols on recent global tropical cyclone (TC) activity is examined in observations, reanalysis, and models (the Coupled Model Intercomparison Project phase 5 - CMIP5 multi-model, and one single model large ensemble). In observations, we find a reduction of TC activity only in the North Atlantic following the last three strong volcanic eruptions; that signal, however, cannot be clearly attributed to volcanoes, as all three eruptions were simultaneous with El Niño events. In reanalyses, we find no robust impact of volcanic eruptions on potential intensity (PI) and genesis indices. In models, we find a reduction in PI after volcanic eruptions in the historical simulations, but this effect is significantly reduced when differences between the model environment and observations are accounted for. Morever, the CMIP5 multi-model historical ensemble shows no effect of volcanic eruptions on a TC genesis index. Finally, there is no robust and consistent reduction in recent TC activity following recent volcanic eruptions in a large set of synthetic TCs downscaled from these simulations. Taken together, these results show that in recent eruptions volcanic aerosols did not reduce global TC activity.

Technological improvements or climate change? Bayesian modeling of time-varying conformance to Benford's Law.

We develop a Bayesian time-varying model that tracks periods at which conformance to Benford’s Law is lower. Our methods are motivated by recent attempts to assess how the quality and homogeneity of large datasets may change over time by using the First-Digit Rule. We resort to a smooth multinomial logistic model which captures the dynamics governing the proportion of first digits, and apply the proposed model to global tropical cyclone tracks over the past two centuries. Our findings indicate that cumulative technological improvements may have only had a moderate influence on the homogeneity of the dataset, and hint that recent heterogeneity could be due to other drivers.

Estimating Tropical Cyclone Intensity by Satellite Imagery Utilizing Convolutional Neural Networks

Abstract Accurately estimating tropical cyclone (TC) intensity is one of the most critical steps in TC forecasting and disaster warning/management. For over 40 years, the Dvorak technique (and several improved versions) has been applied for estimating TC intensity by forecasters worldwide. However, the operational Dvorak techniques primarily used in various agencies have several deficiencies, such as inherent subjectivity leading to inconsistent intensity estimates within various basins. This collaborative study between meteorologists and data scientists has developed a deep-learning model using satellite imagery to estimate TC intensity. The conventional convolutional neural network (CNN), which is a mature technology for object classification, requires several modifications when being used for directly estimating TC intensity (a regression task). Compared to the Dvorak technique, the CNN model proposed here is objective and consistent among various basins; it has been trained with satellite infrared brightness temperature and microwave rain-rate data from 1097 global TCs during 2003–14 and optimized with data from 188 TCs during 2015–16. This paper also introduces an upgraded version that further improves the accuracy by using additional TC information (i.e., basin, day of year, local time, longitude, and latitude) and applying a postsmoothing procedure. An independent testing dataset of 94 global TCs during 2017 has been used to evaluate the model performance. A root-mean-square intensity difference of 8.39 kt (1 kt ≈ 0.51 m s−1) is achieved relative to the best track intensities. For a subset of 482 samples analyzed with reconnaissance observations, a root-mean-square intensity difference of 8.79 kt is achieved.

Impact of air–sea coupling on the simulated global tropical cyclone activity in the high-resolution Community Earth System Model (CESM)

Atmosphere–ocean coupling is critical for tropical cyclones (TC) formation and development. TCs derive their energy from the upper ocean, and the associated TC-ocean interactions can in turn modulate storm evolution. This study assesses the impact of ocean coupling on directly-simulated global TC activity in the high-resolution “TC-permitting” Community Earth System Model (CESM). Model-simulated global TC activity is evaluated in a 30-year fully-coupled CESM simulation (CPL), in which the 0.25° atmosphere component is coupled to the nominal 1° dynamic ocean (with ~ 0.27° horizontal grid spacing in the tropics). An atmosphere-only simulation (ATM) is branched from CPL, with sea surface temperature (SST) specified from CPL, which we use to isolate the effect of ocean coupling on TC activity. We find that the two-way ocean coupling can affect global TC frequency, geographical distribution, storm intensity, and interannual variability. ATM on average simulates 27% more major TC events than CPL globally, and the TC power dissipation is higher than CPL poleward of 12° latitude in both hemispheres. The lack of negative SST feedbacks in ATM allows TCs to have a longer intensification period and reach the maximum intensity at a higher latitude. In CPL, TC interannual variability is heavily influenced by El Nino/La Nina events. This relationship can be captured in ATM under strong events but is less predictable during weak and neutral years. Results help to better understand the connections and feedbacks linking air–sea coupling, tropical variability, and the directly simulated TC activity within the high-resolution Earth System Models.

A 13-Year Global Climatology of Tropical Cyclone Warm-Core Structures from AIRS Data

Abstract There is uncertainty as to whether the typical warm-core structure of tropical cyclones (TCs) is featured as an upper-level warm core or not. It has been hypothesized that data from the satellite-borne Advanced Microwave Sounding Unit (AMSU) are inadequate to resolve a realistic TC warm-core structure. This study first evaluates 13 years of Atmospheric Infrared Sounder (AIRS) temperature retrieval against recent dropsonde measurements in TCs. AIRS can resolve the TC warm-core structure well, comparable to the dropsonde observations, although the AMSU-A retrievals fail to do so. Using 13-yr AIRS data in global TCs, a global climatology of the TC warm-core structure is generated in this study. The typical warm-core height is at the upper level around 300–400 hPa for all TCs and increases with TC intensity: 400 hPa (~8 km) for tropical storms, 300 hPa (~10 km) for category 1–3 hurricanes, 250–300 hPa (~10–11 km) for category 4 hurricanes, and 150 hPa (~14 km) for category 5 hurricanes. The range of warm-core height varies with TC intensity as well. A strong correlation between TC intensity and warm-core strength is found. A weaker but still significant correlation between TC intensity and warm-core height is also found.

Development of Global Stochastic Tropical Cyclone Model by using large ensemble GCM simulation data(d4PDF)

Development of Global Stochastic Tropical Cyclone Model by using large ensemble GCM simulation data(d4PDF)

Global Tropical Cyclone Track Detection and Analysis of the d4PDF Mega-ensemble Projection

Global Tropical Cyclone Track Detection and Analysis of the d4PDF Mega-ensemble Projection

The increasing variability of tropical cyclone lifetime maximum intensity

This study investigates long-term changes in the variability of TC intensity of global tropical cyclones, a topic which has been relatively infrequently studied to date. Our study finds that the variability of global TC lifetime maximum intensity (LMI), as measured by the LMI standard deviation, increases during 1981–2016. The increasing trend in LMI variability is statistically significant for both the Northern and Southern Hemispheres, with three individual TC basins: the western North Pacific, the South Indian and the South Pacific also having statistically significant increases. This increasing trend primarily results from distinct changes in the relative percentages of TCs with different intensities. When comparing two periods: 1981–1998 and 1999–2016, the proportions of weak and strong TCs increase, whereas moderate TCs occur relatively less frequently. This bimodal pattern of observed LMI distribution change is further linked to opposite trends in the average intensities of TCs that undergo rapid intensification (RI) during their lifetime (RI TCs) and those that do not (non-RI TCs). The LMI distributions of RI and non-RI TCs migrate to higher and lower intensities, respectively. Our results demonstrate from an observational perspective that strong TCs have strengthened while weak TCs have weakened as the global climate has warmed since 1981.

The Climatological Effect of Saharan Dust on Global Tropical Cyclones in a Fully Coupled GCM

AbstractClimate in the tropical North Atlantic and West Africa is known to be sensitive to both the atmospheric burden and optical properties of aerosolized mineral dust. We investigate the global climatic response to an idealized perturbation in atmospheric burden of Saharan‐born mineral dust, comparable to the observed changes between the 1960s and 1980s, using simulations with the high‐resolution, fully coupled Geophysical Fluid Dynamics Laboratory Climate Model 2.5, Forecast‐oriented Low Ocean Resolution version, across a range of realistic optical properties, with a specific focus on tropical cyclones. The direct radiative responses at the top of the atmosphere and at the surface along with regional hydrologic and thermodynamic responses are in agreement with previous studies, depending largely on the amount of aerosol absorption versus scattering. In all simulations, dust causes a decrease in tropical cyclone activity across the North Atlantic Ocean, as determined by a tropical cyclone tracking scheme, with the largest response occurring in the most absorbing and scattering optical regimes. These changes are partially corroborated by common local genesis potential indices. However, no clear‐cut explanation can be developed upon inspection of their constituent variables. There are also nonnegligible anomalies in the North Pacific and Indian Oceans in these simulations. A relationship between accumulated cyclone energy and top of the atmosphere radiative flux anomalies is used to explain the North Atlantic anomalies, while analogy to known climate variations can help us understand the far‐field response to the dust forcing.