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The downshear‐left enhancement of tropical cyclone rainfall has been demonstrated previously, but the radial dependence of this effect was not analyzed in detail. This study quantifies the progressive upwind shift of the wavenumber‐1 maximum rain position with radius relative to the vertical wind shear direction. This shift is visualized as a distinctive upwind spiral of the maximum. It is shown that this spiral pattern is generally observed across various storm intensities, shear strength, and ocean basins. Detailed examination revealed that the maximum downwind deflection angle of the wavenumber‐1 rain maximum relative to the shear direction is smaller for tropical storms than hurricanes, but insensitive to hurricane intensity. It is proposed that the spirality is produced by a continuous decline in angular advection of air parcels with radius. The stability of the deflection angle in hurricanes may be accounted for by a corresponding increase in vertical ascent under strengthening angular flow. Plain Language Summary Tropical cyclones (TCs) can produce torrential rainfall that generates floods, causing significant socio‐economic losses. Understanding the spatial structure of the TC rain field is crucial for improving disaster preparedness. The TC rain field can be thought of as the combination of a symmetric and an asymmetric part. By using a technique called Fourier decomposition, we can break down the asymmetric part into individual wavenumber components. The first component, wavenumber‐1 (WN‐1), is dominant and tends to be larger in the downshear quadrants under vertical wind shear. Using 21 years of global WN‐1 rain fields, we produced composite images aligned with the shear direction. We discovered that the positions at which the WN‐1 maximum occurs progressively shift upwind with increasing distance from the TC center, forming a spiral. We provided the first quantification of the observed spirals and showed that this is a general pattern that exists across different TC intensities, shear strength, and ocean basins. We also identified detailed changes in the pattern with storm intensity and introduced simple models as a first attempt to comprehend these changes. The findings can improve weather forecasts and risk predictions, making us better prepared for hazards associated with TC rainfall. Key Points There exists a general and progressive upwind shift in the wavenumber‐1 maximum with radius in global shear‐relative rainfall composites Maximum downwind deflection of the wavenumber‐1 maxima rises with storm intensity up to Category 1 on the Saffir–Simpson Hurricane Scale Linearity between the angular velocity and the vertical velocity of the storm may stabilize the downwind deflection beyond Category 1

Global models comprising the sixth-generation Coupled Climate Model Intercomparison Project (CMIP6) are downscaled using a very high-resolution but simplified coupled atmosphere–ocean tropical cyclone model, as a means of estimating the response of global tropical cyclone activity to increasing greenhouse gases. As with a previous downscaling of CMIP5 models, the results show an increase in both the frequency and severity of tropical cyclones, robust across the models downscaled, in response to increasing greenhouse gases. The increase is strongly weighted to the Northern Hemisphere, and especially noteworthy is a large increase in the higher latitudes of the North Atlantic. Changes are insignificant in the South Pacific across metrics. Although the largest increases in track density are far from land, substantial increases in global landfalling power dissipation are indicated. The incidence of rapid intensification increases rapidly with warming, as predicted by existing theory. Measures of robustness across downscaled climate models are presented, and comparisons to tropical cyclones explicitly simulated in climate models are discussed.

The radius of maximum wind (RMW) defines the location of the maximum winds in a tropical cyclone and is critical to understanding intensity change as well as hazard impacts. A comparison between the Hurricane Analysis and Forecast System (HAFS) models and two statistical models based off the National Hurricane Center official forecast is conducted relative to a new baseline climatology to better understand whether models have skill in forecasting the RMW of North Atlantic tropical cyclones. On average, the HAFS models are less skillful than the climatology and persistence baseline and two statistically derived RMW estimates. The performance of the HAFS models is dependent on intensity with better skill for stronger tropical cyclones compared to weaker tropical cyclones. To further improve guidance of tropical cyclone hazards, more work needs to be done to improve forecasts of tropical cyclone structure. Plain Language Summary The radius of maximum wind (RMW) is a key structural parameter of tropical cyclones that describes how far the strongest winds are from the storm's center. The RMW is closely tied to significant hazards such as wind, storm surge, and rainfall. However, little forecast guidance is provided for the RMW resulting in forecasters using climatological estimates to help communicate hazard risk. In order to better forecast the RMW, we need to understand the performance of the few guidance techniques available. We compare RMW forecasts from the Hurricane Analysis and Forecast System (HAFS) to two statistical models and a climatological estimate. Forecasts of the RMW from HAFS are not competitive with statistical derivations of the RMW with marginally better to comparable skill for stronger tropical cyclones. The results indicate that there is a strong need for future improvements to better predict tropical cyclone structure in addition to track and intensity. Key Points Forecasting the radius of maximum wind (RMW) is important for forecasting tropical cyclone hazards A RMW climatology and persistence model is created to determine forecast skill Statistical RMW forecasts are skillful and outperform dynamical model guidance

The Global Ensemble Forecast System (GEFS) is upgraded to version 12, in which the legacy Global Spectral Model (GSM) is replaced by a model with a new dynamical core—the Finite Volume Cubed-Sphere Dynamical Core (FV3). Extensive tests were performed to determine the optimal model and ensemble configuration. The new GEFS has cubed-sphere grids with a horizontal resolution of about 25 km and an increased ensemble size from 20 to 30. It extends the forecast length from 16 to 35 days to support subseasonal forecasts. The stochastic total tendency perturbation (STTP) scheme is replaced by two model uncertainty schemes: the stochastically perturbed physics tendencies (SPPT) scheme and stochastic kinetic energy backscatter (SKEB) scheme. Forecast verification is performed on a period of more than two years of retrospective runs. The results show that the upgraded GEFS outperforms the operational-at-the-time version by all measures included in the GEFS verification package. The new system has a better ensemble error–spread relationship, significantly improved skills in large-scale environment forecasts, precipitation probability forecasts over CONUS, tropical cyclone track and intensity forecasts, and significantly reduced 2-m temperature biases over North America. GEFSv12 was implemented on 23 September 2020.

A recent study showed that the global average latitude where tropical cyclones achieve their lifetime-maximum intensity has been migrating poleward at a rate of about one-half degree of latitude per decade over the last 30 years in each hemisphere. However, it does not answer a critical question: is the poleward migration of tropical cyclone lifetime-maximum intensity associated with a poleward migration of tropical cyclone genesis? In this study we will examine this question. First we analyze changes in the environmental variables associated with tropical cyclone genesis, namely entropy deficit, potential intensity, vertical wind shear, vorticity, skin temperature and specific humidity at 500 hPa in reanalysis datasets between 1980 and 2013. Then, a selection of these variables is combined into two tropical cyclone genesis indices that empirically relate tropical cyclone genesis to large-scale variables. We find a shift toward greater (smaller) average potential number of genesis at higher (lower) latitudes over most regions of the Pacific Ocean, which is consistent with a migration of tropical cyclone genesis towards higher latitudes. We then examine the global best track archive and find coherent and significant poleward shifts in mean genesis position over the Pacific Ocean basins.

This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial–temporal data with statistical data by extracting features with deep learning encoder–decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and eastern Pacific basins in 2016–19 for 24-h lead-time track and intensity forecasts and show they achieve comparable mean absolute error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve upon the National Hurricane Center’s official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting.

The physical processes governing a tropical cyclone's lifecycle are largely understood, but key processes occur at scales below those resolved by global climate models. Increased resolution may help simulate realistic tropical cyclone intensification. We examined fully coupled, global storm‐resolving models run at resolutions in the range 28–2.8 km in the atmosphere and 28–5 km in the ocean. Simulated tropical cyclone activity, peak intensity, intensification rate, and horizontal wind structure are all more realistic at a resolution of ∼5 km compared with coarser resolutions. Rapid intensification, which is absent at typical climate model resolutions, is also captured, and exhibits sensitivity to how, and if, deep convection is parameterized. Additionally, the observed decrease in inner‐core horizontal size with increasing intensification rate is captured at storm‐resolving resolution. These findings highlight the importance of global storm‐resolving models for quantifying risk and understanding the role of intense tropical cyclones in the climate system. Plain Language Summary Simulating strong tropical storms (i.e., major hurricanes, super typhoons) with climate models is challenging because important processes that act to intensify a storm occur over spatial scales that are too small for global models to capture. Typical models lack sufficient resolution in the atmosphere and ocean, often constrained by computational resources. Recently, in a few models, resolution has increased to a point where each grid cell represents an area of just a few square kilometres, a significant leap of one or two orders of magnitude. We analyzed tropical storms simulated by these state‐of‐the‐art, so‐called storm‐resolving models and found that peak tropical storm intensity and the rate at which storms intensify are both more realistic. These models also simulate the rapid intensification of tropical storms and capture the small eye diameters often seen in the most intense storms. Our work provides evidence that storm‐resolving resolution may help us better understand the role of tropical storms in the climate system and predict their behavior in a warming climate. Key Points Simulated tropical cyclone characteristics analyzed in two fully coupled global climate models at atmospheric resolutions of 28 to 2.8 km Tropical cyclone intensification rate is close to observations at resolutions of 5 km or finer, and rapid intensification is captured Storm‐resolving models also capture the observed relationship between high intensification rate and small inner‐core size

Model 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.

This study examines the interdecadal variations in the frequency and intensity of tropical cyclones (TCs) making landfall in South China (SC) during the period 1975–2018. The annual frequency shows a decrease in 1997 but rises again since 2008 and the annual maximum landfall intensity (MLI) shows an increase since 2012. According to these variations, three subperiods, 1975–1996 (higher frequency but lower MLI), 1997–2011 (lower frequency and MLI) and 2012–2018 (higher frequency and MLI), are defined. The increase in MLI during 2012–2018 is related to the increases in the frequency of (1) TCs undergoing rapid intensification over the South China Sea (SCS) and landfalling in SC, with higher maximum intensity and location of maximum intensity closer to the coast of SC, and (2) intense typhoons (ITYs) over the western North Pacific (WNP), which maintain high intensity before landfall. These changes are closely related to the lower vertical wind shear and higher TC heat potential over the ocean east of the Philippines and the northern part of the SCS. Such an environment is more conducive for TC intensification, leading to the observed increases in the number of rapid-intensifying TCs over the SCS and ITYs over the WNP. Some of these latter TCs move across the SCS and tend to maintain high intensity during landfall in SC. The steering flow also changes, which allows more TCs to enter the SCS, resulting in an increase of ITYs making landfall in SC.

There is no consensus on whether climate change has yet affected the statistics of tropical cyclones, owing to their large natural variability and the limited period of consistent observations. In addition, projections of future tropical cyclone activity are uncertain, because they often rely on coarse-resolution climate models that parameterize convection and hence have difficulty in directly representing tropical cyclones. Here we used convection-permitting regional climate model simulations to investigate whether and how recent destructive tropical cyclones would change if these events had occurred in pre-industrial and in future climates. We found that, relative to pre-industrial conditions, climate change so far has enhanced the average and extreme rainfall of hurricanes Katrina, Irma and Maria, but did not change tropical cyclone wind-speed intensity. In addition, future anthropogenic warming would robustly increase the wind speed and rainfall of 11 of 13 intense tropical cyclones (of 15 events sampled globally). Additional regional climate model simulations suggest that convective parameterization introduces minimal uncertainty into the sign of projected changes in tropical cyclone intensity and rainfall, which allows us to have confidence in projections from global models with parameterized convection and resolution fine enough to include tropical cyclones. Climate model simulations reveal that recent destructive tropical cyclones would have been equally intense in terms of wind speed but would have produced less rainfall if these events had occurred in pre-industrial climates, and in future climates they would have greater wind speeds and rainfall.