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  1. Free, publicly-accessible full text available September 1, 2024
  2. Abstract In 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. 
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    Abstract Earlier studies have proposed many semiempirical relations between climate and tropical cyclone (TC) activity. To explore these relations, this study conducts idealized aquaplanet experiments using both symmetric and asymmetric sea surface temperature (SST) forcings. With zonally symmetric SST forcings that have a maximum at 10°N, reducing meridional SST gradients around an Earth-like reference state leads to a weakening and southward displacement of the intertropical convergence zone. With nearly flat meridional gradients, warm-hemisphere TC numbers increase by nearly 100 times due particularly to elevated high-latitude TC activity. Reduced meridional SST gradients contribute to a poleward expansion of the tropics, which is associated with a poleward migration of the latitudes where TCs form or reach their lifetime maximum intensity. However, these changes cannot be simply attributed to the poleward expansion of Hadley circulation. Introducing zonally asymmetric SST forcings tends to decrease the global TC number. Regional SST warming—prescribed with or without SST cooling at other longitudes—affects local TC activity but does not necessarily increase TC genesis. While regional warming generally suppresses TC activity in remote regions with relatively cold SSTs, one experiment shows a surprisingly large increase of TC genesis. This increase of TC genesis over relatively cold SSTs is related to local tropospheric cooling that reduces static stability near 15°N and vertical wind shear around 25°N. Modeling results are discussed with scaling analyses and have implications for the application of the “convective quasi-equilibrium and weak temperature gradient” framework. 
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  4. Abstract

    Recent climate modeling studies point to an increase in tropical cyclone rainfall rates in response to climate warming. These studies indicate that the percentage increase in tropical cyclone rainfall rates often outpaces the increase in saturation specific humidity expected from the Clausius-Clapeyron relation (~7% °C−1). We explore the change in tropical cyclone rainfall rates over all oceans under global warming using a high-resolution climate model with the ability to simulate the entire intensity spectrum of tropical cyclones. Consistent with previous results, we find a robust increase of tropical cyclone rainfall rates. The percentage increase for inner-core tropical cyclone rainfall rates in our model is markedly larger than the Clausius-Clapeyron rate. However, when the impact of storm intensity is excluded, the rainfall rate increase shows a much better match with the Clausius-Clapeyron rate, suggesting that the “super Clausius-Clapeyron” scaling of rainfall rates with temperature increase is due to the warming-induced increase of tropical cyclone intensity. The increase of tropical cyclone intensity and environmental water vapor, in combination, explain the tropical cyclone rainfall rate increase under global warming.

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  5. Abstract

    Here we present a machine learning–based wind reconstruction model. The model reconstructs hurricane surface winds with XGBoost, which is a decision-tree-based ensemble predictive algorithm. The model treats the symmetric and asymmetric wind fields separately. The symmetric wind field is approximated by a parametric wind profile model and two Bessel function series. The asymmetric field, accounting for asymmetries induced by the storm and its ambient environment, is represented using a small number of Laplacian eigenfunctions. The coefficients associated with Bessel functions and eigenfunctions are predicted by XGBoost based on storm and environmental features taken from NHC best-track and ERA-Interim data, respectively. We use HWIND for the observed wind fields. Three parametric wind profile models are tested in the symmetric wind model. The wind reconstruction model’s performance is insensitive to the choice of the profile model because the Bessel function series correct biases of the parametric profiles. The mean square error of the reconstructed surface winds is smaller than the climatological variance, indicating skillful reconstruction. Storm center location, eyewall size, and translation speed play important roles in controlling the magnitude of the leading asymmetries, while the phase of the asymmetries is mainly affected by storm translation direction. Vertical wind shear impacts the asymmetry phase to a lesser degree. Intended applications of this model include assessing hurricane risk using synthetic storm event sets generated by statistical–dynamical downscaling hurricane models.

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