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Timothy Hall and Emmi Yonekura

Abstract

A statistical–stochastic model of the complete life cycle of North Atlantic (NA) tropical cyclones (TCs) is used to examine the relationship between climate and landfall rates along the North American Atlantic and Gulf Coasts. The model draws on archived data of TCs throughout the North Atlantic to estimate landfall rates at high geographic resolution as a function of the ENSO state and one of two different measures of sea surface temperature (SST): 1) SST averaged over the NA subtropics and the hurricane season and 2) this SST relative to the seasonal global subtropical mean SST (termed relSST). Here, the authors focus on SST by holding ENSO to a neutral state. Jackknife uncertainty tests are employed to test the significance of SST and relSST landfall relationships. There are more TC and major hurricane landfalls overall in warm years than cold, using either SST or relSST, primarily due to a basinwide increase in the number of storms. The signal along the coast, however, is complex. Some regions have large and significant sensitivity (e.g., an approximate doubling of annual major hurricane landfall probability on Texas from −2 to +2 standard deviations in relSST), while other regions have no significant sensitivity (e.g., the U.S. mid-Atlantic and Northeast coasts). This geographic structure is due to both shifts in the regions of primary TC genesis and shifts in TC propagation.

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Emmi Yonekura and Timothy M. Hall

Abstract

Improvements on a statistical tropical cyclone (TC) track model in the western North Pacific Ocean are described. The goal of the model is to study the effect of El Niño–Southern Oscillation (ENSO) on East Asian TC landfall. The model is based on the International Best-Track Archive for Climate Stewardship (IBTrACS) database of TC observations for 1945–2007 and employs local regression of TC formation rates and track increments on the Niño-3.4 index and seasonally varying climate parameters. The main improvements are the inclusion of ENSO dependence in the track propagation and accounting for seasonality in both genesis and tracks. A comparison of simulations of the 1945–2007 period with observations concludes that the model updates improve the skill of this model in simulating TCs. Changes in TC genesis and tracks are analyzed separately and cumulatively in simulations of stationary extreme ENSO states. ENSO effects on regional (100-km scale) landfall are attributed to changes in genesis and tracks. The effect of ENSO on genesis is predominantly a shift in genesis location from the southeast in El Niño years to the northwest in La Niña years, resulting in higher landfall rates for the East Asian coast during La Niña. The effect of ENSO on track propagation varies seasonally and spatially. In the peak activity season (July–October), there are significant changes in mean tracks with ENSO. Landfall-rate changes from genesis– and track–ENSO effects in the Philippines cancel out, while coastal segments of Vietnam, China, the Korean Peninsula, and Japan show enhanced La Niña–year increases.

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Emmi Yonekura and Timothy M. Hall

Abstract

A new statistical model for western North Pacific Ocean tropical cyclone genesis and tracks is developed and applied to estimate regionally resolved tropical cyclone landfall rates along the coasts of the Asian mainland, Japan, and the Philippines. The model is constructed on International Best Track Archive for Climate Stewardship (IBTrACS) 1945–2007 historical data for the western North Pacific. The model is evaluated in several ways, including comparing the stochastic spread in simulated landfall rates with historic landfall rates. Although certain biases have been detected, overall the model performs well on the diagnostic tests, for example, reproducing well the geographic distribution of landfall rates. Western North Pacific cyclogenesis is influenced by El Niño–Southern Oscillation (ENSO). This dependence is incorporated in the model’s genesis component to project the ENSO-genesis dependence onto landfall rates. There is a pronounced shift southeastward in cyclogenesis and a small but significant reduction in basinwide annual counts with increasing ENSO index value. On almost all regions of coast, landfall rates are significantly higher in a negative ENSO state (La Niña).

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Ning Lin, Renzhi Jing, Yuyan Wang, Emmi Yonekura, Jianqing Fan, and Lingzhou Xue

Abstract

A progression of advanced statistical methods is applied to investigate the dependence of the 6-h tropical cyclone (TC) intensity change on various environmental variables, including the recently developed ventilation index (VI). The North Atlantic (NA) and western North Pacific (WNP) observations from 1979 to 2014 are used. As a first step, a model of the intensity change is developed as a linear function of 13 variables used in operational models, obtaining statistical R 2 values of 0.26 for NA and 0.3 for WNP. Statistical variable selection techniques are then applied to significantly reduce the number of predictors (to 5–11), while keeping similar R 2 values with linear or nonlinear models. Further reduction of the number of predictors (to 5–7) and significant improvement of R 2 (0.41–0.53) are obtained with mixture modeling, indicating that the dependence of TC intensification on the environment is nonhomogeneous. Applying VI as the environmental predictor in the mixture modeling gives R 2 results (0.41–0.74) similar to or better than those with more environmental variables, confirming that VI is a dominant environmental variable, although its effect on TC intensification is quite heterogeneous. However, the overall predictive R 2 results of the mixture models are relatively low (<0.3), as the considered environmental variables have limited predictability for the occurrence of extreme/rapid intensification. Finally, nonparametric regression with six predictors [current intensity, previous intensity change, the three components of VI (maximum potential intensity, shear, and entropy deficit), and 200-hPa zonal wind] performs relatively well with predictive R 2 values of 0.37 for NA and 0.36 for WNP. The predictability of these statistical models may be further improved by adding oceanic and inner-core process predictors.

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