Objectively derived resolution-dependent criteria are defined for the detection of tropical cyclones in model simulations and observationally based analyses. These criteria are derived from the wind profiles of observed tropical cyclones, averaged at various resolutions. Both an analytical wind profile model and two-dimensional observed wind analyses are used. The results show that the threshold wind speed of an observed tropical cyclone varies roughly linearly with resolution. The criteria derived here are compared to the numerous different criteria previously employed in climate model simulations. The resulting method provides a simple means of comparing climate model simulations and reanalyses.
Changes in the frequency of U.S. landfalling hurricanes with respect to the El Nino-Southern Oscillation (ENSO) cycle are assessed. Ninety-eight years (1900-97) of U.S. landfalling hurricanes are classified, using sea surface temperature anomaly data from the equatorial Pacific Ocean, as occurring during an El Nino (anomalously warm tropical Pacific waters), La Nina (anomalously cold tropical Pacific waters), or neither (neutral). The mean and variance of U.S. landfalling hurricanes are determined for each ENSO phase. Each grouping is then tested for Poisson distribution using a chi-squared test. Resampling using a "bootstrap" technique is then used to determine the 5% and 95% confidence limits of the results. Last, the frequency of major U.S. landfalling hurricanes (sus-tained winds of 96 kt or more) with respect to ENSO phase is assessed empirically. The results indicated that El Nino events show a reduction in the probability of a U.S. landfalling hurricane, while La Nina shows an increase in the chance of a U.S. hurricane strike. Quantitatively, the probability of two or more landfalling U.S. hurricanes during an El Nino is 28%, of two or more landfalls during neutral conditions is 48%, and of two or more landfalls during La Nina is 66%. The frequencies of landfalling major hurricanes show similar results. The probability of one or more major hurricane landfall during El Nino is 23% but is 58% during neutral conditions and 63% during La Nina.
The very limited instrumental record makes extensive analyses of the natural variability of global tropical cyclone activities difficult in most of the tropical cyclone basins. However, in the two regions where reasonably reliable records exist (the North Atlantic and the western North Pacific), substantial multidecadal variability (particularly for intense Atlantic hurricanes) is found, but there is no clear evidence of long-term trends. Efforts have been initiated to use geological and geomorphological records and analysis of oxygen isotope ratios in rainfall recorded in cave stalactites to establish a paleoclimate of tropical cyclones, but these have not yet produced definitive results. Recent thermodynamical estimation of the maximum potential intensities (MPI) of tropical cyclones shows good agreement with observations. Although there are some uncertainties in these MPI approaches, such as their sensitivity to variations in parameters and failure to include some potentially important interactions such as ocean spray feedbacks, the response of upperoceanic thermal structure, and eye and eyewall dynamics, they do appear to be an objective tool with which to predict present and future maxima of tropical cyclone intensity. Recent studies indicate the MPI of cyclones will remain the same or undergo a modest increase of up to 10%-20%. These predicted changes are small compared with the observed natural variations and fall within the uncertainty range in current studies. Furthermore, the known omissions (ocean spray, momentum restriction, and possibly also surface to 300-hPa lapse rate changes) could all operate to mitigate the predicted intensification. A strong caveat must be placed on analysis of results from current GCM simulations of the "tropical-cyclone-like" vortices. Their realism, and hence prediction skill (and also that of "embedded" mesoscale models), is greatly limited by the coarse resolution of current GCMs and the failure to capture environmental factors that govern cyclone intensity. Little, therefore, can be said about the potential changes of the distribution of intensities as opposed to maximum achievable intensity. Current knowledge and available techniques are too rudimentary for quantitative indications of potential changes in tropical cyclone frequency. The broad geographic regions of cyclogenesis and therefore also the regions affected by tropical cyclones are not expected to change significantly. It is emphasized that the popular belief that the region of cyclogenesis will expand with the 26°C SST isotherm is a fallacy. The very modest available evidence points to an expectation of little or no change in global frequency. Regional and local frequencies could change substantially in either direction, because of the dependence of cyclone genesis and track on other phenomena (e.g., ENSO) that are not yet predictable. Greatly improved skills from coupled global ocean-atmosphere models are required before improved predictions are possible.
A normalization estimates damage from an historical extreme event were that same event to occur under contemporary societal conditions. This paper provides a major update the leading dataset on normalized US hurricane losses in the continental United States from 1900 to 2017. Over this period, hurricanes caused $1.9 trillion in normalized (2017) damage, or just over $16.1 billion annually.Landfalling hurricanes in the continental United States (CONUS) are responsible for more than 2/3 of global catastrophe losses since 1980, according to data from Munich Re, a global reinsurance company. 1 The management of economic risks associated with hurricanes largely relies on "catastrophe models" which estimate losses from modeled storms in the context of contemporary data on exposure and vulnerability. 2,3 As a complement to such model-based approaches, an empirical approach to hurricane risk estimation has been employed since 1998, called "normalization." 4,5 A normalization estimates damage of an historical extreme event were it to occur under contemporary societal conditions. Normalization methodologies are widely
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