James B. Elsner

The increasing intensity of the strongest tropical cyclones

Atlantic tropical cyclones are getting stronger on average, with a 30-year trend that has been related to an increase in ocean temperatures over the Atlantic Ocean and elsewhere. Over the rest of the tropics, however, possible trends in tropical cyclone intensity are less obvious, owing to the unreliability and incompleteness of the observational record and to a restricted focus, in previous trend analyses, on changes in average intensity. Here we overcome these two limitations by examining trends in the upper quantiles of per-cyclone maximum wind speeds (that is, the maximum intensities that cyclones achieve during their lifetimes), estimated from homogeneous data derived from an archive of satellite records. We find significant upward trends for wind speed quantiles above the 70th percentile, with trends as high as 0.3 ± 0.09 m s-1 yr-1 (s.e.) for the strongest cyclones. We note separate upward trends in the estimated lifetime-maximum wind speeds of the very strongest tropical cyclones (99th percentile) over each ocean basin, with the largest increase at this quantile occurring over the North Atlantic, although not all basins show statistically significant increases. Our results are qualitatively consistent with the hypothesis that as the seas warm, the ocean has more energy to convert to tropical cyclone wind.

Effect of El Niño on U.S. Landfalling Hurricanes, Revisited

Abstract 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 (sustained 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 t...

Spatial Variations in Major U.S. Hurricane Activity: Statistics and a Physical Mechanism

The authors provide a statistical and physical basis for understanding regional variations in major hurricane activity along the U.S. coastline on long timescales. Current statistical models of hurricane activity are focused on the frequency of events over the entire North Atlantic basin. The exception is the lead author’s previous work, which models the occurrence of hurricanes over the Caribbean Sea, Gulf of Mexico, and the southeast U.S. coast separately. Here the authors use statistics to analyze data from historical and paleoclimatic records to expand this work. In particular, an inverse correlation in major hurricane activity across latitudes at various timescales is articulated. When activity is above normal at high latitudes it tends to be below normal at low latitudes and vice versa. Past research, paleoclimatic records, and historical data hint at the potential of using the North Atlantic oscillation (NAO) as an indicator of where storms will likely track over long timescales. An excited (relaxed) NAO is associated with higher (lower) latitude recurving (nonrecurving) storms. The Gulf (East) Coast is more susceptible to a major hurricane strike during a relaxed (excited) NAO.

Changes in the rates of North Atlantic major hurricane activity during the 20th century

The authors document and explain changes in the rates of North Atlantic major hurricanes over the 20th century. A change-point analyses identifies two contrasting regimes of activity. The regimes have significantly different occurrence rates that coincide with changes in the climate over the extratropical North Atlantic. In conjunction with the recent Arctic warming and a relaxation of the North Atlantic oscillation, it is speculated that we are beginning a new period of greater major hurricane activity.

Prediction Models for Annual U.S. Hurricane Counts

Abstract The authors build on their efforts to understand and predict coastal hurricane activity by developing statistical seasonal forecast models that can be used operationally. The modeling strategy uses May–June averaged values representing the North Atlantic Oscillation (NAO), the Southern Oscillation index (SOI), and the Atlantic multidecadal oscillation to predict the probabilities of observing U.S. hurricanes in the months ahead (July–November). The models are developed using a Bayesian approach and make use of data that extend back to 1851 with the earlier hurricane counts (prior to 1899) treated as less certain relative to the later counts. Out-of-sample hindcast skill is assessed using the mean-squared prediction error within a hold-one-out cross-validation exercise. Skill levels are compared to climatology. Predictions show skill above climatology, especially using the NAO + SOI and the NAO-only models. When the springtime NAO values are below normal, there is a heightened risk of U.S. hurrica...

Nonlinear Prediction, Chaos, and Noise

Abstract We present a brief overview of some new methodologies for making predictions on time-series data. These ideas stern from two rapidly growing fields: nonlinear dynamics (choas) theory and parallel distributed processing. Examples are presented that show the usefulness of such methods in making short-term predictions. It is suggested that such methodologies are capable of distinguishing between chaos and noise. Implications of these ideas and methods in the study of weather and climate are discussed.

Climatology Models for Extreme Hurricane Winds near the United States

Abstract The rarity of severe coastal hurricanes implies that empirical estimates of extreme wind speed return levels will be unreliable. Here climatology models derived from extreme value theory are estimated using data from the best-track [Hurricane Database (HURDAT)] record. The occurrence of a hurricane above a specified threshold intensity level is assumed to follow a Poisson distribution, and the distribution of the maximum wind is assumed to follow a generalized Pareto distribution. The likelihood function is the product of the generalized Pareto probabilities for each wind speed estimate. A geographic region encompassing the entire U.S. coast vulnerable to Atlantic hurricanes is of primary interest, but the Gulf Coast, Florida, and the East Coast regions are also considered. Model parameters are first estimated using a maximum likelihood (ML) procedure. Results estimate the 100-yr return level for the entire coast at 157 kt (±10 kt), but at 117 kt (±4 kt) for the East Coast region (1 kt = 0.514 m ...

Assessing Forecast Skill through Cross Validation

Abstract This study explains the method of cross validation for assessing forecast skill of empirical prediction models. Cross validation provides a relatively accurate measure of an empirical procedures ability to produce a useful prediction rule from a historical dataset. The method works by omitting observations and then measuring “hindcast” errors from attempts to predict these missing observations from the remaining data. The idea is to remove the information about the omitted observations that would be unavailable in real forecast situations and determine how well the chosen procedure selects prediction rules when such information is deleted. The authors examine the methodology of cross validation and its potential pitfalls in practical applications through a set of examples. The concepts behind cross validation are quite general and need to be considered whenever empirical forecast methods, regardless of their sophistication are employed.

Improving Extended-Range Seasonal Predictions of Intense Atlantic Hurricane Activity

Abstract This study shows that hindcasts of seasonal numbers of intense Atlantic hurricanes made using a nonlinear statistical model are superior to those made by linear statistical models previously described in the literature. A fully cross-validated Poisson model achieves an increase of nearly 40% in hindcast skill when compared to a fully cross-validated linear model. Improvements are most evident for years with relatively large numbers of intense hurricanes. It is suggested that a significant improvement in forecast skill is possible with the Poisson model. A prediction for the 1993 season is made, and calls for two intense hurricanes to visit the Atlantic basin.

Fluctuations in North Atlantic Hurricane Frequency

The annual record of hurricane activity in the North Atlantic basin for the period 1886‐1996 is examined from the perspective of time series analysis. Singular spectrum analysis combined with the maximum entropy method is used on the time series of annual hurricane occurrences over the entire basin to extract the dominant modes of oscillation. The annual frequency of hurricanes is modulated on the biennial, semidecadal, and neardecadal timescales. The biennial and semidecadal oscillations correspond to two well-known physical forcings in the local and global climate. These include a shift in tropical stratospheric winds between an east and west phase [quasi-biennial oscillation (QBO)] and a shift in equatorial Pacific Ocean temperatures between a warm .