Thomas H. Jagger

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.

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...

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 ...

Detecting Shifts in Hurricane Rates Using a Markov Chain Monte Carlo Approach

Time series of annual hurricane counts are examined using a changepoint analysis. The approach simulates posterior distributions of the Poisson-rate parameter using Gibbs sampling. A posterior distribution is a distribution of a parameter conditional on the data. The analysis is first performed on the annual series of major North Atlantic hurricane counts from the twentieth century. Results show significant shifts in hurricane rates during the middle 1940s, the middle 1960s, and at 1995, consistent with earlier published results. The analysis is then applied to U.S. hurricane activity. Results show no abrupt changes in overall coastal hurricane rates during the twentieth century. In contrast, the record of Florida hurricanes indicates downward shifts during the early 1950s and the late 1960s. The shifts result from fewer hurricanes passing through the Bahamas and the western Caribbean Sea. No significant rate shifts are noted along either the Gulf or East Coasts. Climate influences on coastal hurricane activity are then examined. Results show a significant reduction in U.S. hurricane activity

A Dynamic Probability Model of Hurricane Winds in Coastal Counties of the United States

Abstract The authors develop and apply a model that uses hurricane-experience data in counties along the U.S. hurricane coast to give annual exceedence probabilities to maximum tropical cyclone wind events. The model uses a maximum likelihood estimator to determine a linear regression for the scale and shape parameters of the Weibull distribution for maximum wind speed. Model simulations provide quantiles for the probabilities at prescribed hurricane intensities. When the model is run in the raw climatological mode, median probabilities compare favorably with probabilities from the National Hurricane Center’s risk analysis program “HURISK” model. When the model is run in the conditional climatological mode, covariate information in the form of regression equations for the distributional parameters allows probabilities to be estimated that are conditioned on climate factors. Changes to annual hurricane probabilities with respect to a combined effect of a La Nina event and a negative phase of the North Atla...

Comparison of Hurricane Return Levels Using Historical and Geological Records

Abstract Hurricane return levels estimated using historical and geological information are quantitatively compared for Lake Shelby, Alabama. The minimum return level of overwash events recorded in sediment cores is estimated using a modern analog (Hurricane Ivan of 2004) to be 54 m s−1 (105 kt) for a return period of 318 yr based on 11 events over 3500 yr. The expected return level of rare hurricanes in the observed records (1851–2005) at this location and for this return period is estimated using a parametric statistical model and a maximum likelihood procedure to be 73 m s−1 (141 kt), with a lower bound on the 95% confidence interval of 64 m s−1 (124 kt). Results are not significantly different if data are taken from the shorter 1880–2005 period. Thus, the estimated sensitivity of Lake Shelby to overwash events is consistent with the historical record given the model. In fact, assuming the past is similar to the present, the sensitivity of the site to overwash events as estimated from the model is likel...

Hurricanes and Climate Change

1. The tropical cyclone climate model intercomparison project 2. Change of tropical cyclone and seasonal climate state in a global warming experiment with a global cloud-system-resolving model 3. Role of the SST anomaly structures in response of cycogenesis to global warming 4. Tropical cyclones rainfall in the observations, reanalysis and ARPEGE simulations in the North Atlantic basin 5. Tropical cyclones as a critical phenomenon 6. Environmental signals in property damage losses from hurricanes 7. A statistical analysis of the frequency of United States and eastern North Pacific hurricanes related to solar activity 8. Regional typhoon activity as revealed by track patterns and climate change 9. Some atmospheric and oceanic indexes and their relationship with tropical cyclones over the Caribbean 10. On the increasing intensity of the strongest Atlantic hurricanes 11. Frequency and intensity of hurricanes within Floridas threat zone 12. Linking tropical cyclone frequency over the western North Pacific with sea surface temperatures 13. A track-relative climatology of Eglin Air Force Base hurricanes in a variable climate 14. Estimating the impact of climate variability on cumulative hurricane destructive potential through data mining

The increasing efficiency of tornado days in the United States

The authors analyze the historical record of tornado reports in the United States and find evidence for changes in tornado climatology possibly related to global warming. They do this by examining the annual number of days with many tornadoes and the ratio of these days to days with at least one tornado and by examining the annual proportion of tornadoes occurring on days with many tornadoes. Additional evidence of a changing tornado climate is presented by considering tornadoes in geographic clusters and by analyzing the density of tornadoes within the clusters. There is a consistent decrease in the number of days with at least one tornado at the same time as an increase in the number of days with many tornadoes. These changes are interpreted as an increasing proportion of tornadoes occurring on days with many tornadoes. Coincident with these temporal changes are increases in tornado density as defined by the number of tornadoes per area. Trends are insensitive to the begin year of the analysis. The bottom line is that the risk of big tornado days featuring densely concentrated tornado outbreaks is on the rise. The results are broadly consistent with numerical modeling studies that project increases in convective energy within the tornado environment.

Forecasting US insured hurricane losses

Coastal hurricanes generate huge financial losses within the insurance industry. The relative infrequency of severe coastal hurricanes implies that empirical probability estimates of the next big loss will be unreliable. Hurricane climatologists have recently developed statistical models to forecast the level of coastal hurricane activity based on climate conditions prior to the season. Motivated by the usefulness of such models, in this chapter we analyze and model a catalog of normalized insured losses caused by hurricanes affecting the United States. The catalog of losses dates back through the twentieth century. The purpose of this work is to develop a preseason forecast tool that can be used for insurance applications. Although wind speed is directly related to damage potential, the amount of damage depends on both storm intensity and storm size. As anticipated, we found that climate conditions prior to a hurricane season provide information about possible future insured hurricane losses. The models exploit this information to predict the distribution of likely annual losses and the distributionofaworst-casecatastrophiclossaggregatedovertheentireUScoast.