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- Author or Editor: James B. Elsner x
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Abstract
Hurricane activity over the North Atlantic basin during 1995 and 1996 is compared to the combined hurricane activity over the previous four years (1991–94). The earlier period produced a total of 15 hurricanes compared to a total of 20 hurricanes over the latter period. Despite this similarity in numbers, the hurricanes of 1995 and 1996 were generally of the tropical-only variety, which marks a substantial departure from activity during the early 1990s. The return of tropical-only hurricanes to the Atlantic basin is likely the result of several global and local factors, including cool SST conditions in the equatorial central and eastern Pacific and warm SSTs in the tropical Atlantic. The hurricane activity of 1995 and 1996 is more reminiscent of activity of some seasons during the early and mid-1950s.
Abstract
Hurricane activity over the North Atlantic basin during 1995 and 1996 is compared to the combined hurricane activity over the previous four years (1991–94). The earlier period produced a total of 15 hurricanes compared to a total of 20 hurricanes over the latter period. Despite this similarity in numbers, the hurricanes of 1995 and 1996 were generally of the tropical-only variety, which marks a substantial departure from activity during the early 1990s. The return of tropical-only hurricanes to the Atlantic basin is likely the result of several global and local factors, including cool SST conditions in the equatorial central and eastern Pacific and warm SSTs in the tropical Atlantic. The hurricane activity of 1995 and 1996 is more reminiscent of activity of some seasons during the early and mid-1950s.
Abstract
Violent typhoons continue to have catastrophic impacts on economies and welfare, but how they are responding to global warming has yet to be fully understood. Here, an empirical framework is used to explain physically why observations support a tight connection between increasing ocean warmth and the increasing intensity of supertyphoons in the western North Pacific. It is shown that the energy needed for deep convection is on the rise with greater heat and moisture in the lower tropical troposphere but that this energy remains untapped when air pressure is high. Accordingly, tropical cyclone formation is becoming less common, but those that do form are likely to reach extreme intensities from the discharge of stored energy. These thermodynamic changes to the environment most significantly influence the upper portion of extreme typhoon intensities, indicating that supertyphoons are likely to be stronger at the expense of overall tropical cyclone occurrences in the western North Pacific.
Abstract
Violent typhoons continue to have catastrophic impacts on economies and welfare, but how they are responding to global warming has yet to be fully understood. Here, an empirical framework is used to explain physically why observations support a tight connection between increasing ocean warmth and the increasing intensity of supertyphoons in the western North Pacific. It is shown that the energy needed for deep convection is on the rise with greater heat and moisture in the lower tropical troposphere but that this energy remains untapped when air pressure is high. Accordingly, tropical cyclone formation is becoming less common, but those that do form are likely to reach extreme intensities from the discharge of stored energy. These thermodynamic changes to the environment most significantly influence the upper portion of extreme typhoon intensities, indicating that supertyphoons are likely to be stronger at the expense of overall tropical cyclone occurrences in the western North Pacific.
Abstract
Research on trends in western North Pacific tropical cyclone (TC) activity is limited by problems associated with different wind speed conversions used by the various meteorological agencies. This paper uses a quantile method to effectively overcome this conversion problem. Following the assumption that the intensity ranks of TCs are the same among agencies, quantiles at the same probability level in different data sources are regarded as having the same wind speed level. Tropical cyclone data from the Joint Typhoon Warning Center (JTWC) and Japan Meteorological Agency (JMA) are chosen for research and comparison. Trends are diagnosed for the upper 45% of the strongest TCs annually. The 27-yr period beginning with 1984, when the JMA began using the technique, is determined to be the most reliable for achieving consensus among the two agencies regarding these trends. The start year is a compromise between including as many years in the data as possible, but not so many that the period includes observations that result in inconsistent trend estimates. The consensus of TC trends between the two agencies over the period is interpreted as fewer but stronger events since 1984, even with the lower power dissipation index (PDI) in the western North Pacific in recent years.
Abstract
Research on trends in western North Pacific tropical cyclone (TC) activity is limited by problems associated with different wind speed conversions used by the various meteorological agencies. This paper uses a quantile method to effectively overcome this conversion problem. Following the assumption that the intensity ranks of TCs are the same among agencies, quantiles at the same probability level in different data sources are regarded as having the same wind speed level. Tropical cyclone data from the Joint Typhoon Warning Center (JTWC) and Japan Meteorological Agency (JMA) are chosen for research and comparison. Trends are diagnosed for the upper 45% of the strongest TCs annually. The 27-yr period beginning with 1984, when the JMA began using the technique, is determined to be the most reliable for achieving consensus among the two agencies regarding these trends. The start year is a compromise between including as many years in the data as possible, but not so many that the period includes observations that result in inconsistent trend estimates. The consensus of TC trends between the two agencies over the period is interpreted as fewer but stronger events since 1984, even with the lower power dissipation index (PDI) in the western North Pacific in recent years.
Abstract
A hierarchical Bayesian strategy for modeling annual U.S. hurricane counts from the period 1851–2000 is illustrated. The approach is based on a separation of the reliable twentieth-century records from the less precise nineteenth-century records and makes use of Poisson regression. The work extends a recent climatological analysis of U.S. hurricanes by including predictors (covariates) in the form of indices for the El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). Model integration is achieved through a Markov chain Monte Carlo algorithm. A Bayesian strategy that uses only hurricane counts from the twentieth century together with noninformative priors compares favorably to a traditional (frequentist) approach and confirms a statistical relationship between climate patterns and coastal hurricane activity. Coinciding La Niña and negative NAO conditions significantly increase the probability of a U.S. hurricane. Hurricane counts from the nineteenth century are bootstrapped to obtain informative priors on the model parameters. The earlier records, though less reliable, allow for a more precise description of U.S. hurricane activity. This translates to a greater certainty in the authors' belief about the effects of ENSO and NAO on coastal hurricane activity. Similar conclusions are drawn when annual U.S. hurricane counts are disaggregated into regional counts. Contingent on the availability of values for the covariates, the models can be used to make predictive inferences about the hurricane season.
Abstract
A hierarchical Bayesian strategy for modeling annual U.S. hurricane counts from the period 1851–2000 is illustrated. The approach is based on a separation of the reliable twentieth-century records from the less precise nineteenth-century records and makes use of Poisson regression. The work extends a recent climatological analysis of U.S. hurricanes by including predictors (covariates) in the form of indices for the El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). Model integration is achieved through a Markov chain Monte Carlo algorithm. A Bayesian strategy that uses only hurricane counts from the twentieth century together with noninformative priors compares favorably to a traditional (frequentist) approach and confirms a statistical relationship between climate patterns and coastal hurricane activity. Coinciding La Niña and negative NAO conditions significantly increase the probability of a U.S. hurricane. Hurricane counts from the nineteenth century are bootstrapped to obtain informative priors on the model parameters. The earlier records, though less reliable, allow for a more precise description of U.S. hurricane activity. This translates to a greater certainty in the authors' belief about the effects of ENSO and NAO on coastal hurricane activity. Similar conclusions are drawn when annual U.S. hurricane counts are disaggregated into regional counts. Contingent on the availability of values for the covariates, the models can be used to make predictive inferences about the hurricane season.
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. hurricane activity relative to climatology. The preliminary NAO value for 2005 is −0.565 standard deviations so the NAO-only model predicts a 13% increase over climatology of observing three or more U.S. hurricanes.
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. hurricane activity relative to climatology. The preliminary NAO value for 2005 is −0.565 standard deviations so the NAO-only model predicts a 13% increase over climatology of observing three or more U.S. hurricanes.
Abstract
Predictive climate distributions of U.S. landfalling hurricanes are estimated from observational records over the period 1851–2000. The approach is Bayesian, combining the reliable records of hurricane activity during the twentieth century with the less precise accounts of activity during the nineteenth century to produce a best estimate of the posterior distribution on the annual rates. The methodology provides a predictive distribution of future activity that serves as a climatological benchmark. Results are presented for the entire coast as well as for the Gulf Coast, Florida, and the East Coast. Statistics on the observed annual counts of U.S. hurricanes, both for the entire coast and by region, are similar within each of the three consecutive 50-yr periods beginning in 1851. However, evidence indicates that the records during the nineteenth century are less precise. Bayesian theory provides a rational approach for defining hurricane climate that uses all available information and that makes no assumption about whether the 150-yr record of hurricanes has been adequately or uniformly monitored. The analysis shows that the number of major hurricanes expected to reach the U.S. coast over the next 30 yr is 18 and the number of hurricanes expected to hit Florida is 20.
Abstract
Predictive climate distributions of U.S. landfalling hurricanes are estimated from observational records over the period 1851–2000. The approach is Bayesian, combining the reliable records of hurricane activity during the twentieth century with the less precise accounts of activity during the nineteenth century to produce a best estimate of the posterior distribution on the annual rates. The methodology provides a predictive distribution of future activity that serves as a climatological benchmark. Results are presented for the entire coast as well as for the Gulf Coast, Florida, and the East Coast. Statistics on the observed annual counts of U.S. hurricanes, both for the entire coast and by region, are similar within each of the three consecutive 50-yr periods beginning in 1851. However, evidence indicates that the records during the nineteenth century are less precise. Bayesian theory provides a rational approach for defining hurricane climate that uses all available information and that makes no assumption about whether the 150-yr record of hurricanes has been adequately or uniformly monitored. The analysis shows that the number of major hurricanes expected to reach the U.S. coast over the next 30 yr is 18 and the number of hurricanes expected to hit Florida is 20.
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 s−1). Highest wind speed return levels are noted along the Gulf Coast from Texas to Alabama. The study also examines how the extreme wind return levels change depending on climate conditions including El Niño–Southern Oscillation, the Atlantic Multidecadal Oscillation, the North Atlantic Oscillation, and global temperature. The mean 5-yr return level during La Niña (El Niño) conditions is 125 (116) kt, but is 140 (164) kt for the 100-yr return level. This indicates that La Niña years are the most active for the occurrence of strong hurricanes, but that extreme hurricanes are more likely during El Niño years. Although El Niño inhibits hurricane formation in part through wind shear, the accompanying cooler lower stratosphere appears to increase the potential intensity of hurricanes that do form. To take advantage of older, less reliable data, the models are reformulated using Bayesian methods. Gibbs sampling is used to integrate the prior over the likelihood to obtain the posterior distributions for the model parameters conditional on global temperature. Higher temperatures are conditionally associated with more strong hurricanes and higher return levels for the strongest hurricane winds. Results compare favorably with an ML approach as well as with recent modeling and observational studies. The maximum possible near-coastal wind speed is estimated to be 208 kt (183 kt) using the Bayesian (ML) approach.
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 s−1). Highest wind speed return levels are noted along the Gulf Coast from Texas to Alabama. The study also examines how the extreme wind return levels change depending on climate conditions including El Niño–Southern Oscillation, the Atlantic Multidecadal Oscillation, the North Atlantic Oscillation, and global temperature. The mean 5-yr return level during La Niña (El Niño) conditions is 125 (116) kt, but is 140 (164) kt for the 100-yr return level. This indicates that La Niña years are the most active for the occurrence of strong hurricanes, but that extreme hurricanes are more likely during El Niño years. Although El Niño inhibits hurricane formation in part through wind shear, the accompanying cooler lower stratosphere appears to increase the potential intensity of hurricanes that do form. To take advantage of older, less reliable data, the models are reformulated using Bayesian methods. Gibbs sampling is used to integrate the prior over the likelihood to obtain the posterior distributions for the model parameters conditional on global temperature. Higher temperatures are conditionally associated with more strong hurricanes and higher return levels for the strongest hurricane winds. Results compare favorably with an ML approach as well as with recent modeling and observational studies. The maximum possible near-coastal wind speed is estimated to be 208 kt (183 kt) using the Bayesian (ML) approach.
Abstract
The authors apply a procedure called Bayesian model averaging (BMA) for examining the utility of a set of covariates for predicting the distribution of U.S. hurricane counts and demonstrating a consensus model for seasonal prediction. Hurricane counts are derived from near-coastal tropical cyclones over the period 1866–2008. The covariate set consists of the May–October monthly averages of the Atlantic SST, North Atlantic Oscillation (NAO) index, Southern Oscillation index (SOI), and sunspot number (SSN). BMA produces posterior probabilities indicating the likelihood of the model given the set of annual hurricane counts and covariates. The September SSN covariate appears most often in the higher-probability models. The sign of the September SSN parameter is negative indicating that the probability of a U.S. hurricane decreases with more sunspots. A consensus hindcast for the 2007 and 2008 season is made by averaging forecasts from a large subset of models weighted by their corresponding posterior probability. A cross-validation exercise confirms that BMA can provide more accurate forecasts compared to methods that select a single “best” model. More importantly, the BMA procedure incorporates more of the uncertainty associated with making a prediction of this year’s hurricane activity from data.
Abstract
The authors apply a procedure called Bayesian model averaging (BMA) for examining the utility of a set of covariates for predicting the distribution of U.S. hurricane counts and demonstrating a consensus model for seasonal prediction. Hurricane counts are derived from near-coastal tropical cyclones over the period 1866–2008. The covariate set consists of the May–October monthly averages of the Atlantic SST, North Atlantic Oscillation (NAO) index, Southern Oscillation index (SOI), and sunspot number (SSN). BMA produces posterior probabilities indicating the likelihood of the model given the set of annual hurricane counts and covariates. The September SSN covariate appears most often in the higher-probability models. The sign of the September SSN parameter is negative indicating that the probability of a U.S. hurricane decreases with more sunspots. A consensus hindcast for the 2007 and 2008 season is made by averaging forecasts from a large subset of models weighted by their corresponding posterior probability. A cross-validation exercise confirms that BMA can provide more accurate forecasts compared to methods that select a single “best” model. More importantly, the BMA procedure incorporates more of the uncertainty associated with making a prediction of this year’s hurricane activity from data.
Abstract
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 during strong El Niño events and during the positive phase of the North Atlantic Oscillation (NAO). ENSO effects are prominent over Florida while NAO effects are concentrated along the Gulf Coast.
Abstract
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 during strong El Niño events and during the positive phase of the North Atlantic Oscillation (NAO). ENSO effects are prominent over Florida while NAO effects are concentrated along the Gulf Coast.
Abstract
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.
Abstract
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.
