1. Introduction

Contemporary understanding of global climate processes, including the El Niño–Southern Oscillation and the North Atlantic Oscillation among others, suggest that climate may operate in two or more quasi-stationary states (Lockwood 2001; Tsonis et al. 1998; Tsonis and Elsner 1990; Berger and Labeyrie 1987; Charney and DeVore 1979; Namias 1964; Lorenz 1963). Transition between different climate regimes may occur abruptly rather than slowly varying as a consequence of the dissipative, nonlinear, and nonequilibrium properties of the climate system (Vannitsem and Nicolis 1991). Consequently, successive shifts result in an observed variable that appears to exhibit low-frequency oscillations. Another description is randomly occurring change points. In this case data-analytic tools relying on singular value decomposition (SVD) are less appropriate as they assume stationarity and regularly varying changes in the underlying system.

Important in the context of climate variability and change problems, changepoint analysis quantitatively identifies temporal shifts in the mean value of the observations (Solow 1987). Moreover there is widespread interest in grouping years into active and inactive periods based on values of some climate index. Changepoint analysis directly addresses the question of when the change is likely to have occurred. Changepoint analysis can serve to pinpoint potential inhomogeneities in records arising from improved observational technologies and changes in station location (Lund and Reeves 2002). A statistical changepoint analysis provides a tool that is consistent with a physical model supporting abrupt rather than slowly varying transitions. In particular, changepoint analysis can be used to study climate variations. Indeed, geological records show large, widespread, abrupt climate changes (Alley et al. 2003) including sudden shifts in tropical cyclone frequency (Liu et al. 2001).

Lack of widespread interest in changepoint analysis might be due to the ad hoc decisions (choice of parametric model, choice of data transformation) necessary for its implementation. For instance, Elsner et al. (2000a) use a log-linear regression to detect change points in the time series of annual counts of North Atlantic major hurricanes during the period 1900–99. A similar approach is employed by Chu (2002) in examining hurricanes over the central North Pacific. The assumption is that the annual counts of hurricanes after a logarithm transformation is approximately normally distributed.

Changepoint analysis using a Markov chain Monte Carlo (MCMC) approach has received considerable attention from statisticians and engineers. The term Monte Carlo refers to drawing random numbers and the term Markov chain refers to a series of values (a chain) the next of which depends only on the current value. In hydrology, the approach is applied to the analysis of sudden change at an unknown time in a sequence of energy inflows modeled by normally distributed random variables (Perreault et al. 2000a,b). To our knowledge, it has yet to be applied to climate data.

Our intent here is to shed light on the problem of detecting hurricane climate changes while introducing some of the essential ideas behind the MCMC approach to changepoint analysis. Our interest is to better understand coastal hurricane variations by applying the methodology to count data that are nonnormally distributed. The message is that changepoint analysis can provide new insights into climate variability not accessible with other methods. Using this approach, some of the subjective decisions typically associated with changepoint models can be dispensed with in favor of easier interpretation. In section 2 we outline the underlying philosophy of our MCMC approach to changepoint analysis. In section 3 we apply the approach to the annual counts of major North Atlantic hurricanes and in section 4 to annual counts of coastal hurricane activity. In section 5 we show how the methodology can be used to examine covariate relationships. In particular we examine the influence of the El Niño–Southern Oscillation and the North Atlantic Oscillation on coastal hurricane activity. Section 6 provides a summary and list of conclusions.

2. Detecting change points

a. Statistical inference

We begin with a short overview of the fundamentals of statistical inference. Given a data sample (either from the climate or a simulation of the climate), what conclusions can be made about the entire “population”? Inference about a statistical model can be formalized as follows: Let θ be a population parameter, then inference amounts to a supposition about θ on the basis of observing the data. We contend that values of θ which result in high probabilities of observing the data that were collected, y, are more likely than those which assign a low probability to these same observations (maximum likelihood principle). In essence, the inferences are made by specifying a probability distribution of y for a given value of θ, f( y|θ).

In classical statistics, θ is considered a constant, while in Bayesian statistics it is treated as random quantity. Inferences are based on p(θ|y), a probability distribution of θ given the data y, rather than on f( y|θ). This seems natural as we are interested in the probability distribution of the parameter given the data, rather than the data given the parameter. The costs of this approach are the need to specify a prior probability distribution, π (θ), which represents our beliefs about the distribution of θ before we have information from the data, and the need to postulate a particular family of parametric distributions (likelihood). The Bayesian approach combines the likelihood distribution of the data given the parameter with the prior distribution to obtain the probability of θ given the data y

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which is called Bayes's theorem. Having observed y, Bayes's theorem is used to determine the distribution of θ conditional on y. This is called the posterior distribution of θ.

Any feature of the posterior distribution is legitimate for inference, including moments, quantiles, p values, etc. These quantities are expressed in terms of the posterior expectations of functions of θ. The posterior expectation of a function g(θ) is

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Evaluation of the integrals are a source of practical difficulties. Moreover, in most applications, analytic evaluation of the expected value of the posterior density is impossible. The main problem with using numerical methods is that the calculation of the posterior expectation involves a high-dimensional integral if the number of parameters (θ_i) is large. In this case, analytic evaluation of the integral is usually impossible and numerical integration is difficult to apply and time consuming. The MCMC method is employed in statistics to solve this problem. Its use in a wide variety of applications (particularly in the health and social sciences) is illustrated in Congdon (2003).

Monte Carlo integration evaluates E[g(y)] by drawing samples from a probability density. An asymptotic approximation is given by

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Thus the population mean of g(θ_i) is estimated by a sample mean. A stationary (invariant) posterior distribution is guaranteed because the Monte Carlo sampling produces a Markov chain that does not depend on the starting value. The key here is that N is controlled by the analyst; it is not the size of a fixed data sample (Gilks et al. 1996).

c. MCMC changepoint analysis

Given a series of counts, a change point occurs if at some point t in the series the counts come from a distribution with a common rate up to that time and come from the same distribution but with a different rate afterward. A change point is a location in time that divides the rate process into independent epochs. Here we define the changepoint time at the first count of the new epoch. Thus in a series of n annual counts, if a change is detected between time k and k + 1, we say that k + 1 is the changepoint time.

A MCMC algorithm for detecting change points using Gibbs sampling consists of two steps. Step 1 uses the entire record to determine candidate change points based on the expected value of the probability of a change as a function of time. A larger mean probability indicates a greater likelihood of a change point. A plot of the mean probabilities as a function of time along with a minimum probability line identifies the candidate change points against the hypothesis of no change points. The candidate change points have mean probabilities at or exceeding the minimum probability line. Step 2 determines the posterior distributions of the hurricane rates before and after the candidate change point, ignoring the other candidates. The fraction of the posterior density of the difference in rates that are greater (or less) than 0 provides evidence of the direction of change given a change has occurred. From a traditional perspective this amounts to a p value. As with all output associated with the Markov chain, the fraction of hurricane-rate differences greater (or less) than 0 is a random variable so additional runs are used to obtain ensemble averaged values. Thus our algorithm is a modification of the single changepoint analysis. A fully Bayesian multiple changepoint analysis (Lavielle and Labarbier 2001; Rotondi 2002) is not attempted here.

For the present problem we are interested in two parameters; the hurricane rates before and after some changepoint year. Let λ = (λ_b, λ_a) be a vector of two parameters, where λ_a is the mean hurricane rate after the change and λ_b is the mean hurricane rate before the change and we wish to simulate from the posterior f(λ|y) as described previously. More specifically, the data are counts (Y_i) of the annual number of hurricanes during the period 1900–2001 (n = 102). Thus the analysis describes a Poisson distribution with a mean rate λ_b during years i = 1, … , k and a Poisson distribution with a different mean rate λ_a during years i = k + 1, … , n. Since hurricane landfalls occur at different times with little interaction between successive events, and the conditions that generate them do not change significantly, it is reasonable to assume that the number of hurricanes over a given region follows a Poisson process (see e.g., Elsner and Kara 1999; Solow 1989).

Formally, the hierarchical specification for this Poisson/gamma model is given as:

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where λ_b ∼ gamma(α₁, β₁), λ_a ∼ gamma(α₂, β₂), k is discrete uniform over (1, … , 101), each independent, and β₁ ∼ gamma(γ₁, ε₁) and β₂ ∼ gamma(γ₂, ε₂) (Carlin et al. 1992). Note that the two-parameter gamma distribution is continuous and bounded on the left by 0 with a positive skew. The Poisson–gamma relationship is used because the annual counts follow a Poisson distribution with the rate parameter following a gamma distribution (see Elsner and Bossak 2001).

The specification is hierarchical because in stage one the annual counts are random values following a Poisson distribution with an unknown Poisson rate; in stage two the Poisson rate follows a gamma distribution with two unknown parameters. In stage three the unknown scale parameter follows a gamma distribution; and in the fourth stage the parameters of the gamma distributions follow noninformative priors. The specification leads to the following conditional distributions used in the Gibbs sampling (Carlin et al. 1992):

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where the likelihood function is

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Derivation of the likelihood formula is given as an appendix. Starting with some initial (prior) values for α₁, α₂, γ₁, γ₂, ε₁, and ε₂ Gibbs sampling generates sequences of λ_a and λ_b which form Markov chains. The stationary distributions of the chains are the posterior distributions for each of the parameters.

3. North Atlantic major hurricane activity

A hurricane is a tropical cyclone with maximum sustained (1 min) 10-m winds of 33 m s⁻¹ (65 kt) or greater. A major hurricane is one in which winds exceed 50 m s⁻¹ (category 3 or higher on the Saffir–Simpson hurricane destruction potential scale). The long-term average number of major hurricanes over the North Atlantic is close to 2 yr⁻¹. Landsea et al. (1996) note a downward trend in the occurrence of these powerful hurricanes. Using additional years of data, Wilson (1999) suggests a possible increase in activity beginning with 1995. The regression-based changepoint model employed in Elsner et al. (2000a) shows that indeed 1995 is the start of the most recent epoch of greater major hurricane activity.

We first apply the MCMC changepoint analysis on the time series of annual North Atlantic major hurricanes (Fig. 1). Change points were identified in this time series in Elsner et al. (2000a) using a classical approach. It is understood that the counts are likely biased prior to 1943 before the advent of aircraft reconnaissance, but the intention here is to identify shifts in the time series of annual counts regardless of their origin (natural or artificial). In fact one of the points made by Elsner et al. (2000a) is that, if the model is worthwhile it should detect a shift in activity during the middle 1940s. Landsea (1993) suggests that an overestimation of hurricane intensity might have occurred even after 1943 during the period spanning the 1940s through the 1960s based on an inconsistency in central pressures and wind maxima estimates. Since there is still debate on this issue, and since corrections have yet to be made in the best-track dataset, we do not consider the effect of this potential bias in the present study. In any event, this choice does not influence the work presented here.

To examine convergence of the MCMC, we run the analysis using the initial conditions prescribed in the previous section. Values of β₁ and β₂ are plotted for each iteration (Fig. 2). Convergence is quick as there appears to be no trend in the values or their fluctuations across iterations. Thus the distribution of values for both β₁ and β₂ do not change as the chain is run for a greater number of iterations. This is typical. However, it is still good practice to remove the early iterates to allow the chain to “forget” its starting position.

Figure 3 shows the results from the MCMC changepoint algorithm applied to the annual counts of major North Atlantic hurricanes during the period 1900–2001. The expected value of the transition kernel of the Markov chain [ p(k|Y, λ_b, λ_a, β₁, β₂)], which is the probability that year k is a change point given the data and the parameters, is plotted as a function of year. The expected value represents the average posterior probability of the year being the first year of a new epoch. Large probabilities indicate a likely change occurred with year t. The dashed line shows the minimum probability necessary for detecting a change point based on the null model of no change points. The line is smoothed using a 5-yr normal kernel. Years with probabilities above the empirical confidence line include 1906, 1943, and 1995. Note that several years around 1943 are also candidate changepoint years. This indicates that although the algorithm chooses 1943 as the most likely year of the new epoch there is uncertainty surrounding this choice. This is not the case with 1995 or 1906 where no other “nearby” years appear to be in contention. The locally elevated probability at 1965 will be revisited shortly. Also note that years near the beginning and end of the record require substantially larger posterior probabilities to surpass the confidence level. The U-shaped confidence line indicates that there is larger variance on the posterior changepoint probabilities near the ends of the record. Caution is needed when interpreting large probabilities on these years as the chance of a false detection is greater. Ensemble runs of the algorithm can help in this regard.

We examine the influence that the choice of initial values has on the average posterior changepoint probability and thus the selection of candidate years. This is done by running Gibbs sampling 30 times for each value of the α priors equal to 0.1, 0.3, 0.5, and 0.7. Sampling is done for 1050 iterations with the first 50 discarded as burn-in. Figure 4 shows the distribution of probabilities for the candidate years using box and whisker plots. Overall the results demonstrate that the choice of prior values is not a critical factor in identifying candidate years as the chain quickly finds a stationary distribution regardless of where the chain is started. Variability in the average posterior probabilities is largest for candidate years 1906 and 1995. As noted earlier, this results from the fact that these years are near the beginning and end of the time series. Posterior probability distributions based on relatively few data points will have a greater spread.

The procedure continues with a confirmatory analysis of the candidate changepoint years. Once the candidate change point is identified, additional confirmatory analysis is made by comparing the conditional posterior-estimated Poisson rates before and after each change point. Both statistically and from intuition, the confirmatory analysis will demonstrate the direction of change since it has been established that a change has occurred. We look first at 1943 since the jump in annual major hurricane counts at this time is most likely due to the start of aircraft reconnaissance investigations into the storms (Neumann et al. 1999; Jarvinen et al. 1984). Posterior density estimates of the statistics (β₁, β₂, λ_a, λ_b, and λ_a − λ_b) from the Gibbs sampling are shown in Fig. 5. We focus on the probability densities of the annual hurricane-rate parameters before and after (including) 1943. Densities are smoothed versions of the histograms and are based here on a normal kernel with bandwidth equal to 4 times the standard deviation of the values (Venables and Ripley 1999). The choice of bandwidth is a compromise between smoothing enough to remove insignificant bumps but not smoothing too much to hide real peaks.

Gibbs sampling is again run 30 times to get an ensemble average of the mean Poisson rate before 〈λ_b〉 and after 〈λ_a〉 the change point. The ensemble average of the mean rate parameter is 1.51 before 1943 and 2.51 thereafter. The posterior densities of the rate parameters indicate little overlap implying a rate increase beginning with the 1943 season. This is examined directly with the posterior density of the rate differences. Only a negligible fraction of the posterior distribution of λ_a − λ_b is less than 0 (p value). Thus, conditional on a change occurring with 1943, it is clear that the change is in the direction of more frequent major hurricanes beginning with this year. The methodology does not use the posterior-rate differences to test the hypothesis of a change point. This is a confirmatory analysis, with the actual decision based on the probability for each year assuming each epoch would last at least 10 yr. Ten years is based on the time scale of interest (decades). As stated earlier, the increase in major hurricane activity starting in the middle 1940s is likely due in part to the start of aircraft reconnaissance. Based on this fact we ignore the earlier era and continue examining the record from 1943 onward.

Figure 6 shows the results from the MCMC changepoint algorithm applied to the annual counts of major North Atlantic hurricanes during the period 1943–2001. Both 1965 and 1995 are years with high probabilities. Additional high-probability years clustering around 1965 include 1962, 1966, and 1967. Estimates of the posterior densities conditioned on a change at 1965 are shown in Fig. 7. As before, Gibbs sampling produces an ensemble average of the mean Poisson rate before 〈λ_b〉 and after 〈λ_a〉 the change point. The ensemble averaged mean rate is 3.41 before 1965 and 1.97 thereafter. The posterior densities of the rates indicate little overlap implying a decrease in activity beginning with the 1965 season. The ensemble p value is less than 0.001. Similar results are obtained for 1962, 1966, and 1967 indicating that the decline in abundance of major North Atlantic hurricanes might have begun as early as 1962 or as late as 1967 with the most likely year being 1965. Note that although 1965 has a local maximum posterior probability when the longer series is considered, it rises above the noise floor (dashed line) when the earlier, less reliable, portion of the record is removed.

Next we consider 1995. Estimates of the posterior densities are shown in Fig. 8. The ensemble-averaged mean hurricane rate before 1995 is 2.37 (ignoring the earlier change point) and 3.57 thereafter. The density for the hurricane rate since 1995 (λ_a) is considerably flatter owing to the relatively few years of data (7) in the record following this year. The greater uncertainty about the annual rate at the end of the hurricane record creates more overlap on the rate distributions and thus a larger p value on the rate difference. Even still, evidence is convincing that 1995 represents an upward shift in hurricane activity.

Thus a picture emerges of significant quantifiable shifts in the occurrence rates of major North Atlantic hurricanes during the twentieth century. The results for the middle 1940s, 1965, and 1995 are consistent with results obtained using a nonprobabilistic changepoint model (Elsner et al. 2000a). Beginning with the era of aircraft surveillance, we see that major hurricanes occurred at an average annual rate of nearly 3.5 yr⁻¹. The rate dropped significantly to about two major hurricanes per year beginning sometime during the middle 1960s with the new epoch most likely starting with the 1965 season. The modern era of fewer major hurricanes ends abruptly with the 1995 season. For the next seven seasons through 2001 the mean rate is more than 3.5 major hurricanes per year. An advantage of the probabilistic model is that it provides uncertainty estimates on the rates before and after the change point. It also provides density estimates of rate differences. Next we apply the changepoint analysis to the twentieth century U.S. hurricane record.

4. U.S. hurricane activity

In the previous section the record of major North Atlantic hurricanes was examined using a changepoint analysis. Here we apply the analysis to records of U.S. hurricane activity. We are unaware of changepoint studies on these records. Hurricane landfall occurs when all or part of the eyewall (the central ring of deep atmospheric convection, heavy rainfall, and strong winds) passes directly over the coast or adjacent barrier island. A U.S. hurricane is a hurricane that makes at least one landfall. A reliable list of the annual counts of U.S. hurricanes back to 1900 is available from the U.S. National Oceanic and Atmospheric Administration (Neumann et al. 1999). These data represent a blend of historical archives and modern direct measurements. An updated climatology of annual coastal hurricane activity is given in Elsner and Kara (1999) and Elsner and Bossak (2001). Here we do not consider hurricanes affecting Hawaii, Puerto Rico, or the Virgin Islands.

5. ENSO and the NAO

The influence of the El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) on annual coastal hurricane numbers is examined after a small modification to the analysis. Here it is assumed that each year is independent, which is reasonable for annual hurricane counts. The statistical relationship between ENSO and U.S. hurricanes is well known (Bove et al. 1998; Elsner et al. 1999; Elsner and Kara 1999; Jagger et al. 2001), but the relationship between NAO and U.S. hurricanes is less well recognized (Elsner et al. 2000b, 2001).

A reliable time record of the Pacific ENSO is obtained using basin-scale equatorial fluctuations of sea surface temperatures (SST). Average SST anomalies over the region bounded by 6°N–6°S latitude and 90°W–180° longitude are called the “cold tongue index” (CTI; Deser and Wallace 1990). Values of CTI are obtained from the Joint Institute for the Study of the Atmosphere and the Oceans as monthly anomalies (base period: 1950– 79) in hundredths of a degree Celsius. Monthly values of the CTI are strongly correlated with values from other ENSO SST indices. Since the Atlantic hurricane season runs principally from August through October, a 3-month averaged (August–October) CTI from the dataset is used. Values of an index for the NAO are calculated from sea level pressures at Gibraltar and at a station over southwest Iceland (Jones et al. 1997), and are obtained from the Climatic Research Unit. The values are first averaged over the pre- and early-hurricane season months of May and June. This is a compromise between signal strength and timing relative to the hurricane season. The signal-to-noise ratio in the NAO is largest during the boreal winter and spring, whereas the U.S. hurricane season begins in June (see Elsner et al. 2001).

We divide the range of ENSO and NAO values occurring over the 102-yr period into equal-interval tercile values (terciles) describing below-normal, normal, and above-normal years. The upper and lower terciles of the August–October average CTI are 0.90° and −0.23°C, respectively. The upper and lower terciles of the May– June average NAO index are 1.05 and −0.85 standard deviations respectively. Years of above- (below)-normal CTI correspond to El Niño (La Niña) events. Years of above- (below)-normal NAO index values correspond to positive (negative) phases of the NAO. With this specification, there are 14 (39) above (below) normal ENSO years and 11 (34) above- (below)-normal NAO years during the twentieth century. We remove the normal years and create a sequence of hurricane counts. The sequence is a set of hurricane counts for years of above-normal climate conditions followed abruptly by the set of counts for years of below-normal conditions. For instance, assuming an equal number of above, below, and normal years, let Y_a be the counts for years in which NAO is above normal where a = 1, … , n/3 and Y_b be the counts for years in which NAO is below normal, where b = 1, … , n/3. Then we create a new series by concatenating Y_a and Y_b. In this way we create an artificial time series that contains a potential change point and then compare the hurricane rates for years of above-normal (before the change point) and below-normal (after, and including the change point) climate conditions.

Figure 15 shows the posterior densities of the rate differences (above-normal years minus below-normal years). As anticipated we see that during El Niño years (above normal) the annual rate is significantly less than the rate during La Niña years (below normal). A 30-member ensemble gives an average rate of 0.72 hurricanes yr⁻¹ during El Niño years compared with 2.18 hurricanes yr⁻¹ during La Niña years. This difference results in a p value that is less than 0.001 conditioned on a change occurring. The influence of El Niño on hurricanes appears along the entire coast, but is strongest over Florida which has a mean rate of 0.37 hurricanes yr⁻¹ during El Niño compared with 0.93 hurricanes yr⁻¹ during La Niña. This difference corresponds to a p value of 0.011. Figure 15 also shows the effect of NAO on U.S. hurricanes. During its strong phase (above normal), the ensemble average rate is 1.02 hurricanes yr⁻¹ compared with 2.21 hurricanes yr⁻¹ during its weak (or negative) phase (below normal). This provides a p value of 0.003. Unlike ENSO the influence of the NAO is only significant along the Gulf Coast. Here the annual rate is 0.38 hurricanes yr⁻¹ during the NAO strong phase and 0.86 during the NAO weak phase. This is consistent with the NAO as a factor influencing hurricane tracks as hypothesized in Elsner et al. (2000b, 2001). A weaker NAO during boreal spring is associated with a subtropical high pressure cell displaced farther south and west of its mean position (near the Azores) during the following hurricane season. Tropical cyclones forming and remaining equatorward of the subtropical high tend to intensify at low latitudes, crossing through the Caribbean before reaching the Gulf Coast (see Elsner 2003).

6. Summary and conclusions

This paper demonstrates an application of a probabilistic framework for determining sudden changes at unknown times in records involving counts. The approach is through a hierarchical algorithm involving the Poisson and gamma distributions and Gibbs sampling. Gibbs sampling is a commonly used MCMC procedure to sample from conditional distributions. Our purpose is to use the approach for identifying shifts in the rates of coastal hurricane activity. The technique is also applied to the problem of detecting the influence of covariates (ENSO and NAO) on coastal storm activity.

The changepoint analysis is first run on annual counts of major North Atlantic hurricanes. Results are consistent with those from a regression-based changepoint model (Elsner et al. 2000a) including an ominous rate increase starting in 1995. When the algorithm is applied to annual counts of overall U.S. hurricane activity there is little evidence for significant rate changes during the twentieth century. A similar result is found when the counts are grouped by regions including the Gulf and East Coasts. The exception is Florida. Results show significant decreases in the number of Florida hurricanes during the early 1950s and likely again during the late 1960s. A closer examination reveals that this decrease results from fewer storms approaching the southern half of the state from the Bahamas and western Caribbean Sea. The decrease occurs during a period of substantial growth in the state's population.

The analysis is then used to study the influence of ENSO and the NAO on coastal hurricane activity. Climate data representing these two modes of variability are divided into terciles representing above-normal, near-normal, and below-normal conditions. As expected from previous studies, we find a statistically significant link to the ENSO. During El Niño years coastal hurricane rates are reduced from Texas to Maine. The most pronounced effect occurs over Florida. The NAO might also play a role. On average during years in which the NAO index is below normal, more than twice as many hurricanes reach the coast. However, unlike the ENSO's influence which is felt along the entire coast, NAO's influence is significant only for the Gulf Coast from Texas to Alabama.

The conclusions are the following:

• A changepoint model utilizing Gibbs sampling is a useful tool for climate analysis.

• Major North Atlantic hurricanes have become more frequent since 1995, at a level reminiscent of the 1940s and 1950s.

• In general, twentieth-century U.S. hurricane activity shows no abrupt shifts in activity.

• The exception is over Florida where activity decreased during the early 1950s and again during the late 1960s.

• Florida's hurricane decline results from fewer hurricanes passing through the Bahamas and western Caribbean Sea.

• El Niño events tend to suppress hurricane activity along the entire coast with the most pronounced effects over Florida.

• Below-normal NAO conditions are associated with an increase in hurricane activity from Texas to Alabama.