2005 Atlantic hurricane season in motion

Watch the record-setting season in motion.

Better risk models

Important advances are being made to understand and predict hurricane activity. On the seasonal time scale, and to a first order, we know that a warm ocean fuels storm genesis, a calm atmosphere allows storms to intensify, and the position and strength of the subtropical high pressure region paves the tracks for storms that do form. The next generation of risk modelers should incorporate this science into their assessments.

We at the Hurricane Climate Institute at Florida State University (FSU) have made important contributions to this science. We have developed techniques for predicting seasonal hurricane activity (Elsner et al. 1998; 1999; Jagger et al. 2001; 2002), have quantified the statistical association between the North Atlantic oscillation (NAO) and hurricane activity (Elsner et al. 2001; Elsner 2003; Elsner and Jagger 2006), and have demonstrated the utility of Bayesian methods for handling incomplete and missing data (Elsner and Bossak 2001; Elsner et al. 2004; Elsner and Jagger 2004). Our approach is to build models from the available data.

Data models help us understand and predict relationships beyond that accessible with statistical descriptions because they provide a safeguard against cherry-picking the evidence. Data models help us unravel the nuances of climate on hurricanes. Standard meteorological procedures like filtering, trend lines, and empirical orthogonal functions are not up to this task. Data models provide us a context that is consistent with the nature of underlying climate processes, similar to the way the laws of physics provide a context for studying meteorology. In short, data modeling is a scientific way to understanding how the climate works given the available evidence. At issue for risk assessment is how extreme coastal hurricane activity is conditional on climate patterns.

The next big improvement in risk modeling will likely come with data models that assess regional hurricane activity using historical data and numerical prediction output. Indeed, we now successfully model hurricane counts (Elsner and Jagger 2006) and hurricane intensities (Jagger and Elsner 2006) in regions along the coastal United States. Moreover we demonstrate statistical skill in predicting the expected annual insured loss conditional on the state of the NAO and Atlantic ocean temperatures (Jagger et al. 2007).

With our help this science is incorporated in risk models from Accurate Environmental Forecasting (AEF). However, more work is needed to add spatial information and regional predictors. Global predictors include leading modes of variability such as the NAO as well as variables that track the El Nino. Regional predictors like sea temperatures in the Gulf of Mexico and the Caribbean Sea, surface air pressures over Bermuda, and rainfall/soil moisture indicators over eastern North America and western Europe should be considered.

As a consequence of incomplete data and the existence of alternative scientific theories (e.g., climate change versus natural variability), probabilistic risk assessment requires some degree of expert judgment. One approach is to use Bayesian statistics another is to use expert opinion in formal elicitation. Elicitation is practiced in analyzing earthquakes and other geological hazards. Although the physics of climate is better understood than certain geological processes, there remains a sufficient lack of understanding with regard to hurricane risk to cause divergence among researchers.

Formal methods are available for eliciting expert judgment. One method involves a panel of experts who debate and explain the merits of evidence and argument. This approach is based on the assumption that group judgments can improve the validity of forecasts. In any case, the procedures will provide information about the relative risk that is agreeable to the panel. This is done by Risk Management Solutions (RMS) resulting in updates to their hurricane risk assessments that reflects, to some degree, expert opinions about future hurricane activity.

To improve these efforts evidence models should be used to ensure that the experts give credible witness to the data. For example, it is inconsistent for an expert to believe that the most likely number of U.S. hurricanes over the next 5 years will be 10 while at the same time believing there is a 40% chance that the number will be less than 3. The data simply do not conform to this type of distribution.

Averaging expert opinion will not necessarily give a consistent estimate of the hurricane rate either and the method does not account for the uncertainty inherent in the numbers provided by the experts. Moreover, there is some agreement on increased hurricane activity over the basin as a whole for the next few years, but much less agreement on what that means for citizens living along the U.S. coast. This differential in uncertainty also needs to be quantified and incorporated.

As mentioned, a data model can help. One model is to assume that each of the N-year totals from the experts is Poisson with a parameter equal to rate times N. This generates separate estimates for each expert. Another model is to assume that the observed counts have a negative binomial distribution. More work is needed, but future risk models will certainly benefit by utilizing the latest hurricane climate science.

Disclosure: I acknowledge discussions with Thomas H. Jagger on this topic. My financial support comes from the U.S. National Science Foundation and the Risk Prediction Institute of the Bermuda Institute of Ocean Sciences. These opinions are mine and do not necessarily reflect those of the funding agencies. I worked previously under contract with AEF. Currently I have no financial interest in a risk modeling or insurance company.

Comparing hurricane return levels using historical and geological records

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 analogue (Hurricane Ivan of 2004) to be 54 m/s (105 kt) for a return period of 318 years based on 11 events over 3500 years. 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 (141 kt) with a lower bound on the 95% confidence interval of 64 m/s (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 likely more accurately set at 64 m/s. Read more.

U.S. hurricanes and the North Atlantic oscillation

One thing I find surprising about the debate on climate change and hurricanes is the lack of discussion on the North Atlantic oscillation (NAO). Some in politics and insurance suggest greater attention be placed on understanding future hurricane activity as it relates to the United States. We've published 13 scientific papers on this topic since 2001 (here). The research identifies and elucidates the role of the NAO in portending hurricane tracks across the Atlantic Ocean. A weak NAO phase tends to favor tracks that parallel lines of latitude. In contrast a strong NAO phase tends to favor tracks that cross latitudes (hurricanes that, in general, get steered away from the U.S. coast). We speculate the reason for this is related to the position and strength of the subtropical high pressure system.

Interestingly, the NAO was in a positive phase for much of the 1970s and 1980s with historic highs in the early 1990s and speculation about a link to global warming has been made. Osborn et al. (1999) show that the NAO from the 1960s to early 1990s is outside the range of earlier variability in the instrumental record and also outside the range of variability simulated using UK Hadley Centre's numerical model. Thus with greater warmth and perhaps more Atlantic hurricanes it is possible that the threat to the United States as defined by the probability of a strike will remain relatively constant rather than increase.

In fact there is some evidence for this in the historical record of U.S. hurricane counts which show no long term trend but a tendency for a smaller ratio of landfall counts to basin-wide counts. The differential influence of improvements in observing technologies on landfall and total counts tends to confound attempts to understand this tendency as noted in Elsner and Kara (1999). Moreover, conditional on the phase of the NAO, there are statistically significant positive relationships between Atlantic sea-surface temperature (SST) and both U.S. hurricane counts (Elsner and Jagger 2006) and insured losses (Jagger et al. 2007).

Hurricane evidence

The debate on hurricanes and climate change can sometimes devolve into issues of data reliability. Unfortunately some of what is said about these issues is nonsense, or worse, self serving. As one example, during the middle 1990's, the high priest of NOAA's best-track data argued vehemently that the hurricane intensities during the 1950's and '60s were biased upward. I checked with my colleague Noel LaSeur, who flew into these early storms, and he said "If anything, we underestimated the intensity" suggesting a possible downward bias. Noel is correct. With this light, the intensity of the hurricanes of 2004 & 2005 is not that unusual against the backdrop of the formidable mid century hurricanes. Enthusiasts and partisans should not be tinkering with these data. Moreover, while it stands to reason (a priori) that the historical information will be less precise than data collected today with modern technologies, to ignore these earlier records is scientifically indefensible. Inspired by Edward Tufte recommendations for truth-telling in graphical presentations (Visual Explanations, Graphics Press, 1997), I suggest that one way to enforce data standards is to insist that the original, unprocessed data be posted alongside the manipulated data, and that the manipulators and their methods be identified.

Hurricanes and climate change

The World Meteorological Organization has just released their consensus statement on tropical cyclones and climate change which mentions that because of the rapid advances being made in this area findings may be soon superceded by new results. Please consider joining us for the First International Summit on Hurricanes and Climate Change to hear all about the latest discoveries.

Atlantic hurricanes and global warming

The power of Atlantic tropical cyclones has risen rather dramatically and the increase is correlated with an increase in the late summer/early fall sea-surface temperature over the North Atlantic. A debate concerns the nature of these increases with some studies attributing them to a natural climate fluctuation, known as the Atlantic Multidecadal Oscillation (AMO), and others suggesting climate change related in part to anthropogenic increases in radiative forcing from greenhouse-gases. Here I apply tests for causality using the global mean near-surface air temperature (GT) and Atlantic sea-surface temperature (SST) records during the Atlantic hurricane season and find that GT is useful in predicting Atlantic SST, but not the other way around. Thus I concluded that GT "causes" SST providing evidence in support of the climate change hypothesis.

Forecast model of U.S. hurricanes 6 months in advance

Hurricanes are a serious social and economic threat to the
United States. Hurricane Katrina is a grim reminder of this fact.
Recent advances allow skillful forecasts of the U.S. hurricane
threat at (or near) the start of the Atlantic hurricane season.
Skillful forecasts of hurricane landfalls at longer lead times
(forecast horizons) for the complete hurricane season would greatly
benefit risk managers and others interested in acting on these
forecasts. Here we show a model that provides a 6-month forecast
horizon for annual hurricane counts along the U.S. coastline during
the June through November hurricane season. Forecast skill exceeds
that of climatology. The long-lead skill is linked to the
persistence of Atlantic sea-surface temperatures and to
teleconnections between North Atlantic sea-level pressures and
precipitation variability over North America and Europe. The model
is developed using Bayesian regression and therefore incorporates
the full set of Atlantic hurricane data extending back to 1851.

[with R.J. Murnane and T.H. Jagger]

Return periods for Hurricane Katrina

Hurricane Katrina is the most destructive natural disaster in U.S. history. The relative infrequency of severe coastal hurricanes implies that empirical probability estimates of the next big one will be unreliable. Here we use an extreme-value model and show that a hurricane of Katrina's intensity or stronger can be expected to occur, on average, once every 21 years somewhere along the Gulf coast and once every 14 years somewhere along the entire coast from Texas to Maine. The model predicts a 100-year return level of 83 m/s (186 mph) during globally warm years and 75 m/s (168 mph) during globally cool years. The magnitude of this difference is consistent with models predicting an increase in hurricane intensity with increasing greenhouse warming.

[with T.H. Jagger & A.A. Tsonis]

Definition: Hurricane Climate

Hurricane climate is the study of hurricanes that includes the role climate factors play in modulating seasonal, annual, and decadal hurricane activity. Hurricane climatology is the statistics (e.g., mean number of hurricanes, maximum estimated intensity, etc.) of past hurricane activity over some reference time period. The role climate factors play in modulating hurricane activity are examined using empirical, statistical, or dynamical models. For hurricanes occurring over the North Atlantic, climate factors include El Niño, the North Atlantic Oscillation (NAO), the Atlantic sea-surface temperature (SST), and the stratospheric Quasi-Biennial Oscillation (QBO). Hurricane climate also includes the role global warming might have on hurricane activity.