What is Hurricane Climatology?

Hurricane Climatology on Prezi

Geographers poised to lead a new revolution in hurricane climate research

In my talk this week to the West Florida chapter of the American Meteorological Society I make a case that geographers are well positioned to lead a new revolution in hurricane climate research. Watch here.

Body of Work

The figure is a thumbnail sketch of our research efforts over the past 15 years in the area of Atlantic hurricane climate. The top row is the feature of hurricane activity that we've studied, the middle row is the statistical or empirical model that we've used to study the feature, and the third row is a headline of our principal finding. The time axis provides a chronology for our efforts to better understand the hurricane problem. Click here for copies of these and other related papers.

Summary: Summit on Hurricanes and Climate Change

An International Summit on Hurricanes and Climate Change was held May 27-30, 2007 at the Aldemar Knossos Royal Village in Hersonissos, Crete. It was hosted by Aegean Conferences and supported by the Bermuda Institute for Ocean Sciences (BIOS) Risk Prediction Initiative and by the U.S. National Science Foundation. It was organized by myself and Richard Murnane (BIOS). There were 77 participants from at least 18 countries with a mix of academics and stakeholders (insurance and risk modelers). The purpose was to gather leading researchers in the field of tropical cyclone climate for sustained discussions on the state of the science and to elevate the discourse above the fray. In this spirit, it was appropriate to convene at the birth place of the Socratic method.

Traditionally tropical cyclones are analyzed as a passive response to climate forcing: the hurricane as a product of its environment. A warm ocean provides sustenance, a calm atmosphere nurturing, and a subtropical high pressure cell forward direction. An increase in oceanic heat will raise a hurricane's potential intensity, yet an increase in shearing winds could counter by dispersing the heat in a fledgling storm. This perspective is useful for identifying the mechanisms responsible for making some seasons active while others inactive. In this regard it was argued that data modeling is superior to data analysis (trend lines, etc) as it avoids cherry-picking the evidence and provides a framework for making use of older, less reliable data.

For example, a Poisson distribution is useful for modeling tropical storm counts over time. The benefit of this approach is that it provides a context that is consistent with the nature of underlying physical processes, analogous to the way the laws of physics provide a context for studying meteorology. It was shown that smoothing (filtering) the count data introduces low frequency patterns that may not be significant and that a data model of Atlantic hurricanes indicates a recent upswing in the number of strongest hurricanes with little multidecadal variation. The figure above shows annual hurricane rates using a data model of basin-wide Atlantic hurricane counts. Results are shown for different categories of hurricane intensity. Note that for each year the model provides a distribution on the estimated annual rate as indicated by a box plot. The model also provides a distribution for the number of rate changes over the 63-year period (1943-2005). Note the absence of a multidecadal pattern.

Although the question of whether we can ascribe a change in tropical cyclone intensity to anthropogenic climate change (attribution) is still open, it was argued based on data models for extreme winds that the difference in U.S. hurricane intensity between globally warm and cool years is consistent in sign and magnitude with theory and simulations. In this regard it was noted that the discrepancy between numerical model results and observations is likely due to a reliance on data analysis rather than data models.

The collective role that hurricanes play in changing the climate was a point of emphasis at the Summit. In the Atlantic, heat and moisture transport out of the tropics by an ensemble of hurricanes moving poleward in a given season was shown to have a detectable influence on the baroclinic activity at high latitudes the following winter which in turn influences the preferred hurricane track type (recurving or straight-moving) during the subsequent hurricane season. Thus a communication between the tropics and the middle latitudes on the biennial time scale is accomplished through tropical cyclone track changes and middle latitude baroclinicity. Also, the relationship between global warming and ENSO was explained in terms of warming rather than warmth. A warming planet is associated with more El Nino events which on the biennial time scale leads to cooling. These are intriguing hypotheses about climate change and tropical cyclones that merit further investigation. It was also shown that super typhoons in the western North Pacific need a deep ocean mixed layer for rapid intensification only in regions where the sub surface water temperatures are marginally supportive of tropical cyclone intensification. It was demonstrated that high aerosol concentrations lead to an enhancement of the ice/water cloud microphysics leading to the invigoration of the convection inside a tropical cyclone.

Another important theme of the Summit was paleotempestology. Paleotempestology is the study of prehistoric storms from geological and biological evidence. For instance, coastal wetlands and lakes are subject to overwash processes during hurricane strikes when barrier sand dunes are overtopped by storm surge. The assumption is that during landfall the waves and wind-driven storm surge reach high enough over the barrier to deposit sand in the lake. In a sediment core taken from the lake bottom, a sand layer will appear distinct from the fine organic mud that accumulates slowly under normal conditions. Sediment cores taken from the northeastern Caribbean show more sand layers during the second half of the Little Ice Age when sea temperatures near Puerto Rico were a few degrees C cooler than today provides some evidence that today's warmth is not needed for increased storminess. Not surprisingly intervals of more hurricanes correspond with periods of fewer El Nino events. Sedimentary ridges in Australia left behind by ancient tropical cyclones indicate that activity from the last century under represents the continent's stormy past. Proxy techniques based on oxygen isotopes from tree rings and cave deposits also show promise for studying prehistoric tropical cyclone events.

It was mentioned that a spatially limited set of proxies or historical records is not able to resolve changes in overall activity from changes in local activity due to shifts in tracks. While the northeastern Caribbean region is in the direct path of today's hurricanes, was it always? The answer is important as more hurricanes locally could mean changes in steering rather than changes in abundance. Proxy data from the U.S. Gulf coast show a pattern of frequent hurricanes between 3800 and 1000 years ago followed by relatively few hurricanes during the most recent millennium which is explained in terms of the position of the subtropical North Atlantic High. Moreover it was shown that recent increases in typhoon intensities affecting Korea can be explained by an eastward shift in the subtropical North Pacific High allowing the storms to recurve over the warmer waters of the Kuroshio Current rather than the colder subsurface waters of the Yellow Sea. In order to understand how climate influences local changes in tropical cyclone activity, more research is needed to identify factors influencing tropical cyclone tracks.

Results from various high resolution numerical models, including a 20 km-mesh model, were consistent in showing stronger tropical cyclones in a warmer future. Most models indicate an overall decrease in the number of storms, attributable in one study to greater atmospheric stability and a decrease in the vertical mass flux. Not all models agree on the change in individual basin numbers with some models showing an increase in the Atlantic and others a decrease. It was shown that models without tropical cyclones need to remove the oceanic heat in the tropics through stronger trade winds. It was noted that models may be better at identifying changes to the large-scale genesis fields and that models still do not have the resolution to be useful to society. Climate model projections can be downscaled to construct tropical cyclone climatologies using a method that combines rejection sampling of genesis points with simple physical models for storm motion and winds. A few presentations focused on the perception and politics of tropical cyclone risk in a changing climate.

The summit was a success. All sessions were plenary and attendance averaged greater than 90%. There were 4 days of talks with each day broken into an early and late morning session of 4 to 5 speakers each. Coffee, tea, soft drinks, and snacks were served during the poster session between the oral sessions. The posters were available for viewing during the entire summit. Lunch was served on the beach after the late morning session followed by free time. In the evening participants and their company were treated to a full course meal at a local restaurant. Wednesday afternoon featured a tour of the Minoan Palace of Knossos near the city of Heraklion. The Summit concluded with a gala dinner featuring traditional Greek and Cretan music and dancing. Invited speakers were provided full accommodations. Travel awards were given to six students and two additional participants. The conference proceedings will be published by Springer as an edited volume in 2008.

Limitation of risk models in a changing climate

Hurricane risk models used by the insurance industry rely on a catalog of storms that represent the historical data in some way or another. While useful for estimating aggregate portfolio losses from a hypothetical worse case scenario, these catalogs are not easily suited for anticipating losses based on a changing climate. At the core of the catalog is a set of synthetic storms and a way to assign a probability to each. But each synthetic storm in the catalog is a composite of size, track, and intensity so it is difficult to estimate risk at a particular point location. Additionally, it is important to consider how climate influences hurricane risk, but it is not obvious how to condition the multidimensional storm event on climate. Perhaps most importantly, the assigned return rates are empirically driven in that the rates are not connected parametrically to a theoretical distribution. This leads to lower confidence in the estimated rates.

We propose an alternative approach for anticipating losses that produces predict expected wind speed distributions at any location. The parametric distributions which give tighter confidence intervals can be naturally conditioned on preseason climate variables. More importantly this approach could allow the reinsurance industry to examine which coastal regions are most sensitive to the changing climate. For example, with increasing Atlantic sea-surface temperatures is it realistic to expect that the risk of hurricane damage will increase everywhere? The answer to this question has implications for insurance rates and societal vulnerability.

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.