The weather varies, but climate change affects the frequencies with which particular weather occurs, including the frequencies of extreme weather, such as heavy storms, heat waves, and droughts. More frequent occurrence of such events will underlie the most serious physical and economic impacts of climate change. Prudent programs to adapt to current and future climate change must take these changing probabilities into account, while also making risk assessments and devising adaptation measures.
Federal agencies and other bodies charged with estimating the probabilities of such extreme weather events have been deriving their estimates from historical frequency data, which are assumed to reflect future probabilities as well. These estimates have not yet adequately factored in the effects of past and future climate change, despite strong evidence of a changing climate. They have relied on historical data stretching back as far as 50 or 100 years that may be increasingly unrepresentative of future conditions. As a result, the risks of damage from climate change based on these estimates may be badly understated.
In this article, we intend to highlight, by means of a significant illustrative case study, the extent to which these backward-looking probability estimates may be understating future frequencies and risks, and how this might affect assessments of possible adaptive investments. Our analysis shows that government and private organizations that use these probability assessments in designing programs and projects with lifetimes expected to be long may be investing too little to make existing and newly constructed infrastructure resistant to the effects of changing climate. New investments designed around historical risk standards may suffer excess damages and poor returns.
This article illustrates the issue with an economic analysis of the risks of relatively intense hurricanes striking the New York City region. This region is particularly exposed to damage, because of its population density and its concentration of economic activity and infrastructure. Also, there is good evidence that rising sea surface temperatures are changing the frequencies of more intense hurricanes in the North Atlantic. Our underlying goal is to demonstrate the importance of accelerating research programs that link climate change to future probabilities of extreme weather events and, despite remaining uncertainties, to embody the findings in estimates and disseminate them to the public. Program planners, investors, civil engineers, and the public should be alerted to the effects of climate change on the risks of damage from extreme weather events.
How and Why Climate Risks May Be Underestimated
Over the past half-century, temperatures and precipitation in the United States have gradually increased, more of the precipitation has fallen in heavy storms, sea level and sea surface temperatures have risen, and other aspects of climate change have also emerged. A scientific consensus agrees that such changes will continue for many decades, whatever reductions of greenhouse gas emissions are achieved. It is not these gradual changes that are most threatening, however. Organisms and ecosystems can tolerate a range of weather conditions, and manmade structures and systems are designed to do so as well. Within this range of tolerance, weather variability causes little damage, and if change is sufficiently gradual, many systems can adapt or be adapted.
When weather varies outside this range of tolerance, however, damages increase very disproportionately. As floodwaters rise, damages are minimal as long as the levees hold, but when the levees are overtopped, damages can be catastrophic. If roofs are constructed to withstand 80-mph winds, a storm bringing 70-mph winds might only damage a few shingles, but if winds rose to 100 mph, roofs might come off and entire structures be destroyed. Plants can withstand a dry spell with little loss of yield, but a prolonged drought will destroy the entire crop. The very harmful risks of damage from climate change arise from an increasing likelihood of such extreme weather events, not from a gradual change in average conditions.1
Unfortunately, even if weather conditions do not become more volatile as climate changes, which might happen, a shift in average conditions will also bring about a changing probability of weather events far removed from average conditions.2 For example, as more rain falls in heavy storms, the probability rises that deluges that bring about extreme flooding and disastrous damages will occasionally occur. As average temperatures rise, the likelihood of an extreme heat wave also rises.
Weather frequency estimates have not yet come to grips with the changing probabilities of extreme weather. The methodologies in use typically are backward-looking and conservative. The frequencies with which specific weather events occur are estimated from measurements in the historical record going back decades. These frequencies, calculated from past records, are then used to “fit” to the data a probability distribution with a similar mean, variance, and skewness. The probability distribution can then be used to estimate the likelihood of extreme weather, even though there are few, if any, such events in the historical record. Estimating the probability of extreme, and therefore very infrequent, weather events in this way is inherently difficult, because there are so few such events in the measured record. Extrapolating from the occurrence of rarely observed events, the probability of even more extreme events beyond the historical record is unavoidably uncertain.
When climate is changing, an even more serious problem lies in assuming that the future will be like the past and projecting probabilities estimated from historical data into the future.3 Not only are the agencies that are charged with assessing weather distributions making this assumption—that the estimated probability distributions are stationary—they are also ignoring measured trends in historical weather patterns.
They do so for two main reasons. The first is uncertainty—whether an apparent trend is real, is just a poorly understood cyclical phenomenon that will be reversed, or is just a string of random events. The second is the dilemma in giving more weight to recent observations, which might better represent current conditions, but which would provide less data with which to estimate a probability distribution that is representative of extreme and unlikely events.4
Uncertainty about future climate conditions that affect particular localities and weather phenomena is the main reason why weather probability assessments estimates are still based on historical data, despite strong scientific and empirical evidence that the future will not be like the past. Conservative agencies retain methodologies and estimates that are likely to be erroneous rather than make use of scientific projections of future conditions that are still quite uncertain, especially at a regional or local geographic scale. The question bedeviling weather forecasters is “If the future will not be like the past, what will it be like?” Climate models are still unable to provide answers to this question with high reliability.
Nonetheless, weather probability estimates become increasingly outdated as time passes or when projected further into the future. They provide unreliable guidance for the design, placement, and construction of infrastructure that will likely be in place for many decades and vulnerable to extreme weather throughout its useful life. Because they lead planners to underestimate future risks, they also provide unreliable guidance for investment and program decision-makers to help make existing infrastructure and communities more resistant to extreme weather. As a result, according to a new report by a National Research Council panel,
Government agencies, private organizations, and individuals whose futures will be affected by climate change are unprepared, both conceptually and practically, for meeting the challenges and opportunities it presents. Many of their usual practices and decision rules—for building bridges, implementing zoning rules, using private motor vehicles, and so on—assume a stationary climate—a continuation of past climatic conditions, including similar patterns of variation and the same probabilities of extreme events. That assumption, fundamental to the ways people and organizations make their choices, is no longer valid.5
This is a problem of broad and significant scope. Among the public and private sector organizations that are exposed to increasing but under-estimated risks are the following:
Local, state, and federal disaster management agencies
Local, state, and federal agencies that finance and build public infrastructure in vulnerable areas, as well as those that own and operate vulnerable infrastructure
Private investors and owners of vulnerable buildings and other physical property
Property and casualty insurers
Creditors holding vulnerable infrastructure directly or indirectly as collateral
Vulnerable businesses and households
Clearly, this list encompasses a large proportion of the American economy, and an assessment of the vulnerable regions would also extend over a large part of the country, including coastal regions subject to hurricanes, storm surges, and erosion; river basins subject to flooding; and agricultural areas subject to wind, storm, and drought damage.
These underestimated risks should not be neglected in any attempt to adapt to climate change. Research is underway to address this problem but should be accelerated, and efforts to improve climate change forecasts at regional and local scales should be intensified. In these efforts, more emphasis should be placed on forecasts of the likelihood of extreme weather events. Even while these efforts are underway, however, agencies responsible for weather probability assessment should update their estimates, incorporating the best available scientific climate projections that provide guidance regarding future conditions. Uncertainties in these projected weather frequencies should be frankly acknowledged and explained. In addition to their best estimates, agencies should also present plausible uncertainty bands around those probabilities. Finally, vulnerable groups such as those listed above should be encouraged or directed to use these revised probability estimates in their risk assessments, and program and investment planning as an important step toward anticipatory adaptation to climate change.
A Case Study: Hurricane Risk in the New York City Region
To clarify and illustrate the issue, we present a case study of the risks to the New York City metropolitan region from hurricane damage. Of course, the scope of the problem is much wider. Hurricane risks imperil the entire Atlantic seaboard, the Gulf of Mexico, the Caribbean, many Pacific coastal areas, and the Indian Ocean. Nor is the issue just that of hurricane risks; risks of floods, droughts, and severe storms may also be underestimated for the same underlying reasons. This case study nonetheless indicates the severity and magnitude of the problem.
The New York metropolitan region extends across three states and encompasses an extraordinarily dense concentration of infrastructure, physical assets, and business activity. In 2006, for example, the value of insured coastal property in the New York, Connecticut, and New Jersey region was almost $3 trillion.6 The metropolitan region's economy is vulnerable to the more extreme effects of climate change. Storm surges could reach 18–24 feet in a strong hurricane. Low-lying regions, including Kennedy Airport and lower Manhattan, would flood. Roads, subway, and tunnel entrances would be submerged, along with ground-level and underground infrastructure. High winds would do severe damage, partly by blowing dangerous debris through city streets. The New York City government has recognized such risks, and in 2008 created the New York City Panel on Climate Change and the Climate Change Adaptation Task Force to develop an adaptation strategy. Studies toward this objective are underway. Adaptative measures that might be taken as a result of the study include changes in building codes and the siting of infra-structure, changes in flood risk maps, and changes in operating protocols for pollution control plants and other facilities.
The case study builds on several relevant investigations, incorporating them as components in an overall design that shows to what extent hurricane probabilities may be underestimated, how economic damage risk may consequently also be underestimated, how these risks assessments can be updated and projected into the future based on relevant scientific information, and how these updated risk assessments might be used to improve decisions on investments in adaptation.
The starting point is the probability assessment carried out by the National Hurricane Center (NHC), an office within the National Oceanic and Atmospheric Administration. The methodology used for NYC and other coastal regions counts the occurrence of hurricanes of specific intensities (defined in terms of maximum sustained wind speeds) striking within a 75-mile radius during the historical record of approximately 100 years. NHC scientists fitted a particular probability distribution, the Weibull distribution, to these observed frequencies, and the probabilities of hurricanes of various intensities were then read off the fitted probability distribution. There were no actual observations of the most severe hurricanes in the historical record for the New York region, so those probabilities were extrapolations based on the fitted distribution. The results, expressed as the expected return periods, which are the reciprocals of the annual probabilities, are shown in Table 1 for various categories of hurricanes.7
These probability estimates were constructed in 1999. It is questionable whether these estimates were valid in that year, because there has apparently been an upward trend in intense hurricanes in the North Atlantic over at least the past 35 years. The number of Category 4 and 5 hurricanes in the North Atlantic increased from 16 during the period 1975–1989 to 25 from 1990–20048.
Consequently, the earlier years in the historical record used to compute frequencies might not have been representative of the final years.
There is good reason to believe that this increasing frequency of stronger hurricanes in the North Atlantic is linked to climate change through the gradual rise in sea surface temperatures.9 Warming ocean waters provide the energy from which more intense hurricanes are developed and sustained.10 Based on empirical data, doubts have been expressed regarding the consistency of this relationship, though the underlying physics are well-grounded and trends are most clearly evident in the North Atlantic.11,12 The relationship has been accepted by the leading private sector risk management firm and incorporated into its risk assessment.13 According to a recent study, a three degree centigrade increase in sea surface temperature would raise maximum hurricane wind speeds by 15 to 20 percent.14
Measurements throughout the oceans have found a rising trend in sea surface temperatures at a rate of approximately 0.14 degrees centigrade per decade.15 The rate of warming is apparently increasing, however, and the North Atlantic warming has been faster than the global average. According to a recent examination, in the 28-year period from 1981–2009, warming in the North Atlantic has averaged 0.264 degrees centigrade per decade, roughly twice the global average.16 Rising sea surface temperatures in the North Atlantic, the driving force behind the increasing frequency of intense hurricanes, explain why backward-looking historical probability estimates, such as those generated using the NHC's approach, probably do not provide adequate guidance with respect to current and future risks.
This problem is compounded by the rising trend in sea level, itself partly the result of increasing ocean temperatures. Higher sea levels and tides raise the probability of flooding driven by hurricane-force winds. In the North Atlantic between New York and North Carolina, sea level has also risen more rapidly than the global average, at rates between 0.24 and 0.44 centimeters per decade.17
These scientific findings and measurements can be used to project future hurricane frequency estimates. The trend in sea surface temperature, linked to the relationship between sea surface temperature and maximum wind speed, provides a way to forecast changes in the intensity of future hurricanes. High and low estimates can define a range of future probabilities. Though there are considerable uncertainties inherent in forecasts based on this approach, the results are arguably more useful than static estimates based on historical data that fail to incorporate any relevant information about the effects of climate change. At a minimum, this approach can provide a quantitative sensitivity analysis indicating by how much existing estimates may be underestimating future risks.
Table 2 displays some results, based on both the higher and lower estimates of sea surface temperature trends and the relationship between sea surface temperature and maximum wind speeds. The table shows the estimated return periods in years for hurricanes striking the region, based on the 1999 Weibull distribution estimated by the NHC return periods for the New York metropolitan region. (Figures differ slightly from those in Table 1 for less intense storms because of curve-fitting variances.) In addition, it presents return periods for 2010, 2020, and 2030 estimated by indexing the scale parameter of the probability distribution to a time trend based on the rate of sea surface temperature change and its effect on maximum wind speeds. The ranges shown for the decades 2010–2030 are based on the high and low estimates of the rate of sea surface temperature increase.
Climate change will increase the probability of hurricanes striking New York, but especially the more severe hurricanes. By 2030, the probabilities of Category 4 and Category 5 hurricanes striking the New York metropolitan region are likely to have increased by as much as 25 and 30 percent, respectively. For Category 3 storms, what was a one-in-68 chance of a Category 3 hurricane may have become a one-in-58-year event. These changing probabilities have dramatic economic implications.
The Economic Implications of Increasing Weather Risk
A professional risk management consultancy recently estimated that a Category 3 hurricane with a landfall in the New York metropolitan region would probably result in losses of approximately $200 billion in property damage, business losses, and other impacts.18 According to the NHC's 2000 estimates, there is only a 1.5 percent chance of that happening in any year. However, this may be a very misleading portrayal of the economic risk.
A more complete assessment makes use of a tool common in the insurance industry: the loss exceedance curve. This curve represents the annual probability of a loss equal to or greater than specified amounts. It summarizes the probabilities of hurricanes of various intensities and estimates of the damages they would create. A loss exceedance probability of $200 billion represents the chance that a hurricane loss of that amount or more, into the trillions of dollars, might occur. To construct such a loss exceedance function for the New York region, one needs not only the probabilities of Category 1–5 hurricanes, but also the damages that they, respectively, would inflict.
A recent study by Yale economics professor William Nordhaus, based on hurricanes recorded throughout the United States, investigated the relationship between maximum wind speeds and resulting damages.19 Shockingly, this study found that damages increase as the eighth power of the wind speed: if a hurricane with wind speeds of 50 mph would cause $10 billion in damages, then one with maximum winds of 100 mph would cause not twice the damages, $20 billion, but more than $2.5 trillion. The reasons for this dramatic escalation are threefold. First, higher winds will obviously do more damage to everything in their path; second, more intense hurricanes are likely to have impacts over wider areas; and third, their winds are likely to persist at damaging speeds, although not at the maximum, for longer periods of time.
The loss exceedance curve implied by this relationship is plotted in Figure 1 for the year 2000 and for subsequent decades, using the higher estimate of sea surface temperature increase. On the horizontal axis, damages are marked in hundreds of billions of dollars. On the vertical axis are the probabilities of hurricane losses of those amounts or more. One striking feature that is immediately apparent is that the exceedance curve is “fat-tailed”: probabilities decline slowly as heavy losses mount. As maximum wind speeds increase, damages mount very rapidly, offsetting the declining probability of the more intense storms. The probability of losses exceeding a trillion dollars is not half the probability of losses exceeding $500 billion, but substantially more than that. This illustrates how vulnerable to catastrophic hurricane damage the New York metropolitan region is now.
Figure 1. Hurricane loss exceedance curves, 2000–2030.
The second feature that Figure 1 illustrates is that the probabilities of large losses shift upward over time, as climate change makes intense hurricanes more likely. By 2030, the probability of hurricane damages exceeding amounts in the range of $100 to $500 billion could be 30–50 percent greater than current estimates assume. Warming sea surface temperatures and rising sea levels increase the economic risks to coastal cities. In the absence of effective adaptation measures, risks of catastrophic losses will very likely continue to rise over coming decades.
Another way of understanding the increasing economic risks is to ask how much the region should be willing to pay to insure against all hurricane damages, if such comprehensive insurance were available. Even without an aversion to catastrophic risks, the region should be willing to pay an annual insurance premium up to the expected value of losses in the absence of insurance. The expected value of losses is the sum of all possible hurricane losses, weighted by their probabilities of occurring. Because the loss exceedance curve is so “fat-tailed,” with significant probabilities of huge losses, that rational insurance premium, calculated using the outdated 2000 return periods estimated by the National Hurricane Center, is about $33 billion dollars per year. To put that amount in context, the entire 2009 expenditure budget of the City of New York is just over $60 billion. However, as the likelihood of hurricane damage rises, that insurance premium increases to $35–37 billion in 2010, $40–46 billion in 2020, and $47–62 billion in 2030. The ranges reflect the high and low estimates of the pace of sea surface temperature increase. In other words, the expected value of losses could nearly double over three decades, just because of the increasing likelihood of intense hurricanes.
Unfortunately, the reality is even more disturbing. Nordhaus's investigation and others have found an increasing trend of damages over time for the same maximum wind speeds. The rising trend reflects population increases and increasing numbers of buildings and other infrastructure in the coastal zone, and the erosion of barrier beaches and other protection, among other factors. Were vulnerabilities to increase over coming decades at the same pace as in the past, a rate that Nordhaus estimated at 2.9 percent per year for constant hurricane intensities, the region's vulnerabilities and expected losses would obviously become much higher still. By 2010, the expected value of annual damages has risen to $45–47 billion, by 2020 they would be $65–75 billion, and by 2030 they would range from $100–130 billion per year.
Moreover, no one should expect businesses, individuals, or their representatives in government to be indifferent to the risks of catastrophic damage. In many contexts, all manifest a significant aversion to risks of major losses. Individuals and businesses often buy insurance against catastrophic losses with a premium greatly exceeding the actuarial fair value (reflecting the insurance companies' administrative costs, capital costs, and profit margins).20 Attempts by financial economists to explain the large and persistent equity risk premium evident in securities markets, the higher long-term returns to stocks over less risky bonds, have concluded that the coefficient of relative risk aversion must be as high as four. With this degree of risk aversion, society would be willing to give up significant fractions of annual income, well in excess of the expected value of losses, if it were possible to eliminate or reduce significantly the threat of rare disasters.21
Risks to Investors
Investors in infrastructure projects vulnerable to hurricane damage, whether buildings, roads, or other structures, face greater risks than they realize and are likely to experience rates of return from their investments that are dramatically below those that they anticipate. Infrastructure projects are designed and engineered to withstand extreme weather, so it would take an extremely unlikely event to cause major damage. There is a trade-off between an extra margin of safety and the additional cost required to achieve it. Civil engineers and planners are trained to estimate and base decisions on such trade-offs, often going beyond what is strictly required by building codes and other regulations.
Unfortunately, in assessing these trade-offs, civil engineers and planners are still relying on historical frequency estimates and are making the same assumptions that the future will be like the past, despite climate change. Thought leaders in the engineering profession have only recently begun weighing alternative approaches to climate change issues.22 Practicing engineers are predominantly still adhering to “best practice,” a term that implies a continuation with assumptions validated by past experience, not innovative approaches aimed at new challenges.
For example, consider an infrastructure project anticipated to have a 40-year lifetime, which is designed to meet the hurricane frequencies calculated in 2000 (and still promulgated by the National Hurricane Center). The investor might require an expected income stream that would provide a discounted present value return of 12 percent on his investment, taking into account the risks of possible income losses from hurricane damage. Unfortunately, as the years pass over the 40-year lifetime of the project, the probabilities of more intense hurricanes striking the region increase. The initial estimates of risk are no longer valid. The expected returns on the project are dramatically affected, as Table 3 illustrates. These results are based on three alternative real (inflation-free) discount rates: three, five, and eight percent. Three percent represents a discount rate appropriate for public sector investments, five percent is an inflation-free return indicative of private returns on capital, and eight percent is a still higher alternative. Higher discount rates give less weight to future years and to the higher risks of future hurricane damage.
As we stated before, hurricane damages are estimated as a function of maximum wind speed, but the more conservative Carvill index is used, which relates damages to the third power of wind speed rather than to the eighth power, as Nordhaus estimated. The Carvill index is used in this illustration because it underlies some recent financial derivative instruments available to hedge hurricane risk.23 Moreover, it is assumed that hurricane damages sustained in any year are limited to that year. Despite these conservative assumptions, Table 3 shows the dramatic impact of increasing risk on expected returns. The project is not likely to earn the planned 12 percent return. At a three percent discount rate, expected investment returns would be reduced by almost 90 percent, with a significant probability that the project would not repay the capital investment. With a five percent discount rate, the expected project rate of return is reduced by more than two-thirds. With an eight percent discount rate, the expected return is reduced by almost one-half. The message is clear: Designing vulnerable infrastructure projects without adequately estimating future weather risks will lead to significant investment losses.
Investments in Adaptation and Prevention
Not surprisingly, if past frequencies of extreme weather events are projected into the future without taking into account the effects of climate change, the economic value of investments in adaptation and prevention efforts are dramatically underestimated. Using the previous investment analysis as a starting point, imagine that at an additional investment cost, it is possible to strengthen the structure to withstand an additional 10 mph of maximum wind speed without any additional damage. The pay-off to this adaptation investment would be a lower risk of hurricane damage and a higher expected income return. Suppose further that such an investment in adaptation would just break even if the historical hurricane frequencies were projected into the future, over the project's anticipated lifetime. Under these assumptions, adaptation would be considered uneconomic, since it would yield no positive return on investment.
If the effects of climate change were taken into account by anticipating the increasing probabilities of more extreme storms striking the region, then the economic advantage of investing in adaptation and prevention would appear much more attractive. Table 4 shows the expected returns on such an investment that would have been considered only a break-even proposition if historical probabilities were projected into the future.
The differences are dramatic: At a three percent time discount rate, the zero return on adaptation rises to a 68 percent return; at five percent, it becomes a 56 percent rate of return; and at eight percent time discount, it becomes a 43 percent investment return.
Since, with few exceptions, private investors and public agencies at local, state, and federal levels are still relying on static, historically based probability estimates of extreme weather events and have not yet incorporated the effects of climate change into these probability estimates when they evaluate the economics of adaptation investments, these agencies are grossly underestimating the economic case for investments in adaptation. This is one of the reasons why adaptation has lagged and is proceeding so slowly.24
Every year the United States is hit with hurricanes, floods, droughts, and other weather-related disasters such as wildfires and pest outbreaks. These cause many billions of dollars in damages, loss of life, and disruption or displacement of entire communities. Some of these losses can be avoided if preventive and anticipatory actions are taken. If the risks of extreme weather events are underestimated, however, the pace and extent of preventive activities will lag.
Ignoring the effects of climate change on future probabilities of extreme weather events could lead to significant underestimates of future risks to vulnerable communities, infrastructure, and investments. Deriving such probabilities from historical records that go back many decades, with no adjustment for changes in climate extending inevitably into future decades, is likely to produce faulty estimates for planning and investment decisions. Climate change is very likely to affect the frequency with which many forms of extreme weather will occur.
The effects of climate change on weather and storm patterns are still uncertain, particularly at local and regional geographical scales. However, uncertainty is not a justification for paralysis. It should be incorporated into estimates of future risks by establishing plausible ranges for key variables and parameters, as has been done in this study. Adhering to estimates almost certain to be wrong and waiting for uncertainties to be resolved provides misleading information for current decisions. The resulting decision errors can be very costly.
Public and private sector agencies responsible for providing estimates of weather risks are now grappling with the problems of incorporating the effects of climate change, but progress is slow and the bias is toward conservatism—sticking to the historical record until an alternative is clearly established. Moreover, much of the current research into this issue is narrowly focused and is not connected to adaptation program planning. Leadership in the responsible agencies is needed ensure that their frequency estimates, to the extent now possible, reflect current and future probabilities, not past historical conditions, and that their estimates are frequently updated to incorporate new information about climate change effects.
1. T. R. Karl, G. A. Meehl et al., Weather and Climate Extremes in a Changing Climate, U.S. Climate Change Science Program Synthesis & Assessment Product 3.3, Washington, DC, 2008.
2. “In the non-stationary case, a trend in the mean affecting the entire data set must imply a trend in extreme events,” p. 306 from M. Nogaj, S. Parey, and D. Dacunha-Castelle, “Non-Stationary Extreme Models and a Climatic Application,” Nonlinear Processes in Geophysics 14 (June 25, 2007): 305–316;.
3. P. C. D. Milly, J. Betancourt, M. Falkenmark, R. M. Hirsch, Z. W. Kundzewicz, D. P. Lettenmaier, and R. J. Stouffer, “Stationarity Is Dead: Whither Water Management?,” Science 319 (2008): 573–574.
4. R. E. Livezey, K. Y. Vinnikov, and M. M. Timofeyeva, “Estimation and Extrapolation of Climate Normals and Climatic Trends,” Journal of Applied Meteorology and Climatology 46 (2007): 1759–1776.
5. National Research Council, Panel on Strategies and Methods for Climate-Related Decision Support, “Informing Decisions in a Changing Climate” (Washington, DC: National Research Council, 2009), p. S-1.
6. D. L. James Valverde, Jr., “Hurricane Risk in New York City and Long Island,” Insurance Information Institute, 24 February 2006.
7. Data accessed at
8. P. J. Webster et al., “Changes in Tropical Cyclone Number, Duration and Intensity in a Warming Environment,” Science 309 (2005): 1844–1846.
9. Union of Concerned Scientists, “Hurricanes in a Warmer World,” Cambridge, MA, 2006.
10. K. A. Emanuel, “The Dependence of Hurricane Intensity on Climate,” Nature 326, no. 6112 (1987): 483–485; K. A. Emanuel, “Increasing Destructiveness of Tropical Cyclones over the Past 30 Years,” Nature 436 (2005): 686–688; K. A. Emanuel, Divine Wind (New York: Oxford Univ. Press, 2005)
11. C. W. Landsea, B. A. Harper, K. Hoarau, and J. A. Knaff, “Can We Detect Trends in Extreme Tropical Cyclones?,” Science 313 (2006): 452–454.
12. J. P. Kossin et al., “A Globally Consistent Reanalysis of Hurricane Variability and Trends,” Geophysical Research Letters 34 (2007): L04815.
13. “The intensity of tropical cyclones is likely to increase, with larger peak windspeeds, more heavy rainfall and a greater risk of storm surges.” p. 39 in “RMS and the U.S. Hurricane Model Overview,” (Hackensack, NJ: Risk Management Solutions, 21 May 2009).
14. R. Sriverand M. Huber, “Low Frequency Variability in Globally Integrated Tropical Cyclone power dissipation,” Geophysical Research Letters 33 (2006): L11705.
15. K. S. Casey and P. Cornillon, “Global and Regional Sea Surface Temperatures,” Journal of Climate 14 (2001): 3801–3818.
16. R. Tisdale, “The Impact of the North Atlantic and Volcanic Aerosols on Short-term Global Sea-Surface Temperature Trends,” accessed at http://bobtisdale.blogspot.com/2009/02.
17. U.S. Climate Change Science Program, Synthesis and Assessment Product 4.1, “Coastal Sensitivity to Sea Level Rise: A Focus on the Mid-Atlantic Region,” Washington, DC, 15 January 2009.
18. K. M. Clark, CEO AIR Worldwide Corporation, “Major Hurricane Strikes the Northeast: How Large Will the Losses Be?,” presentation to the Northeast Hurricane Conference, 19 July 2006.
19. W. Nordhaus, “The Economics of Hurricanes in the United States,” 21 December 2006; accessed at http://nordhaus.yale.edu/hurr_122106.pdf.
20. See H. C. Kunreuther and E. O. Michel-Kerjan, At War with the Weather (Cambridge, MA: MIT Press, 2009).
21. R. J. Barro, “Rare Disasters, Asset Prices and Welfare Costs,” American Economic Review 99, no. 1 (2009): 243–264.
22. See, for example, United States Geological Survey, “Climate Change and Water Resources: A Federal Perspective,” Washington, DC, 2008.
23. It is assumed that at wind speeds of 30 mph or below, there are no damages, and at wind speeds above 130 mph, there would be a total loss of project income, and between those limits damages would be proportional to the Carvill index.
24. R. Repetto, “The Climate Crisis and the Adaptation Myth,” Yale School of Forestry and Environmental Studies, available at http://environment.research.yale.edu/documents/downloads/v-z/WorkingPaper13.pdfa.