A NATIONAL INDEX FOR THE UNITED STATES

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LOSSES CAUSED BY WEATHER AND CLIMATE EXTREMES: A NATIONAL INDEX FOR THE UNITED STATES a

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Stanley A. Changnon , Joyce M. Changnon & Geoffrey J. D. Hewings a

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Changnon Climatologist Mahomet , Illinois 61853

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Geography Department , University of Illinois , Urbana-Champaign, Illinois 61801 Published online: 15 May 2013.

To cite this article: Stanley A. Changnon , Joyce M. Changnon & Geoffrey J. D. Hewings (2001) LOSSES CAUSED BY WEATHER AND CLIMATE EXTREMES: A NATIONAL INDEX FOR THE UNITED STATES, Physical Geography, 22:1, 1-27 To link to this article: http://dx.doi.org/10.1080/02723646.2001.10642727

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LOSSES CAUSED BY WEATHER AND CLIMATE EXTREMES: A NATIONAL INDEX FOR THE UNITED STATES

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Stanley A. Changnon and Joyce M. Changnon Changnon Climatologist Mahomet, Illinois 61853 Geoffrey J. D. Hewings Geography Department University of Illinois Urbana-Champaign, Illinois 61801 Abstract: An annual index measuring past national losses and capable of measuring future loss from weather and climate extremes was developed to help measure future shifts in climate, a subject of great concern to the global warming issue and the insurance industry. Results from climate models indicate that a changed climate owing to global warming would alter and increase many extremes, and the objective of this study was to develop a national-scale index to monitor change in monetary losses and costs from weather and climate extremes. Forty-four historical variables addressing various aspects of weather and climate extremes and their effects on the U.S. economy since the late 1940s were assessed as potential input to such an index. Frequencies of most severe weather conditions from 1950 to 1997 did not correspond well with their financial impacts. However, hurricane losses did correspond with the frequency of intense hurricanes, and tornado losses corresponded well with the number of violent tornadic storms. Quality insurance loss data for several major storm types (thunder, hail, winter, and wind storms) were available, plus quality data on flood and hurricane losses, and all were adjusted to 1997 dollars. Techniques were developed to assess the effect of seasonal climate extremes on major crop yields and costs for electricity and natural gas consumed. Trends were upward for certain key variables between 1950 and 1997, including the incidence and losses associated with winter storms, flood losses, crop losses, and incidence of heavy rains. Trends were downward for other weather-driven loss variables including hurricane losses, energy costs, thunderstorm losses, wind storm losses, and hail losses. Nine loss variables were selected to develop the U.S. annual loss index, and in rank order, based on their average annual loss values, were energy costs, followed by losses from hurricanes, floods, severe convective storms, crops, tornadoes, winter storms, hail, and wind storms. The variables chosen also will be available in future years to allow a continuing assessment using the index. The 1950-1997 average annual index value was $17.47 billion (1997 dollars), with annual values ranging from a high of $54.4 billion in 1972 to a low of $2.4 billion in 1963. The 48-year trend of the index was unchanging with three notable high loss periods: 19501954, 1970-1974, and 1990-1994. [Key words: climate change, storms, natural hazards, weather extremes.]

INTRODUCTION Concern over global warming and its effects on weather and climate has received wide attention as scientists have attempted to determine whether the climate has become more variable or extreme. A central question identified at a 1998 1 Physical Geography, 2001, 22, 1, pp. 1-27. Copyright © 2001 by V. H. Winston & Son, Inc. All rights reserved.

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international conference focusing on this issue was: "Can we establish indices and indicators of extreme weather and climate?" (Karl et al., 1999, p. 3). Two past studies have developed two climate indices for the conterminous United States: (1) a Climate Extremes Index (Karl et al., 1995); and (2) a Greenhouse Climate Response Index (Karl et al., 1996). Both indices were based on five national weather conditions. The variables in the Greenhouse Climate Response Index (GCRI) were all expressed as the percentage of the conterminous U.S. area and included: (1) much above normal temperatures; (2) much above normal precipitation in the cold season; (3) severe drought in the warm season; (4) a much greater than the normal proportion of total precipitation derived from days with 2-inch (5.1 cm) or heavier precipitation; and (5) much below normal day-to-day temperature differences. These variables were selected because they measured conditions that global climate models had indicated would change as a result of global warming. The selection of variables measuring extremes of different climate conditions also was predicated on a concept that such extremes had an effect on society (economic losses or gains caused by warmer/colder, and/or wetter/drier conditions), but no relationships were presented to define the magnitude of the economic loss (or gain) of the index values in the United States. Hence, these climate-based indices are measures of extreme climate conditions having some likely but unknown economic importance. They were defined for the purpose of detecting temporal changes in their magnitudes. Included among those deeply concerned about the effects of climate change caused by global warming is the nation's insurance industry, which expressly indicated its need for an index based on weather/climate impacts (Changnon et al., 1999). Yet scientists at the 1998 international climate extremes conference, in assessing changes in extremes and the need for indices, focused totally on the use of climate measurements (Karl and Easterling, 1999), with much of the focus on temperature and precipitation extremes (Easterling et al., 1999), not on impactbased indices. Most projections based on global climate models operated under various global warming scenarios indicate there will be more extremes (Nicholls, 1995). Some extremes expected include: (1) more high-temperature events; (2) less low-temperature events in winter; (3) higher probability of intense precipitation; (4) more severe droughts and floods; and (5) changes in storminess, although storm changes were seen as more uncertain (Karl et al., 1999). Several types of indices have been proposed to measure climate change impacts or climate extremes (Easterling and Kates, 1995), but only a few have been developed. One of the extremes indices proposed is the "commonsense" climate index based on climate conditions assumed to have major impacts, such as heating degree days and heavy rainfall (Hansen et al., 1996). This index was designed to monitor global conditions, not just those in the United States. Moreover, this index did not attempt to equate the variables selected (to form the index) with societal or economic impacts. The World Meteorological Organization (1998) announced a special task group to develop indices to detect climate change, including extent of glaciers, surface temperatures, sea levels, and climate extremes. Unfortunately, global climate-based indices cannot be

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equated with economic losses, because loss data do not exist for most parts of the world. The objective of this study was to develop an index based on economic losses and costs resulting from major weather and climate extremes occurring each year in the United States. The index serves as a single national measure of how the climate, as defined by those conditions that cause major financial losses in the United States, has fluctuated and will continue to fluctuate over time. An index was developed after assessing a wide variety of historical data on extremes, each with a duration of 48 years or more. Those weather and climate variables ultimately selected in developing the loss index also had to be available in future years to allow for a continuing assessment of future fluctuations. Excesses in all forms of weather conditions (e.g., temperature, wind, precipitation, storms) create physical effects on the environment, and in turn, create economic impacts. Short, brief periods of extremes of any of these conditions, such as an F5 tornado or a week in July with temperatures exceeding 100°F (37.8°C), cause impacts. Weather extremes, mainly events that last from hours to days, and sometimes weeks, include thunderstorms, hurricanes, hailstorms, tornadoes, most floods, wind storms, and winter storms. Climate extremes, periods of extreme conditions lasting months to years, include droughts, excessive wetness from prolonged above-normal precipitation, a hot summer, a season of great storminess, or a series of cold winters such as those from 1976 to 1980. Considerable recent research has assessed weather and climate extremes, and some of this has focused on the impacts of these extremes (Changnon, 1999b; Kunkel, Pielke, and Changnon, 1999). Such studies have shown that a key to an adequate temporal analysis of weather-economic impact data is to adjust the raw data for societal changes. This requires making adjustments for shifting dollar values and other conditions that affect losses such as property density, property quality, and the population density (Changnon et al., 2000). The extremes index was based on direct losses from various extremes, not on economic gains from certain anomalous conditions, such as the record warm and dry Midwestern winter during El Niño 1997-1998 (Changnon, 1999a). Economic gains from severe storms could not be adequately assessed because there has never been a systematic, long-lasting effort to collect such data on damaging storms. Data on many indirect, and often delayed, losses that occur months and years after a damaging event, also were not included because data on such conditions have seldom been collected (National Academy of Sciences, 1999). A national index-based assessment of climate extremes, or of their economic impacts, integrates regional differences into a single national number. Since many atmospheric extremes in an area as large as the United States are regional, the measure of small-scale yet intense extremes could go undetected or appear diminished in a national assessment. Hence, a national-scale index has some serious limitations and must be used with these potential regional differences in mind. Regardless, the atmospheric sciences community, national policy makers, and the insurance industry consider it important to have a national number for addressing the global warming issue. This paper describes the variables considered for inclu-

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sion in the national extremes impact index, their relationships, and their fluctuations in past years. An index consisting of nine variables is defined and analyzed.

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DATA AND ANALYSIS Three classes of data relate to damaging weather and climate extremes. The first class of data measures events that are totally "physical" in nature, often related to damages but not dependent on impacts for their definition. These include frequencies of thunderstorms, days with hail, numbers of tornadoes, frequencies or intensities of heavy rain events, hurricane frequencies, and extremes of temperature, precipitation, and snowfall. The second class of data also pertains to weather and climate conditions but to those partially defined by their impact relationships. The weather category includes violent tornadoes (F4 or F5 rating based on wind speeds) and intense land-falling hurricanes (scales of 3, 4, or 5 related to the magnitude of atmospheric pressure and wind speeds). The climate events in this class include the number of heating and cooling degree days (based on temperatures above or below certain thresholds when, on the average, heating or cooling begins), and severe drought and wetness indices (defined using precipitation and temperature data to estimate local moisture conditions). All such weather and climate values have been equated with costs or damages using generalized relationships. As impact indicators, however, they do not provide direct measures of losses or costs. The third class of data includes "direct losses or costs"—that is, economic data pertaining to losses from weather extremes such as tornadoes, hurricanes, hail, and floods. They also include climate extremes such as financial losses caused by crop damages and the costs of increased energy use related to extremely high or low seasonal temperatures. More than 60 data sets representing each class, and based on various weather and climate extremes and their impacts, were identified, assembled, and considered as potential candidates for variables in the index. After reviewing these data sets, several were found to be redundant, and 44 were ultimately chosen for evaluation. Data in these 44 individual data sets were evaluated and adjusted where necessary. As noted above, all forms of loss data must be normalized for year-to-year changes that distort efforts to make equitable temporal comparisons. Economic values in the data sets were either modified to normalize them to current (1997) conditions, or they already had been properly normalized when obtained. All values analyzed pertain to national annual values. Crop Yield Values Data on the four major U.S. crops (corn, soybeans, wheat, and cotton) were acquired from the U.S. Department of Agriculture for 1950-1997. Crop yield values were interpreted and analyzed to define those effects of weather that are principally extremes of growing-season moisture and temperature conditions. These four crops account for 92 percent of the nation's crop value.

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Fig. 1. Distribution of 1950-1997 corn yields with a curve statistically fit to the distribution, as an expression of changing farm practices and technology. Also shown is equation of the best-fit distribution.

Annual crop yield values had to be made comparable over time because changing farming practices, seed varieties, and agricultural technologies have created continuing, ever-changing increases in yields over time. This normalization process was accomplished by expressing the annual values as a percentage of the expected yield value determined by statistically fitting curves to the array of historical yield values. Such curves reflect the yield expected owing to agricultural technology under average weather conditions (Thompson, 1986; Offutt et al., 1987). Figure 1 shows the best-fit curve and equation, as calculated for the 1950-1997 yields of corn, illustrating an upward trend over time. Values determined for each year (and for each crop) were the percentage of the actual yield to the expected yield for that year. Figure 2 shows those departures calculated for 1950-1997 soybean yields. By this process, four sets of annual yields were created and expressed as a percentage of expected values for the period of record. Annual departures below expected yields, expressed as percentages for a given crop, were compared with the year's total crop production to determine the amount of production lost as a result of weather. These production amounts were expressed in financial terms by using the current year's unit value (bushels for corn, soybeans and wheat, and pounds for cotton). These annual dollar loss values were adjusted to 1997 levels by using the implicit price deflator of the gross national product. For example, the national corn yield in 1955 was 7 percent below the expected yield (with average weather), and the year's production (2,872,959,000 bushels) was 217 million bushels less than the expected production of 3,089,203,225 bushels (actual production divided by 93 percent). The 1955 price was $1.35 per bushel, representing a 1955 loss of $292.95 million. This value was adjusted to the 1997 level by the price index, creating a loss of $1.530 billion in 1997 dollars. Extreme Moisture Conditions Measurements relating to annual drought and extreme wet conditions were desired because they potentially relate to crop outcomes and floods. National val-

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Fig. 2. Percent departure of actual annual soybean yields from best-fit curves, or expected yields with average weather for soybeans, 1950-1997.

ues developed for the Palmer Drought Severity Intensity Index (PDSI) were selected. This widely used meteorologically based index uses precipitation and temperature data to estimate soil moisture conditions on a monthly basis for various U.S. locations. Departures from average conditions are quantified and values of 3 (or greater) are labeled as "severe drought"on the dry side and as "severe moisture surplus" on the wet side. These two values were used to calculate the annual percentages of the United States experiencing severe drought and severe moisture surplus. Annual values for each condition were developed for 1950-1997. Insurance-Defined Catastrophe Data The property insurance industry's records of 1950-1997 storm catastrophes were acquired. These records are considered the nation's premier property loss data (National Academy of Sciences, 1999). The insurance industry defines catastrophes as events that cause sufficient insured losses to exceed a $5 million loss threshold set by the industry. Individual catastrophe values available for this study, 1,038 events during 1950-1997, had each been modified by insurance experts to adjust for shifts in insurance coverage, changes in property value, and shifts in costs of repairs. Prior research has shown a need to further modify insurance-adjusted catastrophe loss values for changes in population, a reflection of the shifting target at risk in the nation during 1949-1997 (Changnon and Changnon, 1998). Hence, the annual insurance-adjusted catastrophe loss values for 1950-1996 were further adjusted to 1997 values to account for changing population density in the nation. Catastrophes were sorted into six classes based on their major cause. These classes included catastrophes owed to (1) tornadoes; (2) hail and associated winds; (3) winter storms (snow and/or ice storms plus associated flooding); (4) hurricanes and tropical storms; (5) thunderstorms, including accompanying hail, high winds,

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lightning, and heavy rain (flash flooding); and (6) wind storms. Losses were determined for each class.

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Crop-Hail Insurance Data Data from the crop-hail insurance industry were used to measure hail and wind crop losses. The industry computes the national annual total losses, total liability, and a "loss cost" value, annual losses (dollar) divided by annual liability (dollars) multiplied by $100. This value normalizes the loss to exposure and changing dollar values, making it comparable between years (Changnon and Changnon, 1997). Loss cost values were used to adjust the historical crop-hail loss values to the 1997 level. Hail and wind account for 26 percent of losses to corn and soybean yields (Changnon, 1972). Frequencies of Severe Storms Various national measures of annual severe storm frequencies were assessed. These included: (1) the average number of thunderstorm days, 1950-1997, based on data from 66 first-order weather stations located around the nation; (2) number of tornado days reported nationally by the National Weather Service, 1953-1997; (3) number of killer tornadoes (storms leading to one or more deaths), 1953-1997; and (4) number of hail days for 1950-1997, based on data from 1012 weather stations (Changnon and Changnon, 1999). Frequencies of Intense Storm Fvents Measures of the more intense severe storm events were analyzed because much of the annual storm-related loss is from a few extremely intense storms each year. Annual incidences of intense land-falling hurricanes were obtained for 1950-1997. Intense hurricanes have a rating of 3, 4, or 5 on the Saffir/Simpson hurricane scale and account for 80 percent of all hurricane losses (Pielke and Landsea, 1998). A rating of 3 and higher indicates the storm's central pressure is less than 27.91 inches, wind speeds exceed 178 kph, and storm surges are 2.7 meters or higher. Violent tornadoes are defined as those having a wind speed-damage intensity level of F4 or F5 (Grazulis, 1991). Based on damage studies, these levels relate to events with high winds (>333 kph) that cause extensive damage. Annual data were analyzed for 1950-1997 intense events. There was no existing measure of intense thunderstorms, including associated hailstorms, high winds, and lightning activity, but such a measure was developed using thunderstorm catastrophe data. For each year, the amount of loss (adjusted to 1997 dollars) caused by thunderstorm catastrophes was divided by the number of thunderstorm catastrophes, and this annual intensity value was calculated for 1950-1997. Measures of a fourth well-known intense weather condition, the amount of heavy precipitation occurring in a short period of time, were sought. Karl and Knight (1998) presented an annual index defined as the area of the United States

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affected by a much above normal frequency of days with 2 inches (5.1 cm) or more rainfall. Annual values of this 5.1 cm rain-day index were assessed for 1950-1997. Another heavy precipitation measure, the percentage of the United States with 7day heavy precipitation amounts reaching once in 5-year frequency levels (Kunkel, Andsager, and Easterling, 1999), also was tested because 7-day heavy rainfall events were found to correlate well with hydrologic flood peaks in the Midwest (Changnon and Kunkel, 1995). The annual percentage of the nation with these 7day precipitation values was assessed for the years in the 1950-1997 period, and labeled as the "7-day heavy rain index." Hurricane, Tornado, and Flood Losses Data sets obtained measuring the annual monetary losses from hurricanes, tornadoes, and floods were obtained from NCAR (Pielke, 1999). Each data set had been normalized and developed for use in temporal assessments of losses. Lossnormalized data sets acquired and analyzed included: (1) annual U.S. per-capita flood damages, 1950-1997; (2) annual U.S. flood losses per unit wealth, 19501997; (3) annual U.S. flood damages, 1950-1997; (4) annual U.S. normalized hurricane losses, 1950-1997; and (5) annual U.S. normalized tornado losses, 19501997. Effects of Abnormal Temperatures on Energy Consumption Economic effects were assessed for 1950-1997 temperatures that deviated from the average values over prolonged periods (seasons) on the national consumption of electricity and natural gas across the United States. Annual data on the national consumption and prices of each energy form were provided by the Energy Information Administration. Consumption focused on that by residences and commercial businesses. Industrial use of electricity and natural gas was not included because these values are heavily tied to demands other than weather conditions. Annual values of national consumption were adjusted to ever-changing and increasing usage owing to shifting demand resulting from the nation's population growth, technological changes, and shifting economic factors that collectively affect usage and price. An adjustment technique successfully used in agriculture was chosen to handle energy-related variables to assess the weather effect in energy consumption. Curves were statistically fit to the temporal distributions of the annual consumption values of electricity and those of natural gas for 1950-1997. These curves represent the combined influences of shifting economy, changes in energy usage, and technological changes affecting consumption. Hence, the values on the curve with the best fit represent expected consumption values in each year with average temperature conditions. For those years with the annual consumption values appreciably above or below the expected value, the difference was calculated and considered to be the effect of temperature conditions during the year. Figure 3 shows the best-fit curve, a third-order fit to the distribution with an R2 of 0.997, the equation, and annual values for the national electricity consumption from 1949 to 1997. Values above the curve were a result of high temperature

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Fig. 3. Best-fit curve and its equation for distribution of annual electric consumption values, 19491997.

extremes. Note that the 1973 value is well above the curve, and the calculated difference is 75.2 billion kilowatt hours, or 8.4 percent of the year's expected value. The above expected departures in kilowatt hours each year were measured, and costs were assessed based on electricity prices in that year. These annual costs were then adjusted to 1997 dollars using the implicit price deflator of the gross national product. There were no added costs and consumer gains as a result of mild summers and winters in 22 years from 1950 to 1997. The same procedure was used to define natural gas usage. The years in which values exceeded the expected value of electricity or natural gas usage represented added costs owed to temperature extremes. Actual differences above expected consumption levels for both electricity and natural gas were the variables assessed for costs caused by temperature anomalies. The combination of gas and electric costs, as calculated for each year, formed a national "energy use" data set. Of course, these are underestimates because they do not include temperature effects on energy consumption in the industrial sector. RELATIONSHIP BETWEEN VARIABLES The relationships between these 44 variables were determined to assess which ones were interrelated and which were largely independent. Correlation analysis was performed for 622 pairs of the 44 weather-impact variables. Most (556, or 92 percent) of the 604 simple correlation coefficients determined were less than 0.3, revealing that most variables were quite independent of all others. Only 12 coefficients were 0.5 or higher. Three tornado-related relationships had coefficients of 0.6 or higher, and federal disaster relief payments, which show a rapid increase over time since they began in 1953, correlated moderately well with the frequency (+0.51) and losses (+0.71) of winter storm catastrophes. The relationship of the fre-

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quencies of various severe storm conditions (thunderstorms, hailstorms, and tornadoes) and their impacts also was examined. The relationship between thunderstorm-day frequencies and various impact variables (thunderstorm catastrophes, hail catastrophes, and tornado losses) was extremely low, with coefficients less than 0.3; that is, many thunderstorm days in a year did not mean many hail days or considerable hail loss. Annual tornado losses correlated well with the number of killer tornadoes, the number of intense tornadoes, and the number of tornado catastrophes, but tornado measures correlated poorly with other storm conditions. Moderately good correlations were found for three types of the five intense storm measures Thunderstorm intensity values correlated well with costly catastrophes, the number of hurricanes, and hail days. Frequencies of both intense tornadoes and hurricanes correlated with their losses. The 7-day heavy rain index correlated poorly with the 5.1 cm rain-day index, and both correlated poorly with the measurements of extreme wetness or extreme drought. The relationship analysis revealed that most economic impact conditions bore no relationship to any other variable, and this was true also for the various measurements of flood loss. The two heavy precipitation measurements (5.1-cm and 7-day) did not correlate well with any flood loss measurements. This agrees with the findings of Pielke and Downton (2000) that various heavy precipitation values did not relate to damaging floods. Frequencies and losses of winter storm catastrophes and wind storm catastrophes had no significant relationship with any other storm condition. Crop losses correlated poorly with the drought and excess wetness measurements. Drought severity (percentage of the United States) correlation coefficients ranged from 0.24 (soybean yield departures) to 0.41 (corn yield departures). As expected, the wetness severity (percentage of the United States) showed even poorer correlations with crop yield departures. Furthermore, crop loss values were unrelated to the two heavy precipitation indices. Multiple correlation analysis including cooling degree-day departures, moisture surplus, drought index, 7-day heavy rain index, the 2-inch rain-day index, and crop-hail loss value produced correlation coefficients of 0.42 (cotton), 0.52 (wheat), 0.54 (corn), and 0.51 (soybeans). Low correlations between national crop losses and national temperature and moisture indices are not unexpected because each major crop is concentrated in certain U.S. regions. If excessively wet or dry (warm or cool) areas, which never cover the entire nation, do not coincide with these crop areas in a given year, there is no relationship. National annual values for population-weighted heating degree days and cooling degree days, which measure temperature-created demands for heating and cooling, were compared with electricity and natural gas values expressed as percentage departures above and below expected levels. Annual percentage values for natural gas correlated well with the population-weighted heating degree days, with a coefficient of +0.80, similar to earlier results (Quayle and Diaz, 1981). Annual values of electricity consumption in the nation had a correlation of +0.87 with the population-weighted cooling degree days, agreeing closely with results based on analysis of weekly values (Le Comte and Warren, 1981). A multiple correlation analysis of the 1950-1997 electricity consumption against the annual departures of population-weighted average heating degree days and cooling degree days pro-

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duced a coefficient of +0.94. Thus, weather-related energy consumption values agreed closely with the national estimates of demand based heating and cooling degree days. Comparison of the 44 extreme weather-impact variables with the two national climate indices revealed weak relationships. The three highest correlations for 1950-1997 values came with the GCRI, which had correlation coefficients of +0.48 with drought severity, +0.42 with natural gas consumption, and +0.39 with cooling degree days. All other 129 simple correlation coefficients were 0.33 or less. Thus, these two national climate-based indices do not explain the magnitude and variability of any one of the many extreme weather-impact variables (e.g., crop losses, catastrophic losses, flood losses, hurricane losses). A multiple correlation analysis for the two climate indices was performed, involving seven different variables considered to be potentially related to them. The combination of the percentage of average heating and cooling degree-day values (both population weighted) with the two moisture indices (surplus moisture and drought severity) resulted in relatively high correlation coefficients, 0.79 for the CEI and 0.69 for the GCRI. These four variables plus the departure from average of electricity use and natural gas use resulted in moderately higher correlations: 0.83 for the CEI and 0.74 for the GCRI. The addition of flood losses per capita did not improve these correlations. TEMPORAL BEHAVIOR OF VARIABLES Temporal distributions of the 44 variables were assessed to define their long-term fluctuations and trends over the 1950-1997 period. Table 1 classifies the secular trends for each variable and the statistical significance levels attained for trends ranging from 1 to 10 percent. Linear trends were fit using the least squares method and their significance assessed with the Students t test. The hypothesis tested was whether the 48-year linear trends were not stationary. Review of the distributions in Table 1 shows a nearly equal distribution of upward trends (18) and downward trends (19). Seven conditions had flat 48-year trends. A meaningful expression of crop yield change owed to stressful weather conditions is the temporal distribution of yield departures below expected annual values. Temporal distributions of the departures of three crop yields (corn, soybeans, and wheat) all had upward trends for the study period. Hence, when annual losses based on all four crops were combined, the values exhibited a slight upward trend (Fig. 4) that was not statistically significant. Adjusted hurricane losses (Fig. 5) showed a slight downward and statistically insignificant trend from 1950 to 1997, whereas the number of intense hurricanes had a significant downward trend. Losses from wind storm catastrophes also had a statistically significant (10 percent level) downward trend, but the 48-year frequency of wind storm catastrophes indicated a slight upward trend over time. Flood losses (Fig. 6) exhibited an upward trend that was statistically significant at the 1 percent level. The flood loss distribution exhibits two important characteristics: (1) an early 1950-1970 period of annual losses of similar magnitude ($1 billion to $2 billion); and (2) a later 1972-1997 period with higher annual losses ($5 bil-

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Table 1. Classification of Linear Trends during 1950-1997 for 44 Weather and Climate Extremes and Measures of Their Economic Impactsa

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Downward (decreasing over time) Hurricane losses ($) [10%]

Crop-hail loss cost ($) [10%]

Hail catastrophes losses ($)

Number of days with hail [2%]

Number of days with killer tornadoes

Number of violent tornadoes

Tornado catastrophes losses ($)

Intensity of thunderstorms ($) [10%]

Wind catastrophes losses ($) [10%]

Number thunderstorm days [2%]

Electricity use departures [5%]

Crop-hail losses ($)

Cooling degree-day departures

Flood losses, % wealth

Thunderstorm catastrophe losses ($) [10%]

Number of intense hurricanes [10%]

Energy costs [5%]

Drought severity [5%]

Natural gas departures [10%] No change over time Per capita losses ($) with >$100 million catastrophes Cotton yield departures (%) Tornado losses ($)

Tornado catastrophes, number

Corn yield departures (%)

Soybean departures (%)

Wheat yield departures (%) Upward (increasing over time) Number of hail catastrophes

Number of wind catastrophes

Number of tornado days

Federal relief payments ($) [1%]

Number of thunderstorm catastrophes [1%]

Number of winter storm catastrophes [1 %]

Number of hurricane catastrophes [2%]

Winter storm losses ($) [1%]

Flood losses ($) [2%]

2-inch rain index [2%]

Extreme wetness index [1%]

7-day heavy rain index [1%]

Flood losses ($) per capita [5%]

Number of >$100 million catastrophes

Hurricane catastrophe losses [1%]

Heating degree-day departures

Hurricane losses ($) [5%]

Crop losses ($)

a Bracketed percentages indicate statistical significance at the 1 to 10 percent level, and all dollar values are adjusted to 1997 values.

lion to $6 billion annually) with a flat trend over these last 26 years. This jump-shift distribution agrees in general with that of the wetness index, showing wetter conditions in the most recent 25 years. The drought severity index had a significant downward trend, reflecting the inverse of the ever-wetter national climate regime. The two heavy rain indices, the 5.1-cm rain-day index and the 7-day rain index, had statistically significant upward trends like flood losses. However, the correlation

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Fig. 4. Annual losses (billions of 1997 dollars) for all four major crops (corn, soybeans, wheat, and cotton) combined, 1950-1997.

Fig. 5. Annual normalized hurricane losses (millions of 1997 dollars), 1950-1997 (Pielke and Landsea, 1998), and linear trend line.

analysis of these heavy rain indices and the three measures of flood losses showed no relationship. The time distribution based on annual departures of natural gas consumption (Fig. 7) and of electricity consumption had downward trends, and both were statis-

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CHANCNON ET AL.

Fig. 6. Annual adjusted flood losses (billions of 1997 dollars), 1950-1997, and linear trend line.

Fig. 7. Annual natural gas usage in the United States, expressed as a percent of the annual expected value with average weather conditions, 1950-1997, and linear trend line.

tically significant at the 5 percent level. Population-weighted cooling and heating degree days also declined during the 1950-1997 period. Winter storm catastrophes began occurring in most years after 1968. After reaching a peak in 1979, their incidence maintained a constant trend, ranging from 3 to 6 storms per year through 1997. Only one winter storm qualified as a catastrophe

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Fig. 8. Annual normalized tornado losses (millions of 1997 dollars), 1950 to 1997, and linear trend line.

(exceeding $5 million in losses) in the 1950-1968 period. Winter storm losses exhibited a continuing increase over time, and the upward trends of both distributions were statistically significant. Crop losses caused by hail and associated high winds exhibited a significant (10 percent level) decline over time that matched the trends for hail-caused catastrophe losses to property and annual values of the national number of hail days. The normalized tornado losses had a flat trend during 1950-1997 (Fig. 8). However, property losses from tornado catastrophes, the number of killer tornadoes, and the number of intense tornadoes all showed systematic declines over time, but none were statistically significant. The distribution of the number of thunderstorm catastrophes from 1950 to 1997 had a statistically significant increase with a sharp increase from 1960 to 1975 and essentially a flat trend thereafter, similar to flood losses. The average national number of days with thunderstorms did not show an upward trend, declining from 40 days in 1910 to 38 days in 1997, statistically significant at the 2 percent level. Insured property losses owed to thunderstorm catastrophes and thunderstorm intensity values both had downward trends. SELECTION OF INDEX VARIABLES A key issue in establishing an index for economic impacts of weather and climate extremes was the selection of the most economically important variables. The goal was to include those conditions that produce all or most measurable weatherclimate losses in the United States.

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Table 2. Weather-Caused Losses (Billions of Dollars) after Pielke (1997) and White and Haas (1975)

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Conditions

Average annual loss (Pielke, 1997)

Loss (White and Haas, 1975)

Floods

$2.4 (1984-1993 data)

$5.80

Hurricanes

$6.2(1989-1995 data)

$2.30

Tornadoes

$2.9 (1991-1094 data)

$1.20

$2.3 (estimated)

$2.10

Lightning

>$1.0 (estimated)

$0.50

Winter storms

>$1.0 (estimated)

$0.15

Hail

The generally weak relationships between the frequencies of various types of storm days (thunderstorm, tornado, and hail) and the various catastrophic losses led to a decision to exclude them from the variables in the index. Three intense storm measures (thunderstorm, tornado, and hurricane) related moderately well to certain losses. However, they did not offer additional information about the magnitude nor fluctuations in their losses. However, their distributions helped substantiate the fluctuations found in the actual loss values. Data sets available on economic losses were believed to include most major weather- and climate-produced losses in the nation. Most data sets contained values for the 1949-1997 period, or earlier starting years, but one key data set began in 1950 (tornado losses), and this dictated the period to be used in the defining the index, 1950-1997. The selection of each variable was based on the amount of monetary loss in the years during this 48-year period. Obviously, important variables and major candidates for inclusion in the index were the losses caused by floods, hurricanes, and tornadoes. Other variables were selected based on review of various published reports of damaging conditions. For example, Pielke (1997) presented annual average loss values based on recent years, and these are listed in Table 2 along with annual losses based on a 1975 national assessment of the costs of hazards (White and Haas, 1975). Importantly, both assessments identified the same six variables and showed that hail, winter storms, and lightning (thunderstorm) losses were important to consider in addition to the three variables already identified. Sylves (1998) summed the federal disaster payments (in constant 1994 dollars) for each weather condition, based on the primary weather condition causing the loss declaration (defined by the Presidential declarations) for 1953-1997. The ranks of the conditions' average values in Table 2 and the disaster payments agreed, further revealing that flood and hurricane losses are major causes of storm loss in the United States. The federal payment conditions also revealed that drought-induced crop losses were important and needed to be included in the index. Federal payments also showed the importance of incorporating the losses from severe storms (which include hail and lightning) in a national loss index.

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Table 3. Payments for Property Insurance Catastrophes during 1950-1997 (1997 Dollars)

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1950-1997 total, $ billions

Annual average, $ billions

Thunderstorm catastrophes

78,335

1.632

Hurricane catastrophes

59,082

1.205

Tornado catastrophes

40,400

0.898

8,530

0.174

8,452

0.282

8,062

0.168

Hail catastrophes Winter storm catastrophes Wind storm catastrophes

a

a

Winter storm values are based on 1968-1997 data since only one catastrophe was reported in 1950-1967 period. Beginning in 1968, at least one winter storm catastrophe occurred each year.

A recent major assessment of natural disasters in the United States (Mileti, 1999) was reviewed to assess its information on the economic impacts of each form of "climatological hazards." These 13 hazards were identified, and loss values were assigned for the 1975-1994 period of assessment. Three hazards (snow avalanches, fog, and wildfires) produced very minor losses and had poor historical data, and were not considered as candidates for inclusion in the index. Another key loss variable is the energy costs associated with temperature extremes. This variable does not appear among those listed in Table 2, but it was recognized as a major monetary impact that needed to be included in the national index. The Mileti study showed that losses owed to temperature extremes (cold and hot) from 1975 to 1994 were as much as $45 billion. The Federal Emergency Management Agency produced an in-depth assessment of all natural hazards affecting the nation (Federal Emergency Management Agency, 1997) that identified 11 weather hazards, including droughts, floods, winter storms, hurricanes, thunderstorms, hail, winter storms, tornadoes, and wind storms. The report presented the average fatalities owed to certain hazards, but did not identify financial losses except for certain specific past major events. Table 3 presents the property insurance industry's total payments and annual averages (normalized to 1997 dollars), based on catastrophe payments during 1950-1997. Flood losses are not included since flood insurance funding is handled by the federal government. The catastrophe data are limited because they do not represent all the property damage. Insurance experts have estimated that between 5 and 15 percent of all property loss is uninsured against weather hazards, excluding floods (Lecomte, 1999, pers. comm.; Roth, 1999, pers. comm.). Although some property losses also occur during weather events that do not qualify as catastrophes, catastrophes account for approximately 90 percent of all weather-caused insured property losses. The large loss value attributed to thunderstorms (Tables 2 and 3) revealed the need to include an economic loss measure owed to thunder-

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storms along with the other variables already identified for the index. Insurance data further showed sizable losses caused by winter storms and wind storms. Analyses of the 44 various weather and climate values and their impact values led to the conclusion that the available longer term (1950-1997) adjusted economic values covering the nine major extremes covered most major weather losses and could be used as variables to develop the index. All were dollar loss expressions, and as shown, they accounted for most economic impacts from extreme weather and climate conditions occurring in the United States during 1950-1997. The nine measures chosen to calculate the index were: (1) costs of energy (temperature extremes), (2) hurricane losses (crops and property), (3) flood losses (crops and property), (4) crop losses (extremes of moisture and temperature), (5) severe thunderstorm property catastrophe losses, (6) tornado losses, (7) crop-hail losses, (8) winter storm property catastrophe losses, and (9) wind storm property catastrophe losses. All dollar values had been carefully adjusted to the 1997 dollar value, and all historical loss values were adjusted for shifting societal conditions (e.g., population, wealth, insurance coverage, effects of new technologies) during the 19501997 period. This resulted in a 48-year historical record to be used to assess the past variability of the index. Furthermore, each variable will be generated in future years, allowing continuing monitoring. Table 4 presents the annual average losses of the nine selected variables. The sum of the annual values is $1 7.470 billion. Energy use costs rank first, followed by hurricane, flood, and crop losses. Each condition's values also were expressed as a percent of the total, and three conditions—hurricanes, energy costs (from temperature extremes), and floods—accounted for 69 percent of total average loss. When crop losses are included, these four conditions constitute 84 percent of the total average losses resulting from weather extremes. The effects of the nation's major climate extremes are represented by the crop losses and energy costs. Together these represent an average annual value of $7.25 billion, 41 percent of the total losses. Hence, the Climate Extremes Index and Greenhouse Climate Response Index, which largely incorporate seasonal and annual temperature and precipitation extremes, likely represent, on average, less than half the annual economic losses related to severe weather extremes. Sixty percent of the total losses in the United States are caused by weather extremes, which (other than the 5.1-cm heavy rainday index) are not a part of these two climate indices. Furthermore, this study found that the 5.1-cm rain-day index had no relationship with flood losses or crop losses. Comparison of the loss index values with the Climate Extremes and Greenhouse Response indices revealed that no relationship existed. The losses from these nine conditions incorporate most but not all U.S. losses owed to weather and climate extremes. The use of catastrophe losses for severe local storms, winter storms, and wind storms excludes events with property losses under $5 million loss and uninsured property losses. Roth (1999, pers. comm.) and Lecomte (1999, pers. comm.) estimated that weather catastrophe losses account for about 90 percent of all insured U.S. property losses caused by weather. The three catastrophic storm classes used in the index (thunder, winter, and wind) account for $2,082 billion in average losses per year (Table 3), and adding 10 percent of omit-

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Table 4. Average Annual Losses and Costs (1997 Dollars) Based on Major Weather and Climate Conditions Causing Loss in the United States, 1950-1997

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Condition

Average loss, billions dollars

Percent of total loss

Standard deviation

Slope of 48-year trend

Energy costs

4.650

26.6

3.87

-0.08*

Hurricane loss

4.235

24.3

6.89

-0.09*

Flood loss

3.182

18.2

3.46

+0.08*

Crop loss

2.603

14.9

3.77

+0.03

Severe thunderstorm loss

1.632

9.3

2.06

-0.01*

Tornado loss

0.448

2.6

0.36

+0.01

Winter storm loss

0.282

1.6

0.34

+0.01*

Crop hail loss

0.270

1.5

0.30

-0.02

Wind storm loss

0.168

1.0

0.41

-0.01*

*Trend was statistically significant at 1 to 5 percent level.

ted losses to this average value indicates that the unmeasured, insured property losses average about $230 million annually. Insured residential and commercial property losses caused by wind, hail, and lightning account for 95 percent of all U.S. property losses owed to these conditions, since all residential and commercial insurance policies cover these losses and almost all properties are insured for these hazards (Roth, 1999, pers. comm.). Interpretation of these values, in light of the estimated total average insured losses for thunderstorms, high winds, and winter storms ($2.082 billion + $230 million = $2.312 billion annually), suggests that the uninsured property losses nationally average $112 million annually. Thus, the unmeasured and measured property losses from these three storm conditions are estimated to be $2.434 billion annually, $342 million more than the catastrophe losses accounted for in the index. Crop losses caused by precipitation and temperature extremes ($2.603 billion annually) do not include losses to speciality crops and livestock, which are estimated to average between $250 and $300 million annually. Livestock losses in the extremely severe 1988 drought and the record 1993 Midwestern floods were 4 and 3 percent, respectively, of the total agricultural losses. Adams (1997) noted that the record cold temperatures in Florida during the 1983-1984 winter caused losses of $1 billion. The speciality crop losses in the Deep South and California from the record-damaging El Niño weather of 1997-1998 were $160 million (Changnon, 1999a). Annual average weather losses to the nation's vegetable processing industry are $45 million. Major climate extremes also can substantially reduce retail sales, as they did in California during the stormy, wet winter caused by El Niño 997-1998 (Changnon, 1999a). Weather extremes also produce major losses to the nation's transportation systems, including commercial aviation, the trucking industry, riverine shippers, and railroads. For example, the flood of 1993 caused record losses of $409 million

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to the nation's railroads and $610 million to river-based barge industry (Changnon, 1996). If data existed for these various unmeasured property, crop, and business losses, the index's annual average of $17,470 billion would be much higher, $20 billion or more. There are other losses and costs that are not accounted for because no data exists to measure them (National Academy of Sciences, 1999). For example, a season causing yield losses to the nation's soybean crop, which is accounted for in this study as a loss to producers, also results in higher costs for agribusinesses that process soybeans, and in turn, to consumers of soybean products. A winter with mild temperatures that lowers energy consumption and reduces costs to consumers brings losses to utilities. Gains to electricity and natural gas consumers owed to mild winters and summers during 1950-1997 were calculated to amount to $187.7 billion, 84 percent of the total consumer costs, but these consumer gains represented losses to energy producers (Changnon, 2000). U.S. industries reported as "sensitive" to weather and climate contributed $2,530.5 billion (1996 dollars) to the nation's Cross Domestic Product (BASC, 1998). This amount is 29 percent of the nation's total GDP. Thus, the loss index, at $17.47 billion, which represents the direct losses from extremes, is much less than the value of everything labeled as weather sensitive. Part of this difference is owed to the fact that the secondary and delayed losses and costs of extremes are not accounted for in the index because of the lack of data for these values. Furthermore, many weather sensitive activities do not suffer sizable losses from extremes in most years. If data existed for these various unmeasured property, crop, and business losses, the annual average of $17.470 billion might be as high as $20 billion. How do the annual average loss values determined in this study relate to other estimates of losses from weather hazards and extremes? Pielke (1997) estimated that national losses from weather extremes (and not including temperature extremes) averaged $300 million per week. This totals $15.6 billion per year and matches well with the weather losses in the index. The recent hazards report (Mileti, 1999, p. 66) states, "Dollar losses (1975-1994) were between $230 billion and $1 trillion. A conservative estimate is $500 billion per year." If this value is used, the annual average loss would be $25 billion (in 1994 dollars), and since the report states that about 80 percent of all losses are owed to "climatological disasters," the resulting total would have an annual value of $20 billion in losses owed to weather-climate extremes. The annual index values for 1991-1995 also were compared with the annual measures of "weather-related damage in the U.S.," as provided by the National Weather Service (BASC, 1998). The weather loss values from the NWS for these five years appear in Table 5, along with the index values. The index loss values are higher in each year with the greatest one-year difference in 1995 and the least in 1993. Comparison of the 5-year totals shows that the index value was 29 percent higher than the loss value. Some of this difference may be owed to the fact that the index values have been normalized to 1997 dollars and although not known, the 1991-1995 loss values of the NWS may be the loss of the year of occurrence and hence less than if normalized to 1997 dollar values. Importantly, both sets of annual values fluctuate together, with both high in 1992 and 1993 and both lowest in 1994. Interestingly, the NWS-based 5-year average of $17.771 billion is very close

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Table 5. The Annual Loss Index Values in Billions of 1997 Dollars for 1991-1995 and the Annual Weather-Related Damages in the United States as Reported by the National Weather Service in Billions of Dollars (BASC, 1998)

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Year

Index

Loss

Difference

1991

13.151

6.228

6.923

1992

43.576

38.351

5.225

1993

29.688

28.427

1.261 2.930

1994

7.369

4.439

1995

20.648

11.412

9.236

114.332

88.857

25.475

Totals

to the 48-year average index value, $17.47 billion. Comparison of the values assigned to the various causes of loss revealed that in two categories, the index values during 1991-1995 were much higher than the NWS loss values. These included the energy costs, which were much higher than the NWS loss value assessed as resulting from "extreme temperatures," and the index value for severe thunderstorms plus tornadoes losses and crop-hail losses, which exceeded the NWS loss value owed to "convective storms." The 5-year values for floods, hurricanes, winter storms, and droughts were quite similar. In general, this comparison further shows generally good agreement between the two sets of values. Table 6 presents examples of the calculations of the indices for two recent years. The sum of the values for 1993 was $29,688 billion, defined as the 1993 index, and the index (sum) of the1994 values was $7,369 billion, revealing a fourfold difference between the two years. The effect of the large 1993 Midwestern flood losses is quite evident in the 1993 value. Most losses in 1994 were a result of severe local storms and winter storms. Importantly, for each condition, the values for two years were widely different, illustrating the great year-to-year variations with the types of losses. Analysis of the conditions causing the maximum loss in each year during the 1950-1997 period revealed six conditions that rated highest in one or more years. The energy costs rated highest in 12 of the 48 years; hurricane losses also were highest in 12 years; and flood losses also were highest in 12 years. Crop losses ranked first in 7 years, severe storm losses in 4 years, and wind storm losses were highest in 1 year. Various statistical measures of the nine variables appear in Table 4. Most have large standard deviations, many exceeding their mean values, reflecting the great year-to-year variability found in the loss variables. Figure 9 shows the 48 annual index values distributed in time plus a curve based on averages for 5-year periods (e.g., 1950-1954, 1955-1959). There were four major loss years: 1972 ($54.4 billion), 1992 ($43.6 billion), 1954 ($39.8 billion), and 1955 ($39.3 billion). Major loss factors in each of these years varied. For example, in 1972 there were high flood losses ($14.3 billion), sizable energy costs ($26.4 billion), and large hurri-

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Table 6. Calculations of Impact Index for 1993 and 1994 (Billions of Dollars) Variable

1993

1994

Hurricane losses

0.071

1.144

Flood losses

17.168

1.149

Crop losses

5.891

0.258

Severe storm losses

2.125

1.946 0.355

Energy costs

3.032

Tornado losses

0.497

0.355

Crop-hail losses

0.327

0.379

Winter storm losses

0.577

1.572

0.000

0.211

29.688

7.369

Wind storm losses Total losses

canes losses ($11.9 billion). Hurricanes in 1992 caused $36.9 billion in losses, 87 percent of the annual total. Losses in 1955 were high because of hurricanes ($14.7 billion), energy costs ($13.9 billion), and flood losses ($5.2 billion). The highest and lowest annual values during 1950-1997 had a very large range in magnitudes. The lowest three values were $2,371 billion (1963), $3,119 billion (1966), and $3,364 billion (1968). The lowest value is only 4 percent of the highest value, $54 billion. Comparison of the annual values of Figure 9 with the annual average of $17.47 billion reveals slightly skewed values with 27 of the 48 values less than average. There is considerable year-to-year variability in the index (losses) values. The distribution of the 5-year average index values (Fig. 9) shows three peaks (1950-1954, 1970-1974, and 1990-1994), and all are separated by 20 years. The highest 5-year value in the 48-year period occurred in 1970-1974 with an annual average of $32.499 billion, followed by $25.046 billion in 1950-1954, and $20,323 billion in 1990-1994. The lowest average annual value was $9.936 billion in 1960-1964. Statistical testing of the temporal distribution of the loss index values revealed the 48-year period had no statistically significant upward or downward trend. It was essentially flat. This is not unexpected since four major loss conditions had upward trends, whereas five others had downward trends (Table 1). In contrast, the climate data based GCRI indicated a statistically significant increase for the 1910-1990 period (Karl et al., 1996). Hayden (1999) analyzed historical storminess, based on extratropical storm frequency across the United States for 1890-1997, and found no evidence of an increase or decrease over time, including the 1950-1997 period. This supports the concept of an unchanging time distribution of impacts from all types of storm conditions. There is no reason to conclude that the combination of weather and climate extremes that cause the major losses across the nation, after adjustment for the ever-increasing target at risk and shifting dollar values, are changing. Furthermore, study of the time distributions of the physical conditions

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Fig. 9. Annual and 5-year average national loss index values (billions of 1997 dollars), 1950-1997.

causing losses (thunderstorm days, hail days, tornado days, hydrologic floods, and hurricanes) exhibited no marked upward or downward trends, although several have slight decreases in recent years. Measures of certain intense storm conditions (thunderstorms, tornadoes, and hurricanes) showed temporal decreases, whereas heavy rainfall conditions showed increases over time, again reflecting a mixed outcome. CONCLUSIONS Forty-four variables (weather and climate extremes and measures of their economic impacts) were assessed for use in a national loss-based index based on data for 1950-1997. Assessment of more than 600 correlations between these 44 variables revealed that most (92 percent) had coefficients less than 0.3. Hence, most variables did not correlate well and were independent. Total hurricane losses were strongly related to the incidence and costs of hurricanes classed as catastrophic events by the property insurance industry, and to hurricane intensity. The frequency of violent tornadoes correlated well with tornado losses, incidence of killer tornadoes, and insurance-defined tornado catastrophes. However, most physical measures of other types of storms did not correlate well with losses they created. Population-weighted heating and cooling degree days correlated well with the national costs of natural gas and electricity, but the national drought and surplus moisture measures did not correlate well with crop yield losses.

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The 48-year trends of the most costly loss conditions showed mixed outcomes. Flood losses and crop losses showed increases, whereas hurricane losses, severe thunderstorm losses, and energy costs showed temporal decreases during 19501997. In general, the trends of the 39 other variables were rather evenly divided, up and down, but most trends were not statistically significant at the 10 percent level. There were several significant upward trends over time, including the (1) number of costly catastrophes, (2) federal disaster payments, (3) number and losses caused by winter storms, (4) number of hurricane catastrophes, (5) number of thunderstorm catastrophes, (6) flood losses and per capita flood losses, (7) number of wind catastrophes, (8) severe wetness index, and (9) the two heavy precipitation (5.1-cm and 7-day) indices. Conditions with statistically significant downward time trends included: (1) number of thunderstorm days, (2) electricity use, (3) wind storm losses, (4) number of intense hurricanes, (5) losses from thunderstorm catastrophes, (6) crop-hail losses, (7) days with hail, (8) drought severity, (9) energy costs, and (10) thunderstorm intensity. Trends for 7 of the 44 variables were flat during 1950-1997. Nine economic measures of loss caused by weather and climate extremes were selected for use in the impact index. Each had quality data for 1950-1997, and the capability for future measurements through data collection efforts of established entities. Each measure had been adjusted for temporal societal shifts, including the dollar value, which could affect temporal comparisons. The nine values were independent, and none had even a moderate statistical relationship to any of the others. All annual values had been set to 1997 dollar values. The variables selected were: (1) hurricane losses (crops and property), (2) energy costs owed to temperature extremes, (3) flood losses (crops and property), (4) severe storm property losses (based on thunderstorm catastrophes), (5) crop losses largely caused by various moisture and temperature extremes, (6) tornado losses, (7) winter storm property losses (catastrophes), (8) hail-wind losses to crops, and (9) wind storm property losses (catastrophes). All nine loss values in each year from 1950 to 1997 were combined to produce an annual index value. Values ranged widely from a low of $2.4 billion in 1981 to a high of $54.4 billion in 1972. The average annual loss value was $17.47 billion. Twenty-five of the 48 loss values were below the 48-year average. The highest 5year loss period was 1970-1974, with an average of $32.4 billion per year. There were three 5-year periods of high losses (1950-1954, 1970-1974, and 19901994), each 20 years apart. The total average annual loss, $17.47 billion, determined from the nine variables is estimated to be $0.5 to $2 billion less than the nation's total average loss from all atmospheric extremes. Data do not exist to estimate certain unmeasured losses to property, crops, and business. The 48-year linear trend based on the distribution of the annual indices exhibited no significant increase or decrease over time. The nine variables used to compute the index exhibited mixed trends. Those increasing over time included flood losses, crop losses, and winter storm losses; those decreasing over time included thunderstorm losses, energy costs, hurricane losses, and wind storm losses; and those unchanging over time included tornado losses. This study found that values from the existing Climate Extreme Index and the Greenhouse Climate Response Index, as developed for the conterminous United

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States, did not relate well to the loss index, to impact-producing weather extremes, or to the economic losses owed to hurricanes, droughts, floods, crops, temperature extremes, or severe storms, for example. Both of those climate indices had upward trends over time, whereas the loss index exhibited a stationary time trend. The CEI and GCRI may serve as useful climate measures but do not directly measure many of the extremes that cause the nation's significant economic losses. Acknowledgments: Roger A. Pielke, Jr. of the National Center for Atmospheric Research provided useful guidance and extensive amounts of data on flood losses, tornado losses, and hurricane losses. David Easterlingof the National Climate Data Center provided the annual data for the Climate Extremes Index and the Greenhouse Climate Response Index. Jene Robinson of Illinois Power and Dale Heydlauff of American Electric Power provided helpful advice on the electricity and natural gas analysis. Gary Kerney, Eugene Lecomte, and Richard J. Roth, Sr., all national experts in the property insurance industry, were very helpful in providing guidance and information about insured property losses. Kenneth Kunkel of the Midwestern Climate Center provided useful data and advice about Midwestern conditions. The National Crop Insurance Service provided data on crop losses from hail. Experts at Travelers Insurance Company provided the adjusted catastrophe data used in the study, and we are deeply grateful for their assistance. This research was partly supported by a grant from the Electric Power Research Institute.

BIBLIOGRAPHY Adams, C. R. (1997) Impacts of temperature extremes. In R. A. Pielke, ed., Proceedings Workshop on the Social and Economic Impacts of Weather. Boulder, CO: National Center for Atmospheric Research, 11-15. Board on Atmospheric Science and Climate (BASC) (1998) The Atmospheric Sciences Entering the Twenty-First Century. Washington, DC: National Research Council, National Academy Press. Changnon, D. and Changnon, S. A. (1997) Surrogate data to estimate crop hail loss. Journal of Applied Meteorology Vol. 36, 1202-1210. Changnon, D. and Changnon, S. A. (1998) Evaluating weather catastrophe data for use in climate change investigations. Climatic Change, Vol. 38, 435-445. Changnon, S. A. (1972) Examples of economic losses from hail in the United States. Journal of Applied Meteorology, Vol. 11, 1128-1137. Changnon, S. A. (1996) Losers and winners: A summary of the flood's impacts. In S. A. Changnon, ed., The Great Flood of 1993. Boulder, CO: Westview, 276299. Changnon, S. A. (1999a) Impacts of 1997-98 El Niño-generated weather in the United States. Bulletin of the American Meteorological Society, Vol. 80, 18191827. Changnon, S. A. (1999b) Recent high losses for weather disasters in the U.S. during the 1990s: How excessive and why? Natural Hazards, Vol. 18, 287-300. Changnon, S. A. (2000) An Index to Monitor Impacts of Weather and Climate Extremes in the United States. Mahomet, IL: Changnon Climatologist. Changnon, S. A. and Changnon, D. (1999) Developing Long-Term Hail Databases for the United States. Mahomet, IL: Changnon Climatologist. Changnon, S. A., Fosse, E. R., and Lecomte, E. (1999) Interactions between the atmospheric sciences and insurers in the U.S. Climatic Change, Vol. 42, 51-67.

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