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- Author or Editor: Mary W. Downton x
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Abstract
Many climatic applications, including detection of climate change, require temperature time series that are free from discontinuities introduced by nonclimatic events such as relocation of weather stations. Although much attention has been devoted to discontinuities in the mean, possible changes in the variance have not been considered. A method is proposed to test and possibly adjust for nonclimatic inhomogeneities in the variance of temperature time series. The method is somewhat analogous to that developed by Karl and Williams to adjust for nonclimatic inhomogeneities in the mean. It uses the nonparametric bootstrap technique to compute confidence intervals for the discontinuity in variance. The method is tested on 1901–88 summer and winter mean maximum temperature data from 21 weather stations in the midwestern United States. The reasonableness, reliability, and accuracy of the estimated changes in variance are evaluated.
The bootstrap technique is found to be a valuable tool for obtaining confidence limits on the proposed variance adjustment. Inhomogeneities in variance are found to be more frequent than would be expected by chance in the summer temperature data, indicating that variance inhomogeneity is indeed a problem. Precision of the estimates in the test data indicates that changes of about 25%–30% in standard deviation can be detected if sufficient data are available. However, estimates of the changes in the standard deviation may be unreliable when less than 10 years of data are available before or after a potential discontinuity. This statistical test can be a useful tool for screening out stations that have unacceptably large discontinuities in variance.
Abstract
Many climatic applications, including detection of climate change, require temperature time series that are free from discontinuities introduced by nonclimatic events such as relocation of weather stations. Although much attention has been devoted to discontinuities in the mean, possible changes in the variance have not been considered. A method is proposed to test and possibly adjust for nonclimatic inhomogeneities in the variance of temperature time series. The method is somewhat analogous to that developed by Karl and Williams to adjust for nonclimatic inhomogeneities in the mean. It uses the nonparametric bootstrap technique to compute confidence intervals for the discontinuity in variance. The method is tested on 1901–88 summer and winter mean maximum temperature data from 21 weather stations in the midwestern United States. The reasonableness, reliability, and accuracy of the estimated changes in variance are evaluated.
The bootstrap technique is found to be a valuable tool for obtaining confidence limits on the proposed variance adjustment. Inhomogeneities in variance are found to be more frequent than would be expected by chance in the summer temperature data, indicating that variance inhomogeneity is indeed a problem. Precision of the estimates in the test data indicates that changes of about 25%–30% in standard deviation can be detected if sufficient data are available. However, estimates of the changes in the standard deviation may be unreliable when less than 10 years of data are available before or after a potential discontinuity. This statistical test can be a useful tool for screening out stations that have unacceptably large discontinuities in variance.
Abstract
Statistical measures for evaluating the performance of urban air quality models have recently been strongly recommended by several investigators. Problems that were encountered in the use of recommended performance measures in an evaluation of three versions of an urban photochemical model are described. The example demonstrates the importance of designing an evaluation to take into account the way in which the model will be used in regulatory practice, and then choosing performance measures on the basis of that design. The evaluation illustrates some limitations and possible pitfalls in the use and interpretation of statistical measures of model performance. Drawing on this experience, a procedure for evaluation of air quality models for regulatory use is suggested.
Abstract
Statistical measures for evaluating the performance of urban air quality models have recently been strongly recommended by several investigators. Problems that were encountered in the use of recommended performance measures in an evaluation of three versions of an urban photochemical model are described. The example demonstrates the importance of designing an evaluation to take into account the way in which the model will be used in regulatory practice, and then choosing performance measures on the basis of that design. The evaluation illustrates some limitations and possible pitfalls in the use and interpretation of statistical measures of model performance. Drawing on this experience, a procedure for evaluation of air quality models for regulatory use is suggested.
Abstract
Regression models have been used with poor success to detect the effect of emission control programs in ambient concentration measurements of carbon monoxide. An advanced CO regression model is developed whose form is based on an understanding of the physical processes of dispersion. Its performance is shown to be superior to the more traditionally developed regression and time series models. The model separates the effects of emissions change from the effects of fluctuations in meteorological conditions. The separation appears to be most reliable for winter conditions. The model has sufficient precision to identify present trends in emissions ambient concentration data. This model should be useful for detecting changes in emission trends due to implementation of a control program on vehicular emissions such as an inspection and maintenance program.
Abstract
Regression models have been used with poor success to detect the effect of emission control programs in ambient concentration measurements of carbon monoxide. An advanced CO regression model is developed whose form is based on an understanding of the physical processes of dispersion. Its performance is shown to be superior to the more traditionally developed regression and time series models. The model separates the effects of emissions change from the effects of fluctuations in meteorological conditions. The separation appears to be most reliable for winter conditions. The model has sufficient precision to identify present trends in emissions ambient concentration data. This model should be useful for detecting changes in emission trends due to implementation of a control program on vehicular emissions such as an inspection and maintenance program.
Abstract
In the 1980s Florida was struck by an unusual series of severe freezes that caused enormous damage to citrus groves. While citrus acreage in relatively freeze-free parts of the state has expanded rapidly since these freezes, serious questions remain about the commercial viability of growing citrus crops in some central Florida counties. This paper considers the role that freeze risk plays in the investment decisions of citrus growers. A simplified example is used to estimate tolerable levels of freeze risk for individuals evaluating the investment at different discount rates, and to show the impact of changes in the risk level. Changes in estimated freeze risk in the 1980s are computed over the historical temperature record, and related to the growers’ replanting decisions. It is concluded that the computed changes in the probability of a killing freeze would be sufficient to alter the citrus planting decisions of some investors. Furthermore, the longest available climate record should be used to estimate the risk of such low-probability extreme events.
Abstract
In the 1980s Florida was struck by an unusual series of severe freezes that caused enormous damage to citrus groves. While citrus acreage in relatively freeze-free parts of the state has expanded rapidly since these freezes, serious questions remain about the commercial viability of growing citrus crops in some central Florida counties. This paper considers the role that freeze risk plays in the investment decisions of citrus growers. A simplified example is used to estimate tolerable levels of freeze risk for individuals evaluating the investment at different discount rates, and to show the impact of changes in the risk level. Changes in estimated freeze risk in the 1980s are computed over the historical temperature record, and related to the growers’ replanting decisions. It is concluded that the computed changes in the probability of a killing freeze would be sufficient to alter the citrus planting decisions of some investors. Furthermore, the longest available climate record should be used to estimate the risk of such low-probability extreme events.
Abstract
Severe freezes are a serious problem for the citrus growers of central Florida. To investigate possible climatic causes of intermittent freezes, this paper examines the influence of several atmospheric circulation patterns on winter temperatures in Florida. The Pacific/North American pattern is shown to be particularly influential and the North Atlantic Oscillation also to be significant, while the Southern Oscillation does not show a direct effect. A decreasing trend in Florida winter temperatures since 1947 can be explained by fluctuations in the former two circulation patterns. Climate model studies to investigate possible changes in the frequency or location of these circulation patterns could suggest potential changes in the freeze risk associated with climatic change.
Abstract
Severe freezes are a serious problem for the citrus growers of central Florida. To investigate possible climatic causes of intermittent freezes, this paper examines the influence of several atmospheric circulation patterns on winter temperatures in Florida. The Pacific/North American pattern is shown to be particularly influential and the North Atlantic Oscillation also to be significant, while the Southern Oscillation does not show a direct effect. A decreasing trend in Florida winter temperatures since 1947 can be explained by fluctuations in the former two circulation patterns. Climate model studies to investigate possible changes in the frequency or location of these circulation patterns could suggest potential changes in the freeze risk associated with climatic change.
Abstract
The poor relationship between what climatologists, hydrologists, and other physical scientists call floods, and those floods that actually cause damage to life or property, has limited what can be reliably said about the causes of observed trends in damaging floods. It further limits what can be said about future impacts of floods on society based on predicted changes in the global hydrological cycle. This paper presents a conceptual framework for the systematic assessment of the factors that condition observed trends in flood damage. Using the framework, it assesses the role that variability in precipitation has in damaging flooding in the United States at national and regional levels. Three different measures of flood damage—absolute, per capita, and per unit wealth—each lead to different conclusions about the nature of the flood problem. At a national level, of the 10 precipitation measures examined in this study, the ones most closely related to flood damage are the number of 2-day heavy rainfall events and the number of wet days. Heavy rainfall events are defined relative to a measure of average rainfall in each area, not as absolute thresholds. The study indicates that the growth in recent decades in total damage is related to both climate factors and societal factors: increased damage is associated with increased precipitation and with increasing population and wealth. At the regional level, this study reports a stronger relationship between precipitation measures and flood damage, and indicates that different measures of precipitation are most closely related to damage in different regions. This study suggests that climate plays an important, but by no means determining, role in the growth in damaging floods in the United States in recent decades.
Abstract
The poor relationship between what climatologists, hydrologists, and other physical scientists call floods, and those floods that actually cause damage to life or property, has limited what can be reliably said about the causes of observed trends in damaging floods. It further limits what can be said about future impacts of floods on society based on predicted changes in the global hydrological cycle. This paper presents a conceptual framework for the systematic assessment of the factors that condition observed trends in flood damage. Using the framework, it assesses the role that variability in precipitation has in damaging flooding in the United States at national and regional levels. Three different measures of flood damage—absolute, per capita, and per unit wealth—each lead to different conclusions about the nature of the flood problem. At a national level, of the 10 precipitation measures examined in this study, the ones most closely related to flood damage are the number of 2-day heavy rainfall events and the number of wet days. Heavy rainfall events are defined relative to a measure of average rainfall in each area, not as absolute thresholds. The study indicates that the growth in recent decades in total damage is related to both climate factors and societal factors: increased damage is associated with increased precipitation and with increasing population and wealth. At the regional level, this study reports a stronger relationship between precipitation measures and flood damage, and indicates that different measures of precipitation are most closely related to damage in different regions. This study suggests that climate plays an important, but by no means determining, role in the growth in damaging floods in the United States in recent decades.
The magnitude of flood damage in the United States, combined with the uncertainty in current estimates of flood risk, suggest that society could benefit from improved scientific information about flood risk. To help address this perceived need, a group of researchers initiated an interdisciplinary study of climate variability, scientific uncertainty, and hydrometeorological information for flood-risk decision making, focused on Colorado's Rocky Mountain Front Range urban corridor. We began by investigating scientific research directions that were likely to benefit flood-risk estimation and management, through consultation with climatologists, hydrologists, engineers, and planners. In doing so, we identified several challenges involved in generating new scientific information to aid flood management in the presence of significant scientific and societal uncertainty. This essay presents lessons learned from this study, along with our observations on the complex interactions among scientific information, uncertainty, and societal decision making. It closes by proposing a modification to the “end to end” approach to conducting societally relevant scientific research. Although we illustrate points using examples from flood management, the concepts may be applicable to other arenas, such as global climate change.
The magnitude of flood damage in the United States, combined with the uncertainty in current estimates of flood risk, suggest that society could benefit from improved scientific information about flood risk. To help address this perceived need, a group of researchers initiated an interdisciplinary study of climate variability, scientific uncertainty, and hydrometeorological information for flood-risk decision making, focused on Colorado's Rocky Mountain Front Range urban corridor. We began by investigating scientific research directions that were likely to benefit flood-risk estimation and management, through consultation with climatologists, hydrologists, engineers, and planners. In doing so, we identified several challenges involved in generating new scientific information to aid flood management in the presence of significant scientific and societal uncertainty. This essay presents lessons learned from this study, along with our observations on the complex interactions among scientific information, uncertainty, and societal decision making. It closes by proposing a modification to the “end to end” approach to conducting societally relevant scientific research. Although we illustrate points using examples from flood management, the concepts may be applicable to other arenas, such as global climate change.
