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- Author or Editor: Olga V. Wilhelmi x
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Abstract
Climate change is projected to increase the number of days producing excessive heat across the southwestern United States, increasing population exposure to extreme heat events. Extreme heat is currently the main cause of weather-related mortality in the United States, where the negative health effects of extreme heat are disproportionately distributed among geographic regions and demographic groups. To more effectively identify vulnerability to extreme heat, complementary local-level studies of adaptive capacity within a population are needed to augment census-based demographic data and downscaled weather and climate models. This pilot study, conducted in August 2009 in Phoenix, Arizona, reports responses from 359 households in three U.S. Census block groups identified as heat-vulnerable based on heat distress calls, decedent records, and demographic characteristics. This study sought to understand social vulnerability to extreme heat at the local level as a complex phenomenon with explicit characterization of coping and adaptive capacity among urban residents.
Abstract
Climate change is projected to increase the number of days producing excessive heat across the southwestern United States, increasing population exposure to extreme heat events. Extreme heat is currently the main cause of weather-related mortality in the United States, where the negative health effects of extreme heat are disproportionately distributed among geographic regions and demographic groups. To more effectively identify vulnerability to extreme heat, complementary local-level studies of adaptive capacity within a population are needed to augment census-based demographic data and downscaled weather and climate models. This pilot study, conducted in August 2009 in Phoenix, Arizona, reports responses from 359 households in three U.S. Census block groups identified as heat-vulnerable based on heat distress calls, decedent records, and demographic characteristics. This study sought to understand social vulnerability to extreme heat at the local level as a complex phenomenon with explicit characterization of coping and adaptive capacity among urban residents.
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.
Abstract
There is growing use of limited-area models (LAMs) for high-resolution (<10 km) applications, for which consistent mapping of input terrestrial and meteorological datasets is critical for accurate simulations. The geographic coordinate systems of most input datasets are based on spheroid-shaped (i.e., elliptical) Earth models, while LAMs generally assume a perfectly sphere-shaped Earth. This distinction is often neglected during preprocessing, when input data are remapped to LAM domains, leading to geolocation discrepancies that can exceed 20 km at midlatitudes.
A variety of terrestrial (topography and land use) input dataset configurations is employed to explore the impact of Earth model assumptions on a series of 1-km LAM simulations over Colorado. For the same terrestrial datasets, the ~20-km geolocation discrepancy between spheroidal-versus-spherical Earth models over the domain leads to simulated differences in near-surface and midtropospheric air temperature, humidity, and wind speed that are larger and more widespread than those due to using different topography and land use datasets altogether but not changing the Earth model. Simulated differences are caused by the shift of static fields with respect to boundary conditions, and altered Coriolis forcing and topographic gradients.
The sensitivity of high-resolution LAM simulations to Earth model assumptions emphasizes the importance for users to ensure terrestrial and meteorological input data are consistently mapped during preprocessing (i.e., datasets share a common geographic coordinate system before remapping to the LAM domain). Concurrently, the modeling community should update preprocessing systems to make sure input data are correctly mapped for all global and limited-area simulation domains.
Abstract
There is growing use of limited-area models (LAMs) for high-resolution (<10 km) applications, for which consistent mapping of input terrestrial and meteorological datasets is critical for accurate simulations. The geographic coordinate systems of most input datasets are based on spheroid-shaped (i.e., elliptical) Earth models, while LAMs generally assume a perfectly sphere-shaped Earth. This distinction is often neglected during preprocessing, when input data are remapped to LAM domains, leading to geolocation discrepancies that can exceed 20 km at midlatitudes.
A variety of terrestrial (topography and land use) input dataset configurations is employed to explore the impact of Earth model assumptions on a series of 1-km LAM simulations over Colorado. For the same terrestrial datasets, the ~20-km geolocation discrepancy between spheroidal-versus-spherical Earth models over the domain leads to simulated differences in near-surface and midtropospheric air temperature, humidity, and wind speed that are larger and more widespread than those due to using different topography and land use datasets altogether but not changing the Earth model. Simulated differences are caused by the shift of static fields with respect to boundary conditions, and altered Coriolis forcing and topographic gradients.
The sensitivity of high-resolution LAM simulations to Earth model assumptions emphasizes the importance for users to ensure terrestrial and meteorological input data are consistently mapped during preprocessing (i.e., datasets share a common geographic coordinate system before remapping to the LAM domain). Concurrently, the modeling community should update preprocessing systems to make sure input data are correctly mapped for all global and limited-area simulation domains.
Abstract
Extreme heat is the leading cause of weather-related mortality in the United States, suggesting the necessity for better understanding population vulnerability to extreme heat. The work presented here is part of a larger study examining vulnerability to extreme heat in current and future climates [System for Integrated Modeling of Metropolitan Extreme Heat Risk (SIMMER)] and was undertaken to assess Houston, Texas, residents’ adaptive capacity to extreme heat. A comprehensive, semistructured survey was conducted by telephone at 901 households in Houston in 2011. Frequency and logistic regression analyses were conducted. Results show that 20% of the survey respondents reported heat-related symptoms in the summer of 2011 despite widespread air conditioning availability throughout Houston. Of those reporting heat-related symptoms experienced in the home (n = 56), the majority could not afford to use air conditioning because of the high cost of electricity. This research highlights the efficacy of community-based surveys to better understand adaptive capacity at the household level; this survey contextualizes population vulnerability and identifies more targeted intervention strategies and adaptation actions.
Abstract
Extreme heat is the leading cause of weather-related mortality in the United States, suggesting the necessity for better understanding population vulnerability to extreme heat. The work presented here is part of a larger study examining vulnerability to extreme heat in current and future climates [System for Integrated Modeling of Metropolitan Extreme Heat Risk (SIMMER)] and was undertaken to assess Houston, Texas, residents’ adaptive capacity to extreme heat. A comprehensive, semistructured survey was conducted by telephone at 901 households in Houston in 2011. Frequency and logistic regression analyses were conducted. Results show that 20% of the survey respondents reported heat-related symptoms in the summer of 2011 despite widespread air conditioning availability throughout Houston. Of those reporting heat-related symptoms experienced in the home (n = 56), the majority could not afford to use air conditioning because of the high cost of electricity. This research highlights the efficacy of community-based surveys to better understand adaptive capacity at the household level; this survey contextualizes population vulnerability and identifies more targeted intervention strategies and adaptation actions.
Abstract
During the last few decades, scientific capabilities for understanding and predicting weather and climate risks have advanced rapidly. At the same time, technological advances, such as the Internet, mobile devices, and social media, are transforming how people exchange and interact with information. In this modern information environment, risk communication, interpretation, and decision-making are rapidly evolving processes that intersect across space, time, and society. Instead of a linear or iterative process in which individual members of the public assess and respond to distinct pieces of weather forecast or warning information, this article conceives of weather prediction, communication, and decision-making as an interconnected dynamic system. In this expanded framework, information and uncertainty evolve in conjunction with people’s risk perceptions, vulnerabilities, and decisions as a hazardous weather threat approaches; these processes are intertwined with evolving social interactions in the physical and digital worlds. Along with the framework, the article presents two interdisciplinary research approaches for advancing the understanding of this complex system and the processes within it: analysis of social media streams and computational natural–human system modeling. Examples from ongoing research are used to demonstrate these approaches and illustrate the types of new insights they can reveal. This expanded perspective together with research approaches, such as those introduced, can help researchers and practitioners understand and improve the creation and communication of information in atmospheric science and other fields.
Abstract
During the last few decades, scientific capabilities for understanding and predicting weather and climate risks have advanced rapidly. At the same time, technological advances, such as the Internet, mobile devices, and social media, are transforming how people exchange and interact with information. In this modern information environment, risk communication, interpretation, and decision-making are rapidly evolving processes that intersect across space, time, and society. Instead of a linear or iterative process in which individual members of the public assess and respond to distinct pieces of weather forecast or warning information, this article conceives of weather prediction, communication, and decision-making as an interconnected dynamic system. In this expanded framework, information and uncertainty evolve in conjunction with people’s risk perceptions, vulnerabilities, and decisions as a hazardous weather threat approaches; these processes are intertwined with evolving social interactions in the physical and digital worlds. Along with the framework, the article presents two interdisciplinary research approaches for advancing the understanding of this complex system and the processes within it: analysis of social media streams and computational natural–human system modeling. Examples from ongoing research are used to demonstrate these approaches and illustrate the types of new insights they can reveal. This expanded perspective together with research approaches, such as those introduced, can help researchers and practitioners understand and improve the creation and communication of information in atmospheric science and other fields.
