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Andrew Grundstein
and
John Dowd

Abstract

Biometeorological indices, such as the apparent temperature, are widely used in studies of heat-related mortality to quantify the human sensation to the environmental conditions. Increases in the frequency of environmentally stressful days as indicated by biometeorological indices may augment the risk for heat-related morbidity and mortality. This study examines trends in the frequency of days with extreme maximum and minimum apparent temperatures across the United States for 1949–2010. An increase in occurrence of 1-day extreme minimum apparent temperatures is particularly notable, especially in the eastern and western United States, with 44% of stations exhibiting positive trends. About 20% of stations have positive trends in 1-day extreme maximum apparent temperature, mostly in the western United States. The median trend for both 1-day extreme maximum and minimum apparent temperature is approximately 2 days per 10 yr, indicating that by 2010 there were 12 more days with extreme apparent temperatures than there were in 1949. Few stations with trends in 4-day extreme minimum or maximum apparent temperatures were noted. An important finding is that there has been a 53% increase in stations with positive trends in 1-day extreme minimum apparent temperatures and a 63% increase in stations with positive trends in 1-day extreme maximum apparent temperatures since a similar study by Gaffen and Ross was conducted using the period 1949–95. Although there is a clear increase in the hazard for days with extreme apparent temperatures, changes in health outcomes are modulated by factors, such as the age of the population and access to air conditioning, that affect social vulnerability.

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Andrew Grundstein
,
John Dowd
, and
Vernon Meentemeyer

Thirty-seven children on average die each year in the United States from vehicle-related hyperthermia. In many cases, the parent or caregiver intentionally left the child unattended in the car, unaware of how quickly temperatures may reach deadly levels. To better quantify how quickly temperatures may increase within a car, maximum rates of temperature change were computed from data collected on 14 clear days in Athens, Georgia. Also, a human thermal exchange model was used in a case study to investigate the influence of different meteorological factors on the heat stress of a child in a hot vehicle. Results indicate that a car may heat up by approximately 4°C in 5 min, 7°C in 10 min, 16°C in 30 min, and 26°C in 60 min. Within the vehicle, the dominant energy transfers toward the child are via longwave radiation and conduction from the hot interior surfaces of the car. Modeling simulations show that sun exposure and high-humidity conditions further increase the heat stress on the child but that a negative feedback involving evaporated perspiration reduces the influence of variations in humidity on net heat storage. Last, a table of vehicle temperature changes is included that may help public officials and the media communicate the dangers of vehicle-related hyperthermia in children.

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Andrew J. Grundstein
and
Mace L. Bentley

Abstract

A hydroclimatology, or description of long-term means and interannual variation, that focuses on soil moisture deficits was constructed for the period of 1895–1998 for a six-state region composing the Ohio Valley. The term “deficit” is considered from an agricultural point of view whereby moisture-induced crop stress is a combination of insufficient precipitation and soil moisture. Of particular concern are deficits that occur during the growing season (May–September) when vegetation is most susceptible to moisture-induced stress. Evidence suggests that there is considerable temporal variability but no long-term trend toward either wetter or drier conditions in the Ohio Valley. The pattern of growing-season deficit is characterized by multiyear and multidecadal cycles of wet and dry periods. Decreases in precipitation during years with anomalously large growing-season deficits, however, are associated more with the reduced frequency of precipitation events than with any changes in intensity. These variations in precipitation frequency and the conditions conducive to droughts are intimately linked with large-scale atmospheric conditions, including the low-level and upper-level flow patterns.

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Andrew Grundstein
,
J. Marshall Shepherd
, and
Sarah Duzinski

Abstract

Inflatable bounce houses provide a popular summer activity for children. Injuries such as sprains and fractures are widely acknowledged, but there is less awareness about possible hazards from excessive heat exposure. This study aims to identify whether conditions in the bounce house are more oppressive than ambient conditions on a typical summer day in Athens, Georgia. Results show that maximum air temperatures in the bounce house can reach up to 3.7°C (6.7°F) greater than ambient conditions, and peak heat index values may exceed outdoor conditions by 4.5°C (8.1°F). When considered within the context of the National Weather Service heat index safety categories, the bounce house reached the “danger” level in more than half of the observations, compared with only 7% of observations for ambient conditions. Parents and caregivers should be aware of heat-related hazards in bounce houses and closely monitor children, adjusting or canceling activities as conditions become more oppressive.

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Andrew J. Grundstein
,
Qi Qi Lu
, and
Robert Lund

Abstract

This paper estimates return levels of extreme snow water equivalents (SWE) in the northern Great Plains region, containing North and South Dakota, Iowa, Minnesota, and Nebraska. The return levels are estimated from extreme-value methods using a new hybrid SWE dataset that improves the spatial resolution of existing data in the area. A novel aspect of the methods is the use of standard error margins to spatially smooth the estimated SWE return levels computed on individual grid cells. The end product is a reliable return-level estimate that controls for uncertainties in the raw observations. The methods should prove useful in analyses of other geographical regions.

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Andrew Grundstein
,
Marshall Shepherd
,
Paul Miller
, and
Stefanie Ebelt Sarnat

Abstract

A major thunderstorm asthma epidemic struck Melbourne and surrounding Victoria, Australia, on 21 November 2016, which led to multiple deaths, a flood of residents seeking medical attention for respiratory problems, and an overwhelmed emergency management system. This case day had all the classic ingredients for an epidemic, including high rye grass pollen concentrations, a strong multicellular thunderstorm system moving across the region, and a large population of several million people in the vicinity of Melbourne. A particular characteristic of this event was the strong, gusty winds that likely spread the pollen grains and/or allergenic contents widely across the region to increase population exposure. This exploratory case study is the first to examine the usefulness of low-to-middle-atmospheric thermodynamic information for anticipating epidemic thunderstorm asthma outbreaks by allowing the forecast of strong downdraft winds. The authors investigated the utility of several mesoscale products derived from atmospheric soundings such as downdraft convective available potential energy (DCAPE) and indices for predicting surface wind gusts such as microburst wind speed potential index (MWPI) and a wind gust index (GUSTEX). These results indicate that DCAPE levels reached “high” to “very high” thresholds for strong downdraft winds in the lead-up to the thunderstorm, and the MWPI and GUSTEX indices accurately predicted the high maximum surface wind observations. This information may be useful for diagnostic and prognostic assessment of epidemic thunderstorm asthma and in providing an early warning to health practitioners, emergency management officials, and residents in affected areas.

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Daniel J. Leathers
,
Daniel Graybeal
,
Thomas Mote
,
Andrew Grundstein
, and
David Robinson

Abstract

A one-dimensional snowpack model, a unique airmass identification scheme, and surface weather observations are used to investigate large ablation events in the central Appalachian Mountains of North America. Data from cooperative observing stations are used to identify large ablation events within a 1° latitude × 1° longitude grid box that covers the majority of the Lycoming Creek basin in northern Pennsylvania. All 1-day ablation events greater than or equal to 7.6 cm (3 in.) are identified for the period of 1950 through 2001. Seventy-one events are identified, and these days are matched with a daily airmass type derived using the Spatial Synoptic Classification technique. Average meteorological characteristics on ablation days of each airmass type are calculated in an effort to understand the diverse meteorological influences that led to the large ablation events. A one-dimensional mass and energy balance snowpack model (“SNTHERM”) is used to calculate surface/atmosphere energy fluxes responsible for ablation under each airmass type. Results indicate that large ablation events take place under diverse airmass/synoptic conditions in the central Appalachians. Five airmass types account for the 71 large ablation events over the 52-yr period. Forty-three of the events occurred under “moist” airmass types and 28 under “dry” airmass conditions. Large ablation events under dry airmass types are driven primarily by daytime net radiation receipt, especially net solar radiation. These events tend to occur early and late in the snow cover season when solar radiation receipt is highest and are characterized by relatively clear skies, warm daytime temperatures, and low dewpoint temperatures. Moist airmass types are characterized by cloudy, windy conditions with higher dewpoint temperatures and often with liquid precipitation. During these events sensible heat flux is most often the dominant energy flux to the snowpack during ablation episodes. However, in many cases there is also a significant input of energy to the snowpack associated with condensation. Combinations of high sensible and latent heat fluxes often result in extreme ablation episodes, similar to those witnessed in this area in January 1996.

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Emily L. Pauline
,
John A. Knox
,
Lynne Seymour
, and
Andrew J. Grundstein

Capsule

Where are climate extremes happening? This information is urgently needed. We combine this information with social demographic data to create an index identifying U.S. locations vulnerable to climate extremes.

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George Maier
,
Andrew Grundstein
,
Woncheol Jang
,
Chao Li
,
Luke P. Naeher
, and
Marshall Shepherd

Abstract

Extreme heat is the leading weather-related killer in the United States. Vulnerability to extreme heat has previously been identified and mapped in urban areas to improve heat morbidity and mortality prevention efforts. However, only limited work has examined vulnerability outside of urban locations. This study seeks to broaden the geographic context of earlier work and compute heat vulnerability across the state of Georgia, which offers diverse landscapes and populations with varying sociodemographic characteristics. Here, a modified heat vulnerability index (HVI) developed by Reid et al. is used to characterize vulnerability by county. About half of counties with the greatest heat vulnerability index scores contain the larger cities in the state (i.e., Athens, Atlanta, Augusta, Columbus, Macon, and Savannah), while the other half of high-vulnerability counties are located in more rural counties clustered in southwestern and east-central Georgia. The source of vulnerability varied between the more urban and rural high-vulnerability counties, with poverty and population of nonwhite residents driving vulnerability in the more urban counties and social isolation/population of elderly/poor health the dominant factor in the more rural counties. Additionally, the effectiveness of the HVI in identifying vulnerable populations was investigated by examining the effect of modification of the vulnerability index score with mortality during extreme heat. Except for the least vulnerable categories, the relative risk of mortality increases with increasing vulnerability. For the highest-vulnerability counties, oppressively hot days lead to a 7.7% increase in mortality.

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Emily L. Pauline
,
John A. Knox
,
Lynne Seymour
, and
Andrew J. Grundstein

Abstract

The occurrence of extreme weather and climate events has increased in recent decades. This increasing frequency has adversely impacted economic and health outcomes, leading to an increasingly urgent need to study climate extremes. The National Centers for Environmental Information (NCEI) created the Climate Extremes Index (CEI) in 1996 to quantify climate extremes. In this article, we explore the potential for enhancing the CEI via the use of the Z-score statistic to calculate the CEI on a numerical scale, to increase usability at smaller spatial scales, and to allow the creation of a new climate Extremes Vulnerability Index (EVI). The EVI combines the results from the revised CEI with values from the Social Vulnerability Index from the Centers for Disease Control and Prevention (CDC). The EVI can be used by policy-makers, planners, and the public to understand a subregion’s vulnerability to climate extremes. This information from the EVI could then be used to implement policies and changes in infrastructure that mitigate risk in vulnerable climate divisions. In a trial application, it is found that the southeastern and portions of the central United States had the highest levels of vulnerability for the abnormal month of December 2015.

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