Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5

Buzan, J. R.; Oleson, Keith W.; Huber, Matthew

doi:10.5194/gmd-8-151-2015

Cited by 171 publications

(241 citation statements)

References 92 publications

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“…We estimate the thermal comfort by using a feels‐like temperature—HI, which is defined as (Buzan et al, ; Rothfusz, )

HI = - 42.379 + 2.04901523 T_{F} + 10.14333127 RH - 0.22475541 T_{F} RH - 6.83783 \times 10^{- 3} T_{F}^{2} - 5.481717 \times 10^{- 2} {RH}^{2} + 1.22874 \times 10^{- 3} T_{F}^{2} RH + 8.5282 \times 10^{- 4} T_{F} {RH}^{2} - 1.99 \times 10^{- 6} T_{F}^{2} {RH}^{2},

where T F and RH denote air temperature (in degrees Fahrenheit) and relative humidity (in %), respectively. Additional adjustments are applied for the HI calculations (https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml).…”

Section: Methodsmentioning

confidence: 99%

Modeling the Impacts of Urbanization on Summer Thermal Comfort: The Role of Urban Land Use and Anthropogenic Heat

Yang

Leung

et al. 2019

JGR Atmospheres

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Urban land use and anthropogenic heat (AH) emission can considerably influence the human thermal comfort during extreme heat events. In this study, a spatially heterogeneous AH emission data and updated urban land use data are integrated into the Weather Research and Forecasting model to simulate the physical processes of urban warming during summer. Simulations conducted in the Yangtze River Delta (YRD) of east China suggest that the mean urban heat island intensity reaches 1.49 °C in urbanized areas during summer, with AH emission making a considerable contribution. The warming effect due to urban land use is intensified during extremely hot days, but in contrast, the AH effects are slightly reduced. Urban development increases the total thermal discomfort hours by 27% in the urban areas of YRD, with AH and urban land use contributing nearly equal amount. By limiting the daytime latent heat release, urban land use reduces the daily maximum heat stress particularly during extremely hot days; however, such alleviations can be offset by the AH emission. Strategies for mitigation of urban heat island effect and heat stress in cities should therefore include measures to reduce AH emission.

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“…We estimate the thermal comfort by using a feels‐like temperature—HI, which is defined as (Buzan et al, ; Rothfusz, )

HI = - 42.379 + 2.04901523 T_{F} + 10.14333127 RH - 0.22475541 T_{F} RH - 6.83783 \times 10^{- 3} T_{F}^{2} - 5.481717 \times 10^{- 2} {RH}^{2} + 1.22874 \times 10^{- 3} T_{F}^{2} RH + 8.5282 \times 10^{- 4} T_{F} {RH}^{2} - 1.99 \times 10^{- 6} T_{F}^{2} {RH}^{2},

Section: Methodsmentioning

confidence: 99%

Modeling the Impacts of Urbanization on Summer Thermal Comfort: The Role of Urban Land Use and Anthropogenic Heat

Yang

Leung

et al. 2019

JGR Atmospheres

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“…However, temperature alone may not fully capture the effect of heat stress on participation in physical activity [35][36][37][38] . Heat stress indices, composites between temperature and relative humidity, might indicate that participation in physical activity declines in instances of both high temperature and high humidity 39 .…”

mentioning

confidence: 99%

Climate change may alter human physical activity patterns

2017

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Regular physical activity supports healthy human functioning 1-3 . Might climate change-by modifying the environmental determinants of human physical activity-alter exercise rates in the future 4 ? Here we conduct an empirical investigation of the relationship between meteorological conditions, physical activity and future climate change. Using data on reported participation in recreational physical activity from over 1.9 million US survey respondents between 2002 and 2012, coupled with daily meteorological data, we show that both cold and acutely hot temperatures, as well as precipitation days, reduce physical activity. We combine our historical estimates with output from 21 climate models and project the possible physical activity effects of future climatic changes by 2050 and 2099. Our projection indicates that warming over the course of this century may increase net recreational physical activity in the United States. Activity may increase most during the winter in northern states and decline most during the summer in southern states.At a physiological level, too little exercise can produce costly health outcomes like obesity 1 , cardiovascular disease 3 and diabetes 2 , and insufficient physical activity is a leading cause of death in the United States 5 . Sedentary lifestyles are also associated with psychological concerns such as impaired cognitive performance 6 and a greater risk of clinical depression and anxiety 7 . Human well-being clearly suffers from insufficient physical activity. Yet even in spite of its substantial benefits, people in many countries participate in below-recommended levels of physical activity 8 and are becoming increasingly sedentary 9 .Further, external factors such as urban and workplace design 10 , social relations 11 and environmental characteristics shape behaviours regarding when, where and the extent to which humans are physically active. Of the environmental factors affecting physical activity rates, temperature plays a noteworthy role 12 . When it is too cold or too hot, adults perform less physical activity, resulting in more sedentary lifestyles. This reduction is mostly due to the nature of adult physical activity: the vast majority of exercise-related physical activity occurs outdoors 13 . When it is too cold or to hot to go outdoors-for a walk, a jog, or to garden-many simply forgo physical activity entirely.These facts combine to suggest that weather and climate may be substantial determinants of behavioural patterns that underlie costly public health crises. Here we report on the effect of meteorological conditions on participation in recreational physical activity of over 1.9 million US residents between 2002 and 2012. Using these data, we examine four questions. First, what weather conditions are most conducive to individual participation in physical activities? Second, do the effects of temperature on physical activity vary by demographic factors like weight and age? Third, how might climate change alter the distribution of physical activity throughout the month...

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“…However, the model excluding SM exhibits a particularly poor performance (i.e., low HR) in these regions. The causes of the poor model performance in eastern TX and SC are currently not fully understood, although it is worthy of note that data from MERRA-2 grid cells in SC exhibit a relatively low overall frequency of exceedance of this threshold and are also characterized by comparatively low 99th percentile θ e in an analysis of heat indices derived from the Community Land Model v4.5 (Buzan et al, 2015). Grid cells along the Gulf coast and over the states of MO, IA and IL exhibit high HR for prediction of extreme maximum θ e and substantial improvement in HR is noted in IA, IL and MO (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Very few statistical downscaling analyses focus on integrative variables such as θ e that explicitly incorporate covariability of T and q, but such variables have direct applications to climate change impact analyses (such as analysis of heat waves; Buzan et al, 2015). Further, this is an application of climate downscaling where statistical approaches may be particularly useful given evidence that even when nested within observed lateral boundary conditions regional climate models (RCMs) have difficulty in capturing the joint probability distributions of T and q and thus in accurately representing either the probability distribution of static energy or the spatiotemporal variability therein (Pryor and Schoof, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Given the importance of extreme heat to human health, we further examine the ability of the models to capture the occurrence of very high θ e . In this analysis we set a threshold of 347 K (73 • C) to indicate extreme maximum θ e (based on information provided in (Buzan et al, 2015) for the eastern USA) and a threshold of 337 K for minimum θ e . A contingency table approach is used to evaluate the accuracy of the model predictions of extreme θ e using the hit rate (HR) (Wilks, 2011):…”

Section: Methodsmentioning

confidence: 99%

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Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

2017

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Abstract. The static energy content of the atmosphere is increasing on a global scale, but exhibits important subglobal and subregional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called "warming holes" (i.e., locations with decreasing daily maximum air temperatures (T ) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θ e ) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. New nonlinear statistical models for summertime daily maximum and minimum θ e are developed and used to advance understanding of drivers of historical change and variability over the eastern USA. The predictor variables are an index of the daily global mean temperature, daily indices of the synoptic-scale meteorology derived from T and specific humidity (Q) at 850 and 500 hPa geopotential heights (Z), and spatiotemporally averaged soil moisture (SM). SM is particularly important in determining the magnitude of θ e over regions that have previously been identified as exhibiting warming holes, confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θ e . Consistent with our a priori expectations, models built using artificial neural networks (ANNs) out-perform linear models that do not permit interaction of the predictor variables (global T , synoptic-scale meteorological conditions and SM). This is particularly marked in regions with high variability in minimum and maximum θ e , where more complex models built using ANN with multiple hidden layers are better able to capture the day-to-day variability in θ e and the occurrence of extreme maximum θ e . Over the entire domain, the ANN with three hidden layers exhibits high accuracy in predicting maximum θ e > 347 K. The median hit rate for maximum θ e > 347 K is > 0.60, while the median false alarm rate is ≈ 0.08.

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Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5

Cited by 171 publications

References 92 publications

Modeling the Impacts of Urbanization on Summer Thermal Comfort: The Role of Urban Land Use and Anthropogenic Heat

Modeling the Impacts of Urbanization on Summer Thermal Comfort: The Role of Urban Land Use and Anthropogenic Heat

Climate change may alter human physical activity patterns

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

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