Explicit Calculations of Wet‐Bulb Globe Temperature Compared With Approximations and Why It Matters for Labor Productivity

Kong, Qinqin; Huber, Matthew

doi:10.1029/2021ef002334

Cited by 31 publications

(35 citation statements)

References 97 publications

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“…With this method, values higher than 32.3 • C should be considered as dangerous for exercising outside (table 1 from [11]). One must keep in mind that differences exist between the full and simplified formulations, especially for specific hot-humid or hotdry areas [24]. Results presented in this study are only valid for the simplified version WBGT * .…”

Section: Heat Stress Metricsmentioning

confidence: 94%

Robust increase in population exposure to heat stress with increasing global warming

Freychet

Hegerl

Lord

et al. 2022

Environ. Res. Lett.

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Extreme heat, particularly if combined with humidity, poses a severe risk to human health. To estimate future global risk of extreme heat with humidity on health, we calculate indicators of heat stress that have been commonly used; the Heat Index, the Wet-Bulb Globe Temperature and the Wet-Bulb Temperature, from the latest Climate Model Intercomparison Project (CMIP6) projections. We analyze how and where different levels of heat stress hazards will change, from severe to deadly, and how results are sensitive to the choice of the index used. We evaluate this risk at country-level and use population and GDP growth scenario to estimate the vulnerability of each nation. Consistent with previous studies, we find that South and East Asia, and the Middle-East, are highly exposed to heat stress hazards, and that this exposure increases by 20\% to 60\% with global mean temperature change from 1.5 to 3 degrees Celsius. However, we also find substantial increases in heat health risk for some vulnerable countries with less adaptive capacity, such as West Africa, and Central and South America. For these regions, about 20 to more than 50\% of the population could be exposed to severe heat stress each year on average, independent of the index used. For global warming of 3 degrees, European countries and the USA will also be exposed several times per year to conditions with daily mean heat stress level equal to the maximum heat stress of the 2003 heat wave.

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Section: Heat Stress Metricsmentioning

confidence: 94%

Robust increase in population exposure to heat stress with increasing global warming

Freychet

Hegerl

Lord

et al. 2022

Environ. Res. Lett.

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“…One key variable that is necessary in the calculation of T g (both in Equations and ) is the cosine of the solar zenith angle. In previous studies it is found that radiation in the Liljegren T g methodology has inaccuracies at sunrise and sunset due to the method used to calculate this variable (Kong & Huber, 2022; Lemke & Kjellstrom, 2012). These inaccuracies are known to become greater in a numerical weather prediction service time step (a period of several hours) (Brimicombe, Quintino, et al., 2022; Hogan & Hirahara, 2016).…”

Section: Methodsmentioning

confidence: 99%

Wet Bulb Globe Temperature: Indicating Extreme Heat Risk on a Global Grid

Brimicombe

Pappenberger

et al. 2023

GeoHealth

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The Wet Bulb Globe Temperature (WBGT) is an international standard heat index used by the health, industrial, sports, and climate sectors to assess thermal comfort during heat extremes. Observations of its components, the globe and the wet bulb temperature (WBT), are however sparse. Therefore WBGT is difficult to derive, making it common to rely on approximations, such as the ones developed by Liljegren et al. (2008, https://doi.org/10.1080/15459620802310770, normalWnormalBnormalGnormalTnormalLnormalinormallnormaljnormalenormalgnormalrnormalenormaln ${\mathrm{W}\mathrm{B}\mathrm{G}\mathrm{T}}_{\mathrm{L}\mathrm{i}\mathrm{l}\mathrm{j}\mathrm{e}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{n}}$) and by the American College of Sports Medicine (normalWnormalBnormalGnormalTnormalAnormalCnormalSnormalM87 ${\mathrm{W}\mathrm{B}\mathrm{G}\mathrm{T}}_{\mathrm{A}\mathrm{C}\mathrm{S}\mathrm{M}87}$). In this study, a global data set is created by implementing an updated WBGT method using ECMWF ERA5 gridded meteorological variables and is evaluated against existing WBGT methods. The new method, normalWnormalBnormalGnormalTnormalBnormalrnormalinormalmnormalinormalcnormalonormalmnormalbnormale ${\mathrm{W}\mathrm{B}\mathrm{G}\mathrm{T}}_{\mathrm{B}\mathrm{r}\mathrm{i}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{b}\mathrm{e}}$, uses globe temperature calculated using mean radiant temperature and is found to be accurate in comparison to normalWnormalBnormalGnormalTnormalLnormalinormallnormaljnormalenormalgnormalrnormalenormaln ${\mathrm{W}\mathrm{B}\mathrm{G}\mathrm{T}}_{\mathrm{L}\mathrm{i}\mathrm{l}\mathrm{j}\mathrm{e}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{n}}$ across three heatwave case studies. In addition, it is found that normalWnormalBnormalGnormalTnormalAnormalCnormalSnormalM87 ${\mathrm{W}\mathrm{B}\mathrm{G}\mathrm{T}}_{\mathrm{A}\mathrm{C}\mathrm{S}\mathrm{M}87}$ is not an adequate approximation of WBGT. Our new method is a candidate for a global forecasting early warning system.

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“…Liljegren's model has been extensively calibrated and validated (with an RMS difference of less than 1°C) and has seen increasing applications in recent years (Casanueva et al, 2020;Takakura et al, 2017). Kong and Huber (2022) developed a Python implementation of Liljegren's code (originally in FORTRAN and C language), which is especially suitable for processing large-size climate model output and is adopted in this paper. For the detailed calculation procedure of WBGT, see Liljegren et al (2008) and Kong and Huber (2022).…”

Section: Estimation Of Heat Stressmentioning

confidence: 99%

“…Kong and Huber (2022) developed a Python implementation of Liljegren's code (originally in FORTRAN and C language), which is especially suitable for processing large-size climate model output and is adopted in this paper. For the detailed calculation procedure of WBGT, see Liljegren et al (2008) and Kong and Huber (2022).…”

Section: Estimation Of Heat Stressmentioning

confidence: 99%

The Poverty Impacts of Labor Heat Stress in West Africa Under a Warming Climate

et al. 2022

Self Cite

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This paper assesses the poverty implications of heat stress‐related labor capacity losses based on simulations using a global general equilibrium economic model. Compared with past studies, we use a more precise measurement of heat stress, assign labor capacity losses to specific labor types by sector, and employ an economic model that contains highly disaggregated economic sectors and regions. This model allows us to determine global and regional economic impacts that account for international dependencies. We focus attention on seven West African countries for which we determine the implied changes in real incomes of households near the poverty line. For these countries, we use household microsimulations to determine potential impacts on the poverty headcount. In our results, poverty impacts are heterogeneous across countries and earning sources‐based household strata. A key channel behind this heterogeneity is how loss of labor productivity affects the relative returns to factors of production. We find that unskilled agricultural wages could increase, as loss of productivity in the face of inelastic food demand induces increased labor demand in order to dampen agricultural output losses. In our experiments, even neglecting potential increases in mortality and morbidity, poverty increases range from 2.3% in Cameroon to up to 7.2%–9.2% in Ghana and Nigeria. In one of the seven countries considered, Guinea, poverty sees little change due to the mitigating effects of rising labor wages.

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Explicit Calculations of Wet‐Bulb Globe Temperature Compared With Approximations and Why It Matters for Labor Productivity

Cited by 31 publications

References 97 publications

Robust increase in population exposure to heat stress with increasing global warming

Robust increase in population exposure to heat stress with increasing global warming

Wet Bulb Globe Temperature: Indicating Extreme Heat Risk on a Global Grid

The Poverty Impacts of Labor Heat Stress in West Africa Under a Warming Climate

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