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For each of these models, a single realization has been taken into account for each simulation. We obtained the evolution of heat exposure from daily data to compute annual statistics. And only then, we interpolated the results onto a common grid for analyses purposes CanESM2 grid, with a 2.

No bias correction has been applied to climate model outputs, similarly to other studies on CCE [e.

Introduction

It takes into account variations in temperature and relative humidity of the atmosphere. It is defined as:. This relation has the advantage to be valid for every model configuration 23 :. S4 in the Supporting Information. S1 in the Supporting Information. Relationships between temperature thresholds mentioned above and health recommendations on labour productivity have been built in previous studies 2 , 3 , 4 , 5. No recommendations has been published for the threshold considered as deadly 1 , and there is no scientific study recommending to stop working for the entire day 5. With this information, we can estimate the additional working time lost per year due to the increase in heat exposure in each grid cell g by the following indicator:.

Online Cumulative Subject Index To Volumes 195 198 And 200 227 1995

Due to the fact that we use daily mean WBGT values and that working hours are generally warmer than the daily mean, our estimate represents a lower band of the effect of heat exposure on working time loss due to increasing resting times. From statistics of the International Labour Organization 25 , we estimate the change in annual labour productivity expressed as a relative annual loss of total GDP for each country c and each economic sector s considered as vulnerable to heat exposure agriculture, mining and quarrying, manufacturing and construction.


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Statistics of economic outputs are considered fixed to values of In our analysis, we thus assume that all socio-economic conditions remain equal to current ones and that no additional adaptation measures are implemented in the vulnerable sectors selected. The annual loss in labour productivity due to the increase in heat exposure is computed as follows for a specific country:. Both are expressed in constant international dollars in PPP. Vacation time is considered in statistics. For countries with missing values, working hours are assumed to be equal to the global average i.

Days with heat exposure are considered equally distributed among working days, weekends and holidays. Finally, the annual loss in labour productivity due to the increase in heat exposure world-wide and aggregated by countries of the same income class can be obtained by the following equation:. Simulations of each climate model have been considered equally in the multi-model analysis, without weighting any simulation more than another.

Spatially represented signals on Fig. For indicators of labour productivity loss, we consider that all socio-economic conditions remain equal to current ones i. Throughout the manuscript, a relationship between CCE and an indicator of the increase in heat exposure is considered robustly linear, only if the linearity has been verified by the accuracy of a Pearson correlation test:. The actual value of this limit varies with the number of samples n in the distribution. No account is needed to get access. After the publication, the code that supports the findings of this study will be available from the corresponding author upon reasonable request.

Mora, C. Global risk of deadly heat. Nature Climate Change 7 , Kjellstrom, T. Workplace heat stress, health and productivity-an increasing challenge for low and middle-income countries during climate change. Global Health Action 2 , 1—6 Dunne, J. Reductions in labour capacity from heat stress under climate warming. Nature Clim. Change 3 , — Annual Review of Public Health 37 , 97— Occupational Exposure to Heat and Hot Environments tech.

Watts, N. The report of the Lancet Countdown on health and climate change: shaping the health of nations for centuries to come. The Lancet Harrington, L. Poorest countries experience earlier anthropogenic emergence of daily temperature extremes. Environmental Research Letters 11 , 1—8 Seneviratne, S. Allowable CO 2 emissions based on regional and impact-related climate targets. Nature , — Allen, M. Warming caused by cumulative carbon emissions towards the trillionth tonne.

Matthews, H. The proportionality of global warming to cumulative carbon emissions. Nature , —32 Meinshausen, M. Greenhouse-gas emission targets for limiting global warming to 2C. Zickfeld, K. Setting cumulative emissions targets to reduce the risk of dangerous climate change. Proceedings of the National Academy of Sciences , — Cumulative carbon as a policy framework for achieving climate stabilization. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences , — Collins, M. Gillett, N. Journal of Climate 26 , — Frame, D.

Cumulative emissions and climate policy. Nature Geoscience 7 , — Leduc, M. Quantifying the limits of a linear temperature response to cumulative CO2 emissions. Journal of Climate 28 , — MacDougall, A. Curr Clim Change Rep 2 , 39—47 Regional estimates of the transient climate response to cumulative CO2 emissions. Nature Climate Change 6 , — Partanen, A. Seasonal climate change patterns due to cumulative CO2 emissions. Environmental Research Letters 12 , Yaglou, C. Control of heat casualties at military training centers.

Buzan, J. Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4. Geoscientific Model Development 8 , — Zhao, Y.

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Estimating heat stress from climate-based indicators: present-day biases and future spreads in the CMIP5 global climate model ensemble. Environmental Research Letters 10 , Taylor, K. An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society 93 , — International Labour Organization database, www.

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