DOI: 10.5281/zenodo.5594469
title: Dataset from 'Increased labor losses and decreased adaptation potential in a warmer world'

description: <strong>Abstract:<br> Working in hot and potentially humid conditions creates health and well-being risks that will increase as the planet warms. It has been proposed that workers could adapt to increasing temperatures by moving labor from midday to cooler hours.</strong> <strong>Here we use reanalysis data to show that in the current climate, globally approximately 30% of heavy labor losses in the workday could be recovered by moving labor from the hottest hours of the day. However, we show that this particular workshift adaptation potential is lost at a rate of about 2% per degree of global warming as early morning heat exposure rises to unsafe levels for continuous work, with worker productivity losses accelerating under higher warming levels. These findings emphasize the importance of finding alternative adaptation mechanisms to keep workers safe, as well as the importance of limiting global warming.</strong>
description: All files are in netcdf format, except the annual global sums of heavy labor losses (era5_Annual_ILO_GlobalSum_Hours_Wages_Productivity_Lost_NOCUTOFF_heavy_MonthlyPatterns_gwf0_1979_2020.npz), which are saved as npz file format. Scripts to load and plot data provided at: https://github.com/LukeAParsons/Warming_Adaptation_Limits Files with names starting with 'hours_lost_' are annual sums of heavy labor productivity lost (units in hours/person/year) calculated from hourly simplified Wet Bulb Globe Temperatures (sWBGT) from ERA5 reanalysis data. Exposure response function from Kjellstrom et al. (2018)/Watts et al. (2020: Lancet Countdown) are used to calculate heavy labor productivity losses. File 'swbgt_patterns_CMIP6_1pctCO2_MMMedian_Annual_monthly_era5_grid.nc' contains the CMIP6 multi-model median warming patterns (annual, and months 1-12 for Jan-Dec) regridded to the ERA5 horizontal grid resolution. These are the patterns used to 'pattern scale' the era5 sWBGT data to calculate labor losses under 1,2,3,4 C of additional warming. Files 'swbgt_patterns_CMIP6_1pctCO2_MMMedian_Annual_JJA_DJF_75p_95p_99p_1x1_grid.nc', 'huss_patterns_CMIP6_1pctCO2_MMMedian_Annual_JJA_DJF_75p_95p_99p_1x1_grid.nc', and 'tas_patterns_CMIP6_1pctCO2_MMMedian_Annual_JJA_DJF_75p_95p_99p_1x1_grid.nc' contain the CMIP6 multi-model median warming patterns for swbgt, huss, and tas annual mean, JJA, DJF, the 75th percentile, the 95th percentile, and the 99th percentile regridded to a common 1x1 degree horizontal resolution. Individual sWBGT model warming patterns (on each CMIP6 model's native grid resolution) are also included in files such as 'WBGT_patterns_monthly_35_149_day_ACCESS-CM2_1pctCO2_r1i1p1f1_gn_09500101-10991231.nc'. All CMIP6 warming patterns are from years 35-149 from the 1pctCO2 simulations. File 'heavylaborlost_global_populationweighted_annual_hours_lost_mean_present_warming_1_2_3_4_C_grid_05x05.nc' contains climatological (20-year) mean labor losses multiplied by GPWv4 working age population in agriculture + construction industries (units: lost hours/year). Provided on a 0.5x0.5 degree horizontal grid structure. File 'heavylaborlost_global_populationweighted_annual_productivity2017PPP_lost_mean_present_warming_1_2_3_4_C_grid_05x05.nc' contains climatological (20-year) mean productivity (2017PPP$) losses in agriculture + construction/industry sectors (units: 2017PPP$/year). Provided on a 0.5x0.5 degree horizontal grid structure. File 'swbgt_diurnalcycle_2001_2020_Mean.nc' contains the climatological mean (2001-2020) 24-hour diurnal cycle of sWBGT for each month (Jan-Dec) from the ERA5 data. Provided at the ERA5 horizontal grid structure. File 'etopo_regridded_mask_1x1_grid.nc' used in GitHub scripts to mask CMIP6 warming patterns over oceans. See the Methods section in Parsons et al. for further details.