Datasets and code from: 'Geophysical Uncertainties in Air Pollution Exposure and Benefits of Emissions Reductions for Global Health'

Data and code from Earth's Future publication titled: ''Geophysical Uncertainties in Air Pollution Exposure and Benefits of Emissions Reductions for Global Health''. Multiply the PM scaler for each 5-year time slice by the CMIP6 gridded PM data to 'debias' the 0.5 degree CMIP6 data as described in the main text methods.The new version of this upload includes the 'adjusted' (debiased) CMIP6 5-year mean PM2.5 saved as netcdf files (zipped all together by ssp: ssp126 or ssp370). Abstract Exposure to fine particulate matter (PM2.5) air pollution is associated with large-scale health consequences, but the geophysical uncertainties in estimates of PM2.5-related global premature mortality remain understudied. Using four observation-based PM2.5 datasets and six Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models, we compare uncertainties in current PM2.5-related mortality estimates based on model/observation combinations to the uncertainties associated with projected emissions reductions and the resulting impacts on global health. Although estimates of current mortality are sensitive to the PM2.5 dataset (6.54 to 9.23 million/year using the central estimate from the Global Exposure Mortality Model), the projected near-term and long-term benefits of emissions reductions for reduced mortality are much more certain. Specifically, uncertainties in projected avoided deaths are consistently less than half the magnitude of uncertainties in current mortality estimates. Under a low-emissions scenario, avoided cumulative deaths would exceed a quarter-billion by 2100.