Abstract/Summary

New satellite missions promise global reductions in the uncertainties of aerosol optical properties but it is unclear how those reductions will propagate to uncertainties in the shortwave (SW) direct aerosol radiative effect (DARE) and radiative forcing (DARF), which are currently large, on the order of at least 20%. In this work we build a Monte-Carlo framework to calculate the impact of uncertainties in aerosol optical depth (AOD), single scattering albedo (SSA) and asymmetry parameter on the uncertainty in shortwave DARE and DARF. This framework uses the results of over 2.3 million radiative transfer simulations to calculate global clear-sky DARE and DARF based on a range of uncertainties in present-day and pre-industrial aerosol optical properties, representative of existing and future global observing systems. We find the one-sigma uncertainty varies between ±0.23 to ±1.91 Wm-2 (5 and 42%) for the top of atmosphere (TOA) clear-sky DARE and between ±0.08 to ±0.47 Wm-2 (9 and 52%) for the TOA DARF. At the TOA, AOD uncertainty is the main contributor to overall uncertainty, except over bright surfaces where SSA uncertainty contributes most. We apply regionally varying uncertainties to represent current measurement uncertainties, finding that aerosol optical property uncertainties represent 24% of TOA DARE and DARF. Reducing regionally varying optical property uncertainties by a factor of two would reduce their contributions to TOA DARE and DARF uncertainty proportionally. Applying a simple scaling to all-sky conditions, aerosol optical property uncertainty contributes to about 25% total uncertainty in TOA, all-sky SW DARE and DARF. Compared to previous studies which considered uncertainties in non-aerosol variables, our results suggest that the aerosol optical property uncertainty accounts for a third to a half of total direct SW uncertainty. Recent and future progress in constraining aerosol optical properties using ground-based or satellite retrievals could be translated into DARE and DARF uncertainty using our freely available framework.