Aerosol particles, including soot and sulfur dioxide from burning fossil fuels, essentially mask the effects of greenhouse gases and are at the heart of the biggest uncertainty in climate change prediction. New research from the University of Michigan shows that satellite-based projections of aerosols’ effect on Earth’s climate significantly underestimate their impacts.
The findings will be published online the week of Aug. 1 in the early edition of the Proceedings of the National Academy of Sciences.
Full citation for the paper: Joyce E. Penner, Li Xu, and Minghuai Wang (2011). “Satellite methods underestimate indirect climate forcing by aerosols.” Proceedings of the National Academy of Sciences Published online before print August 1, 2011, doi: 10.1073/pnas.1018526108.
Abstract: Satellite-based estimates of the aerosol indirect effect (AIE) are consistently smaller than the estimates from global aerosol models, and, partly as a result of these differences, the assessment of this climate forcing includes large uncertainties. Satellite estimates typically use the present-day (PD) relationship between observed cloud drop number concentrations (Nc) and aerosol optical depths (AODs) to determine the preindustrial (PI) values of Nc. These values are then used to determine the PD and PI cloud albedos and, thus, the effect of anthropogenic aerosols on top of the atmosphere radiative fluxes. Here, we use a model with realistic aerosol and cloud processes to show that empirical relationships for ln(Nc) versus ln(AOD) derived from PD results do not represent the atmospheric perturbation caused by the addition of anthropogenic aerosols to the preindustrial atmosphere. As a result, the model estimates based on satellite methods of the AIE are between a factor of 3 to more than a factor of 6 smaller than model estimates based on actual PD and PI values for Nc. Using ln(Nc) versus ln(AI) (Aerosol Index, or the optical depth times angstrom exponent) to estimate preindustrial values for Nc provides estimates for Nc and forcing that are closer to the values predicted by the model. Nevertheless, the AIE using ln(Nc) versus ln(AI) may be substantially incorrect on a regional basis and may underestimate or overestimate the global average forcing by 25 to 35%.