Estimation of global black carbon direct radiative forcing and its uncertainty constrained by observations

Black carbon (BC) contributes to global warming by absorbing sunlight. However, the size of this contribution, namely, the direct radiative forcing (RF), ranges from +0.1 to +1.0 W m(-2), largely due to differences between bottom-up and observation-based estimates. Current global models systematically underestimate BC radiation absorption relative to observations, which is often attributed to the underestimation of BC emissions. Several studies that adjusted emissions to correct biases of global aerosol models resulted in a revised upward estimate of the BC RF. However, the BC RF was never optimized against observations in a rigorous mathematical manner. Here we simulated the absorption of solar radiation by BC from all sources at the 10 km resolution by combining a highly disaggregated emission inventory with a nested aerosol climate model and a downscaling method. As a result, the normalized mean bias in BC radiation absorption was reduced from -56% to -5% in Asia and from -71% to -46% elsewhere. We applied a Bayesian method that makes the best account of all model, representativeness, and observational uncertainties to estimate the BC RF and its uncertainty. Using the new emission inventory and high-resolution model reduces uncertainty in BC RF from -109%/+172% to -77%/+78% over Asia and from -83%/+114% to -64%/+70% over other continental regions. Finally, we derived an observationally constrained BC RF of 0.53 Wm(-2) (0.14 to 1.19 as 90% confidence) as our best estimate, less than previous estimates. Our estimate implies that reduction in BC emissions would contribute to slow down global warming, but the contribution could be less than previously thought.National Natural Science Foundation of China [41390240, 41130754]; 111 program [B14001]; Agence Nationale de la Recherche' [ANR-09-CEP-005-03/PAPRIKA]; National Aeronautics and Space Administration [09-GLORY09-0028]; GENCI-TGCC [2013-t2013012201]SCI(E)ARTICLEtaos@pku.edu.cn105948-597112