Abstract
Abstract. Accurate estimates of the emissions and distribution of black carbon (BC) in the region referred to here as Southeastern Asia (70–150° E, 11° S–55° N) are critical to studies of the atmospheric environment and climate change. Analysis of modeled BC concentrations compared to in situ observations indicates levels are underestimated over most of Southeast Asia when using any of four different emission inventories. We thus attempt to reduce uncertainties in BC emissions and improve BC model simulations by developing top-down, spatially resolved, estimates of BC emissions through assimilation of OMI (Ozone Monitoring Instrument) observations of aerosol absorption optical depth (AAOD) with the GEOS-Chem (Goddard Earth Observing System – chemistry) model and its adjoint for April and October 2006. Overwhelming enhancements, up to 500 %, in anthropogenic BC emissions are shown after optimization over broad areas of Southeast Asia in April. In October, the optimization of anthropogenic emissions yields a slight reduction (1–5 %) over India and parts of southern China, while emissions increase by 10–50 % over eastern China. Observational data from in situ measurements and AERONET (Aerosol Robotic Network) observations are used to evaluate the BC inversions and assess the bias between OMI and AERONET AAOD. Low biases in BC concentrations are improved or corrected in most eastern and central sites over China after optimization, while the constrained model still underestimates concentrations in Indian sites in both April and October, possibly as a consequence of low prior emissions. Model resolution errors may contribute up to a factor of 2.5 to the underestimation of surface BC concentrations over northern India. We also compare the optimized results using different anthropogenic emission inventories and discuss the sensitivity of top-down constraints on anthropogenic emissions with respect to biomass burning emissions. In addition, the impacts of brown carbon, the formulation of the observation operator, and different a priori constraints on the optimization are investigated. Overall, despite these limitations and uncertainties, using OMI AAOD to constrain BC sources improves model representation of BC distributions, particularly over China.
Highlights
Black carbon (BC) is a product of incomplete combustion of carbonaceous fuels, enhanced concentrations of which have led to a present-day overall positive radiative forcing and climate warming (Charlson and Pilat, 1969; Satheesh and Ramanathan, 2000; Bond et al, 2013)
A typical fresh particle mass absorption cross section (MABS; essentially the column of BC absorption divided by the load) of about 7.5 m2 g−1, a value recommended by Bond and Bergstrom (2006), is not represented in most models (Koch et al, 2009)
Our study focuses on April and October to compare seasons when the dust loading over Southeast Asia is relatively large and small
Summary
Black carbon (BC) is a product of incomplete combustion of carbonaceous fuels, enhanced concentrations of which have led to a present-day overall positive radiative forcing and climate warming (Charlson and Pilat, 1969; Satheesh and Ramanathan, 2000; Bond et al, 2013). To reduce uncertainties in BC emissions and improve the poor representation of BC in model simulations, different top-down approaches have been used to constrain bottom-up BC emissions, such as the linear constraints between concentrations and emissions (Park et al, 2003; Kondo et al, 2011; Fu et al, 2012; Wang et al, 2013), inverse modeling using the decoupled direct method (Hu et al, 2009a, b), the Kalman filter technique (Cohen and Wang, 2014), and the adjointbased 4-D variational approach (Hakami et al, 2005) These studies have exclusively used in situ measurements or airborne observations, which can provide accurate observations of aerosol properties.
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