AERONET-based models of smoke-dominated aerosol near source regions and transported over oceans, and implications for satellite retrievals of aerosol optical depth

Abstract

Abstract. Smoke aerosols from biomass burning are an important component of the global aerosol system. Analysis of Aerosol Robotic Network (AERONET) retrievals of aerosol microphysical/optical parameters at 10 sites reveals variety between biomass burning aerosols in different global source regions, in terms of aerosol particle size and single scatter albedo (SSA). Case studies of smoke observed at coastal/island AERONET sites also mostly lie within the range of variability at the near-source sites. Differences between sites tend to be larger than variability at an individual site, although optical properties for some sites in different regions can be quite similar. Across the sites, typical midvisible SSA ranges from ∼ 0.95-0.97 (sites dominated by boreal forest or peat burning, typically with larger fine-mode particle radius and spread) to ∼ 0.88-0.9 (sites most influenced by grass, shrub, or crop burning, typically smaller finemode particle radius and spread). The tropical forest site Alta Floresta (Brazil) is closer to this second category, although with intermediate SSA ∼ 0.92. The strongest absorption is seen in southern African savannah at Mongu (Zambia), with average midvisible SSA ∼ 0.85. Sites with stronger absorption also tend to have stronger spectral gradients in SSA, becoming more absorbing at longer wavelengths. Microphysical/optical models are presented in detail so as to facilitate their use in radiative transfer calculations, including extension to UV (ultraviolet) wavelengths, and lidar ratios. One intended application is to serve as candidate optical models for use in satellite aerosol optical depth (AOD) retrieval algorithms. The models presently adopted by these algorithms over ocean often have insufficient absorption (i.e. too high SSA) to represent these biomass burning aerosols. The underestimates in satellite-retrieved AOD in smoke outflow regions, which have important consequences for applications of these satellite data sets, are consistent with the level of underestimated absorption.

Deriving brown carbon from multiwavelength absorption measurements: method and application to AERONET and Aethalometer observations

“…The AERONET observations of wavelength-dependent absorption are retrieved from the direct and diffuse radiation measured by sun/sky radiometers, but they do not include any aerosol assumptions such as those used in the AERONET retrieval of refractive index and size distribution. AERONET AAOD is widely used to investigate the sources, compositions, and properties of aerosols (Russell et al, 2010;Bond et al, 2014;Sayer et al, 2014). However, we show that the retrieval is an indirect measure of aerosol absorption and that uncertainties and assumptions in the retrieval scheme may impact the reported multiwavelength absorption and introduce subtle inconsistencies with our assumed population of particles.…”

Evaluating the Height of Biomass Burning Smoke Aerosols Retrieved from Synergistic Use of Multiple Satellite Sensors over Southeast Asia

“…The VIIRS-retrieved size information, such as AE and fine-mode AOD fraction, can help to constrain the size information, in particular over ocean where size information is more reliable than over land. Meanwhile, the spectral absorption characteristics can be constrained by climatological data derived from AERONET (e.g., Sayer et al, 2014a) or other sources.…”

“…In this region, smoke aerosols originate from scattered burning sources as seen by the MODIS fire mask (Giglio et al, 2003), which makes their optical and microphysical properties complicated. Smoke properties can vary according to material burned, combustion type, and aging processes (e.g., Dubovik et al, 2002;Reid et al, 2005;Lee et al, 2010;Sayer et al, 2014a). For the test case, the smoke layer with AOD generally higher than 0.8 (not shown) resides at altitudes ranging from 2-5 km (Fig.…”

Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations

“…Since biases in FMF are directly related to the biases in AOD at UV wavelengths, a similar tendency to AOD errors is observed (i.e., retrieval bias in the same direction). For AAE, we assume an uncertainty of 0.4, which is an intermediate value between the standard deviation of AAE of smoke aerosols from different source regions [Sayer et al, 2014a] and that of dust [Russell et al, 2010]. The positive (negative) bias in AAE leads to higher (lower) UVAI for a given aerosol loading condition, thereby resulting in negative (positive) bias in ATH.…”