Jungbin Mok

Effects of ozone and aerosol on surface UV radiation variability.

Global (direct+diffuse) spectral ultraviolet (UV, 290-363nm) and total ozone measurements made on the roof of the Main Science Building, Yonsei University at Seoul (37.57°, 128.98°E) were analyzed to quantify the effects of ozone and aerosol on the variability of surface erythemal UV (EUV) irradiance. The measurements have been made with a Brewer Spectrophotometer MKIV (SCI-TEC#148) and a Dobson Ozone Spectrophotometer (Beck#123), respectively, during 2004-2008. The overall mean radiation amplification factor, RAF(AOD, SZA) [23,24] due to total ozone (O(3)) (hereafter O(3) RAF) shows that 1% decrease in total ozone results in an increase of 1.18±0.02% in the EUV irradiance with the range of 0.67-1.74% depending on solar zenith angles (SZAs) (40-70°) and on aerosol optical depths (AODs) (<4.0), under both clear (cloud cover<25%) and all sky conditions. For the mean AOD, the O(3) RAFs(SZA) for both sky conditions increased as SZA increased from 40° to 60°, and then decreased for higher SZA 70°, where the patterns are consistent with results of the previous studies [2,10]. A similar analysis of the RAF(O(3), SZA) due to AOD (hereafter AOD RAF) under clear and all-sky conditions shows that on average, a 1% increase in AOD forces a decrease of 0.29±0.06% in the EUV irradiance with the maximum range 0.18-0.63% depending on SZAs and O(3). Thus, overall sensitivity of UV to ozone (O(3), RAF) was estimated to be about four times higher than to the aerosol (AOD RAF). At the mean O(3), the AOD RAFs(SZA) for both skies appears to be almost independent of SZAs. It is shown that the O(3) RAFs are nearly independent of the sky conditions, whereas the AOD RAFs depend distinctly on the sky conditions with the larger values for all skies. Under cloud free conditions, the overall mean ratio for measured-to-modeled O(3), RAF(AOD, SZA) is 1.13, whereas the ratio for AOD RAF(O(3), SZA) shows 0.82 in the EUV irradiance. Overall, the RAF measurements are corroborated by radiative transfer model calculations under clear-sky conditions.

Impacts of brown carbon from biomass burning on surface UV and ozone photochemistry in the Amazon Basin.

The spectral dependence of light absorption by atmospheric particulate matter has major implications for air quality and climate forcing, but remains uncertain especially in tropical areas with extensive biomass burning. In the September-October 2007 biomass-burning season in Santa Cruz, Bolivia, we studied light absorbing (chromophoric) organic or “brown” carbon (BrC) with surface and space-based remote sensing. We found that BrC has negligible absorption at visible wavelengths, but significant absorption and strong spectral dependence at UV wavelengths. Using the ground-based inversion of column effective imaginary refractive index in the range 305–368 nm, we quantified a strong spectral dependence of absorption by BrC in the UV and diminished ultraviolet B (UV-B) radiation reaching the surface. Reduced UV-B means less erythema, plant damage, and slower photolysis rates. We use a photochemical box model to show that relative to black carbon (BC) alone, the combined optical properties of BrC and BC slow the net rate of production of ozone by up to 18% and lead to reduced concentrations of radicals OH, HO2, and RO2 by up to 17%, 15%, and 14%, respectively. The optical properties of BrC aerosol change in subtle ways the generally adverse effects of smoke from biomass burning.

Validation of aerosol type classification from satellite remote sensing

Inter-comparison of various satellite data is performed for the purpose of validation of aerosol type classification algorithm from satellite remote sensing, so called, MODIS-OMI algorithm (MOA hereafter). Infrared Optical Depth Index (IODI), correlation coefficient between carbon monoxide (CO) column density and black carbon (BC) aerosol optical thickness (AOT), and aerosol types from 4-channel algorithm and CALIOP measurements are used to validate dust, BC, and aerosol type from MOA, respectively. The agreement of dust pixels between IODI and MOA ranges 0.1 to 0.6 with respect to AOT constraint, and it is inferred that IODI is less sensitive to optically thin dust layer. Increase of the correlation coefficient between AOT and CO column density when BC pixels are taken into account supports the performance of MOA to detect BC aerosol. The agreement of aerosol types from MOA and 4CA showed reasonable consistency, and the difference can be described by different absorptivity test and retrieval accuracy of AE. Intercomparison of aerosol types between MOA and CALIOP measurements represented reasonable consistency when AOT greater than 0.5, and height dependence of MOA is inferred from consistency analysis with respect to aerosol layer height from CALIOP measurements. Inter-comparisons among different satellite data showed feasible future for validating aerosol type classification algorithm from satellite remote sensing.

Validation of Aerosol Type Classification from Satellite Remote Sensing

Inter‐comparison of various satellite data is performed for the purpose of validation of aerosol type classification algorithm from satellite remote sensing, so called, MODIS‐OMI algorithm (MOA hereafter). Infrared Optical Depth Index (IODI), correlation coefficient between carbon monoxide (CO) column density and black carbon (BC) aerosol optical thickness (AOT), and aerosol types from 4‐channel algorithm and CALIOP measurements are used to validate dust, BC, and aerosol type from MOA, respectively. The agreement of dust pixels between IODI and MOA ranges 0.1 to 0.6 with respect to AOT constraint, and it is inferred that IODI is less sensitive to optically thin dust layer. Increase of the correlation coefficient between AOT and CO column density when BC pixels are taken into account supports the performance of MOA to detect BC aerosol. The agreement of aerosol types from MOA and 4 CA showed reasonable consistency, and the difference can be described by different absorptivity test and retrieval accuracy of...

Correlation between BC and CO from satellite remote sensing

Main sources of both CO and BC are related to incomplete combustion which results from biomass burning and anthropogenic industrial emission, but sinks are different. This research investigates how the correlation between CO and BC varies according to these two sources, season and regions.