Shortcomings and uncertainties in the model representation of atmospheric transformations (the aging) of organic aerosol (OA) have long been identified as one of the potential sources of considerable uncertainty in OA simulations with both global and regional models. However, the impact of this uncertainty on predictions of radiative and climate effects of both anthropogenic and biomass burning (BB) aerosol yet needs to be understood. This study examines the importance of the model representation of OA for simulating the direct radiative effect (DRE) of Siberian BB aerosol in the eastern Arctic. We employ a regional coupled chemistry-meteorology model and a global fire emission database to simulate the optical properties and DRE of BB aerosol emitted from intense Siberian fires in July 2016 and compare the DRE estimates that were obtained using two alternative representations of Siberian BB OA. One of them is a default OA representation that predicts very little secondary OA (SOA), and another involves a simple original OA parameterization that has been developed previously within the volatility basis set (VBS) framework and features a strong production of SOA. The simulations of the aerosol optical properties are evaluated against satellite observations of the aerosol optical depth (AOD) in Siberia and the Arctic as well as against values of the single scattering albedo derived from in situ observations of the aerosol absorption and scattering coefficients at four Arctic sites. While the simulations with the default OA representation are found to strongly underestimate AOD both in Siberia and the eastern Arctic, the use of the VBS parameterization considerably improves the agreement between the AOD simulations and observations in both regions. Simulations of the single scattering albedo are found to be overall rather adequate with both representations. Differences in the OA representations are found to result in major differences in the estimates of the DRE of Siberian BB aerosol in the eastern Arctic. Specifically, although the simulations with both representations predict that the DRE is predominantly negative at the top of the atmosphere (TOA), the magnitude of the mean DRE is found to be more than twice as large (6.0 W m-2) with the VBS parameterization than with the default OA representation (2.8 W m-2). An even larger difference (by a factor of 3.5) is found between the estimates of the DRE over the snow-or ice-covered areas. The different treatments of the BB OA evolution are associated also with considerably different contributions of black and brown carbon to the DRE estimates. Overall, our results indicate that model estimates of the DRE of Siberian BB aerosol in the eastern Arctic are strongly sensitive to the assumptions regarding the evolution of OA in Siberian BB plumes and that the SOA formation in these plumes is one of the major factors determining the magnitude of the radiative effects of Siberian BB aerosol in the real atmosphere.

Studies on optical properties of aerosols can reduce the uncertainty for modelling direct radiative forcing (DRF) and improve the accuracy for discussing aerosols effects on the Tibetan Plateau (TP) climate. This study investigated the spatiotemporal variation of aerosol optical and microphysical properties over TP based on OMI and MERRA2, and assessed the influence of aerosol optical properties on DRF at NamCo station (30 degrees 46.440N, 90 degrees 59.310E, 4730 m) in the central TP from 2006 to 2017 based on a long measurement of AERONET and the modelling of SBDART model. The results show that aerosol optical depth (AOD) exhibits obvious seasonal variation over TP, with higher AOD500nm (>0.75) during spring and summer, and lower value (<0.25) in autumn and winter. The aerosol concentrations show a fluctuated rising from 1980 to 2000, significant increasing from 2000 to 2010 and slight declining trend after 2013. Based on sensitivity experiments, it is found that AOD and single scattering albedo (SSA) have more important impact on the DRF compared with a values and ASY. When AOD440nm increases by 60%, DRF at the TOA and ATM is increased by 57.2% and 60.2%, respectively. When SSA440nm increases by 20%, DRF at the TOA and ATM decreases by 121% and 96.7%, respectively. (c) 2022 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

The uncertainty of passive microwave retrievals of snowfall is notoriously high where high-frequency surface emissivity is significantly reduced and varies markedly in response to the changes in snowpack physical properties. Using the dense media radiative transfer theory, this article studies the potential effects of terrestrial snow-cover depth, density, and grain size on high-frequency channels 89 and 166 GHz of the radiometer onboard the Global Precipitation Measurement (GPM) core satellite, which are commonly used to capture snowfall scattering signals. Integrating the inference across all feasible grain sizes, ranges of snowpack density and depth are identified over which snowfall scattering signatures can be time-varying and potentially obscured. Using ten years of reanalysis data, the seasonal vulnerability of snowfall retrievals to the changes in snowpack emissivity in the Northern Hemisphere is mapped in a probabilistic sense and connections are made with the uncertainties of the GPM passive microwave snowfall retrievals. It is found that among different snow classes, relatively light Arctic tundra snow in fall, with a density below 260 kg m(-3), and shallow prairie snow during the winter, with a depth of less than 40 cm, can reduce the surface emissivity and obscure the snowfall passive microwave signatures. It is demonstrated that during winter, the highly vulnerable areas are over Kazakhstan and Mongolia with taiga and prairie snow. In the fall, these areas are largely over tundra and taiga snow in north of Russia and the Arctic Archipelagos as well as prairies in Canada and the Great Plains in the United States.

We present the first box model simulation results aimed at identification of possible effects of the atmospheric photochemical evolution of the organic component of biomass burning (BB) aerosol on the aerosol radiative forcing (ARF) and its efficiency (ARFE). The simulations of the dynamics of the optical characteristics of the organic aerosol (OA) were performed using a simple parameterization developed within the volatility basis set framework and adapted to simulate the multiday BB aerosol evolution in idealized isolated smoke plumes from Siberian fires (without dilution). Our results indicate that the aerosol optical depth can be used as a good proxy for studying the effect of the OA evolution on the ARF, but variations in the scattering and absorbing properties of BB aerosol can also affect its radiative effects, as evidenced by variations in the ARFE. Changes in the single scattering albedo (SSA) and asymmetry factor, which occur as a result of the BB OA photochemical evolution, may either reduce or enhance the ARFE as a result of their competing effects, depending on the initial concentration OA, the ratio of black carbon to OA mass concentrations and the aerosol photochemical age in a complex way. Our simulation results also reveal that (1) the ARFE at the top of the atmosphere is not significantly affected by the OA oxidation processes compared to the ARFE at the bottom of the atmosphere, and (2) the dependence of ARFE in the atmospheric column and on the BB aerosol photochemical ages almost mirrors the corresponding dependence of SSA.

Using a balance model of the snow layer, we estimate the concentration of black carbon (BC) in the snow; then, with the help of radiative transfer model SNICAR, we calculate the snow albedo and radiative forcing (RF) from snow darkening with BC. Data from an ensemble simulation with INMCM5, the 5th version of the climate model of Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences for the period 1998-2002 are used as input, which include snow both on land and on sea ice. The regionally averaged results are compared with other model data and field measurements.

The broadband surface albedo of snow can greatly be reduced by the deposition of light-absorbing impurities, such as black carbon on or near its surface. Such a reduction increases the absorption of solar radiation and may initiate or accelerate snowmelt and snow albedo feedback. Coincident measurements of both black carbon concentration and broadband snow albedo may be difficult to obtain in field studies; however, using the relationship developed in this simple model sensitivity study, black carbon mass densities deposited can be estimated from changes in measured broadband snow albedo, and vice versa. Here, the relationship between the areal mass density of black carbon found near the snow surface to the amount of albedo reduction was investigated using the popular snow radiative transfer model Snow, Ice, and Aerosol Radiation (SNICAR). We found this relationship to be linear for realistic amounts of black carbon mass concentrations, such as those found in snow at remote locations. We applied this relationship to measurements of broadband albedo in the Chilean Andes to estimate how vehicular emissions contributed to black carbon (BC) deposition that was previously unquantified.

The ability of light-absorbing impurities (LAI) to darken snow had been known for decades, even inspiring practical applications, but quantification of the radiative forcing awaited radiative-transfer modeling in 1980 and measurement of soot in Arctic snow in 1983-4. Climate-modeling interest in this forcing began in 2004, spurring a modern explosion of research on several topics: methods to measure black carbon (BC) and other LAI, Arctic air pollution, measurement of BC mixing ratio in snow over large areas, and radiative transfer modeling of this forcing and its climatic and hydrological effects. The BC-content of snow in large remote regions of the northern hemisphere is on the order of 20 parts per billion, causing albedo reductions of similar to 1-2%. This reduction is climatically significant but difficult to detect by remote sensing, so quantification requires fieldwork to collect and analyze snow samples. This review is a personal account of early research at the National Center for Atmospheric Research and the University of Washington, followed by a brief summary of recent work by the author and his colleagues.

High-latitude areas are very sensitive to global warming, which has significant impacts on soil temperatures and associated processes governing permafrost evolution. This study aims to improve first-layer soil temperature retrievals during winter. This key surface state variable is strongly affected by snow's geophysical properties and their associated uncertainties (e.g., thermal conductivity) in land surface climate models. We used infrared MODIS land-surface temperatures (LST) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) brightness temperatures (Tb) at 10.7 and 18.7 GHz to constrain the Canadian Land Surface Scheme (CLASS), driven by meteorological reanalysis data and coupled with a simple radiative transfer model. The Tb polarization ratio (horizontal/vertical) at 10.7 GHz was selected to improve snowpack density, which is linked to the thermal conductivity representation in the model. Referencing meteorological station soil temperature measurements, we validated the approach at four different sites in the North American tundra over a period of up to 8 years. Results show that the proposed method improves simulations of the soil temperature under snow (Tg) by 64% when using remote sensing (RS) data to constrain the model, compared to model outputs without satellite data information. The root mean square error (RMSE) between measured and simulated Tg under the snow ranges from 1.8 to 3.5 K when using RS data. Improved temporal monitoring of the soil thermal state, along with changes in snow properties, will improve our understanding of the various processes governing soil biological, hydrological, and permafrost evolution.

The influence of Arctic vegetation on albedo, latent and sensible heat fluxes, and active layer thickness is a crucial link between boundary layer climate and permafrost in the context of climate change. Shrubs have been observed to lower the albedo as compared to lichen or graminoid-tundra. Despite its importance, the quantification of the effect of shrubification on summer albedo has not been addressed in much detail. We manipulated shrub density and height in an Arctic dwarf birch (Betula nana) shrub canopy to test the effect on shortwave radiative fluxes and on the normalized difference vegetation index (NDVI), a proxy for vegetation productivity used in satellite-based studies. Additionally, we parametrised and validated the 3D radiative transfer model DART to simulate the amount of solar radiation reflected and transmitted by an Arctic shrub canopy. We compared results of model runs of different complexities to measured data from North-East Siberia. We achieved comparably good results with simple turbid medium approaches, including both leaf and branch optical property media, and detailed object based model parameterisations. It was important to explicitly parameterise branches as they accounted for up to 71% of the total canopy absorption and thus contributed significantly to soil shading. Increasing leaf biomass resulted in a significant increase of the NDVI, decrease of transmitted photosynthetically active radiation, and repartitioning of the absorption of shortwave radiation by the canopy components. However, experimental and modelling results show that canopy broadband nadir reflectance and albedo are not significantly decreasing with increasing shrub biomass. We conclude that the leaf to branch ratio, canopy background, and vegetation type replaced by shrubs need to be considered when predicting feedbacks of shrubification to summer albedo, permafrost thaw, and climate warming. (C) 2014 Elsevier Inc. All rights reserved.

Aerosol radiative forcing (ARE) over intense mining area in Indian monsoon trough region, computed based on the aerosol optical properties obtained through Prede (POM-1L) sky radiometer and radiative transfer model, are analysed for the year 2011 based on 21 clear sky days spread through seasons. Due to active mining and varied minerals ARF is expected to be significantly modulated by single scattering albedo (SSA). Our studies show that radiative forcing normalized by aerosol optical depth (ADD) is highly correlated with SSA (0.96) while ARF at the surface with AOD by 0.92. Our results indicate that for a given AOD, limits or range of ARF are determined by SSA, hence endorses the need to obtain SSA accurately, preferably derived through observations concurrent with AOD. Noticeably, ARE at the top-of the atmosphere is well connected to SSA (r = 0.77) than AOD (r = 0.6). Relation between observed black carbon and SSA are investigated. A possible over estimation of SSA by the inversion algorithm, SKYRAD.pack 4.2, used in the current study is also discussed. Choice of atmospheric profiles deviating from tropical to mid altitude summer or winter does not appear to be sensitive in ARE calculation by SBDART. Based on the 21 clear sky days, a multiple linear regression equation is obtained for ARF(bot) as a function of AOD and SSA with a bias of +/- 2.7 Wm(-2). This equation is verified with an independent data set of seasonal mean AOD and SSA to calculate seasonal ARF that compares well with the modeled ARE within +/- 4 Wm(-2). (C) 2013 Elsevier Ltd. All rights reserved.