Monitoring of snow surface near-infrared bidirectional reflectance factors with added light-absorbing particles

Abstract

Abstract. Broadband snow albedo can range from 0.3 to 0.9 depending on microphysical properties and light-absorbing particle (LAP) concentrations. Beyond the widely observed direct and visibly apparent effect of darkening snow, it is still unclear how LAPs influence snow albedo feedbacks. To investigate LAPs' indirect effect on snow albedo feedbacks, we developed and calibrated the Near-Infrared Emitting and Reflectance-Monitoring Dome (NERD) and monitored bidirectional reflectance factors (BRFs) hourly after depositing dust and black carbon (BC) particles onto experimental snow surfaces. After comparing snow infrared BRFs to snow specific surface areas (SSAs), we found that both measured and modeled snow infrared BRFs are correlated with snow SSA. These results, however, demonstrate a considerable uncertainty of ±10 m2 kg−1 in the determination of snow SSA from our BRF measurements. The nondestructive technique for snow SSA retrieval that we present here can be further developed for science applications that require rapid in situ snow SSA measurements. After adding large amounts of dust and BC to snow, we found more rapid decreasing of snow BRFs and SSAs in snow with added LAPs compared to natural (clean) snow but only during clear-sky conditions. These results suggest that deposition of LAPs onto snow can accelerate snow metamorphism via a net positive snow grain-size feedback.

Further improvement of wet process treatments in GEOS-Chem v12.6.0: impact on global distributions of aerosols and aerosol precursors

“…However, uptake of aerosol by ice and by snow is different. Schneider et al (2019) found specific surface area (SSA) of ice crystals is approximately one-fifth of SSA of snow. Therefore, in this work, we roughly assume washout rate by ice (T < 248 K) is one-fifth of that by snow.…”

“…For EANET observations, due to too much missing data, the criterion is loosened to monthly mean data available for each month during a 3-year period (2010)(2011)(2012). Seto et al (2007) pointed out that EANET observations at urban sites are much higher than those at remote sites. Since the number of the Asian sites is very limited, to make the validation more appropriate, only remote and rural sites are used for model validation.…”

Further improvement of wet process treatments in GEOS-Chem v12.6.0: Impact on global distributions of aerosol precursors and aerosols

“…However, uptake of aerosol by ice and by snow are different Schneider et al (2019). found specific surface area (SSA) of ice crystal is ~1/5 of SSA of snow.…”

The retrieval of snow properties from SLSTR/Sentinel-3 – part 1: method description and sensitivity study

“…Although the global classification snow crystal, ice crystal, and solid precipitation particles suggested in Kikuchi et al (2013) consist of the 121 particle types, we restrict ourselves, in the retrieval algorithm, with nine shapes of ice crystals, for which optical characteristics are represented in database (Yang et al, 2013). And these nine shapes have been proven to be used to reproduce typical wavelength/angular features of snow reflectance in reality, especially from satellite observations (Rä isä nen et al, 2015; Pirazzini et al, 2015;Saito et al, 2019;Schneider et al, 2019;Pohl et al, 2020b). To futher illustrate that the selected dataset is able to reproduce the BRF of different snow types, we compared the simulated and measured BRF of fresh (Dumont et al, 2010) and aged (Peltoniemi et al, 2009) snow samples.…”

“…demonstrated that they can also be used for snow studies (Rä isä nen et al, 2015;Pirazzini et al, 2015;Saito et al, 2019;Schneider et al, 2019;Pohl et al, 2020b). In fact, the single-scattering properties of ice crystals in Yang et al, (2013) database are determined solely by given particle size, shape, and refractive index.…”