Snow Albedo Research Articles

Quantifying the effects of freeze-thaw transitions and snowpack melt on land surface albedo and energy exchange over Alaska and Western Canada

Variations in land surface albedo and snow-cover strongly impact the global biosphere, particularly through the snow-albedo feedback on climate. The seasonal freeze-thaw (FT) transition is coupled with snowpack melt dynamics and strongly impacts surface water mobility and the energy budget in the northern (≥45°N) arctic and boreal region (ABR). However, understanding of the regional variation in snowmelt and its effect on the surface energy budget are limited due to sparse in situ measurements of these processes and environmental constraints on effective monitoring within the ABR. In this study, we combined synergistic observations from overlapping satellite optical-infrared and microwave sensor records to quantify the regional patterns and seasonal progression in wet snow conditions during the spring snowmelt and autumn snow accumulation periods across Alaska and western Canada. The integrated satellite record included daily landscape FT status from AMSR microwave brightness temperature retrievals; and snow-cover extent, black sky albedo and net shortwave solar radiation (Rsnet) derived from MODIS and AVHRR observations. The integrated satellite records were analyzed with in situ surface air temperature and humidity observations from regional weather stations over a two-year study period (2015–2016) overlapping with the NASA ABoVE (Arctic Boreal Vulnerability Experiment). Our results show a large (79%) mean decline in land surface albedo between dry snow and snow-free conditions during the spring (March–June) and autumn (August–November) transition periods. Onset of diurnal thawing and refreezing of the surface snow layer and associated wet snow conditions in spring contributed to an approximate 25% decrease in snow cover albedo that extended over a seven to 21 week snowpack depletion period. The lower wet snow albedo enhances Rsnet by approximately 74% (9–10 MJ m−2 d−1) relative to dry snow conditions, reinforcing snowmelt and surface warming, and contributing to growing season onset and activation of biological and hydrological processes in the ABR. These results contribute to better understanding of snow albedo feedbacks to Arctic amplification, and the representation of these processes in global Earth system models.

Why Do Models Produce Spread in Snow Albedo Feedback?

Key Points Structurally varying snowpack, vegetation, and albedo parameterizations drive most of the intermodel spread in snow albedo feedback Many models now exhibit a more realistic feedback than in the past, but issues with two models limit the reduction in ensemble spread Preliminary signs from ongoing model development are positive and suggest a likely reduction in spread among upcoming models

Spring warming in Yukon mountains is not amplified by the snow albedo feedback

Decreasing spring snow cover may amplify Arctic warming through the snow albedo feedback. To examine the impact of snowmelt on increasing temperature we used a 5,000 m elevation gradient in Yukon, Canada, extending from valley-bottom conifer forests, through middle elevation tundra, to high elevation icefields, to compare validated downscaled reanalysis air temperature patterns across elevational bands characterized by different patterns of spring snowmelt. From 2000 to 2014 we observed surface warming of 0.01 °C/a·1,000 m in May (0.14 °C/a at 1,000 m to 0.19 °C/a at 5,000 m), and uniform cooling of 0.09 °C/a in June at all elevations. May temperature trends across elevationally dependent land cover types were highly correlated with each other despite large variations in albedo and snow cover trends. Furthermore, a clear dependency of infrared skin temperature on snow cover mediated albedo decline was observed in tundra, but this was insufficient to influence average diurnal air temperature. We observed negative June temperature trends which we attribute to increasing daytime cloud cover because albedo and snow cover trends were unchanging. We conclude that 8-day and monthly averaged Spring air temperature trends are responding to a synoptic external forcing that is much stronger than the snow albedo feedback in sub-Arctic mountains.

Albedo of a Small Ice-Covered Boreal Lake: Daily, Meso-Scale and Interannual Variability on the Background of Regional Climate

In winter, the reflective properties of ice-and-snow-covered lakes and the surrounding surface covered with snow differ little. Estimation of the albedo becomes especially important in spring when the reflectivity of the ice-covered lakes differs markedly from the surrounding snow-free surface. The measurements of the snow and ice albedo in situ are necessary to ensure adequate analysis of remote sensing data and monitoring of the state of the land ice. A wide range of surface albedo of small boreal ice-covered Lake Vendyurskoe (North-Western Russia) is calculated from observational data of solar radiation obtained in 1995–2000 and 2002–2017. A noticeable variability of albedo due to spring melting and changes in weather conditions is described. Climatic variability (North Atlantic Oscillation (NAO) index, the average monthly air temperature and sum of precipitation in December-March) is analyzed in the context of possible influence on the maximal thickness of snow and white ice, and, consequently, on the spring lake albedo dynamics. In current work to estimate the external heat flux that comes from the atmosphere to the surface of the lake and presumably determines the dynamics of albedo during spring melting, a simple parameter was used: the accumulated sum of positive temperatures, starting from March 1. The obtained prognostic equation of the cubic dependence of albedo on the named parameter demonstrates a different rate of albedo decrease in the melting of snow, white, and congelation ice.

Spring snow albedo feedback over northern Eurasia: Comparing in situ measurements with reanalysis products

Abstract. This study uses daily observations and modern reanalyses in order to evaluate reanalysis products over northern Eurasia regarding the spring snow albedo feedback (SAF) during the period from 2000 to 2013. We used the state-of-the-art reanalyses from ERA-Interim/Land and the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) as well as an experimental set-up of ERA-Interim/Land with prescribed short grass as land cover to enhance the comparability with the station data while underlining the caveats of comparing in situ observations with gridded data. Snow depth statistics derived from daily station data are well reproduced in all three reanalyses. However day-to-day albedo variability is notably higher at the stations than for any reanalysis product. The ERA-Interim grass set-up shows improved performance when representing albedo variability and generates comparable estimates for the snow albedo in spring. We find that modern reanalyses show a physically consistent representation of SAF, with realistic spatial patterns and area-averaged sensitivity estimates. However, station-based SAF values are significantly higher than in the reanalyses, which is mostly driven by the stronger contrast between snow and snow-free albedo. Switching to grass-only vegetation in ERA-Interim/Land increases the SAF values up to the level of station-based estimates. We found no significant trend in the examined 14-year time series of SAF, but interannual changes of about 0.5 % K−1 in both station-based and reanalysis estimates were derived. This interannual variability is primarily dominated by the variability in the snowmelt sensitivity, which is correctly captured in reanalysis products. Although modern reanalyses perform well for snow variables, efforts should be made to improve the representation of dynamic albedo changes.

The importance of snow albedo for ice sheet evolution over the last glacial cycle

Abstract. The surface energy and mass balance of ice sheets strongly depends on the amount of solar radiation absorbed at the surface, which is mainly controlled by the albedo of snow and ice. Here, using an Earth system model of intermediate complexity, we explore the role played by surface albedo for the simulation of glacial cycles. We show that the evolution of the Northern Hemisphere ice sheets over the last glacial cycle is very sensitive to the representation of snow albedo in the model. It is well known that the albedo of snow depends strongly on snow grain size and the content of light-absorbing impurities. Excluding either the snow aging effect or the dust darkening effect on snow albedo leads to an excessive ice build-up during glacial times and consequently to a failure in simulating deglaciation. While the effect of snow grain growth on snow albedo is well constrained, the albedo reduction due to the presence of dust in snow is much more uncertain because the light-absorbing properties of dust vary widely as a function of dust mineral composition. We also show that assuming slightly different optical properties of dust leads to very different ice sheet and climate evolutions in the model. Conversely, ice sheet evolution is less sensitive to the choice of ice albedo in the model. We conclude that a proper representation of snow albedo is a fundamental prerequisite for a successful simulation of glacial cycles.

Influence of canopy shading and snow coverage on effective albedo in a snow-dominated evergreen needleleaf forest

The presence of a forest canopy above highly reflective snow results in overall lower surface albedo, even when snow is intercepted by the forest canopy. The effective forest snow albedo (FSA) explains the overall upwelling radiation from the forest relative to the incoming radiation. FSA is strongly influenced by the complex pathways of radiation as it travels through the 3D canopy structure. Current errors in calculations of FSA arise due to uncertainties in how models should treat masking of snow by vegetation. Improvement of distributed models is currently limited by a lack of measurements that demonstrate both spatial and temporal variability over forests. We present above-canopy measurements of winter-time effective forest snow albedo using up- and down-looking radiometers mounted on an octocopter UAV for a total of fifteen flights on eight different days. Ground-view fractions across the flight path were between 0.12 and 0.81. Correlations between FSA and both ground-view fraction and maximum canopy height were statistically significant during 14 out of 15 flights, but correlation strength varied between flights as a function of solar angle and snow cover. Measured effective albedo across the flight path differed by up to 0.33 during snow-on canopy conditions. A subsequent comparison between maximum interception and no interception showed effective albedo differed by up 0.27. A similar variation (0.26) in effective FSA was measured during low (44°) and high (67°) solar zenith angles. This study therefore demonstrates that temporal and spatial variations in effective albedo caused by canopy shading of the snow surface are therefore as important as temporal variations caused by interception of snow by the canopy. Calculation of effective albedo of forested areas requires careful consideration of canopy height, canopy coverage, solar angle and interception coverage. The results of this study should be used to inform snow albedo and canopy structure parametrizations in local and larger scale land surface models.

Assessing Satellite‐Derived Radiative Forcing From Snow Impurities Through Inverse Hydrologic Modeling

AbstractLight‐absorbing impurities in snow and ice (LAISI) lower the snow albedo and cause accelerated snowmelt. The radiative forcing caused by LAISI is in this connection the key variable in understanding LAISI‐snowpack dynamics. Here we present an approach combining distributed hydrologic model simulations and remotely sensed radiative forcing from LAISI in order to improve model predictions of radiative forcing impacts. In a case study, we assess the seasonal cycle of instantaneous at‐surface clear‐sky radiative forcing from LAISI as predicted by model and satellite observations for a river basin located at the southern slope of the Himalayas. By scaling dust depositions, we optimize simulated radiative forcing conditioned on satellite observations. The optimized model predicts that LAISI‐induced radiative forcing in snow contributes to 4.1% to 5.8% of the annual discharge. The presented approach has a wide range of applications as it provides a novel method to constrain and evaluate measures of LAISI‐induced radiative forcing.

Global radiative effects of solid fuel cookstove aerosol emissions

Abstract. We apply the NCAR CAM5-Chem global aerosol-climate model to quantify the net global radiative effects of black and organic carbon aerosols from global and Indian solid fuel cookstove emissions for the year 2010. Our assessment accounts for the direct radiative effects, changes to cloud albedo and lifetime (aerosol indirect effect, AIE), impacts on clouds via the vertical temperature profile (semi-direct effect, SDE) and changes in the surface albedo of snow and ice (surface albedo effect). In addition, we provide the first estimate of household solid fuel black carbon emission effects on ice clouds. Anthropogenic emissions are from the IIASA GAINS ECLIPSE V5a inventory. A global dataset of black carbon (BC) and organic aerosol (OA) measurements from surface sites and aerosol optical depth (AOD) from AERONET is used to evaluate the model skill. Compared with observations, the model successfully reproduces the spatial patterns of atmospheric BC and OA concentrations, and agrees with measurements to within a factor of 2. Globally, the simulated AOD agrees well with observations, with a normalized mean bias close to zero. However, the model tends to underestimate AOD over India and China by ∼ 19 ± 4 % but overestimate it over Africa by ∼ 25 ± 11 % (± represents modeled temporal standard deviations for n = 5 run years). Without BC serving as ice nuclei (IN), global and Indian solid fuel cookstove aerosol emissions have net global cooling radiative effects of −141 ± 4 mW m−2 and −12 ± 4 mW m−2, respectively (± represents modeled temporal standard deviations for n = 5 run years). The net radiative impacts are dominated by the AIE and SDE mechanisms, which originate from enhanced cloud condensation nuclei concentrations for the formation of liquid and mixed-phase clouds, and a suppression of convective transport of water vapor from the lower troposphere to the upper troposphere/lower stratosphere that in turn leads to reduced ice cloud formation. When BC is allowed to behave as a source of IN, the net global radiative impacts of the global and Indian solid fuel cookstove emissions range from −275 to +154 mW m−2 and −33 to +24 mW m−2, with globally averaged values of −59 ± 215 and 0.3 ± 29 mW m−2, respectively. Here, the uncertainty range is based on sensitivity simulations that alter the maximum freezing efficiency of BC across a plausible range: 0.01, 0.05 and 0.1. BC–ice cloud interactions lead to substantial increases in high cloud (< 500 hPa) fractions. Thus, the net sign of the impacts of carbonaceous aerosols from solid fuel cookstoves on global climate (warming or cooling) remains ambiguous until improved constraints on BC interactions with mixed-phase and ice clouds are available.

An Assessment of High-Resolution Gridded Temperature Datasets over California

Abstract High-resolution gridded datasets are in high demand because they are spatially complete and include important finescale details. Previous assessments have been limited to two to three gridded datasets or analyzed the datasets only at the station locations. Here, eight high-resolution gridded temperature datasets are assessed two ways: at the stations, by comparing with Global Historical Climatology Network–Daily data; and away from the stations, using physical principles. This assessment includes six station-based datasets, one interpolated reanalysis, and one dynamically downscaled reanalysis. California is used as a test domain because of its complex terrain and coastlines, features known to differentiate gridded datasets. As expected, climatologies of station-based datasets agree closely with station data. However, away from stations, spread in climatologies can exceed 6°C. Some station-based datasets are very likely biased near the coast and in complex terrain, due to inaccurate lapse rates. Many station-based datasets have large unphysical trends (>1°C decade−1) due to unhomogenized or missing station data—an issue that has been fixed in some datasets by using homogenization algorithms. Meanwhile, reanalysis-based gridded datasets have systematic biases relative to station data. Dynamically downscaled reanalysis has smaller biases than interpolated reanalysis, and has more realistic variability and trends. Dynamical downscaling also captures snow–albedo feedback, which station-based datasets miss. Overall, these results indicate that 1) gridded dataset choice can be a substantial source of uncertainty, and 2) some datasets are better suited for certain applications.

Concentrations and source regions of light-absorbing particles in snow/ice in northern Pakistan and their impact on snow albedo

Abstract. Black carbon (BC), water-insoluble organic carbon (OC), and mineral dust are important particles in snow and ice which significantly reduce albedo and accelerate melting. Surface snow and ice samples were collected from the Karakoram–Himalayan region of northern Pakistan during 2015 and 2016 in summer (six glaciers), autumn (two glaciers), and winter (six mountain valleys). The average BC concentration overall was 2130 ± 1560 ng g−1 in summer samples, 2883 ± 3439 ng g−1 in autumn samples, and 992 ± 883 ng g−1 in winter samples. The average water-insoluble OC concentration overall was 1839 ± 1108 ng g−1 in summer samples, 1423 ± 208 ng g−1 in autumn samples, and 1342 ± 672 ng g−1 in winter samples. The overall concentration of BC, OC, and dust in aged snow samples collected during the summer campaign was higher than the concentration in ice samples. The values are relatively high compared to reports by others for the Himalayas and the Tibetan Plateau. This is probably the result of taking more representative samples at lower elevation where deposition is higher and the effects of ageing and enrichment are more marked. A reduction in snow albedo of 0.1–8.3 % for fresh snow and 0.9–32.5 % for aged snow was calculated for selected solar zenith angles during daytime using the Snow, Ice, and Aerosol Radiation (SNICAR) model. The daily mean albedo was reduced by 0.07–12.0 %. The calculated radiative forcing ranged from 0.16 to 43.45 W m−2 depending on snow type, solar zenith angle, and location. The potential source regions of the deposited pollutants were identified using spatial variance in wind vector maps, emission inventories coupled with backward air trajectories, and simple region-tagged chemical transport modeling. Central, south, and west Asia were the major sources of pollutants during the sampling months, with only a small contribution from east Asia. Analysis based on the Weather Research and Forecasting (WRF-STEM) chemical transport model identified a significant contribution (more than 70 %) from south Asia at selected sites. Research into the presence and effect of pollutants in the glaciated areas of Pakistan is economically significant because the surface water resources in the country mainly depend on the rivers (the Indus and its tributaries) that flow from this glaciated area.

The regional climate model REMO (v2015) coupled with the 1-D freshwater lake model FLake (v1): Fenno-Scandinavian climate and lakes

Abstract. The regional climate model REMO was coupled with the FLake lake model to include an interactive treatment of lakes. Using this new version, the Fenno-Scandinavian climate and lake characteristics were studied in a set of 35-year hindcast simulations. Additionally, sensitivity tests related to the parameterization of snow albedo were conducted. Our results show that overall the new model version improves the representation of the Fenno-Scandinavian climate in terms of 2 m temperature and precipitation, but the downside is that an existing wintertime cold bias in the model is enhanced. The lake surface water temperature, ice depth and ice season length were analyzed in detail for 10 Finnish, 4 Swedish and 2 Russian lakes and 1 Estonian lake. The results show that the model can reproduce these characteristics with reasonably high accuracy. The cold bias during winter causes overestimation of ice layer thickness, for example, at several of the studied lakes, but overall the values from the model are realistic and represent the lake physics well in a long-term simulation. We also analyzed the snow depth on ice from 10 Finnish lakes and vertical temperature profiles from 5 Finnish lakes and the model results are realistic.

A Multilayer Surface Temperature, Surface Albedo and Water Vapor Product of Greenland from MODIS.

A multilayer, daily ice-surface temperature (IST)-albedo-water vapor product of Greenland, extending from March 2000 through December 2016, has been developed using standard MODerate-resolution Imaging Spectroradiometer (MODIS) data products from the Terra satellite. To meet the needs of the ice sheet modeling community, this new Earth Science Data Record (ESDR) is provided in a polar stereographic projection in NetCDF format, and includes the existing standard MODIS Collection-6.1 IST and derived melt maps, and Collection 6 snow albedo and water vapor maps, along with ancillary data, and is provided at a spatial resolution of ~0.78 km. This ESDR enables relationships between IST, surface melt, albedo and water vapor to be evaluated easily. We show examples of the components of the ESDR and describe some uses of the ESDR such as for comparison with skin temperature, albedo and water vapor output from Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2). Additionally we show validation of the MODIS IST using in situ and aircraft data, and validation of MERRA-2 skin temperature maps using MODIS IST and in situ data. The ESDR has been assigned a DOI and will be available through the National Snow and Ice Data Center by the summer of 2018.

Simulated cold bias being improved by using MODIS time-varying albedo in the Tibetan Plateau in WRF model

Systematic cold biases exist in the simulation for 2 m air temperature in the Tibetan Plateau (TP) when using regional climate models and global atmospheric general circulation models. We updated the albedo in the Weather Research and Forecasting (WRF) Model lower boundary condition using the Global LAnd Surface Satellite Moderate-Resolution Imaging Spectroradiometer albedo products and demonstrated evident improvement for cold temperature biases in the TP. It is the large overestimation of albedo in winter and spring in the WRF model that resulted in the large cold temperature biases. The overestimated albedo was caused by the simulated precipitation biases and over-parameterization of snow albedo. Furthermore, light-absorbing aerosols can result in a large reduction of albedo in snow and ice cover. The results suggest the necessity of developing snow albedo parameterization using observations in the TP, where snow cover and melting are very different from other low-elevation regions, and the influence of aerosols should be considered as well. In addition to defining snow albedo, our results show an urgent call for improving precipitation simulation in the TP.

Resolving Size Distribution of Black Carbon Internally Mixed With Snow: Impact on Snow Optical Properties and Albedo

AbstractWe develop a stochastic aerosol‐snow albedo model that explicitly resolves size distribution of aerosols internally mixed with various snow grains. We use the model to quantify black carbon (BC) size effects on snow albedo and optical properties for BC‐snow internal mixing. Results show that BC‐induced snow single‐scattering coalbedo enhancement and albedo reduction decrease by a factor of 2–3 with increasing BC effective radii from 0.05 to 0.25 μm, while polydisperse BC results in up to 40% smaller visible single‐scattering coalbedo enhancement and albedo reduction compared to monodisperse BC with equivalent effective radii. We further develop parameterizations for BC size effects for application to climate models. Compared with a realistic polydisperse assumption and observed shifts to larger BC sizes in snow, respectively, assuming monodisperse BC and typical atmospheric BC effective radii could lead to overestimates of ~24% and ~40% in BC‐snow albedo forcing averaged over different BC and snow conditions.

Snow cover and snow albedo changes in the central Andes of Chile and Argentina from daily MODIS observations (2000–2016)

The variables of snow cover extent (SCE), snow cover duration (SCD), and snow albedo (SAL) are primary factors determining the surface energy balance and hydrological response of the cryosphere, influencing snow pack and glacier mass-balance, melt, and runoff conditions. This study examines spatiotemporal patterns and trends in SCE, SCD, and SAL (2000–2016; 16 years) for central Chilean and Argentinean Andes using the MODIS MOD10A1 C6 daily snow product. Observed changes in these variables are analyzed in relation to climatic variability by using ground truth observations (meteorological data from the El Yeso Embalse and Valle Nevado weather stations) and the Multivariate El Niño index (MEI) data. We identified significant downward trends in both SCE and SAL, especially during the onset and offset of snow seasons. SCE and SAL showed high inter-annual variability which correlate significantly with MEI applied with a one-month time-lag. SCE and SCD decreased by an average of ~13 ± 2% and 43 ± 20 days respectively, over the study period. Analysis of spatial pattern of SCE indicates a slightly greater reduction on the eastern side (~14 ± 2%) of the Andes Cordillera compared to the western side (~12 ± 3%). The downward SCE, SAL, and SCD trends identified in this study are likely to have adverse impacts on downstream water resource availability to agricultural and densely populated regions in central Chile and Argentina.

Bioavailability of Mineral-Bound Iron to a Snow Algal-Bacterial Coculture and Implications for Albedo-Altering Snow Algal Blooms.

Snow algae can form large-scale blooms across the snowpack surface and near-surface environments. These pigmented blooms can decrease snow albedo and increase local melt rates, and they may impact the global heat budget and water cycle. Yet, the underlying causes for the geospatial occurrence of these blooms remain unconstrained. One possible factor contributing to snow algal blooms is the presence of mineral dust as a micronutrient source. We investigated the bioavailability of iron (Fe)-bearing minerals, including forsterite (Fo90, Mg1.8Fe0.2SiO4), goethite, smectite, and pyrite as Fe sources for a Chloromonas brevispina-bacterial coculture through laboratory-based experimentation. Fo90 was capable of stimulating snow algal growth and increased the algal growth rate in otherwise Fe-depleted cocultures. Fo90-bearing systems also exhibited a decrease in the ratio of bacteria to algae compared to those of Fe-depleted conditions, suggesting a shift in microbial community structure. The C. brevispina coculture also increased the rate of Fo90 dissolution relative to that of an abiotic control. Analysis of 16S rRNA genes in the coculture identified Gammaproteobacteria, Betaproteobacteria, and Sphingobacteria, all of which are commonly found in snow and ice environments. Archaea were not detected. Collimonas and Pseudomonas, which are known to enhance mineral weathering rates, comprised two of the top eight (>1%) operational taxonomic units (OTUs). These data provide unequivocal evidence that mineral dust can support elevated snow algal growth under otherwise Fe-depleted growth conditions and that snow algal microbial communities can enhance mineral dissolution under these conditions.IMPORTANCE Fe, a key micronutrient for photosynthetic growth, is necessary to support the formation of high-density snow algal blooms. The laboratory experiments described herein allow for a systematic investigation of the interactions of snow algae, bacteria, and minerals and their ability to mobilize and uptake mineral-bound Fe. Results provide unequivocal and comprehensive evidence that mineral-bound Fe in Fe-bearing Fo90 was bioavailable to Chloromonas brevispina snow algae within an algal-bacterial coculture. This evidence includes (i) an observed increase in snow algal density and growth rate, (ii) decreased ratios of bacteria to algae in Fo90-containing cultures relative to those of cultures grown under similarly Fe-depleted conditions with no mineral-bound Fe present, and (iii) increased Fo90 dissolution rates in the presence of algal-bacterial cocultures relative to those of abiotic mineral controls. These results have important implications for the role of mineral dust in supplying micronutrients to the snow microbiome, which may help support dense snow algal blooms capable of lowering snow albedo and increasing snow melt rates on regional, and possibly global, scales.

Scavenging ratio of black carbon in the Arctic and the Antarctic

Long-term monitoring of atmospheric aerosols and their interaction with radiation, cloud, and cryosphere over the Arctic and the Antarctic are very important for the global climate change related issues. In this regard, for conducting aerosol measurements, India has extended the concerted efforts to the Svalbard region of the Norwegian Arctic (Himadri, 78°55′N 11°56′E, 8 m a.s.l.) in the northern hemisphere and the Larsemann Hills of coastal Antarctic (Bharati, 69°24.4′S 76°11.7′E, 40 m a.s.l.) in the southern hemisphere. In the present study, we have examined the role of black carbon (BC) deposition in darkening the polar snow in different sunlit seasons and estimated the scavenging ratio of BC over both the poles from simultaneous measurements of atmospheric and snow deposited BC concentrations. The study reveals distinct spatio-temporal variability of BC in polar snow, even though the concentrations are, in general, low (<12 ppbw, parts per billion by weight). During local summer seasons, the BC in snow at the Arctic (median ∼ 7.98 ppbw) was higher than that at the Antarctica (median ∼ 1.70 ppbw). Concurrent with this, the scavenging ratio (SR) also showed large variability over both the poles. Relatively higher values of SR over the Antarctica (mean ∼ 119.54 ± 23.04; during southern hemispheric summer) in comparison to that over the Arctic (mean ∼ 69.48 ± 4.79; during northern hemispheric spring) clearly indicate the difference in removal mechanisms (aerosol mixing, aging and size distribution) of BC from the atmosphere over distinct polar environments. Measurement of spectral incoming and reflected radiances over the Arctic snow during the early spring season of 2017 indicated the values of surface broadband albedo varying between 0.64 and 0.79. The Snow, Ice and Aerosol Radiative (SNICAR) model simulated values of spectral albedo correlated well with the measured ones and indicated the role of dust absorption, in addition to that of BC, in changing the snow albedo. This information needs to be accurately incorporated in the radiative transfer models for the accurate estimation of snow albedo forcing over the Polar Regions.

Hourly mass and snow energy balance measurements from Mammoth Mountain, CA USA, 2011–2017

Abstract. The mass and energy balance of the snowpack govern its evolution. Direct measurement of these fluxes is essential for modeling the snowpack, yet there are few sites where all the relevant measurements are taken. Mammoth Mountain, CA USA, is home to the Cold Regions Research and Engineering Laboratory and University of California – Santa Barbara Energy Site (CUES), one of five energy balance monitoring sites in the western US. There is a ski patrol study site on Mammoth Mountain, called the Sesame Street Snow Study Plot, with automated snow and meteorological instruments where new snow is hand-weighed to measure its water content. There is also a site at Mammoth Pass with automated precipitation instruments. For this dataset, we present a clean and continuous hourly record of selected measurements from the three sites covering the 2011–2017 water years. Then, we model the snow mass balance at CUES and compare model runs to snow pillow measurements. The 2011–2017 period was marked by exceptional variability in precipitation, even for an area that has high year-to-year variability. The driest year on record, and one of the wettest years, occurred during this time period, making it ideal for studying climatic extremes. This dataset complements a previously published dataset from CUES containing a smaller subset of daily measurements. In addition to the hand-weighed SWE, novel measurements include hourly broadband snow albedo corrected for terrain and other measurement biases. This dataset is available with a digital object identifier: https://doi.org/10.21424/R4159Q.

Light-absorption of dust and elemental carbon in snow in the Indian Himalayas and the Finnish Arctic

Abstract. Light-absorbing impurities (LAIs) deposited in snow have the potential to substantially affect the snow radiation budget, with subsequent implications for snow melt. To more accurately quantify the snow albedo, the contribution from different LAIs needs to be assessed. Here we estimate the main LAI components, elemental carbon (EC) (as a proxy for black carbon) and mineral dust in snow from the Indian Himalayas and paired the results with snow samples from Arctic Finland. The impurities are collected onto quartz filters and are analyzed thermal–optically for EC, as well as with an additional optical measurement to estimate the light-absorption of dust separately on the filters. Laboratory tests were conducted using substrates containing soot and mineral particles, especially prepared to test the experimental setup. Analyzed ambient snow samples show EC concentrations that are in the same range as presented by previous research, for each respective region. In terms of the mass absorption cross section (MAC) our ambient EC surprisingly had about half of the MAC value compared to our laboratory standard EC (chimney soot), suggesting a less light absorptive EC in the snow, which has consequences for the snow albedo reduction caused by EC. In the Himalayan samples, larger contributions by dust (in the range of 50 % or greater for the light absorption caused by the LAI) highlighted the importance of dust acting as a light absorber in the snow. Moreover, EC concentrations in the Indian samples, acquired from a 120 cm deep snow pit (possibly covering the last five years of snow fall), suggest an increase in both EC and dust deposition. This work emphasizes the complexity in determining the snow albedo, showing that LAI concentrations alone might not be sufficient, but additional transient effects on the light-absorbing properties of the EC need to be considered and studied in the snow. Equally as imperative is the confirmation of the spatial and temporal representativeness of these data by comparing data from several and deeper pits explored at the same time.