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In recent decades, surface melting on the inland Greenland ice sheet has increased, leading to significant meltwater-refreezing in the snow and firn. Increased knowledge of the physical and chemical characteristics of these melt features (i.e., ice layers) is needed to help estimate future global sea-level rise. Here, using a combination of microscopy and spectroscopy, we investigate the size, shape, location, and chemical compositions of inclusions in 12 ice layers of the SIGMA-A ice core in the northwestern Greenland ice sheet (78°03′06″N, 67°37′42″W, 1490 m a.s.l.). In the ice layers, we found inclusions exceeding 30 μm diameter that could only be formed by melting–refreezing, which we classify into columnar-, particle-, and rod-like inclusions. We propose that the smaller columnar- and particle-like inclusions of solid Na2SO4⋅10H2O and CaSO4⋅2H2O form first, within the ice grains, followed by the larger rod-like inclusions of brines with mainly Na+ and Cl− in grain boundaries. Our results suggest a new proxy that may help identify past warm climates in deeper ice cores in Greenland and for studying future ice sheet melting behavior.

Glacier phototroph blooms on the surfaces of ice sheets and glaciers cause albedo reduction, leading to increased melting rates. We observed seasonal changes in the abundance of phototrophs on the Qaanaaq Ice Cap in northwestern Greenland from June to August 2014, and reproduced these changes using numerical and empirical models. The phototroph community on the ice surface mainly consisted of the glacier alga Ancylonema nordenskioldii and the cyanobacterium Phormidesmis priestleyi. The glacier alga appeared on the ice surface in late June, after which its abundance increased exponentially throughout the melting period. A logistic growth model designed for snow algal growth reproduced the measured exponential increases, suggesting that growth could be explained using the model as a function of the ice melting duration. Cyanobacteria appeared and their abundance increased in late July but did not change exponentially thereafter. The abundance of cyanobacteria was explained with an empirical model expressed as a function of the amount of mineral dust on the bare ice surface. Our numerical and empirical models for reproducing glacier algae and cyanobacteria could be useful for quantifying the albedo reduction caused by their growth and the melt rates of the Greenland ice sheet and glaciers in the future.

In situ meteorological data are essential to better understand ongoing environmental changes in the Arctic. Here, we present a dataset of quality-controlled meteorological observations by two automatic weather stations in northwest Greenland from July 2012 to the end of August 2020. The stations were installed in an accumulation area on the Greenland Ice Sheet (SIGMA-A site, 1490 m a.s.l.) and near the equilibrium line of the Qaanaaq Ice Cap (SIGMA-B site, 944 m a.s.l.). We describe the two-step sequence of quality-control procedures that we used to create increasingly reliable datasets by masking erroneous data records. We analyzed the resulting 2012–2020 time series of air temperature, positive degree-days, snow height, surface albedo, and histograms of longwave radiation (a proxy of cloud formation frequency). We found that snow height increased and albedo remained steady at the SIGMA-A site, whereas high air temperatures and clear-sky conditions prevailed while snow height and albedo decreased in the summers of 2015, 2019, and 2020 at the SIGMA-B site. Therefore, it appears that these weather conditions led to notable snow height degradation at the SIGMA-B site but not at the SIGMA-A site. We anticipate that this quality-control method and these datasets will aid in climate studies of northwest Greenland as well as contribute to the advancement of broader polar climate studies.

Cryoconite holes (CHs) are water-filled cylindrical holes with cryoconite (dark-coloured sediment) deposited at their bottoms, forming on ablating ice surfaces of glaciers and ice sheets worldwide. Because the collapse of CHs may disperse cryoconite on the ice surface, thereby decreasing the ice surface albedo, accurate simulation of the temporal changes in CH depth is essential for understanding ice surface melt. We established a novel model that simulates the temporal changes in CH depth using heat budgets calculated independently at the ice surface and CH bottom based on hole-shape geometry. We evaluated the model with in situ observations of the CH depths on the Qaanaaq ice cap in Northwest Greenland during the 2012, 2014, and 2017 melt seasons. The model reproduced well the observed depth changes and timing of CH collapse. Although earlier models have shown that CH depth tends to be deeper when downward shortwave radiation is intense, our sensitivity tests suggest that deeper CH tends to form when the diffuse component of downward shortwave radiation is dominant, whereas CHs tend to be shallower when the direct component is dominant. In addition, the total heat flux to the CH bottom is dominated by shortwave radiation transmitted through ice rather than that directly from the CH mouths when the CH is deeper than 10 cm. Furthermore, the tests highlight that the ice surface albedo is a key parameter for accurately reproducing the timing of CH collapse because 0.1 decrease in albedo induces the CH collapse one day earlier. Heat component analysis suggests that CH depth is governed by the balance between the intensity of the diffuse component of downward shortwave radiation and the wind speed. Therefore, these meteorological conditions may be important factors contributing to the recent surface darkening of the Greenland ice sheet and other glaciers via the redistribution of CHs. Coupling the CH model proposed in this study with a climate model should improve our understanding of glacier-surface darkening.

The 13th Symposium on Polar Science/Interdisciplinary sessions [IA] Arctic research, Fri. 18 Nov.

The 13th Symposium on Polar Science/Interdisciplinary sessions [IA] Arctic research, Thu. 17 Nov.

The 13th Symposium on Polar Science/Ordinary sessions [OM] Polar meteorology and glaciology, Wed. 16 Nov.

The 12th Symposium on Polar Science, 15–18 Nov. 2021, National Institute of Polar Research (NIPR) Research Organization of Information and Systems (ROIS)

Key Points: • First ever measurements with a high‐accuracy single‐particle soot photometer of black carbon (BC) concentrations in Arctic snowpack • Topography and BC emission flux strongly influenced latitudinal variations of mass concentrations and size distributions of BC • Measured BC mass concentrations 2–25 times lower than previously reported show the importance of revalidating climate models Black carbon (BC) deposited on snow lowers its albedo, potentially contributing to warming in the Arctic. Atmospheric distributions of BC and inorganic aerosols, which contribute directly and indirectly to radiative forcing, are also greatly influenced by depositions. To quantify these effects, accurate measurement of the spatial distributions of BC and ionic species representative of inorganic aerosols (ionic species hereafter) in snowpack in various regions of the Arctic is needed, but few such measurements are available. We measured mass concentrations of size-resolved BC (CMBC) and ionic species in snowpack by using a single-particle soot photometer and ion chromatography, respectively, over Finland, Alaska, Siberia, Greenland, and Spitsbergen during early spring in 2012–2016. Total BC mass deposited per unit area (DEPMBC) during snow accumulation periods was derived from CMBC and snow water equivalent (SWE). Our analyses showed that the spatial distributions of anthropogenic BC emission flux, total precipitable water, and topography strongly influenced latitudinal variations of CMBC, BC size distributions, SWE, and DEPMBC. The average size distributions of BC in Arctic snowpack shifted to smaller sizes with decreasing CMBC due to an increase in the removal efficiency of larger BC particles during transport from major sources. Our measurements of CMBC were lower by a factor of ~13 than previous measurements made with an Integrating Sphere/Integrating Sandwich spectrophotometer due mainly to interference from coexisting non-BC particles such as mineral dust. The SP2 data presented here will be useful for constraining climate models that estimate the effects of BC on the Arctic climate. Plain Language Summary Black carbon (BC) particles, commonly known as soot, are emitted from incomplete combustion of fossil fuels and biomass. They efficiently absorb solar radiation and thus heat the atmosphere. BC particles emitted at midlatitudes and in the Arctic are deposited onto snow in the Arctic, accelerating snowmelt in early spring by absorbing solar radiation. These processes contribute to warming in the Arctic. Calculations of this warming effect by using numerical models need to be validated by comparison with observed BC concentrations in snowpack. However, there are very few accurate records of concentrations of BC in snow because of technical difficulties in making these measurements. We developed a new laser-induced incandescence technique to measure BC concentrations in snowpack and applied it for the first time in six Arctic regions (Finland, Alaska, North and South Siberia, Greenland, and Spitsbergen). The BC concentrations we measured were highest in Finland and South Siberia, which are closer to large anthropogenic BC sources than the other regions, where our measured BC concentrations were much lower. On average, our BC concentrations were much lower than those previously measured by different techniques. Therefore, previous comparisons of modeled and observed BC concentrations need to be re-evaluated using the present data.

In countries like Japan, particular solid precipitation particles (PPs), such as unrimed PPs and graupel, often form a weak layer in snow, which triggers slab avalanches. An understanding of weak PP layers is therefore essential for avalanche prevention authorities to design a predictive model for slab avalanches triggered by those layers. Specific surface area (SSA) is a parameter that could characterize the physical properties of PPs. The SSAs of solid PPs in Nagaoka – a city in Japan experiencing the heaviest snowfall in the country – were measured for four winters (from 2013/2014 to 2016/2017). More than 100 SSAs of PP were measured during the study period using the gas absorption method. The measured SSA values range from 42 to 153 m2 kg−1. Under melting conditions, PPs showed comparatively smaller values. Unrimed and slightly rimed PPs exhibited low SSA, whereas heavily rimed PPs and graupel exhibited high SSA. The degree of PP riming depends on the synoptic meteorological conditions. Based on the potential of weak PP layer formation with respect to the degree of riming of PPs, the results indicate that SSA is a useful parameter for describing the characteristics of PP, and consequently predicting avalanches triggered by weak PP layers. The study found that the values of SSA strongly depend on wind speed (WS) and wet-bulb temperature (Tw) on the ground. SSA increases with increase in WS and decreases with increase in Tw. An equation to empirically estimate the SSA of fresh PPs in Nagaoka using WS and Tw was established. The equation successfully reproduced the fluctuation of SSA. The SSA equation, along with the meteorological data, is an efficient first step toward describing the development of weak PP layers in the snow cover models.

In countries like Japan, particular solid precipitation particles (PPs), such as unrimed PPs and graupel, often form a weak layer in snow, which triggers slab avalanches. An understanding of weak PP layers is therefore essential for avalanche prevention authorities to design a predictive model for slab avalanches triggered by those layers. Specific surface area (SSA) is a parameter that could characterize the physical properties of PPs. The SSAs of solid PPs in Nagaoka – a city in Japan experiencing the heaviest snowfall in the country – were measured for four winters (from 2013/2014 to 2016/2017). More than 100 SSAs of PP were measured during the study period using the gas absorption method. The measured SSA values range from 42 to 153 m2 kg−1. Under melting conditions, PPs showed comparatively smaller values. Unrimed and slightly rimed PPs exhibited low SSA, whereas heavily rimed PPs and graupel exhibited high SSA. The degree of PP riming depends on the synoptic meteorological conditions. Based on the potential of weak PP layer formation with respect to the degree of riming of PPs, the results indicate that SSA is a useful parameter for describing the characteristics of PP, and consequently predicting avalanches triggered by weak PP layers. The study found that the values of SSA strongly depend on wind speed (WS) and wet-bulb temperature (Tw) on the ground. SSA increases with increase in WS and decreases with increase in Tw. An equation to empirically estimate the SSA of fresh PPs in Nagaoka using WS and Tw was established. The equation successfully reproduced the fluctuation of SSA. The SSA equation, along with the meteorological data, is an efficient first step toward describing the development of weak PP layers in the snow cover models.

Depositions of light-absorbing particles (LAPs), such as black carbon (BC) and dust, on the snow surface modulate the snow albedo; therefore, they are considered key factors of snow-atmosphere interaction in the present-day climate system. However, their detailed roles have not yet been fully elucidated, mainly due to the lack of in-situ measurements. Here, we develop a new model chain NHM-Chem-SMAP, which is composed of a detailed regional meteorology-chemistry model and a multilayered physical snowpack model, and evaluate it using LAPs concentrations data measured at Sapporo, Japan during the 2011-2012 winter. NHM-Chem-SMAP successfully reproduces the in-situ measured seasonal variations in the mass concentrations of BC and dust in the surface snowpack. Furthermore, we find that LAPs from domestic and foreign sources played a role in shortening the snow cover duration by 5 and 10 days, respectively, compared to the completely pure snow condition.

Previous reconstructions of summer temperatures from ice cores have relied on a statistical relationship between a melt layer and temperature observed at nearby stations. This study presents a novel method for reconstructing summer temperatures from melt layers in ice cores using an energy balance model that incorporates heat conduction and meltwater refreezing in the firn. We use the seasonal patterns in the ERA‐Interim reanalysis data set for an ice core site to calculate the amounts of refreezing water within the firn under various summer mean temperature (SMT) and annual precipitation conditions, and prepared calibration tables containing these three variables. We then estimate the SMTs from the refreezing amount and annual accumulation, both of which can be obtained from an ice core. We apply this method to four ice cores that were recovered from sites with different climates: two sites on the Greenland Ice Sheet, one in Alaska, and one in Russian Altai. The reconstructed SMTs show comparable variations with those of observed temperatures at nearby stations. The nonlinear relationship between SMT and melt layer thickness differs between sites, indicating that a single linear approximation cannot be employed to estimate SMT. Sensitivity analyses suggest that the annual temperature range, amount of annual precipitation, and firn albedo (which is a time‐invariant value in the model) significantly affect the relationship between SMT and melt layer thickness. This new method provides an alternative to existing approaches and yields an independent estimate of SMT from ice cores that have been affected by melting.

Clouds have been recognized to enhance surface melt on the Greenland Ice Sheet (GrIS). However, quantitative estimates of the effects of clouds on the GrIS melt area and ice-sheet-wide surface mass balance are still lacking. Here we assess the effects of clouds with a state-of-the-art regional climate model, conducting a numerical sensitivity test in which adiabatic atmospheric conditions as well as zero cloud water/ice amounts are assumed (i.e., clear-sky conditions), although the precipitation rate is the same as in the control all-sky simulation. By including or excluding clouds, we quantify time-integrated feedbacks for the first time. We find that clouds were responsible for a 3.1%, 0.3%, and 0.7% increase in surface melt extent (of the total GrIS area) in 2012, 2013, and 2014, respectively. During the same periods, clouds reduced solar heating and thus daily runoff by 1.6, 0.8, and 1.0 Gt day−1, respectively: clouds did not enhance surface mass loss. In the ablation areas, the presence of clouds results in a reduction of downward latent heat flux at the snow/ice surface so that much less energy is available for surface melt, which highlights the importance of indirect time-integrated feedbacks of cloud radiative effects.

Cloud detection and screening constitute critically important first steps required to derive many satellite data products. Traditional threshold-based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and they have difficulties over areas partially covered with snow/ice. Exploiting advances in machine learning techniques and radiative transfer modeling of coupled environmental systems, we have developed a new, threshold-free cloud mask algorithm based on a neural network classifier driven by extensive radiative transfer simulations. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over snow-covered areas in the mid-latitudes. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors. Compared to threshold-based methods and previous machine-learning approaches, this new cloud mask (i) does not rely on thresholds, (ii) needs fewer satellite channels, (iii) has superior performance during winter seasons in mid-latitude areas, and (iv) can easily be applied to different sensors.

Cryoconite holes are water-filled cylindrical holes formed on ablation ice surfaces and commonly observed on glaciers worldwide. Temporal changes of cryoconite holes characteristically −2, 40–55% of the incoming flux). In contrast, holes developed in sunny conditions correspond to high solar radiation (186–278 W m−2, 63–88%). Results suggest that the dimensions of holes drastically changed depending on the weather conditions and that frequent cloudy, warm and windy conditions would cause a decay of holes and weathering crust, inducing an increase in the cryoconite coverage on the ice, consequently darkening the glacier surface.

Our study is the first to demonstrate a high-temporal-resolution record of mineral composition in a Greenland ice core over the past 100 years. To reconstruct the past variations in the sources and transportation processes of mineral dust in northwestern Greenland, we analyzed the morphology and mineralogical composition of dust in an ice core from 1915 to 2013 using Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDS). Analysis of the SEM-EDS reveals that the ice core dust mainly consisted of silicate minerals and the composition varied substantially on multi-decadal and inter-decadal scales, suggesting that the geological origin of the ice core minerals changed periodically during the past 100 years. The multi-decadal variation trend differed among mineral types: kaolinite generally formed in low- or middle-latitude areas were abundant in the colder periods (1950 to 2000), whereas mica, chlorite, feldspars, mafic minerals, and quartzes formed in arid, high-latitude, and local areas were abundant in the warmer periods (1915 to 1949 and 2005 to 2013). This indicates that the multi-decadal variation of the relative abundance of the minerals can be attributed to the local temperature changes in Greenland. The trajectory analysis shows that the minerals were mainly transported from the western coast of Greenland in the two warming periods, which was likely due to an increase of dust sourced from local ice-free areas. On the other hand, the abundant kaolinite was likely derived from old sediments at higher latitudes in North America, rather than from low and middle latitudes.

The 11th Symposium on Polar Science/Ordinary sessions: [OM] Polar Meteorology and Glaciology, Thu. 3 Dec.

The 11th Symposium on Polar Science/Special session: [S] Accelerating Arctic research: Recent progress and future prospect of Arctic research, Tue. 1 Dec.

The 11th Symposium on Polar Science/Special session: [S] Accelerating Arctic research: Recent progress and future prospect of Arctic research, Tue. 1 Dec.

The 11th Symposium on Polar Science/Special session: [S] Accelerating Arctic research: Recent progress and future prospect of Arctic research, Poster presentations, Mon. 16 Nov. - Fri. 18 Dec.

The 11th Symposium on Polar Science/Ordinary sessions: [OM] Polar Meteorology and Glaciology, Poster presentations, Mon. 16 Nov. - Fri. 18 Dec.

The 11th Symposium on Polar Science/Ordinary sessions: [OM] Polar Meteorology and Glaciology, Poster presentations, Mon. 16 Nov. - Fri. 18 Dec.

The Greenland Ice Sheet (GrIS) mass loss has been accelerating at a rate of about 20 ± 10 Gt/yr2 since the end of the 1990's, with around 60 % of this mass loss directly attributed to enhanced surface meltwater runoff. However, in the climate and glaciology communities, different approaches exist on how to model the different surface mass balance (SMB) components using: (1) complex physically-based climate models which are computationally expensive; (2) intermediate complexity energy balance models; (3) simple and fast positive degree day models which base their inferences on statistical principles and are computationally highly efficient. Additionally, many of these models compute the SMB components based on different spatial and temporal resolutions, with different forcing fields as well as different ice sheet topographies and extents, making inter-comparison difficult. In the GrIS SMB model intercomparison project (GrSMBMIP) we address these issues by forcing each model with the same data (i.e., the ERA-Interim reanalysis) except for two global models for which this forcing is limited to the oceanic conditions, and at the same time by interpolating all modelled results onto a common ice sheet mask at 1 km horizontal resolution for the common period 1980–2012. The SMB outputs from 13 models are then compared over the GrIS to (1) SMB estimates using a combination of gravimetric remote sensing data from GRACE and measured ice discharge, (2) ice cores, snow pits, in-situ SMB observations, and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Our results reveal that the mean GrIS SMB of all 13 models has been positive between 1980 and 2012 with an average of 340 ± Gt/yr, but has decreased at an average rate of −7.3 Gt/yr2 (with a significance of 96 %), mainly driven by an increase of 8.0 Gt/yr2 (with a significance of 98 %) in meltwater runoff. Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting the need for accurate representation of the GrIS ablation zone extent and processes driving the surface melt. In addition, a higher density of in-situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 mWE/yr due to large discrepancies in modelled snowfall accumulation. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of same order than RCMs with observations and remain then useful tools for long-term simulations. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present day SMB relative to observations, suggesting that biases are not systematic among models.

Surface albedo of snow and ice is substantially reduced by inorganic impurities, such as aeolian mineral dust (MD) and black carbon (BC), and also by organic impurities, such as microbes that live in the snow. In this paper, we present the temporal changes of surface albedo, snow grain size, MD, BC and snow algal cell concentration observed on a snowpack in northwest Greenland during the ablation season of 2014 and our attempt to reproduce the changes in albedo with a physically based snow albedo model. We also attempt to reproduce the effects of inorganic impurities and the red snow algae (Sanguina nivaloides) on albedo. Concentrations of MD and red snow algae in the surface snow were found to increase in early August, while snow grain size and BC were found to not significantly change throughout the ablation season. Surface albedo was found to have decreased by 0.08 from late July to early August. The albedo simulated by the model agreed with the albedo observed during the study period. However, red snow algae exerted little effect on surface albedo in early August. This is probably owing to the abundance of smaller cells (4.9×104 cells L−1) when compared with the cell abundance of red snow reported by previous studies in the Arctic region (∼108 cells L−1). The simulation of snow albedo until the end of the melting season, with a snow algae model, revealed that the reduction in albedo attributed to red snow algae could equal 0.004, out of a total reduction of 0.102 arising from the three impurities on a snowpack in northwest Greenland. Finally, we conducted scenario simulations using the snow albedo model, coupled with the snow algae model, in order to simulate the possible effects of red snow blooming on snow albedo under warm conditions in northwest Greenland. The result suggests that albedo reduction by red snow algal growth under warm conditions (surface snow temperature of +1.5 ∘C) reached 0.04, equivalent to a radiative forcing of 7.5 W m−2 during the ablation season of 2014. This coupled albedo model has the potential to dynamically simulate snow albedo, including the effect of organic and inorganic impurities, leading to proper estimates of the surface albedo of snow cover in Greenland.

Possible albedo reduction due to light absorbing impurities (LAI) in snowpack observed at various sites in the world are investigated. Reviewing previously measured black carbon (BC) concentrations, their values distribute in a range of 0.07-0.25 ppbw (ng of BC in g of snow) in Antarctica, 0.55-20 ppbw in Greenland Ice Sheet (GrIS), 4.4-87.6 ppbw for the other Arctic except GrIS, and 4-1221 ppbw for mid-latitudes. As albedo reduction rate by LAI depends on snow grain size, it is more enhanced by larger grain snow such as melt form (melting snow) than smaller grain snow such as precipitation particles (new snow). By assuming two typical snow grain radii rs = 1000 and 50 µm, respectively for those snow grain shapes, the albedo reduction as a function of BC concentration can be calculated with physically based snow albedo model. The result indicates that albedo in Antarctic snow is not affected by BC in any case of snow grain radius. In GrIS albedo reduction due to BC is small around 0.006 for rs = 50 µm (new snow) but it rises to 0.026 for rs = 1000 µm (melting snow), suggesting a few percent of albedo reduction could occur under warmer climate condition due to enhanced snow metamorphism. In the other Arctic except GrIS, the maximum albedo reductions for rs = 50 µm (1000 µm) are 0.015 (0.064) at the maximum BC concentration (87.6 ppbw). For. mid-latitudes, it is 0.070 (0.24) for rs = 50 µm (1000 µm) at the maximum BC concentration (1221 ppbw). These results mean albedo reduction in highly polluted area of mid-latitudes cannot be ignored even in case of new snow and is more serious for melting snow.We have conducted energy budget and snow pit observations at Sapporo (43°N, 141°E, 15 m a.s.l), Japan since 2005. In addition, elemental carbon (EC~BC) and mineral dust concentrations in snowpack were also monitored for snow samples collected twice a week from 2007 by the thermal optical reflectance (TOT) method and gravimetric measurement of a filter. During 10 years from 2007 to 2017, the medians of EC and dust concentrations are 196 ppbw and 2700 ppbw, respectively. Using those data, contribution of LAI to albedo reduction and the radiative forcing (RF) were estimated. The 10-year-mean albedo reduction and RF due to BC+dust are 0.053 and +6.7 Wm-2, respectively, in which BC effect on albedo reduction is 5.6 times larger than dust. The albedo reduction by BC+dust for only melting period is 0.151, that is 4.8 times larger than that for accumulation period. The effect of LAI on albedo reduction is enhanced by snow grain growth as well as an increase of LAI in melting period compared to that for accumulation season.

The Tenth Symposium on Polar Science/Ordinary sessions: [OM] Polar Meteorology and Glaciology, Wed. 4 Dec. / 2F Auditorium, National Institute of Polar Research

The Tenth Symposium on Polar Science/Ordinary sessions: [OM] Polar Meteorology and Glaciology, Wed. 4 Dec. / Entrance Hall (1st floor) , National Institute of Polar Research

Surface albedo of snow and ice is substantially reduced by inorganic impurities, such as aeolian mineral dust (MD) and black carbon (BC), and also by organic impurities, such as microbes that live in the snow. In this paper, we present the temporal changes of surface albedo, snow grain size, MD, BC, and snow algal cell concentration observed on a snowpack in northwest Greenland during the ablation season of 2014 and our attempt to reproduce the changes in albedo with a physically based snow albedo model. We also attempt to reproduce the effects of inorganic impurities and the red snow algae (Chlamydomonas nivalis) on albedo. Concentrations of MD and red snow algae in the surface snow were found to increase in early August, while snow grain size and BC were found to not significantly change throughout the ablation season. Surface albedo was found to have decreased by 0.08 from late July to early August. The albedo simulated by the model agreed with the albedo observed during the study period. However, red snow algae exerted little effect on surface albedo in early August. This is probably owing to the abundance of smaller cells (4.9 × 104 cells L^-1) when compared with the cell abundance of typical red algal snow (~ 108 cells L−1). The simulation of snow albedo until the end of the melting season, with an algal growth model, revealed that the reduction in albedo attribute to red algae could equal 0.004, out of a total reduction of 0.102 arising from the three impurities on a snowpack in northwest Greenland. Finally, we conducted scenario simulations using the snow albedo model, coupled with the algal growth model, in order to simulate the possible effects of red algal blooming on snow albedo under warm conditions in northwest Greenland. The result suggests that albedo reduction by red snow algal growth under warm conditions (surface snow temperature of +1.5 °C) reached 0.04, equivalent to a radiative forcing of 7.5 W m−2 during the ablation season of 2014. This coupled albedo model has the potential to dynamically simulate snow albedo, including the effect of organic and inorganic impurities, leading to proper estimates of the surface albedo of snow cover in Greenland.

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing (http://step.esa.int/main/toolboxes/snap/). In this work we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400-1020nm, we derive important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on the spatial grid of 300m. The algorithm also incorporates cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long – term snow property records essential for climate monitoring and data assimilation studies - especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.