Search

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.

This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions modelling at ten sites. The long-term datasets (one maritime, one arctic, three boreal and five mid-latitude alpine) are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project. Periods covered by the in situ data vary between seven and twenty years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature and surface temperature) available at varying temporal intervals. 30-year (1980–2010) time-series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to one-hour timesteps and bias corrected. Although applied to all sites, the bias corrections are particularly important for mountain sites that are hundreds of meters higher than the grid elevations; as a result, uncorrected air temperatures are too high and snowfall amounts are too low in comparison with in situ measurements. The discussion considers the importance of data sharing to the identification of errors and how the publication of these datasets contributes to good practice, consistency and reproducibility in Geosciences. Supplementary material provides information on instrumentation, an estimate of the percentages of missing values, and gap-filling methods at each site. It is hoped that these datasets will be used as benchmarks for future model development and that their ease of use and availability will help model developers quantify model uncertainties and reduce model errors. The data are published in the repository PANGAEA and available at: https://doi.org/10.1594/PANGAEA.897575.

To improve surface mass balance (SMB) estimates for the Greenland Ice Sheet (GrIS), we developed a 5 km resolution regional climate model combining the Japan Meteorological Agency Non-Hydrostatic atmospheric Model and the Snow Metamorphism and Albedo Process model (NHM–SMAP) with an output interval of 1 h, forced by the Japanese 55-year reanalysis (JRA-55). We used in situ data to evaluate NHM–SMAP in the GrIS during the 2011–2014 mass balance years. We investigated two options for the lower boundary conditions of the atmosphere: an offline configuration using snow, firn, and ice albedo, surface temperature data from JRA-55, and an online configuration using values from SMAP. The online configuration improved model performance in simulating 2 m air temperature, suggesting that the surface analysis provided by JRA-55 is inadequate for the GrIS and that SMAP results can better simulate physical conditions of snow/firn/ice. It also reproduced the measured features of the GrIS climate, diurnal variations, and even a strong mesoscale wind event. In particular, it successfully reproduced the temporal evolution of the GrIS surface melt area extent as well as the record melt event around 12 July 2012, at which time the simulated melt area extent reached 92.4 %. Sensitivity tests showed that the choice of calculation schemes for vertical water movement in snow and firn has an effect as great as 200 Gt year−1 in the GrIS-wide accumulated SMB estimates; a scheme based on the Richards equation provided the best performance.

The Ninth Symposium on Polar Science/Interdisciplinary sessions: [IF] Toward Future Plan of the Arctic and Antarctic Science, Thu. 6 Dec. / 2F Auditorium, National Institute of Polar Research

The Ninth Symposium on Polar Science/Ordinary sessions: [OM] Polar meteorology and glaciology, Wed. 5 Dec. / Entrance Hall (1st floor), National Institute of Polar Research

The Ninth Symposium on Polar Science/Ordinary sessions: [OM] Polar meteorology and glaciology, Wed. 5 Dec. / 3F Multipurpose Conference room, National Institute of Polar Research

Environments along the coast of Greenland are rapidly changing under the influence of a warming climate in the Arctic. To better understand the changes in the coastal environments, we performed researches in the Qaanaaq region in northwestern Greenland as a part of the ArCS (Arctic Challenge for Sustainability) Project. Mass loss of ice caps and marine-terminating outlet glaciers were quantified by field and satellite observations. Measurements and sampling in fjords revealed the important role of glacial meltwater discharge in marine ecosystems. Flooding of a glacial stream in Qaanaaq and landslides in a nearby settlement were investigated to identify the drivers of the incidents. Our study observed rapid changes in the coastal environments, and their critical impact on the society in Qaanaaq. We organized workshops with the residents to absorb local and indigenous knowledge, as well as to share the results and data obtained in the project. Continuous effort towards obtaining long-term observations requiring involvement of local communities is crucial to contribute to a sustainable future in Greenland.

Albedo parameterization is of fundamental importance for accurate representation of high-latitude climate variability by modeling studies. Field observations show that near-infrared snow albedo decreases dramatically when surface air temperature exceeds −2 °C. This can influence reproduction of sea ice simulations taking into consideration the importance of the drastic change in albedo in early melt season for the seasonal change of sea ice extent. Therefore, we conducted global ocean data-assimilative simulation experiments using a modified snow albedo parameterization. The modified parameterization reduced the albedo directly and achieved a comparable indirect reduction via changes in the modeled snow and sea ice distributions (ice–albedo feedback). As a result, sea ice thickness was reduced by more than 0.4–1 cm over most of the central Arctic Ocean. Sea ice velocities were also reduced by enhanced ocean drag with weakened surface ocean circulation in the Beaufort Gyre. In the Southern Ocean, the modified parameterization caused snow thicknesses to be decreased by up to 2 cm in the Weddell Sea. These impacts, which were generally larger than the spread of ensemble experiment results and therefore robust, at least in our model, provide useful information for quantifying the results of albedo modification in climate modeling studies.

The Greenland ice sheet (GrIS) is known to be contributing to sea level rise in a warming climate. The snow cover on the ice sheet is the direct link between a potentially warmer atmosphere and the ice itself. However, little is known about the microstructure and especially about the spatial and temporal variability of the snow cover, except from indirect evidence from remote sensing. The detailed snowpack stratigraphy is relevant for processes such as the albedo feedback, water infiltration and firn densification. During a field campaign in 2015, spatially distributed snow observations of the GrIS were gathered at stations belonging to the Greenland Climate Network (GC-Net). High-resolution snow profiles of density, specific surface area and hardness were measured. Hardness was measured with the SnowMicroPen, which was also used to assess the spatial variability of the snow density with depth. The snow cover model SNOWPACK was forced with reanalysis data from the model NHM-SMAP. The measured mean density of the upper snow cover was in good agreement with the simulations using constant densities for snow accumulation, i.e. new snow, depending on the geographical location on the GrIS. However, the observed stratigraphy in terms of density and SSA could not be reproduced. We found that for a one-dimensional snowpack model it is difficult to parameterize for snowpacks undergoing multiple erosion and redeposition events, as is typical for the GrIS and other perennial polar snowpacks. This limitation may be a drawback to understanding past and future changes of the snow, and the associated processes.

As part of the terrestrial branch of the Japan-funded Arctic Climate Change Research Project (GRENE-TEA), which aims to clarify the role and function of the Arctic terrestrial system in the climate system, and assess the influence of its changes on a global scale, this model intercomparison project (GTMIP) is planned and being conducted to (1) enhance communication and understanding between the "minds and hands" (i.e., between the modelling and field scientists) and (2) assess the uncertainty and variations stemming from variability in model implementation/design and in model outputs due to climatic and historical conditions in the Arctic terrestrial regions. This paper provides an overview and the experiment protocol of Stage 1 of the project, site simulations driven by statistically fitted data created using the GRENE-TEA site observations for the last three decades. The target metrics for the model evaluation cover key processes in both physics and biogeochemistry, including energy budgets, snow, permafrost, phenology, and carbon budgets. The preliminary results on four metrics (annual mean latent heat flux, annual maximum snow depth, gross primary production, and net ecosystem production) already demonstrate the range of variations in reproducibility among existing models and sites. Full analysis on annual as well as seasonal time scales, to be conducted upon completion of model outputs submission, will delineate inter-dependence among the key processes, and provide the clue for improving the model performance.

The surface energy balance (SEB) from 30 June to 14 July 2012 at site SIGMA (Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic)-A, (78 deg 03 min N, 67 deg 38 min W; 1490 m a.s.l.) on the northwest Greenland Ice Sheet (GrIS) was investigated by using in situ atmospheric and snow measurements as well as numerical modeling with a one-dimensional multi-layered physical snowpack model called SMAP (Snow Metamorphism and Albedo Process). At SIGMA-A, remarkable near-surface snowmelt and continuous heavy rainfall (accumulated precipitation between 10 and 14 July was estimated to be 100 mm) were observed after 10 July 2012. Application of the SMAP model to the GrIS snowpack was evaluated based on the snow temperature profile, snow surface temperature, surface snow grain size, and shortwave albedo, all of which the model simulated reasonably well. Above all, the fact that the SMAP model successfully reproduced frequently observed rapid increases in snow albedo under cloudy conditions highlights the advantage of the physically based snow albedo model (PBSAM) incorporated in the SMAP model. Using such data and model, we estimated the SEB at SIGMA-A from 30 June to 14 July 2012. Radiation-related fluxes were obtained from in situ measurements, whereas other fluxes were calculated with the SMAP model. By examining the components of the SEB, we determined that low-level clouds accompanied by a significant temperature increase played an important role in the melt event observed at SIGMA-A. These conditions induced a remarkable surface heating via cloud radiative forcing in the polar region., 形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: PA1610008000

Snow algal bloom is a common phenomenon on melting snowpacks in polar and alpine regions and can substantially increase melting rates of the snow due to the effect of albedo reduction on the snow surface. In order to reproduce algal growth on the snow surface using a numerical model, temporal changes in snow algal abundance were investigated on the Qaanaaq Glacier in northwest Greenland from June to August 2014. Snow algae first appeared at the study sites in late June, which was approximately 94 hours after air temperatures exceeded the melting point. Algal abundance increased exponentially after the appearance, but the increasing rate became slow after late July, and finally reached 3.5 × 107 cells m-2 in early August. We applied a logistic model to the algal growth curve and found that the algae could be reproduced with an initial cell concentration of 6.9 × 102 cells m-2, a growth rate of 0.42 d-1, and a carrying capacity of 3.5 × 107 cells m-2 on this glacier. This model has the potential to simulate algal blooms from meteorological data sets and to evaluate their impact on the melting of seasonal snowpacks and glaciers.

To improve surface mass balance (SMB) estimates for the Greenland Ice Sheet (GrIS), we developed a 5 km resolution regional climate model combining the Japan Meteorological Agency Non-Hydrostatic atmospheric Model and the Snow Metamorphism and Albedo Process model (NHM-SMAP) with an output interval of 1 h, forced by the Japanese 55year Reanalysis (JRA-55). We used in situ data to evaluate NHM-SMAP in the GrIS during the 2011–2014 mass balance years. We investigated two options for the lower boundary conditions of the atmosphere, an "off-line" configuration using snow/firn/ice albedo and surface temperature data from JRA-55 and an "on-line" configuration using values from SMAP. The on-line configuration improved model performance in simulating 2 m air temperature, suggesting that the surface analysis provided by JRA-55 is inadequate for the GrIS and that SMAP results can better simulate snow/firn/ice physical conditions. It also reproduced the measured features of the GrIS climate, diurnal variations, and even a meso-scale strong wind event. In particular, it reproduced the GrIS surface melt area extent well. Sensitivity tests showed that the choice of calculation schemes for vertical water movement in snow and firn has an effect as great as 200 Gt year–1 in the GrIS-wide accumulated SMB estimates; a scheme based on the Richards equation provided the best performance.

Glaciological observations were conducted in 2012 and 2013 at the SIGMA-A site on the northwest Greenland ice sheet (78°03’06”N, 67°37’42”W, 1490 m a.s.l.) as part of the Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic (SIGMA) project. The meteorological conditions during the two observations were quite different. The meteorological condition during the 2012 observation period was warm, and heavy rainfall occurred during the observation period, thus the snow was very wet. In contrast, the meteorological condition during the observation period in 2013 was cold, with a blowing snow event, thus the snow was quite dry. The glaciological observations in 2012 consisted of 1) snow-stake measurements, 2) snow pit observations, 3) grain size observations for validation of satellite-derived snow products, 4) snow specific surface area measurements using a near-infrared camera, 5) snow sampling for chemical analyses, and 6) drilling of firn cores with a hand auger. The glaciological observations in 2013 consisted of 1) snow-stake measurements, 2) snow pit observations, and 3) snow sampling for chemical analyses.

Light-absorbing snow impurities of elemental carbon (EC), organic carbon (OC), and mineral dust have been measured at three locations at elevations from 1,469 to 1,992 m on August 1, 2011, and at the site SIGMA-A (78°N, 68°W, elevation 1,490 m) on the northwest Greenland ice sheet (GrIS) during the period from June 28 to July 12, 2012. At SIGMA-A, a remarkable snow surface lowering together with snow melting was observed during the observation period in 2012, when a record surface melting event occurred over the GrIS. The concentrations in the surface were 0.9, 3.8, and 107 ppbw for EC, OC, and dust, respectively, at the beginning of the period, which increased to 4.9, 17.2, and 1327 ppbw for EC, OC, and dust, respectively, at the end. The EC and dust concentrations were remarkably higher than those at the three locations in 2011 and the recent measurements at Summit. However, our measurements for EC and OC could be underestimated because a recent study indicates that the collection efficiency of a quartz fiber filter, which we employed, is low. We confirm that the snow surface impurity concentrations were enhanced in the observation period, which can be explained by the effects of sublimation/evaporation and snow melt amplification associated with drastic melting. Scanning electron microscopy analysis of surface snow impurities on July 12 revealed that the major component of snow impurities is mineral dust with size larger than 5 μm, which suggests possible emission source areas are peripheral bare soil regions of Greenland and/or the Canadian Arctic.

The specific surface area (SSA) of snow is of particular interest to researchers because SSA is strongly related to snow albedo and is a comparatively better indicator of snow’s complexity than grain size. The time variation of SSA for fresh snow samples was observed in the laboratory under isothermal conditions at 226 K and 254 K using the gas adsorption method and Brunauer-Emmett-Teller theory. The SSA of the snow samples decreased with time under isothermal metamorphism. The decrease in SSA was fitted with the logarithmic equation proposed by Legagneux et al. (2003), and adjustable parameters were obtained. The rate of decrease in SSA depended on the shape of the initial snow type and temperature. Dendritic snow samples exhibited large initial SSAs, and their SSAs decreased faster compared with those of fragmented (collected from drifting snow) and plate-like precipitation particles with relatively small initial SSAs. The rate of decrease in SSA was lower at 226 K than that at 254 K.

The potential of the thermal infrared (TIR) remote sensing for discriminating surface snow types was examined by analyzing TIR radiances acquired from space over the Greenland ice sheet. The brightness temperature difference (BTD) between TIR wavelengths of 11 and 12μm was found to increase in accordance with in situ observed evolutions of surface snow type. Spatial and temporal distributions of BTD over the entire ice sheet indicated that BTD has a sensitivity of about 1.2 K for variations of the possible snow types. The observed behaviors of BTD were coincident with those predicted by a radiative transfer calculation using previous in situ measured snow emissivities, although some biases on the order of 0.1-0.3 K remain. The dependence of BTD on the surface snow type was also consistent with the behaviors of snow reflectance at the shortwave infrared (SWIR) wavelength 1.6μm, which is a measure of snow grain size, except for the case of melting wet snow. The inconsistency in the wet snow case was considered to be due to the different optical responses of the TIR and SWIR signals to wet snow, which suggested the possibility of using TIR signals to discriminate wet/dry conditions of snow cover in an old stage. As a result, it is determined that TIR remote sensing has potential not only as an approach supplementary to the SWIR method for assessing surface snow types in daytime but also as the only method for simultaneous retrieval of snow type and surface temperature in nighttime., 資料番号: PA1510067000

To clarify the effect of light absorbing impurities including glacial microbes spectral albedo measurements using a spectrometer for spectral domains of the ultraviolet, visible and near-infrared have been carried out on ablation area in Qaanaaq Glacier in northwestern Greenland in July 2011. The almost glacier surfaces in the ablation area were covered with cryoconite (biogenic dust) on thin ice grain layer above bare ice. There were also snow-covered surfaces including red snow (snow algae). The measured spectral albedos had a remarkable contrast between red snow surface and cryoconite-covered ice surface in the spectral domain from the ultraviolet to the visible, where red snow albedo increased rapidly with the wavelength, while the cryoconite albedo was relatively flat to the wavelength. We simulated the spectral albedos of these surfaces with a radiative transfer model for the atmosphere-snow system. The single scattering properties are calculated with Mie theory by assuming red snow gains to be spherical and with geometric optics by assuming ice grains of cryoconite surface to be non-spherical Voronoi aggregates. We calculated the effect of glacial microbes as snow (ice) impurities using a mineral dust model by changing the imaginary part of refractive index so as to fit the theoretically calculated spectral albedo to the measurement. Finally the imaginary part of refractive indices for red snow and cryoconite at the wavelengths less than 1.0 μm were retrieved. It was found that cryoconite has uniformly higher light absorption compared to mineral dust and red snow has strong light absorption at the wavelengths less than 0.6 μm.

第3回極域科学シンポジウム/特別セッション「これからの北極研究」11月28日（水） 国立極地研究所 2階大会議室

[1] We developed a multilayered physical snowpack model named Snow Metamorphism and Albedo Process (SMAP), which is intended to be incorporated into general circulation models for climate simulations. To simulate realistic physical states of snowpack, SMAP incorporates a state-of-the-art physically based snow albedo model, which calculates snow albedo and solar heating profile in snowpack considering effects of snow grain size and snow impurities explicitly. We evaluated the performance of SMAP with meteorological and snow impurities (black carbon and dust) input data measured at Sapporo, Japan during two winters: 2007–2008 and 2008–2009, and found SMAP successfully reproduced all observed variations of physical properties of snowpack for both winters. We have thus confirmed that SMAP is suitable for climate simulations. With SMAP, we also investigated the effects of snow impurities on snowmelt at Sapporo during the two winters. We found that snowpack durations at Sapporo were shortened by 19 days during the 2007–2008 winter and by 16 days during the 2008–2009 winter due to radiative forcings caused by snow impurities. The estimated radiative forcings due to snow impurities during the accumulation periods were 3.7 W/m2 (it corresponds to albedo reduction in 0.05) and 3.2 W/m2 (albedo reduction in 0.05) for the 2007–2008 and 2008–2009 winters, respectively. While during the ablation periods they were 25.9 W/m2 (albedo reduction in 0.18) and 21.0 W/m2 (albedo reduction in 0.17) for each winter, respectively.

[1] Snow surface roughness such as sastrugi on the Antarctic ice sheet can be a cause of error for remote sensing of snow parameters. The effect of sastrugi on snow bidirectional reflectance was assessed by a field experiment, model simulations, and satellite measurements. The hemispherical-directional reflectance factor (HDRF) of artificial sastrugi-like linear ridges measured at Nakasatsunai, Hokkaido, Japan, exhibited different patterns from that of a flat surface, with the difference of more than ±50% for some geometries. A 3-D Monte Carlo radiative transfer model (MC model) reproduced both the HDRF measurements for the artificial ideal sastrugi and previous measurements for natural sastrugi at the South Pole. Furthermore, the sastrugi effect was applied to remote sensing. Failure to include the surface roughness in models for developing snow-grain-size lookup tables can lead to order-of-magnitude retrieval errors. Using the MC model and multiangle data derived from the Moderate Resolution Imaging Spectrometer over the South Pole during the 2003–2004 summer, the sastrugi and snow parameters were retrieved. The height-to-width ratio of sastrugi reduced from 0.1 to 0.02, whereas the azimuth angle was nearly constant within the range of 0°–30° during the summer. The snow grain size showed a seasonal variation, which depended on the spectral channel. These retrieved parameters were consistent with existing ground measurements. The results suggest that a combination of multiangle data and a 3-D radiative transfer model can be used to quantitatively estimate surface roughness, along with snow grain size, on ice sheets.

Ground-based measurements of spectral degree of linear polarization (DLP) of various snow types were made during intensive field campaigns in a snowfield in Hokkaido, Japan, and on the northwest Greenland ice sheet in 2012. Spectral measurements were conducted under the solar zenith angle of approximately the Brewster angle in order to quantify the polarization properties of light reflected from snow. We obtained spectral DLPs for five different snow types in both field campaigns including precipitation particles, needles, surface hoar, melt forms, and melt freeze crust covering the snow surface. The measurements showed that in the visible region the spectral dependence of the DLP was small while in the near infrared region it increased with increasing snow grain size with some distinct local peaks. The angular dependence indicated that the DLP exhibited small angular dependence in the visible region while in the near-infrared region it exhibited large and broad peaks in the forward direction. Especially for the melt-freeze crust, the DLP approached 1.0 at wavelengths close to λ = 1.5 and 2.0 μm. These features can be explained by (1) the relative contribution of surface versus volume scattering to the reflected light, (2) the incident angle (solar zenith angle) of approximately the Brewster angle, and (3) the ratio between direct and diffuse components of the solar radiation incident on the snow surface. The spectral DLP was found to be quiet sensitive to the incident solar radiation and solar elevation as well as snow optical properties. Comparison between the spectral DLP and snow grain size obtained by snow pit work shows that the DLP for λ > 1.5 μm was very sensitive to large snow grains close to the surface. This finding suggests that polarization measurements obtained from airborne/satellite polarimeters will be useful for surface snow grain size retrievals and help improve the accuracy of such retrievals based on the intensity-only measurements, especially for the large snow grain sizes., 資料番号: PA1510023000