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Twenty-seven models participated in the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modeling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modeling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parameterizations are problematic, and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behavior and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.

Current sea-level rise partly stems from increased surface melting and meltwater runoff from the Greenland ice sheet. Multi-year snow, also known as firn, covers about 80% of the ice sheet and retains part of the surface meltwater. Since the firn cold content integrates its physical and thermal characteristics, it is a valuable tool for determining the meltwater-retention potential of firn. We use gap-filled climatological data from nine automatic weather stations in the ice-sheet accumulation area to drive a surface-energy-budget and firn model, validated against firn density and temperature observations, over the 1998-2017 period. Our results show a stable top 20 m firn cold content (CC20) at most sites. Only at the lower-elevation Dye-2 site did CC20 decrease, by 24% in 2012, before recovering to its original value by 2017. Heat conduction towards the surface is the main process feeding CC20 at all nine sites, while CC20 reduction occurs through low-cold-content fresh-snow addition at the surface during snowfall and latent-heat release when meltwater refreezes. Our simulations suggest that firn densification, while reducing pore space for meltwater retention, increases the firn cold content, enhances near-surface meltwater refreezing and potentially sets favourable conditions for ice-slab formation., SCOPUS: ar.j, info:eu-repo/semantics/published

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.

Perennial snow, or firn, covers 80 % of the Greenland ice sheet and has the capacity to retain part of the surface meltwater, buffering the ice sheet’s contribution to sea level. Multi-layer firn models are traditionally used to simulate the firn processes and estimate meltwater retention. We present the output from nine firn models, forced by weather-station-derived mass and energy fluxes at four sites representative of the dry snow, percolation, ice slab and firn aquifer areas. We compare the model outputs and evaluate them against in situ observations. Models that explicitly account for deep meltwater percolation overestimate percolation depth and consequently firn temperature at the percolation and ice slab sites although they accurately simulate the recharge of the firn aquifer. Models using Darcy's law and a bucket scheme compare favourably to observations at the percolation site but only the Darcy models accurately simulate firn temperature and thus meltwater percolation at the ice slab site. We find that Eulerian models, that transfer firn through fixed layers, smooth sharp gradients in firn temperature and density over time. From the model spread, we find that simulated densities (respectively temperature) have an uncertainty envelope of ±60 kg m−3 (resp. ±14 °C) in the dry snow area and up to ±280 kg m−3 (resp. ±15–18 °C) at warmer sites.

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.

During spring 2017, we conducted research expeditions to the SIGMA-A site, which is located on the northwestern Greenland Ice Sheet. We maintained an automated weather station (AWS) to enable continuous meteorological observations. We extended 1.5-m long poles of the AWS and replaced two aerovane sensors, two thermo-hydrometers and an ultrasonic snow gauge. We also drilled an ice core and recovered a core with a total length of 60.06 m, conducted stratigraphic observations, and measured the density of the ice core. In addition, we conducted snow-pit observations and snow sampling, measured the specific surface area of snow using near-infrared reflectance, performed sunphotometry observations, and measured the spectral albedo. To schedule research activities in the field camp and helicopter pick-up flights, we received weather forecasts from the Meteorological Research Institute of Japan through the Internet using a satellite phone every day. We took a male dog to the field camp to alert us to approaching animals.

To better understand the processes controlling recent mass loss of peripheral glaciers and ice caps in northwestern Greenland, we measured surface mass balance (SMB), ice velocity and near-surface ice temperature on Qaanaaq Ice Cap in the summers of 2012–16. The measurements were performed along a survey route spanning the terminus of an outlet glacier to the upper reaches (243–968 m a.s.l.). The ice-cap-wide SMB ranged from −1.10 ± 0.29 to −0.13 ± 0.26 m w.e. a−1for the years from 2012/13 to 2015/16. Mass balance showed substantially large fluctuations over the study period under the influence of summer temperature and snow accumulation. Ice velocity showed seasonal speedup only in the summer of 2012, suggesting an extraordinary amount of meltwater penetrated to the bed and enhanced basal ice motion. Ice temperature at a depth of 13 m was −8.0°C at 944 m a.s.l., which was 2.5°C warmer than that at 243 m a.s.l., suggesting that ice temperature in the upper reaches was elevated by refreezing and percolation of meltwater. Our study provided in situ data from a relatively unstudied region in Greenland, and demonstrated the importance of continued monitoring of these processes for longer timespans in the future.

A long-term Northern Hemisphere (NH) daily 5-km snow cover extent (SCE) product (JASMES) was developed by the application of a consistent objective snow cover mapping algorithm to data from historical optical sensors on polar orbiting satellites from 1978 to 2015. A conventional decision tree algorithm with multiple threshold tests was employed to analyze radiances for the five spectral bands available across the full analysis period. The accuracies of the analyzed SCE maps were evaluated against in-situ snow data measured at ground stations along with the SCE maps from the National Oceanic and Atmospheric Administration Climate Data Record (NOAA-CDR) product. The evaluation showed the JASMES product to have a more temporally stable producer's accuracy (PA; 1–omission error) than NOAA, which is a key factor in the analysis of long-term SCE trends. Comparison of seasonal NH SCE trends from the two products showed NOAA to have opposite trends to those of JASMES in the fall and winter seasons, and to have overestimated SCE decreasing trends in the spring and summer. These tendencies are consistent with the increasing spatial and temporal resolutions of information over time, which were used in generating the NOAA snow analysis. An estimation of unbiased SCEs based on the accuracies of SCE maps also endorses the long-term trends of the JASMES product. The JASMES NH seasonal SCE exhibited negative slopes in all seasons but was only statistically significant in the summer (JJA) and fall (SON). Delayed snow cover onset was observed to be the main driver of decreasing annual snow duration (SCD) trends. The spatial pattern of annual SCD trends exhibited noticeable asymmetry between continents, with the largest significant decreases observed over western Eurasia with relatively few statistically significant decreases over North America.

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.

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.

Monitoring particulate matter is essential to alert the public about health risks. The Terra/MODIS true color image clearly captured a yellow band over Hokkaido prefecture in Japan on 7 March 2016. We investigated whether this event was an Asian dust (Kosa) transport or not with the ground-based observations in Sapporo and Takikawa in Hokkaido and NASA's MERRA-2 re-analysis data. The timing of increased particle number concentrations (PNCs; greater than 0.5 mu m) was clearly measured by a low-cost aerosol sensor at Sapporo and Takikawa in the early afternoon. For this particle size range, the PNC by this aerosol sensor had greater agreement with another commercial instrument for the 1-hourly mean data. The lidar data at Takikawa and NASA's AERONET at Sapporo also implied the increased dust particles (i.e., dominance of non-spherical and coarse particles, respectively), which supported that the PNC increase was due to the dust transport. The hourly PM2.5 data in Sapporo significantly increased in the evening rather than around the noon to early afternoon. We concluded that this event was judged as an Asian dust (Kosa) event in Hokkaido starting from the early afternoon, which was, however, not reported by Japan Meteorological Agency (JMA) based on their visible observations.

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.

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 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.