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Biological processes on snow and glacier surfaces in the Arctic region play a key role causing albedo reduction called as “Bio-albedo effect” due to blooms of snow and glacier phototrophs. Because the bio-albedo effect varies temporally and spatially due to their biological properties including growth, death and migration, the biological processes need to be separated from accumulation processes of the other impurities such as aeolian mineral dust and black carbon. In addition, different processes causing the bio-albedo effect, which are known as red snow, dark ice and cryoconite holes, are observed in the Arctic snowpacks and glaciers. To understand the bio-albedo effect quantitatively, a numerical model to reproduce such biological processes as well as a physically based albedo model should be established. We recently established several numerical models: the snow algae model to simulate red snow phenomena caused by snow algal blooms (Onuma et al., 2020; 2022a), the glacier algae model to simulate dark ice phenomena caused by glacier algal blooms (Onuma et al., 2022b) and the cryoconite hole model to simulate vertical dynamics of cryoconite holes (Onuma et al., In prep.). In this study, we simulate spatio-temporal changes in algal abundance and bio-albedo effect in Greenland Ice Sheet since 2000 using regional climate or land surface models coupling with the established models. The simulated spatio-temporal changes are evaluated using a polar-orbit satellite, Global Change Observation Mission for Climate (GCOM-C) which carries an optical sensor capable of multi-channel observation at wavelengths from near-UV to thermal infrared wavelengths (380nm to 12µm). In addition, we also use GCOM-W satellite with a microwave sensor to discuss relationship between snow/ice surface melt periods and algal blooms. The detailed discussion will be presented at the meeting.References[1] Y. Onuma, N. Takeuchi, S. Tanaka, N. Nagatsuka, M. Niwano and T. Aoki, Physically based model of the contribution of red snow algal cells to temporal changes in albedo in northwest Greenland. The Cryosphere, 14, 2087-2101. doi:10. 5194/tc-14-2087-2020 (2020)[2] Y. Onuma, K. Yoshimura and N. Takeuchi, Global simulation of snow algal blooming by coupling a land surface and newly developed snow algae models, Journal of Geophysical Research: Biogeosciences, 127 (2), e2021JG006339. doi:10.1029/2021JG006339 (2022a).[3] Y. Onuma, N. Takeuchi, J. Uetake, M. Niwano, S. Tanaka, N. Nagatsuka and T. Aoki, Modeling seasonal growth of phototrophs on bare ice on the Qaanaaq Ice Cap, northwestern Greenland. Journal of Glaciology, 1-13. doi:10.1017/jog.2022.76 (2022b)

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.

We reconstructed surface mass balance (SMB) around Dome Fuji, Antarctica, over the last 5000 years using the data from 15 shallow ice cores and seven snow pits. The depth–age relationships for the ice cores were determined by synchronizing them with a layer-counted ice core from West Antarctica (WAIS Divide ice core) using volcanic signals. The reconstructed SMB records for the last 4000 years show spatial patterns that may be affected by their locations relative to the ice divides around Dome Fuji, proximity to the ocean, and wind direction. The SMB records from the individual ice cores and snow pits were stacked to reconstruct the SMB history in the Dome Fuji area. The stacked record exhibits a long-term decreasing trend at -0.037±0.005 kg m−2 per century over the last 5000 years in the preindustrial period. The decreasing trend may be the result of long-term surface cooling over East Antarctica and the Southern Ocean and sea ice expansion in the water vapor source areas. The multidecadal to centennial variations of the Dome Fuji SMB after detrending the record shows four distinct periods during the last millennium: a mostly negative period before 1300 CE, a slightly positive period from 1300 to 1450 CE, a slightly negative period from 1450 to 1850 CE with a weak maximum around 1600 CE, and a strong increase after 1850 CE. These variations are consistent with those of previously reconstructed SMB records in the East Antarctic plateau. The low accumulation rate periods tend to coincide with the combination of strong volcanic forcings and solar minima for the last 1000 years, but the correspondence is not clear for the older periods, possibly because of the lack of coincidence of volcanic and solar forcings or the deterioration of the SMB record due to a smaller number of stacked cores.

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, Thu. 17 Nov.

The 13th Symposium on Polar Science/Special session [S] Towards the next six-year Japanese Antarctic Research Project (phase X), Tue. 15 Nov.

We reconstructed surface mass balance (SMB) around Dome Fuji, Antarctica, over the last 5000 years using the data from 15 shallow ice cores and 7 snow pits. The depth-age relationships for the ice cores were determined by synchronizing them with a layer-counted ice core from West Antarctica (WAIS Divide ice core) using volcanic signals. The reconstructed SMB records for the last 4000 years show spatial patterns that may be affected by their locations relative to the ice divides around Dome Fuji, proximity to the ocean, and wind direction. The SMB records from the individual ice cores and snow pits were stacked to reconstruct the SMB history in the Dome Fuji area. The stacked record exhibits a long-term decreasing trend at −0.037±0.005 kg m-2 per century over the last 5000 years in the preindustrial period. The decreasing trend may be the result of long-term surface cooling over East Antarctica and the Southern Ocean, and sea-ice expansion in the water vapor source areas. The multidecadal to centennial variations of the Dome Fuji SMB after detrending the record shows four distinct periods during the last millennium: mostly negative period before 1300 C.E., slightly positive for 1300–1450 C.E., slightly negative for 1450–1850 C.E. with a weak maximum around 1600 C.E., and strong increase after 1850 C.E. These variations are consistent with those of previously reconstructed SMB records in the East Antarctic plateau. The low accumulation rate periods tend to coincide with the combination of strong volcanic forcings and solar minima for the last 1000 years, but the correspondence is not clear for the older periods, possibly because of the lack of coincidence of volcanic and solar forcings, or the deterioration of the SMB record due to smaller number of stacked cores.

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

Glaciers are key components of the Asian water towers and provide water to large downstream communities for domestic, agricultural and industrial uses. In the Nepal Himalaya, the Indian Summer Monsoon dominates climate, and results in a complex meteorology and simultaneous accumulation and ablation that complicate the quantification of snow processes. Assessing solid precipitation input, especially in the upper accumulation area (> 6000 m a.s.l.), remains key to understanding recent mass losses. Catchment-scale glacio-hydrological modelling in the Himalaya has to date mostly relied on temperature-index or intermediate-complexity enhanced temperature-index methods, but recent studies have shown that such approaches can lead to inaccurate amounts of melt, especially at high elevations where refreezing, sublimation and avalanches influence the snow depth variability. The Trakarding–Trambau Glacier system experienced significant mass loss over the last decades, and recent field measurements of meteorology and glacier change present the opportunity to examine these problems with physically-based and spatially-resolved atmospheric and glacio-hydrological modelling.We combine a novel non-hydrostatic atmospheric model (NHM; atmospheric core of the cryosphere-oriented regional climate model NHM-SMAP) and an advanced land surface model at cloud-permitting hyper-resolution (~ 100 m) to explore the role of snow processes in the water balance of this glacierized catchment. We force the land-surface model of the catchment with dynamically downscaled, hourly outputs from NHM for the 2018-2019 hydrological year. We evaluate the NHM output using available in-situ meteorological observations and evaluate the land surface model skills and process representation with in-situ mass balance observations, remotely sensed surface elevation change and snow cover. Coupling of the two types of models is unprecedented in the Himalaya, and holds promise to reveal processes that cannot be explicitly assessed by simpler models or forcing data. We investigate the contribution of sublimation and precipitation partition to the glacier mass balance and catchment runoff, and analyze the difference in mass balance and its drivers between the debris-covered and debris free-glaciers. To place this very novel type of simulations into the context of current research, we compare our NHM-forced simulations with simulations forced by station data and ERA5-Land reanalysis. Finally, we evaluate the effect of spatial resolution (50 m, 100 m, 200 m) on model performance and process representation. Our results highlight the potential of sophisticated models based on the calculations of energy and mass fluxes to unravel the complex processes that shape the response of Himalayan catchments, and provide an assessment of their skills as a function of spatial resolution.

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.

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.

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.

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.

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

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.