ALBERT

Description: We present a reconstruction of historical (1950–2014) surface mass balance (SMB) of the Greenland ice sheet (GrIS) using a high-resolution regional climate model (RACMO2; ~ 11 km) to dynamically downscale the climate of the Community Earth System Model version 2 (CESM2; ~ 111 km). After further statistical downscaling to 1 km spatial resolution, evaluation using in situ SMB measurements and remotely sensed GrIS mass change shows good agreement, including the recently observed acceleration in surface mass loss (2003–2014). Comparison with an ensemble of eight previously conducted RACMO2 simulations forced by climate reanalysis demonstrates that the current product accurately reproduces the long term average and inter-annual variability of individual SMB components, and captures the recent increase in meltwater runoff that accelerated GrIS mass loss. This means that, for the first time, an Earth System Model (CESM2), without assimilating observations, can be used to reconstruct historical GrIS SMB and the mass loss acceleration that started in the 1990s. This paves the way for attribution studies of future GrIS mass loss projections and contribution to sea level rise.

Description: Melt occurrence in Antarctica is derived from L-band observations from the Soil Moisture and Ocean Salinity (SMOS) satellite between the austral summer 2010/11 and 2017/18. The detection algorithm is adapted from a threshold method previously developed for 19 GHz passive microwave measurements from Special Sensor Microwave Imagers (SSM/I, SSMIS). The comparison of daily melt occurrence retrieved from 1.4 GHz and 19 GHz observations shows an overall close agreement, but a lag of few days is usually observed by SMOS at the beginning of the melt season. To understand the difference, we performed a theoretical analysis using a microwave emission radiative transfer model that shows that the sensitivity of 1.4 GHz signal to liquid water is significantly weaker than at 19 GHz if the water is only present in the uppermost tens of centimeters of the snowpack. Conversely, 1.4 GHz measurements are sensitive to water when spread over at least 1 m and when present at depth, up to hundreds of meters. This is explained by the large penetration depth in dry snow and by the long wavelength (21 cm). We conclude that SMOS and higher frequency radiometers provide interesting complementary information on melt occurrence and on the location of the water in the snowpack.

Description: The mass balance of many valley glaciers is enhanced by the presence of melt hotspots within otherwise continuous debris cover. We assess the effect of debris, melt hotspots, and ice dynamics on the thinning of Kennicott Glacier in three companion papers. In Part A we report in situ measurements from the debris-covered tongue. In Part B, we develop a method to delineate ice cliffs using high-resolution imagery and produce distributed mass balance estimates. Here in Part C we describe feedbacks controlling rapid thinning under thick debris. Despite the extreme abundance of ice cliffs on Kennicott Glacier, average melt rates are strongly suppressed downglacier due to thick debris. The estimated melt pattern therefore appears to reflect Østrem’s curve (the debris thickness-melt relationship). As Kennicott Glacier has thinned over the last century Østrem’s curve has manifested itself in two process domains on the glacier surface. The portion of the glacier affected by the upper-limb of Østrem’s curve corresponds to high melt, melt gradients, and ice dynamics, as well as high ice cliff and stream occurrence. The portion of the glacier affected by the lower-limb of Østrem’s curve corresponds to low melt, melt gradients, and ice dynamics, as well as high ice cliff and stream occurrence. The upglacier end of the zone of maximum thinning on Kennicott Glacier occurs at the boundary between these process domains and the bend in Østrem’s curve. The expansion of debris upglacier appears to be linked to changes in the surface velocity pattern through time. In response to climate warming, debris itself may therefore control where rapid thinning occurs on debris-covered glaciers. Ice cliffs are most abundant at the upglacier end of the zone of maximum thinning.

Description: The mass balance of many valley glaciers is enhanced by the presence of ice cliffs within otherwise continuous debris cover. We assess the effect of debris and ice cliffs on the thinning of Kennicott Glacier in three companion papers. In Part A we report in situ measurements from the debris-covered tongue. Here, in Part B, we develop a method to delineate ice cliffs using high-resolution imagery and use empirical relationships from Part A to produce distributed mass balance estimates. In Part C we describe feedbacks that contribute to rapid thinning under thick debris. Ice cliffs cover 11.7 % of the debris-covered tongue, the most of any glacier studied to date, and they contribute 19 % of total melt. Ice cliffs contribute an increasing percentage of melt the thicker the debris cover. In the lowest 4 km of the glacier, where debris thicknesses are greater than 20 cm, ice cliffs contribute 40 % of total melt. Surface lake coverage doubled between 1957 and 2009, but lakes do not occur across the full extent of the zone of maximum glacier thinning. Despite abundant ice cliffs and expanding surface lakes, average melt rates are suppressed by debris, the pattern of which appears to reflect the debris thickness-melt relationship (or Østrem’s curve). This suggests that, in addition to melt hotspots, the decline in ice discharge from upglacier is an important contributor to the thinning of Kennicott glacier under thick debris.

Description: The mass balance of many Alaskan glaciers is perturbed by debris cover. Yet the effect of debris on glacier response to climate change in Alaska has largely been overlooked. In three companion papers we assess the role of debris, ice dynamics, and surface processes in thinning Kennicott Glacier. In Part A, we report in situ measurements from the glacier surface. In Part B, we develop a method to delineate ice cliffs using high-resolution imagery and produce distributed mass balance estimates. In Part C we explore feedbacks that contribute to glacier thinning. Here in Part A, we describe data collected in the summer of 2011. We measured debris thickness (109 locations), sub-debris melt (74), and ice cliff backwasting (60) data from the debris-covered tongue. We also measured air-temperature (3 locations) and internal-debris temperature (10). The mean debris thermal conductivity was 1.06 W (m C)−1, increasing non-linearly with debris thickness. Mean debris thicknesses increase toward the terminus and margin where surface velocities are low. Despite the relatively high air temperatures above thick debris, the melt-insulating effect of debris dominates. Sub-debris melt rates ranged from 6.5 cm d−1 where debris is thin to 1.25 cm d−1 where debris is thick near the terminus. Ice cliff backwasting rates varied from 3 to 14 cm d−1 with a mean of 7.1 cm d−1 and tended to increase as elevation declined and debris thickness increased. Ice cliff backwasting rates are similar to those measured on debris-covered glaciers in High Mountain Asia and the Alps.

Description: The roughness of glacier surfaces is an important factor governing surface albedo and turbulent heat transfer. Previous studies have not directly observed spatial and temporal variation in surface roughness (z0) over a whole glacier and whole melting season. Such observations can do much to help us understand variation in z0 and thus variations in albedo and turbulent heat transfer. This study, at the August-one ice cap in the Qilian mountains, collected three-dimensional ice surface data at plot-scale, using both automatic and manual close-range digital photogrammetry. Data was collected from sampling sites spanning the whole glacier for the whole of the melting season. The automatic site collected daily photogrammetric measurements from July to September of 2018 for a plot near the center of the ice cap; during this time, snow cover gave place to ice and then returned to snow. Z0 was calculated from this data. Manual measurements were taken at sites dotted from terminal to top; they showed that z0 was larger at the snow and ice transition zone than in areas fully snow or ice covered. This zone moved up the ice cap during the melting season. It is clear that persistent snowfall and rainfall both reduce z0. Using data from a meteorological station near the automatic photogrammetry site, we were able to calculate surface energy balances over the course of the melting season. We found that high or rising turbulent heat as a component of surface energy balance tended to produce a smooth ice surface; low or decreasing turbulent heat tended to produce a rougher surface.

Description: The accurate knowledge of variations of melt ponds is important for understanding Arctic energy budget due to its albedo-transmittance-melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) from the MODIS surface reflectance. We construct an ensemble-based deep neural network and use in-situ observations of MPF from multi-sources to train the network. The results show that our derived MPF is in good agreement with the observations, and relatively outperforms the MPF retrieved by University of Hamburg. Built on this, we create a new MPF data from 2000 to 2017 (the longest data in our knowledge), and analyze the spatial and temporal variability of MPF. It is found that the MPF has significant increasing trends from late July to early September, which is largely contributed by the MPF over the first-year sea ice. The analysis based on our MPF during 2000–2017 confirms that the integrated MPF to late June does promise to improve the prediction skill of seasonal Arctic sea ice minimum. However, our MPF data shows concentrated significant correlations first appear in a band, extending from the eastern Beaufort Sea, through the central Arctic, to the northern East Siberian and Laptev Seas in early-mid June, and then shifts towards large areas of the Beaufort Sea, Canadian Arctic, the northern Greenland Sea and the central Arctic basin.

Description: Inuit have reported greater inter-annual variability in seasonal sea ice conditions. For Deception Bay (Nunavik), an area prized for seal and caribou hunting, an increase in solid precipitation and a shorter snow cover period is expected in the near future. In this context, and considering ice-breaking transport in the fjord by mining companies, we monitored sea ice in the area for three seasons of ice between 2015 and 2018. This article presents a case study for the combined use of TerraSAR- X and time-lapse photography time-series in order to monitor snow-covered sea ice seasonal processes. The X-band median backscattering is shown to reproduce the seasonal evolution expected from C-band data. Two different freeze-up and breakup processes are characterized. New X-band backscattering values from newly formed ice types are reported. The monitoring approach presented in this article has the potential to be applied in other remote locations, and processes outlined here may inform our understanding of other fjords or bays where ice-breakers transit.

Description: Combining multiple data sources with multi-physics simulation frameworks offers new potential to extend snow model inter-comparison efforts to the Himalaya. This study evaluates the importance and performance of different snowpack process representations for simulating snow cover and runoff dynamics in the region. Focusing on the Astore catchment in the upper Indus basin, a spatially distributed version of the Factorial Snowpack Model (FSM) was driven by climate fields from the High Asia Refined Analysis (HAR) dynamical downscaling product. Ensemble performance was evaluated using observed runoff and MODIS remote sensing of snow-covered area, albedo and land surface temperature. The results show that FSM ensemble spread depends primarily on the interactions between parameterisations of albedo and snowpack hydrology when applied in the western Himalaya. These interactions incur variation in the importance of other model choices, most notably the atmospheric stability adjustment. Although no single FSM configuration performs best in all years, applying the prognostic albedo parameterisation while accounting for liquid water retention, refreezing and drainage leads to the highest overall performance. Years when this is not the case tend to coincide with probable inaccuracies in HAR climate inputs. While the results indicate that ensemble spread and errors may be notably lower in anomaly space, FSM configurations show substantial differences in their absolute sensitivity to climate variation. Therefore, a subset of the ensemble should be retained for climate change projections, namely those members including both prognostic albedo and snowpack hydrology, while additional stability adjustment options should be tested.

Description: Based on a snow depth (SD) dataset retrieved from meteorological stations, this experiment explored snow indices including SD, snow covered days (SCDs), and snow phenology variations in China from 1952 to 2012. The results indicated that the snow in China exhibits regional differences, and the snow cover is mainly concentrated in three snow cover areas in Northeast China, northern Xinjiang and the Tibetan Plateau. In China, the annual average SD showed an increasing trend, and the increases in the average snow depth (SDaverage), cumulative snow depth (SDoverall) and maximum snow depth (SDmax) reached 0.04 cm, 0.05 cm and 0.07 cm per decade, respectively. The significant increases were mainly concentrated in areas higher than 40° N latitude, especially in Northeast China. The SDaverage, SDoveralland SDmax jump points are mainly in 1956, 1957, 1978, and 1987. In the first main period, the SDoverall oscillation in China is relatively stable, and its average period is approximately 13 years. The SCDs showed an increasing trend, with an increase of 0.5 days per decade. The significant increases in SCDs were also concentrated in areas higher than 40° N latitude, especially in Northeast China.However, in the Tibetan Plateau, the decrease in the SCDs reached 0.1 days per decade. In snow phenology, the snow duration days (SDDs) of China decreased, and 17.4 % of the meteorological stations showed significant decreasing trends. This result is mainly caused by the postponement of the snow onset date (SOD) and the advancement of the snow end date (SED). Geographical factors, including latitude, longitude and altitude, affect snow cover distribution directly and indirectly. The squared multiple correlations of SDDs and SCDs are greater than 0.9. Among the effects of SDDs and SCDs, the largest standardized total effect is from altitude on the SDDs, and the effect reaches 0.8.