Investigating cold based summit glaciers through direct access to basal ice: A case study constraining the maximum age of Chli Titlis glacier, Switzerland

Cold glaciers at the highest locations of the European Alps have been investigated with great success by drilling ice cores to retrieve their stratigraphic climate records. Findings like the Oetztal ice man have demonstrated that small ice bodies at summit locations of comparatively lower altitudes may also contain old ice if comprising ice frozen to the underlying bedrock. In this case, constraining the maximum age of their basal ice part may help to identify past periods with minimum ice extent in the Alps. Facing ongoing warming and recent years with extremely negative glacier mass balance, these sites may not preserve their unique climate information for much longer, however. Since sampling and dating the lowermost ice is essential, and usually requires substantial logistical (drilling) effort, we utilize here the direct access to basal ice offered by an existing ice cave at Chli Titlis (3030 m), Central Switzerland. Our dedicated approach comprises a combination of standard glaciological tools with the analysis of the isotopic and physical properties and sophisticated radiocarbon dating techniques. By this means we demonstrate that, in comparison to an earlier study at Chli Titlis, stagnant cold basal ice conditions still exist fairly unchanged more than 25 years after the pioneering exploration. Our radiocarbon analysis constrains the maximum age of the ice at Chli Titlis to about 5000 years before present. By this means, the approach presented here will contribute to a future systematic investigation of cold-based summit glaciers also targeting the Eastern Alps.

Attribution of Greenland's ablating ice surfaces on ice sheet albedo using unmanned aerial systems

Surface albedo, a primary control on the amount of energy available for melt, has considerable spatial heterogeneity across the Greenland ice sheet ablation area. However, the relative importance of distinct surface types on albedo remains unclear. In this study, the causes of mesoscale (10² to 10³ m) albedo variability are assessed using high resolution (decimetre-scale) digital imagery and broadband albedo data acquired by a fixed-wing unmanned aerial system. We characterize the reflectance properties and terrain roughness associated with six distinct surface types identified from a 25 km longitudinal transect across the ablating dark region of the Kangerlussuaq sector. Principal component analysis of the fractional area of each surface type versus coincident Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data reveals the relative importance of each surface type. The highest correlation with mesoscale albedo was the fractional area of distributed impurities. Although not the darkest surface type, their extensive coverage meant that they could explain 65 % of the albedo variability across the survey transect including the presence of the dark region. In contrast, the 2 % mean surface water coverage across our survey transect could only explain 12 % of albedo variation and crevasses, only 17 %. Localised cryoconite patches have the lowest albedo signature but comprise less than 1 % of the survey area and do not appear to reduce mesoscale albedo. We anticipate further reduction in ablation area albedo under future warming as localized areas of distributed impurities, supraglacial water and crevassing increase in extent and conclude that current bare ice area albedo models may advance significantly by representing the evolution of the surface types identified in this study.

Sea ice drift from Sentinel-1 SAR imagery using open source feature tracking

A computational efficient, open source feature tracking algorithm, called ORB, is adopted and tuned for sea ice drift retrieval from Sentinel-1 SAR images. The best suitable setting and parameter values have been found using four representative Sentinel-1 image pairs. A new quality measure for feature tracking algorithms is introduced utilising the distribution of the resulting vector field. The performance of the algorithm is compared with two other feature tracking algorithms (SIFT and SURF). Applied on a test image pair acquired over Fram Strait, the tuned ORB algorithm produces the highest number of vectors (6920, SIFT: 1585 and SURF: 518) while being computational most efficient (66 s, SIFT: 182 s and SURF: 99 s using a 2,7 GHz processor with 8 GB memory). For validation purpose, 350 manually drawn vectors have been compared with the closest calculated vectors and the resulting root mean square distance is 609.9 m (equivalent to 7.5 pixel). All test image pairs show a significant better performance of the HV channel. On average, around 4 times more vectors have been found using HV polarisation. All software requirements necessary for applying the presented feature tracking algorithm are open source to ensure a free and easy implementation.

Non-climatic signal in ice core records: lessons from Antarctic mega-dunes

We present the results of glaciological investigations in the mega-dune area located 30 km to the east from Vostok Station (central East Antarctica) implemented during the 58th, 59th and 60th Russian Antarctic Expedition (January 2013–January 2015). Snow accumulation rate and isotope content (δD, δ18O and δ17O) were measured along the 2 km profile across the mega-dune ridge accompanied by precise GPS altitude measurements and GPR survey. It is shown that the spatial variability of snow accumulation and isotope content covaries with the surface slope. The accumulation rate regularly changes by one order of magnitude within the distance < 1 km, with the reduced accumulation at the leeward slope of the dune and increased accumulation in the hollow between the dunes. At the same time, the accumulation rate averaged over the length of a dune wave (22 mm we) corresponds well with the value obtained at Vostok Station, which suggests no additional wind-driven snow sublimation in the mega-dunes compared to the surrounding plateau. The snow isotopic composition is in negative correlation with the snow accumulation. Analyzing dxs/δD and 17O-excess/δD slopes, we conclude that the spatial variability of the snow isotopic composition in the mega-dune area could be explained by post-depositional snow modifications. Using the GPR data, we estimated the apparent dune drift velocity (4.6 ± 1.1 m yr−1). The full cycle of the dune drift is thus about 410 years. Since the spatial anomalies of snow accumulation and isotopic composition are supposed to drift with the dune, an ice core drilled in the mega-dune area would exhibit the non-climatic 410 year cycle of these two parameters. We simulated a vertical profile of snow isotopic composition with such a non-climatic variability, using the data on the dune size and velocity. This artificial profile is then compared with the real vertical profile of snow isotopic composition obtained from a core drilled in the mega-dune area. We note that the two profiles are very similar. The obtained results are discussed in terms of interpretation of data obtained from ice cores drilled beyond the mega-dune areas.

Which are the highest peaks in the US Arctic? Fodar settles the debate

While creation of the United States Geological Survey's topographic maps of the eastern Alaska Arctic were an outstanding accomplishment for their time, they nonetheless contained significant errors when made in the late 1950s. One notable discrepancy relates to the tallest peak in the US Arctic: USGS maps of different scale alternate between Mt Chamberlin and Mt Isto. Given that many of the peaks here are close in height and covered with glaciers, recent climate change may also have changed their height and their order. We resolved these questions using fodar, a new airborne photogrammetric technique that utilizes Structure-from-Motion (SfM) software and requires no ground control, and validated it using GPS measurements on the peaks and using airborne lidar. Here we show that Mt Chamberlin is currently the 3rd tallest peak and that the order and elevations of the five tallest mountains in the US Arctic are Mt Isto (2735.6 m), Mt. Hubley (2717.6 m), Mt. Chamberlin (2712.3 m), Mt. Michelson (2698.1 m), and an unnamed peak (2694.9 m); these orthometric heights relative to the NAVD88 vertical datum, established with use of GEOID12B. We find that it is indeed plausible that this ranking has changed over time and may continue to change as summit glaciers continue to shrink, though Mt Isto will remain the highest under current climate trends. Mt Isto is also over 100 m higher than the highest peak in the Canadian Arctic, making it the highest peak in the North American Arctic. Fodar elevations compared to within a few centimeters of our ground-based GPS measurements of the peaks made a few days later and our complete validation assessment indicates a measurement uncertainty of better than ±20 cm (95 % RMSE). By analyzing time-series of fodar maps, we were able to detect topographic change on the centimeter-level on these steep slopes, indicating that fodar can be used to measure mountain snow packs for water resource availability or avalanche danger, to measure glacier volume change and slope subsidence, and many other applications of benefit to society. Compared to lidar, the current state-of-the-art in airborne topographic mapping, we found this SfM technique as accurate, more scientifically useful, and significantly less expensive, suggesting that fodar is a disruptive innovation that will enjoy widespread usage in the future.

Greenland annual accumulation along the EGIG line, 1959–2004, from ASIRAS airborne radar and detailed neutron-probe density measurements

We report annual snow accumulation rates from 1959 to 2004 along a 250 km segment of the Expéditions Glaciologiques Internationales au Groenland (EGIG) line across central Greenland using Airborne SAR/Interferometric Radar Altimeter System (ASIRAS) radar layers and detailed neutron-probe (NP) density profiles. ASIRAS-NP accumulation rates are not statistically different (C.I. 95 %) from in situ EGIG accumulation measurements from 1985 to 2004. Below 3000 m elevation, ASIRAS-NP increases by 20 % for the period 1995 to 2004 compared to 1985 to 1994. Above 3000 m elevation, accumulation increases by 13 % for 1995–2004 compared to 1985–1994. Model snow accumulation results from the calibrated Fifth Generation Mesoscale Model modified for polar climates (Polar MM5) underestimate mean annual accumulation by 16 % compared to ASIRAS-NP from 1985 to 2004. We test radar-derived accumulation rates sensitivity to density using modelled density profiles in place of detailed NP data. ASIRAS radar layers combined with Herron and Langway (1980) model density profiles (ASIRAS-HL) produce accumulation rates within 3.5 % of ASIRAS-NP estimates. We suggest using Herron and Langway (1980) density profiles to calibrate radar layers detected in dry snow regions of ice sheets lacking detailed in situ density measurements, such as those observed by the IceBridge campaign.

On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model

This paper examines the ability of optical reflectance data assimilation to improve snow depth and snow water equivalent simulations from a detailed multilayer snowpack model. The direct use of reflectance data, instead of higher level snow products, rules out uncertainties due to commonly used retrieval algorithms. Data assimilation is performed with an ensemble-based method, the Sequential Importance Resampling Particle filter, to represent simulation uncertainties. Here, model uncertainties are essentially ascribed to meteorological forcings. An original method of stochastic perturbation is implemented to explicitly simulate the consequences of these uncertainties on the snowpack estimates. The assimilation of spectral reflectances from the MODerate Imaging Spectrometer (MODIS) sensor is examined, through twin experiments based on synthetic observations, over five seasons at the Col du Lautaret, located in the French Alps. Overall, the assimilation of MODIS-like data reduces root mean square errors (RMSE) on snow depth and snow water equivalent by a factor of 2. At this study site, the lack of MODIS data on cloudy days does not affect the assimilation performance significantly. The combined assimilation of MODIS-like reflectances and a few snow depth measurements throughout the 2010/11 season further reduces RMSEs by a factor of roughly 3.5. This work suggests that the assimilation of optical reflectances should become an essential component of spatialized snowpack simulation and forecast systems. The assimilation of real MODIS data will be investigated in future works.

Impacts of snow and organic soils parameterization on North-Eurasian soil temperature profiles simulated by the ISBA land surface model

In this study we analysed how an improved representation of snowpack processes and soil properties in the multi-layer snow and soil schemes of the ISBA land surface model impacts the simulation of soil temperature profiles over North-Eurasian regions. For this purpose, we refine ISBA's snow layering algorithm and propose a parameterization of snow albedo and snow compaction/densification adapted from the detailed Crocus snowpack model. We also include a dependency on soil organic carbon content for ISBA's hydraulic and thermal soil properties. First, changes in the snowpack parameterization are evaluated against snow depth, snow water equivalent, surface albedo, and soil temperature at a 10 cm depth observed at the Col de Porte field site in the French Alps. Next, the new model version including all of the changes is used over Northern-Eurasia to evaluate the model's ability to simulate the snow depth, the soil temperature profile and the permafrost characteristics. The results confirm that an adequate simulation of snow layering and snow compaction/densification significantly impacts the snowpack characteristics and the soil temperature profile during winter, while the impact of the more accurate snow albedo computation is dominant during the spring. In summer, the accounting for the effect of soil organic carbon on hydraulic and thermal soil properties improves the simulation of the soil temperature profile. Finally, the results confirm that this last process strongly influences the simulation of the permafrost active layer thickness and its spatial distribution.

Annual Greenland accumulation rates (2009–2012) from airborne Snow Radar

Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet (GrIS) through increasing surface melt, emphasizing the need to closely monitor surface mass balance (SMB) in order to improve sea-level rise predictions. Here, we quantify accumulation rates, the largest component of GrIS SMB, at a higher spatial resolution than currently available, using Snow Radar stratigraphy. We use a semi-automated method to derive annual-net accumulation rates from airborne Snow Radar data collected by NASA's Operation IceBridge from 2009 to 2012. An initial comparison of the accumulation rates from the Snow Radar and the outputs of a regional climate model (MAR) shows that, in general, the radar-derived accumulation matches closely with MAR in the interior of the ice sheet but MAR estimates are high over the southeast GrIS. Comparing the radar-derived accumulation with contemporaneous ice cores reveals that the radar captures the annual and long-term mean. The radar-derived accumulation rates resolve large-scale patterns across the GrIS with uncertainties of up to 11 %, attributed mostly to uncertainty in the snow/firn density profile.

Small-scale variation of snow in a regional permafrost model

The strong winds prevalent in high altitude and arctic environments heavily redistribute the snow cover, causing a small-scale pattern of highly variable snow depths. This has profound implications for the ground thermal regime, resulting in highly variable near-surface ground temperatures on the meter scale. Asymmetric snow distributions combined with the non-linear insulating effect of snow also mean that the spatial average ground temperature in a 1 km2 area can not necessarily be determined based on the average snow cover for that area. Land surface or permafrost models employing a coarsely classified average snow depth will therefore not yield a realistic representation of ground temperatures. In this study we employ statistically derived snow distributions within 1 km2 grid cells as input to a regional permafrost model in order to represent sub-grid variability of ground temperatures. This is shown to improve the representation of both the average and the total range of ground temperatures: the model results show that we reproduce observed sub-grid ground temperature variations of up to 6 °C, with 98 % of borehole observations within the modelled temperature range. Based on this more faithful representation of ground temperatures, we find the total permafrost area of mainland Norway to be nearly twice as large as what is modelled without a sub-grid approach.