Snow Melting Performance of Graphene Composite Conductive Concrete in Severe Cold Environment

The use of conductive concrete is an effective way to address snow and ice accretion on roads in cold regions because of its energy saving and high efficiency without interruption of traffic. Composite conductive concrete was prepared using graphene, carbon fiber, and steel fiber, and the optimum dosage of graphene was explored with resistivity as the criterion. Subsequently, under the conditions of an initial temperature of −15 °C and a wind speed of 20 km/h, the extremely severe snow event environment in cold regions was simulated. The effects of electrode spacing and electric voltage on snow melting performance of conductive concrete slab were explored. Results showed that graphene can significantly improve the conductivity of conductive concrete; the optimal content of graphene was 0.4% of cement mass in terms of resistivity. The snow-melting power of conductive concrete slab decreased with increase in electrode spacing and increased with increase in on-voltage. For an optimal input voltage of 156 V and an optimal electrode spacing of 10 cm, the time required to melt a 24 h snow thickness (21 cm), accumulated during a simulated severe snow event, was only 2 h, which provides an empirical basis for the application of graphene composite conductive concrete to pavement snow melting in cold regions.

A Distributed Temperature Profiling System for Vertically and Laterally Dense Acquisition of Soil and Snow Temperature

Measuring soil and snow temperature with high vertical and lateral resolution is critical for advancing the predictive understanding of thermal and hydro-biogeochemical processes that govern the behavior of environmental systems. Vertically resolved soil temperature measurements enable the estimation of soil thermal regimes, freeze/thaw layer thickness, thermal parameters, and heat and/or water fluxes. Similarly, they can be used to capture the snow thickness and the snowpack thermal parameters and fluxes. However, these measurements are challenging to acquire using conventional approaches due to their total cost, their limited vertical resolution, and their large installation footprint. This study presents the development and validation of a novel Distributed Temperature Profiling (DTP) system that addresses these challenges. The system leverages digital temperature sensors to provide unprecedented, finely resolved depth-profiles of temperature measurements with flexibility in system geometry and vertical resolution. The integrated miniaturized logger enables automated data acquisition, management, and wireless transfer. A novel calibration approach adapted to the DTP system confirms the factory-assured sensor accuracy of +/−0.1 °C and enables improving it to +/−0.015 °C. Numerical experiments indicate that, under normal environmental conditions, an additional error of 0.01 % in amplitude and 70 seconds time delay in amplitude for a diurnal period can be expected, owing to the DTP housing. We demonstrate the DTP systems capability at two field sites, one focused on understanding how snow dynamics influence mountainous water resources, and the other focused on understanding how soil properties influence carbon cycling. Results indicate that the DTP system reliably captures the dynamics in snow thickness, and soil freezing and thawing depth, enabling advances in understanding the intensity and timing in surface processes and their impact on subsurface thermal-hydrological regimes. Overall, the DTP system fulfills the needs for data accuracy, minimal power consumption, and low total cost, enabling advances in the multiscale understanding of various cryospheric and hydro-biogeochemical processes.

MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years

We combine satellite data products to provide a first and general overview of the physical sea ice conditions along the drift of the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition and a comparison with previous years (2005–2006 to 2018–2019). We find that the MOSAiC drift was around 20 % faster than the climatological mean drift, as a consequence of large-scale low-pressure anomalies prevailing around the Barents–Kara–Laptev sea region between January and March. In winter (October–April), satellite observations show that the sea ice in the vicinity of the Central Observatory (CO; 50 km radius) was rather thin compared to the previous years along the same trajectory. Unlike ice thickness, satellite-derived sea ice concentration, lead frequency and snow thickness during winter months were close to the long-term mean with little variability. With the onset of spring and decreasing distance to the Fram Strait, variability in ice concentration and lead activity increased. In addition, the frequency and strength of deformation events (divergence, convergence and shear) were higher during summer than during winter. Overall, we find that sea ice conditions observed within 5 km distance of the CO are representative for the wider (50 and 100 km) surroundings. An exception is the ice thickness; here we find that sea ice within 50 km radius of the CO was thinner than sea ice within a 100 km radius by a small but consistent factor (4 %) for successive monthly averages. Moreover, satellite acquisitions indicate that the formation of large melt ponds began earlier on the MOSAiC floe than on neighbouring floes.

Snow Patches and Their Influence on Coastal Erosion at Baydaratskaya Bay Coast, Kara Sea, Russian Arctic

The Arctic coastal environment is a very dynamic system and sensitive to any changes. In our research we demonstrate that nivation (snow patch activity) impacts the Arctic landscape especially in the coastal dynamic at the western part of Russian Arctic. During fieldwork, snowbanks were described and studied and their qualitative role in the development of coastal systems was revealed for Baydaratskaya Bay coast, the Kara Sea. On one side, the large snow cover protects the coastal slope from thermodenudation and thermoabrasion; on the other side, a thick layer of snow affects the ground temperature regime. During snow melting, snow patches contribute to the removal of material from the coastal slope. The quantitative effect of snow on the ground temperature regime was assessed according to numerical simulations. The critical snow thickness was determined based on a calculation. Critical snow thicknesses based on simulation and field data correlated well. The numerical simulation showed the talik formation under the snow patch. Talik size essentially depends on the freezing temperature of sediment (influenced by salinity). The changes of ground temperature regime might further generate thawing settlement of sediment under snow and contribute to beach topography, which might be a trigger for thermoabrasion.

The MOSAiC Drift: Ice conditions from space and comparison with previous years

We combine satellite data products to provide a first and general overview of the sea-ice conditions along the MOSAiC drift and a comparison with previous years. We find that the MOSAiC drift was around 25 % faster than the climatological mean drift, as a consequence of large-scale low-pressure anomalies prevailing around the Barents-Kara-Laptev Sea region between January and March. In winter (October–April), satellite observations show that the sea-ice in the vicinity of the Central Observatory (CO) was rather thin compared to the previous years along the same trajectory. Unlike ice thickness, satellite-derived sea-ice concentration, lead frequency, and snow thickness during winter month were close to the long-term mean with little variability. With the onset of spring and decreasing distance to Fram Strait, variability in ice concentration and lead activity increased. In addition, frequency and strength of deformation events (divergence and shear) were higher during summer than during winter. Overall, we find that sea-ice conditions observed close (~ 5 km) to the CO are representative for the wider (50 km and 100 km) surroundings. An exception is the ice thickness: Here we find that sea-ice near the CO (50 km radius) was 4 % thinner than sea-ice within a 100 km radius. Moreover, satellite acquisitions indicate that the formation of large melt ponds began earlier on the MOSAiC floe than on neighbouring floes.

Multi-Frequency Satellite Approaches for Snow on Sea Ice: first results from the POLAR+ Snow on Sea Ice ESA project

<p>Abstract: We propose new methods for multi-frequency snow thickness retrievals building on the legacy of the Arctic+ Snow project where we developed two products: the dual-altimetry Snow Thickness (DuST) and the Snow on Drifting Sea Ice (SnoDSI). The primary objective of this project is to investigate multi-frequency approaches to retrieve snow thickness over all types of sea ice surfaces in the Arctic and provide a state-of-the-art snow product. Our approach follows ESA ITT recommendations to prioritise satellite-based products and will benefit from the recent ‘golden era in polar altimetry’ with the successful launch of the laser altimeter ICESat-2 in 2018 complementing data provided by the rich fleet of radar altimeters, CryoSat-2, Sentinel-3 A/B, AltiKa. Our primary objective is to produce an optimal snow product over the recent ‘operational‘ period. This will be complemented by additional snow products covering a longer periods of climate relevance and making use of historical altimeters (Envisat, ICESat-1) and passive microwave radiometers for comparison purposes (SMOS, AMSRE, AMSR-2). In addition to snow thickness, and as a secondary objective, we will explore other snow characteristics (snow density, snow metamorphism, scattering horizon, roughness, etc) and compare these results with in-situ, airborne and other snow on sea ice products including from model studies and reanalysis on drifting sea ice products. In preparation to future multi-frequency mission we will put an emphasis on uncertainty analysis of our snow product, the impact of the snow on the sea ice thickness retrieval, and on climate physics via model runs with snow initialisation and data assimilation. Finally, learning from past and present campaings (i.e. CryoVex, MOSAiC) we will propose methodologies for effective future snow and sea ice thickness airborne validation campaigns via innovative inverse modelling approaches and airborne retrackers.</p><p> </p>

Improving Arctic Sea Ice Prediction Though Freeboard Assimilation

<p>With the presented work we aim to improve sea ice forecasts and our understanding of Arcitc sea ice formation though freeboard assimilation. Over the last years understanding Arctic sea ice changes and being able to make a reliable sea ice forecast has gained in importance. The central roll of Arctic sea ice extent in climate warming makes it a highly discussed topic in the climate research community. However a reliable Arctic sea ice forecast both on short term to seasonal time scales remains a challenge to be mastered, hinting that there are still many processes at play to be better understood. <br>One promising approach to improve forecasts has been to assimilate satellite sea ice data into numerical sea ice models. Mainly two parameters measured by satellites have been used for assimilation: Sea ice concentration, which is competitively easy to obtain from satellites measuring passive microwave emissions as for example obtained by the SMOS satellite, and sea ice thickness, which is not directly measured, but has to be calculated from surface elevation measurements, as for example obtained by Cryosat 2. Compering the skill, of assimilation products using sea ice thickness and sea ice concentration shows that sea ice thickness has a longer memory and is over all leading to a better performance then sea ice concentration assimilation. Knowing this, sea ice thickness assimilation is far from being straight forward. Surface elevation measurements, obtained from satellite altemitry measurements, have to be separated into snow and ice freeborad, by assuming a snow thickness, to derive sea ice thickness from. Most of the time this is done using a snow thickness climatology obtained from Soviet drift stations measuring snow over multi year ice during the period 1954-1991 with adaption over first year sea ice, where this climatology has proven to be overestimating snow thickness. The technique is widely used jet known to introduce an error. <br>To avoid errors caused by wrongly assumed snow covers the DMI and Aalborg University and DTU are at the moment collaborating on assimilating freebord instead of sea ice thickness into the CICE-NEMO modeling frame work using LARS NGen (LARS the Advanced Retracking System, Next Generation) sate of the art retracing software. In the presented work we will show first results of freeboard assimilation with a focus how this assimilation influences winter sea ice formation as well as the upper Arctic Ocean dynamics.</p>