Seasonal ice mass-balance buoys: adapting tools to the changing Arctic

Monitoring the local mass balance of Arctic sea ice provides opportunities to attribute the observed changes in a particular floe’s mass balance to specific forcing phenomena. A shift from multi- year to seasonal ice in large portions of the Arctic presents a challenge for the existing Lagrangian array of autonomous ice mass-balance buoys, which were designed with a perennial ice cover in mind. This work identifies the anticipated challenges of operation in seasonal ice and presents a new autonomous buoy designed to monitor ice mass balance in the seasonal ice zone. The new design presented incorporates features which allow the buoy to operate in thin ice and open water, and reduce its vulnerability to ice dynamics and wildlife damage, while enhancing ease of deployment. A test deployment undertaken from April to June 2009 is discussed and results are presented with analysis to illustrate both the features and limitations of the buoy’s abilities

Bonded discrete element simulations of sea ice with non-local failure: Applications to Nares Strait

The discrete element method (DEM) can provide detailed descriptions of sea ice dynamics that explicitly model floes and discontinuities in the ice, which can be challenging to represent accurately with current models. However, floe-scale stresses that inform lead formation in sea ice are difficult to calculate in current DEM implementations. In this paper, we use the ParticLS software library to develop a DEM that models the sea ice as a collection of discrete rigid particles that are initially bonded together using a cohesive beam model that approximates the response of an Euler-Bernoulli beam located between particle centroids. Ice fracture and lead formation are determined based on the value of a non-local Cauchy stress state around each particle and a Mohr-Coulomb fracture model. Therefore, large ice floes are modeled as continuous objects made up of many bonded particles that can interact with each other, deform, and fracture. We generate particle configurations by discretizing the ice in MODIS satellite imagery into polygonal floes that fill the observed ice shape and extent. The model is tested on ice advecting through an idealized channel and through Nares Strait. The results indicate that the bonded DEM model is capable of qualitatively capturing the dynamic sea ice patterns through constrictions such as ice bridges, arch kinematic features, and lead formation. In addition, we apply spatial and temporal scaling analyses to illustrate the model's ability to capture heterogeneity and intermittency in the simulated ice deformation

Neither dust nor black carbon causing apparent albedo decline in Greenland\u27s dry snow zone: Implications for MODIS C5 surface reflectance

Remote sensing observations suggest Greenland ice sheet (GrIS) albedo has declined since 2001, even in the dry snow zone. We seek to explain the apparent dry snow albedo decline. We analyze samples representing 2012–2014 snowfall across NW Greenland for black carbon and dust light-absorbing impurities (LAI) and model their impacts on snow albedo. Albedo reductions due to LAI are small, averaging 0.003, with episodic enhancements resulting in reductions of 0.01–0.02. No significant increase in black carbon or dust concentrations relative to recent decades is found. Enhanced deposition of LAI is not, therefore, causing significant dry snow albedo reduction or driving melt events. Analysis of Collection 5 Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data indicates that the decline and spectral shift in dry snow albedo contains important contributions from uncorrected Terra sensor degradation. Though discrepancies are mostly below the stated accuracy of MODIS products, they will require revisiting some prior conclusions with C6 data

High-Resolution Snow Depth on Arctic Sea Ice From Low-Altitude Airborne Microwave Radar Data

We present new high-resolution snow depth data on Arctic sea ice derived from airborne microwave radar measurements from the IceBird campaigns of the Alfred Wegener Institute (AWI) together with a new retrieval method using signal peakiness based on an intercomparison exercise of colocated data at different altitudes. We aim to demonstrate the capabilities and potential improvements of radar data, which were acquired at a lower altitude (200 ft) and slower speed (110 kn) and had a smaller radar footprint size (2-m diameter) than previous airborne snow radar data. So far, AWI Snow Radar data have been derived using a 2-18-GHz ultrawideband frequency-modulated continuous-wave (FMCW) radar in 2017-2019. Our results show that our method in combination with thorough calibration through coherent noise removal and system response deconvolution significantly improves the quality of the radar-derived snow depth data. The validation against a 2-D grid of in situ snow depth measurements on level landfast first-year ice indicates a mean bias of only 0.86 cm between radar and ground truth. Comparison between the radar-derived snow depth estimates from different altitudes shows good consistency. We conclude that the AWI Snow Radar aboard the IceBird campaigns is able to measure the snow depth on Arctic sea ice accurately at higher spatial resolution than but consistent with the existing airborne snow radar data of NASA Operation IceBridge. Together with the simultaneous measurements of the total ice thickness and surface freeboard, the IceBird campaign data will be able to describe the whole sea-ice column on regional scales

The influence of snow on sea ice as assessed from simulations of CESM2

We assess the influence of snow on sea ice in experiments using the Community Earth System Model version 2 for a preindustrial and a 2xCO2 climate state. In the preindustrial climate, we find that increasing simulated snow accumulation on sea ice results in thicker sea ice and a cooler climate in both hemispheres. The sea ice mass budget response differs fundamentally between the two hemispheres. In the Arctic, increasing snow results in a decrease in both congelation sea ice growth and surface sea ice melt due to the snow\u27s impact on conductive heat transfer and albedo, respectively. These factors dominate in regions of perennial ice but have a smaller influence in seasonal ice areas. Overall, the mass budget changes lead to a reduced amplitude in the annual cycle of ice thickness. In the Antarctic, with increasing snow, ice growth increases due to snow-ice formation and is balanced by larger basal ice melt, which primarily occurs in regions of seasonal ice. In a warmer 2xCO2 climate, the Arctic sea ice sensitivity to snow depth is small and reduced relative to that of the preindustrial climate. In contrast, in the Antarctic, the sensitivity to snow on sea ice in the 2xCO2 climate is qualitatively similar to the sensitivity in the preindustrial climate. These results underscore the importance of accurately representing snow accumulation on sea ice in coupled Earth system models due to its impact on a number of competing processes and feedbacks that affect the melt and growth of sea ice

Simulation of the melt season using a resolved sea ice model with snow cover and melt ponds

A three-dimensional sea ice model is presented with resolved snow thickness variations and melt ponds. The model calculates heating from solar radiative transfer and simulates the formation and movement of brine/melt water through the ice system. Initialization for the model is based on observations of snow topography made during the summer melt seasons of 2009, 2010, and 2012 from a location off the coast of Barrow, AK. Experiments are conducted to examine the importance of snow properties and snow and ice thickness by comparing observed and modeled pond fraction and albedo. One key process simulated by the model is the formation of frozen layers in the ice as relatively warm fresh water grid cells freeze when cooled by adjacent, cold brine-filled grid cells. These layers prevent vertical drainage and lead to flooding of melt water commonly observed at the beginning of the melt season. Flooding persists until enough heat is absorbed to melt through the frozen layer. The resulting long-term melt pond coverage is sensitive to both the spatial variability of snow cover and the minimum snow depth. For thin snow cover, initial melting results in earlier, reduced flooding with a small change in pond fraction after drainage of the melt water. Deeper snow tends to generate a delayed, larger peak pond fraction before drainage.This is the publisher’s final pdf. The article is copyrighted by American Geophysical Union and published by John Wiley & Sons, Inc. It can be found at: http://agupubs.onlinelibrary.wiley.com/agu/jgr/journal/10.1002/%28ISSN%292169-9291/. Supporting Information S2 (Auxiliary Material) is available at: http://onlinelibrary.wiley.com/doi/10.1002/2014JC010569/abstrac

Snow depth on Arctic sea ice derived from airborne radar measurements

The snow layer on sea ice has high importance for polar climate as it affects heat, radiation, and fresh-water budgets. Additionally, snow loading is a critical parameter for the sea-ice freeboard-to-thickness conversion for satellite radar and laser altimeters. Despite its importance, there is a lack of snow observations spanning different spatial and temporal scales, thus introducing a significant source of uncertainty to altimetric sea-ice thickness retrievals. The ultra-wideband microwave radar (UWBM) Snow Radar, a 2–18 GHz airborne frequency-modulated continuous-wave (FMCW) radar developed by the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas, can accurately detect the air/snow and snow/ice interfaces to measure snow thickness. Since 2009, an airborne Snow Radar has been operated onboard NASA’s Operation IceBridge (OIB) campaigns. In 2017, the UWBM Snow Radar was operated for the first time on an Alfred Wegener Institute (AWI) research aircraft, together with an airborne laser scanner for surface topography and freeboard measurements and an electromagnetic induction sounding instrument (EM Bird) to measure total ice thickness. The AWI airborne surveys operate at a low survey altitude (60 m a.g.l.) and slow aircraft speed, enabling fine-resolution mapping of the snow layer. Furthermore, the unique instrument setup on board the AWI research aircraft and the concurrent measurements of snow freeboard, total sea-ice thickness and snow depth allow us to directly investigate the freeboard-to-thickness conversion on regional scales for the first time. Here, we evaluate the performance of the radar installation and present radar-derived snow depth retrieved with a wavelet technique from recent airborne campaigns, PAMARCMiP2017 and IceBird winter 2019, over Arctic sea ice in the Greenland, Lincoln, Beaufort and Chukchi Seas and the central Arctic Ocean in March–April of the respective years

Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.

The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these polar regions covered by sea ice, the wind is relatively strong due to the absence of obstructions and redistributes a large part of the deposited snow mass, which complicates estimates for precipitation hardly distinguishable from blowing or drifting snow. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove the snow mass balance uncertainties (i.e., snow transport contribution) in the Arctic environment. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open-source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial laser scan observations of surface dynamics to simulate snow deposition in a MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate) sea ice domain with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against scanner measurements. However, the approximations imposed by the numerical framework, together with potential measurement errors (precipitation), give rise to quantitative inaccuracies, which should be addressed in future work. The modeling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic. &nbsp;</p

Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice

Wind-driven redistribution of snow on sea ice alters its topography and microstructure, yet the impact of these processes on radar signatures is poorly understood. Here, we examine the effects of snow redistribution over Arctic sea ice on radar waveforms and backscatter signatures obtained from a surface-based, fully polarimetric Ka- and Ku-band radar at incidence angles between 0∘ (nadir) and 50∘. Two wind events in November 2019 during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition are evaluated. During both events, changes in Ka- and Ku-band radar waveforms and backscatter coefficients at nadir are observed, coincident with surface topography changes measured by a terrestrial laser scanner. At both frequencies, redistribution caused snow densification at the surface and the uppermost layers, increasing the scattering at the air–snow interface at nadir and its prevalence as the dominant radar scattering surface. The waveform data also detected the presence of previous air–snow interfaces, buried beneath newly deposited snow. The additional scattering from previous air–snow interfaces could therefore affect the range retrieved from Ka- and Ku-band satellite altimeters. With increasing incidence angles, the relative scattering contribution of the air–snow interface decreases, and the snow–sea ice interface scattering increases. Relative to pre-wind event conditions, azimuthally averaged backscatter at nadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Results show substantial backscatter variability within the scan area at all incidence angles and polarizations, in response to increasing wind speed and changes in wind direction. Our results show that snow redistribution and wind compaction need to be accounted for to interpret airborne and satellite radar measurements of snow-covered sea ice

Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice

Wind-driven redistribution of snow on sea ice alters its topography and microstructure, yet the impact of these processes on radar signatures is poorly understood. Here, we examine the effects of snow redistribution over Arctic sea ice on radar waveforms and backscatter signatures obtained from a surface-based, fully polarimetric Ka- and Ku-band radar at incidence angles between 0∘ (nadir) and 50∘. Two wind events in November 2019 during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition are evaluated. During both events, changes in Ka- and Ku-band radar waveforms and backscatter coefficients at nadir are observed, coincident with surface topography changes measured by a terrestrial laser scanner. At both frequencies, redistribution caused snow densification at the surface and the uppermost layers, increasing the scattering at the air–snow interface at nadir and its prevalence as the dominant radar scattering surface. The waveform data also detected the presence of previous air–snow interfaces, buried beneath newly deposited snow. The additional scattering from previous air–snow interfaces could therefore affect the range retrieved from Ka- and Ku-band satellite altimeters. With increasing incidence angles, the relative scattering contribution of the air–snow interface decreases, and the snow–sea ice interface scattering increases. Relative to pre-wind event conditions, azimuthally averaged backscatter at nadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Results show substantial backscatter variability within the scan area at all incidence angles and polarizations, in response to increasing wind speed and changes in wind direction. Our results show that snow redistribution and wind compaction need to be accounted for to interpret airborne and satellite radar measurements of snow-covered sea ice