On the contribution of grain boundary sliding type creep to firn densification – an assessment using an optimization approach

Simulation approaches to firn densification often rely on the assumption that grain boundary sliding is the leading process driving the first stage of densification. Alley (1987) first developed a process-based material model of firn that describes this process. However, often so-called semi-empirical models are favored over the physical description of grain boundary sliding owing to their simplicity and the uncertainties regarding model parameters. In this study, we assessed the applicability of the grain boundary sliding model of Alley (1987) to firn using a numeric firn densification model and an optimization approach, for which we formulated variants of the constitutive relation of Alley (1987). An efficient model implementation based on an updated Lagrangian numerical scheme enabled us to perform a large number of simulations to test different model parameters and identify the simulation results that best reproduced 159 firn density profiles from Greenland and Antarctica. For most of the investigated locations, the simulated and measured firn density profiles were in good agreement. This result implies that the constitutive relation of Alley (1987) characterizes the first stage of firn densification well when suitable model parameters are used. An analysis of the parameters that result in the best agreement revealed a dependence on the mean surface mass balance. This finding may indicate that the load is insufficiently described, as the lateral components of the stress tensor are usually neglected in one-dimensional models of the firn column.

On the energy budget of a low-Arctic snowpack

Arctic landscapes are covered in snow for at least 6 months of the year. The energy balance of the snow cover plays a key role in these environments, influencing the surface albedo, the thermal regime of the permafrost, and other factors. Our goal is to quantify all major heat fluxes above, within, and below a low-Arctic snowpack at a shrub tundra site on the east coast of Hudson Bay in eastern Canada. The study is based on observations from a flux tower that uses the eddy covariance approach and from profiles of temperature and thermal conductivity in the snow and soil. Additionally, we compared the observations with simulations produced using the Crocus snow model. We found that radiative losses due to negative longwave radiation are mostly counterbalanced by the sensible heat flux, whereas the latent heat flux is minimal. At the snow surface, the heat flux into the snow is similar in magnitude to the sensible heat flux. Because the snow cover stores very little heat, the majority of the upward heat flux in the snow is used to cool the soil. Overall, the model was able to reproduce the observed energy balance, but due to the effects of atmospheric stratification, it showed some deficiencies when simulating turbulent heat fluxes at an hourly timescale.

An empirical algorithm to map perennial firn aquifers and ice slabs within the Greenland Ice Sheet using satellite L-band microwave radiometry

Perennial firn aquifers are subsurface meltwater reservoirs consisting of a meters-thick water-saturated firn layer that can form on spatial scales as large as tens of kilometers. They have been observed within the percolation facies of glaciated regions experiencing intense seasonal surface melting and high snow accumulation. Widespread perennial firn aquifers have been identified within the Greenland Ice Sheet (GrIS) via field expeditions, airborne ice-penetrating radar surveys, and satellite microwave sensors. In contrast, ice slabs are nearly continuous ice layers that can also form on spatial scales as large as tens of kilometers as a result of surface and subsurface water-saturated snow and firn layers sequentially refreezing following multiple melting seasons. They have been observed within the percolation facies of glaciated regions experiencing intense seasonal surface melting but in areas where snow accumulation is at least 25 % lower as compared to perennial firn aquifer areas. Widespread ice slabs have recently been identified within the GrIS via field expeditions and airborne ice-penetrating radar surveys, specifically in areas where perennial firn aquifers typically do not form. However, ice slabs have yet to be identified from space. Together, these two ice sheet features represent distinct, but related, sub-facies within the broader percolation facies of the GrIS that can be defined primarily by differences in snow accumulation, which influences the englacial hydrology and thermal characteristics of firn layers at depth. Here, for the first time, we use enhanced-resolution vertically polarized L-band brightness temperature (TVB) imagery (2015–2019) generated using observations collected over the GrIS by NASA's Soil Moisture Active Passive (SMAP) satellite to map perennial firn aquifer and ice slab areas together as a continuous englacial hydrological system. We use an empirical algorithm previously developed to map the extent of Greenland's perennial firn aquifers via fitting exponentially decreasing temporal L-band signatures to a set of sigmoidal curves. This algorithm is recalibrated to also map the extent of ice slab areas using airborne ice-penetrating radar surveys collected by NASA's Operation IceBridge (OIB) campaigns (2010–2017). Our SMAP-derived maps show that between 2015 and 2019, perennial firn aquifer areas extended over 64 000 km2, and ice slab areas extended over 76 000 km2. Combined together, these sub-facies are the equivalent of 24 % of the percolation facies of the GrIS. As Greenland's climate continues to warm, seasonal surface melting will increase in extent, intensity, and duration. Quantifying the possible rapid expansion of these sub-facies using satellite L-band microwave radiometry has significant implications for understanding ice-sheet-wide variability in englacial hydrology that may drive meltwater-induced hydrofracturing and accelerated ice flow as well as high-elevation meltwater runoff that can impact the mass balance and stability of the GrIS.

Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.

Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parameterized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a remotely piloted aircraft system (RPAS). Distributions of snow specific surface area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than in Cambridge Bay (CB), where TVC is at a lower latitude with a subarctic shrub tundra compared to CB, which is a graminoid tundra. DHFs were fitted with a Gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. SMRT simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE)-Grid 2.0 enhanced resolution at 37 GHz.

Brief communication: Increased glacier mass loss in the Russian High Arctic (2010–2017)

Glaciers in the Russian High Arctic have been subject to extensive atmospheric warming due to global climate change, yet their contribution to sea level rise has been relatively small over the past decades. Here we show surface elevation change measurements and geodetic mass balances of 93 % of all glacierized areas of Novaya Zemlya, Severnaya Zemlya, and Franz Josef Land using interferometric synthetic aperture radar measurements taken between 2010 and 2017. We calculate an overall mass loss rate of -22±6 Gt a−1, corresponding to a sea level rise contribution of 0.06±0.02 mm a−1. Compared to measurements prior to 2010, mass loss of glaciers on the Russian archipelagos has doubled in recent years.

Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements

It is well understood that the distribution and quantity of liquid water in snow is relevant for snow hydrology and avalanche forecasting, yet detecting and quantifying liquid water in snow remains a challenge from the micro- to the macro-scale. Using near-infrared (NIR) spectral reflectance measurements, previous case studies have demonstrated the capability to retrieve surface liquid water content (LWC) of wet snow by leveraging shifts in the complex refractive index between ice and water. However, different models to represent mixed-phase optical properties have been proposed, including (1) internally mixed ice and water spheres, (2) internally mixed water-coated ice spheres, and (3) externally mixed interstitial ice and water spheres. Here, from within a controlled laboratory environment, we determined the optimal mixed-phase optical property model for simulating wet snow reflectance using a combination of NIR hyperspectral imaging, radiative transfer simulations (Discrete Ordinate Radiative Transfer model, DISORT), and an independent dielectric LWC measurement (SLF Snow Sensor). Maps of LWC were produced by finding the lowest residual between measured reflectance and simulated reflectance in spectral libraries, generated for each model with varying LWC and grain size, and assessed against the in situ LWC sensor. Our results show that the externally mixed model performed the best, retrieving LWC with an uncertainty of ∼1 %, while the simultaneously retrieved grain size better represented wet snow relative to the established scaled band area method. Furthermore, the LWC retrieval method was demonstrated in the field by imaging a snowpit sidewall during melt conditions and mapping LWC distribution in unprecedented detail, allowing for visualization of pooling water and flow features.

Uncertainties in projected surface mass balance over the polar ice sheets from dynamically downscaled EC-Earth models

The future rates of ice sheet melt in Greenland and Antarctica are an important factor when making estimates of the likely rate of sea level rise. Global climate models that took part in the fifth Coupled Model Intercomparison Project (CMIP5) have generally been unable to replicate observed rates of ice sheet melt. With the advent of the sixth Coupled Model Intercomparison Project (CMIP6), with a general increase in the equilibrium climate sensitivity, we here compare two versions of the global climate model EC-Earth using the regional climate model HIRHAM5 downscaling of EC-Earth for Greenland and Antarctica. One version (v2) of EC-Earth is taken from CMIP5 for the high-emissions Representative Concentration Pathway 8.5 (RCP8.5) scenario and the other (v3) from CMIP6 for the comparable high-emissions Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5) scenario. For Greenland, we downscale the two versions of EC-Earth for the historical period 1991–2010 and for the scenario period 2081–2100. For Antarctica, the periods are 1971–2000 and 2071–2100, respectively. For the Greenland Ice Sheet, we find that the mean change in temperature is 5.9 ∘C when downscaling EC-Earth v2 and 6.8 ∘C when downscaling EC-Earth v3. Corresponding values for Antarctica are 4.1 ∘C for v2 and 4.8 ∘C for v3. The mean change in surface mass balance at the end of the century under these high-emissions scenarios is found to be −290 Gt yr−1 (v2) and −1640 Gt yr−1 (v3) for Greenland and 420 Gt yr−1 (v2) and 80 Gt yr−1 (v3) for Antarctica. These distinct differences in temperature change and particularly surface mass balance change are a result of the higher equilibrium climate sensitivity in EC-Earth v3 (4.3 K) compared with 3.3 K in EC-Earth v2 and the differences in greenhouse gas concentrations between the RCP8.5 and the SSP5-8.5 scenarios.

Assessing volumetric change distributions and scaling relations of retrogressive thaw slumps across the Arctic

Arctic ice-rich permafrost is becoming increasingly vulnerable to terrain-altering thermokarst, and among the most rapid and dramatic of these changes are retrogressive thaw slumps (RTSs). They initiate when ice-rich soils are exposed and thaw, leading to the formation of a steep headwall which retreats during the summer months. The impacts and the distribution and scaling laws governing RTS changes within and between regions are unknown. Using TanDEM-X-derived digital elevation models, we estimated RTS volume and area changes over a 5-year time period from winter 2011/12 to winter 2016/17 and used for the first time probability density functions to describe their distributions. We found that over this time period all 1853 RTSs mobilized a combined volume of 17×106 m3 yr−1, corresponding to a volumetric change density of 77 m3 yr−1 km−2. Our remote sensing data reveal inter-regional differences in mobilized volumes, scaling laws, and terrain controls. The distributions of RTS area and volumetric change rates follow an inverse gamma function with a distinct peak and an exponential decrease for the largest RTSs. We found that the distributions in the high Arctic are shifted towards larger values than at other study sites We observed that the area-to-volume scaling was well described by a power law with an exponent of 1.15 across all study sites; however the individual sites had scaling exponents ranging from 1.05 to 1.37, indicating that regional characteristics need to be taken into account when estimating RTS volumetric changes from area changes. Among the terrain controls on RTS distributions that we examined, which included slope, adjacency to waterbodies, and aspect, the latter showed the greatest but regionally variable association with RTS occurrence. Accounting for the observed regional differences in volumetric change distributions, scaling relations, and terrain controls may enhance the modelling and monitoring of Arctic carbon, nutrient, and sediment cycles.

Effect of snowfall on changes in relative seismic velocity measured by ambient noise correlation

In mountainous, cold temperate and polar sites, the presence of snow cover can affect relative seismic velocity changes (dV/V) derived from ambient noise correlation, but this relation is relatively poorly documented and ambiguous. In this study, we analyzed raw seismic recordings from a snowy flat field site located above Davos (Switzerland), during one entire winter season (from December 2018 to June 2019). We identified three snowfall events with a substantial response of dV/V measurements (drops of several percent between 15 and 25 Hz), suggesting a detectable change in elastic properties of the medium due to the additional fresh snow. To better interpret the measurements, we used a physical model to compute frequency-dependent changes in the Rayleigh wave velocity computed before and after the events. Elastic parameters of the ground subsurface were obtained from a seismic refraction survey, whereas snow cover properties were obtained from the snow cover model SNOWPACK. The decrease in dV/V due to a snowfall was well reproduced, with the same order of magnitude as observed values, confirming the importance of the effect of fresh and dry snow on seismic measurements. We also observed a decrease in dV/V with snowmelt periods, but we were not able to reproduce those changes with our model. Overall, our results highlight the effect of the snow cover on seismic measurements, but more work is needed to accurately model this response, in particular for the presence of liquid water in the snowpack.