Dr. Martin Schneebeli

Photochemical release of iodine from snow has been suggested as a source of reactive iodine to the Arctic atmosphere, however understanding of the underlying mechanism and potential source strength is hindered by a lack of measurements of iodine concentration and speciation in snow. Moreover, the origin of snow iodine is also unknown. Here, we report iodine speciation measurements in Arctic snow on sea ice at a range of snow depths from 177 samples, representing 80 sampling events, from December 2019 to October 2020 collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. We demonstrate that while there appears to be a source of iodine, in particular iodate, to the base of the snow over first year ice, this does not influence iodine concentration in the surface snow. There is instead evidence of a top-down source of iodine, potentially from iodine-enriched marine aerosol, as well as some evidence for episodic influx of iodate with dust. The potential for photochemical release of molecular iodine (I₂) from iodide in surface snow was investigated, and it was demonstrated that this could provide an iodine emission flux to the Arctic atmosphere comparable to oceanic fluxes. Knowledge of the prevalence and speciation of iodine in Arctic snow will contribute to better understanding of its contribution to observed concentrations of polar iodine oxide (IO), and hence its contribution to the depletion of tropospheric ozone in the Arctic.

Snowpack stratigraphy measurements are essential for avalanche forecasting, snow-sports, and various cryospheric science applications. Such measurements are needed to assess slope stability and provide ground-truth data for remote sensing and numerical snowpack models. To date, many different techniques for acquiring snowpack stratigraphy measurements exist. The most common ones are the manual  now pit observation, the high-resolution penetrometer SnowMicroPen® (SMP), and the micro Computer Tomography (microCT), all of them requiring experienced operators and time. Since all these techniques come at a high cost, as they are labor intensive and/or require expensive tools, the number of available measurements is often very limited.
To overcome the challenge of rapidly acquiring snow profiles, and ultimately increase the number of available measurements, we developed the SnowSoundPen (SSP). The SSP implements a novel measurement technology. It is based on a probe with a sensing tip which is inserted into the snowpack and integrates a microphone which records the breaking sound of snow. Since it is known from microCT measurements that snow is a sintered 3D ice structure, we propose to use the acoustic response of breaking ice structures to assess the snowpack’s hardness, instead of using the mechanical response as the SMP and other devices do. This method allows to replace the expensive and fragile force sensor, traditionally used, with a cheaper microphone, that is protected by a fully enclosed tip, making it thus even usable under melt-freeze conditions. The SSP is lightweight and portable, can be used in a hand-driven or motor-driven configuration, and most importantly, it can be produced at a fraction of the cost of devices like the SMP.
Here we show that the audio signal acquired with the SSP can be translated into an SMP-like hardness signal. Estimating such parameters is key to show the SSP’s usefulness for operational measurements. The SSP enables cheap and yet precise measurements of snow stratigraphy, and can thus ultimately be a tool to rapidly assess the snowpack’s characteristics. By providing faster snowpack stratigraphy assessments, we hope that the SSP will contribute towards improving avalanche forecasting and establishing safer snow-sport practices.

The Arctic Ocean is an exceptional environment where hydrosphere, cryosphere, and atmosphere are closely interconnected. Changes in sea-ice extent and thickness affect ocean currents, as well as moisture and heat exchange with the atmosphere. Energy and water fluxes impact the formation and melting of sea ice and snow cover. Here, we present a comprehensive statistical analysis of the stable water isotopes of various hydrological components in the central Arctic obtained during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020, including the understudied Arctic winter. Our dataset comprises >2200 water, snow, and ice samples. Snow had the most depleted and variable isotopic composition, with d¹⁸O (–16.3%) increasing consistently from surface (–22.5%) to bottom (–9.7%) of the snowpack, suggesting that snow metamorphism and wind-induced transport may overprint the original precipitation isotope values. In the Arctic Ocean, isotopes also help to distinguish between different sea-ice types, and whether there is a meteoric contribution. The isotopic composition and salinity of surface seawater indicated relative contributions from different freshwater sources: lower d¹⁸O (approximately –3.0%) and salinities were observed near the eastern Siberian shelves and towards the center of the Transpolar Drift due to river discharge. Higher d¹⁸O (approximately –1.5%) and salinities were associated with an Atlantic source when the RV Polarstern crossed the Gakkel Ridge into the Nansen Basin. These changes were driven mainly by the shifts within the Transpolar Drift that carried the Polarstern across the Arctic Ocean. Our isotopic analysis highlights the importance of investigating isotope fractionation effects, for example, during sea-ice formation and melting. A systematic full-year sampling for water isotopes from different components strengthens our understanding of the Arctic water cycle and provides crucial insights into the interaction between atmosphere, sea ice, and ocean and their spatio-temporal variations during MOSAiC.

The Arctic poses many challenges for Earth system and snow physics models, which are commonly unable to simulate crucial Arctic snowpack processes,such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost, and the global carbon budget. Recognizing that models are shaped by human choices, 18 Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack but reflected the broader research context. Inadequate and insufficient resources, partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities - an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development - and anchored in the past. This anchoring and non-epistemic values led to diverging opinions about whether existing models were "good enough"and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations currently impeding progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice.

We use a tracer method involving the cosmogenic radioisotope beryllium-7 (half-life = 53.3 days) to follow the deposition of aerosols and the fate of snow on the MOSAiC ice floe during winter and spring 2019–2020. When examined alongside data from earlier studies in the Arctic Ocean that covered summer and fall, Be-7 inventories indicate a summertime peak for aerosol Be-7 deposition fluxes coinciding with seasonal minima boundary-level aerosol concentrations, which suggests that deposition fluxes are primarily controlled by precipitation. This conclusion is supported by the linear relationship between Be-7 fluxes and precipitation rates derived from data from the MOSAiC and SHEBA expeditions. Inventories of Be-7 within the snow column exhibited evidence of significant redistribution. Be-7 deficits, relative to the flux, were observed in areas of level sea ice while excess Be-7 was found associated with deformed ice features such as pressure ridges, leading to the following estimates for the distribution of snow on the ice floe in May 2020: 75–93% of the snow mass is found on deformed sea ice with the remainder on level ice. Furthermore, uncertainties associated with measurements of Be-7 concentrations within the ocean mixed layer would allow for losses of snow through open leads of up to approximately 20% of the flux. Our snow distribution estimates agree with data from repeat snow depth transect measurements. These results suggest that Be-7 can be a useful tool in studying snow redistribution.

The amount of snow on Arctic sea ice impacts the ice mass budget. Wind redistribution of snow into open water in leads is hypothesized to cause significant wintertime snow loss. However, there are no direct measurements of snow loss into Arctic leads. We measured the snow lost in four leads in the Central Arctic in winter 2020. We find, contrary to expectations, that under typical winter conditions, minimal snow was lost into leads. However, during a cyclone that delivered warm air temperatures, high winds, and snowfall, 35.0 ± 1.1 cm snow water equivalent (SWE) was lost into a lead (per unit lead area). This corresponded to a removal of 0.7-1.1 cm SWE from the entire surface - ∼6%-10% of this site's annual snow precipitation. Warm air temperatures, which increase the length of time that wintertime leads remain unfrozen, may be an underappreciated factor in snow loss into leads.

Saharan dust outbreaks have profound effects on ecosystems, climate, human health, and the cryosphere in Europe. However, the spatial deposition pattern of Saharan dust is poorly known due to a sparse network of ground measurements. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This somewhat improvised campaign triggered wide interest since 152 samples were collected from the snow in the Pyrenees, the French Alps, and the Swiss Alps in less than 4 weeks. Among the 152 samples, 113 in total could be analysed, corresponding to 70 different locations. The analysis of the samples showed a large variability in the dust properties and amount. We found a decrease in the deposited mass and particle sizes with distance from the source along the transport path. This spatial trend was also evident in the elemental composition of the dust as the iron mass fraction decreased from 11% in the Pyrenees to 2% in the Swiss Alps. At the local scale, we found a higher dust mass on south-facing slopes, in agreement with estimates from high-resolution remote sensing data. This unique dataset, which resulted from the collaboration of several research laboratories and citizens, is provided as an open dataset to benefit a large community and to enable further scientific investigations. Data presented in this study are available at 10.5281/zenodo.7969515 .

Hydrogen peroxide is a primary atmospheric oxidant significant in terminating gas-phase chemistry and sulfate formation in the condensed phase. Laboratory experiments have shown an unexpected oxidation acceleration by hydrogen peroxide in grain boundaries. While grain boundaries are frequent in natural snow and ice and are known to host impurities, it remains unclear how and to which extent hydrogen peroxide enters this reservoir. We present the first experimental evidence for the diffusive uptake of hydrogen peroxide into grain boundaries directly from the gas phase. We have machined a novel flow reactor system featuring a drilled ice flow tube that allows us to discern the effect of the ice grain boundary content on the uptake. Further, adsorption to the ice surface for temperatures from 235 to 258 K was quantified. Disentangling the contribution of these two uptake processes shows that the transfer of hydrogen peroxide from the atmosphere to snow at temperatures relevant to polar environments is considerably more pronounced than previously thought. Further, diffusive uptake to grain boundaries appears to be a novel mechanism for non-acidic trace gases to fill the highly reactive impurity reservoirs in snow's grain boundaries.

Repeated transects have become the backbone of spatially distributed ice and snow thickness measurements crucial for understanding of ice mass balance. Here we detail the transects at the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) 2019-2020, which represent the first such measurements collected across an entire season. Compared with similar historical transects, the snow at MOSAiC was thin (mean depths of approximately 0.1-0.3 m), while the sea ice was relatively thick first-year ice (FYI) and second-year ice (SYI). SYI was of two distinct types: relatively thin level ice formed from surfaces with extensive melt pond cover, and relatively thick deformed ice. On level SYI, spatial signatures of refrozen melt ponds remained detectable in January. At the beginning of winter the thinnest ice also had the thinnest snow, with winter growth rates of thin ice (0.33 m month^-1 for FYI, 0.24 m month^-1 for previously ponded SYI) exceeding that of thick ice (0.2 m month^-1). By January, FYI already had a greater modal ice thickness (1.1 m) than previously ponded SYI (0.9 m). By February, modal thickness of all SYI and FYI became indistinguishable at about 1.4 m. The largest modal thicknesses were measured in May at 1.7 m. Transects included deformed ice, where largest volumes of snow accumulated by April. The remaining snow on level ice exhibited typical spatial heterogeneity in the form of snow dunes. Spatial correlation length scales for snow and sea ice ranged from 20 to 40 m or 60 to 90 m, depending on the sampling direction, which suggests that the known anisotropy of snow dunes also manifests in spatial patterns in sea ice thickness. The diverse snow and ice thickness data obtained from the MOSAiC transects represent an invaluable resource for model and remote sensing product development.

Snow-layer segmentation and classification are essential diagnostic tasks for various cryospheric applications. The SnowMicroPen (SMP) measures the snowpack's penetration force at submillimeter intervals in snow depth. The resulting depth–force profile can be parameterized for density and specific surface area. However, no information on traditional snow types is currently extracted automatically. The labeling of snow types is a time-intensive task that requires practice and becomes infeasible for large datasets. Previous work showed that automated segmentation and classification is, in theory, possible but cannot be applied to data straight from the field or needs additional time-costly information, such as from classified snow pits. We evaluate how well machine learning models can automatically segment and classify SMP profiles to address this gap. We trained 14 models, among them semi-supervised models and artificial neural networks (ANNs), on the MOSAiC SMP dataset, an extensive collection of snow profiles on Arctic sea ice. SMP profiles can be successfully segmented and classified into snow classes based solely on the SMP's signal. The model comparison provided in this study enables SMP users to choose a suitable model for their task and dataset. The findings presented will facilitate and accelerate snow type identification through SMP profiles. Anyone can access the tools and models needed to automate snow type identification via the software repository “snowdragon”. Overall, snowdragon creates a link between traditional snow classification and high-resolution force–depth profiles. Traditional snow profile observations can be compared to SMP profiles with such a tool.

Sea ice ridges are one of the most under-sampled and poorly understood components of the Arctic sea ice system. Yet, ridges play a crucial role in the sea ice mass balance and have been identified as ecological hotspots for ice-associated flora and fauna in the Arctic. To better understand the mass balance of sea ice ridges, we drilled and sampled two different first-year ice (FYI) ridges in June-July 2020 during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). Ice cores were cut into 5 cm sections, melted, then analyzed for salinity and oxygen (δ¹⁸O) isotope composition. Combined with isotope data of snow samples, we used a mixing model to quantify the contribution of snow to the consolidated sea ice ridge mass. Our results demonstrate that snow meltwater is important for summer consolidation and overall ice mass balance of FYI ridges during the melt season, representing 6%-11% of total ridged ice mass or an ice thickness equivalent of 0.37-0.53 m. These findings demonstrate that snowmelt contributes to consolidation of FYI ridges and is a mechanism resulting in a relative increase of sea ice volume in summer. This mechanism can also affect the mechanical strength and survivability of ridges, but also contribute to reduction of the habitable space and light levels within FYI ridges. We proposed a combination of two pathways for the transport of snow meltwater and incorporation into ridge keels: percolation downward through the ridge and/or lateral transport from the under-ice meltwater layer. Whether only one pathway or a combination of both pathways is most likely remains unclear based on our observations, warranting further research on ridge morphology.

Snow significantly impacts the seasonal growth of Arctic sea ice due to its thermally insulating properties. Various measurements and parameterizations of thermal properties exist, but an assessment of the entire seasonal evolution of thermal conductivity and snow resistance is hitherto lacking. Using the comprehensive snow dataset from the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have evaluated for the first time the seasonal evolution of the snow's and denser snow-ice interface layers' thermal conductivity above different ice ages (refrozen leads, first-year ice, and second-year ice) and topographic features (ridges). Our dataset has a density range of snow and ice between 50 and 900 kg m⁻³, and corresponding anisotropy measurements, meaning we can test the current parameterizations of thermal conductivity for this density range. Combining different measurement parameterizations and assessing the robustness against spatial heterogeneity, we found the average thermal conductivity of snow (<550 kg m⁻³) on sea ice remains approximately constant (0.26 ± 0.05 ) over time irrespective of underlying ice type, with substantial spatial and vertical variability. Due to this consistency, we can state that the thermal resistance is mainly influenced by snow height, resulting in a 2.7 times higher average thermal resistance on ridges (1.42 m² K W⁻¹) compared to first-year level ice (0.51 m² K W⁻¹). Our findings explain how the scatter of thermal conductivity values directly results from structural properties. Now, the only step is to find a quick method to measure snow anisotropy in the field. Suggestions to do this are listed in the discussion.

Snow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system’s energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition.

The microstructure of the uppermost portions of a melting Arctic sea ice cover has a disproportionately large influence on how incident sunlight is reflected and absorbed in the ice/ocean system. The surface scattering layer (SSL) effectively backscatters solar radiation and keeps the surface albedo of melting ice relatively high compared to ice with the SSL manually removed. Measurements of albedo provide information on how incoming shortwave radiation is partitioned by the SSL and have been pivotal to improving climate model parameterizations. However, the relationship between the physical and optical properties of the SSL is still poorly constrained. Until now, radiative transfer models have been the only way to infer the microstructure of the SSL. During the MOSAiC expedition of 2019-2020, we took samples and, for the first time, directly measured the microstructure of the SSL on bare sea ice using X-ray micro-computed tomography. We show that the SSL has a highly anisotropic, coarse, and porous structure, with a small optical diameter and density at the surface, increasing with depth. As the melting surface ablates, the SSL regenerates, maintaining some aspects of its microstructure throughout the melt season. We used the microstructure measurements with a radiative transfer model to improve our understanding of the relationship between physical properties and optical properties at 850 nm wavelength. When the microstructure is used as model input, we see a 10-15% overestimation of the reflectance at 850 nm.This comparison suggests that either a) spatial variability at the meter scale is important for the two in situ optical measurements and therefore a larger sample size is needed to represent the microstructure or b) future work should investigate either i) using a ray-tracing approach instead of explicitly solving the radiative transfer equation or ii) using a more appropriate radiative transfer model.

The specific surface area (SSA) of snow can be directly measured by X-ray computed tomography or indirectly measured using the reflectance of near-infrared light. The IceCube (IC) is a well-established spectroscopic instrument that uses a near-infrared wavelength of 1310 nm. We compared the SSA of six snow types measured with both instruments. We measured significantly higher values with the IC, with a relative percentage difference of between 20 % and 52 % for snow types with an SSA between 5 and 25 m² kg⁻¹. We found no significant difference for snow with an SSA between 30 and 80 m² kg⁻¹. The difference is statistically significant between snow types but not uniquely related to the SSA. We suspected that particles artificially created during the sample preparation were the source of the difference. We sampled, measured and counted these particles to conduct numerical simulations with the TwostreAm Radiative TransfEr in Snow (TARTES) radiation transfer solver. The results support the hypothesis that these small, artificial particles can significantly increase the reflectivity at 1310 nm and, therefore, lead to an overestimation of the SSA.

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.

The Greenland Climate Network (GC-Net) consists of 31 automatic weather stations (AWSs) at 30 sites across the Greenland Ice Sheet. The first site was initiated in 1990, and the project has operated almost continuously since 1995 under the leadership of the late Konrad Steffen. The GC-Net AWS measured air temperature, relative humidity, wind speed, atmospheric pressure, downward and reflected shortwave irradiance, net radiation, and ice and firn temperatures. The majority of the GC-Net sites were located in the ice sheet accumulation area (17 AWSs), while 11 AWSs were located in the ablation area, and two sites (three AWSs) were located close to the equilibrium line altitude. Additionally, three AWSs of similar design to the GC-Net AWS were installed by Konrad Steffen's team on the Larsen C ice shelf, Antarctica. After more than 3 decades of operation, the GC-Net AWSs are being decommissioned and replaced by new AWSs operated by the Geological Survey of Denmark and Greenland (GEUS). Therefore, making a reassessment of the historical GC-Net AWS data is necessary. We present a full reprocessing of the historical GC-Net AWS dataset with increased attention to the filtering of erroneous measurements, data correction and derivation of additional variables: continuous surface height, instrument heights, surface albedo, turbulent heat fluxes, and 10 m ice and firn temperatures. This new augmented GC-Net level-1 (L1) AWS dataset is now available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2023) and will continue to be refined. The processing scripts, latest data and a data user forum are available at https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing (last access: 30 November 2023). In addition to the AWS data, a comprehensive compilation of valuable metadata is provided: maintenance reports, yearly pictures of the stations and the station positions through time. This unique dataset provides more than 320 station years of high-quality atmospheric data and is available following FAIR (findable, accessible, interoperable, reusable) data and code practices.

Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry-derived sea ice thickness. During winter of the MOSAiC Expedition, the “KuKa” dual-frequency, fully polarized Ku- and Ka-band radar was deployed in "stare" nadir-looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual-frequency, dual-polarization and waveform shape, and compared to independent snow depth measurements. Novel dual-polarization approaches yielded r² values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub-banded to CryoSat-2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co-polarized dual-frequency approaches were at least a factor of four too small and had a r² 0.15 or lower. r² for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters.

Stable water isotopes are used as a paleothermometer in ice cores for climate reconstructions over the past millennia. The underlying physical processes involved in the isotope-temperature relation, however, unfold at much shorter timescales. Here, we study the temporary archival of frontal passages in the seasonal Alpine snow cover. We combine five snow profiles sampled in winter 2017 at the Weissfluhjoch with a quantitative snow layer age reconstruction and atmospheric reanalysis data to characterize the circulation and clouds associated with the precipitation producing synoptic-scale cold and warm fronts. We find that the vertical cloud structure and the air parcels' net cooling during transport leave a distinct imprint in the δ¹⁸O and δD vertical profile in the snow. The near-surface humidity gradient at the moisture source is reflected in the second order isotope parameter deuterium excess. In the cold season, these environmental conditions during cloud formation and at the moisture source are preserved in the snow. In the melt season, significant post-depositional effects due to wet snow metamorphism, however, leads to an enrichment in heavy isotopes in the snow and a strong smoothing of the initial atmospheric imprint. These findings show that the isotope signal archived in the dry snow cover is strongly modulated by individual weather systems prior to deposition. Major shifts in the upper-level jet stream and cyclone tracks likely leading to changes in moisture source regions and conditions, could therefore be detectable in the isotope composition of Alpine ice.