Matthias Jaggi

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.

Snow and ice topography impact and are impacted by fluxes of mass, energy, and momentum in Arctic sea ice. We measured the topography on approximately a 0.5 km² drifting parcel of Arctic sea ice on 42 separate days from 18 October 2019 to 9 May 2020 via Terrestrial Laser Scanning (TLS). These data are aligned into an ice-fixed, lagrangian reference frame such that topographic changes (e.g., snow accumulation) can be observed for time periods of up to six months. Using in-situ measurements, we have validated the vertical accuracy of the alignment to ± 0.011 m. This data collection and processing workflow is the culmination of several prior measurement campaigns and may be generally applied for repeat TLS measurements on drifting sea ice. We present a description of the data, a software package written to process and align these data, and the philosophy of the data processing. These data can be used to investigate snow accumulation and redistribution, ice dynamics, surface roughness, and they can provide valuable context for co-located measurements.

Precise measurements of Arctic sea ice mass balance are necessary to understand the rapidly changing sea ice cover and its representation in climate models. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we made repeat point measurements of snow and ice thickness on primarily level first- and second-year ice (FYI, SYI) using ablation stakes and ice thickness gauges. This technique enabled us to distinguish surface and bottom (basal) melt and characterize the importance of oceanic versus atmospheric forcing. We also evaluated the time series of ice growth and melt in the context of other MOSAiC observations and historical mass balance observations from the Surface Heat Budget of the Arctic (SHEBA) campaign and the North Pole Environmental Observatory (NPEO). Despite similar freezing degree days, average ice growth at MOSAiC was greater on FYI (1.67 m) and SYI (1.23 m) than at SHEBA (1.45 m, 0.53 m), due in part to initially thinner ice and snow conditions on MOSAiC. Our estimates of effective snow thermal conductivity, which agree with SHEBA results and other MOSAiC observations, are unlikely to explain the difference. On MOSAiC, FYI grew more and faster than SYI, demonstrating a feedback loop that acts to increase ice production after multi-year ice loss. Surface melt on MOSAiC (mean of 0.50 m) was greater than at NPEO (0.18 m), with considerable spatial variability that correlated with surface albedo variability. Basal melt was relatively small (mean of 0.12 m), and higher than NPEO observations (0.07 m). Finally, we present observations showing that false bottoms reduced basal melt rates in some FYI cases, in agreement with other observations at MOSAiC. These detailed mass balance observations will allow further investigation into connections between the carefully observed surface energy budget, ocean heat fluxes, sea ice, and ecosystem at MOSAiC and during other campaigns.

Compression of snow at low strain rates governs the natural densification of snow and firn on Earth. Different deformation mechanisms (e.g., grain boundary sliding and dislocation creep) are widely employed in models and are mainly believed to change depending on relative density or snow type. To explore this picture, we conducted compression experiments with a nominally constant strain rate ɛ̇ ≈ 10^-6 s^-1 and systematically varied snow type, density, and specific surface area. We used a micro-compression stage to enable X-ray micro-tomography imaging and microstructure characterization before and after the experiment. Using repeated load-relaxation cycles, we eliminate changes in the microstructure that occur during the first loading, allowing us to probe the viscoplastic behavior of the intact ice matrix. To evaluate the effective mechanical behavior of snow, we derived an implicit, analytical solution of a non-linear scalar model that characterizes loading and relaxation in terms of the stress exponent n in Glen's law. Our results evidence a sharp transition of n as a function of the geometrical grain size, and rule out that this transition is caused by density or snow type. The derived values of n ≈ 1.9 for small grains to n≈4.4 for large grains are consistent with a transition from grain boundary sliding to dislocation creep at a transition grain size of around 0.5-0.6 mm. Our results shed new light on the old discussion about deformation mechanisms in snow and firn.

Time series of SAR tomographic profiling measurements of seasonal snow in the Swiss Alps were acquired with the tower-mounted ESA Wideband Scatterometer (WBScat) during an entire snow season as part of the ESA SnowLab campaign. The wide range of frequency bands (1-40 GHz) covered in combination with the depth-resolving capability of the tomographic SAR measurements provides new insights into relative change of location and intensity of radar backscatter within the snowpack during melting and refreezing cycles as a function of time and various parameters obtained by auxiliary measurements such as snow accumulation, snow mass (SWE), snow surface temperature, and liquid water content. It is found that at both, Ku-band and Ka-band, the tomographic profiles provide evidence of substantial backscatter at melt/freeze crust interfaces within the seasonal snowpack. Varying penetration depths and (re-)appearing backscattering layers can be observed throughout several cycles of snow melting and gradual refreezing.

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 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.

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.

The sub-kilometre scale distribution of snow depth on Arctic sea ice impacts atmosphere-ice fluxes of energy and mass, and is of importance for satellite estimates of sea-ice thickness from both radar and lidar altimeters. While information about the mean of this distribution is increasingly available from modelling and remote sensing, the full distribution cannot yet be resolved. We analyse 33 539 snow depth measurements from 499 transects taken at Soviet drifting stations between 1955 and 1991 and derive a simple statistical distribution for snow depth over multi-year ice as a function of only the mean snow depth. We then evaluate this snow depth distribution against snow depth transects that span first-year ice to multiyear ice from the MOSAiC, SHEBA and AMSR-Ice field campaigns. Because the distribution can be generated using only the mean snow depth, it can be used in the downscaling of several existing snow depth products for use in flux modelling and altimetry studies.

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 m SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 m and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98-114 m.

The necessity of characterizing snow through objective, physically motivated parameters has led to new model formulations and new measurement techniques. Consequently, essential structural parameters such as density and specific surface area (for basic characterization) or mechanical parameters such as the critical crack length (for avalanche stability characterization) gradually replace the semiempirical indices acquired from traditional stratigraphy. These advances come along with new demands and potentials for validation. To this end, we conducted the RHOSSA field campaign, in reference to density (ρ) and specific surface area (SSA), at the Weissfluhjoch research site in the Swiss Alps to provide a multi-instrument, multi-resolution dataset of density, SSA and critical crack length over the complete winter season of 2015-2016. In this paper, we present the design of the campaign and a basic analysis of the measurements alongside predictions from the model SNOWPACK. To bridge between traditional and new methods, the campaign comprises traditional profiles, density cutter, IceCube, SnowMicroPen (SMP), micro-computed-tomography, propagation saw tests and compression tests. To bridge between different temporal resolutions, the traditional weekly to biweekly (every 2 weeks, used in this sense throughout the paper) snow pits were complemented by daily SMP measurements. From the latter, we derived a recalibration of the statistical retrieval of density and SSA for SMP version 4 that yields an unprecedented spatiotemporal picture of the seasonal evolution of density and SSA in a snowpack. Finally, we provide an intercomparison of measured and modeled estimates of density and SSA for four characteristic layers over the entire season to demonstrate the potential of high-temporal-resolution monitoring for snowpack model validation.

The role of near-surface snow processes for the formation of climate signals through densification into deep polar firn is still barely understood. To this end we have analyzed a shallow snow pit (0-3 meters) from EastGRIP (Greenland) and derived high-resolution profiles of different types of mechanically relevant fabric tensors. The structural fabric, which characterizes the anisotropic geometry of ice matrix and pore space, was obtained by X-ray tomography. The crystallographic fabric, which characterizes the anisotropic distribution of the c-axis (or optical axis) orientations of snow crystals, was obtained from automatic analysis of thin sections. The structural fabric profile unambiguously reveals the seasonal cycles at EastGRIP, as a consequence of temperature gradient metamorphism, and in contrast to featureless signals of parameters like density or specific surface area. The crystallographic fabric profile unambiguously reveals a signal of cluster-type texture already at shallow depth. We make use of order of magnitude estimates for the formation time of both fabric signals and discuss potential coupling effects in the context of snow and firn densification.

The aim of the ESA SnowLab project is to provide a comprehensive multi-frequency, multi-polarisation, multi-temporal dataset of active microwave measurements over snow-covered grounds to investigate the relationship between effective snow- and ground parameters and the resultant signals detected by microwave radar. An important part for the development of microwave models is the microstructural characterisation. This characterisation can only be done by repeated measurements by SnowMicroPen and more completely, but also much more expensive, by X-ray micro-tomography. Within this project we complemented the microwave measurements of Alpine snow in Switzerland with extensive effective snow- and ground parameters and meteorological data. Microwave backscatter measurements were conducted using the 9 - 18 GHz ESA SnowScat instrument and since December 2018 the recently built ESA WBScat instrument. WBScat allows to extend the spectral coverage to 1 - 40 GHz.

Recently, different efforts were dedicated to improve various components of snowpack models, notably, by including more objective parameters of snow microstructure. To contribute with a dataset for this purpose, we designed a new measurement campaign for the winter 2015-2016 at Weissfluhjoch, Switzerland. We focused on density and specific surface area (SSA) of snow, two fundamental microstructural parameters from which many physical snow properties can be estimated. Weekly measurements of density (density cutter) and SSA (IceCube) profiles at 3 cm vertical resolution now extend the traditional snow measurements. To investigate also short time evolutions, daily SnowMicroPen measurements were additionally done from which proxies of density and SSA can be calculated at 1 mm vertical resolution. Occasionally, snow samples were also taken from the snow pit for micro-tomography measurements to investigate specific snow cover features, such as weak layers, graupel or crusts at a 10 micrometers resolution. Finally, Propagation Saw Tests were performed nearly on a weekly basis. In this paper, we present an overview of this measurement campaign carried out from December 2015 to March 2016. We show preliminary results of the density profile evolution that highlight the advantage of daily measurements compared to weekly ones when aiming at a highly continuous picture. The present dataset offers new opportunities for calibrating and validating physically based snowpack models, as well as for a better understanding of processes such as fracture propagation, snow densification, or crust formation.

We present an improved machine to produce nature-identical snow in a cold laboratory for reproducible experiments. The machine is based on the common supersaturation principle of blowing cold air over a heated water basin. The moist airstream is directed into a chamber, where it cools and the nucleation of ice crystals is promoted on stretched nylon wires. Snow crystals grow on the wires and are harvested regularly by a new automatic brush rack. Depending on the settings, different snow crystals can be produced, which are shown to be consistent with the Nakaya diagram. The main snow types are dendrites and needles. We prepared specimens from the snow produced by the snowmaker and analyzed them using microcomputer tomography. For dendrites we show that there are natural snow samples that have the same crystal shape and similar microstructural parameters, namely density and specific surface area. The machine can produce suitable amounts of snow for laboratory experiments in an efficient way. As an advantage over previous designs, uniform and reproducible snow samples can be generated under well-defined conditions.

Traditionally, the shape of snow crystals is studied on a crystal screen. Therefore, snow is implicitly considered as a material composed of individual grains. However, doing this, the structural information is lost, comparable to trying to understand the significance of structure from a pile of rubble. Most mechanical and physical properties of the snow can only be explained by considering the three-dimensional (3D) architecture of the snow microstructure. We give an overview of the methods used to visualize the 3D structure of the snow, which can be easily used in a class room, as anaglyph images and animations. We will present and explain images of the major snow classes and several interfaces and weak layers. We also give an overview of current techniques which can be used to collect samples in the field and how they can be processed later using high-resolution micro-tomography (micro-CT). The imaging of layers at the scale of a few centimeters is now possible with the newly developed replica technique and fast computers. This makes the technique available to image layer transitions, as they often occur in weak layers.