Snow Metamorphism Research Articles

Isotopic evolution of a seasonal snowcover and its melt by isotopic exchange between liquid water and ice

Understanding an isotopic evolution of a snowpack is important for both climate and hydrological studies, because the snowmelt is a significant component of groundwater and surface runoff in temperate areas. In this work, we studied oxygen and hydrogen isotopic evolution from new snow to snow profile and to meltwater through two winter seasons (1998 and 2001) at the Central Sierra Snow Laboratory, California, USA. The slopes of the δD vs. δ 18O regression for the new snow are similar to that of the global meteoric water line (GMWL) of 8. However, this slope decreases in the snow profile and decreases further in the meltwater. We attribute this systematic slope changes to the isotopic exchange between ice and liquid water that is generated at the snow surface by melting and flows through the snowpack by percolation. A physically-based one-dimensional model, including melting of snow at the surface and isotopic exchange between percolating water and ice, was used to simulate isotopic variation of snowmelt in 2001. A successful simulation was obtained for the δD– δ 18O slope of snowmelt (6.5), which is significantly lower than the slope of the meteoric water line (8.2) defined by the new snow. This result indicates that the liquid water evaporation should not be considered as the only process that yields slopes of the δD vs. δ 18O relationship in surface water and groundwater. The d-excess of the snowmelt is changed from the original snow because of the δD– δ 18O relationship controlled by ice–liquid exchange. With a δD– δ 18O slope less than 8, the d-excess would be anti-correlated with δD or δ 18O. The model is also used to examine how isotopic heterogeneity of a snowpack affects the isotopic redistribution in the pore water, ice and meltwater of the snowpack. The results show that isotopic heterogeneity of the snowpack may significantly affect the temporal changes in the δD– δ 18O slopes, and a measured slope at a given time is a combined result of meteorological conditions, which affect both isotopic composition of the original snow and the process of snow metamorphism, and the melting history of the snowpack.

Breeding snow: an instrumented sample holder for simultaneous tomographic and thermal studies

To study the recrystallization processes during temperature gradient metamorphism of snow, we developed a sample holder that allows applying well-defined and stable thermal gradients to a snow sample while it is scanned in an x-ray micro-tomograph. To this end, both the thermal insulation of the sample as well as image contrast and resolution of the tomography had to be optimized. We solved this conflict by using thin aluminum cylinders in combination with highly insulating foam. This design is light, does not corrupt image quality and provides very good thermal decoupling from the environment. The sample holder was instrumented to measure the effective conductivity of the snow sample and calibrated using five materials of known conductivity. Finite element simulations were consistent with the calibration measurements and gave insight into the internal temperature and heat flux fields. With this setup, geometric and thermal evolution of snow under realistic thermal boundary conditions like alternating temperature gradients can be measured.

An adjustment for kinetic growth metamorphism to improve shear strength parameterization in the SNOWPACK model

Shear strength is an important parameter for interpreting the stability of simulated snow covers. In the SNOWPACK model, snow shear strength is estimated as a function of snow density using expressions for different grain types. In the model, shear strength changes discontinuously as grain type changes from rounded to faceted grains (and vice versa), but in nature, shear strength changes take place more gradually. An experiment on the growth of depth hoar allows a new parameterization of continuous changes in shear strength. A parameter is induced in the expression for shear strength as a function of water vapor transport. It ranges from 0 (rounded grains) to 1 (depth hoar) depending on the metamorphic stage of the snow. The parameterization is incorporated into the SNOWPACK model to calculate the progressive change in shear strength during snow metamorphism. The calculated shear strength using the improved SNOWPACK model agreed well with that measured in cold-room experiments using artificial snow. This model, which can calculate gradually changing shear strength, is expected to improve the accuracy of avalanche forecasting.

Phase-field modeling of dry snow metamorphism

Snow on the ground is a complex three-dimensional porous medium consisting of an ice matrix formed by sintered snow crystals and a pore space filled with air and water vapor. If a temperature gradient is imposed on the snow, a water vapor gradient in the pore space is induced and the snow microstructure changes due to diffusion, sublimation, and resublimation: the snow metamorphoses. The snow microstructure, in turn, determines macroscopic snow properties such as the thermal conductivity of a snowpack. We develop a phase-field model for snow metamorphism that operates on natural snow microstructures as observed by computed x-ray microtomography. The model takes into account heat and mass diffusion within the ice matrix and pore space, as well as phase changes at the ice-air interfaces. Its construction is inspired by phase-field models for alloy solidification, which allows us to relate the phase-field to a sharp-interface formulation of the problem without performing formal matched asymptotics. To overcome the computational difficulties created by the large difference between diffusional and interface-migration time scales, we introduce a method for accelerating the numerical simulations that formally amounts to reducing the heat- and mass-diffusion coefficients while maintaining the correct interface velocities. The model is validated by simulations for simple one- and two-dimensional test cases. Furthermore, we perform qualitative metamorphism simulations on natural snow structures to demonstrate the potential of the approach.

Impact of the microstructure of snow on its temperature: A model validation with measurements from Summit, Greenland

The influence of snow microstructure on thermal and radiative transfer in snow has not been thoroughly investigated as the tools necessary to efficiently measure microstructural geometry at millimeter resolution have not yet been available. Here we investigate the impact of snow microstructure on the thermal and radiative properties of snow and specifically determine the depth resolution of snow stratigraphy measurements needed to adequately model snow temperatures. To address this subject, detailed information on physical properties of the snow cover was collected at Summit, Greenland, in summer 2003. We present a new set of snow microstructure data, measured with a high‐resolution penetrometer (SnowMicroPen (SMP)). The penetration resistance can be used to estimate the thickness of individual layers but also reflects the thickness and the number of bonds in the snowpack. SMP is a motor‐driven, constant speed micropenetrometer acquiring 256 hardness measurements per millimeter. The data were used to describe the physical properties of snow and to compute snow temperatures in the topmost meter of the snowpack using the snow cover model SNOWPACK. The spatial resolution of the snow microstructure and the subsequent parametrization of SMP‐estimated density strongly affected the modeled temperature and temperature gradients. The SMP‐estimated density was used as an input to SNOWPACK and affected the thermal conductivity and radiative transfer in the snowpack. Our results thus show that highly resolved stratigraphic input data on the order of at least 1 cm are necessary to adequately simulate snow temperatures and snow metamorphism, including the observed formation of subsurface hoar in the high Arctic snow cover.

Snow physics as relevant to snow photochemistry

Abstract. Snow on the ground is a complex multiphase photochemical reactor that dramatically modifies the chemical composition of the overlying atmosphere. A quantitative description of the emissions of reactive gases by snow requires knowledge of snow physical properties. This overview details our current understanding of how those physical properties relevant to snow photochemistry vary during snow metamorphism. Properties discussed are density, specific surface area, thermal conductivity, permeability, gas diffusivity and optical properties. Inasmuch as possible, equations to parameterize these properties as functions of climatic variables are proposed, based on field measurements, laboratory experiments and theory. The potential of remote sensing methods to obtain information on some snow physical variables such as grain size, liquid water content and snow depth are discussed. The possibilities for and difficulties of building a snow photochemistry model by adapting current snow physics models are explored. Elaborate snow physics models already exist, and including variables of particular interest to snow photochemistry such as light fluxes and specific surface area appears possible. On the other hand, understanding the nature and location of reactive molecules in snow seems to be the greatest difficulty modelers will have to face for lack of experimental data, and progress on this aspect will require the detailed study of natural snow samples.

Explicit iterative computation of diffusive vapour field in the 3-D snow matrix: preliminary results for low flux metamorphism

AbstractThe metamorphism of seasonal snow is classically considered as limited by vapour diffusion in the pore phase. To account for the lack of knowledge of the ice–vapour reaction coefficient near 0°C, the assumption of a reaction-limited metamorphism was first tested in three-dimensional simulations at low and very low temperature gradients; however, the validity of such results is difficult to verify experimentally. By a reasoned use of traditional iterative schemes, vapour diffusion is now simulated in three dimensions on tomographic snow data, mapping the gradient of vapour pressure near the grains. Repeating this process may provide a way to simulate the isothermal metamorphism without grain packing at a reasonable expense of computation time. Preliminary results are compared with existing computations made within the reaction-limited hypothesis.

The temperature-gradient metamorphism of snow: vapour diffusion model and application to tomographic images

AbstractA simple physical model describing the temperature-gradient metamorphism of snow is presented. This model, based on Kelvin’s equation and Fick’s law, takes into account the local variation of the saturating vapour pressure with temperature. It can determine locally whether the ice is condensing or subliming, depending on both the pressure and temperature fields in the snow structure. This model can also explain the formation of facets that occurs during the metamorphism. Using X-ray microtomographic images of snow samples obtained under low to moderate temperature-gradient conditions, this model has been tested and compared to the reaction-limited model proposed in a previous work.

Observation of isothermal metamorphism of new snow and interpretation as a sintering process

Isothermal snow metamorphism is based on the fundamental process of sintering. However, the relative weights of the physical processes responsible for sintering between ice grains are still debated. The most active are expected to be grain boundary diffusion or sublimation‐condensation. We performed four isothermal experiments, starting with fresh snow, at temperatures of −1.6, −8.3, −19.1, and −54 °C during nearly 1 year. Monthly, we imaged the snow samples by X‐ray microtomography. We monitored the changes in density, specific surface area, structure model index, and several structural parameters based on the distance transform of the ice matrix and pore space in the snow, as the trabecular number and thickness. At −54 °C, the metamorphism process was very slow, and during 1 year the specific surface area decreased by only 19%. For the other samples, the results can be interpreted as a two‐stage process with a first phase of rapid change in trabecular number and structure model index and a second phase where the trabecular number and structure model index were constant. An increase in density and trabecular thickness together with a decrease of the specific surface area following a power law were observed throughout the experiments. The increase in ice thickness (coarsening) related linearly to the densification. The continuous densification implies that volume processes like grain boundary diffusion have to occur. The linear relation between densification and coarsening suggests that the same mechanism governs both processes. The logarithmic decrease of the specific surface area is an indication that the coarsening is rate limiting.

Inversion of a passive microwave snow emission model for water equivalent estimation using airborne and satellite data

Snow Water Equivalent (SWE) is an important variable in climate and hydrological studies in northern latitudes. To date, remote sensing tools, especially in the microwave domain, have shown their potential for monitoring this parameter. However, operational use at the regional and global scales needs further improvements. Strong uncertainties remain, mainly due to snow metamorphism as well to the presence of vegetation cover. The objective of this paper is to use the semi-empirical Helsinski University of Technology (HUT) snow emission model to estimate SWE with a minimum number of ancillary ground information. Non linear inversion techniques were used to compute SWE considering snow grain size as a free parameter, and initializing the inversion process with rough values of snow depth and density. Results were validated by comparing SWE estimations derived from airborne and spaceborne 19.35 and 37.0 GHz brightness temperatures to ground based SWE measurements over Central Canada. We showed that remotely sensed SWE estimations, retrieved with an average root mean square error of 24 mm, permit the monitoring of the seasonal snow pack evolution, as well as interannual variation (tested over 11 years), useful for hydrological applications.

Snow characterization at a global scale with passive microwave satellite observations

The sensitivity of passive microwave satellite observations to snow characteristics is evaluated, between 19 and 85 GHz, for a winter season, for the Northern Hemisphere. The surface emissivities derived from the Special Sensor Microwave/Imager measurements are systematically compared with in situ snow measurements at 2784 stations, in North America and Eurasia. In addition, coincident satellite responses from active microwave sensors (ERS scatterometer) and visible observations (AVHRR) are also analyzed. Vegetation interferes with the signal that is received by the satellites. Snow emissivities also react to scattering by the snow grain growth that is related to the snow metamorphism during the winter. This phenomenon increases with frequency and is already very sensitive at 37 GHz. Passive microwaves at high frequency (85 GHz) are very sensitive to the presence of snow on the ground, even for very low snow depth. None of the tested satellite measurements is well correlated to the snow depth at a global scale, making snow depth retrieval from these observations very difficult on a global basis. The sensitivity of the satellite observations to snow characteristics depends on local conditions. To partly alleviate these difficulties, a neural network inversion scheme based on local statistics is developed to combine satellite observations, in situ measurements, and land surface model outputs. The combination of different wavelengths partly limits the ambiguities related to the individual sensitivity of each satellite observation to the various sources of variabilities. The final retrieval algorithm is compatible with an assimilation strategy that would better constrain the behavior of surface models. Finally, a clustering algorithm is applied to the suite of satellite observations and clearly shows a strong sensitivity to the snow characteristics and metamorphism during the winter. Characterization of the snowpack using satellite observation classification can yield qualitative information for snow model parameterization.

Correlation between the specific surface area and the short wave infrared (SWIR) reflectance of snow

Abstract The albedo of snow is determined in part by the size and shape of snow crystals, especially in the short wave infrared (SWIR). Many models of snow albedo represent snow crystals by spheres of surface/volume (S/V) ratio equal to that of snow crystals. However, the actual S/V ratio of snow has never been measured simultaneously with the albedo, for a thorough test of models. Using CH4 adsorption at 77 K, we have measured the specific surface area (SSA) of snow samples, i.e. its ratio S/(V · ρ), where ρ is the density of ice, together with the snow spectral albedo using a field radiometer with nadir viewing, at Ny-Alesund, Svalbard. Tests are performed at 1310, 1629, 1740 and 2260 nm, and we find a good correlation between the SSA and the snow spectral albedo in the SWIR (linear correlation coefficient R2 > 0.98 for the last 3 wavelengths). Snow samples having varied crystals shapes such as rounded crystals in windpacks and hollow faceted crystals in depth hoar were studied and crystal shape did not affect the correlation in a detectable manner. An interest in using SSA rather than crystal size to predict SWIR albedo is that the reflectance of large hollow crystals such as depth hoar or surface hoar will be correctly predicted from their SSA, while considering their large dimensions would underestimate reflectance. We compare these correlations to those predicted by commonly used optical models. The best agreement is found when we compare our data to the modeled hemispheric reflectance, corrected by an adjustable factor that shows a small wavelength dependence. We propose that, once these results have been confirmed by more studies, it may be possible to design a rapid and simple optical method to measure snow SSA in the field. Our results may also allow a more detailed use of remote sensing data to study snow metamorphism, air–snow exchanges of gases, and climate.

Measuring the Release of Organic Contaminants from Melting Snow under Controlled Conditions

The release of organic contaminants from a melting snowpack may result in temporary concentration peaks in receiving water bodies and respective pulse exposure of aquatic organisms. It is thus of considerable interest to gain a mechanistic and quantitative understanding of the processes determining the dynamic behavior of organic chemicals during snowmelt. Uniformly structured and contaminated snow was produced with the help of a newly designed snow gun and exposed to predetermined temperature conditions in a temperature-controlled cold room. The dry density and liquid water content during four freeze-thaw cycles was recorded continuously at different layers within the snowpack using time domain reflectometry, providing information on meltwater production and propagation as well as snow metamorphism. Fractionated meltwater samples were filtered and the dissolved and particle phase analyzed for five polycyclic aromatic hydrocarbons (PAHs) using gas chromatography/ mass spectrometry. The distribution of the PAHs between the dissolved and particulate fractions of the meltwater was strongly related to their hydrophobicity. Particle-bound PAHs were released late during the snowmelt, whereas PAHs in the dissolved phase were released uniformly during a two day melting period. Even though conductivity measurements indicated a preferential early elution of ions in the first meltwater fractions, no such "first flush" behavior was observed for soluble PAH. The developed laboratory-based approach opens up for the first time the possibility of reproducible experiments on organic contaminant behavior in snow. Future experiments will explore, in detail, how the properties of organic chemicals, the physical and chemical properties of the snowpack, and the temperature variations before and during the time of melting interact to determine the timing of chemical release from a snowpack.

Evaluation of Snow Depth and Soil Temperatures Predicted by the Hydro–Thermodynamic Soil–Vegetation Scheme Coupled with the Fifth-Generation Pennsylvania State University–NCAR Mesoscale Model

Abstract The Hydro–Thermodynamic Soil–Vegetation Scheme (HTSVS) coupled in a two-way mode with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Meteorological Model (MM5) is evaluated for a typical snowmelt episode in the Baltic region by means of observations at 25 soil temperature, 355 snow-depth, and 344 precipitation sites that have, in total, 1000, 1775, and 1720 measurements, respectively. The performance with respect to predicted near-surface meteorological fields is evaluated using reanalysis data. Snow depth depends on snow metamorphism, sublimation, and snowfall. Because in the coupled model these processes are affected by the predicted surface radiation fluxes and cloud and precipitation processes, sensitivity studies are performed with two different cloud microphysical schemes and/or radiation schemes. Skill scores are calculated as a quality measure for the coupled model’s performance for a typical forecast range of 120 h for a typical spring (snowmelt) weather situation in the Baltic region. Discrepancies between predicted and observed snow-depth changes relate to the coupling. Enhanced water supply to the atmosphere, which results from water that was assumed to be open in MM5 but was actually ice covered in nature, finally leads to an overestimation of snowfall (input to HTSVS) and changes in snow depth (output). The resolution-dependent discrepancies between the terrain height in the model and real world also lead to snowfall where none occurred. For heavy snowfall the performance of the coupled model with respect to predicted snow-depth changes becomes nearly independent of the choice of the cloud microphysical and radiation schemes. As compared with observed changes in snow depth, the coupled model simulation using the Schultz scheme in conjunction with the radiation scheme from the Community Climate Model, version 2, (CCM2) predicts snow-depth changes of less than 2.5 mm considerably better than the other combinations that were tested. For thick snowpacks, the accuracy of the snow-depth decrease resulting from metamorphism strongly depends on the initial value of snow density. The coupled model acceptably captures the soil temperature diurnal cycles, the observed soil temperature increase with time, and the soil temperature behavior with depth. In general, discrepancies between simulated and observed soil temperatures decrease with soil depth. Simulations performed with the so-called CLOUD radiation scheme capture soil temperature minima and maxima better than do simulations performed with the CCM2 scheme.

A mean field model of the decrease of the specific surface area of dry snow during isothermal metamorphism

The surface area of snow that is accessible to gases is an essential parameter for quantifying the exchange of trace gases between the snowpack and the atmosphere and is called the specific surface area (SSA). Snow SSA decreases during metamorphism, but this is not described in current snow models owing to the complexity of the physics and geometry of snow. In this paper, we test whether it is possible to model snow SSA changes during isothermal metamorphism without accounting for all the complexity of the three‐dimensional (3‐D) structure of real snow. We have developed a mean field model of snow metamorphism under isothermal conditions, grounded in the theoretical framework of transient Ostwald ripening and representing snow as a distribution of spherical particles. Analytical expressions of the growth rates of these spheres are obtained, and the evolution of two measurable parameters that characterize snow geometry, the SSA and the distribution of radii of curvature (DRC), are simulated and compared to experimental data obtained by X‐ray tomography. The qualitative effects of temperature, snow density, and the condensation coefficient on the rate of SSA decrease are examined. The model predicts very well the rate of evolution of the particle size distribution, which validates our physical description of isothermal metamorphism. In particular, we find that vapor phase diffusion is rate limiting. However, the calculation of the SSA from the DRC appears delicate and evidences too crude approximations in our description of the 3‐D geometry of snow. Finally, it is stressed that the initial DRC can greatly influence the rate of SSA decrease, while experimental measurements of the rate of SSA decrease suggest that all snow types evolve in a similar way. It is thus proposed that most natural fresh snows have similar DRCs.

Evaluation of a high-resolution regional climate simulation over Greenland

A simulation of the 1991 summer has been performed over south Greenland with a coupled atmosphere–snow regional climate model (RCM) forced by the ECMWF re-analysis. The simulation is evaluated with in-situ coastal and ice-sheet atmospheric and glaciological observations. Modelled air temperature, specific humidity, wind speed and radiative fluxes are in good agreement with the available observations, although uncertainties in the radiative transfer scheme need further investigation to improve the model’s performance. In the sub-surface snow-ice model, surface albedo is calculated from the simulated snow grain shape and size, snow depth, meltwater accumulation, cloudiness and ice albedo. The use of snow metamorphism processes allows a realistic modelling of the temporal variations in the surface albedo during both melting periods and accumulation events. Concerning the surface albedo, the main finding is that an accurate albedo simulation during the melting season strongly depends on a proper initialization of the surface conditions which mainly result from winter accumulation processes. Furthermore, in a sensitivity experiment with a constant 0.8 albedo over the whole ice sheet, the average amount of melt decreased by more than 60%, which highlights the importance of a correctly simulated surface albedo. The use of this coupled atmosphere snow RCM offers new perspectives in the study of the Greenland surface mass balance due to the represented feedback between the surface climate and the surface albedo, which is the most sensitive parameter in energybalance-based ablation calculations.

Evaluation of a high-resolution regional climate simulation over Greenland

A simulation of the 1991 summer has been performed over south Greenland with a coupled atmosphere–snow regional climate model (RCM) forced by the ECMWF re-analysis. The simulation is evaluated with in-situ coastal and ice-sheet atmospheric and glaciological observations. Modelled air temperature, specific humidity, wind speed and radiative fluxes are in good agreement with the available observations, although uncertainties in the radiative transfer scheme need further investigation to improve the model’s performance. In the sub-surface snow-ice model, surface albedo is calculated from the simulated snow grain shape and size, snow depth, meltwater accumulation, cloudiness and ice albedo. The use of snow metamorphism processes allows a realistic modelling of the temporal variations in the surface albedo during both melting periods and accumulation events. Concerning the surface albedo, the main finding is that an accurate albedo simulation during the melting season strongly depends on a proper initialization of the surface conditions which mainly result from winter accumulation processes. Furthermore, in a sensitivity experiment with a constant 0.8 albedo over the whole ice sheet, the average amount of melt decreased by more than 60%, which highlights the importance of a correctly simulated surface albedo. The use of this coupled atmosphere snow RCM offers new perspectives in the study of the Greenland surface mass balance due to the represented feedback between the surface climate and the surface albedo, which is the most sensitive parameter in energybalance-based ablation calculations.

Interpretation of AMSU microwave measurements for the retrievals of snow water equivalent and snow depth

The objective of this paper is to interpret microwave scattering signatures over snow cover as observed by the Advanced Microwave Sounding Unit (AMSU) for the retrievals of snow water equivalent and snow depth. A case study involving seasonal snow cover over the U.S. Great Plains was analyzed in detail. Area‐wide analysis of the relationship between snow depth and the AMSU scattering signatures in the 23–150 GHz window region showed weak correlation, deteriorated by the dependence of these signatures on snow metamorphism. The lower frequency scattering index, computed as the difference in the brightness temperature between 23 and 31 GHz channels, was low and insensitive to fresh snow predominant in December, but increased later in the season, and thus was more sensitive to snow depth for older snow cover. However, this seasonal increase in microwave scattering was observed for every snow depth range, suggesting a strong dependence on snow metamorphism. In contrast, the 89 GHz scattering index responded to relatively shallow snow cover in December, but was less sensitive to snow depth variability later in the season. A snow hydrology model was applied at specific locations to estimate snow water equivalent (other than snow depth) for comparisons with the AMSU measurements. Overall, the lower frequency index was the best predictor of snow water equivalent and snow depth. However, correlation was higher for snow density and snow water equivalent. This was attributed to the response of this scattering index to the grain size evolution with time, which correlated better with the snow density and water equivalent changes in the snow cover than snow depth. Correlation between the snow water equivalent and the lower frequency index for fresh snow cover was significantly improved by switching to the higher frequency index at 89 GHz as predictor when the lower frequency index at 31 GHz was less than the 5 K threshold. Correlation further improved for fresh snow cover associated with a positive 150 GHz scattering index. These results suggest that the utility of AMSU measurements for the retrievals of snow depth and snow water equivalent could be better optimized by proper selection of the scattering thresholds that minimize the variability of important snow parameters such as grain size.

Tomography of temperature gradient metamorphism of snow and associated changes in heat conductivity

AbstractTemperature gradient metamorphism is one of the dominant processes changing the structure of natural dry snow. The structure of snow regulates the thermal and mechanical properties. Physical models and numerical simulations of the evolution of the snow cover require a thorough understanding of the interplay between structure and physical properties. The structure of snow and the heat conductivity were measured simultaneously without disturbance in a miniature snow breeder. The structure was measured by microtomography, and heat conductivity by measuring heat fluxes and temperatures. A temperature gradient from 25 to 100 K m−1 was applied to the snow. The snow density range of the samples varied from 150 to 500 kg m−3. The density in the observed volume remained constant during the experiments under temperature gradient conditions. The structure was analysed with respect to the size of typical ice structures and air pores, specific surface area, curvature and anisotropy of the ice matrix. The temporal changes in structure and heat conductivity are compared. The heat conductivity changed by as much as twice its initial value, caused by changes in structure and texture, but not due to changes in density. This shows the enormous importance of structure in the evolution of the heat conductivity. The observed changes are not in good agreement with the current understanding of the metamorphic process, because heat conductivity increased during temperature gradient metamorphism, instead of the expected decrease due to a shrinking of the bonds. We also observed a plateau in the evolution of the heat conductivity coefficient, which indicates a quasi‐steady state of the structural evolution with respect to thermophysical properties of snow. Copyright © 2004 John Wiley & Sons, Ltd.

Bomb-test 36Cl measurements in Vostok snow (Antarctica) and the use of 36Cl as a dating tool for deep ice cores

A large pulse of atmospheric 36 Cl generated by a limited number of nuclear tests peaked in the late 1950s to early 1960s. The corresponding enhanced 36 Cl deposition is seen in various glaciological archives in the Northern Hemisphere. The profile of the bomb spike recorded in firn layers at Vostok Station, central East Antarctica, has been measured by employing accelerator mass spectrometry (AMS). The records obtained from two well-dated data sets collected in snow pits in 1997 and 1998 show a broad 36 Cl peak, beginning as early as the 1940s and reaching its maximum in the 1960s. The signal is followed by a long-lasting tail up to the surface. This pattern is totally unexpected. We show that the results, unlike the Greenland data, can be explained by a mobility of HCl in the Antarctic firn. This experiment demonstrates the instability of gaseous Cl − deposits, a phenomenon which has important implications for the use of natural cosmogenic 36 Cl radionuclides as a reliable dating tool for deep ice cores from low-accumulation areas. However, during glacial times, under favourable atmospheric chemistry conditions this dating method may still be applicable. Snow metamorphism and ventilation are assumed to be the two main physical processes responsible for the observed patterns. DOI: 10.1111/j.1600-0889.2004.00109.x