The depth of snow cover is temporally and spatially heterogeneous at different scales in Alpine regions. For snow hydrology/climatology the spatial variability of snow depths is a key parameter for capturing the total amount of snow in a given area. Here a scale analysis of the spatial variability of snow depths during the accumulation period is investigated. The development of the variability is characterized by a parameter, β, describing the relationship between the standard deviation and the mean of snow depths. The analysis includes two datasets: (1) 141 snow-depth point measurements representing flat-field observations, and (2) snow precipitation from the numerical weather prediction model COSMO-7. The results reveal that β is almost invariant at scales between 10 and 300 km. The COSMO-7 data exhibit the same scale invariance above 50 km, indicating that the spatial variability of snow depths is formed by the precipitation pattern at these scales. The scaling analysis of β allows determination of the absolute accuracy of estimating the total amount of snow in a given area and helps to validate different snow models or remote-sensing techniques by ground truth verification.

In October 2003 a campaign on board the Australian icebreaker Aurora Australis had the objective to validate standard Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice products. Additionally, the satellite laser altimeter on the Ice, Cloud and land Elevation Satellite (ICESat) was in operation. To capture the large-scale information on the sea-ice conditions necessary for satellite validation, the measurement strategy was to obtain large-scale sea-ice statistics using extensive sea-ice measurements in a Lagrangian approach. A drifting buoy array, spanning initially 50 km × 100 km, was surveyed during the campaign. In situ measurements consisted of 12 transects, 50–500 m, with detailed snow and ice measurements as well as random snow depth sampling of floes within the buoy array using helicopters. In order to increase the amount of coincident in situ and satellite data an approach has been developed to extrapolate measurements in time and in space. Assuming no change in snow depth and freeboard occurred during the period of the campaign on the floes surveyed, we use buoy ice-drift information as well as daily estimates of thin-ice fraction and rough-ice vs smooth-ice fractions from AMSR-E and QuikSCAT, respectively, to estimate kilometer-scale snow depth and freeboard for other days. the results show that ICESat freeboard estimates have a mean difference of 1.8 cm when compared with the in situ data and a correlation coefficient of 0.6. Furthermore, incorporating ICESat roughness information into the AMSR-E snow depth algorithm significantly improves snow depth retrievals. Snow depth retrievals using a combination of AMSR-E and ICESat data agree with in situ data with a mean difference of 2.3 cm and a correlation coefficient of 0.84 with a negligible bias.

Sea-ice melting processes were inferred from in situ sea-ice and ocean condition data obtained in the Arctic in summer 2006 and 2007. the relationship between ice concentration observed by on-board ice watches and water temperature showed negative correlations. This implies that as ice concentration decreases, the upper ocean becomes warmer due to greater absorption of solar radiation into open water, which promotes ice melting. However, heating of surface water is significant even in regions that were almost completely ice-covered, suggesting that transmitted solar radiation through the ice is also effective at melting sea ice. A simplified ice–upper-ocean coupled model was applied to examine the effect of heat input from open water, thick ice and thin ice. the ponded thin ice is estimated to transmit approximately three times more solar radiation than ponded thick ice. Model results suggest that transmission of solar radiation through ponded ice amplified the ice-albedo feedback mechanism, particularly in thin ice regions. Recently, the extent of old and thick multi-year ice in the Arctic Ocean has been rapidly reduced. As a result, heat input to the upper ocean through the ice is enhanced and ice melt is further accelerated.

Wind-transported snow is a common phenomenon in cold windy areas, creating snowdrifts and contributing significantly to the loading of avalanche release areas. It is therefore necessary to take into account snowdrift formation both in terms of predicting and controlling drift patterns. Particularly in an Alpine context, drifting snow is a nonstationary phenomenon, which has not been taken into account in physical modeling carried out in wind tunnels or in numerical simulations. Only a few studies have been conducted to address the relation between wind gusts and drifting-snow gusts. Consequently, the present study was conducted at the Lac Blanc pass (2700ma.s.l.) experimental site in the French Alps using a snow particle counter and a cup anemometer in order to investigate drifting-snow gusts. First, it was shown that the behavior of the wind gust factor was coherent with previous studies. Then the definition of wind gust factor was extended to a drifting-snow gust factor. Sporadic drifting-snow events were removed from the analysis to avoid artificially high drifting-snow gust factors. Two trends were identified: (1) A high 1 s peak and a mean 10 min drifting-snow gust factor, greater than expected, were observed for events that exhibited a gamma distribution on the particle width histogram. The values of drifting-snow gust factors decreased with increasing gust duration. (2) Small drifting-snow gusts (i.e. smaller than or of the same order of magnitude as wind gusts) were also observed. However, in this case, they were systematically characterized by a snow particle size distribution that differed from the two-parameter gamma probability density function.

Modern satellite measurements of sea-ice thickness are based on altimeter measurements of the difference in elevation between the snow or ice surface and the local sea surface. For retrieval of sea-ice thickness, it is assumed that the ice is in hydrostatic equilibrium, and that the snow load on the ice and the density of the sea ice and sea water are known. This study presents data from in situ sea-ice thickness drillings and snow and ice density measurements from Fram Strait, the Barents Sea and the Svalbard coast, in the European Arctic. the error in the altimetry ice thickness products is assessed based on the spatial variability of snow and ice density and snow thickness data. Ice thickness uncertainty related to snow depth was found to be ∼40 cm (radar altimeter) and ∼90 cm (laser altimeter), while uncertainty related to ice density is 25 cm for both techniques. the assumption of hydrostatic equilibrium at the scales of the measurements (10–100 m) was found to hold better in the case of level landfast ice near Svalbard than for Fram Strait drift ice, which consists of mixed ice types, where the deviation between the calculated and measured ice thicknesses was on average ∼0.5 m.

Snow hides its true structure from easy visual observation. One reason is that ice is transparent, so the three-dimensional (3-D) structure is impossible to disentangle. 3-D reconstruction is essential to understand the physical and mechanical properties of snow. In recent years, the techniques to measure and visualize snow in 3-D have improved tremendously. X-ray microtomography is much more user-friendly than the older microtome sectioning techniques. We show different techniques to measure cast and natural snow samples and the steps necessary to produce high-quality data. The simplest way is 3-D renderings. Imaging snow in 3-D definitively challenges our previous view of a snowpack consisting of particles as traditionally seen on a crystal card.

Snow distribution is a dominating factor in sea-ice mass balance in the Bellingshausen Sea, Antarctica, through its roles in insulating the ice and contributing to snow-ice production. the wind has long been qualitatively recognized to influence the distribution of snow accumulation on sea ice, but the relative importance of drifting and blowing snow has not been quantified over Antarctic sea ice prior to this study. the presence and magnitude of drifting snow were monitored continuously along with wind speeds at two sites on an ice floe in the Bellingshausen Sea during the October 2007 Sea Ice Mass Balance in the Antarctic (SIMBA) experiment. Contemporaneous precipitation measurements collected on board the RVIB Nathaniel B. Palmer and accumulation measurements by automated ice mass-balance buoys (IMBs) allow us to document the proportion of snowfall that accumulated on level ice surfaces in the presence of high winds and blowing-snow conditions. Accumulation on the sea ice during the experiment averaged <0.01 m w.e. at both IMB sites, during a period when European Centre for Medium-Range Weather Forecasts analyses predicted >0.03 m w.e. of precipitation on the ice floe. Accumulation changes on the ice floe were clearly associated with drifting snow and high winds. Drifting-snow transport during the SIMBA experiment was supply-limited. Using these results to inform a preliminary study using a blowing-snow model, we show that over the entire Southern Ocean approximately half of the precipitation over sea ice could be lost to leads.

With decreases in Arctic sea-ice extent in recent years, the Northern Sea Route (NSR) and Northwest Passage (NWP), which we collectively term the Arctic Sea Route (ASR), have become open for navigation more frequently. The ASR connects the Pacific and Atlantic Oceans, with the NSR following the Siberian coast, and the NWP following the north coast of North America. This study evaluated long-term ice concentrations along both routes using microwave data from the SMMR and SSM/I sensors, and analyzed details using data from the AMSR-E passive microwave sensor. The data were used to determine the number of navigable days according to various sea-ice concentrations. Analysis of SMMR and SSM/I data showed a remarkably large number of navigable days on the NSR since 1995. For the NWP, the low resolution of the SMMR and SSM/I data for the Canadian Arctic Archipelago made analysis difficult, but long-term change in the sea-ice distribution on the ASR was indicated. Analysis of the AMSR-E microwave sensor data revealed navigable days along the NSR in 2002 and from 2005 to 2009 (except 2007). For navigation purposes, the sea-ice decrease in specific regions is important, as well as the decrease across the Arctic Ocean as a whole. For the NWP, numerous navigable days were identified in the period 2006–08.

Advanced Microwave Scanning Radiometer (AMSR-E) snow-depth data for Antarctic sea ice are compared with ship-based visual observations of snow depth, ice type and ridged-ice fraction, and with satellite C-band and Ku-band radar backscatter observations for two ship cruises into the Weddell Sea (ISPOL 2004–05,WWOS 2006) and one cruise into the Bellingshausen Sea (SIMBA 2007) during late winter/spring. Most (>75%) AMSR-E and ship-based snow-depth observations agree within 0.2 m during WWOS and SIMBA. Remaining observations indicate substantial underestimations of snow depths by AMSR-E data. These underestimations tend to increase with the ridged-ice fraction for WWOS and SIMBA. In areas with large snow depths, a combination of relatively stable low C-band radar backscatter and variable Ku-band radar backscatter is associated with undeformed first-year ice and may indicate snow metamorphism at this time of year during SIMBA. In areas with small snow depths, a combination of relatively stable low Ku-band radar backscatter, high C-band radar backscatter and low C-band radar backscatter standard deviations is associated with rough first-year ice during SIMBA. This information can help to better understand causes of the observed AMSR-E snow-depth bias during late-winter/spring conditions with decreasing average snow depth and to delineate areas where this bias occurs.

Wintertime (April–September) area estimates of the Terra Nova Bay polynya (TNBP), Antarctica, based on satellite microwave radiometry are compared with in situ observations of water salinity, temperature and currents at a mooring in Terra Nova Bay in 1996 and 1997. In 1996, polynya area anomalies and associated anomalies in polynya ice production are significantly correlated with salinity anomalies at the mooring. Salinity anomalies lag area and/or ice production anomalies by about 3 days. Up to 50% of the variability in the salinity at the mooring position can be explained by area and/or ice production anomalies in the TNBP for April–September 1996. This value increases to about 70% when considering shorter periods like April–June or May–July, but reduces to 30% later, for example July–September, together with a slight increase in time lag. In 1997, correlations are smaller, less significant and occur at a different time lag. Analysis of ocean currents at the mooring suggests that in 1996 conditions were more favourable than in 1997 for observing the impact of descending plumes of salt-enriched water formed in the polynya during ice formation on the water masses at the mooring depth.

The fractionation of major sea-water ions, or deviation in their relative concentrations from Standard Mean Ocean Water ratios, has been frequently observed in sea ice. It is generally thought to be associated with precipitation of solid salts at certain eutectic temperatures. the variability found in bulk sea-ice samples indicates that the fractionation of ions depends on the often unknown thermal history of sea ice, which affects the structure of pore networks and fate of solid salts within them. Here we investigate the distribution of ions in Arctic sea ice that is a few weeks old with a reconstructible thermal history. We separate the centrifugable (interconnected) and entrapped (likely disconnected) contributions to the ice salinity and determine their ion fractionation signatures. the results indicate that differential diffusion of ions, rather than eutectic precipitation of cryohydrates, has led to significant ion fractionation. the finding emphasizes the role of coupled diffusive–convective salt transport through complex pore networks in shaping the biogeochemistry of sea ice.

Sea-ice drift fields were obtained from sequences of synthetic aperture radar (SAR) images using a method based on pattern recognition. the accuracy of the method was estimated for two image products of the Envisat Advanced SAR (ASAR) with 25 m and 150 m pixel size. For data from the winter season it was found that 99% of the south–north and west–east components of the determined displacement vector are within ±3–5 pixels of a manually derived reference dataset, independent of the image resolution. For an image pair with 25 m resolution acquired during summer, the corresponding value is 12 pixels. Using the same resolution cell dimensions for the displacement fields in both image types, the estimated displacement components differed by 150–300 m. the use of different texture parameters for predicting the performance of the algorithm dependent on ice conditions and image characteristics was studied. It was found that high entropy values indicate a good performance.

Antarctic Sea Ice Processes and Climate (ASPeCt) ship-based ice observations, conducted during the Sea Ice Mass Balance in the Antarctic (SIMBA) and Sea Ice Physics and Ecosystem eXperiment (SIPEX) International Polar Year (IPY) cruises (September–October 2007), are used to validate remote-sensing measurements of ice extent and concentration. Observations include varied sea-ice types at and inside the ice edge of West (~90˚ W) and East (~120˚ E) Antarctica. Time series of Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) ice extents and US National Ice Center (NIC) ice edges were obtained for the 2007–08 periods bracketing the period these cruises were conducted. A comparison between passive microwave satellite imagery and ASPeCt observations of sea-ice concentration during two cruises was also performed. In 90˚W regions, the concentrated pack ice indicated good correlation between ship observations and passive microwave estimates of the ice concentration (R2 = 0.80). In the marginal zone of West Antarctica and over nearly the entire sea-ice zone of East Antarctica, correlation dropped to R2 < 0.60. These findings are consistent with other studies comparing passive microwave and ship observations and further verify that the East Antarctic sea-ice zone is more marginal in character. There are significant ice-edge differences between AMSR-E and NIC between late November 2007 and early March 2008 such that the AMSR-E sea-ice extent estimate is 1–2 × 106 km2 less than the NIC estimate.

Envisat Advanced Synthetic Aperture Radar (ASAR) Wide Swath Mode (WSM) images are used to derive C-band HH-polarization normalized radar cross sections (NRCS). These are compared with ice-core analysis and visual ship-based observations of snow and ice properties observed according to the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol during two International Polar Year summer cruises (Oden 2008 and Palmer 2009) in West Antarctica. Thick first-year (TFY) and multi-year (MY) ice were the dominant ice types. the NRCS value ranges between –16.3 ± 1.1 and –7.6 ± 1.0 dB for TFY ice, and is –12.6 ± 1.3 dB for MY ice; for TFY ice, NRCS values increase from –~15 dB to –9 dB from December/January to mid-February. In situ and ASPeCt observations are not, however, detailed enough to interpret the observed NRCS change over time. Co-located Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) vertically polarized 37 GHz brightness temperatures (TB37V), 7 day and 1 day averages as well as the TB37V difference between ascending and descending AMSR-E overpasses suggest the low NRCS values (–15 dB) are associated with snowmelt being still in progress, while the change towards higher NRCS values (–9 dB) is caused by commencement of melt– refreeze cycles after about mid-January.

This paper assesses the capabilities of a new one-dimensional snow scheme developed for the thermodynamic component of the Louvain-la-Neuve sea-Ice Model (LIM). the model is validated at Point Barrow, Alaska, and at Ice Station Polarstern (ISPOL) in the western Weddell Sea, Southern Ocean. the new snow thermodynamic scheme leads to better snow internal temperature profiles, with a set-up-dependent increase in the correlation between simulated and observed temperature profiles. on average over all runs, these correlations are 27% better with the six-layer configuration. the model’s ability to reproduce observed temperatures improves with the number of snow layers, but stabilizes after a threshold layer number is reached. the lowest and highest values for this threshold are 3 (at Point Barrow) and 6 (at ISPOL), respectively. Overall, the improvement of the model’s ability to simulate sea-ice thickness is not as significant as for snow temperature, probably because of the rather crude representation of the snow stratigraphy in the model.

Using results stemming from the International Trans-Antarctic Scientific Expedition (ITASE) ice-core array plus data from ice cores from the South Pole and Siple Dome we investigate the use of sodium (Na+), non-sea-salt sulfate (nssSO42–) and methylsulfonate (MS–) as proxies for Antarctic sea-ice extent (SIE). Maximum and mean annual chemistry concentrations for these three species correlate significantly with maximum, mean and minimum annual SIE, offering more information and clarification than single ice-core and single species approaches. Significant correlations greater than 90% exist between Na+ and maximum SIE; nssSO42– with minimum and mean SIE; and MS– with mean SIE. Correlations with SIE within large geographic regions are in the same direction for all ice-core sites for Na+ and nssSO42– but not MS–. All ice cores display an SIE correlation with nssSO42– and MS–, but not all correlate with Na+. This multi-core multi-parameter study provides the initial step in determining which chemical species can be used reliably and in which regions as a building block for embedding other ice-core records. Once established, the resulting temporal and spatial matrix can be used to relate ice extents, atmospheric patterns, biological productivity and site conditions.

There has been a marked decline in the summer extent of Arctic sea ice over the past few decades. Data from autonomous ice mass-balance buoys can enhance our understanding of this decline. These buoys monitor changes in snow deposition and ablation, ice growth, and ice surface and bottom melt. Results from the summer of 2008 showed considerable large-scale spatial variability in the amount of surface and bottom melt. Small amounts of melting were observed north of Greenland, while melting in the southern Beaufort Sea was quite large. Comparison of net solar heat input to the ice and heat required for surface ablation showed only modest correlation. However, there was a strong correlation between solar heat input to the ocean and bottom melting. As the ice concentration in the Beaufort Sea region decreased, there was an increase in solar heat to the ocean and an increase in bottom melting.

In late March 2007 an array of GPS ice drifters was deployed in the Beaufort Sea as part of the Sea Ice Experiment: Dynamic Nature of the Arctic (SEDNA). the drifters were deployed in an array designed to resolve four, nested spatial scales of sea-ice deformation, from 10 to 140 km, with the arrays maintaining appropriate shape for strain-rate calculation until mid-June. In this paper, we test whether sea-ice deformation displays fractal properties in the vicinity of SEDNA. We identify that deformation time series have different spectral properties depending on the spatial scale. At the scales around 100 km, deformation is a red-noise process, indicating the importance of the ice-pack surface forcing in determining the deformation rate of sea ice at this scale. At smaller scales, the deformation becomes an increasingly whiter process (it has pink noise properties), which suggests an increasing role of dissipative processes at smaller scales. At spatial scales of 10–100 km, and sub-daily scales, there is no deformation coherence across scales; coherence only becomes apparent at longer scales greater than 100 km. the lack of coherence at small scales aids in understanding previous observations where correlation between 10km regions adjacent to each other varied widely, with correlation coefficients between –0.3 and 1. This suggests it is not appropriate to think of sea ice as having a decorrelation length scale for deformation. We find that lead scale observations of deformation are required when estimating ice growth in leads and ridging time series. For the two SEDNA arrays, we find coherence between 140 and 20 km scale deformation up to periods of 16 days. This suggests sea-ice deformation displays coherent deformation between 100 km scale and the scale of the Beaufort Sea (of order 1000 km), over synoptic time periods (daily to weekly timescales). Organization of leads at synoptic and larger scales is an emergent feature of the deformation field that is caused by the smooth variation of surface forcing (wind) on the ice pack.

Snow depth is a key parameter for assessing the sea-ice mass budget in the Arctic and for the surface energy balance at the atmosphere–snow–ice–ocean interfaces. However, scientific expeditions to the high Arctic Ocean are rare, and for large parts of the year no snow and ice data are collected in situ in most regions. Therefore any additional in situ observations of snow depth are of interest to the scientific community. Arctic adventurers and tourists are among the most frequent visitors to the Arctic Ocean and North Pole. If properly trained and carefully adhering to standard protocols, they could collect valuable snow-depth data from large regions. Here we analyse such data from four Arctic basin ski traverses carried out between 1994 and 2007. Individual datasets show characteristic regional differences of snow thickness, which provide invaluable information for the validation of models and satellite data. the observations were made applying a continuously upgraded observation guideline scheme. Earlier observations were based on a relatively broad view of easily observable snow and ice parameters. Improvements included requirements of more detailed snow-thickness surveys in order to observe the spatial variability over varying sea-ice surfaces in a better way. Possibilities for, and limitations of, ice-thickness estimates and measurements are also discussed. Often, assessment of ice thickness is more problematic since measurements are either time-consuming or biased, meaning that possibilities for collecting large datasets are limited.