Twenty-seven studies on the thermal conductivity of snow (Keff) have been published since 1886. Combined, they comprise 354 values of Keff, and have been used to derive over 13 regression equation and predicting Keff vs density. Due to large (and largely undocumented) differences in measurement methods and accuracy, sample temperature and snow type, it is not possible to know what part of the variability in this data set is the result of snow microstructure. We present a new data set containing 488 measurements for which the temperature, type and measurement accuracy are known. A quadratic equation,where ρ is in g cm−3, and Keff is in W m−1K−1, can be fit to the new data (R2 = 0.79). A logarithmic expression,can also be used. The first regression is better when estimating values beyond the limits of the data; the second when estimating values for low-density snow. Within the data set, snow types resulting from kinetic growth show density-independent behavior. Rounded-grain and wind-blown snow show strong density dependence. The new data set has a higher mean value of density but a lower mean value of thermal conductivity than the old set. This shift is attributed to differences in snow types and sample temperatures in the sets. Using both data sets, we show that there are well-defined limits to the geometric configurations that natural seasonal snow can take.

Twenty-seven studies on the thermal conductivity of snow (Keff) have been published since 1886. Combined, they comprise 354 values ofKeff, and have been used to derive over 13 regression equation and predictingKeffvs density. Due to large (and largely undocumented) differences in measurement methods and accuracy, sample temperature and snow type, it is not possible to know what part of the variability in this data set is the result of snow microstructure. We present a new data set containing 488 measurements for which the temperature, type and measurement accuracy are known. A quadratic equation,whereρis in g cm−3, andKeffis in W m−1K−1, can be fit to the new data (R2= 0.79). A logarithmic expression,can also be used. The first regression is better when estimating values beyond the limits of the data; the second when estimating values for low-density snow. Within the data set, snow types resulting from kinetic growth show density-independent behavior. Rounded-grain and wind-blown snow show strong density dependence. The new data set has a higher mean value of density but a lower mean value of thermal conductivity than the old set. This shift is attributed to differences in snow types and sample temperatures in the sets. Using both data sets, we show that there are well-defined limits to the geometric configurations that natural seasonal snow can take.

Abstract. We present a multi-temporal digital inventory of Svalbard glaciers with the most recent from the late 2000s containing 33 775 km 2 of glaciers covering 57 % of the total land area of the archipelago. At present, 68 % of the glacierized area of Svalbard drains through tidewater glaciers that have a total terminus width of ∼ 740 km. The glacierized area over the entire archipelago has decreased by an average of 80 km 2 a −1 over the past ∼ 30 yr, representing a reduction of 7 %. For a sample of ∼ 400 glaciers (10 000 km 2 ) in the south and west of Spitsbergen, three digital inventories are available from the 1930/60s, 1990 and 2007 from which we calculate average changes during 2 epochs. In the more recent epoch, the terminus retreat was larger than in the earlier epoch, while area shrinkage was smaller. The contrasting pattern may be explained by the decreased lateral wastage of the glacier tongues. Retreat rates for individual glaciers show a mix of accelerating and decelerating trends, reflecting the large spatial variability of glacier types and climatic/dynamic response times in Svalbard. Lastly, retreat rates estimated by dividing glacier area changes by the tongue width are larger than centerline retreat due to a more encompassing frontal change estimate with inclusion of lateral area loss.

Abstract. Satellite remote sensing is a convenient tool for studying snow and glacier ice, allowing us to conduct research over large and otherwise inaccessible areas. This paper reviews various methods for measuring snow and glacier ice properties with satellite remote sensing. These methods have been improving with the use of new satellite sensors, like the synthetic aperture radar (SAR) during the last decade, leading to the development of new and powerful methods, such as SAR interferometry for glacier velocity, digital elevation model generation of ice sheets, or snow cover mapping. Some methods still try to overcome the limitations of present sensors, but future satellites will have much increased capability, for example, the ability to measure the whole optical spectrum or SAR sensors with multiple polarization or frequencies. Among the methods presented are the satellite-derived determination of surface albedo, snow extent, snow volume, snow grain size, surface temperature, glacier facies, glacier velocities, glacier extent, and ice sheet topography. In this review, emphasis is put on the principles and theory of each satellite remote sensing method. An extensive list of references, with an emphasis on studies from the 1990s, allows the reader to delve into specific topics. INTRODUCTIONThere have been tremendous technological achievements in the twentieth century that enable scientists to undertake research at virtually every spot on Earth. In parallel, advances in space technology during the last decades have provided us with a rapidly increasing number of satellite platforms that can be used to study complex physical processes of the Earth-atmosphere system. The development within this field concerns not only the growing number of satellites but also the rapid progression of sensor capabilities. In the future a major challenge will be connected with combining various sources of information gathered from space, i.e., data assimilation, and to make use of this information in a systematic, repetitive manner to monitor temporal and spatial variability, for example, in climate-change research.In the field of glaciology, satellite remote sensing has proven to be a particularly useful tool because areas of interest are often inaccessible. Further, in many regions at high latitudes, like the Greenland and Antarctic ice sheets, it is only during parts of the year that effective ground-based research can be carried out due to the harsh climate environment and the lack of daylight. Satellite remote sensing often permits real-time, yearround, and long-term studies. Also, the large spatial coverage of satellite remotely sensed data enables monitoring and process studies over large areas. In this way, satellite data help in understanding processes and teleconnections on the regional, continental, or even global scale, for example, global satellite-derived maps of snow cover. Such products are particularly important because they assist interpretation and analysis concerning global change.Also, on smaller scales, sate...

We present a multi-temporal digital inventory of Svalbard glaciers with the most recent from the late 2000s containing 33 775 km² of glaciers, or 57% of the total land area of the archipelago. At present, 68% of the glaciated area of Svalbard drains through tidewater glaciers that have a summed terminus width of ~ 740 km. The glaciated area over the entire archipelago has decreased by an average of 80 km² a⁻¹ over the past ~ 30 yr, representing a reduction of 7%. For a sample of ~ 400 glaciers (10 000 km²) in the south and west of Spitsbergen, three digital inventories are available from 1930/60s, 1990 and 2007 from which we calculate average changes during 2 epochs. In the more recent epoch, the terminus retreat was larger than in the earlier epoch while area shrinkage was smaller. The contrasting pattern may be explained by the decreased lateral wastage of the glacier tongues. Temporal retreat rates for individual glaciers show a mix of accelerating and decelerating trends, reflecting the large spatial variability of glacier types and climatic/dynamic response times in Svalbard. Last, retreat rates estimated by dividing glacier area changes by the tongue width are larger than centerline retreat due to a more encompassing frontal change estimate with inclusion of lateral area loss