Mean Snow Depth Research Articles

Traiter l'incertitude des projections climatiques (essai)

Handling the uncertainty of climate change projections (essay) The uncertainties associated with climate projections cannot be ignored when those projections are used. They arise from uncertainties surrounding developments for greenhouse gases, uncertainties arising from the climate models and the impact models (hydrology, biodiversity etc.), as well as the natural variability of the climate. To describe these different sources of uncertainty, and take them into account is not easy for engineers and managers, who are more accustomed to reasoning in a determinist framework. This article aims to demonstrate that statistics offers a number of approaches which make it possible to use a given multiscenario, multimodel climate projection. The simplest propose a descriptive summary of the information, while the more complex approaches can test the significance of the expected changes, to separate the different causes of uncertainty and measure each one's contribution to the overall variability. The approach is illustrated by a set of projections for temperature, precipitation and mean snow depth for a mountainous region of the French Alps. It can be easily applied to any variable whose projections have been generated through an impact model which simulates the consequences of a set of climate projections for a given sector defined in social and ecosystem terms.

Distribution of snow depth variability

Snow depth is the easiest snowpack property to measure in the field and is used to estimate the distribution of snow for quantifying snow storage. Often the mean of three snow depth measurements is used to represent snow depth at a location. This location is used as a proxy for an area, typically a digital elevation model (DEM) or remotely sensed pixel. Here, 11, 17, or 21 snow depth measurements were used to represent the mean snow depth of a 30-m DEM pixel. Using the center snow depth measurement for each sampling set was not adequate to represent the pixel mean, and while the use of three snow depth measurements improved the estimate of mean, there is still large error for some pixels. These measurements were then used to determine the variability of snow depth across a pixel. Estimating variability from few points rather than all in a measurement was not sufficient. The sampling size was increased from one to the total per pixel (11, 17, or 21) to determine how many point samples were necessary to approximate the mean snow depth per pixel within five percent. Binary regression trees were constructed to determine which terrain and canopy variables dictated the spatial distribution of the snow depth, the standard deviation of snow depth, and the sample size to within 5% of the mean per pixel. One location was measured in two years just prior to peak accumulation, and it is shown that there was little to no inter-annual consistency in the mean or standard deviation.

Small and transient response of winter soil respiration and microbial communities to altered snow depth in a mid-temperate forest

Global climate change is altering snow depth in winter, which could significantly affect soil respiration and microbial communities. However, belowground responses are still uncertain as they depend on the thermal effects on soils, the acclimation of soil microbial communities and ecosystem type. Here, we conducted a snow manipulation experiment including 50% removal of snowpack (mean snow depth after treatment was 3.1 ± 0.7 cm), ambient snow (mean snow depth was 6.3 ± 0.7 cm), and 50% increase of snowpack (mean snow depth after treatment was 9.6 ± 1.5 cm) to explore the effects of altered snow depth on winter soil respiration and microbial communities in a mid-latitude plantation forest with continental climate with dry winters. Winter soil CO2 effluxes varied from 0.09 to 0.84 µmol m−2 s−1 with a mean of 0.32 ± 0.07 µmol m−2 s−1. The cumulative soil CO2 effluxes from 11 December 2014 to 21 March 2015 were 27.3 ± 1.1, 26.5 ± 2.1, and 29.5 ± 1.3 g C m−2 under reduced, ambient and added snowpack, which corresponded to 5.7 ± 0.2%, 5.5 ± 0.3%, and 5.8 ± 0.1% of the annual soil CO2 effluxes, respectively. Our one-year observation results suggested that although snow reduction decreased soil temperature, microbial biomass carbon (MBC) and soil respiration did not change, suggesting microbial adaptation to cold conditions between −4 °C and −1 °C. In contrast, snow addition increased soil temperature, MBC, and soil respiration. Microbial community structure (F/B, ratio of fungi to bacteria) was also changed and soil enzymatic (β-glucosidase) activities increased under snow addition. However, these effects were short-lived and disappeared when soil temperature did not differ between the addition and control plots at the 14th day after treatment. These results indicated that the responses of soil microbial communities and respiratory activities to changing soil temperature were rapid and the response of soil respiration to snow addition was transient. Consequently, altered snow depth did not affect cumulative soil CO2 effluxes. Our study suggests that wintertime soil respiration rates are generally low and snow manipulation has minor effects on soil CO2 efflux, soil temperature (the determinant driver of wintertime soil CO2 efflux) and soil microbial biomass at our site.

Reconstruction of Snow on Arctic Sea Ice

AbstractSnow on Arctic sea ice is a poorly observed variable that plays an important role in the Arctic climate system and impacts the remote sensing systems that monitor Arctic sea ice. We present and validate a reconstruction of Arctic snow depth based on observed sea ice motion and snowfall derived from reanalysis data. Overall, the reconstruction is in good agreement with direct measurements of snow depth from Operation IceBridge, slightly underestimating mean IceBridge snow depth. At the local scale (10 km), the reconstruction is more skilled than a weighted climatology over first year ice, but underestimates deeper snow over multi‐year ice. Reconstructions of single buoy snow depths are unskilled, but the reconstruction shows skill in simulating the mean snow depth across all buoys. Spring snow depths show a low‐to‐high cross‐Arctic gradient and tend to be greatest in the Atlantic sector of the eastern Arctic. The relationship between ice type (multiyear or first‐year ice) and snow depth previously documented in the western Arctic is not evident in the eastern Arctic. Using ice type to weight snow depths for satellite ice thickness retrievals may not be justifiable in the eastern Arctic. Reconstructed snow depth across the Arctic shows significant interannual variability, suggesting that use of a fixed snow depth climatology may lead to biases in retrieved ice thickness and its variability. However, interannual variability in pan‐Arctic mean snow depth is comparable or smaller than the uncertainty in both the reconstruction and IceBridge, highlighting the need for high accuracy snow depth products and reconstructions.

Multi-component ensembles of future meteorological and natural snow conditions for 1500 m altitude in the Chartreuse mountain range, Northern French Alps

Abstract. This article investigates the climatic response of a series of indicators for characterizing annual snow conditions and corresponding meteorological drivers at 1500 m altitude in the Chartreuse mountain range in the Northern French Alps. Past and future changes were computed based on reanalysis and observations from 1958 to 2016, and using CMIP5–EURO-CORDEX GCM–RCM pairs spanning historical (1950–2005) and RCP2.6 (4), RCP4.5 and RCP8.5 (13 each) future scenarios (2006–2100). The adjusted climate model runs were used to drive the multiphysics ensemble configuration of the detailed snowpack model Crocus. Uncertainty arising from physical modeling of snow accounts for 20 % typically, although the multiphysics is likely to have a much smaller impact on trends. Ensembles of climate projections are rather similar until the middle of the 21st century, and all show a continuation of the ongoing reduction in average snow conditions, and sustained interannual variability. The impact of the RCPs becomes significant for the second half of the 21st century, with overall stable conditions with RCP2.6, and continued degradation of snow conditions for RCP4.5 and 8.5, the latter leading to more frequent ephemeral snow conditions. Changes in local meteorological and snow conditions show significant correlation with global temperature changes. Global temperature levels 1.5 and 2 ∘C above preindustrial levels correspond to a 25 and 32 % reduction, respectively, of winter mean snow depth with respect to the reference period 1986–2005. Larger reduction rates are expected for global temperature levels exceeding 2 ∘C. The method can address other geographical areas and sectorial indicators, in the field of water resources, mountain tourism or natural hazards.

Thin Sea Ice, Thick Snow, and Widespread Negative Freeboard Observed During N‐ICE2015 North of Svalbard

AbstractIn recent years, sea‐ice conditions in the Arctic Ocean changed substantially toward a younger and thinner sea‐ice cover. To capture the scope of these changes and identify the differences between individual regions, in situ observations from expeditions are a valuable data source. We present a continuous time series of in situ measurements from the N‐ICE2015 expedition from January to June 2015 in the Arctic Basin north of Svalbard, comprising snow buoy and ice mass balance buoy data and local and regional data gained from electromagnetic induction (EM) surveys and snow probe measurements from four distinct drifts. The observed mean snow depth of 0.53 m for April to early June is 73% above the average value of 0.30 m from historical and recent observations in this region, covering the years 1955–2017. The modal total ice and snow thicknesses, of 1.6 and 1.7 m measured with ground‐based EM and airborne EM measurements in April, May, and June 2015, respectively, lie below the values ranging from 1.8 to 2.7 m, reported in historical observations from the same region and time of year. The thick snow cover slows thermodynamic growth of the underlying sea ice. In combination with a thin sea‐ice cover this leads to an imbalance between snow and ice thickness, which causes widespread negative freeboard with subsequent flooding and a potential for snow‐ice formation. With certainty, 29% of randomly located drill holes on level ice had negative freeboard.

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

Abstract. Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term (1966–2012) ground-based measurements from 1814 stations. Spatially, long-term (1971–2000) mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade−1 from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50° N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales.

Shrub-induced snowpack variability alters wintertime soil respiration across a simulated tundra landscape

ABSTRACTAcross the arctic tundra, the expansion of tall shrubs is expected to alter soil microbial activity in winter through shrub effects on snow redistribution. Tall shrubs act as a windbreak, trapping deep snowdrifts that insulate the ground from extreme cold and elevate soil respiration within shrub patches. However, this windbreak effect may reduce both snow cover and soil respiration in open tundra areas outside shrub patches. The net impact of increasing shrub cover on soil respiration across landscapes with heterogeneous vegetation cover and snow depths remains unknown. Here, I use a set of tundra landscape simulations to address this knowledge gap. The simulations vary shrub cover, mean snow depth and the strength of shrub windbreak effect. I show that for mean snowfall depths 40 cm or greater, increasing shrub cover usually reduced landscape-level soil respiration due to greater heterogeneity in snow depth. These simulations suggest that there is no overarching positive relationship between shr...

Scale-dependent effects of post-fire canopy cover on snowpack depth in montane coniferous forests.

Winter snowpack in dry montane regions provides a valuable ecosystem service by storing water into the growing season. Wildfire in coniferous montane forests has the potential to indirectly affect snowpack accumulation and ablation (mass loss) rates by reducing canopy cover, which reduces canopy interception of snow but also increases solar radiation and wind speed. These counteracting effects create uncertainty regarding the canopy conditions that maximize post-fire snowpack duration, which is of concern as montane regions across the western United States experience increasingly warm, dry winters with below-average snowpack. The net effect of wildfire on snowpack depth and duration across the landscape is uncertain, and likely scale dependent. In this study, I tested whether intermediate levels of wildfire severity maximize snowpack depth by increasing accumulation while slowing ablation, using gridded, repeated snow depth measurements from three fires in the Sierra Nevada of California. Increasing fire severity had a strong negative effect on snowpack depth, suggesting that increased ablation after fire, rather than increased accumulation, was the dominant control over snowpack duration. Contrary to expectations, the unburned forest condition had the highest overall snowpack depth, and mean snow depth among all site visits was reduced by 78% from unburned forest to high-severity fire. However, at the individual tree scale, snowpack depth was greater under canopy openings than underneath canopy, controlling for effects of fire severity and aspect. This apparent paradox in snowpack response to fire at the stand vs. individual tree scales is likely due to greater variation in canopy cover within unburned and very low severity areas, which creates smaller areas for snow accumulation while reducing ablation via shading. Management efforts to maximize snowpack duration in montane forests should focus on retaining fine-scale heterogeneity in forest structure.

Autumn irruptions of Eurasian Jay (Garrulus glandarius) in Norway in relation to acorn production and weather

In Scandinavia, the Eurasian Jay (Garrulus glandarius) is a resident forest-dwelling species, which in some years show eruptivemovements in the autumn. Such irruptions seem to be related to the variation in seed crops of oaks (Quercus spp.).When there has been a high seed crop, termed mast, Jays use hoarded acorns not only during winter, but also as food for their young in the succeeding spring. However, whether this leads to an irruption in a succeeding year may also depend on other factors that affect survival and reproduction. AJay hunting index (1995–2015) and a Jay irruption index (observations at a coastal ornithological station 1976–2015) from southern Norway peaked in years withmast failure, but there also was a significant negative relationship with mean snow depth in January– February and precipitation in April–May. Much snow is assumed to reduce winter survival, bymaking hoarded acorns less available, whereas heavy rain in spring is known to have a negative effect on passerine breeding success in general.

Snow deposition patterns on southern Spitsbergen glaciers, Svalbard, in relation to recent meteorological conditions and local topography

ABSTRACTWe present a detailed study on snow cover on six different glaciers in southern Spitsbergen, Svalbard: Amundsenisen, Flatbreen, Hansbreen, Nannbreen, Storbreen and Werenskioldbreen. Fieldwork was carried out in April–May 2013, at the end of the accumulation season, to determine large-scale spatial distribution patterns of snow cover on glaciers surrounding the Hornsund Fjord. Snow depth was measured using an 800 MHz ground-penetrating radar (GPR). In addition, the structure of the snowpack was determined by digging snow pits and collecting snow cores from different glacier facies. These samples were subsequently analysed against circulation types and meteorological data from selected sites. In particular, snow patterns were compared against rain-on-snow events. The mean snow depth measures ranged from 1.90 m (Werenskioldbreen) to 3.80 m (Amundsenisen), whereas the accumulation gradient ranged from 15 cm 100 m−1 (Storbreen) to 74 cm 100 m−1 (Nannbreen). These results followed previous observations, suggesting a decrease in snow accumulation from coastal areas towards the island’s interior. The estimated snow water equivalent values were lower than those measured in the 1990s and during 2010 (Hansbreen: c. −48%), with the exception of Amundsenisen (c. +17%). The relatively high densities found in the snowpack did not compensate for the shallow snow depths measured, which can be ascribed to low precipitation totals and an increased frequency of rain events during the winter months.

Subgrid parameterization for snow depth over mountainous terrain from flat field snow depth

AbstractSnow depth is an important variable for a variety of models including land‐surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) most often at lower or mid elevation. While these sites have provided a wealth of information, various studies have questioned the representativity of such flat field measurements for the surrounding topography, especially in alpine regions. Using highly resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees, we developed two parameterizations to estimate domain‐averaged snow depth in coarse‐scale model applications over complex topography using easy to derive topographic parameters. The first parameterization uses a commonly applied linear lapse rate. Removing the dominant elevation gradient in mean snow depth revealed remaining underlying correlations with other topographic parameters, in particular the sky view factor. The second parameterization combines a power law elevation trend scaled with the subgrid parameterized sky view factor. Using a variety of statistic measures showed that the more complex parameterization performs better when using mean high‐resolution flat field snow depth. The performances slightly decreased when formulating the parameterizations for a single flat field snow depth measurement. Nevertheless, the more complex parameterization still outperformed the linear lapse rate model. As the parameterization was developed independently of a specific geographic region, we suggest it could be used to assimilate flat field snow depth or snowfall into coarse‐scale snow model frameworks.

Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle

Abstract. Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land–atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from differencing snow-covered and non-snow-covered DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided useful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to-noise ratio. The application of SfM to UAV photographs returns meaningful information in areas with mean snow depth > 30 cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 10 m s−1, clear skies, high sun angles and surfaces with negligible vegetation cover.

Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice

AbstractSnow distribution over sea ice is an important control on sea ice physical and biological processes. We combine measurements of the atmospheric boundary layer and blowing snow on an Antarctic sea ice floe with terrestrial laser scanning to characterize a typical storm and its influence on the spatial patterns of snow distribution at resolutions of 1–10 cm over an area of 100 m × 100 m. The pre‐storm surface exhibits multidirectional elongated snow dunes formed behind aerodynamic obstacles. Newly deposited dunes are elongated parallel to the predominant wind direction during the storm. Snow erosion and deposition occur over 62% and 38% of the area, respectively. Snow deposition volume is more than twice that of erosion (351 m3versus 158 m3), resulting in a modest increase of 2 ± 1 cm in mean snow depth, indicating a small net mass gain despite large mass relocation. Despite significant local snow depth changes due to deposition and erosion, the statistical distributions of elevation and the two‐dimensional correlation functions remain similar to those of the pre‐storm surface. Pre‐storm and post‐storm surfaces also exhibit spectral power law relationships with little change in spectral exponents. These observations suggest that for sea ice floes with mature snow cover features under conditions similar to those observed in this study, spatial statistics and scaling properties of snow surface morphology may be relatively invariant. Such an observation, if confirmed for other ice types and conditions, may be a useful tool for model parameterizations of the subgrid variability of sea ice surfaces.

Spatial and temporal variation of bulk snow properties in northern boreal and tundra environments based on extensive field measurements

Abstract. An extensive in situ data set of snow depth, snow water equivalent (SWE), and snow density collected in support of the European Space Agency (ESA) SnowSAR-2 airborne campaigns in northern Finland during the winter of 2011–2012 is presented (ESA Earth Observation Campaigns data 2000–2016). The suitability of the in situ measurement protocol to provide an accurate reference for the simultaneous airborne SAR (synthetic aperture radar) data products over different land cover types was analysed in the context of spatial scale, sample spacing, and uncertainty. The analysis was executed by applying autocorrelation analysis and root mean square difference (RMSD) error estimations. The results showed overall higher variability for all the three bulk snow parameters over tundra, open bogs and lakes (due to wind processes); however, snow depth tended to vary over shorter distances in forests (due to snow–vegetation interactions). Sample spacing/sample size had a statistically significant effect on the mean snow depth over all land cover types. Analysis executed for 50, 100, and 200 m transects revealed that in most cases less than five samples were adequate to describe the snow depth mean with RMSD < 5 %, but for land cover with high overall variability an indication of increased sample size of 1.5–3 times larger was gained depending on the scale and the desired maximum RMSD. Errors for most of the land cover types reached ∼ 10 % if only three measurements were considered. The collected measurements, which are available via the ESA website upon registration, compose an exceptionally large manually collected snow data set in Scandinavian taiga and tundra environments. This information represents a valuable contribution to the snow research community and can be applied to various snow studies.

Design of a scanning laser meter for monitoring the spatio-temporal evolution of snow depth and its application in the Alps and in Antarctica

Abstract. Although both the temporal and spatial variations of the snow depth are usually of interest for numerous applications, available measurement techniques are either space-oriented (e.g. terrestrial laser scans) or time-oriented (e.g. ultrasonic ranging probe). Because of snow heterogeneity, measuring depth in a single point is insufficient to provide accurate and representative estimates. We present a cost-effective automatic instrument to acquire spatio-temporal variations of snow depth. The device comprises a laser meter mounted on a 2-axis stage and can scan ≈ 200 000 points over an area of 100–200 m2 in 4 h. Two instruments, installed in Antarctica (Dome C) and the French Alps (Col de Porte), have been operating continuously and unattended over 2015 with a success rate of 65 and 90 % respectively. The precision of single point measurements and long-term stability were evaluated to be about 1 cm and the accuracy to be 5 cm or better. The spatial variability in the scanned area reached 7–10 cm (root mean square) at both sites, which means that the number of measurements is sufficient to average out the spatial variability and yield precise mean snow depth. With such high precision, it was possible for the first time at Dome C to (1) observe a 3-month period of regular and slow increase of snow depth without apparent link to snowfalls and (2) highlight that most of the annual accumulation stems from a single event although several snowfall and strong wind events were predicted by the ERA-Interim reanalysis. Finally the paper discusses the benefit of laser scanning compared to multiplying single-point sensors in the context of monitoring snow depth.

Estimating small-scale snow depth and ice thickness from total freeboard for East Antarctic sea ice

Deriving the snow depth on Antarctic sea ice is a key factor in estimating sea-ice thickness distributions from space or airborne altimeters. Using a linear regression to model snow depth from observed ‘total freeboard’, or the snow/ice surface elevation relative to sea level is an efficient and promising method for the estimation of snow depth for instruments which only detect the uppermost surface of the sea-ice conglomerate (e.g. laser altimetry). However the Antarctic pack-ice zone is subject to substantial variability due to synoptic-scale weather forcing. Ice formation, motion and melt undergo large spatio-temporal variability throughout the year. In this paper we estimate snow depth from total freeboard for the ARISE (2003), SIPEX (2007) and SIPEX-II (2012) research voyages to the East Antarctic pack-ice zone. Using in situ data we investigate variability in snow depth and show that for East Antarctica, relationships between snow depth and total freeboard vary between each voyage. At a resolution of metres to tens of metres, we show how regression-based snow-depth models track total freeboard and generally over-estimate snow depth, especially on highly deformed sea ice and at sites where ice freeboard makes a substantial contribution to total freeboard. For a set of 3192 records we obtain an in situ mean snow depth of 0.21m (σ=0.19m). Using a regression model derived from all in situ points we obtain the same mean, with a slightly lower variability (σ=0.16m). Using voyage-specific subsets of the data to derive regression models and estimate snow depth, mean snow depths ranged from 0.19m (model derived from SIPEX observations) to 0.25m (model derived from SIPEX-II observations). While small, these discrepancies impact ice thickness estimation using the assumption of hydrostatic equilibrium. Mean in situ ice thickness for all samples is 1.44m (σ=1.19m). Using empirical models for snow depth, ice thickness varies from 1.0 to 1.8m with the best match to the in situ mean given when snow depth is derived using a snow depth model from all observations (1.53m, σ=1.55m). However, mean values only tell part of the story when investigating the sea-ice thickness distribution. Here we explicitly show how modelling snow depth and ice thickness based on a total freeboard signal compares with in situ observations. This provides insight into the confidence we place in ice thickness distributions derived using a total freeboard signal and empirically-derived models for snow depth.

Satellite Remote Sensing of Snow Depth on Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches

Snow on Antarctic sea ice plays a key role for sea ice physical processes and complicates retrieval of sea ice thickness using altimetry. Current methods of snow depth retrieval are based on satellite microwave radiometry, which perform best for dry, homogeneous snow packs on level sea ice. We introduce an alternative approach based on in-situ measurements of total (sea ice plus snow) freeboard and snow depth, which we use to compute snow depth on sea ice from Ice, Cloud, and land Elevation Satellite (ICESat) total freeboard observations. We compare ICESat snow depth for early winter and spring of the years 2004 through 2006 with the Advanced Scanning Microwave Radiometer aboard EOS (AMSR-E) snow depth product. We find ICESat snow depths agree more closely with ship-based visual and air-borne snow radar observations than AMSR-E snow depths. We obtain average modal and mean ICESat snow depths, which exceed AMSR-E snow depths by 5–10 cm in winter and 10–15 cm in spring. We observe an increase in ICESat snow depth from winter to spring for most Antarctic regions in accordance with ground-based observations, in contrast to AMSR-E snow depths, which we find to stay constant or to decrease. We suggest satellite laser altimetry as an alternative method to derive snow depth on Antarctic sea ice, which is independent of snow physical properties.

On the extraordinary snow on the sea ice off East Antarctica in late winter, 2012

In late winter-early spring 2012, the second Sea Ice Physics and Ecosystems Experiment (SIPEX II) was conducted off Wilkes Land, East Antarctica, onboard R/V Aurora Australis. The sea-ice conditions were characterized by significantly thick first-year ice and snow, trapping the ship for about 10 days in the near coastal region. The deep snow cover was particularly remarkable, in that its average value of 0.45m was almost three times that observed between 1992 and 2007 in the region. To reveal factors responsible, we used in situ observations and ERA-Interim reanalysis (1990–2012) to examine the relative contribution of the different components of the local-regional snow mass balance equation i.e., snow accumulation on sea ice, precipitation minus evaporation (P–E), and loss by (i) snow-ice formation and (ii) entering into leads due to drifting snow. Results show no evidence for significantly high P–E in the winter of 2012. Ice core analysis has shown that although the snow-ice layer was relatively thin, indicating less transformation from snow to snow-ice in 2012 as compared to measurements from 2007, the difference was not enough to explain the extraordinarily deep snow. Based on these results, we deduce that lower loss of snow into leads was probably responsible for the extraordinary snow in 2012. Statistical analysis and satellite images suggest that the reduction in loss of snow into leads is attributed to rough ice surface associated with active deformation processes and larger floe size due to sea-ice expansion. This highlights the importance of snow-sea ice interaction in determining the mean snow depth on Antarctic sea ice.

Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

Abstract. Airborne light detection and ranging (lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak-snow-accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow-accumulation zone, with a total surveyed area of over 106 km2. The percentage of pixels with at least one snow-depth measurement was observed to increase from 65–90 to 99 % as the sampling resolution of the lidar point cloud was increased from 1 to 5 m. However, a coarser resolution risks undersampling the under-canopy snow relative to snow in open areas and was estimated to result in at least a 10 cm overestimate of snow depth over the main snow-accumulation region between 2000 and 3000 m, where 28 % of the area had no measurements. Analysis of the 1 m gridded data showed consistent patterns across the four sites, dominated by orographic effects on precipitation. Elevation explained 43 % of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14 % over the elevation range of 1500–3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under-canopy areas increased with elevation in the rain–snow transition zone (1500–1800 m) and was about 35 ± 10 cm above 1800 m. Lidar has the potential to transform estimation of snow depth across mountain basins, and including local canopy effects is both feasible and important for accurate assessments.