Snow Depth and Ice Thickness Measurements From the Beaufort and Chukchi Seas Collected During the AMSR-Ice03 Campaign

Sturm, Matthew; Maslanik, James A; Perovich, D. K.; Stroeve, Julienne; Richter-Menge, J.; Markus, T.; Holmgren, Jon; Heinrichs, J.; Tape, K. D.

doi:10.1109/tgrs.2006.878236

Cited by 65 publications

(42 citation statements)

References 30 publications

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“…Although the data in Fig. 6 represent level ice in the landing areas excluding local variations like sastrugi, the results are in agreement with other studies of in situ data (Sturm et al, 2006;Farrell et al, 2012;Newman et al, 2014) and data from IceBridge airborne surveys (Kurtz and Farrel, 2011;Kwok, 2017). It is noteworthy that the snow depth distribution in The Cryosphere Discuss., https://doi.org /10.5194/tc-2017-278 Manuscript under review for journal The Cryosphere Discussion started: 22 December 2017 c Author(s) 2017.…”

Section: Depth Of Undisturbed Snow Coversupporting

confidence: 90%

“…the Chukchi Sea agrees very well with the data collected during the AMSR-Ice03 validation campaign, covering a smaller part of that sea (Sturm et al, 2006).…”

Section: Depth Of Undisturbed Snow Coversupporting

confidence: 79%

“…Snow depth variations on local scale are caused by wind forcing, resulting in sastrugi formation and increased snow depth near ice ridges and hummocks and reduced snow depth on level ice. On larger scale snow depth variations are synoptic in origin (Sturm et al, 2006) or caused by different age of ice where snow cover has been built up.…”

Section: Methodsmentioning

confidence: 99%

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Snow depth on Arctic sea ice from historical in situ data

Shalina¹,

Sandven²

2017

Preprint

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Abstract.In this paper we analyze snow data from Soviet airborne expeditions Sever that was collected in the Arctic around places of landings in March, April and May and cover much wider area than the region of observations of Soviet North Pole drifting stations. Particularly, there were a lot of Sever observations in the Eurasian seas. We investigate the following snow parameters: average snow depth on the level ice, height and area of sastrugi, depth of snow dunes attached to ice ridges and 10 depth of snow on hummocks. We have built new snow depth climatology for the late winter that was calculated using both Sever expedition and North Pole drifting station observations. Our result refines the description of snow depth in the central Arctic and provides detailed information on snow depth in the marginal seas. In the 1970s-80s the snow cover in the central Arctic had the following characteristics: the snow depth of the undisturbed snow was 21.2 cm, the depth of sastrugi (that occupied about 36% of the ice surface) was 36.2 cm and the depth of snow assembled near the hummocks and ridges was 15 about 65 cm. For the marginal seas Sever observations revealed that the average depth of undisturbed snow on the level ice changed from 9.8 cm in the Laptev Sea to 15.3 cm in the East Siberian Sea, the topmost value in the East Siberian Sea is explained by the highest proportion of multiyear ice there. Observations demonstrated a very high spatial variability of snow depth in the marginal seas characterized by standard deviation changing from 66 to 100%. Average height of sastrugi in the Eurasian seas varied from 23 cm to about 32 cm with standard deviation from 50 to 56%. Average area covered by sastrugi 20 in the marginal seas was estimated as 36.5% of the area of the ice floe where those features have been observed. The snow map introduced here as a new climatology is built from Sever and North Pole data, with the latter amounted to 6.1% of the whole data set. On the whole, our snow depth map reveals lower values comparing to Warren climatology in the central Arctic and shows refined information for the Eurasian seas.

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Section: Depth Of Undisturbed Snow Coversupporting

confidence: 90%

“…the Chukchi Sea agrees very well with the data collected during the AMSR-Ice03 validation campaign, covering a smaller part of that sea (Sturm et al, 2006).…”

Section: Depth Of Undisturbed Snow Coversupporting

confidence: 79%

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Snow depth on Arctic sea ice from historical in situ data

Shalina¹,

Sandven²

2017

Preprint

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“…Field measurements were conducted on smooth, first-year sea ice on Elson Lagoon, a location where landfast ice undergoes little deformation. Following the OIB pass on 15 March 2012, snow depths were measured every 1-5 m in a two-dimensional layout along two transects using a GPS-enabled Magna probe unit (Sturm and Holmgren, 1999); the probe has an accuracy of 0.3 cm over level sea ice and snow (Sturm et al, 2006). The first transect (used here) consisted of three lines ∼ 1000 m in length, each 5 m apart, for a width of 10 m. Snow density was measured every ∼ 100 m with a Federal Sampler (Marr, 1940); the average density was 306 ± 91 kg m −3 .…”

Section: Bromex 2012mentioning

confidence: 99%

“…Previous understanding of snow depth over Arctic sea ice has been derived from various field surveys (e.g., Sturm et al, 2002Sturm et al, , 2006 and the climatology based on snow data from drifting stations that operated in 1937and 1954-1991(Warren et al, 1999. The W99 snow climatology is still widely used in ice thickness retrievals.…”

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confidence: 99%

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

et al. 2017

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Abstract. Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air-snow (a-s) and snow-ice (s-i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two groundbased campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.

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Sensitivity of simulated Arctic sea ice to realistic ice thickness distributions and snow parameterizations

et al. 2014

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[1] Sea ice and snow on sea ice to a large extent determine the surface heat budget in the Arctic Ocean. In spite of the advances in modeling sea-ice thermodynamics, a good number of models still rely on simple parameterizations of the thermodynamics of ice and snow. Based on simulations with an Arctic sea-ice model coupled to an ocean general circulation model, we analyzed the impact of changing two sea-ice parameterizations: (1) the prescribed ice thickness distribution (ITD) for surface heat budget calculations, and (2) the description of the snow layer. For the former, we prescribed a realistic ITD derived from airborne electromagnetic induction sounding measurements. For the latter, two different types of parameterizations were tested: (1) snow thickness independent of the sea-ice thickness below, and (2) a distribution proportional to the prescribed ITD. Our results show that changing the ITD from seven uniform categories to fifteen nonuniform categories derived from field measurements, and distributing the snow layer according to the ITD, leads to an increase in average Arctic-wide ice thickness by 0.56 m and an increase by 1 m in the Canadian Arctic Archipelago and Canadian Basin. This increase is found to be a direct consequence of 524 km 3 extra thermodynamic growth during the months of ice formation (January, February, and March). Our results emphasize that these parameterizations are a key factor in sea-ice modeling to improve the representation of the sea-ice energy balance.Citation: Castro-Morales, K., F. Kauker, M. Losch, S. Hendricks, K. Riemann-Campe, and R. Gerdes (2014), Sensitivity of simulated Arctic sea ice to realistic ice thickness distributions and snow parameterizations,

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Snow Depth and Ice Thickness Measurements From the Beaufort and Chukchi Seas Collected During the AMSR-Ice03 Campaign

Cited by 65 publications

References 30 publications

Snow depth on Arctic sea ice from historical in situ data

Snow depth on Arctic sea ice from historical in situ data

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

Sensitivity of simulated Arctic sea ice to realistic ice thickness distributions and snow parameterizations

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