Snow Depth on Arctic Sea Ice

Warren, Stephen G.; Rigor, Ignatius; Untersteiner, Norbert; Радионов, В. Ф.; Bryazgin, Nikolay N.; Aleksandrov, Yevgeniy I.; Colony, R.

doi:10.1175/1520-0442(1999)012<1814:sdoasi>2.0.co;2

Cited by 526 publications

(772 citation statements)

References 27 publications

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“…The principal challenges in deriving an accurate sea ice thickness using satellite altimetry are the discrimination of ice and open water, retracking radar waveforms to obtain height estimates, constructing sea surface height beneath the ice, and estimating the depth of the snow 20 cover. Ice thickness is retrieved from freeboard by processing CS2 Level 1B data, with a footprint of approximately 300 m by 1700 m (Wingham et al, 2006), and assuming snow density and snow depth from the Warren et al (1999) climatology (hereafter W99), modified for the distribution of multi-year versus first-year ice (see Laxon et al, 2013 andTilling et al, 2018 for data processing details).…”

Section: Ice Thickness Distribution From Cryosat-2mentioning

confidence: 99%

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New insight from CryoSat-2 sea ice thickness for sea ice modelling

Feltham¹,

Schröder²,

Tsamados³

2018

Preprint

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Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone 10 simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of 15 positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50% of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does 20 not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

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Section: Ice Thickness Distribution From Cryosat-2mentioning

confidence: 99%

“…While for individual years and regions the W99 snow load can differ from reality by more than 0.1 m (Warren et al, 1999), the average snow conditions are accurately represented over multi-year ice (e.g. Haas et al, 2017).…”

Section: Defining Region For Comparing Model Sea Ice Thickness With Cmentioning

confidence: 99%

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Feltham¹,

Schröder²,

Tsamados³

2018

Preprint

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“…Stake line and depth probe measurements taken at many drifting stations [e.g., Buzuev et al, 1979;Radionov et al, 1996;Warren et al, 1998] show that the snow cover in May on unridged ice averages 35 cm in depth, with a standard deviation of 17 cm. The actual variability of snow depth is much higher because much of the snow is collected on the leeward side of pressure ridges where depths of I m and more are typical [Hanson, 1980].…”

Section: Snowmentioning

confidence: 99%

Observations of melt ponds on Arctic sea ice

Fetterer¹,

1998

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Abstract. In an introductory section we review the physical processes influencing the formation and evolution of melt ponds on sea ice during the Arctic summer. As melt progresses, the changing properties of the surface interact strongly with the surface heat balance. The small interannual variability of the seasonal ice extent suggests an interannual variability of the surface heat balance of _+ 1 W m -2 or less. The interannual variance of atmospheric forcing represented by the transport of moist static energy into the Arctic is an order of magnitude greater. This appears to contradict the notion of a highly sensitive sea ice cover and emphasizes the need to generate albedo as an important internal variable in interactive models. Observations of melt ponds are needed in order to derive improved relationships between surface albedo and parameters such as the amount of snow, the onset and termination of melting, the ice thickness distribution, and ice deformation. Here classified (National Technical Means) imagery is used to measure melt pond coverage as it evolves over a summer on ice surrounding a drifting buoy. Local variability of pond cover is greatest at the beginning of the melt season, that is, pond coverage from 5% to 50% depending on ice type, as previously found by Russian investigators. An important distinction is found in the temporal change of pond cover: it decreases with time on thick ice, and it increases with time on thin ice (eventually leading to the disappearance of thin ice at the end of summer). An attempt to relate pond coverage to ice concentrations derived from passive microwave data proved unsuccessful. IntroductionIn the central Arctic, summer starts in late May when increasing solar radiation rapidly warms the snow that covers the ice. Melting, which starts in early June, lowers the albedo and further enhances the absorption of short-wave radiation. By mid-June a significant fraction of the pack ice is covered by melt water ponds. As melting progresses, the ponds deepen, their area diminishes, and much of the thin ice melts completely so that more of the dark ocean surface is exposed. Freeze-up occurs in late August or early September.The progression of melt controls the albedo of the ice surface. Thickness and mass balance of sea ice are extremely sensitive to the summertime short-wave radiation balance, making surface albedo a powerful tuning parameter in sea ice models. While the processes affecting the surface albedo are qualitatively known, the available data on regional and temporal differences are sparse and unsatisfactory for validating efforts toward modeling the albedo as an internal variable derived from surface processes such as available snow, melt rate, ice topography, thickness distribution, and ice deformation. At the same time, there is an acute need for an improved representation of sea ice processes in models.The computational requirements of fully interactive models of the global climate system for the prediction of, for instance, the effects of increased greenhou...

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“…Snow cover changed during the station lifetime and line measurements allowed "to obtain a representative distribution of snow depths, passing through sastrugi, snow dunes, and pressure ridges as well as level snow" (Warren et al, 1999). The 10 advantage of the NP line measurements is that natural local variability on the same ice is captured as well as the time evolution over the year.…”

Section: Factors 25mentioning

confidence: 99%

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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Snow Depth on Arctic Sea Ice

Cited by 526 publications

References 27 publications

New insight from CryoSat-2 sea ice thickness for sea ice modelling

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Observations of melt ponds on Arctic sea ice

Snow depth on Arctic sea ice from historical in situ data

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