Supplementary material to "Inter-comparison of snow depth over sea ice from multiple methods"

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

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The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

10.5194/egusphere-egu21-12240 ◽

2021 ◽

Author(s):

Isolde Glissenaar ◽

Jack Landy ◽

Alek Petty ◽

Nathan Kurtz ◽

Julienne Stroeve

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Satellite Altimetry ◽

Region Of Interest ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Baffin Bay ◽

The Impact

The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.

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Simulation of Snow on Arctic Sea Ice Using a Coupled Snow–Ice Model

Journal of Hydrometeorology ◽

10.1175/2009jhm1112.1 ◽

2010 ◽

Vol 11 (1) ◽

pp. 199-210 ◽

Cited By ~ 10

Author(s):

Yi-Ching Chung ◽

Stéphane Bélair ◽

Jocelyn Mailhot

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Single Layer ◽

Coupled System ◽

The Arctic ◽

Ice Thickness ◽

The Arctic Ocean ◽

Snow Model ◽

Ice Model

Abstract The new Recherche Prévision Numérique (NEW-RPN) model, a coupled system including a multilayer snow thermal model (SNTHERM) and the sea ice model currently used in the Meteorological Service of Canada (MSC) operational forecasting system, was evaluated in a one-dimensional mode using meteorological observations from the Surface Heat Budget of the Arctic Ocean (SHEBA)’s Pittsburgh site in the Arctic Ocean collected during 1997/98. Two parameters simulated by NEW-RPN (i.e., snow depth and ice thickness) are compared with SHEBA’s observations and with simulations from RPN, MSC’s current coupled system (the same sea ice model and a single-layer snow model). Results show that NEW-RPN exhibits better agreement for the timing of snow depletion and for ice thickness. The profiles of snow thermal conductivity in NEW-RPN show considerable variability across the snow layers, but the mean value (0.39 W m−1 K−1) is within the range of reported observations for SHEBA. This value is larger than 0.31 W m−1 K−1, which is commonly used in single-layer snow models. Of particular interest in NEW-RPN’s simulation is the strong temperature stratification of the snowpack, which indicates that a multilayer snow model is needed in the SHEBA scenario. A sensitivity analysis indicates that snow compaction is also a crucial process for a realistic representation of the snowpack within the snow/sea ice system. NEW-RPN’s overestimation of snow depth may be related to other processes not included in the study, such as small-scale horizontal variability of snow depth and blowing snow processes.

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Characteristics of Sea-ice thickness and Snow-depth distributions of the Summer landfast ice in Lützow-Holm Bay, East Antarctica

Annals of Glaciology ◽

10.3189/172756406781811240 ◽

2006 ◽

Vol 44 ◽

pp. 281-287 ◽

Cited By ~ 9

Author(s):

Shotaro Uto ◽

Haruhito Shimoda ◽

Shuki Ushio

Keyword(s):

Sea Ice ◽

Interannual Variability ◽

Snow Depth ◽

East Antarctica ◽

Ice Thickness ◽

First Year ◽

Total Thickness ◽

Sea Ice Thickness ◽

Landfast Ice ◽

Northeasterly Wind

AbstractSea-ice observations have been conducted on board icebreaker shirase as a part of the Scientific programs of the Japanese Antarctic Research Expedition. We Summarize these to investigate Spatial and interannual variability of ice thickness and Snow depth of the Summer landfast ice in Lützow-Holm Bay, East Antarctica. Electromagnetic–inductive observations, which have been conducted Since 2000, provide total thickness distributions with high Spatial resolution. A clear discontinuity, which Separates thin first-year ice from thick multi-year ice, was observed in the total thickness distributions in two voyages. Comparison with Satellite images revealed that Such phenomena reflected the past breakup of the landfast ice. Within 20–30km from the Shore, total thickness as well as Snow depth decrease toward the Shore. This is due to the Snowdrift by the Strong northeasterly wind. Video observations of Sea-ice thickness and Snow depth were conducted on 11 voyages Since December 1987. Probability density functions derived from total thickness distributions in each year are categorized into three types: a thin-ice, thick-ice and intermediate type. Such interannual variability primarily depends on the extent and duration of the Successive break-up events.

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Supplementary material to "Benthic foraminifera as tracers of brine production in Storfjorden sea ice factory"

10.5194/bg-2019-405-supplement ◽

2019 ◽

Author(s):

Eleonora Fossile ◽

Maria Pia Nardelli ◽

Arbia Jouini ◽

Bruno Lansard ◽

Antonio Pusceddu ◽

...

Keyword(s):

Sea Ice ◽

Benthic Foraminifera ◽

Supplementary Material

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Supplementary material to "The NASA Eulerian Snow on Sea Ice Model (NESOSIM): Initial model development and analysis"

10.5194/gmd-2018-84-supplement ◽

2018 ◽

Author(s):

Alek A. Petty ◽

Melinda Webster ◽

Linette Boisvert ◽

Thorsten Markus

Keyword(s):

Sea Ice ◽

Model Development ◽

Initial Model ◽

Supplementary Material ◽

Ice Model

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Supplementary material to "Climate information preserved in seasonal water isotope at NEEM: relationships with temperature, circulation and sea ice"

10.5194/cp-2018-8-supplement ◽

2018 ◽

Author(s):

Minjie Zheng ◽

Jesper Sjolte ◽

Florian Adolphi ◽

Bo Møllesøe Vinther ◽

Hans Christian Steen-Larsen ◽

...

Keyword(s):

Sea Ice ◽

Climate Information ◽

Supplementary Material ◽

Water Isotope

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Spatial variation of biogeochemical properties of landfast sea ice in the Gulf of Bothnia, Baltic Sea

Annals of Glaciology ◽

10.3189/172756406781811169 ◽

2006 ◽

Vol 44 ◽

pp. 80-87 ◽

Cited By ~ 15

Author(s):

M. Steffens ◽

M.A. Granskog ◽

H. Kaartokallio ◽

H. Kuosa ◽

K. Luodekari ◽

...

Keyword(s):

Sea Ice ◽

Baltic Sea ◽

Snow Depth ◽

Large Scale ◽

Spatial Scales ◽

Thick Layer ◽

Ice Cores ◽

Ice Thickness ◽

Gulf Of Bothnia ◽

Landfast Sea Ice

AbstractHorizontal variation of landfast sea-ice properties was studied in the Gulf of Bothnia, Baltic Sea, during March 2004. In order to estimate their variability among and within different spatial levels, 72 ice cores were sampled on five spatial scales (with spacings of 10 cm, 2.5 m, 25 m, 250m and 2.5 km) using a hierarchical sampling design. Entire cores were melted, and bulk-ice salinity, concentrations of chlorophylla(Chla), phaeophytin (Phaeo), dissolved nitrate plus nitrite (DIN) as well as dissolved organic carbon (DOC) and nitrogen (DON) were determined. All sampling sites were covered by a 5.5–23 cm thick layer of snow. Ice thicknesses of cores varied from 26 to 58 cm, with bulk-ice salinities ranging between 0.2 and 0.7 as is typical for Baltic Sea ice. Observed values for Chla(range: 0.8–6.0 mg ChlaL–1; median: 2.9 mg ChlaL–1) and DOC (range: 37–397 μM; median: 95 μM) were comparable to values reported by previous sea-ice studies from the Baltic Sea. Analysis of variance among different spatial levels revealed significant differences on the 2.5km scale for ice thickness, DOC and Phaeo (with the latter two being positively correlated with ice thickness). For salinity and Chla, the 250 m scale was found to be the largest scale where significant differences could be detected, while snow depth only varied significantly on the 25 m scale. Variability on the 2.5 m scale contributed significantly to the total variation for ice thickness, salinity, Chlaand DIN. In the case of DON, none of the investigated levels exhibited variation that was significantly different from the considerable amount of variation found between replicate cores. Results from a principal component analysis suggest that ice thickness is one of the main elements structuring the investigated ice habitat on a large scale, while snow depth, nutrients and salinity seem to be of secondary importance.

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