Coastal landfast sea ice decay and breakup in northern Alaska: Key processes and seasonal prediction

Abstract The surface spectral albedo was measured over coastal landfast sea ice in Prydz Bay (off Zhongshan Station), East Antarctica from 5 October to 26 November of 2016. The mean albedo decreased from late-spring to early-summer, mainly responding to the change in surface conditions from dry (phase I) to wet (phase II). The evolution of the albedo was strongly influenced by the surface conditions, with alternation of frequent snowfall events and katabatic wind that induce snow blowing at the surface. The two phases and day-to-day albedo variability were more pronounced in the near-infrared albedo wavelengths than in the visible ones, as the near-infrared photons are more sensitive to snow metamorphism, and to changes in the uppermost millimeters and water content of the surface. The albedo diurnal cycle during clear sky conditions was asymmetric with respect to noon, decreasing from morning to evening over full and patchy snow cover, and decreasing more rapidly in the morning over bare ice. We conclude that snow and ice metamorphism and surface melting dominated over the solar elevation angle dependency in shaping the albedo evolution. However, we realize that more detailed surface observations are needed to clarify and quantify the role of the various surface processes.

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Seasonal evolution and salt/freshwater fluxes of first-year sea ice: Comparison between pack ice and landfast sea ice

10.5194/egusphere-egu21-10798 ◽

2021 ◽

Author(s):

Marc Oggier ◽

Hajo Eicken ◽

Robert Rember ◽

Allison Fong ◽

Dmitry V. Divine ◽

...

Keyword(s):

Sea Ice ◽

Ice Cores ◽

Upper Ocean ◽

First Year ◽

Ice Melt ◽

Ice Surface ◽

Freshwater Fluxes ◽

Exchange Processes ◽

Bulk Salinity ◽

Landfast Sea Ice

<p>Sea ice affects the exchange of energy and matter between the atmosphere and the ocean from local to hemispheric scales. Salt fluxes across the ice-ocean interface that drive thermohaline mixing beneath growing sea ice are important elements of upper ocean nutrient and carbon exchange. Sea-ice melt releases freshwater into the upper ocean and results in formation of melt ponds that affect gas and energy transfer across the atmosphere-ice interface. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) provided an opportunity to follow sea-ice evolution and exchange processes over a full seasonal cycle in a rapidly changing ice cover. To this end, approximately 25 sea-ice cores were collected at 2 distinct sites, representing first-year and multi-year ice, to monitor physical, biological and geochemical processes relevant to atmosphere-ice-ocean exchange processes. Here we compare the growth and decay of first-year ice in the Central Arctic during the winter 2019-2020 to that of landfast first-year ice at Utqia&#289;vik, Alaska, from 1998 to 2016. Ice stratigraphy was similar at both sites with about 15 cm of granular ice on top of columnar ice, with a comparable growth history with a similar maximum ice thickness of 1.6-1.7 m. We aggregated the sea-ice bulk salinity and temperature profiles using a degree-day approach, and examined brine and freshwater fluxes at lower and upper interfaces of the ice, respectively. Preliminary results show lower sea-ice bulk salinity during the growth season and greater desalination at the ice surface during the melt season at the MOSAiC floe in comparison to Utqia&#289;vik.</p>

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Physical controls on the storage of methane in landfast sea ice

The Cryosphere ◽

10.5194/tc-8-1019-2014 ◽

2014 ◽

Vol 8 (3) ◽

pp. 1019-1029 ◽

Cited By ~ 9

Author(s):

J. Zhou ◽

J.-L. Tison ◽

G. Carnat ◽

N.-X. Geilfus ◽

B. Delille

Keyword(s):

Sea Ice ◽

Bubble Formation ◽

Gas Bubble ◽

Physical Processes ◽

Ice Melt ◽

Physical Controls ◽

The Difference ◽

Landfast Sea Ice ◽

Ice Water ◽

Brine Concentration

Abstract. We report on methane (CH4) dynamics in landfast sea ice, brine and under-ice seawater at Barrow in 2009. The CH4 concentrations in under-ice water ranged from 25.9 to 116.4 nmol L−1sw, indicating a supersaturation of 700 to 3100% relative to the atmosphere. In comparison, the CH4 concentrations in sea ice ranged from 3.4 to 17.2 nmol L−1ice and the deduced CH4 concentrations in brine from 13.2 to 677.7 nmol L−1brine. We investigated the processes underlying the difference in CH4 concentrations between sea ice, brine and under-ice water and suggest that biological controls on the storage of CH4 in ice were minor in comparison to the physical controls. Two physical processes regulated the storage of CH4 in our landfast ice samples: bubble formation within the ice and sea ice permeability. Gas bubble formation due to brine concentration and solubility decrease favoured the accumulation of CH4 in the ice at the beginning of ice growth. CH4 retention in sea ice was then twice as efficient as that of salt; this also explains the overall higher CH4 concentrations in brine than in the under-ice water. As sea ice thickened, gas bubble formation became less efficient, CH4 was then mainly trapped in the dissolved state. The increase of sea ice permeability during ice melt marked the end of CH4 storage.

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Chlorophyll-a in Antarctic Landfast Sea Ice: A First Synthesis of Historical Ice Core Data

Journal of Geophysical Research Oceans ◽

10.1029/2018jc014245 ◽

2018 ◽

Vol 123 (11) ◽

pp. 8444-8459 ◽

Cited By ~ 11

Author(s):

K. M. Meiners ◽

M. Vancoppenolle ◽

G. Carnat ◽

G. Castellani ◽

B. Delille ◽

...

Keyword(s):

Sea Ice ◽

Chlorophyll A ◽

Ice Core ◽

Core Data ◽

Landfast Sea Ice

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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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Sea-ice freeboard or thickness? Design choices in the context of data assimilation in the coupled numerical prediction system EC-Earth3 for seasonal Arctic sea ice prediction

10.5194/egusphere-egu21-8317 ◽

2021 ◽

Author(s):

Francois Massonnet ◽

Sara Fleury ◽

Florent Garnier ◽

Ed Blockley ◽

Pablo Ortega Montilla ◽

...

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

General Circulation ◽

Seasonal Prediction ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Concentration

<p>It is well established that winter and spring Arctic sea-ice thickness anomalies are a key source of predictability for late summer sea-ice concentration. While numerical general circulation models (GCMs) are increasingly used to perform seasonal predictions, they are not systematically taking advantage of the wealth of polar observations available. Data assimilation, the study of how to constrain GCMs to produce a physically consistent state given observations and their uncertainties, remains, therefore, an active area of research in the field of seasonal prediction. With the recent advent of satellite laser and radar altimetry, large-scale estimates of sea-ice thickness have become available for data assimilation in GCMs. However, the sea-ice thickness is never directly observed by altimeters, but rather deduced from the measured sea-ice freeboard (the height of the emerged part of the sea ice floe) based on several assumptions like the depth of snow on sea ice and its density, which are both often poorly estimated. Thus, observed sea-ice thickness estimates are potentially less reliable than sea-ice freeboard estimates. Here, using the EC-Earth3 coupled forecasting system and an ensemble Kalman filter, we perform a set of sensitivity tests to answer the following questions: (1) Does the assimilation of late spring observed sea-ice freeboard or thickness information yield more skilful predictions than no assimilation at all? (2) Should the sea-ice freeboard assimilation be preferred over sea-ice thickness assimilation? (3) Does the assimilation of observed sea-ice concentration provide further constraints on the prediction? We address these questions in the context of a realistic test case, the prediction of 2012 summer conditions, which led to the all-time record low in Arctic sea-ice extent. We finally formulate a set of recommendations for practitioners and future users of sea ice observations in the context of seasonal prediction.</p>

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Evaluation of the Rapid Refresh Numerical Weather Prediction Model Over Arctic Alaska

Weather and Forecasting ◽

10.1175/waf-d-20-0169.1 ◽

2021 ◽

Author(s):

Matthew T. Bray ◽

David D. Turner ◽

Gijs de Boer

Keyword(s):

Boundary Layer ◽

Sea Ice ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Numerical Weather ◽

Arctic Alaska ◽

Short Term Forecasting ◽

Northern Alaska ◽

Mixed Phase Clouds

AbstractDespite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary-layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary-layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.

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The Ice of Crown Prince Gustav Channel, Graham Land, Antarctica

Journal of Glaciology ◽

10.1017/s0022143000012703 ◽

1950 ◽

Vol 1 (08) ◽

pp. 404-409 ◽

Cited By ~ 18

Author(s):

Alan Reece

Keyword(s):

Sea Ice ◽

East Coast ◽

Falkland Islands ◽

Northern Half ◽

Southern Half ◽

Landfast Sea Ice ◽

Crown Prince

The author describes the ice in Crown Prince Gustav Channel on the east coast of Graham Land, Antarctica, based mainly on observations made by members of the Falkland Islands Dependencies Survey in 1945–48. He considers that the ice in the northern half of the Channel is landfast sea ice which may persist for more than one season, and, that in the southern half it is shelf ice of the same origin as the Larsen Shelf Ice. He concludes that this shelf ice in the southern part of the Channel is part of the Larsen Shelf Ice.

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Sounds and vibrations recorded on and beneath landfast sea ice during construction of an artificial gravel island for oil production in the Alaska Beaufort Sea

The Journal of the Acoustical Society of America ◽

10.1121/1.4808676 ◽

2004 ◽

Vol 116 (4) ◽

pp. 2648-2649

Author(s):

Charles R. Greene

Keyword(s):

Sea Ice ◽

Oil Production ◽

Beaufort Sea ◽

Landfast Sea Ice

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Erroneous sea-ice concentration retrieval in the East Antarctic

Annals of Glaciology ◽

10.1017/aog.2018.1 ◽

2018 ◽

Vol 59 (76pt2) ◽

pp. 201-212 ◽

Cited By ~ 1

Author(s):

Hoi Ming Lam ◽

Gunnar Spreen ◽

Georg Heygster ◽

Christian Melsheimer ◽

Neal W. Young

Keyword(s):

Sea Ice ◽

Microwave Remote Sensing ◽

Atmospheric Effects ◽

Sea Ice Concentration ◽

Microwave Brightness Temperature ◽

Antarctic Sea Ice ◽

Optical Images ◽

Ice Concentration ◽

Retrieval Algorithms ◽

Landfast Sea Ice

ABSTRACTLarge discrepancies have been observed between satellite-derived sea-ice concentrations(IC) from passive microwave remote sensing and those derived from optical images at several locations in the East Antarctic, between February and April 2014. These artefacts, that resemble polynyas in the IC maps, appear in areas where optical satellite data show that there is landfast sea ice. The IC datasets and the corresponding retrieval algorithms are investigated together with microwave brightness temperature, air temperature, snowfall and bathymetry to understand the failure of the IC retrieval. The artefacts are the result of the application of weather filters in retrieval algorithms. These filters use the 37 and 19 GHz channels to correct for atmospheric effects on the retrieval. These channels show significant departures from typical ranges when the artefacts occur. A melt–refreeze cycle with associated snow metamorphism is proposed as the most likely cause. Together, the areas of the artefacts account for up to 0.5% of the Antarctic sea-ice area and thus cause a bias in sea-IC time series. In addition, erroneous sea ICs can adversely affect shipping operations.

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