Landfast sea ice extent in the Chukchi and Beaufort Seas: The annual cycle and decadal variability

Abstract Well-established satellite-derived Arctic and Antarctic sea ice extents are combined to create the global picture of sea ice extents and their changes over the 35-yr period 1979–2013. Results yield a global annual sea ice cycle more in line with the high-amplitude Antarctic annual cycle than the lower-amplitude Arctic annual cycle but trends more in line with the high-magnitude negative Arctic trends than the lower-magnitude positive Antarctic trends. Globally, monthly sea ice extent reaches a minimum in February and a maximum generally in October or November. All 12 months show negative trends over the 35-yr period, with the largest magnitude monthly trend being the September trend, at −68 200 ± 10 500 km2 yr−1 (−2.62% ± 0.40% decade−1), and the yearly average trend being −35 000 ± 5900 km2 yr−1 (−1.47% ± 0.25% decade−1).

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Modeling the annual cycle of daily Antarctic sea ice extent

The Cryosphere ◽

10.5194/tc-14-2159-2020 ◽

2020 ◽

Vol 14 (7) ◽

pp. 2159-2172 ◽

Cited By ~ 1

Author(s):

Mark S. Handcock ◽

Marilyn N. Raphael

Keyword(s):

Sea Ice ◽

Annual Cycle ◽

Traditional Method ◽

Linear Trend ◽

Phase Variation ◽

Positive Trend ◽

Concentration Data ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Antarctic Sea Ice

Abstract. The total Antarctic sea ice extent (SIE) experiences a distinct annual cycle, peaking in September and reaching its minimum in February. In this paper we propose a mathematical and statistical decomposition of this temporal variation in SIE. Each component is interpretable and, when combined, gives a complete picture of the variation in the sea ice. We consider timescales varying from the instantaneous and not previously defined to the multi-decadal curvilinear trend, the longest. Because our representation is daily, these timescales of variability give precise information about the timing and rates of advance and retreat of the ice and may be used to diagnose physical contributors to variability in the sea ice. We define a number of annual cycles each capturing different components of variation, especially the yearly amplitude and phase that are major contributors to SIE variation. Using daily sea ice concentration data, we show that our proposed invariant annual cycle explains 29 % more of the variation in daily SIE than the traditional method. The proposed annual cycle that incorporates amplitude and phase variation explains 77 % more variation than the traditional method. The variation in phase explains more of the variability in SIE than the amplitude. Using our methodology, we show that the anomalous decay of sea ice in 2016 was associated largely with a change of phase rather than amplitude. We show that the long term trend in Antarctic sea ice extent is strongly curvilinear and the reported positive linear trend is small and dependent strongly on a positive trend that began around 2011 and continued until 2016.

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Modelling landfast sea ice and its influence on ocean-ice interactions in the area of the Totten Glacier, East Antarctica

10.5194/egusphere-egu21-8846 ◽

2021 ◽

Author(s):

Guillian Van Achter ◽

Thierry Fichefet ◽

Hugues Goosse ◽

Charles Pelletier ◽

Jean Sterlin ◽

...

Keyword(s):

Sea Ice ◽

Climate Models ◽

Mixed Layer Depth ◽

Global Climate Models ◽

East Antarctica ◽

Ice Shelf ◽

Sea Ice Extent ◽

Coastal Polynya ◽

Landfast Sea Ice ◽

The Impact

The Totten Glacier in East Antarctica is of major climate interest because of the large fluctuation of its grounding line and of its potential vulnerability to climate change. The ocean above the continental shelf in front of the Totten ice shelf exhibits large extents of landfast sea ice with low interannual variability. Landfast sea ice is mostly not or sole crudely represented in current climate models. These models are potentially omitting or misrepresenting important effects related to this type of sea ice, such as its influence on coastal polynya locations. Yet, the impact of the landfast sea ice on the ocean &#8211; ice shelf interactions is poorly understood. Using a series of high-resolution, regional NEMO-LIM-based experiments including an explicit treatment of ocean &#8211; ice shelf interactions over the years 2001-2010, we simulate a realistic landfast sea ice extent in the area of Totten Glacier through a combination of a sea ice tensile strength parameterisation and a grounded iceberg representation. We show that the presence of landfast sea ice impacts seriously both the location of coastal polynyas and the ocean mixed layer depth along the coast, in addition to favouring the intrusion of mixed Circumpolar Deep Water into the ice shelf cavities. Depending on the local bathymetry and the landfast sea ice distribution, landfast sea ice affects ice shelf cavities in different ways, either by increasing the ice melt (+28% for the Moscow University ice shelf) or by reducing its seasonal cycle (+10% in March-May for the Totten ice shelf). This highlights the importance of including an accurate landfast sea ice representation in regional and eventually global climate models

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Observation and thermodynamic modeling of the influence of snow cover on landfast sea ice thickness in Prydz Bay, East Antarctica

10.5194/egusphere-egu2020-1715 ◽

2020 ◽

Author(s):

Jiechen Zhao ◽

Bin Cheng ◽

Timo Vihma ◽

Qinghua Yang ◽

Fengming Hui ◽

...

Keyword(s):

Sea Ice ◽

Snow Cover ◽

Annual Cycle ◽

Snow Depth ◽

Snow Accumulation ◽

East Antarctica ◽

Prydz Bay ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Landfast Sea Ice

The observed snow depth and ice thickness on landfast sea ice in Prydz Bay, East Antarctica, were used to determine the role of snow in (a) the annual cycle of sea ice thickness at a fixed location (SIP) where snow usually blows away after snowfall and (b) early summer sea ice thickness within the transportation route surveys (TRS) domain farther from coast, where annual snow accumulation is substantial. The annual mean snow depth and maximum ice thickness had a negative relationship (r = &#8722;0.58, p < 0.05) at SIP, indicating a primary insulation effect of snow on ice thickness. However, in the TRS domain, this effect was negligible because snow contributes to ice thickness. A one-dimensional thermodynamic sea ice model, forced by local weather observations, reproduced the annual cycle of ice thickness at SIP well. During the freeze season, the modeled maximum difference of ice thickness using different snowfall scenarios ranged from 0.53&#8211;0.61 m. Snow cover delayed ice surface and ice bottom melting by 45 and 24 days, respectively. The modeled snow ice and superimposed ice accounted for 4&#8211;23% and 5&#8211;8% of the total maximum ice thickness on an annual basis in the case of initial ice thickness ranging from 0.05&#8211;2 m, respectively.

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Mapping pan-Arctic landfast sea ice stability using Sentinel-1 interferometry

The Cryosphere ◽

10.5194/tc-13-557-2019 ◽

2019 ◽

Vol 13 (2) ◽

pp. 557-577 ◽

Cited By ~ 11

Author(s):

Dyre O. Dammann ◽

Leif E. B. Eriksson ◽

Andrew R. Mahoney ◽

Hajo Eicken ◽

Franz J. Meyer

Keyword(s):

Sea Ice ◽

The Arctic ◽

Sea Ice Extent ◽

Marginal Seas ◽

Nares Strait ◽

Novel Approach ◽

Landfast Ice ◽

Landfast Sea Ice ◽

Fringe Patterns ◽

The Stability

Abstract. Arctic landfast sea ice has undergone substantial changes in recent decades, affecting ice stability and including potential impacts on ice travel by coastal populations and on industry ice roads. We present a novel approach for evaluating landfast sea ice stability on a pan-Arctic scale using Synthetic Aperture Radar Interferometry (InSAR). Using Sentinel-1 images from spring 2017, we discriminate between bottomfast, stabilized, and nonstabilized landfast ice over the main marginal seas of the Arctic Ocean (Beaufort, Chukchi, East Siberian, Laptev, and Kara seas). This approach draws on the evaluation of relative changes in interferometric fringe patterns. This first comprehensive assessment of Arctic bottomfast sea ice extent has revealed that most of the bottomfast sea ice is situated around river mouths and coastal shallows. The Laptev and East Siberian seas dominate the aerial extent, covering roughly 4100 and 5100 km2, respectively. These seas also contain the largest extent of stabilized and nonstabilized landfast ice, but are subject to the largest uncertainties surrounding the mapping scheme. Even so, we demonstrate the potential for using InSAR for assessing the stability of landfast ice in several key regions around the Arctic, providing a new understanding of how stability may vary between regions. InSAR-derived stability may serve for strategic planning and tactical decision support for different uses of coastal ice. In a case study of the Nares Strait, we demonstrate that interferograms may reveal early-warning signals for the breakup of stationary sea ice.

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Review of “Modeling the annual cycle of daily Antarctic sea ice extent” by Handcock and Raphael

10.5194/tc-2019-203-rc1 ◽

2019 ◽

Author(s):

Walter Meier

Keyword(s):

Sea Ice ◽

Annual Cycle ◽

Sea Ice Extent ◽

Antarctic Sea Ice

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The Arctic sea ice extent change connected to Pacific decadal variability

The Cryosphere ◽

10.5194/tc-14-693-2020 ◽

2020 ◽

Vol 14 (2) ◽

pp. 693-708

Author(s):

Xiao-Yi Yang ◽

Guihua Wang ◽

Noel Keenlyside

Keyword(s):

Sea Ice ◽

Sea Level ◽

Annual Cycle ◽

Bering Sea ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Extent ◽

Sea Level Pressure ◽

Level Pressure ◽

Arctic Sea

Abstract. After an unprecedented retreat, the total Arctic sea ice cover for the post-2007 period is characterized by low extent and a remarkable increase in annual cycle amplitude. We have identified the leading role of spring Bering Sea ice in explaining the changes in the amplitude of the annual cycle of total Arctic sea ice. In particular, these changes are related to the recent occurrence of multiyear variability in spring Bering Sea ice extent. This is due to the phase-locking of the North Pacific Gyre Oscillation (NPGO) and the Pacific Decadal Oscillation (PDO) after about 2007, with a correlation coefficient reaching −0.6. Furthermore, there emerge notable changes in the sea level pressure and sea surface temperature patterns associated with the NPGO in the recent decade. After 2007, the NPGO is related to a quadrupole of sea level pressure (SLP) anomalies that is associated with the wind stress curl and Ekman pumping rate anomalies in the Bering deep basin; these account for the change in Bering Sea subsurface variability that contribute to the decadal oscillation of the spring Bering Sea ice extent.

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Fram Strait sea ice export variability and September Arctic sea ice extent over the last 80 years

The Cryosphere ◽

10.5194/tc-11-65-2017 ◽

2017 ◽

Vol 11 (1) ◽

pp. 65-79 ◽

Cited By ~ 65

Author(s):

Lars H. Smedsrud ◽

Mari H. Halvorsen ◽

Julienne C. Stroeve ◽

Rong Zhang ◽

Kjell Kloster

Keyword(s):

Sea Ice ◽

Surface Pressure ◽

Decadal Variability ◽

Arctic Sea Ice ◽

The Arctic ◽

Fram Strait ◽

Sea Ice Extent ◽

Radar Images ◽

Data Record

Abstract. A new long-term data record of Fram Strait sea ice area export from 1935 to 2014 is developed using a combination of satellite radar images and station observations of surface pressure across Fram Strait. This data record shows that the long-term annual mean export is about 880 000 km2, representing 10 % of the sea-ice-covered area inside the basin. The time series has large interannual and multi-decadal variability but no long-term trend. However, during the last decades, the amount of ice exported has increased, with several years having annual ice exports that exceeded 1 million km2. This increase is a result of faster southward ice drift speeds due to stronger southward geostrophic winds, largely explained by increasing surface pressure over Greenland. Evaluating the trend onwards from 1979 reveals an increase in annual ice export of about +6 % per decade, with spring and summer showing larger changes in ice export (+11 % per decade) compared to autumn and winter (+2.6 % per decade). Increased ice export during winter will generally result in new ice growth and contributes to thinning inside the Arctic Basin. Increased ice export during summer or spring will, in contrast, contribute directly to open water further north and a reduced summer sea ice extent through the ice–albedo feedback. Relatively low spring and summer export from 1950 to 1970 is thus consistent with a higher mid-September sea ice extent for these years. Our results are not sensitive to long-term change in Fram Strait sea ice concentration. We find a general moderate influence between export anomalies and the following September sea ice extent, explaining 18 % of the variance between 1935 and 2014, but with higher values since 2004.

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