scholarly journals Brief communication: lack of agreement in remote sensing detection of cyclonic drift caused by Atlantic weather in Antarctic sea ice

2021 ◽  
Author(s):  
Wayne de Jager ◽  
Marcello Vichi
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Alternative Methods ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Dynamics ◽  
Antarctic Sea Ice ◽  
Ice Drift ◽  
The Impact

Abstract. Sea-ice extent variability, a measure based on satellite-derived sea ice concentration measurements, has traditionally been used as an essential climate variable to evaluate the impact of climate change on polar regions. However, concentration- based measurements of ice variability do not allow to discriminate the relative contributions made by thermodynamic and dynamic processes, prompting the need to use sea-ice drift products and develop alternative methods to quantify changes in sea ice dynamics that would indicate trends in Antarctic ice characteristics. Here, we present a new method to automate the detection of rotational drift features in Antarctic sea ice at daily timescales using currently available remote sensing ice motion products from EUMETSAT OSI SAF. Results show that there is a large discrepancy in the detection of cyclonic drift features between products, both in terms of intensity and year-to-year distributions, thus diminishing the confidence at which ice drift variability can be further analysed. Product comparisons showed that there was good agreement in detecting anticyclonic drift, and cyclonic drift features were measured to be 1.5–2.2 times more intense than anticyclonic features. The most intense features were detected by the merged product, suggesting that the processing chain used for this product could be injecting additional rotational momentum into the resultant drift vectors. We conclude that it is therefore necessary to better understand why the products lack agreement before further trend analysis of these drift features and their climatic significance can be assessed.

scholarly journals Impacts of freshwater changes on Antarctic sea ice in an eddy-permitting sea-ice–ocean model

2017 ◽  
Vol 11 (3) ◽  
pp. 1387-1402 ◽  
Author(s):  
Verena Haid ◽  
Doroteaciro Iovino ◽  
Simona Masina
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Great Influence ◽  
Ocean Model ◽  
Freshwater Input ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Dynamics ◽  
Antarctic Sea Ice ◽  
The Antarctic

Abstract. In a warming climate, satellite data indicate that the sea ice extent around Antarctica has increased over the last decades. One of the suggested explanations is the stabilizing effect of increased mass loss of the Antarctic ice sheet. Here, we investigate the sea ice response to changes in both the amount and the spatial distribution of freshwater input to the ocean by comparing a set of numerical sensitivity simulations with additional supply of water at the Antarctic ocean surface. We analyze the short-term response of the sea ice cover and the on-shelf water column to variations in the amount and distribution of the prescribed surface freshwater flux.Our results confirm that enhancing the freshwater input can increase the sea ice extent. Our experiments show a negative development of the sea ice extent only for extreme freshwater additions. We find that the spatial distribution of freshwater is of great influence on sea ice concentration and thickness as it affects sea ice dynamics and thermodynamics. For strong regional contrasts in the freshwater addition the dynamic response dominates the local change in sea ice, which generally opposes the thermodynamic response. Furthermore, we find that additional coastal runoff generally leads to fresher and warmer dense shelf waters.

scholarly journals Better constraints on the sea-ice state using global sea-ice data assimilation

2012 ◽  
Vol 5 (2) ◽  
pp. 1627-1667 ◽  
Author(s):  
P. Mathiot ◽  
C. König Beatty ◽  
T. Fichefet ◽  
H. Goosse ◽  
F. Massonnet ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Ice Thickness ◽  
Initial State ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
The Impact

Abstract. Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using ice-ocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an Ensemble Kalman Filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged sea ice) data into a global coupled ocean–sea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the assimilation of synthetic data (i.e., model-generated data) and, secondly, through the assimilation of satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005–2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.

Comparison of sea ice concentrations from ASI, BT and NT2 algorithms with ERA-Interim dataset in the Arctic and Antarctic regions

2020 ◽  
Author(s):  
Shuang Liang ◽  
Jiangyuan Zeng ◽  
Zhen Li
Keyword(s):  
Sea Ice ◽  
Temporal Distribution ◽  
Arctic Region ◽  
The Arctic ◽  
Polar Regions ◽  
Significant Discrepancy ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Long Time ◽  
Mean Square Difference

<p>Evaluating the performance and consistency of passive microwave (PM) sea ice concentration (SIC) products derived from different algorithms is critical since a good knowledge of the quality of the satellite SIC products is essential for their application and improvement. To comprehensively evaluate the performance of satellite SIC in long time series and the whole polar regions (both Arctic and Antarctic), in the study we examined the spatial and temporal distribution of the discrepancy between four PM satellite SIC products with the ERA-Interim sea ice fraction dataset (ERA SIC) during the period of 2015-2018. The four PM SIC products include the DMSP SSMIS with Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm (SSMIS/ASI), the GCOM-W AMSR2 with NASA Bootstrap (BT) algorithm (AMSR2/BT), the Chinese Feng Yun-3B with enhanced NASA Team (NT2) sea ice algorithm (FY3B/NT2), and the Chinese Feng Yun-3C with NT2 (FY3C/NT2) at a spatial resolution of 12.5 km.</p><p>The results show the spatial patterns of PM SIC products are generally in good agreement with ERA SIC. The comparison of monthly and annual SIC shows that the largest bias and root mean square difference (RMSD) for the PM SIC products mainly occur in summer and the marginal ice zone, indicating that there are still many uncertainties in PM SIC products in such period and region. Meanwhile, the daily sea ice extent (SIE) and sea ice area (SIA) derived from the four PM SIC products can generally well reflect the variation trend of SIE and SIA in Arctic and Antarctic. The largest bias of SIE and SIA are above 4×10<sup>6</sup> km<sup>2</sup> when the sea ice reaches the maximum and minimum value, and the daily bias of SIE and SIA vary seasonally and regionally, which is mainly concentrated from June to October in Arctic. In general, among the four PM SIC products, the SSMIS/ASI product performs the best compared with ERA SIC though it usually underestimates SIC with a negative bias. The FY3B/NT2 and FY3C/NT2 products show more significant discrepancy with higher RMSD and bias in Arctic and Antarctic compared with the SSMIS/ASI and AMSR2/BT. The AMSR2/BT product performs much better in Antarctic than in Arctic and it always overestimates ERA SIC with a positive bias. The consistency of the four PM products concerning ERA SIC in the Antarctic region is generally superior to that in Arctic region.</p>

A circumpolar coupled ocean – Antarctic ice sheet configuration for investigating recent changes in Southern Ocean heat content

2020 ◽  
Author(s):  
Charles Pelletier ◽  
Lars Zipf ◽  
Konstanze Haubner ◽  
Hugues Goosse ◽  
Frank Pattyn ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Ice Sheet ◽  
Heat Content ◽  
Ice Shelf ◽  
Sea Ice Extent ◽  
Antarctic Ice Sheet ◽  
Antarctic Sea Ice ◽  
Physical Phenomena ◽  
The Impact

<p>From 2016 on, observed tendencies of Southern Ocean sea surface temperatures and Antarctic sea ice extent (SIE) have shifted from cooling down (with SIE increase) to warming up (SIE decrease). This change of Southern Ocean surface thermal properties has been sustained since, which indicates that it is not solely due to the interannual variability of the atmosphere, but also to modifications in the ocean itself. Among other physical phenomena, the acceleration of continental ice shelf melt, through its subsequent impact on the Southern Ocean stratification, has been proposed as one of the potential meaningful drivers of the sea ice changes. Reciprocally, recent studies suggest that besides atmosphere forcings, the upper ocean thermal content bears significant impact on ice shelf melt rates and dynamics. Here we present a new circumpolar coupled Southern Ocean – Antarctic ice sheet configuration aiming at investigating the impact of this ocean – continental ice feedback, developed within the framework of the PARAMOUR project. Our setting relies on the ocean and sea ice model NEMO3.6-LIM3 sending ice shelf melt rates to the Antarctic ice sheet model f.ETISh v1.5, who in turn responds to it and provides updated ice shelf cavity geometry. Both technical aspects and first coupled results are presented.</p>

scholarly journals Modeling the annual cycle of daily Antarctic sea ice extent

2020 ◽  
Vol 14 (7) ◽  
pp. 2159-2172 ◽  
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.

scholarly journals Pack-Ice Drift off East Antarctica and some Implications

1989 ◽  
Vol 12 ◽  
pp. 1-8 ◽  
Author(s):  
Allison Ian
Keyword(s):  
Sea Ice ◽  
Bottom Topography ◽  
Regional Climate ◽  
Qualitative Assessment ◽  
Ocean Bottom ◽  
Ice Dynamics ◽  
Antarctic Sea Ice ◽  
Ice Drift ◽  
Region Data ◽  
Autumn And Winter

Three satellite-tracked data buoys were deployed between 70° and 80°E south of 65°S in February-March 1985. These buoys were subsequently trapped within the expanding seasonal sea ice and drifted with the ice. The buoys measured air temperature and pressure, and water temperatures to 100 m depth. Data from the buoys are used to describe the ice drift and environment within the winter sea-ice zone in this region, both north and south of the Antarctic Divergence. Additional preliminary data from a further six buoys deployed in the same area in March 1987 are also presented. Besides providing information on the broad-scale drift of the ice in the Prydz Bay region, data from the buoys have shown: (i) the important role that ice drift plays in determining the autumn and winter expansion of Antarctic sea ice; (ii) the highly mobile nature of the ice, even hundreds of kilometres from the ice edge; (iii) the role that ocean-bottom topography has in determining ice drift over the continental shelf; and (iv) the modifying influence that an ice cover has on regional climate. A qualitative assessment is made of the relative importance of the major forces driving the ice, although the data are insufficient for a detailed study of the ice dynamics.

Impact of wave-induced sea ice fragmentation on sea ice dynamics in the MIZ

2020 ◽  
Author(s):  
Guillaume Boutin ◽  
Timothy Williams ◽  
Pierre Rampal ◽  
Einar Olason ◽  
Camille Lique
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Ice Dynamics ◽  
Sea Ice Dynamics ◽  
Wave Induced ◽  
The Impact

<p>The decrease in Arctic sea ice extent is associated with an increase of the area where sea ice and open ocean interact, commonly referred to as the Marginal Ice Zone (MIZ). In this area, sea ice is particularly exposed to waves that can penetrate over tens to hundreds of kilometres into the ice cover. Waves are known to play a major role in the fragmentation of sea ice in the MIZ, and the interactions between wave-induced sea ice fragmentation and lateral melting have received particular attention in recent years. The impact of this fragmentation on sea ice dynamics, however, remains mostly unknown, although it is thought that fragmented sea ice experiences less resistance to deformation than pack ice. In this presentation, we will introduce a new coupled framework involving the spectral wave model WAVEWATCH III and the sea ice model neXtSIM, which includes a Maxwell-Elasto Brittle rheology. We use this coupled modelling system to investigate the potential impact of wave-induced sea ice fragmentation on sea ice dynamics. Focusing on the Barents Sea, we find that the decrease of the internal stress of sea ice resulting from its fragmentation by waves results in a more dynamical MIZ, in particular in areas where sea ice is compact. Sea ice drift is enhanced for both on-ice and off-ice wind conditions. Our results stress the importance of considering wave–sea-ice interactions for forecast applications. They also suggest that waves likely modulate the area of sea ice that is advected away from the pack by ocean (sub-)mesoscale eddies near the ice edge, potentially contributing to the observed past, current and future sea ice cover decline in the Arctic. </p>

scholarly journals Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model

2020 ◽  
Vol 61 (82) ◽  
pp. 97-105
Author(s):  
Jun Ono ◽  
Yoshiki Komuro ◽  
Hiroaki Tatebe
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Predictive Skill ◽  
The Pacific ◽  
The Impact

AbstractThe impact of April sea-ice thickness (SIT) initialization on the predictability of September sea-ice extent (SIE) is investigated based on a series of perfect model ensemble experiments using the MIROC5.2 climate model. Ensembles with April SIT initialization can accurately predict the September SIE for greater lead times than in cases without the initialization – up to 2 years ahead. The persistence of SIT correctly initialized in April contributes to the skilful prediction of SIE in the first September. On the other hand, errors in the initialization of SIT in April cause errors in the predicted sea-ice concentration and thickness in the Pacific sector from July to September and consequently influence the predictive skill with respect to SIE in September. The present study suggests that initialization of the April SIT in the Pacific sector significantly improves the accuracy of the September SIE forecasts by decreasing the errors in sea-ice fields from July to September.

scholarly journals Wave–sea-ice interactions in a brittle rheological framework

2020 ◽  
Author(s):  
Guillaume Boutin ◽  
Timothy Williams ◽  
Pierre Rampal ◽  
Einar Olason ◽  
Camille Lique
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Ice Dynamics ◽  
Sea Ice Dynamics ◽  
Wave Induced ◽  
The Impact

Abstract. The decrease in Arctic sea ice extent is associated with an increase of the area where sea ice and open ocean interact, commonly referred to as the Marginal Ice Zone (MIZ). In this area, sea ice is particularly exposed to waves that can penetrate over tens to hundreds of kilometres into the ice cover. Waves are known to play a major role in the fragmentation of sea ice in the MIZ, and the interactions between wave-induced sea ice fragmentation and lateral melting have received particular attention in recent years. The impact of this fragmentation on sea ice dynamics, however, remains mostly unknown, although it is thought that fragmented sea ice experiences less resistance to deformation than pack ice. Here, we introduce a new coupled framework involving the spectral wave model WAVEWATCH III and the sea ice model neXtSIM, which includes a Maxwell-Elasto Brittle rheology. We use this coupled modelling system to investigate the potential impact of wave-induced sea ice fragmentation on sea ice dynamics. Focusing on the Barents Sea, we find that the decrease of the internal stress of sea ice resulting from its fragmentation by waves results in a more dynamical MIZ, in particular in areas where sea ice is compact. Sea ice drift is enhanced for both on-ice and off-ice wind conditions. Our results stress the importance of considering wave–sea-ice interactions for forecast applications. They also suggest that waves likely modulate the area of sea ice that is advected away from the pack by ocean (sub-)mesoscale eddies near the ice edge, potentially contributing to the observed past, current and future sea ice cover decline in the Arctic.

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