scholarly journals The MOSAiC Drift: Ice conditions from space and comparison with previous years

2021 ◽  
Author(s):  
Thomas Krumpen ◽  
Luisa von Albedyll ◽  
Helge F. Goessling ◽  
Stefan Hendricks ◽  
Bennet Juhls ◽  
...  
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Ice Conditions ◽  
Snow Thickness ◽  
Lead Activity

Abstract. We combine satellite data products to provide a first and general overview of the sea-ice conditions along the MOSAiC drift and a comparison with previous years. We find that the MOSAiC drift was around 25 % faster than the climatological mean drift, as a consequence of large-scale low-pressure anomalies prevailing around the Barents-Kara-Laptev Sea region between January and March. In winter (October–April), satellite observations show that the sea-ice in the vicinity of the Central Observatory (CO) was rather thin compared to the previous years along the same trajectory. Unlike ice thickness, satellite-derived sea-ice concentration, lead frequency, and snow thickness during winter month were close to the long-term mean with little variability. With the onset of spring and decreasing distance to Fram Strait, variability in ice concentration and lead activity increased. In addition, frequency and strength of deformation events (divergence and shear) were higher during summer than during winter. Overall, we find that sea-ice conditions observed close (~ 5 km) to the CO are representative for the wider (50 km and 100 km) surroundings. An exception is the ice thickness: Here we find that sea-ice near the CO (50 km radius) was 4 % thinner than sea-ice within a 100 km radius. Moreover, satellite acquisitions indicate that the formation of large melt ponds began earlier on the MOSAiC floe than on neighbouring floes.

scholarly journals MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years

2021 ◽  
Vol 15 (8) ◽  
pp. 3897-3920 ◽  
Author(s):  
Thomas Krumpen ◽  
Luisa von Albedyll ◽  
Helge F. Goessling ◽  
Stefan Hendricks ◽  
Bennet Juhls ◽  
...  
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Ice Conditions ◽  
Snow Thickness ◽  
Lead Activity

Abstract. We combine satellite data products to provide a first and general overview of the physical sea ice conditions along the drift of the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition and a comparison with previous years (2005–2006 to 2018–2019). We find that the MOSAiC drift was around 20 % faster than the climatological mean drift, as a consequence of large-scale low-pressure anomalies prevailing around the Barents–Kara–Laptev sea region between January and March. In winter (October–April), satellite observations show that the sea ice in the vicinity of the Central Observatory (CO; 50 km radius) was rather thin compared to the previous years along the same trajectory. Unlike ice thickness, satellite-derived sea ice concentration, lead frequency and snow thickness during winter months were close to the long-term mean with little variability. With the onset of spring and decreasing distance to the Fram Strait, variability in ice concentration and lead activity increased. In addition, the frequency and strength of deformation events (divergence, convergence and shear) were higher during summer than during winter. Overall, we find that sea ice conditions observed within 5 km distance of the CO are representative for the wider (50 and 100 km) surroundings. An exception is the ice thickness; here we find that sea ice within 50 km radius of the CO was thinner than sea ice within a 100 km radius by a small but consistent factor (4 %) for successive monthly averages. Moreover, satellite acquisitions indicate that the formation of large melt ponds began earlier on the MOSAiC floe than on neighbouring floes.

Improving Arctic Sea Ice Prediction Though Freeboard Assimilation

2021 ◽  
Author(s):  
Imke Sievers ◽  
Till Rasmussen ◽  
Lars Stenseng
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Surface Elevation ◽  
Ice Formation ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Snow Thickness

<p>With the presented work we aim to improve sea ice forecasts and our understanding of Arcitc sea ice formation though freeboard assimilation. Over the last years understanding Arctic sea ice changes and being able to make a reliable sea ice forecast has gained in importance. The central roll of Arctic sea ice extent in climate warming makes it a highly discussed topic in the climate research community. However a reliable Arctic sea ice forecast both on short term to seasonal time scales remains a challenge to be mastered, hinting that there are still many processes at play to be better understood. <br>One promising approach to improve forecasts has been to assimilate satellite sea ice data into numerical sea ice models. Mainly two parameters measured by satellites have been used for assimilation: Sea ice concentration, which is competitively easy to obtain from satellites measuring passive microwave emissions as for example obtained by the SMOS satellite, and sea ice thickness, which is not directly measured, but has to be calculated from surface elevation measurements, as for example obtained by Cryosat 2. Compering the skill, of assimilation products using sea ice thickness and sea ice concentration shows that sea ice thickness has a longer memory and is over all leading to a better performance then sea ice concentration assimilation. Knowing this, sea ice thickness assimilation is far from being straight forward. Surface elevation measurements, obtained from satellite altemitry measurements, have to be separated into snow and ice freeborad, by assuming a snow thickness, to derive sea ice thickness from. Most of the time this is done using a snow thickness climatology obtained from Soviet drift stations measuring snow over multi year ice during the period 1954-1991 with adaption over first year sea ice, where this climatology has proven to be overestimating snow thickness. The technique is widely used jet known to introduce an error. <br>To avoid errors caused by wrongly assumed snow covers the DMI and Aalborg University and DTU are at the moment collaborating on assimilating freebord instead of sea ice thickness into the CICE-NEMO modeling frame work using LARS NGen (LARS the Advanced Retracking System, Next Generation) sate of the art retracing software. In the presented work we will show first results of freeboard assimilation with a focus how this assimilation influences winter sea ice formation as well as the upper Arctic Ocean dynamics.</p>

scholarly journals Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic

2021 ◽  
Vol 13 (19) ◽  
pp. 3882
Author(s):  
Jiechen Zhao ◽  
Yining Yu ◽  
Jingjing Cheng ◽  
Honglin Guo ◽  
Chunhua Li ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
High Concentration ◽  
Sea Ice Concentration ◽  
Melt Pond ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Microwave Imager

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.

scholarly journals Long-term variation of sea ice and its response to thermodynamic factors in the Northwest Passage of the Canadian Arctic Archipelago

2020 ◽  
Author(s):  
Xinyi Shen ◽  
Yu Zhang ◽  
Changsheng Chen ◽  
Song Hu
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Northwest Passage ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Ice Conditions ◽  
Thermodynamic Factors

Abstract. Sea ice conditions in the Canadian Arctic Archipelago (CAA) play a key role in the navigation of the Northwest Passage (NWP). Based on the observed and simulated sea ice concentration and thickness data, we studied the temporal and spatial characteristics of sea ice from 1979 to 2017 in the NWP of the CAA and evaluated the sea ice conditions along the southern and northern routes of the NWP. Against the background of the rapid retreat of Arctic sea ice, the 39-year observed sea ice concentration of the NWP exhibited a relatively large decreasing trend in summer and fall, while heavy sea ice conditions were maintained in winter and spring, with a slight increasing trend. Consistent with Arctic sea ice, the sea ice extent in the NWP displayed a decreasing trend of −2.34 %/10 a, with its minimum occurring in 2012. The sea ice thickness in most subregions of the NWP showed a decreasing trend, with the exception of Lancaster Sound. The decreasing trend of sea ice thickness in the NWP was estimated to −0.16 m/10 a. Based on the sea ice concentration and thickness, however, the sea ice conditions were heavier along the northern route than the southern route. This study considered both of these routes, and we selected and evaluated more specific pathways. The correlation results between the sea ice and atmospheric and oceanic thermodynamic factors in the NWP suggested that the thermodynamic factors had a greater impact on sea ice in the summer and fall, and the variations of sea ice concentration were more closely correlated with the thermodynamic factors than sea ice thickness. The sea surface temperature (SST) had a higher correlation with sea ice concentration than surface air temperature (SAT), while SAT exhibited a higher correlation with sea ice thickness than SST. The residual amount of sea ice concentration and thickness in the fall, associated with the fall SAT and SST, contributed to the formation of sea ice in the following winter and spring.

scholarly journals Cryospheric Impacts of Soviet River Diversion Schemes

1984 ◽  
Vol 5 ◽  
pp. 61-68 ◽  
Author(s):  
T. Holt ◽  
P. M. Kelly ◽  
B. S. G. Cherry
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
River Discharge ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Arctic Ocean ◽  
The Impact

Soviet plans to divert water from rivers flowing into the Arctic Ocean have led to research into the impact of a reduction in discharge on Arctic sea ice. We consider the mechanisms by which discharge reductions might affect sea-ice cover and then test various hypotheses related to these mechanisms. We find several large areas over which sea-ice concentration correlates significantly with variations in river discharge, supporting two particular hypotheses. The first hypothesis concerns the area where the initial impacts are likely to which is the Kara Sea. Reduced riverflow is associated occur, with decreased sea-ice concentration in October, at the time of ice formation. This is believed to be the result of decreased freshening of the surface layer. The second hypothesis concerns possible effects on the large-scale current system of the Arctic Ocean and, in particular, on the inflow of Atlantic and Pacific water. These effects occur as a result of changes in the strength of northward-flowing gradient currents associated with variations in river discharge. Although it is still not certain that substantial transfers of riverflow will take place, it is concluded that the possibility of significant cryospheric effects and, hence, large-scale climate impact should not be neglected.

scholarly journals Evolution of the Amundsen Sea Polynya, Antarctica, 2016–2021

2021 ◽  
Author(s):  
Grant J. Macdonald ◽  
Stephen F. Ackley ◽  
Alberto M. Mestas-Nuñez
Keyword(s):  
Sea Ice ◽  
Reanalysis Data ◽  
Sea Ice Concentration ◽  
Amundsen Sea ◽  
Annual Variations ◽  
Ice Concentration ◽  
Ice Conditions ◽  
Sar Imagery ◽  
Key Sites ◽  
Ice Production

Abstract. Polynyas are key sites of ice production during the winter and are important sites of biological activity and carbon sequestration during the summer. The Amundsen Sea Polynya (ASP) is the fourth largest Antarctic polynya, has recorded the highest primary productivity and lies in an embayment of key oceanographic significance. However, knowledge of its dynamics, and of sub-annual variations in its area and ice production, is limited. In this study we primarily utilize Sentinel-1 SAR imagery, sea ice concentration products and climate reanalysis data, along with bathymetric data, to analyze the ASP over the period November 2016–March 2021. Specifically, we analyze (i) qualitative changes in the ASP's characteristics and dynamics, and quantitative changes in (ii) summer polynya area, (iii) winter polynya area and ice production. From our analysis of SAR imagery we find that ice produced by the ASP becomes stuck in the vicinity of the polynya and sometimes flows back into the polynya, contributing to its closure and limiting further ice production. The polynya forms westward off a persistent chain of grounded icebergs that are located at the site of a bathymetric high. Grounded icebergs also influence the outflow of ice and facilitate the formation of a 'secondary polynya' at times. Additionally, unlike some polynyas, ice produced by the polynya flows westward after formation, along the coast and into the neighboring sea sector. During the summer and early winter, broader regional sea ice conditions can play an important role in the polynya. The polynya opens in all summers, but record-low sea ice conditions in 2016/17 cause it to become part of the open ocean. During the winter, an average of 78 % of ice production occurs in April–May and September–October, but large polynya events often associated with high winds can cause ice production throughout the winter. While passive microwave data or daily sea ice concentration products remain key for analyzing variations in polynya area and ice production, we find that the ability to directly observe and qualitatively analyze the polynya at a high temporal and spatial resolution with Sentinel-1 imagery provides important insights about the behavior of the polynya that are not possible with those datasets.

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

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

&lt;p&gt;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.&lt;/p&gt;

Unexpectedly high dimethyl sulfide concentration in high-latitude Arctic sea ice melt ponds

2019 ◽  
Vol 21 (10) ◽  
pp. 1642-1649 ◽  
Author(s):  
Keyhong Park ◽  
Intae Kim ◽  
Jung-Ok Choi ◽  
Youngju Lee ◽  
Jinyoung Jung ◽  
...  
Keyword(s):  
Sea Ice ◽  
High Latitude ◽  
Dimethyl Sulfide ◽  
Arctic Sea Ice ◽  
Sulfide Concentration ◽  
Sea Ice Concentration ◽  
Ice Melt ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Ice Concentration

Dimethyl sulfide (DMS) production in the northern Arctic Ocean has been considered to be minimal because of high sea ice concentration and extremely low productivity.

scholarly journals On the timescales and length scales of the Arctic sea ice thickness anomalies: a study based on 14 reanalyses

2019 ◽  
Vol 13 (2) ◽  
pp. 521-543 ◽  
Author(s):  
Leandro Ponsoni ◽  
François Massonnet ◽  
Thierry Fichefet ◽  
Matthieu Chevallier ◽  
David Docquier
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Length Scales ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Arctic Sea Ice Thickness

Abstract. The ocean–sea ice reanalyses are one of the main sources of Arctic sea ice thickness data both in terms of spatial and temporal resolution, since observations are still sparse in time and space. In this work, we first aim at comparing how the sea ice thickness from an ensemble of 14 reanalyses compares with different sources of observations, such as moored upward-looking sonars, submarines, airbornes, satellites, and ice boreholes. Second, based on the same reanalyses, we intend to characterize the timescales (persistence) and length scales of sea ice thickness anomalies. We investigate whether data assimilation of sea ice concentration by the reanalyses impacts the realism of sea ice thickness as well as its respective timescales and length scales. The results suggest that reanalyses with sea ice data assimilation do not necessarily perform better in terms of sea ice thickness compared with the reanalyses which do not assimilate sea ice concentration. However, data assimilation has a clear impact on the timescales and length scales: reanalyses built with sea ice data assimilation present shorter timescales and length scales. The mean timescales and length scales for reanalyses with data assimilation vary from 2.5 to 5.0 months and 337.0 to 732.5 km, respectively, while reanalyses with no data assimilation are characterized by values from 4.9 to 7.8 months and 846.7 to 935.7 km, respectively.

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