scholarly journals Tracing Atlantic Water Signature in the Arctic Sea Ice Cover East of Svalbard

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Vladimir V. Ivanov ◽  
Vladimir A. Alexeev ◽  
Irina Repina ◽  
Nikolay V. Koldunov ◽  
Alexander Smirnov
Keyword(s):  
Heat Flux ◽  
Time Series ◽  
Sea Ice ◽  
Ice Cover ◽  
Atlantic Water ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Ice Concentration ◽  
Ice Conditions

We focus on the Arctic Ocean between Svalbard and Franz Joseph Land in order to elucidate the possible role of Atlantic water (AW) inflow in shaping ice conditions. Ice conditions substantially affect the temperature regime of the Spitsbergen archipelago, particularly in winter. We test the hypothesis that intensive vertical mixing at the upper AW boundary releases substantial heat upwards that eventually reaches the under-ice water layer, thinning the ice cover. We examine spatial and temporal variation of ice concentration against time series of wind, air temperature, and AW temperature. Analysis of 1979–2011 ice properties revealed a general tendency of decreasing ice concentration that commenced after the mid-1990s. AW temperature time series in Fram Strait feature a monotonic increase after the mid-1990s, consistent with shrinking ice cover. Ice thins due to increased sensible heat flux from AW; ice erosion from below allows wind and local currents to more effectively break ice. The winter spatial pattern of sea ice concentration is collocated with patterns of surface heat flux anomalies. Winter minimum sea ice thickness occurs in the ice pack interior above the AW path, clearly indicating AW influence on ice thickness. Our study indicates that in the AW inflow region heat flux from the ocean reduces the ice thickness.

Landfast ice zoning from SAR imagery

2020 ◽  
Author(s):  
Valeria Selyuzhenok ◽  
Denis Demchev ◽  
Thomas Krumpen
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Drift ◽  
Landfast Ice ◽  
Sea Ice Drift ◽  
Ice Conditions ◽  
Sar Imagery

<p>Landfast sea ice is a dominant sea ice feature of the Arctic coastal region. As a part of Arctic sea ice cover, landfast ice is an important part of coastal ecosystem, it provides functions as a climate regulator and platform for human activity. Recent changes in sea ice conditions in the Arctic have also affected landfast ice regime. At the same time, industrial interest in the Arctic shelf seas continue to increase. Knowledge on local landfast ice conditions are required to ensure safety of on ice operations and accurate forecasting.  In order to obtain a comprehensive information on landfast ice state we use a time series of wide swath SAR imagery.  An automatic sea ice tracking algorithm was applied to the sequential SAR images during the development stage of landfast ice cover. The analysis of resultant time series of sea ice drift allows to classify homogeneous sea ice drift fields and timing of their attachment to the landfast ice. In addition, the drift data allows to locate areas of formation of grounded sea ice accumulation called stamukha. This information сan be useful for local landfast ice stability assessment. The study is supported by the Russian Foundation for Basic Research (RFBR) grant 19-35-60033.</p>

scholarly journals Fate of sea ice in the 'New Arctic': A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions

2021 ◽  
Author(s):  
Sean Horvath ◽  
Linette Boisvert ◽  
Chelsea Parker ◽  
Melinda Webster ◽  
Patrick Taylor ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Mass Balance ◽  
Air Temperature ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Ice Mass Balance ◽  
Temperature And Pressure

Abstract. Since the early 2000s, sea ice has experienced an increased rate of decline in thickness and extent and transitioned to a seasonal ice cover. This shift to thinner, seasonal ice in the 'New Arctic' is accompanied by a reshuffling of energy flows at the surface. Understanding the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with daily sea ice parcel drift tracks produced in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states. Building on previous work, this dataset includes remotely sensed radiative and turbulent fluxes from which the surface energy budget can be calculated. Additionally, flags indicate when sea ice parcels travel within cyclones, recording distance and direction from the cyclone center. The database drift track was evaluated by comparison with sea ice mass balance buoys. Results show ice parcels generally remain within 100km of the corresponding buoy, with a mean distance of 82.6 km and median distance of 54 km. The sea ice mass balance buoys also provide recordings of sea ice thickness, snow depth, and air temperature and pressure which were compared to this database. Ice thickness and snow depth typically are less accurate than air temperature and pressure due to the high spatial variability of the former two quantities when compared to a point measurement. The correlations between the ice parcel and buoy data are high, which highlights the accuracy of this Lagrangian database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic. Applications such as these may shed light on the atmosphere-snow-sea ice interactions in the changing Arctic environment.

scholarly journals Interannual variability in Transpolar Drift ice thickness and potential impact of Atlantification

2020 ◽  
Author(s):  
H. Jakob Belter ◽  
Thomas Krumpen ◽  
Luisa von Albedyll ◽  
Tatiana A. Alekseeva ◽  
Sergei V. Frolov ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Ice Growth ◽  
Arctic Sea ◽  
Transpolar Drift ◽  
The Impact

Abstract. Changes in Arctic sea ice thickness are the result of complex interactions of the dynamic and variable ice cover with atmosphere and ocean. Most of the sea ice exits the Arctic Ocean through Fram Strait, which is why long-term measurements of ice thickness at the end of the Transpolar Drift provide insight into the integrated signals of thermodynamic and dynamic influences along the pathways of Arctic sea ice. We present an updated time series of extensive ice thickness surveys carried out at the end of the Transpolar Drift between 2001 and 2020. Overall, we see a more than 20 % thinning of modal ice thickness since 2001. A comparison with first preliminary results from the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) shows that the modal summer thickness of the MOSAiC floe and its wider vicinity are consistent with measurements from previous years. By combining this unique time series with the Lagrangian sea ice tracking tool, ICETrack, and a simple thermodynamic sea ice growth model, we link the observed interannual ice thickness variability north of Fram Strait to increased drift speeds along the Transpolar Drift and the consequential variations in sea ice age and number of freezing degree days. We also show that the increased influence of upward-directed ocean heat flux in the eastern marginal ice zones, termed Atlantification, is not only responsible for sea ice thinning in and around the Laptev Sea, but also that the induced thickness anomalies persist beyond the Russian shelves and are potentially still measurable at the end of the Transpolar Drift after more than a year. With a tendency towards an even faster Transpolar Drift, winter sea ice growth will have less time to compensate the impact of Atlantification on sea ice growth in the eastern marginal ice zone, which will increasingly be felt in other parts of the sea ice covered Arctic.

scholarly journals North Atlantic warming and declining volume of arctic sea ice

2013 ◽  
Vol 7 (1) ◽  
pp. 245-265 ◽  
Author(s):  
V. A. Alexeev ◽  
V. V. Ivanov ◽  
R. Kwok ◽  
L. H. Smedsrud
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Atlantic Water ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
First Year ◽  
Pacific Water ◽  
Svalbard Archipelago ◽  
Arctic Sea

Abstract. Long-term thinning of arctic sea ice over the last few decades has resulted in significant declines in the coverage of thick multi-year ice accompanied by a proportional increase in thinner first-year ice. This change is often attributed to changes in the arctic atmosphere, both in composition and large-scale circulation, and greater inflow of warmer Pacific water through the Bering Strait. The Atlantic Water (AW) entering the Arctic through Fram Strait has often been considered less important because of strong stratification in the Arctic Ocean and the deeper location of AW compared to Pacific water. In our combined examination of oceanographic measurements and satellite observations of ice concentration and thickness, we find evidence that AW has a direct impact on the thinning of arctic sea ice downstream of Svalbard Archipelago. The affected area extends as far as Severnaya Zemlya Archipelago. The imprints of AW appear as local minima in sea ice thickness; ice thickness is significantly less than that expected of first-year ice. Our lower-end conservative estimates indicate that the recent AW warming episode could have contributed up to 150–200 km3 of sea ice melt per year, which would constitute about 20% of the total 900 km3yr−1 negative trend in sea ice volume since 2004.

scholarly journals On the assimilation of ice velocity and concentration data into large-scale sea ice models

Ocean Science ◽  
2007 ◽  
Vol 3 (2) ◽  
pp. 321-335 ◽  
Author(s):  
V. Dulière ◽  
T. Fichefet
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Optimal Interpolation ◽  
Ice Thickness ◽  
Concentration Data ◽  
Ice Concentration ◽  
Ice Velocity

Abstract. Data assimilation into sea ice models designed for climate studies has started about 15 years ago. In most of the studies conducted so far, it is assumed that the improvement brought by the assimilation is straightforward. However, some studies suggest this might not be true. In order to elucidate this question and to find an appropriate way to further assimilate sea ice concentration and velocity observations into a global sea ice-ocean model, we analyze here results from a number of twin experiments (i.e. experiments in which the assimilated data are model outputs) carried out with a simplified model of the Arctic sea ice pack. Our objective is to determine to what degree the assimilation of ice velocity and/or concentration data improves the global performance of the model and, more specifically, reduces the error in the computed ice thickness. A simple optimal interpolation scheme is used, and outputs from a control run and from perturbed experiments without and with data assimilation are thoroughly compared. Our results indicate that, under certain conditions depending on the assimilation weights and the type of model error, the assimilation of ice velocity data enhances the model performance. The assimilation of ice concentration data can also help in improving the model behavior, but it has to be handled with care because of the strong connection between ice concentration and ice thickness. This study is first step towards real data assimilation into NEMO-LIM, a global sea ice-ocean model.

scholarly journals A means-corrected estimate for the Arctic sea-ice volume in 1990–2019

2021 ◽  
Author(s):  
Petteri Uotila ◽  
Joula Siponen ◽  
Eero Rinne ◽  
Steffen Tietsche
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Satellite Altimetry ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Basin Scale ◽  
Arctic Sea

<p>Decadal changes in sea-ice thickness are one of the most visible signs of climate variability and change. To gain a comprehensive understanding of mechanisms involved, long time series, preferably with good uncertainty estimates, are needed. Importantly, the development of accurate predictions of sea ice in the Arctic requires good observational products. To assist this, a new sea-ice thickness product by ESA Climate Change Initiative (CCI) is compared to a set of five ocean reanalysis (ECCO-V4r4, GLORYS12V1, ORAS5 and PIOMAS).</p><p>The CCI product is based on two satellite altimetry missions, CryoSat-2 and ENVISAT, which are combined to the longest continuous satellite altimetry time series of Arctic-wide sea-ice thickness, 2002–2017. The CCI product performs well in the validation of the reanalyses: overall root-mean-square difference (RMSD) between monthly sea-ice thickness from CCI and the reanalyses ranges from 0.4–1.2 m. The differences are a sum of reanalysis biases, such as incorrect physics or forcing, as well as uncertainties in satellite altimetry, such as the snow climatology used in the thickness retrieval.</p><p>The CCI and reanalysis basin-scale sea-ice volumes have a good match in terms of year-to-year variability and long-term trends but rather different monthly mean climatologies. These findings provide a rationale to construct a multi-decadal sea-ice volume time series for the Arctic Ocean and its sub-basins from 1990–2019 by adjusting the ocean reanalyses ensemble toward CCI observations. Such a time series, including its uncertainty estimate, provides new insights to the evolution of the Arctic sea-ice volume during the past 30 years.</p>

scholarly journals Arctic sea ice cover data from spaceborne SAR by deep learning

2020 ◽  
Author(s):  
Yi-Ran Wang ◽  
Xiao-Ming Li
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Sea Ice ◽  
Spatial Resolution ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Absolute Difference ◽  
Sea Ice Concentration ◽  
Ice Concentration

Abstract. Widely used sea ice concentration and sea ice cover in polar regions are derived mainly from spaceborne microwave radiometer and scatterometer data, and the typical spatial resolution of these products ranges from several to dozens of kilometers. Due to dramatic changes in polar sea ice, high-resolution sea ice cover data are drawing increasing attention for polar navigation, environmental research, and offshore operations. In this paper, we focused on developing an approach for deriving a high-resolution sea ice cover product for the Arctic using Sentinel-1 (S1) dual-polarization (horizontal-horizontal, HH, and horizontal-vertical, HV) data in extra wide swath (EW) mode. The approach for discriminating sea ice from open water by synthetic aperture radar (SAR) data is based on a modified U-Net architecture, a deep learning network. By employing an integrated stacking model to combine multiple U-Net classifiers with diverse specializations, sea ice segmentation is achieved with superior accuracy over any individual classifier. We applied the proposed approach to over 28,000 S1 EW images acquired in 2019 to obtain sea ice cover products in a high spatial resolution of 400 m. By converting the S1-derived sea ice cover to concentration and then compared with Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration data, showing an average absolute difference of 5.55 % with seasonal fluctuations. A direct comparison with Interactive Multisensor Snow and Ice Mapping System (IMS) daily sea ice cover data achieves an average accuracy of 93.98 %. These results show that the developed S1-derived sea ice cover results are comparable to the AMSR and IMS data in terms of overall accuracy but superior to these data in presenting detailed sea ice cover information, particularly in the marginal ice zone (MIZ). Data are available at: https://doi.org/10.11922/sciencedb.00273 (Wang and Li, 2020).

scholarly journals Interannual variability in Transpolar Drift summer sea ice thickness and potential impact of Atlantification

2021 ◽  
Vol 15 (6) ◽  
pp. 2575-2591
Author(s):  
H. Jakob Belter ◽  
Thomas Krumpen ◽  
Luisa von Albedyll ◽  
Tatiana A. Alekseeva ◽  
Gerit Birnbaum ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Arctic Sea ◽  
Transpolar Drift

Abstract. Changes in Arctic sea ice thickness are the result of complex interactions of the dynamic and variable ice cover with atmosphere and ocean. Most of the sea ice exiting the Arctic Ocean does so through Fram Strait, which is why long-term measurements of ice thickness at the end of the Transpolar Drift provide insight into the integrated signals of thermodynamic and dynamic influences along the pathways of Arctic sea ice. We present an updated summer (July–August) time series of extensive ice thickness surveys carried out at the end of the Transpolar Drift between 2001 and 2020. Overall, we see a more than 20 % thinning of modal ice thickness since 2001. A comparison of this time series with first preliminary results from the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) shows that the modal summer thickness of the MOSAiC floe and its wider vicinity are consistent with measurements from previous years at the end of the Transpolar Drift. By combining this unique time series with the Lagrangian sea ice tracking tool, ICETrack, and a simple thermodynamic sea ice growth model, we link the observed interannual ice thickness variability north of Fram Strait to increased drift speeds along the Transpolar Drift and the consequential variations in sea ice age. We also show that the increased influence of upward-directed ocean heat flux in the eastern marginal ice zones, termed Atlantification, is not only responsible for sea ice thinning in and around the Laptev Sea but also that the induced thickness anomalies persist beyond the Russian shelves and are potentially still measurable at the end of the Transpolar Drift after more than a year. With a tendency towards an even faster Transpolar Drift, winter sea ice growth will have less time to compensate for the impact processes, such as Atlantification, have on sea ice thickness in the eastern marginal ice zone, which will increasingly be felt in other parts of the sea-ice-covered Arctic.

Assessment of decadal prediction skills and sensitivity to sea-ice thickness initialization in the Arctic when seasonal ice becomes dominant in the Arctic

2020 ◽  
Author(s):  
Tian Tian ◽  
Shuting Yang ◽  
Pasha Karami ◽  
François Massonnet ◽  
Tim Kruschke ◽  
...  
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
Ice Cover ◽  
Climate Prediction ◽  
Arctic Sea Ice ◽  
Added Value ◽  
The Arctic ◽  
Ice Thickness ◽  
Decadal Prediction ◽  
Sea Ice Thickness

<p>The Arctic has lost more than 50% multiyear sea ice (MYI) area during 1999-2017. Observation analysis suggests that if the decline of the MYI coverage continues, changes in the Arctic ice cover (i.e. area and volume) will be more controlled by seasonal ice than the effect of global warming. To investigate how large and where the source of Arctic prediction skill is given a large losses of thick MYI during the last two decades, we explore the decadal prediction skills and sensitivity to sea ice thickness (SIT) initialization from the EC-Earth3 Climate Prediction System with Anomaly Initialization (EC-Earth3-CPSAI). Three sets of ensemble hind-cast experiments following the protocol for the CMIP6 Decadal Climate Prediction Project (DCPP) are carried out in which the predictions start from: 1) a baseline system with ocean only initialization; 2) with ocean and sea ice concentration (SIC) initialization; 3) with ocean, SIC and SIT initialization. The hind-cast experiments are initialized and validated based on the ERA-Interim-reanalysis for the atmosphere and ORAS5 for ocean and sea-ice, with a focus period 1997-2016. All initialized experiments show better agreement with ORAS5 than the CMIP6 historical run (i.e. the Free run) for the first winter sea ice forecast. The SIT initialized experiments show the best skill in predicting SIT (or volume) and the added value by greatly reducing errors of near surface air temperature over the Greenland and its surrounding waters. In the Central Arctic, the Beaufort and East Siberian Seas, there are only minor differences in prediction skills on seasonal to decadal time scales between the ocean-only initialized and the SIT initialized experiments, indicating that the source of predictability in these regions are mainly from the ocean; while the ocean-only initialization degrades skill with larger RMSE than the Free run, e.g. during the ice-freezing season in the GIN and Barents Seas, or at  the summer minimum in the Kara Sea, the added value from the SIT initialized experiment is present, and it may have long-term effect (>4 years) probably associated with sea-ice recirculation. In all cases, the improvement from the ocean-only initialization to also including SIC initialization is found negligible, even somehow degrading the skills. This highlights the important use of SIT in predicting changes in the Arctic sea ice cover at various time scales during the study period. Therefore, the sea-ice initialization with constraint on SIT is recommended as the most effective initialization strategy in our EC-Earth3-CPSAI for present climate prediction from seasonal to decadal time scales.</p>

The Sea Ice Tracking System (SITU): A Community Tool for the Arctic and Antarctic

2020 ◽  
Author(s):  
Bruno Tremblay ◽  
Stephanie Pfirman ◽  
Garrett Campbell ◽  
Robert Newton ◽  
Walt Meier
Keyword(s):  
Sea Ice ◽  
Tracking System ◽  
Indigenous Communities ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
The Arctic ◽  
Optimal Interpolation ◽  
Wide Range ◽  
Ice Concentration ◽  
Ice Conditions

<p>The Sea Ice Tracking System (SITU), formerly known as the IceTracker or Lagrangian Ice Tracking System, has been expanded to include new functions facilitating a wide range of new applications (http://icemotion.labs.nsidc.org/SITU/). Ice motion vectors are calculated from an optimal interpolation of satellite-derived, free-drift and buoy drift estimates (Polar Pathfinder dataset, version 4, https://nsidc.org/data/nsidc-0116; International Arctic Buoy Program, http://iabp.apl.washington.edu/; NCEP/NCAR reanalysis, https://www.esrl.noaa.gov/). SITU now calculates forward and backward trajectories of Antarctic as well as Arctic sea ice from 1979 to 2018 and incorporates basin-wide contextual information including timeseries of bathymetry, ice concentration, ice age, ice motion, air temperature, pressure, and wind speed, along the tracks. A new animated background option allows users to visualize these basin-wide changing environmental conditions as the tracking progresses. SITU can be used by researchers, educators, local and indigenous communities, policy and planning professionals, and industries.  For instance, geologists can use SITU to determine the provenance of sediment transported by sea-ice and deposited at an ocean core site; biologists can identify source region of biomass transported by sea-ice and seeding algal bloom in a given sea, or overlay bear and birds tracks over ice conditions or ice types animated in the background; coastal communities can backtrack ice to reveal age, origin and other factors that influence habitats of ice-associated species; people planning future expeditions can review recent ice conditions along potential cruise tracks, historians can compare current air temperatures, wind conditions, and ice concentration with past expeditions; students can learn about sea ice motion in the Arctic or compare recent ice drift (Tara or MOSAIC) with that of the epic expedition of Nansen. A new Eulerian option allows users to see changing conditions at one point over the full satellite record (1978 to present). This Eulerian depiction reveals variability as well as trends, and can provide context for data retrieved from a mooring, sediment trap, or sediment core. Publically hosted on the NSIDC Labs webpage, data can be downloaded graphically or in spreadsheet format for deeper analysis.</p>

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