scholarly journals Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012

2014 ◽  
Vol 6 (6) ◽  
pp. 5520-5540 ◽  
Author(s):  
Jihye Ahn ◽  
Sungwook Hong ◽  
Jaeil Cho ◽  
Yang-Won Lee ◽  
Hosang Lee
Keyword(s):  
Sea Ice ◽  
Statistical Modeling ◽  
Satellite Imagery ◽  
Reanalysis Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Barents And Kara Seas

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.

scholarly journals Impact of Winter Ural Blocking on Arctic Sea Ice: Short-Time Variability

2018 ◽  
Vol 31 (6) ◽  
pp. 2267-2282 ◽  
Author(s):  
Xiaodan Chen ◽  
Dehai Luo ◽  
Steven B. Feldstein ◽  
Sukyoung Lee
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Sensible Heat Flux ◽  
Reanalysis Data ◽  
Arctic Sea Ice ◽  
Time Variability ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Barents And Kara Seas ◽  
The Impact

Using daily reanalysis data from 1979 to 2015, this paper examines the impact of winter Ural blocking (UB) on winter Arctic sea ice concentration (SIC) change over the Barents and Kara Seas (BKS). A case study of the sea ice variability in the BKS in the 2015/16 and 2016/17 winters is first presented to establish a link between the BKS sea ice variability and UB events. Then the UB events are classified into quasi-stationary (QUB), westward-shifting (WUB), and eastward-shifting (EUB) UB types. It is found that the frequency of the QUB events increases significantly during 1999–2015, whereas the WUB events show a decreasing frequency trend during 1979–2015. Moreover, it is shown that the variation of the BKS-SIC is related to downward infrared radiation (IR) and surface sensible and latent heat flux changes due to different zonal movements of the UB. Calculations show that the downward IR is the main driver of the BKS-SIC decline for QUB events, while the downward IR and surface sensible heat flux make comparable contributions to the BKS-SIC variation for WUB and EUB events. The SIC decline peak lags the QUB and EUB peaks by about 3 days, though QUB and EUB require lesser prior SIC. The QUB gives rise to the largest SIC decline likely because of its longer persistence, whereas the BKS-SIC decline is relatively weak for the EUB. The WUB is found to cause a SIC decline during its growth phase and an increase during its decay phase. Thus, the zonal movement of the UB has an important impact on the SIC variability in BKS.

Drivers of interannual sea ice variability on the Arctic continental margin north of Svalbard

2020 ◽  
Author(s):  
Øyvind Lundesgaard ◽  
Arild Sundfjord ◽  
Angelika H. H. Renner
Keyword(s):  
Sea Ice ◽  
Continental Margin ◽  
Barents Sea ◽  
Atlantic Water ◽  
Reanalysis Data ◽  
The Arctic ◽  
Upper Ocean ◽  
Ocean Temperature ◽  
Sea Ice Concentration ◽  
Ice Concentration

<p>Sea ice concentration along the Arctic continental margin north of Svalbard is in decline, but superimposed on this trend is considerable interannual variability. Many factors impact sea ice in this region, including atmospheric cooling and heating, winds, sea ice advection, and oceanic heat transport associated with the inflow of Atlantic Water, and regional sea ice cover remains difficult to predict. We present observations of upper ocean temperature between 2012 and 2017 from an ocean mooring located on the continental shelf break north of the Barents Sea, together with concurrent time series of atmospheric variables and sea ice concentration, drift, and thickness, derived from satellite and reanalysis data. While the inflow of Atlantic Water undoubtedly plays a key role in maintaining the area north of Svalbard ice-free through much of the year, variations in upper ocean temperature do not explain major interannual sea ice anomalies during the study period. Instead, we find that the magnitude of sea ice advection from the north and east was a major driver of interannual sea ice variability during our study.</p>

Statistical modeling of sea ice concentration in the northwest region of the Antarctic Peninsula

2021 ◽  
Vol 193 (2) ◽  
Author(s):  
Fernando Luis Hillebrand ◽  
Ulisses Franz Bremer ◽  
Marcos Wellausen Dias de Freitas ◽  
Juliana Costi ◽  
Cláudio Wilson Mendes Júnior ◽  
...  
Keyword(s):  
Sea Ice ◽  
Statistical Modeling ◽  
Antarctic Peninsula ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Northwest Region ◽  
The Antarctic

scholarly journals Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network

2021 ◽  
Vol 9 (3) ◽  
pp. 330
Author(s):  
Quanhong Liu ◽  
Ren Zhang ◽  
Yangjun Wang ◽  
Hengqian Yan ◽  
Mei Hong
Keyword(s):  
Sea Ice ◽  
Short Term Memory ◽  
Prediction Method ◽  
Reanalysis Data ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Lstm Network ◽  
Better Than

To meet the increasing sailing demand of the Northeast Passage of the Arctic, a daily prediction model of sea ice concentration (SIC) based on the convolutional long short-term memory network (ConvLSTM) algorithm was proposed in this study. Previously, similar deep learning algorithms (such as convolutional neural networks; CNNs) were frequently used to predict monthly changes in sea ice. To verify the validity of the model, the ConvLSTM and CNNs models were compared based on their spatiotemporal scale by calculating the spatial structure similarity, root-mean-square-error, and correlation coefficient. The results show that in the entire test set, the single prediction effect of ConvLSTM was better than that of CNNs. Taking 15 December 2018 as an example, ConvLSTM was superior to CNNs in simulating the local variations in the sea ice concentration in the Northeast Passage, particularly in the vicinity of the East Siberian Sea. Finally, the predictability of ConvLSTM and CNNs was analysed following the iteration prediction method, demonstrating that the predictability of ConvLSTM was better than that of CNNs.

scholarly journals DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Sea Ice ◽  
Coupled Model ◽  
Sea Ice Concentration ◽  
Model Free ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Data Assimilation System ◽  
Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

scholarly journals Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

2021 ◽  
Vol 13 (6) ◽  
pp. 1139
Author(s):  
David Llaveria ◽  
Juan Francesc Munoz-Martin ◽  
Christoph Herbert ◽  
Miriam Pablos ◽  
Hyuk Park ◽  
...  
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Cost Effective ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Neural Network Approach ◽  
Ice Concentration ◽  
L Band ◽  
Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

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