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>

Influence of Nordic Seas dynamics on the Atlantic Water propagation and its impacts on sea ice concentration.

2021 ◽  
Author(s):  
Sourav Chatterjee ◽  
Roshin P Raj ◽  
Laurent Bertino ◽  
Nuncio Murukesh
Keyword(s):  
Sea Ice ◽  
Deep Water ◽  
Atlantic Water ◽  
The Arctic ◽  
Deep Water Formation ◽  
Sea Ice Concentration ◽  
Nordic Seas ◽  
Ice Concentration ◽  
Water Formation

<p>Enhanced intrusion of warm and saline Atlantic Water (AW) to the Arctic Ocean (AO) in recent years has drawn wide interest of the scientific community owing to its potential role in ‘Arctic Amplification’. Not only the AW has warmed over the last few decades , but its transfer efficiency have also undergone significant modifications due to changes in atmosphere and ocean dynamics at regional to large scales. The Nordic Seas (NS), in this regard, play a vital role as the major exchange of polar and sub-polar waters takes place in this region. Further, the AW and its significant modification on its way to AO via the Nordic Seas has large scale implications on e.g., deep water formation, air-sea heat fluxes. Previous studies have suggested that a change in the sub-polar gyre dynamics in the North Atlantic controls the AW anomalies that enter the NS and eventually end up in the AO. However, the role of NS dynamics in resulting in the modifications of these AW anomalies are not well studied. Here in this study, we show that the Nordic Seas are not only a passive conduit of AW anomalies but the ocean circulations in the Nordic Seas, particularly the Greenland Sea Gyre (GSG) circulation can significantly change the AW characteristics between the entry and exit point of AW in the NS. Further, it is shown that the change in GSG circulation can modify the AW heat distribution in the Nordic Seas and can potentially influence the sea ice concentration therein. Projected enhanced atmospheric forcing in the NS in a warming Arctic scenario and the warming trend of the AW can amplify the role of NS circulation in AW propagation and its impact on sea ice, freshwater budget and deep water formation.</p>

The Role of Moist Intrusions in Winter Arctic Warming and Sea Ice Decline

2016 ◽  
Vol 29 (12) ◽  
pp. 4473-4485 ◽  
Author(s):  
Cian Woods ◽  
Rodrigo Caballero
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Surface Air Temperature ◽  
The Arctic ◽  
Positive Trend ◽  
Sea Ice Concentration ◽  
Local Conditions ◽  
The Barents Sea ◽  
Strong Inversion ◽  
Ice Concentration

Abstract This paper examines the trajectories followed by intense intrusions of moist air into the Arctic polar region during autumn and winter and their impact on local temperature and sea ice concentration. It is found that the vertical structure of the warming associated with moist intrusions is bottom amplified, corresponding to a transition of local conditions from a “cold clear” state with a strong inversion to a “warm opaque” state with a weaker inversion. In the marginal sea ice zone of the Barents Sea, the passage of an intrusion also causes a retreat of the ice margin, which persists for many days after the intrusion has passed. The authors find that there is a positive trend in the number of intrusion events crossing 70°N during December and January that can explain roughly 45% of the surface air temperature and 30% of the sea ice concentration trends observed in the Barents Sea during the past two decades.

scholarly journals Employing Satellite-Derived Sea Ice Concentration to Constrain Upper-Ocean Temperature in a Global Ocean GCM

2008 ◽  
Vol 21 (17) ◽  
pp. 4498-4513 ◽  
Author(s):  
Achim Stössel
Keyword(s):  
Sea Ice ◽  
Deep Ocean ◽  
Global Ocean ◽  
Upper Ocean ◽  
Ocean Temperature ◽  
Freshwater Input ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Ocean Properties

Abstract The quality of Southern Ocean sea ice simulations in a global ocean general circulation model (GCM) depends decisively on the simulated upper-ocean temperature. This is confirmed by assimilating satellite-derived sea ice concentration to constrain the upper-layer temperature of a sea ice–ocean GCM. The resolution of the model’s sea ice component is about 22 km and thus comparable to the pixel resolution of the satellite data. The ocean component is coarse resolution to afford long-term integrations for investigations of the deep-ocean equilibrium response. Besides improving the sea ice simulation considerably, the simulations with constrained upper-ocean temperature yield much more realistic global deep-ocean properties, in particular when combined with glacial freshwater input. Both outcomes are relatively insensitive to the passive-microwave algorithm used to retrieve the ice concentration being assimilated. The sensitivity of the long-term global deep-ocean properties and circulation to the possible freshwater input from ice shelves and to the parameterization of vertical mixing in the Southern Ocean is reevaluated under the new constraint.

scholarly journals Model-data comparison and data assimilation of mid-Holocene Arctic sea-ice concentration

2013 ◽  
Vol 9 (6) ◽  
pp. 6515-6549 ◽  
Author(s):  
F. Klein ◽  
H. Goosse ◽  
A. Mairesse ◽  
A. de Vernal
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Atmospheric Circulation ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Model Data ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration

Abstract. The consistency between a new quantitative reconstruction of Arctic sea-ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The comparison shows that the simulated sea-ice changes are weaker and spatially more homogeneous than the recorded ones. Furthermore, although the model-data agreement is relatively good in some regions such as the Labrador Sea, the skill of the models at local scale is low. The response of the models follows mainly the increase in summer insolation at large scale. This is modulated by changes in atmospheric circulation leading to differences between regions in the models that are albeit smaller than in the reconstruction. Performing simulations with data assimilation using the model LOVECLIM amplifies those regional differences, mainly through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin of the Arctic. This leads to an increase in the ice concentration in the Barents and Chukchi Seas and a better agreement with the reconstructions. This underlines the potential role of atmospheric circulation to explain the reconstructed changes during the Holocene.

Relationships between wintertime sea ice cover in the Barents Sea and ocean temperature anomalies in the era of satellite observations

2020 ◽  
pp. 1-65
Author(s):  
Pawel Schlichtholz
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Early Period ◽  
Ice Cover ◽  
Atlantic Water ◽  
The Arctic ◽  
Ocean Temperature ◽  
Temperature Anomalies ◽  
The Barents Sea ◽  
Skill Scores

AbstractInvestigation of the predictability of sea ice cover in the Barents Sea is of paramount importance since sea ice changes in this part of the Arctic not only affect local marine ecosystems and human activities but may also influence weather and climate in northern mid-latitudes. Here, observational data from the period 1981-2018 are used to identify statistical linkages of wintertime sea ice cover in the Barents Sea region to preceding sea surface temperature (SST) and Atlantic water temperature anomalies in that region. We find that the ocean temperature anomalies formed by local air-sea interactions during the winter-to-spring season are a significant source of predictability for sea ice area (SIA) in the Barents Sea region the following winter. Optimal areas for constructing SST predictors of Barents Sea SIA and skill scores from retrospective statistical forecasts are shown to differ between the periods to and since the onset of rapid sea ice decline in the region. In the EARLY period (1982-2003), springtime SSTs in the western Barents Sea predicted 44% of the variance of the following winter Barents Sea SIA. In the LATE period (2003-2017), springtime SSTs in the southern Barents Sea predicted 70% of the variance of the following winter Barents Sea SIA. Regression analysis suggests that feedbacks from anomalous winds may be important for the predictability of wintertime sea ice cover in the Barents Sea region.

scholarly journals Sea-Ice Indicators of Polar Bear Habitat

2016 ◽  
Author(s):  
Harry L. Stern ◽  
Kristin L. Laidre
Keyword(s):  
Sea Ice ◽  
Polar Bear ◽  
Barents Sea ◽  
Arctic Basin ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
The Barents Sea ◽  
Ice Concentration ◽  
Ice Retreat ◽  
Sea Ice Retreat

Abstract. Abstract. Nineteen distinct subpopulations of polar bears (Ursus maritimus) are found throughout the Arctic, and in all regions they depend on sea ice as a platform for traveling, hunting, and breeding. Therefore polar bear phenology – the cycle of biological events – is tied to the timing of sea-ice retreat in spring and advance in fall. We analyzed the dates of sea-ice retreat and advance in all 19 polar bear subpopulation regions from 1979 to 2014, using daily sea-ice concentration data from satellite passive microwave instruments. We define the dates of sea-ice retreat and advance in a region as the dates when the area of sea ice drops below a certain threshold (retreat) on its way to the summer minimum, or rises above the threshold (advance) on its way to the winter maximum. The threshold is chosen to be halfway between the historical (1979–2014) mean September and mean March sea-ice areas. In all 19 regions there is a trend toward earlier sea-ice retreat and later sea-ice advance. Trends generally range from −3 to −9 days decade−1 in spring, and from +3 to +9 days decade−1 in fall, with larger trends in the Barents Sea and central Arctic Basin. The trends are not sensitive to the threshold. We also calculated the number of days per year that the sea-ice area exceeded the threshold (termed ice-covered days), and the average sea-ice concentration from 1 June through 31 October. The number of ice-covered days is declining in all regions at the rate of −7 to −19 days decade−1, with larger trends in the Barents Sea and central Arctic Basin. The June–October sea-ice concentration is declining in all regions at rates ranging from −1 to −9 percent decade−1. These sea-ice metrics (or indicators of change in marine mammal habitat) were designed to be useful for management agencies. We recommend that the National Climate Assessment include the timing of sea-ice retreat and advance in future reports.

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 Sea ice, temperature, and prey effects on annual variations in mean lengths of a key Arctic fish, Boreogadus saida, in the Barents Sea

2020 ◽  
Vol 77 (5) ◽  
pp. 1796-1805
Author(s):  
Nicolas Dupont ◽  
Joël M Durant ◽  
Øystein Langangen ◽  
Harald Gjøsæter ◽  
Leif Christian Stige
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Trophic Levels ◽  
The Arctic ◽  
Polar Cod ◽  
Boreogadus Saida ◽  
Sea Ice Concentration ◽  
The Barents Sea ◽  
Ice Concentration ◽  
High Length

Abstract Oceanographic conditions in the Arctic are changing, with sea ice cover decreasing and sea temperatures increasing. Our understanding of the effects on marine populations in the area is, however, limited. Here, we focus on the Barents Sea stock of polar cod (Boreogadus saida). Polar cod is a key fish species for the transfer of energy from zooplankton to higher trophic levels in the Arctic food web. We analyse the relationships between 30-year data series on the length-at-age of polar cod cohorts (ages 0–4) and sea surface temperature, sea ice concentration, prey biomasses, predator indices, and length-at-age the previous year using multiple linear regression. Results for several ages showed that high length-at-age is significantly associated with low sea ice concentration and high length-at-age the previous year. Only length-at-age for age 1 shows a positive significant relationship with prey biomass. Our results suggest that retreating sea ice has positive effects on the growth of polar cod in the Barents Sea despite previous observations of a stagnating stock biomass and decreasing stock abundance. Our results contribute to identifying mechanisms by which climate variability affects the polar cod population, with implications for our understanding of how future climate change may affect Arctic ecosystems.

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.

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

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