scholarly journals Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea

2019 ◽  
Vol 25 ◽  
pp. 26-35
Author(s):  
Sergey A. Lebedev ◽  
Andrey G. Kostianoy ◽  
Sergey K. Popov
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Barents Sea ◽  
Ice Cover ◽  
Water Dynamics ◽  
The Barents Sea ◽  
Time Period ◽  
Ice Edge ◽  
Edge Displacement ◽  
Satellite Altimetry Data

Satellite altimetry data are used for investigation of the sea level variability and sea ice cover retreat in the Barents Sea in 1992-2018. The data from ERS − 1/2, ENVISAT, SARAL/AltiKa, and Sentinel-3A/3B satellites were used in this study. An increasing trend of the sea level of about 2.31 mm/yr was observed in this time period, which caused a total increase in the Barents Sea level by about 6 cm. Linear trends of the sea level change varied from 1.84 mm/yr in July to 4.29 mm/yr in September. The average velocity of the ice edge retreat along the tracks in the northeastern direction is of 10.9 km/yr for the same period. It was found that the ice edge displacement rate tends to increase by 0.30 km/yr per a degree in longitude in the eastward direction. Thus, the ice edge retreat along the “eastern” tracks goes faster than along the “western” ones, which is likely explained by a change in the water dynamics in the Barents Sea.

Satellite altimetry of the Barents sea

2021 ◽  
pp. 194-212
Author(s):  
S.A. Lebedev ◽  
◽  
A.G. Kostianoy ◽  
S.K. Popov ◽  
◽  
...  
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Average Velocity ◽  
Barents Sea ◽  
Water Dynamics ◽  
Altimetry Data ◽  
The Barents Sea ◽  
Time Period ◽  
Ice Edge ◽  
Edge Displacement

Satellite altimetry data are used for investigation of the sea level variability and sea ice cover retreat in the Barents Sea in 1992−2018. The data from ERS−1/2, ENVISAT, SARAL/AltiKa, and Sentinel-3A/3B satellites were used in this study. An increasing trend of the sea level of about 2.31 mm/yr was observed in this time period, which caused a total increase in the Barents Sea level by about 6 cm. Linear trends of the sea level change varied from 1.84 mm/yr in July to 4.29 mm/yr in September. The average velocity of the ice edge retreat along the tracks in the northeastern direction is of 10.9 km/yr for the same period. It was found that the ice edge displacement rate tends to increase by 0.30 km/yr per a degree in longitude in the eastward direction. Thus, the ice edge retreat along the “eastern” tracks goes faster than along the “western” ones, which is likely explained by a change in the water dynamics in the Barents Sea.

Method application of optimal multi-parameter analysis to assess the distribution of water masses as an example of CTD data of the Barents Sea in summer 2017

2019 ◽  
pp. 38-51
Author(s):  
Valeriy G. Yakubenko ◽  
Anna L. Chultsova
Keyword(s):  
Barents Sea ◽  
Field Measurements ◽  
Structural Similarity ◽  
Water Masses ◽  
Parameter Analysis ◽  
Water Dynamics ◽  
Phosphorus Concentration ◽  
Nitrogen And Phosphorus ◽  
The Barents Sea ◽  
Expert Assessments

Identification of water masses in areas with complex water dynamics is a complex task, which is usually solved by the method of expert assessments. In this paper, it is proposed to use a formal procedure based on the application of the method of optimal multiparametric analysis (OMP analysis). The data of field measurements obtained in the 68th cruise of the R/V “Academician Mstislav Keldysh” in the summer of 2017 in the Barents Sea on the distribution of temperature, salinity, oxygen, silicates, nitrogen, and phosphorus concentration are used as a data for research. A comparison of the results with data on the distribution of water masses in literature based on expert assessments (Oziel et al., 2017), allows us to conclude about their close structural similarity. Some differences are related to spatial and temporal shifts of measurements. This indicates the feasibility of using the OMP analysis technique in oceanological studies to obtain quantitative data on the spatial distribution of different water masses.

Regularities and features of ice conditions of the Barents Sea in the second half of XX – early XXI century

2021 ◽  
pp. 179-194
Author(s):  
I.O. Dumanskaya ◽  
Keyword(s):  
Barents Sea ◽  
Ice Cover ◽  
Heat Fluxes ◽  
The Arctic ◽  
Cycle Period ◽  
Arctic Seas ◽  
Sea Ice Extent ◽  
The Barents Sea ◽  
Western Border ◽  
Quantitative Changes

The warming of the Arctic, especially intensified at the beginning of the XXI century, is accompanied by a significant decrease in the area of ice cover in the Arctic seas. The article shows the quantitative changes in the ice parameters of the Barents Sea, as well as factors affecting the formation of ice cover in recent years. In the twenty-first century the frequency of occurrence of mild winters has increased by 17%, the frequency of severe winters has decreased by 19%. Significantly increased the temperature at the meteorological station Malye Karmakuly, water temperature at transect "Kola Meridian", atmospheric and oceanic heat fluxes, and speed of sea currents on the Western border of the Barents sea. The duration of the ice period decreased by an average of 2–3 weeks, and the rate of reduction of ice cover was 7.2% for 10 years. This is the highest speed compared to other Arctic seas. The article shows that the variability of the ice cover of the Barents Sea and other parameters of the natural environment in the region has the cyclic character. Presumably, the cycle period is close to 84 years, which corresponds to the orbital period of Uranium. The minimum sea ice extent after 1935–1945 is expected in the period 2019–2029.

Palynological evidence of sea-surface conditions in the Barents Sea off northeast Svalbard during the postglacial period

2020 ◽  
pp. 1-15
Author(s):  
Camille Brice ◽  
Anne de Vernal ◽  
Elena Ivanova ◽  
Simon van Bellen ◽  
Nicolas Van Nieuwenhove
Keyword(s):  
Sea Ice ◽  
Surface Waters ◽  
Barents Sea ◽  
Ice Cover ◽  
Atlantic Water ◽  
Transitional Period ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Conditions ◽  
The Barents Sea

Abstract Postglacial changes in sea-surface conditions, including sea-ice cover, summer temperature, salinity, and productivity were reconstructed from the analyses of dinocyst assemblages in core S2528 collected in the northwestern Barents Sea. The results show glaciomarine-type conditions until about 11,300 ± 300 cal yr BP and limited influence of Atlantic water at the surface into the Barents Sea possibly due to the proximity of the Svalbard-Barents Sea ice sheet. This was followed by a transitional period generally characterized by cold conditions with dense sea-ice cover and low-salinity pulses likely related to episodic freshwater or meltwater discharge, which lasted until 8700 ± 700 cal yr BP. The onset of “interglacial” conditions in surface waters was marked by a major change in dinocyst assemblages, from dominant heterotrophic to dominant phototrophic taxa. Until 4100 ± 150 cal yr BP, however, sea-surface conditions remained cold, while sea-surface salinity and sea-ice cover recorded large amplitude variations. By ~4000 cal yr BP optimum sea-surface temperature of up to 4°C in summer and maximum salinity of ~34 psu suggest enhanced influence of Atlantic water, and productivity reached up to 150 gC/m2/yr. After 2200 ± 1300 cal yr BP, a distinct cooling trend accompanied by sea-ice spreading characterized surface waters. Hence, during the Holocene, with exception of an interval spanning about 4000 to 2000 cal yr BP, the northern Barents Sea experienced harsh environments, relatively low productivity, and unstable conditions probably unsuitable for human settlements.

scholarly journals More confounders at global and decadal scales in detecting recent sea level accelerations

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
H. Bâki Iz
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Almost Periodic ◽  
Altimetry Data ◽  
Tide Gauge Data ◽  
Gauge Data ◽  
Close Range ◽  
Globally Distributed ◽  
Satellite Altimetry Data

AbstractThe residuals of 27 globally distributed long tide gauge recordswere scrutinized after removing the globally compounding effect of the periodic lunar node tides and almost periodic solar radiation’s sub and superharmonics from the tide gauge data. The spectral analysis of the residuals revealed additional unmodeled periodicities at decadal scales, 19 of which are within the close range of 12–14 years, at 27 tide gauge stations. The amplitudes of the periodicitieswere subsequently estimated for the spectrally detected periods and they were found to be statistically significant (p «0.05) for 18 out of 27 globally distributed tide gauge stations. It was shown that the estimated amplitudes at different localities may have biased the outcome of all the previous studies based on tide gauge or satellite altimetry data that did not account for these periodicities, within the range −0.5 – 0.5 mm/yr., acting as another confounder in detecting 21st century sea level rise.

Sea level in Thule measured with tide gauge, GNSS-IR and Satellite Altimetry

2020 ◽  
Author(s):  
Trine S. Dahl-Jensen ◽  
Shfaqat Abbas Khan ◽  
Simon D.P. Williams ◽  
Ole B. Andersen ◽  
Carsten A. Ludwigsen
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Sea Level Changes ◽  
Level Measurement ◽  
Time Period ◽  
The Right ◽  
Gps Station

<p>Recent studies show that under the right conditions relative sea level can be measured using GNSS interferometric reflectometry (GNSS-IR). We test the possibility of using an existing GNET GPS station in Thule, Greenland, to measure inter annual changes in sea level by comparing sea level measurements from GNSS-IR with tide gauge observations and satellite altimetry data. GNET is a network of 56 permanent GPS stations positioned on the bedrock around the edge fo the Greenland ice sheet with the main purpose of monitoring ice mass changes. Currently, Thule is the only location in Greenland where we have both a tide gauge and a GPS station that is suitable for sea level measurement covering the same time period for more than a couple of years. If successful a number of other GPS stations are also expected to be suitable for GNSS-IR measurements of sea level. However, they lack the tide gauge station for testing.<br>We compare the measured sea level with uplift measured using the GPS and modeled from height changes of the Greenland ice sheet as well as sea surface temperatures and modeled sea level changes from gravimetry, in order to investigate the origin of sea level changes in the region.  <br> </p>

scholarly journals Atlantic water temperature and climate in the Barents Sea, 2000–2009

2012 ◽  
Vol 69 (5) ◽  
pp. 833-840 ◽  
Author(s):  
Vladimir D. Boitsov ◽  
Alexey L. Karsakov ◽  
Alexander G. Trofimov
Keyword(s):  
Water Temperature ◽  
Barents Sea ◽  
Ice Cover ◽  
Atlantic Water ◽  
Climate Index ◽  
Mean Annual Temperature ◽  
Positive Trend ◽  
Late 20Th Century ◽  
The Barents Sea ◽  
Using Data

Abstract Boitsov, V. D., Karsakov, A. L., and Trofimov, A. G. 2012. Atlantic water temperature and climate in the Barents Sea, 2000–2009. – ICES Journal of Marine Science, 69: 833–840. Year-to-year variability in the temperature of Atlantic water (AW), which has a strong influence on the marine climate and ecosystem of the Barents Sea, was analysed using data from the Kola Section. With a positive trend in mean annual temperature during the late 20th century, only positive anomalies were registered during the past decade. In nine of those years, the temperature was warmer than the 1951–2000 long-term mean by 0.5–1.2°C, and in 2006, the historical maximum for the 110-year period of observations along the section was recorded. High air and water temperature coincided with reduced sea-ice cover, especially between October and April, when there is seasonal enlargement of the ice-covered area. An integral climate index (CI) of the Barents Sea based on the variability in temperature of AW, air temperature, and ice cover is presented. A prediction of future Barents Sea climate to 2020 is given by extrapolating the sixth degree polynomial approximating the CI.

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