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76 results for “Barents Sea”
Figure 10 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 10. Interannual variability of the North Atlantic Current Index (red) and monthly average temperature anomalies of the Barents Sea (blue) at a depth of 5 meters for the period 1948-2016. after applying the Butterworth bandpass filter from 12 to 16 years (above), and the cross-correlation picture of their material transformations without filtering (below). Preliminary removal of linear trends, centering and normalization of the series to their standard deviations were performed.
Figure 5 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 5. Changes in the monthly average temperature anomalies of the Barents Sea at depths of 5 meters (above) and 105 meters (below) for the period 1948-2016, smoothed by 2-year (orange) and 7-year (purple) low-frequency Butterworth filters. Their linear trend is shown by blackline and the accumulated sum of anomalies after the removal of the linear trend by a green line. The circles indicate the average values of the anomalies for the warm (May-October) (red) and cold (November-April) (blue) seasons.
Figure 7 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 7. Pictures of the wavelet transform of the monthly average temperature anomalies of the Barents Sea at depths of 5 meters (above) and 105 meters (below) for the period 1948 -2016. The preliminary normalization of the series to their standard deviations was made.
Figure 8 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 8. Interannual variability of the Global Atmospheric Oscillation Index (red) and monthly average temperature anomalies of the Barents Sea (blue) at a depth of 5 meters for the period 1948-2016 after applying the Butterworth bandpass filter from 2 to 7 years (above), and the cross-correlation picture of their material transformations without
Figure 4 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 4. Changes in the monthly average temperature of the Barents Sea (red) and their linear trend (blue) at depths of 5 meters (above) and 105 meters (below) for the period 1948-2016.
Figure 3 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 3. Fields of changes in the average monthly temperature of the Barents Sea (°C over 10 years) at depths of 5 meters (above) and 105 meters (below) calculated from the linear trends of its monthly average anomalies for 1948 - 2016.
Figure 9 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 9. Interannual variability of the North Atlantic Oscillation Index (red) and monthly average temperature anomalies of the Barents Sea (blue) at a depth of 5 meters for the period 1948-2016 after applying the Butterworth bandpass filter from 7 to 10 years (above), and the cross-correlation picture of their material transformations without filtering (below). Preliminary removal of linear trends, centering and normalization of the series to their standard deviations were performed.
Figure 2 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 2. Fields of average temperature of the Barents Sea at depths of 5 meters (above) and 105 meters (below) for 1948-2016.
Figure 6 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 6. Energy spectra (blue) of the monthly average temperature anomalies of the Barents Sea at depths of 5 meters (above) and 105 meters (below) for 1948-2016. A confidence level from 5% (black line at the bottom) to 95% (black line at the top) and a spectrum of red noise (red line between them) were drown. The preliminary normalization of the series to their standard deviations was made.
Figure 1 in Seasonal and Interannual Variability of the Barents Sea Temperature
Figure 1. Series of monthly average temperature anomalies for the Barents Sea at depths of 5, 55, 105, 154 and 222 meters for the period 1948-2016 after applying the 2-year low-frequency Butterworth filter.
Pelagic Fish at the Barents Sea Polar Front in May 2022
<p>## Methods</p> <p>### Study area</p> <p>This dataset is the result from sampling at 5 stations at the Polar Front in the western part of the Barents Sea.</p> <p>### Time coverage</p> <p>The samples were collected between 20 May 2022 and 25 May 2022.</p> <p>### Sampling</p> <p>5 pelagic trawl samples were collected with a Harstad pelagic trawl, which has an effective height of 9-11 m and width of 10-12 m when towed at ca. 3 knots. The mesh size of the inner liner of the cod end was 10 mm. The pelagic trawl was towed at ca. 3 knots for 20-30 min and abundances were standardized by converting to catch per unit effort (expressed in kilograms per cubic meter).</p> <p>### Sample analysis</p> <p>All organisms were identified to the nearest species or genus onboard. Throughout all stations, capelin had a large size distribution, so individuals similar in length were sorted into approximate size classes (small, medium, and large). The total number and weight of each species was recorded. For large catches, subsamples of 20-30 individuals were taken with representing length distributions of the catch. The standard length, height at the anus (up to the nearest 1 mm), and weight (up to the nearest 0.1 g) were measured for all specimens in the (sub)sample.</p> <p>### Fish stomach content analysis</p> <p>The stomachs were isolated and immediately preserved in 70% ethanol. For each individual stomach, the level of fullness (from 0: empty, to 4: full), prey composition (the count and % volume each prey item takes up in the stomach), and the level of digestion for each prey item (from 1: newly eaten, to 5: digested or non-identifiable) were estimated and recorded.</p>
Macrozooplankton at the Barents Sea Polar Front in May 2022
<p># Macrozooplankton across the Barents Sea Polar Front in May 2022</p> <p>## Methods</p> <p>### Study area</p> <p>This dataset is the result from sampling at 6 stations across the Polar Front in the western part of the Barents Sea.</p> <p>### Time coverage</p> <p>The samples were collected between 19 May 2022 and 25 May 2022.</p> <p>### Sampling</p> <p>11 macrozooplankton samples were collected with a Tucker trawl (1 m<sup>2</sup> opening and 1500 μm mesh size) and towed for 10 minutes at ca. 2 knots. The targeted depth at each station was determined from the sound scattering layer identified in the echogram from the vessel's echosounder.</p> <p>### Sample analysis</p> <p>Relative abundance from the Tucker trawl samples were analyzed per station, and taxa were counted and dried at 60 C° in pre-weighted recipients for dry weight measurements. The obtained abundances were standardized and converted to catch per unit effort, expressed in milligrams per cubic meter.</p>
Dataset: Cyclones Modulate the Control of the North Atlantic Oscillation on Transports into the Barents Sea
<p>This repository contains the data presented in '<strong>Heukamp et al., 2023 - Cyclones Modulate the Control of the North Atlantic Oscillation on Transports into the Barents Sea'</strong>.</p>
Data from: Meroplankton diversity, seasonality and life-history traits across the Barents Sea Polar Front revealed by high-throughput DNA barcoding
Open the record for dataset details and reuse information.
Midnight Sun to Polar Night: A model of seasonal light in the Barents Sea - Supplementary Data
<p><strong>Supplementary Data</strong></p> <p>Midnight Sun to Polar Night: A model of seasonal light in the Barents Sea. Connan-McGinty, S., Banas, N.S., Berge, J., Cottier, F., Grant, S., Johnsen, G., Kopec, T.P., Porter, M., McKee, D. (2022).</p> <p>All data and Python code required to re-create figures from the above manuscript.</p>
FESOM model data and particle tracking data used in publication 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean'
<p>FESOM model data and particle tracking data used in the paper 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean" by Andreas Rogge at al.</p> <p>1) FESOM velocity fields averaged over the top 200 m water depth, averaged over the time period 2015-2018.</p> <p>2) FESOM transect at 95°E in the Arctic Ocean (temperature, salinity and velocity), averaged over the time period 2015-2018.</p> <p>3a) Particle back-tracking data based on daily FESOM velocity fields in netcdf format. Particles were released at 95°E every 14 days during the year 2018 and tracked until they reached the surface. Three different constant sinking velocities were used, representative for small and large non-ballasted particles and small ballasted particles.</p> <p>3b) Distribution of particles at the surface for the experiments with three different sinking velocities as mat files. </p>
Figure 1 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 1. Map of the Barents Sea. The red dashed line shows the boundaries of the Barents Sea.
Sea ice reduction drives genetic differentiation among Barents Sea polar bears
<p>Loss of Arctic sea ice due to climate change is predicted to reduce both genetic diversity and gene flow in ice-dependent species, with potential negative consequences for their long-term viability. Here, we tested for the population-genetic impacts of reduced sea ice cover on the polar bear (<i>Ursus maritimus</i>) sampled across two decades (1995–2016) from the Svalbard Archipelago, Norway, an area that is affected by rapid sea ice loss in the Arctic Barents Sea. We analysed genetic variation at 22 microsatellite loci for 626 polar bears from four sampling areas within the archipelago. Our results revealed a 3-10% loss of genetic diversity across the study period, accompanied by a near 200% in genetic differentiation across regions. These effects may best be explained by a decrease in gene flow caused by habitat fragmentation due to loss of sea ice coverage, resulting in increased inbreeding of local polar bears within the focal sampling areas in the Svalbard Archipelago. This study illustrates the importance of genetic monitoring for developing adaptive management strategies for polar bears and other ice-dependent species.</p>
Data for "Observed winter Barents Kara Sea ice variations induce prominent sub-decadal variability and a multidecadal trend in the Warm Arctic Cold Eurasia pattern"
<p>Here the model experiment data used to create the figures in the article "Observed winter Barents Kara Sea ice variations induce prominent sub-decadal variability and a multi-decadal trend in the Warm Arctic Cold Eurasia pattern" are provided.</p>
Figure 1 in Impact of the bird-manure caused eutrophication on the abundance and diversity of chironomid larvae (Diptera; Chironomidae) in lakes of the Bolshoy Aynov Island (Russia, Barents Sea)
Figure 1. Landscape of the Greater Ainu Island.
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