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Keywords = Arctic Gateways

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17 pages, 3622 KB  
Article
Using DNA Metabarcoding to Characterize the Prey Spectrum of Two Co-Occurring Themisto Amphipods in the Rapidly Changing Atlantic-Arctic Gateway Fram Strait
by Annkathrin Dischereit, Owen S. Wangensteen, Kim Præbel, Holger Auel and Charlotte Havermans
Genes 2022, 13(11), 2035; https://doi.org/10.3390/genes13112035 - 4 Nov 2022
Cited by 10 | Viewed by 3140
Abstract
The two congeneric hyperiids Themisto libellula and T. abyssorum provide an important trophic link between lower and higher trophic levels in the rapidly changing Arctic marine ecosystem. These amphipods are characterized by distinct hydrographic affinities and are hence anticipated to be impacted differently [...] Read more.
The two congeneric hyperiids Themisto libellula and T. abyssorum provide an important trophic link between lower and higher trophic levels in the rapidly changing Arctic marine ecosystem. These amphipods are characterized by distinct hydrographic affinities and are hence anticipated to be impacted differently by environmental changes, with major consequences for the Arctic food web. In this study, we applied DNA metabarcoding to the stomach contents of these Themisto species, to comprehensively reveal their prey spectra at an unprecedented-high-taxonomic-resolution and assess the regional variation in their diet across the Fram Strait. Both species feed on a wide variety of prey but their diet strongly differed in the investigated summer season, showing overlap for only a few prey taxa, such as calanoid copepods. The spatially structured prey field of T. libellula clearly differentiated it from T. abyssorum, of which the diet was mainly dominated by chaetognaths. Our approach also allowed the detection of previously overlooked prey in the diet of T. libellula, such as fish species and gelatinous zooplankton. We discuss the reasons for the differences in prey spectra and which consequences these may have in the light of ongoing environmental changes. Full article
(This article belongs to the Special Issue Polar Genomics)
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22 pages, 8348 KB  
Article
Arctic Multiyear Ice Areal Flux and Its Connection with Large-Scale Atmospheric Circulations in the Winters of 2002–2021
by Huiyan Kuang, Yanbing Luo, Yufang Ye, Mohammed Shokr, Zhuoqi Chen, Shaoyin Wang, Fengming Hui, Haibo Bi and Xiao Cheng
Remote Sens. 2022, 14(15), 3742; https://doi.org/10.3390/rs14153742 - 4 Aug 2022
Cited by 6 | Viewed by 2665
Abstract
Arctic sea ice, especially the multiyear ice (MYI), is decreasing rapidly, partly due to melting triggered by global warming, in turn partly due to the possible acceleration of ice export from the Arctic Ocean to southern latitudes through identifiable gates. In this study, [...] Read more.
Arctic sea ice, especially the multiyear ice (MYI), is decreasing rapidly, partly due to melting triggered by global warming, in turn partly due to the possible acceleration of ice export from the Arctic Ocean to southern latitudes through identifiable gates. In this study, MYI and total sea ice areal flux through six Arctic gateways over the winters (October–April) of 2002–2021 were estimated using daily sea ice motion and MYI/total sea ice concentration data. Inconsistencies caused by different data sources were considered for the estimate of MYI flux. Results showed that, there is a slight declining trend in the Arctic MYI areal flux over the past two decades, which is attributable to the decrease in MYI concentration. Overall speaking, MYI flux through Fram Strait accounts for ~87% of the Arctic MYI outflow, with an average of ~325.92 × 103 km2 for the winters of 2002–2021. The monthly MYI areal flux through Fram Strait is characterized with a peak in March (~55.56 × 103 km2) and a trough in April (~40.97 × 103 km2), with a major contribution from MYI concentration. The connections between sea ice outflow and large-scale atmospheric circulations such as Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and Dipole Anomaly (DA) were investigated. High correlation coefficients (CCs) were found in winter months such as January and February. While AO and NAO (especially NAO) exhibited generally weak correlations with the MYI/total sea ice flux, DA presented strong correlations with the areal flux, especially for MYI (CC up to 0.90 in January). However, the atmospheric circulation patterns are sometimes not fully characterized by the specific indices, which could have different effects on sea ice flux and its correlation with the atmospheric indices. Full article
(This article belongs to the Special Issue Remote Sensing of Ice Loss Tracking at the Poles)
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23 pages, 23484 KB  
Article
The Potential and Challenges of Using Soil Moisture Active Passive (SMAP) Sea Surface Salinity to Monitor Arctic Ocean Freshwater Changes
by Wenqing Tang, Simon Yueh, Daqing Yang, Alexander Fore, Akiko Hayashi, Tong Lee, Severine Fournier and Benjamin Holt
Remote Sens. 2018, 10(6), 869; https://doi.org/10.3390/rs10060869 - 4 Jun 2018
Cited by 70 | Viewed by 7554
Abstract
Sea surface salinity (SSS) links various components of the Arctic freshwater system. SSS responds to freshwater inputs from river discharge, sea ice change, precipitation and evaporation, and oceanic transport through the open straits of the Pacific and Atlantic oceans. However, in situ SSS [...] Read more.
Sea surface salinity (SSS) links various components of the Arctic freshwater system. SSS responds to freshwater inputs from river discharge, sea ice change, precipitation and evaporation, and oceanic transport through the open straits of the Pacific and Atlantic oceans. However, in situ SSS data in the Arctic Ocean are very sparse and insufficient to depict the large-scale variability to address the critical question of how climate variability and change affect the Arctic Ocean freshwater. The L-band microwave radiometer on board the NASA Soil Moisture Active Passive (SMAP) mission has been providing SSS measurements since April 2015, at approximately 60 km resolution with Arctic Ocean coverage in 1–2 days. With improved land/ice correction, the SMAP SSS algorithm that was developed at the Jet Propulsion Laboratory (JPL) is able to retrieve SSS in ice-free regions 35 km of the coast. SMAP observes a large-scale contrast in salinity between the Atlantic and Pacific sides of the Arctic Ocean, while retrievals within the Arctic Circle vary over time, depending on the sea ice coverage and river runoff. We assess the accuracy of SMAP SSS through comparative analysis with in situ salinity data collected by Argo floats, ships, gliders, and in field campaigns. Results derived from nearly 20,000 pairs of SMAP and in situ data North of 50°N collocated within a 12.5-km radius and daily time window indicate a Root Mean Square Difference (RMSD) less than ~1 psu with a correlation coefficient of 0.82 and a near unity regression slope over the entire range of salinity. In contrast, the Hybrid Coordinate Ocean Model (HYCOM) has a smaller RMSD with Argo. However, there are clear systematic biases in the HYCOM for salinity in the range of 25–30 psu, leading to a regression slope of about 0.5. In the region North of 65°N, the number of collocated samples drops more than 70%, resulting in an RMSD of about 1.2 psu. SMAP SSS in the Kara Sea shows a consistent response to discharge anomalies from the Ob’ and Yenisei rivers between 2015 and 2016, providing an assessment of runoff impact in a region where no in situ salinity data are available for validation. The Kara Sea SSS anomaly observed by SMAP is missing in the HYCOM SSS, which assimilates climatological runoffs without interannual changes. We explored the feasibility of using SMAP SSS to monitor the sea surface salinity variability at the major Arctic Ocean gateways. Results show that although the SMAP SSS is limited to about 1 psu accuracy, many large salinity changes are observable. This may lead to the potential application of satellite SSS in the Arctic monitoring system as a proxy of the upper ocean layer freshwater exchanges with subarctic oceans. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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