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  • 1
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    A model hindcast of bottom environmental conditions in the North Atlantic Ocean 1948 to 2014 (2018)
    PANGAEA
    In:  Nansen Environmental and Remote Sensing Center
    Details
    Publication Date: 2023-02-24
    Description: This dataset contains a model simulation of the environmental conditions close to the sea-floor from January 1948-April 2015. The simulations relies on the coupled physcial-biogeochemical HYCOM-ECOSMO and has been forced by a Global High Resolution Climate Reconstruction (ECHAM6). The dataset is monthly, it consist of temperature, salinity, currents, oxygen, nitrate, phosphate and silicate all interpolated to 1 meter above the sea floor. Additionally the dataset contains gross primary and secondary production integrated over the water column.
    Keywords: Deep-sea Sponge Grounds Ecosystems of the North Atlantic; File content; File format; File name; File size; NorthAtlantic; SponGES; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 55 data points
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  • 2
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    Occurrence records of Geodia species (Porifera, Demospongiae, Tetractinellida) from the North Atlantic and Arctic Oceans (2021)
    PANGAEA
    Details
    Publication Date: 2024-02-02
    Description: This dataset contains occurrence records (i.e., species name, latitude, longitude, depth (where available), and metadata) for six species of the demosponge genus Geodia Lamarck, 1815, belonging to the Geodiidae family: Geodia atlantica (Stephens, 1915); Geodia barretti Bowerbank, 1858; Geodia macandrewii Bowerbank, 1858; Geodia phlegraei (Sollas, 1880); Geodia hentscheli Cárdenas et al. 2010; and Geodia parva Hansen, 1885. The records fall in the North Atlantic and Arctic Oceans, and are used/described in the linked article by Roberts et al. (2021). Note that the dataset provided has not been subjected to any of the filtering stages applied in that manuscript for the purposes of various novel biogeographical analyses (i.e., it is original and complete), and the taxonomic identifications have been rigorously checked (as described therein). Definitions of institution abbreviations used in the 'Museum Specimen / Picture Information' column of the dataset spreadsheet have been provided in an accompanying table (see Comment field below). Where records are derived from earlier literature sources, full references for citations given in the 'Campaign / Source' column (and further general information on many of the records) may be found in the articles by Cárdenas et al. (2010; 2013) and Cárdenas & Rapp (2015). An earlier version of this dataset may be accessed at the DRYAD repository: Cárdenas P, Rapp HT, Klitgaard AB, Best M, Thollesson M, Tendal OS (2013), Data from: Taxonomy, biogeography and DNA barcodes of Geodia species (Porifera, Demospongiae, Tetractinellida) in the Atlantic boreo-arctic region, Dryad, Dataset, doi:10.5061/dryad.td8sb
    Keywords: 87PA0028; 87PA0067; 87PA0078; 92PA0160002; 92PA0160005; 92PA0160014; 92PA0160028; 92PA0160050; 92PA0160052; 94PA0090001; 94PA0090002; 94PA0090009; 94PA0090010; 94PA0090019; 94PA0090020; 94PA0090026; 94PA0090039; 94PA0090041; 94PA0090043; 94PA0090045; 94PA0090049; 94PA0090062; Agassiz Trawl; AGT; Arctic Ocean; ARK-VII/2; ARK-XXII/1a; Barents Sea; BEAM; Beam trawl; BIODEEP2007_Dredge2; BIODEEP2007_ROV10; BIODEEP2007_ROV9; BIOFAR_St117; BIOFAR_St119; BIOFAR_St120; BIOFAR_St122; BIOFAR_St234; BIOFAR_St279; BIOFAR_St287; BIOFAR_St297; BIOFAR_St298; BIOFAR_St375; BIOFAR_St379; BIOFAR_St389; BIOFAR_St43; BIOFAR_St451; BIOFAR_St452; BIOFAR_St486; BIOFAR_St487; BIOFAR_St498; BIOFAR_St526; BIOFAR_St530; BIOFAR_St531; BIOFAR_St535; BIOFAR_St540; BIOFAR_St550; BIOFAR_St69; BIOFAR_St734; BIOFAR_St756; BIOFAR_St89; BIOFAR_St901; BIOICE_St2022; BIOICE_St2023; BIOICE_St2218; BIOICE_St2292; BIOICE_St2293; BIOICE_St2368; BIOICE_St2374; BIOICE_St2499; BIOICE_St2501; BIOICE_St2516; BIOICE_St2518; BIOICE_St2700; BIOICE_St2728; BIOICE_St2747; BIOICE_St2756; BIOICE_St2768; BIOICE_St2769; BIOICE_St2923; BIOICE_St2926; BIOICE_St2928; BIOICE_St3227; BIOICE_St3659; BIOICE_St3661; BIOSKAG2006_St20; BIOSYS2006_DR182; BIOSYS2006_VG20-1; Blacker1957_11; Blacker1957_130; Blacker1957_131; Blacker1957_14; Blacker1957_16; Blacker1957_164; Blacker1957_165; Blacker1957_168; Blacker1957_20; Blacker1957_21; Blacker1957_22; Blacker1957_24; Blacker1957_25; Blacker1957_27; Blacker1957_28; Blacker1957_33; Blacker1957_35; Blacker1957_36; Blacker1957_44; Blacker1957_45; Blacker1957_46; Blacker1957_53; Blacker1957_55; Blacker1957_56; Blacker1957_60; Blacker1957_61; Blacker1957_62; Blacker1957_68; Blacker1957_75; Blacker1957_8; Blacker1957_80; Blacker1957_81; Blacker1957_84; Blacker1957_9; Blacker1957_94; BMT19; Boury-Esnaultetal1994_CP62; Boury-Esnaultetal1994_CP63; Boury-Esnaultetal1994_CP92; Boury-Esnaultetal1994_CP98; Bowerbank1872a_Vikna; Bowerbank1872aPlateXI_Vikna; Brattholmen_St230407; Breitfuss1930_St1237; Breitfuss1930_St1347; Breitfuss1930_St1385; Burton1934_St548; Burton1959_EIceland; Burton1959_SEIceland; Campaign; CD80_St178; CD80_St18; CD80_St91; CE13008; CE13008_ROV32; CE2008-11_M11GHaul22; CE2008-11_M11GHaul23; Celtic Explorer; Celtic Sea; CENTOBBiogasII_DS33; CGB2011_11c-16-DR01; CGB2011_11c-19-ROV05; CGB2011_11c-30-DR05; CGB2011_11c-31-DR06; Comment; CorSeaCan_B12_CG_ACH_P01_20100809; CorSeaCan_B13_MOI-ACH-P06; CV13012_51; Dana_St6001; Davis Strait; Deep-sea Sponge Grounds Ecosystems of the North Atlantic; Depth, bottom/max; Depth, top/min; DEPTH, water; Dyrelivihavet2008_SandsfjordRogaland; E17044_SP17E44001; EBS; EcosystemBarentsSea2007_St2562; Epibenthic sledge; Event label; FRVScotia2012_S12_469; FRVScotia2012_S12/469; FRVScotia2012_S12-469; G. O. Sars (2003); Giant box corer; GKG; Greenland Sea; GS06/112; GS112_BMT19; GS14; GS14-AGT03; GS14-AGT07; GS14-DR02; GS14-DR09; GS14-DR12; H2DEEP2008_ROV5; HakonMosby_St237; HakonMosby_St242; HakonMosby_St245; HakonMosby_St86072701; HakonMosby_St93060602; HakonMosby_St93060612; HakonMosby_St93060613; HakonMosby_St93061106; Hentschel1929_St40; Hentschel1929_St41; Hentschel1929_St42; Howelletal2010_WSC11; Howelletal2010_WSCE10B; Howelletal2010_WSCE3; Howelletal2010_WSCE4; HUD2007-025_DiveR1059; HUD2010-029; HUD2010-029_R1335; HUD2010-029_R1336-07; HUD2010-029_R1339-10; HUD2010-029_R1340-12; HUD2010-029_R1340-4; HUD2010-029_R1341-18; HUD2013/29; HUD2013-029_DS1-I; Hudson; Iceland Sea; Identification; IngolfExpdt_St1; IngolfExpdt_St125; IngolfExpdt_St21; IngolfExpdt_St78; IngolfExpdt_St90; IngolfExpdt_St92; JAGO; Kara Sea; Kingstonetal1979_LabradorCoast; Koltun1964_St1; Koltun1964_St10; Koltun1964_St11; Koltun1964_St26; Koltun1964_St46; Koltun1964_St7; Koltun1964_St8; Koltun1964_St9; Koltun1966_NofFranzJosephLand; Koltun1966_NofKaraSea; Koltun1966_NWofLaptevSea; Labrador Sea; Langenuen_SteinnesetSt31; Laptev Sea; LATITUDE; LONGITUDE; Lundbeck1909_Angmagsalik; Lynch_St1971; Lynch_St1972; Lynch_St1973; Lynch_St721008; Lysefjord_Uksen; M85/3; M85/3_1123; M85/3_1132; M85/3_1136; M85/3_1219; M85/3_1223; MA0200057_St90; MagnusHeinason_St150990; MAR310_St1; Mareano_StR228-12; Mareano_StR262VL282; Mareano_StR828; Mareano_StR863; Mareano2009_StR469VL491; Mareano2011_StR729VL756; Mareano2011_StR731VL759; Mareano2011_StR744VL772; Mareano2011_StR758VL786; MAR-Eco2004_St50-373; MAR-Eco2004_St70_385; MAR-Eco2004_St70-385; MAR-Eco2004_St72-386; MedSeaCan_B7_MG_PO2_20090523; MedSeaCan_B7_PA_ACH_P02_20090519; Meteor (1986); More2005_St46; MULT; Multiple investigations; NEREIDA0609_BC89; NEREIDA0710_BC237; Nereida2009-2010_BC04; Nereida2009-2010_DR04-001; Nereida2009-2010_DR07-025; Nereida2009-2010_DR10; Nereida2009-2010_DR12; Nereida2009-2010_DR18; Nereida2009-2010_DR19; Nereida2009-2010_DR20; Nereida2009-2010_DR22; Nereida2009-2010_DR23; Nereida2009-2010_DR24; Nereida2009-2010_DR3; Nereida2009-2010_DR32; Nereida2009-2010_DR38; Nereida2009-2010_DR4; Nereida2009-2010_DR6; Nereida2009-2010_DR64; Nereida2009-2010_DR66; Nereida2009-2010_DR7; Nereida2009-2010_DR70; Nereida2009-2010_DR70_BOTTOM; Nereida2009-2010_DR74; Nereida2009-2010_DR74_BOTTOM; North Greenland Sea; North Sea; Norwegian Sea; PA2010-009_Set075; PA2010-009_Set104; PA2010-009_Set105; PA2010-009_Set108; PA2010-009_Set109; PA2010-009_Set111; PA2010-009_Set113; PA2010-009_Set114; PA2010-009_Set115; PA2010-009_Set116; PA2010-009_Set126; PA2010-009_Set141; PA2010-009_Set155; PA2010-009_Set156; PA2010-009_Set157; PA2010-009_Set159; PA2010-009_Set160; PA2010-009_Set161; PA2010-009_Set162; PA2010-009_Set163; PA2010-009_Set164; PA2010-009_Set167; PA2010-009_Set168; PAA2011007; PAA2011007_127_39; PAA2011007_225_114; PAA2011007_255_126; PAA2011007_262_128; PAA2011007_533_23; PAA2011007_634_139; PAA2013008; PAA2013008_157_44; PAA2013008_169_46; PAA2013008_174_47; PAA2013008_176_48; PAA2013008_177_50; PAA2013008_302_141; PAA2013008_305_142; PAA2013008_31_10; PAA2014007; PAA2014007_278_125; PAA2014007_286_127; PAA2014007_321_136; PAA2014007_514_152; PAA2015007; PAA2015007_126_32; PAA2015007_289_60; PAA2015007_299_62; PAA2015007_303_64; Paamiut; Polarstern; PS17; PS17/223; PS70; PS70/002-2; PS70/006-1; PS70/014-4; PS70/015-1; PS70/016-1; PS70/027-1; PS70/040-4; RVMichaelSars_St102; RVMichaelSars_St76; RVMichaelSars_St85; S10176_SP10176001; S11073_SP11073001; S11471_SP11471001; S12135_SP12135001; S12444_SP12444001; S12446_SP12446001; S12447; S15A13; S16185_SP16185001; S16379_SP16379003; S16A03_SP16A03017; S16A03_SP16A03029; S16A03_SP16A03039; S16A03_SP16A03041; S18A02; S18A03; Scotland Sea; ShinkaiMaru_St004; ShinkaiMaru_St109; ShinkaiMaru_St110; ShinkaiMaru_St15; ShinkaiMaru_St18; ShinkaiMaru_St1976; ShinkaiMaru_St21; ShinkaiMaru_St26; ShinkaiMaru_St29; ShinkaiMaru_St3; ShinkaiMaru_St32; ShinkaiMaru_St43; ShinkaiMaru_St50; ShinkaiMaru_St63; ShinkaiMaru_St70; ShinkaiMaru_St79; ShinkaiMaru_St9; ShinkaiMaru1987_St104; ShinkaiMaru1987_St67; Skagerrak; South Atlantic Ocean; Species; SponGES; St89SI0240086; Station label; Submersible JAGO; SwedishArcticExp1871_St37; T0406066; T8903301; T8905093; T8905125; T8905127; T8905185; T9405259; T9405264; T9405276; T9405305; T9405315; T9405317; T9406031; T9406032; T9406036; T9406066; ThalassaZ_Z407; ThalassaZ_Z408; Traena Deep; Trollholmflua; Tromso_Haugbernes; Western Basin; WH_St569; WH47566; WH47572; ZoolPolarExp1900_St30
    Type: Dataset
    Format: text/tab-separated-values, 2307 data points
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  • 3
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    Agulhas Current Meanders Facilitate Shelf-Slope Exchange on the Eastern Agulhas Bank (2018)
    American Geophysical Union
    Details
    Publication Date: 2018-06-08
    Print ISSN: 2169-9275
    Electronic ISSN: 2169-9291
    Topics: Geosciences , Physics
    Published by American Geophysical Union
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  • 4
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    Ocean Biogeochemical Predictions—Initialization and Limits of Predictability (2020)
    Frontiers Media
    Details
    Publication Date: 2020-06-16
    Electronic ISSN: 2296-7745
    Topics: Biology
    Published by Frontiers Media
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  • 5
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    Evaluation of Arctic Ocean surface salinities from SMOS and two CMEMS reanalyses against in-situ data sets (2019)
    Copernicus
    Details
    Publication Date: 2019-01-18
    Description: Although the stratification of the upper Arctic Ocean is mostly salinity-driven, the sea surface salinity (SSS) is still poorly known in the Arctic, due to its strong variability and the sparseness of in-situ observations. Recently, two gridded SSS products have been derived from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, independently developed by the Barcelona Expert Centre (BEC) in Spain and the Ocean Salinity Expertise Center (CECOS) of the Centre Aval de Traitemenent des Donnees SMOS (CATDS) in France, respectively. In parallel, there are two reanalysis products providing the Arctic SSS in the framework of the Copernicus Marine Environment Monitoring Services (CMEMS), one global, and another regional product. While the regional Arctic TOPAZ4 system assimilates a large set of sea-ice and ocean observations with an Ensemble Kalman Filter, the global reanalysis combines in-situ and satellite data using a multivariate ensemble optimal interpolation method. In this study, focused on the Arctic Ocean, these four salinity products, together with the climatology both World Ocean Atlas (WOA) of 2013 and Polar science center Hydrographic Climatology (PHC), are evaluated against in-situ datasets during 2011–2013. For the validation the in-situ observations are divided in two; those that have been assimilated and those that have not. The deviations of SSS between the different products and against the in-situ observations show largest disagreements below the sea-ice and in the marginal ice zone (MIZ), especially during the summer months. In the Beaufort Sea, the summer SSS from the BEC product has the smallest – saline – bias (~0.6 psu) with the smallest root mean squared difference (RSMD) of 2.6 psu. This suggests a potential value of assimilating of this product into the forthcoming Arctic reanalyses. Keywords: Arctic Ocean; sea surface salinity; SMOS; reanalysis; absolute deviation;
    Print ISSN: 1812-0806
    Electronic ISSN: 1812-0822
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 6
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    Delineation of marine ecosystem zones in the northern Arabian Sea using an objective method (2017)
    Copernicus
    Details
    Publication Date: 2017-07-14
    Description: The spatial and temporal variability of marine autotrophic abundance, expressed as chlorophyll concentration, is monitored from space and used to delineate the surface signature of marine ecosystem zones with distinct optical characteristics. An objective zoning method is presented and applied to satellite-derived Chlorophyll a (Chl-a) data from the northern Arabian Sea (50°–75° E and 15°–30° N) during the winter months (November–March). Principal Component Analysis (PCA) and Cluster Analysis (CA) were used to statistically delineate the Chl-a into zones with similar surface distribution patterns and temporal variability. The PCA identifies principal components of variability and the CA splits these into zones based on similar characteristics. Based on the temporal variability of Chl-a pattern within the study area, the statistical clustering revealed six distinct ecological zones. The obtained zones are related to the Longhurst provinces to evaluate how these compared to established ecological provinces. The Chl-a variability within each zone was then compared with the variability of oceanic and atmospheric properties viz. mixed-layer depth (MLD), wind speed, sea-surface temperature (SST), Photosynthetically Active Radiation (PAR), nitrate and Dust Optical Thickness (DOT) as an indication of atmospheric input of iron to the ocean. The analysis showed that in all zones, peak values of Chl-a coincided with low SST and deep MLD. Rate of decrease in SST and deepening of MLD are observed to trigger the intensity of the algae bloom events in the first four zones. Lagged cross-correlation analysis shows that peak Chl-a follows peak MLD and SST minima. The MLD time-lag is shorter than the SST lag by eight days, indicating that the cool surface conditions might have enhanced mixing, leading to increased primary production in the study area. An analysis of monthly climatological nitrate values showed increased concentrations associated with the deepening of the mixed-layer. The input of iron seems to be important in both the open ocean and coastal areas of the northern and north-western part of the Northern Arabian Sea, where the seasonal variability of the Chl-a pattern closely follows the variability of iron deposition.
    Print ISSN: 1810-6277
    Electronic ISSN: 1810-6285
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 7
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    Mesoscale Eddies Are Oases for Higher Trophic Marine Life (2012)
    Public Library of Science
    Details
    Publication Date: 2012-01-17
    Electronic ISSN: 1932-6203
    Topics: Medicine , Natural Sciences in General
    Published by Public Library of Science
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  • 8
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    Evaluation of Arctic Ocean surface salinities from the Soil Moisture and Ocean Salinity (SMOS) mission against a regional reanalysis and in situ data (2019)
    Copernicus
    Details
    Publication Date: 2019-09-06
    Description: Recently two gridded sea surface salinity (SSS) products that cover the Arctic Ocean have been derived from the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) mission: one developed by the Barcelona Expert Centre (BEC) and the other developed by the Ocean Salinity Expertise Center of the Centre Aval de Traitement des Données SMOS at IFREMER (The French Research Institute for Exploitation of the Sea) (CEC). The uncertainties of these two SSS products are quantified during the period of 2011–2013 against other SSS products: one data assimilative regional reanalysis; one data-driven reprocessing in the framework of the Copernicus Marine Environment Monitoring Services (CMEMS); two climatologies – the 2013 World Ocean Atlas (WOA) and the Polar science center Hydrographic Climatology (PHC); and in situ datasets, both assimilated and independent. The CMEMS reanalysis comes from the TOPAZ4 system, which assimilates a large set of ocean and sea-ice observations using an ensemble Kalman filter (EnKF). Another CMEMS product is the Multi-OBservations reprocessing (MOB), a multivariate objective analysis combining in situ data with satellite SSS. The monthly root mean squared deviations (RMSD) of both SMOS products, compared to the TOPAZ4 reanalysis, reach 1.5 psu in the Arctic summer, while in the winter months the BEC SSS is closer to TOPAZ4 with a deviation of 0.5 psu. The comparison of CEC satellite SSS against in situ data shows Atlantic Water that is too fresh in the Barents Sea, the Nordic Seas, and in the northern North Atlantic Ocean, consistent with the abnormally fresh deviations from TOPAZ4. When compared to independent in situ data in the Beaufort Sea, the BEC product shows the smallest bias (
    Print ISSN: 1812-0784
    Electronic ISSN: 1812-0792
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 9
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    Risk of oil contamination of fish eggs and larvae under different oceanic and weather conditions (2019)
    Oxford University Press
    Details
    Publication Date: 2019-03-21
    Description: An oil drift model is applied to determine the spread of oil spills from different locations along ship lanes off southern Norway every month for 20 years. These results are combined with results from an egg- and larvae drift model for Atlantic cod (Gadus morhua) to determine their risk of being impacted by oil. The number of eggs and larvae exposed to oil contamination is connected to environmental conditions. The highest risk of overlap between an oil spill and cod in early life stages occurs during March and April when the eggs and larvae concentrations are highest. Spills off the west coast pose a greater risk because of the ship lanes’ proximity to the spawning grounds, but there is large interannual variability. For some spill locations the interannual variability can be explained by variability in wind and ocean currents. Simultaneously occurring onshore transports lead to a high-risk situation because both oil and larvae are concentrated towards the coast. This study demonstrates how results from oil drift and biological models can be combined to estimate the risks of oil contamination for marine organisms, based on the location and timing of the oil spill, weather/ocean conditions, and knowledge of the organisms’ life cycle.
    Print ISSN: 1054-3139
    Electronic ISSN: 1095-9289
    Topics: Biology , Geosciences , Physics
    Published by Oxford University Press on behalf of International Council for the Exploration of the Sea.
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  • 10
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    Hurricane-driven alteration in plankton community size structure in the Gulf of Mexico: A modeling study (2009)
    American Geophysical Union
    Details
    Publication Date: 2009-04-03
    Print ISSN: 0094-8276
    Electronic ISSN: 1944-8007
    Topics: Geosciences , Physics
    Published by American Geophysical Union
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