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  • 11
    Publication Date: 2023-03-01
    Keywords: ALOHA2000-07-26; ALOHA2000-11-30; ALOHA2001-03-21; ALOHA2001-06-14; Calculated after Luo et al. (2012); Comment; Date/Time of event; DEPTH, water; Event label; Hawaiian Islands, North Central Pacific; Latitude of event; Longitude of event; MAREDAT_Diazotrophs_Collection; Nitrate; Nitrogen Fixation (C2H2 Reduction); Nitrogen fixation rate, total; Nitrogen fixation rate, whole seawater; Phosphate; Salinity; Temperature, water; Unicellular cyanobacteria, nitrogen fixation rate
    Type: Dataset
    Format: text/tab-separated-values, 170 data points
    Location Call Number Expected Availability
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  • 12
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Luo, Yawei; Doney, Scott C; Anderson, L A; Benavides, Mar; Berman-Frank, I; Bode, Antonio; Bonnet, S; Boström, Kjärstin H; Böttjer, D; Capone, D G; Carpenter, E J; Chen, Yaw-Lin; Church, Matthew J; Dore, John E; Falcón, Luisa I; Fernández, A; Foster, R A; Furuya, Ken; Gomez, Fernando; Gundersen, Kjell; Hynes, Annette M; Karl, David Michael; Kitajima, Satoshi; Langlois, Rebecca; LaRoche, Julie; Letelier, Ricardo M; Marañón, Emilio; McGillicuddy Jr, Dennis J; Moisander, Pia H; Moore, C Mark; Mouriño-Carballido, Beatriz; Mulholland, Margaret R; Needoba, Joseph A; Orcutt, Karen M; Poulton, Alex J; Rahav, Eyal; Raimbault, Patrick; Rees, Andrew; Riemann, Lasse; Shiozaki, Takuhei; Subramaniam, Ajit; Tyrrell, Toby; Turk-Kubo, Kendra A; Varela, Manuel; Villareal, Tracy A; Webb, Eric A; White, Angelicque E; Wu, Jingfeng; Zehr, Jonathan P (2012): Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates. Earth System Science Data, 4, 47-73, https://doi.org/10.5194/essd-4-47-2012
    Publication Date: 2023-03-27
    Description: The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. This is a gridded data product about diazotrophic organisms . There are 6 variables. Each variable is gridded on a dimension of 360 (longitude) * 180 (latitude) * 33 (depth) * 12 (month). The first group of 3 variables are: (1) number of biomass observations, (2) biomass, and (3) special nifH-gene-based biomass. The second group of 3 variables is same as the first group except that it only grids non-zero data. We have constructed a database on diazotrophic organisms in the global pelagic upper ocean by compiling more than 11,000 direct field measurements including 3 sub-databases: (1) nitrogen fixation rates, (2) cyanobacterial diazotroph abundances from cell counts and (3) cyanobacterial diazotroph abundances from qPCR assays targeting nifH genes. Biomass conversion factors are estimated based on cell sizes to convert abundance data to diazotrophic biomass. Data are assigned to 3 groups including Trichodesmium, unicellular diazotrophic cyanobacteria (group A, B and C when applicable) and heterocystous cyanobacteria (Richelia and Calothrix). Total nitrogen fixation rates and diazotrophic biomass are calculated by summing the values from all the groups. Some of nitrogen fixation rates are whole seawater measurements and are used as total nitrogen fixation rates. Both volumetric and depth-integrated values were reported. Depth-integrated values are also calculated for those vertical profiles with values at 3 or more depths.
    Keywords: MAREMIP; MARine Ecosystem Model Intercomparison Project
    Type: Dataset
    Format: application/zip, 1.7 MBytes
    Location Call Number Expected Availability
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  • 13
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Luo, Yawei; Doney, Scott C; Anderson, L A; Benavides, Mar; Berman-Frank, I; Bode, Antonio; Bonnet, S; Boström, Kjärstin H; Böttjer, D; Capone, D G; Carpenter, E J; Chen, Yaw-Lin; Church, Matthew J; Dore, John E; Falcón, Luisa I; Fernández, A; Foster, R A; Furuya, Ken; Gomez, Fernando; Gundersen, Kjell; Hynes, Annette M; Karl, David Michael; Kitajima, Satoshi; Langlois, Rebecca; LaRoche, Julie; Letelier, Ricardo M; Marañón, Emilio; McGillicuddy Jr, Dennis J; Moisander, Pia H; Moore, C Mark; Mouriño-Carballido, Beatriz; Mulholland, Margaret R; Needoba, Joseph A; Orcutt, Karen M; Poulton, Alex J; Rahav, Eyal; Raimbault, Patrick; Rees, Andrew; Riemann, Lasse; Shiozaki, Takuhei; Subramaniam, Ajit; Tyrrell, Toby; Turk-Kubo, Kendra A; Varela, Manuel; Villareal, Tracy A; Webb, Eric A; White, Angelicque E; Wu, Jingfeng; Zehr, Jonathan P (2012): Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates. Earth System Science Data, 4, 47-73, https://doi.org/10.5194/essd-4-47-2012
    Publication Date: 2023-12-09
    Description: The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present data set presents depth integrated values of diazotrophs abundance and biomass, computed from a collection of source data sets.
    Keywords: 33KB20020923; 33RR20030714; A2/19921126; A2/1992-11-27; AMT8/1999-05-05; AMT8/1999-05-06; AMT8/1999-05-07; AMT8/1999-05-08; AMT8/1999-05-10; AMT8/1999-05-12; AMT8/1999-05-13; AMT8/1999-05-18; AMT8/1999-05-20; AMT8/1999-05-21; AMT8/1999-05-23; AMT8/1999-05-25; AMT8/1999-05-26; AMT8/1999-05-28; AMT8/1999-05-29; AT19641122; AT19641123; AT19641202; AT19641203; AT19641208; Atlantic; Barbados; Barbados_1974-07-09_1; Barbados_1974-07-16_1; Barbados_1974-07-23_1; Barbados_1974-07-28_1; Barbados_1974-08-07_1; Barbados_1974-08-11_1; Barbados_1974-08-21_1; Barbados_1974-08-27_1; Barbados_1974-09-03_1; Barbados_1974-09-10_1; Barbados_1974-09-17_1; Barbados_1974-09-24_1; Barbados_1974-10-03_1; Barbados_1974-10-08_1; Barbados_1974-10-15_1; Barbados_1974-10-22_1; Barbados_1974-10-29_1; Barbados_1974-11-05_1; Barbados_1974-11-12_1; Barbados_1974-11-19_1; Barbados_1974-11-29_1; Barbados_1974-12-03_1; Barbados_1974-12-10_1; Barbados_1974-12-17_1; Barbados_1974-12-23_1; Barbados_1974-12-30_1; Barbados_1975-01-07_1; Barbados_1975-01-14_1; Barbados_1975-01-21_1; Barbados_1975-01-31_1; Barbados_1975-02-04_1; Barbados_1975-02-11_1; Barbados_1975-02-15_1; Barbados_1975-03-05_1; Barbados_1975-03-18_1; Barbados_1975-04-01_1; Barbados_1975-04-18_1; Barbados_1975-04-29_1; Barbados_1975-05-13_1; Barbados_1975-05-21_1; Barbados_1975-05-27_1; Barbados_1975-06-10_1; Barbados_1975-06-24_1; Barbados_1975-07-08_1; Barbados_1975-08-05_1; Barbados_1975-08-25_1; Barbados_1975-10-15_1; Barbados_1975-11-17_1; Barbados_1975-12-10_1; Barbados_1976-01-02_1; Barbados_1976-01-19_1; Barbados_1976-02-10_1; Barbados_1976-03-12_1; Barbados_1976-04-15_1; Barbados_1976-05-14_1; BATS1995-05-15; BATS1996-10-10; Bermuda, Atlantic Ocean; Bottle, Niskin; CAIBEX-I; CAIBEX-I_2; CAIBEX-I_3; CAIBEX-I_5; CAIBEX-I_6; CAIBEX-II; CAIBEX-II_01; CAIBEX-II_02; CAIBEX-II_03; CAIBEX-II_04; CAIBEX-II_05; CAIBEX-II_06; CAIBEX-II_07; CAIBEX-II_08; CAIBOX; CAIBOX_01; CAIBOX_02; CAIBOX_03; CAIBOX_04; CAIBOX_05; CAIBOX_06; CAIBOX_07; CAIBOX_08; CAIBOX_09; CAIBOX_10; CAIBOX_11; CAIBOX_12; CAIBOX_13; CAIBOX_14; CAIBOX_15; CAIBOX_16; CAIBOX_17; Calculated; Calothrix, associated species; Calothrix, carbon per cell; Calothrix abundance, cells; China Sea; Chlorophyll total, areal concentration; CTD, Seabird; CTD/Rosette; CTD-R; CTD-RO; Date/Time of event; Depth, bottom/max; Depth, top/min; DEPTH, water; Diazotrophs, total biomass as carbon; East China Sea; ECS1993-11-15_1; ECS1993-11-15_2; ECS1993-11-15_3; ECS1993-11-15_4; ECS1993-11-15_5; ECS1993-11-15_6; ECS1994-03-15_1; ECS1994-03-15_2; ECS1994-03-15_3; ECS1994-03-15_4; ECS1994-03-15_5; ECS1994-05-05_1; ECS1994-05-05_2; ECS1994-07-05_1; ECS1994-07-05_2; ECS1994-07-05_3; ECS1994-07-05_4; ECS1995-03-28_1; ECS1995-03-28_2; ECS1995-04-17_1; ECS1995-04-17_2; ECS1995-04-17_3; ECS1995-04-17_4; ECS1995-04-17_5; ECS1995-10-01_1; ECS1995-10-01_10; ECS1995-10-01_11; ECS1995-10-01_12; ECS1995-10-01_13; ECS1995-10-01_2; ECS1995-10-01_3; ECS1995-10-01_4; ECS1995-10-01_5; ECS1995-10-01_6; ECS1995-10-01_7; ECS1995-10-01_8; ECS1995-10-01_9; ECS1996-01-04; ECS1996-04-26_1; ECS1996-04-26_2; ECS1996-04-26_3; ECS1996-04-26_4; ECS1996-04-26_5; ECS1996-04-26_6; ECS1996-04-26_7; ECS1996-04-26_8; ECS1996-04-26_9; Event label; GOFLO; Go-Flo bottles; Gomez2004-10-26; Gomez2004-10-30; Gomez2004-11-03; Gomez2004-11-07; Gomez2004-11-11; Gomez2004-11-15; Gomez2004-11-19; Gomez2004-11-23; Gomez2004-11-27; Gomez2004-12-01; Gomez2004-12-05; Gomez2004-12-09; HakuhoMaru2002-12-07; HakuhoMaru2002-12-09; HakuhoMaru2002-12-11; HakuhoMaru2002-12-13; HakuhoMaru2002-12-15; HakuhoMaru2002-12-17; HakuhoMaru2002-12-18; Heterocyst, biomass; Indian Ocean; Iron; Latitude of event; Longitude of event; MAREMIP; MARine Ecosystem Model Intercomparison Project; Measured at sea surface; Meville2002-06-24; Meville2002-06-26; Meville2002-06-28; Meville2002-06-30; Meville2002-07-02; Meville2002-07-03; Meville2002-07-04; Meville2002-07-05; Meville2002-07-06; Meville2002-07-07; Meville2002-07-08; Meville2002-07-11; Meville2002-07-12; Mirai2003-01-15; Mirai2003-01-17; Mirai2003-01-18; Mirai2003-01-20; Mirai2003-01-21; Mirai2003-01-23; Mirai2003-01-24; Mirai2003-01-26; Mirai2003-01-28; MP-6; MP-6_01; MP-6_02; MP-6_03; MP-6_04; MP-6_05; MP-6_06; MP-6_07; MP-6_08; MP-6_09; MP-6_10; MP-6_11; MP-6_12; MP-6_13; MP-6_14; MP-6_15; MP-6_16; MP-6_17; MP-6_19; MP-6_20; MP-6_21; MP-6_22; MP-6_23; MP-9; MP-9_01; MP-9_02; MP-9_03; MP-9_04; MP-9_05; MP-9_06; MP-9_08; MP-9_09; MP-9_10; MP-9_11; MP-9_12; MP-9_13; MP-9_14; MP-9_15; MP-9_16; MP-9_17; MP-9_18; MP-9_19; MP-9_20; MP-9_21; MP-9_22; MP-9_23; MP-9_24; MP-9_25; MP-9_27; MULT; Multiple investigations; MW19950822_21; NA1975-05-25; NA19750526; NA19750527; NA19750528; NA1975-05-29; NA19750530; NA19750531; NA1975-06-01; NA1975-06-02; NA1975-06-03; NA1975-06-04; NA1975-06-05; NA1975-06-06; NewHorizon2003-08-22; NewHorizon2003-08-25; NewHorizon2003-08-26; NewHorizon2003-08-27; NewHorizon2003-08-28; NewHorizon2003-08-30; NewHorizon2003-08-31; NewHorizon2003-09-01; NewHorizon2003-09-03; NewHorizon2003-09-04; NewHorizon2003-09-05; NewHorizon2003-09-07; NewHorizon2003-09-08; NewHorizon2003-09-09; NewHorizon2003-09-11; NewHorizon2003-09-12; NewHorizon2003-09-13; NewHorizon2003-09-14; NIS; Nitrate; North Atlantic; Northeast Atlantic; North Pacific; North Pacific Ocean; Northwest Pacific; NPO1969-08-28; NPO1969-09-01; NPO1969-09-05; NPO1969-09-09; NPO1969-09-11; NPO1969-09-14; NPO1969-09-17; NPO1969-09-19; NPO1969-09-23; NPO1969-09-27; NPO1969-10-01; NPO1969-10-05; NPO1969-10-10; NWP2002-10-21_1; NWP2002-10-21_2; NWP2002-10-21_3; NWP2002-10-21_4; NWP2002-10-21_5; NWP2004-02-11; NWP2004-02-22; NWP2004-05-05; NWP2004-06-26; NWP2004-06-30; NWP2004-07-04; NWP2004-08-07; NWP2004-11-06; NWP2005-03-31; NWP2005-04-22; NWP2005-04-23; NWP2005-04-24; NWP2005-04-25_1; NWP2005-04-25_2; NWP2005-04-26; NWP2005-04-27; NWP2005-04-28; NWP2005-04-29; NWP2005-04-30_1; NWP2005-04-30_2; NWP2005-05-01; NWP2005-08-10; NWP2005-08-15; NWP2005-11-10; NWP2005-12-26; NWP2006-07-03; NWP2006-10-21; NWP2006-12-20; NWP2006-12-25; NWP2007-01-15; OR-I/414_1; OR-I/414_2; OR-I/448; OR-II/034; OR-II/111_1; OR-II/111_2; OR-II/149_1; OR-II/149_2; Phosphate; Richelia, associated species; Richelia, carbon per cell; Richelia abundance, cells; Roger A. Revelle; RV Kilo Moana; Salinity; Sample comment; Sample method; Sargasso Sea; SargassoSea_1973-09-17; SargassoSea_1973-09-19; SargassoSea_1973-09-20; SargassoSea_1973-09-21; SargassoSea_1973-09-28; SargassoSea_1973-09-29; SargassoSea_1973-10-01; SargassoSea_1973-10-02; SargassoSea_1973-10-03; SargassoSea_1974-02-06; SargassoSea_1974-02-08; SargassoSea_1974-02-11; SargassoSea_1974-02-12; SargassoSea_1974-02-13; SargassoSea_1974-02-14; SargassoSea_1974-02-16; SargassoSea_1974-02-17; SargassoSea_1974-02-18; SargassoSea_1974-02-19; SargassoSea_1974-02-20; SargassoSea_1974-02-21; SargassoSea_1974-02-22; SargassoSea_1974-02-26; SargassoSea_1974-02-27; SargassoSea_1974-03-01; SargassoSea_1974-03-02; SargassoSea_1974-03-03; SargassoSea_1974-03-04; SargassoSea_1974-03-05; SargassoSea_1974-08-08; SargassoSea_1974-08-09; SargassoSea_1974-08-10_1; SargassoSea_1974-08-10_2; SargassoSea_1974-08-11; SargassoSea_1974-08-12; SargassoSea_1974-08-13; SargassoSea_1974-08-14; SargassoSea_1974-08-15; SargassoSea_1974-08-16; SargassoSea_1974-08-17; SargassoSea_1974-08-18; SargassoSea_1974-08-19; SargassoSea_1974-08-20; SargassoSea_1974-08-21; Sarmiento de Gamboa; SCS2000-07-04; SCS2000-07-08; SCS2000-07-12; SCS2000-10-05; SCS2000-10-06; SCS2000-10-07; SCS2000-10-08; SCS2000-10-09; SCS2000-10-10; SCS2000-10-11; SCS2000-10-12; SCS2001-03-21; SCS2001-03-22; SCS2001-03-23; SCS2001-03-24; SCS2001-03-25; SCS2001-03-26; SCS2001-03-27; SCS2001-03-28; SCS2001-03-29; SCS2001-03-30; SCS2001-06-28; SCS2001-06-30; SCS2001-07-02; SCS2001-07-04; SCS2001-07-06; SCS2001-10-23; SCS2001-10-25; SCS2001-10-27; SCS2001-10-29; SCS2001-10-31; SCS2002-03-04; SCS2002-03-05; SCS2002-03-06; SCS2002-03-07; SCS2002-03-08; SCS2002-03-09; SCS2002-03-10;
    Type: Dataset
    Format: text/tab-separated-values, 8546 data points
    Location Call Number Expected Availability
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  • 14
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Luo, Yawei; Doney, Scott C; Anderson, L A; Benavides, Mar; Berman-Frank, I; Bode, Antonio; Bonnet, S; Boström, Kjärstin H; Böttjer, D; Capone, D G; Carpenter, E J; Chen, Yaw-Lin; Church, Matthew J; Dore, John E; Falcón, Luisa I; Fernández, A; Foster, R A; Furuya, Ken; Gomez, Fernando; Gundersen, Kjell; Hynes, Annette M; Karl, David Michael; Kitajima, Satoshi; Langlois, Rebecca; LaRoche, Julie; Letelier, Ricardo M; Marañón, Emilio; McGillicuddy Jr, Dennis J; Moisander, Pia H; Moore, C Mark; Mouriño-Carballido, Beatriz; Mulholland, Margaret R; Needoba, Joseph A; Orcutt, Karen M; Poulton, Alex J; Rahav, Eyal; Raimbault, Patrick; Rees, Andrew; Riemann, Lasse; Shiozaki, Takuhei; Subramaniam, Ajit; Tyrrell, Toby; Turk-Kubo, Kendra A; Varela, Manuel; Villareal, Tracy A; Webb, Eric A; White, Angelicque E; Wu, Jingfeng; Zehr, Jonathan P (2012): Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates. Earth System Science Data, 4, 47-73, https://doi.org/10.5194/essd-4-47-2012
    Publication Date: 2023-12-18
    Description: The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present data set presents depth integrated values of diazotrophs nitrogen fixation rates, computed from a collection of source data sets.
    Keywords: 33KB20020923; 33RR20030714; Alis; ALOHA2000-07-26; ALOHA2000-11-30; ALOHA2001-03-21; ALOHA2001-06-14; ALOHA2004-11-28; ALOHA2005-02-02; ALOHA2005-03-05; ALOHA2005-06-15; ALOHA2005-07-16; ALOHA2005-08-14; ALOHA2005-09-09; ALOHA2005-10-08; ALOHA2005-11-16; ALOHA2005-12-13; ALOHA2006-01-25; ALOHA2006-02-15; ALOHA2006-03-10; ALOHA2006-04-01; ALOHA2006-05-26; ALOHA2006-06-13; ALOHA2006-07-12; ALOHA2006-08-08; ALOHA2006-09-15; ALOHA2006-10-19; ALOHA2006-11-08; ALOHA2006-12-09; ALOHA2007-02-06; ALOHA2007-03-20; ALOHA2007-05-03; ALOHA2007-06-09; ALOHA2007-07-07; ALOHA2007-08-02; ALOHA2007-09-02; ALOHA2007-12-20; ALOHA2008-01-28; ALOHA2008-02-23; ALOHA2008-05-27; ALOHA2008-06-25; ALOHA2008-07-26; ALOHA2008-08-17; ALOHA2008-10-11; ALOHA2008-12-01; ALOHA2009-01-21; ALOHA2009-02-18; ALOHA2009-04-29; ALOHA2009-05-28; ALOHA2009-07-04; ALOHA2009-07-25; ALOHA2009-09-26; ALOHA2009-11-05; ALOHA2010-04-08; ALOHA2010-05-20; ALOHA2010-06-10; ALOHA2010-07-10; ALOHA2010-08-09; ALOHA2010-09-05; ALOHA2010-10-05; AMT17/01; AMT17/02; AMT17/03; AMT17/04; AMT17/05; AMT17/06; AMT17/07; AMT17/08; AMT17/09; AMT17/10; Arabian Sea; AT19641122; AT19641123; AT19641202; AT19641203; Atalante20080627; Atalante20080628; Atalante20080704; Atalante20080705; Atalante20080709/1; Atalante20080710; Atalante20080712; Atalante20080713; Atalante20080714; Atlantic; BIOSOPE_EGY; BIOSOPE_GYR; BIOSOPE_HLNC; BIOSOPE_MAR; BIOSOPE_UPW; BIOSOPE04-10-28; BIOSOPE04-10-30; BIOSOPE04-11-03; BIOSOPE04-11-04; BIOSOPE04-11-06; BIOSOPE04-11-07; BIOSOPE04-11-08; BIOSOPE04-11-10; BIOSOPE04-11-12; BIOSOPE04-11-20; BIOSOPE04-11-21; BIOSOPE04-11-23; BIOSOPE04-11-24; BIOSOPE04-11-28; BIOSOPE04-12-01; BIOSOPE04-12-02; BIOSOPE04-12-03; BIOSOPE04-12-04; BIOSOPE04-12-05; Bottle, Niskin; CAIBEX-I; CAIBEX-I_1; CAIBEX-I_2; CAIBEX-I_3; CAIBEX-I_4; CAIBEX-I_5; CAIBEX-I_6; CAIBEX-I_7; CAIBEX-II; CAIBEX-II_01; CAIBEX-II_02; CAIBEX-II_03; CAIBEX-II_04; CAIBEX-II_05; CAIBEX-II_06; CAIBEX-II_07; CAIBEX-II_08; CAIBOX; CAIBOX_01; CAIBOX_02; CAIBOX_03; CAIBOX_04; CAIBOX_05; CAIBOX_06; CAIBOX_07; CAIBOX_08; CAIBOX_09; CAIBOX_10; CAIBOX_11; CAIBOX_12; CAIBOX_13; CAIBOX_14; CAIBOX_15; CAIBOX_16; CAIBOX_17; Calculated; Cape Verde; CATO-I/9; Chlorophyll total, areal concentration; CLIMAX_VII/1973-08-18; CLIMAX_VII/1973-08-27; CLIMAX_VII/1973-08-29; CLIMAX_VII/1973-08-31; CLIMAX_VII/1973-09-02; CLIMAX_VII/1973-09-04; CLIMAX_VII/1973-09-07; CLIMAX_VII/1973-09-09; Cook25_7; CTD/Rosette; CTD-RO; D325_Stn-A-01; D325_Stn-C-01; D325_Stn-D-07; D325_Stn-E-01; D325_Stn-F-07; Date/Time of event; Depth, bottom/max; Depth, top/min; DEPTH, water; Diapalis-3; Diapalis-3_1; Diapalis-3_2; Diapalis-3_3; Diapalis-3_4; Diapalis-4; Diapalis-4_1; Diapalis-4_2; Diapalis-4_3; Diapalis-4_4; Diapalis-5; Diapalis-5_1; Diapalis-5_3; Diapalis-5_4; Diapalis-5_5; Diapalis-6; Diapalis-6_1; Diapalis-6_2; Diapalis-6_3; Diapalis-6_4; Diapalis-6_5; Diapalis-6_6; Diapalis-7; Diapalis-7_1; Diapalis-7_2; Diapalis-7_3; Diapalis-7_4; Diapalis-7_6; Diapalis-7_7; Diapalis-9; Diapalis-9_1; Diapalis-9_2; Diapalis-9_3; Diapalis-9_4; Diapalis-9_5; DIAPAZON_Diapalis-3; DIAPAZON_Diapalis-4; DIAPAZON_Diapalis-5; DIAPAZON_Diapalis-6; DIAPAZON_Diapalis-7; DIAPAZON_Diapalis-9; DYFAMED2003-03-26; DYFAMED2003-03-30; DYFAMED2004-01-25; DYFAMED2004-02-24; DYFAMED2004-04-25; DYFAMED2004-05-27; DYFAMED2004-07-01; DYFAMED2004-07-31; DYFAMED2004-08-31; DYFAMED2004-09-18; DYFAMED2004-10-14; Equatorial Pacific; Event label; GoA_StnA2010-03-18; GOFLO; Go-Flo bottles; Gulf of Aqaba; Gundersen_1; Gundersen_2; Hawaiian Islands, North Central Pacific; Hesperides_03a; Hesperides_05a; Hesperides_06a; Hesperides_07a; Hesperides_08a; Hesperides_12a; Hesperides_13a; Hesperides_14a; Hesperides_17a; Hesperides_18a; Hesperides_19a; Hesperides_20a; Hesperides_21a; Hesperides_23a; Hesperides_24a; Hesperides_25a; Hesperides_26a; Hesperides_27a; Hesperides_28a; Hesperides_29a; Hesperides_30a; Hesperides_31a; Hesperides_32a; Hesperides_33a; Hesperides_34a; Hesperides_36a; Hesperides_37a; Hesperides_38a; Hesperides_39a; Hesperides_40a; Hesperides_41a; Hesperides_42a; Heterocyst, nitrogen fixation rate; Iron; KiloMoana20060609/1; KiloMoana20060609/2; KiloMoana20060821; KiloMoana20060826; KiloMoana20060922; KiloMoana20060923; KiloMoana20060925; KiloMoana20060927; KiloMoana20060930; KiloMoana20061009; Latitude of event; LB2008-09-12; LB2008-09-16; Levantine Basin; Ligurian Sea, Mediterranean; Longitude of event; MAREMIP; MARine Ecosystem Model Intercomparison Project; Measured at sea surface; Mediterranean Sea; Mooring (long time); MOORY; MP-6; MP-6_01; MP-6_02; MP-6_03; MP-6_04; MP-6_05; MP-6_06; MP-6_07; MP-6_08; MP-6_09; MP-6_10; MP-6_11; MP-6_12; MP-6_13; MP-6_14; MP-6_15; MP-6_16; MP-6_18; MP-6_19; MP-6_20; MP-6_21; MP-6_22; MP-6_23; MP-9; MP-9_01; MP-9_02; MP-9_03; MP-9_04; MP-9_05; MP-9_06; MP-9_07; MP-9_09; MP-9_10; MP-9_11; MP-9_12; MP-9_13; MP-9_14; MP-9_15; MP-9_16; MP-9_17; MP-9_18; MP-9_19; MP-9_20; MP-9_21; MP-9_22; MP-9_23; MP-9_24; MP-9_25; MP-9_26; MP-9_27; MR07-01/02; MR07-01/03; MR07-01/04; MR07-01/05; MR07-01/06; MR07-01/07; MR07-01/08; MR07-01/09; MR07-01/10; MR07-01/11; Mulholland_2006-07-01; Mulholland_2006-07-02; Mulholland_2006-07-03; Mulholland_2006-07-04; Mulholland_2006-07-05; Mulholland_2006-07-06; Mulholland_2006-07-07; Mulholland_2006-07-08; Mulholland_2006-07-09; Mulholland_2006-07-10; Mulholland_2006-07-11; Mulholland_2006-07-12; Mulholland_2006-07-13; Mulholland_2006-07-14; Mulholland_2006-10-25; Mulholland_2006-10-26; Mulholland_2006-10-27; Mulholland_2006-10-28; Mulholland_2006-10-29; Mulholland_2006-10-30; Mulholland_2006-10-31; Mulholland_2006-11-01; Mulholland_2006-11-02; Mulholland_2006-11-03; Mulholland_2006-11-04; Mulholland_2006-11-05; Mulholland_2006-11-06; Mulholland_2006-11-07; Mulholland_2006-11-08; Mulholland_2006-11-09; Mulholland_2008-05-03_1; Mulholland_2008-05-04_1; Mulholland_2008-05-05_1; Mulholland_2008-05-05_2; Mulholland_2008-05-06_1; Mulholland_2008-05-07_1; Mulholland_2008-05-10_1; Mulholland_2008-05-11_1; Mulholland_2008-05-12_1; Mulholland_2008-05-13_1; Mulholland_2008-05-14_1; Mulholland_2008-05-15_1; Mulholland_2008-05-16_1; Mulholland_2008-05-17_1; Mulholland_2008-05-18_1; Mulholland_2008-05-19_1; Mulholland_2008-05-20_1; Mulholland_2008-05-21_1; Mulholland_2008-05-22_1; Mulholland_2008-05-24_1; Mulholland_2009-08-17_1; Mulholland_2009-08-18_1; Mulholland_2009-08-18_2; Mulholland_2009-08-19_1; Mulholland_2009-08-19_2; Mulholland_2009-08-20_1; Mulholland_2009-08-20_3; Mulholland_2009-08-21_1; Mulholland_2009-08-21_3; Mulholland_2009-08-22_1; Mulholland_2009-08-22_3; Mulholland_2009-08-23; Mulholland_2009-08-24_1; Mulholland_2009-08-24_3; Mulholland_2009-08-25_3; Mulholland_2009-08-26_3; Mulholland_2009-08-27_2; Mulholland_2009-08-27_3; Mulholland_2009-11-04_2; Mulholland_2009-11-05_1; Mulholland_2009-11-08_1; Mulholland_2009-11-09_3; Mulholland_2009-11-10_3; Mulholland_2009-11-11_1; Mulholland_2009-11-18_1; Mulholland_2009-11-18_3; NA19750526; NA19750527; NA19750528; NA19750530; NA19750531; NIS; Nitrate; Nitrogen fixation rate, integrated per day; Nitrogen fixation rate, whole seawater; North Atlantic; Northeast Atlantic; North Pacific; Pacific; Phosphate; PUMP; Rahav_2009-07-13_1; Rahav_2009-07-14_1; Rahav_2009-07-16_1; Rahav_2009-12-07_1; Rees2004-03-05/01; Rees2004-04-05; Rees2004-05-16; Rees2004-05-19/01; Rees2004-05-21/01; Rees2004-07-05/01; Rees2004-09-05/01; Roger A. Revelle; RV Kilo Moana; Salinity; Sample comment; Sample method; Sargasso Sea; SargassoSea_1973-09-17; SargassoSea_1973-09-19; SargassoSea_1973-09-20; SargassoSea_1973-09-21; SargassoSea_1973-09-28; SargassoSea_1973-09-29; SargassoSea_1973-10-01; SargassoSea_1973-10-02; SargassoSea_1973-10-03; SargassoSea_1974-02-06; SargassoSea_1974-02-08; SargassoSea_1974-02-11; SargassoSea_1974-02-13; SargassoSea_1974-02-14; SargassoSea_1974-02-16; SargassoSea_1974-02-17; SargassoSea_1974-02-18; SargassoSea_1974-02-19; SargassoSea_1974-02-20; SargassoSea_1974-02-21; SargassoSea_1974-02-26;
    Type: Dataset
    Format: text/tab-separated-values, 5926 data points
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  • 15
    Publication Date: 2024-03-30
    Description: The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present collection presents the original data sets used to compile Global distributions of diazotrophs abundance, biomass and nitrogen fixation rates
    Keywords: MAREDAT_Diazotrophs_Collection; MAREMIP; MARine Ecosystem Model Intercomparison Project
    Type: Dataset
    Format: application/zip, 94 datasets
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  • 16
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Luo, Yawei; Doney, Scott C; Anderson, L A; Benavides, Mar; Berman-Frank, I; Bode, Antonio; Bonnet, S; Boström, Kjärstin H; Böttjer, D; Capone, D G; Carpenter, E J; Chen, Yaw-Lin; Church, Matthew J; Dore, John E; Falcón, Luisa I; Fernández, A; Foster, R A; Furuya, Ken; Gomez, Fernando; Gundersen, Kjell; Hynes, Annette M; Karl, David Michael; Kitajima, Satoshi; Langlois, Rebecca; LaRoche, Julie; Letelier, Ricardo M; Marañón, Emilio; McGillicuddy Jr, Dennis J; Moisander, Pia H; Moore, C Mark; Mouriño-Carballido, Beatriz; Mulholland, Margaret R; Needoba, Joseph A; Orcutt, Karen M; Poulton, Alex J; Rahav, Eyal; Raimbault, Patrick; Rees, Andrew; Riemann, Lasse; Shiozaki, Takuhei; Subramaniam, Ajit; Tyrrell, Toby; Turk-Kubo, Kendra A; Varela, Manuel; Villareal, Tracy A; Webb, Eric A; White, Angelicque E; Wu, Jingfeng; Zehr, Jonathan P (2012): Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates. Earth System Science Data, 4, 47-73, https://doi.org/10.5194/essd-4-47-2012
    Publication Date: 2024-03-30
    Description: The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present data set presents depth integrated values of diazotrophs Gamma-A nifH genes abundance, computed from a collection of source data sets.
    Keywords: 06MT60_5; 06MT60_5/158; 06MT60_5/159; 06MT60_5/161; 06MT60_5/173; 06MT60_5/180; 06MT60_5/181; 06MT60_5/184; 06MT60_5/187; 06MT60_5/188; 06MT60_5/189; 06MT60_5/190; 06MT60_5/192; 06MT60_5/199; ALOHA2002-12-13; ALOHA2002-12-14; ALOHA2005-07-16; ALOHA2005-07-26_01; ALOHA2005-07-26_02; ALOHA2005-07-26_03; ALOHA2005-07-26_04; ALOHA2005-07-26_05; ALOHA2005-07-26_06; ALOHA2005-07-26_07; ALOHA2005-07-26_08; ALOHA2005-08-13; Arabian Sea; Bottle, Niskin; Calculated; Calothrix, abundance expressed in number of nifH gene copies; Calothrix, associated species; Calothrix, biological trait, ratio expressed in mass of carbon per amount of nifH gene copies; CD132; CD132 _AMBITION; CD132_AMBITION/1; CD132_AMBITION/2; CD132_AMBITION/3; CD132_AMBITION/4; CD132/1; CD132/2; CD132/3; CD132/4; Charles Darwin; China Sea; Chlorophyll a; CTD, Seabird; CTD/Rosette; CTD-R; CTD-RO; Date/Time of event; Depth, bottom/max; Depth, top/min; DEPTH, water; Diazotrophs, total biomass as carbon; Eastern equatorial Atlantic; EEA2007-06-14_Stn8; EEA2007-06-15_Stn9; Event label; Foster2008-07-12; Foster2008-07-14; Foster2008-07-15; Foster2008-07-18; Hawaiian Islands, North Central Pacific; Heterocyst, biomass; In situ pump; Iron; ISP; Latitude of event; Longitude of event; M55_30a; M55_36a; M55_38a; M55_44a; M55_45; M55_48a; M55/1; M60/5; M60/5_158; M60/5_159; M60/5_161; M60/5_163a; M60/5_173; M60/5_180; M60/5_181; M60/5_184; M60/5_187; M60/5_188; M60/5_189; M60/5_190; M60/5_192; M60/5_199; MAREMIP; MARine Ecosystem Model Intercomparison Project; Measured at sea surface; Meteor (1986); NIS; Nitrate; North Atlantic sub-tropical gyre; North Pacific; Phosphate; Proteobacteria, abundance expressed in number of nifH gene copies; Richelia, abundance expressed in number of nifH gene copies; Richelia, associated species; Richelia, biological trait, ratio expressed in mass of carbon per amount of nifH gene copies; Salinity; Sample comment; Sample method; SCS2009-08-10; SCS2009-08-15; SCS2009-08-20; SCS2009-08-25; SO187/2; SO187/2_33-1; SO187/2_44-1; SO187/2_45-1-1a; SO187/2_45-4; SO187/2_46-1; SO187/2_48-1; SO187/2_53-1a; SO187/2_54-2; Sonne; South China Sea; South Pacific Ocean; SPO2003-03-17; SPO2003-03-18; SPO2003-03-19; SPO2003-03-20; SPO2003-03-21; SPO2003-03-22-1; SPO2003-03-22-2; SPO2003-03-24; SPO2003-03-25; SPO2003-03-28; SPO2003-03-29; SPO2003-03-30; SPO2003-03-31; SPO2003-04-02; SPO2003-04-03; SPO2003-04-05; SPO2003-04-06; SPO2003-04-07; SPO2003-04-08; SPO2003-04-09; SPO2003-04-10; SPO2003-04-12; SPO2003-04-13; SW2006-06-22; SW2006-06-23; SW2006-06-27; SW2006-06-28; SW2006-06-29; SW2006-06-30; SW2006-07-01; SW2006-07-03; SW2006-07-04; SW2006-07-06; SW2006-07-07; SW2006-07-12; SW2006-07-13; SW2006-07-14_1; SW2006-07-14_2; SW2006-07-15; SW2006-07-17; SW2006-07-19; SW2006-07-20; SW2006-07-21; Temperature, water; Trichodesmium, abundance expressed in number of nifH gene copies; Trichodesmium, biological trait, ratio expressed in mass of carbon per amount of nifH gene copies; Trichodesmium, biomass as carbon; Tropical Atlantic; Unicellular cyanobacteria, biomass; Unicellular cyanobacteria-A, abundance expressed in number of nifH gene copies; Unicellular cyanobacteria-B, abundance expressed in number of nifH gene copies; Unicellular cyanobacteria-B, biological trait, ratio expressed in mass of carbon per amount of nifH gene copies; Unicellular cyanobacteria-C, abundance expressed in number of nifH gene copies; Unicellular cyanobacteria-C, biological trait, ratio expressed in mass of carbon per amount of nifH gene copies; Uniform resource locator/link to source data file; VIETNAM; Water sample; WesternFlyer2005-10-25; Western tropical north Atlantic; WS; WTNA2003-04-24_01; WTNA2003-04-26; WTNA2003-05-01; WTNA2003-05-04; WTNA2003-05-11; WTNA2003-05-12; WTNA2003-05-13; WTNA2003-05-14; WTNA2003-05-18; WTNA2003-05-20
    Type: Dataset
    Format: text/tab-separated-values, 2032 data points
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  • 17
    Publication Date: 2021-06-29
    Description: The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 18
    Publication Date: 2022-10-26
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Priscu, J. C., Kalin, J., Winans, J., Campbell, T., Siegfried, M. R., Skidmore, M., Dore, J. E., Leventer, A., Harwood, D. M., Duling, D., Zook, R., Burnett, J., Gibson, D., Krula, E., Mironov, A., McManis, J., Roberts, G., Rosenheim, B. E., Christner, B. C., Kasic, K., Fricker, H. A., Lyons, W. B., Barker, J., Bowling, M., Collins, B., Davis, C., Gagnon, A., Gardner, C., Gustafson, C., Kim, O-S., Li, W., Michaud, A., Patterson, M. O., Tranter, M., Ryan Venturelli, R., Trista Vick-Majors, T., & Elsworth, C. Scientific access into Mercer Subglacial Lake: scientific objectives, drilling operations and initial observations. Annals of Glaciology, 62(85–86), (2021): 340–352, https://doi.org/10.1017/aog.2021.10.
    Description: The Subglacial Antarctic Lakes Scientific Access (SALSA) Project accessed Mercer Subglacial Lake using environmentally clean hot-water drilling to examine interactions among ice, water, sediment, rock, microbes and carbon reservoirs within the lake water column and underlying sediments. A ~0.4 m diameter borehole was melted through 1087 m of ice and maintained over ~10 days, allowing observation of ice properties and collection of water and sediment with various tools. Over this period, SALSA collected: 60 L of lake water and 10 L of deep borehole water; microbes 〉0.2 μm in diameter from in situ filtration of ~100 L of lake water; 10 multicores 0.32–0.49 m long; 1.0 and 1.76 m long gravity cores; three conductivity–temperature–depth profiles of borehole and lake water; five discrete depth current meter measurements in the lake and images of ice, the lake water–ice interface and lake sediments. Temperature and conductivity data showed the hydrodynamic character of water mixing between the borehole and lake after entry. Models simulating melting of the ~6 m thick basal accreted ice layer imply that debris fall-out through the ~15 m water column to the lake sediments from borehole melting had little effect on the stratigraphy of surficial sediment cores.
    Description: This material is based upon work supported by the US National Science Foundation, Section for Antarctic Sciences, Antarctic Integrated System Science program as part of the interdisciplinary (Subglacial Antarctic Lakes Scientific Access (SALSA): Integrated study of carbon cycling in hydrologically-active subglacial environments) project (NSF-OPP 1543537, 1543396, 1543405, 1543453 and 1543441). Ok-Sun Kim was funded by the Korean Polar Research Institute. We are particularly thankful to the SALSA traverse personnel for crucial technical and logistical support. The United States Antarctic Program enabled our fieldwork; the New York Air National Guard and Kenn Borek Air provided air support; UNAVCO provided geodetic instrument support. Hot water drilling activities, including repair and upgrade modifications of the WISSARD hot water drill system, for the SALSA project were supported by a subaward from the Ice Drilling Program of Dartmouth College (NSF-PLR 1327315) to the University of Nebraska-Lincoln. J. Lawrence assisted with manuscript preparation. Finally, we are grateful to C. Dean, the SALSA Project Manager, and R. Ricards, SALSA Project Coordinator at McMurdo Station, for their organizational skills, and B. Huber of Lamont-Doherty Earth Observatory for providing the SBE39 PT sensors and the Nortek Aquadopp current meter and assisting with interpretation of the data. B. Huber also provided helpful input on programing and calibrating the SBE19PlusV2 6112 CTD.
    Keywords: Antarctic glaciology ; Basal ice ; Biogeochemistry ; Glacial sedimentology ; Subglacial lakes
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 19
    Publication Date: 2023-12-22
    Description: Die klimapolitischen Ziele Deutschlands und der EU machen eine sehr schnelle und tiefgreifende Transformation sowohl der Energieversorgung als auch der energieverbrauchenden Sektoren notwendig. Diese Transformationsherausforderung betrifft nicht zuletzt die energieintensive Industrie in Deutschland, die vor grundlegenden technologischen Veränderungen wichtiger Produktionsprozesse steht. Die Herausforderungen für die Industrie werden durch die aktuelle Energiekrise weiter verschärft. Vor diesem Hintergrund stellt das hier vorgestellte Klimaschutzszenario "SCI4climate.NRW-Klimaneutralität" (S4C-KN), das im Rahmen des vom Land NRW finanzierten Forschungsprojekts "SCI4climate.NRW" entwickelt wurde, die möglichen künftigen Entwicklungen in der energieintensiven Industrie in den Mittelpunkt der Analyse. Das Szenario analysiert diese Entwicklungen im Kontext eines gesamtwirtschaftlichen Transformationspfads hin zu einem klimaneutralen Deutschland im Jahr 2045.
    Keywords: ddc:600
    Repository Name: Wuppertal Institut für Klima, Umwelt, Energie
    Language: German
    Type: report , doc-type:report
    Format: application/pdf
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  • 20
    Publication Date: 2021-06-11
    Description: Trace elements sustain biological productivity, yet the significance of trace element mobilization and export in subglacial runoff from ice sheets is poorly constrained at present. Here, we present size-fractionated (0.02, 0.22, and 0.45 µm) concentrations of trace elements in subglacial waters from the Greenland Ice Sheet (GrIS) and the Antarctic Ice Sheet (AIS). Concentrations of immobile trace elements (e.g., Al, Fe, Ti) far exceed global riverine and open ocean mean values and highlight the importance of subglacial aluminosilicate mineral weathering and lack of retention of these species in sediments. Concentrations are higher from the AIS than the GrIS, highlighting the geochemical consequences of prolonged water residence times and hydrological isolation that characterize the former. The enrichment of trace elements (e.g., Co, Fe, Mn, and Zn) in subglacial meltwaters compared with seawater and typical riverine systems, together with the likely sensitivity to future ice sheet melting, suggests that their export in glacial runoff is likely to be important for biological productivity. For example, our dissolved Fe concentration (20,900 nM) and associated flux values (1.4 Gmol y−1) from AIS to the Fe-deplete Southern Ocean exceed most previous estimates by an order of magnitude. The ultimate fate of these micronutrients will depend on the reactivity of the dominant colloidal size fraction (likely controlled by nanoparticulate Al and Fe oxyhydroxide minerals) and estuarine processing. We contend that ice sheets create highly geochemically reactive particulates in subglacial environments, which play a key role in trace elemental cycles, with potentially important consequences for global carbon cycling.
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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