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Dataset Title:  A compilation of dissolved noble gas and N2/Ar ratio measurements collected
from 1999-2016 in locations spanning the globe
  RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_743867)
Range: longitude = -159.9952 to 178.9985°E, latitude = -68.1081 to 78.9988°N, depth = 0.8 to 5840.5m
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  sequence {
    Byte _FillValue 127;
    Byte actual_range 1, 93;
    String description "a sequential numerical identifier for each cruise, unique to this database.";
    String ioos_category "Unknown";
    String long_name "Sequence";
    String units "unitless";
  }
  cruise_name {
    String description "a string consisting of: the EXPO number is listed first, followed by a colon, followed by colloquial cruise names, followed by a colon, followed by the ship name.";
    String ioos_category "Unknown";
    String long_name "Cruise Name";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range -68.1081, 78.9988;
    String axis "Y";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "the latitude of the station in degrees North.";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -159.9952, 178.9985;
    String axis "X";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "the longitude of the station in degrees East. Negative numbers indicate degrees West.";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  event {
    Int16 _FillValue 32767;
    Int16 actual_range 2, 332;
    String description "number of the event that the water samples were drawn from. Event is used when each cast in a cruise has its own unique number.";
    String ioos_category "Unknown";
    String long_name "Event";
    String units "unitless";
  }
  station {
    Int16 _FillValue 32767;
    Int16 actual_range 1, 154;
    String description "number of the station that the water samples were drawn from. Station is used when each station (location) in a cruise has its own unique number but multiple casts occurred at a given station number.";
    String ioos_category "Identifier";
    String long_name "Station";
    String units "unitless";
  }
  cast {
    Byte _FillValue 127;
    Byte actual_range 1, 18;
    String description "number of the cast at an individual station that the water samples were drawn from. Cast is used when multiple casts occurred at a given station number.";
    String ioos_category "Unknown";
    String long_name "Cast";
    String units "unitless";
  }
  niskin {
    Byte _FillValue 127;
    Byte actual_range 1, 36;
    String description "number of the niskin bottle or rosette position that the water samples were drawn from.";
    String ioos_category "Unknown";
    String long_name "Niskin";
    String units "unitless";
  }
  year {
    Int16 _FillValue 32767;
    Int16 actual_range 1999, 2016;
    String description "year; together the year, month, and day columns are the date that samples were collected.";
    String ioos_category "Time";
    String long_name "Year";
    String units "unitless";
  }
  month {
    Byte _FillValue 127;
    Byte actual_range 1, 12;
    String description "month; together the year, month, and day columns are the date that samples were collected.";
    String ioos_category "Time";
    String long_name "Month";
    String units "unitless";
  }
  day {
    Byte _FillValue 127;
    Byte actual_range 1, 31;
    String description "day; together the year, month, and day columns are the date that samples were collected.";
    String ioos_category "Time";
    String long_name "Day";
    String units "unitless";
  }
  press {
    Float32 _FillValue NaN;
    Float32 actual_range 0.8, 5958.6;
    String description "pressure in dbar";
    String ioos_category "Unknown";
    String long_name "Press";
    String units "decibars (dbar)";
  }
  CTDtemp {
    Float32 _FillValue NaN;
    Float32 actual_range -1.7671, 29.047;
    String description "in situ temperature measured by the CTD in degrees C on the ITS-90 Temperature Scale.";
    String ioos_category "Unknown";
    String long_name "CTDtemp";
    String units "degrees Celsius";
  }
  CTDsal {
    Float32 _FillValue NaN;
    Float32 actual_range 27.5322, 37.6311;
    String description "salinity measured by the CTD, expressed on the PSS-78 scale.";
    String ioos_category "Unknown";
    String long_name "CTDsal";
    String units "unitless";
  }
  analysis_lab {
    Byte _FillValue 127;
    Byte actual_range 1, 4;
    String description "a number indicating which lab the analyses were performed in. 1 = University of Victoria, 2 = Woods Hole Oceanographic Institution, 3 = Scripps Institution of Oceanography, 4 = University of Washington.";
    String ioos_category "Unknown";
    String long_name "Analysis Lab";
    String units "unitless";
  }
  secondary_analysis_lab {
    Byte _FillValue 127;
    Byte actual_range 4, 4;
    String description "for cruises where Ar concentration or N2/Ar ratio were measured in more than one lab, this number indicates which lab the analyses listed in the \"secondary\" columns were performed in. 1 = University of Victoria, 2 = Woods Hole Oceanographic Institution, 3 = Scripps Institution of Oceanography, 4 = University of Washington.";
    String ioos_category "Unknown";
    String long_name "Secondary Analysis Lab";
    String units "unitless";
  }
  He_conc {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0016028, 0.0020003;
    String description "dissolved He concentration in umol/kg. These concentration values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "He Conc";
    String units "micromoles per kilogram (umol/kg)";
  }
  He_conc2 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0016481, 0.0019137;
    String description "dissolved He concentration in umol/kg (duplicate value)";
    String ioos_category "Unknown";
    String long_name "He Conc2";
    String units "micromoles per kilogram (umol/kg)";
  }
  Ne_conc {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0064541, 0.0086643;
    String description "dissolved Ne concentration in umol/kg. These concentration values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Ne Conc";
    String units "micromoles per kilogram (umol/kg)";
  }
  Ne_conc2 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0065521, 0.0086946;
    String description "dissolved Ne concentration in umol/kg (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Ne Conc2";
    String units "micromoles per kilogram (umol/kg)";
  }
  Ar_conc {
    Float32 _FillValue NaN;
    Float32 actual_range 9.5, 18.73;
    String description "dissolved Ar concentration in umol/kg. These concentration values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Ar Conc";
    String units "micromoles per kilogram (umol/kg)";
  }
  Ar_conc2 {
    Float32 _FillValue NaN;
    Float32 actual_range 9.752, 18.775;
    String description "dissolved Ar concentration in umol/kg (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Ar Conc2";
    String units "micromoles per kilogram (umol/kg)";
  }
  Ar_conc_secondary {
    Float32 _FillValue NaN;
    Float32 actual_range 10.313, 17.308;
    String description "same as for Ar_conc but data is from independent samples collected from the same cruise and analyzed in a second laboratory.";
    String ioos_category "Unknown";
    String long_name "Ar Conc Secondary";
    String units "micromoles per kilogram (umol/kg)";
  }
  Ar_conc_secondary2 {
    Float32 _FillValue NaN;
    Float32 actual_range 10.32, 17.297;
    String description "same as for Ar_conc2 but data is from independent samples collected from the same cruise and analyzed in a second laboratory.";
    String ioos_category "Unknown";
    String long_name "Ar Conc Secondary2";
    String units "micromoles per kilogram (umol/kg)";
  }
  Kr_conc {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0020326, 0.0046696;
    String description "dissolved Kr concentration in umol/kg. These concentration values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Kr Conc";
    String units "micromoles per kilogram (umol/kg)";
  }
  Kr_conc2 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0020964, 0.0046796;
    String description "dissolved Kr concentration in umol/kg (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Kr Conc2";
    String units "micromoles per kilogram (umol/kg)";
  }
  Xe_conc {
    Float32 _FillValue NaN;
    Float32 actual_range 2.315e-4, 6.761e-4;
    String description "dissolved Xe concentration in umol/kg. These concentration values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Xe Conc";
    String units "micromoles per kilogram (umol/kg)";
  }
  Xe_conc2 {
    Float32 _FillValue NaN;
    Float32 actual_range 2.722e-4, 6.403e-4;
    String description "dissolved Xe concentration in umol/kg (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Xe Conc2";
    String units "micromoles per kilogram (umol/kg)";
  }
  Ne_Ar {
    Float32 _FillValue NaN;
    Float32 actual_range 4.626e-4, 6.5765e-4;
    String description "dissolved Ne/Ar ratio with no units. These ratio values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Ne Ar";
    String units "unitless";
  }
  Ne_Ar2 {
    Float32 _FillValue NaN;
    Float32 actual_range 4.6311e-4, 6.655e-4;
    String description "dissolved Ne/Ar ratio with no units (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Ne Ar2";
    String units "unitless";
  }
  Kr_Ar {
    Float32 _FillValue NaN;
    Float32 actual_range 2.1086e-4, 2.4932e-4;
    String description "dissolved Kr/Ar ratio with no units. These ratio values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Kr Ar";
    String units "unitless";
  }
  Kr_Ar2 {
    Float32 _FillValue NaN;
    Float32 actual_range 2.1116e-4, 2.4925e-4;
    String description "dissolved Kr/Ar ratio with no units (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Kr Ar2";
    String units "unitless";
  }
  N2_Ar {
    Float32 _FillValue NaN;
    Float32 actual_range 36.725, 38.651;
    String description "dissolved N2/Ar ratio with no units. These ratio values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "N2 Ar";
    String units "unitless";
  }
  N2_Ar2 {
    Float32 _FillValue NaN;
    Float32 actual_range 36.727, 38.683;
    String description "dissolved N2/Ar ratio with no units (duplicate value)";
    String ioos_category "Unknown";
    String long_name "N2 Ar2";
    String units "unitless";
  }
  N2_Ar_secondary {
    Float32 _FillValue NaN;
    Float32 actual_range 36.565, 38.274;
    String description "same as for N2_Ar but data is from independent samples collected from the same cruise and analyzed in a second laboratory.";
    String ioos_category "Unknown";
    String long_name "N2 Ar Secondary";
    String units "unitless";
  }
  N2_Ar_secondary2 {
    Float32 _FillValue NaN;
    Float32 actual_range 36.591, 38.234;
    String description "same as for N2_Ar2 but data is from independent samples collected from the same cruise and analyzed in a second laboratory.";
    String ioos_category "Unknown";
    String long_name "N2 Ar Secondary2";
    String units "unitless";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 0.8, 5840.5;
    String axis "Z";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "depth in meters";
    String ioos_category "Location";
    String long_name "Depth";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  potential_temp {
    Float32 _FillValue NaN;
    Float32 actual_range -1.769, 29.0387;
    String description "Potential temperature in degrees C and referenced to the surface.";
    String ioos_category "Unknown";
    String long_name "Potential Temp";
    String units "degrees Celsius";
  }
  sigma_theta {
    Float32 _FillValue NaN;
    Float32 actual_range 21.2658, 28.0965;
    String description "Potential density of the seawater expressed in sigma units and referenced to the surface.";
    String ioos_category "Physical Oceanography";
    String long_name "Sea Water Sigma Theta";
    String units "sigma units";
  }
  Hesat {
    Float32 _FillValue NaN;
    Float32 actual_range -1.792, 13.784;
    String description "Saturation anomaly of He in percent. 0% indicates that the He concentration is equal to that expected at equilibrium for the potential temperature and salinity of the water. ie. Hesat = (He/Heeq - 1) *100 The He saturation anomaly is calculated relative to the solubility curve of Weiss, R.F. (1971) \"Solubility of Helium and Neon in Water and Seawater\", Journal of Chemical and Engineering Data, 16(2), 235-241. These saturation anomaly values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Hesat";
    String units "unitless (percent)";
  }
  Hesat2 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.689, 10.529;
    String description "Saturation anomaly of He in percent (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Hesat2";
    String units "unitless (percent)";
  }
  Nesat {
    Float32 _FillValue NaN;
    Float32 actual_range -3.82, 7.522;
    String description "Saturation anomaly of Ne in percent. 0% indicates that the Ne concentration is equal to that expected at equilibrium for the potential temperature and salinity of the water. ie. Nesat = (Ne/Neeq - 1) *100  The Ne saturation anomaly is calculated relative to the solubility curve of Hamme, R.C., S.R. Emerson (2004) \"The solubility of neon, nitrogen and argon in distilled water and seawater\", Deep-Sea Research I, 51(11), p. 1517-1528. These saturation anomaly values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Nesat";
    String units "unitless (percent)";
  }
  Nesat2 {
    Float32 _FillValue NaN;
    Float32 actual_range -1.956, 6.304;
    String description "Saturation anomaly of Ne in percent (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Nesat2";
    String units "unitless (percent)";
  }
  Arsat {
    Float32 _FillValue NaN;
    Float32 actual_range -4.49, 7.638;
    String description "Saturation anomaly of Ar in percent. 0% indicates that the Ar concentration is equal to that expected at equilibrium for the potential temperature and salinity of the water.  ie. Arsat = (Ar/Areq - 1) *100  The Ar saturation anomaly is calculated relative to the solubility curve of Hamme, R.C., S.R. Emerson (2004) \"The solubility of neon, nitrogen and argon in distilled water and seawater\", Deep-Sea Research I, 51(11), p. 1517-1528. These saturation anomaly values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Arsat";
    String units "unitless (percent)";
  }
  Arsat2 {
    Float32 _FillValue NaN;
    Float32 actual_range -2.978, 7.098;
    String description "Saturation anomaly of Ar in percent (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Arsat2";
    String units "unitless (percent)";
  }
  Arsat_secondary {
    Float32 _FillValue NaN;
    Float32 actual_range -2.445, 4.212;
    String description "same as for Arsat but data is from independent samples collected from the same cruise and analyzed in a second laboratory";
    String ioos_category "Unknown";
    String long_name "Arsat Secondary";
    String units "unitless (percent)";
  }
  Arsat_secondary2 {
    Float32 _FillValue NaN;
    Float32 actual_range -2.569, 4.179;
    String description "same as for Arsat2 but data is from independent samples collected from the same cruise and analyzed in a second laboratory";
    String ioos_category "Unknown";
    String long_name "Arsat Secondary2";
    String units "unitless (percent)";
  }
  Krsat {
    Float32 _FillValue NaN;
    Float32 actual_range -6.213, 7.855;
    String description "Saturation anomaly of Kr in percent. 0% indicates that the Kr concentration is equal to that expected at equilibrium for the potential temperature and salinity of the water. ie. Krsat = (Kr/Kreq - 1) *100 Kr saturation anomaly is calculated relative to the solubility curve of Weiss, R.F., and T.K. Kyser (1978) \"Solubility of Krypton in Water and Seawater\", Journal of Chemical Thermodynamics, 23(1), 69-72. These saturation anomaly values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Krsat";
    String units "unitless (percent)";
  }
  Krsat2 {
    Float32 _FillValue NaN;
    Float32 actual_range -4.232, 5.498;
    String description "Saturation anomaly of Kr in percent (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Krsat2";
    String units "unitless (percent)";
  }
  Xesat {
    Float32 _FillValue NaN;
    Float32 actual_range -14.614, 9.677;
    String description "Saturation anomaly of Xe in percent. 0% indicates that the He concentration is equal to that expected at equilibrium for the potential temperature and salinity of the water. ie. Xesat = (Xe/Xeeq - 1) *100� The Xe saturation anomaly is calculated relative to the solubility curve of D. Wood and R. Caputi (1966) \"Solubilities of Kr and Xe in fresh and sea water\", U.S. Naval Radiological Defense Laboratory, Technical Report USNRDL-TR-988, San Francisco, CA, pp. 14.�These saturation anomaly values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Xesat";
    String units "unitless (percent)";
  }
  Xesat2 {
    Float32 _FillValue NaN;
    Float32 actual_range -6.618, 8.535;
    String description "Saturation anomaly of Xe in percent (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Xesat2";
    String units "unitless (percent)";
  }
  Ne_Arsat {
    Float32 _FillValue NaN;
    Float32 actual_range -0.618, 4.267;
    String description "Saturation anomaly of Ne/Ar ratio in percent. 0% indicates that the Ne/Ar ratio is equal to that expected at equilibrium for the potential temperature and salinity of the water, ie. Ne/Arsat = ((Ne/Ar) / (Neeq/Areq) - 1) * 100.  Ne/Ar saturation anomaly is calculated relative to the solubility curves of Hamme, R.C., S.R. Emerson (2004) \"The solubility of neon, nitrogen and argon in distilled water and seawater\", Deep-Sea Research I, 51(11), p. 1517-1528. These saturation anomaly values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Ne Arsat";
    String units "unitless (percent)";
  }
  Ne_Arsat2 {
    Float32 _FillValue NaN;
    Float32 actual_range -0.879, 4.435;
    String description "Saturation anomaly of Ne/Ar ratio in percent (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Ne Arsat2";
    String units "unitless (percent)";
  }
  Kr_Arsat {
    Float32 _FillValue NaN;
    Float32 actual_range -1.415, 0.894;
    String description "Saturation anomaly of Kr/Ar ratio in percent. 0% indicates that the Kr/Ar ratio is equal to that expected at equilibrium for the potential temperature and salinity of the water, ie. Kr/Arsat = ((Kr/Ar) / (Kreq/Areq) - 1) * 100. Kr/Ar saturation anomaly is calculated relative to the Ne solubility curve of Hamme, R.C., S.R. Emerson (2004) \"The solubility of neon, nitrogen and argon in distilled water and seawater\", Deep-Sea Research I, 51(11), p. 1517-1528 and the Kr solubility curve of Weiss, R.F., and T.K. Kyser (1978) \"Solubility of Krypton in Water and Seawater\", Journal of Chemical Thermodynamics, 23(1), 69-72. These saturation anomaly values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "Kr Arsat";
    String units "unitless (percent)";
  }
  Kr_Arsat2 {
    Float32 _FillValue NaN;
    Float32 actual_range -1.386, 0.962;
    String description "Saturation anomaly of Kr/Ar ratio in percent (duplicate value)";
    String ioos_category "Unknown";
    String long_name "Kr Arsat2";
    String units "unitless (percent)";
  }
  N2_Arsat {
    Float32 _FillValue NaN;
    Float32 actual_range -0.523, 1.82;
    String description "Saturation anomaly of N2/Ar ratio in percent.  0% indicates that the N2/Ar ratio is equal to that expected at equilibrium for the potential temperature and salinity of the water, ie. N2Arsat = ((N2/Ar) / (N2eq/Areq) - 1) * 100.  N2/Ar saturation anomaly is calculated relative to the solubility curves of Hamme, R.C., S.R. Emerson (2004) \"The solubility of neon, nitrogen and argon in distilled water and seawater\", Deep-Sea Research I, 51(11), p. 1517-1528. These saturation anomaly values are from individual samples. Where a duplicate from the same Niskin was collected and analyzed, the duplicate's value is listed in the second column with the same label appended with \"2\".";
    String ioos_category "Unknown";
    String long_name "N2 Arsat";
    String units "unitless (percent)";
  }
  N2_Arsat2 {
    Float32 _FillValue NaN;
    Float32 actual_range -0.528, 1.723;
    String description "Saturation anomaly of N2/Ar ratio in percent (duplicate value)";
    String ioos_category "Unknown";
    String long_name "N2 Arsat2";
    String units "unitless (percent)";
  }
  N2Arsat_secondary {
    Float32 _FillValue NaN;
    Float32 actual_range -0.274, 1.664;
    String description "same as for N2_Arsat but data is from independent samples collected from the same cruise and analyzed in a second laboratory.";
    String ioos_category "Unknown";
    String long_name "N2 Arsat Secondary";
    String units "unitless (percent)";
  }
  N2Arsat_secondary2 {
    Float32 _FillValue NaN;
    Float32 actual_range -0.295, 1.577;
    String description "same as for N2_Arsat2 but data is from independent samples collected from the same cruise and analyzed in a second laboratory.";
    String ioos_category "Unknown";
    String long_name "N2 Arsat Secondary2";
    String units "unitless (percent)";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Methods (extracted from original Readme file
\\\"[Readme_Hammeetal2019.txt](\\\\\"http://datadocs.bco-
dmo.org/docs/hamme/global_noble_gases/data_docs/743867/1/Readme_Hammeetal2019.txt\\\\\")\\\"):
 
University of Victoria \\- Water samples were collected through CO2-flushed
tubing into evacuated flasks until half-full. The water was equilibrated with
the headspace and then removed. Noble gas samples were determined following a
method similar to that described in (Hamme, R.C., and J.P. Severinghaus 2007)
but with a helium rather than nitrogen balance gas. Gas samples were purified
through a -90\\u00b0C trap to remove water vapor and exposed to a hot getter to
remove all but the noble gases. A calibrated aliquot of 38Ar was added along
with compressed helium to bring the pressure back up. Samples were then
measured for Ar isotopes and Ne/Ar and Kr/Ar ratios on a MAT 253 isotope ratio
mass spectrometer. Absolute Ar concentrations were determined by Ar isotope
dilution with the added 38Ar, while the ratio measurements were combined with
the absolute Ar concentrations to yield Ne and Kr concentrations. Noble gas
standards were calibrated relative to air with assumed dry mole fractions of
1.818e-5 for Ne, 9.34e-3 for Ar, and 1.141e-6 for Kr. N2/Ar measurements were
determined following the method described in (Emerson et al. 1999). Gas
samples were purified though a trap in liquid nitrogen to remove water vapor
and carbon dioxide. Samples were then measured for N2/Ar ratios on a MAT 253
mass spectrometer. N2/Ar standard gases were calibrated relative to air with
assumed dry mole fractions of 9.34e-3 for Ar and 0.78084 for N2.
 
Woods Hole Oceanographic Institution \\- Noble gas samples analyzed at Woods
Hole Oceanographic Institution were determined following variants of the
method described in (Stanley R.H.R., B. Baschek, D.E. Lott, and W.J. Jenkins
2009). Water samples were collected into stainless steel containers for
cruises in 2008 and prior (Bermuda Atlantic Time-series Study cruises and
CLIVAR I6S) or into crimped copper tubes for cruises occurring in 2009 and
later. All the dissolved gas was extracted from the water and then purified
through a cryotrap to remove water vapor and exposed to a hot getter to remove
all the the noble gases. The noble gases were then frozen into two cryotraps,
allowing each noble gas to be sequentially released for analysis in a
quadrupole mass spectrometer. Noble gas concentrations were determined by peak
height manometry for all gases and samples, except the most recent Kr and Xe
data measured from the eastern tropical Pacific, which use a new isotope
dilution method. Noble gas standards were calibrated relative to air with
assumed dry mole fractions of 5.24e-6 for He, 1.818e-5 for Ne, 9.34e-3 for Ar,
1.141e-6 for Kr, and 8.7e-8 for Xe.
 
Scripps Institution of Oceanography \\- Water samples were collected through
CO2-flushed tubing into evacuated flasks until half-full. The water was
equilibrated with the headspace and then removed. Noble gas samples were
determined following the method described in (Hamme, R.C., and J.P.
Severinghaus 2007). Gas samples were purified through a -90\\u00b0C trap to
remove water vapor and exposed to a hot getter to remove all but the noble
gases. A calibrated aliquot of 38Ar was added along with compressed nitrogen
to bring the pressure back up. Samples were then measured for Ar isotopes and
Kr/Ar ratios on a MAT 252 isotope ratio mass spectrometer. Absolute Ar
concentrations were determined by Ar isotope dilution with the added 38Ar,
while the ratio measurements were combined with the absolute Ar concentrations
to yield Kr concentrations. Noble gas standards were calibrated relative to
air with assumed dry mole fractions of 9.34e-3 for Ar, and 1.141e-6 for Kr.
N2/Ar measurements were determined following the method described in (Kobashi,
T., J.P. Severinghaus, and K. Kawamura 2008). Gas samples were purified though
a trap in liquid nitrogen to remove water vapor and carbon dioxide and then
through heated copper to remove oxygen. Samples were then measured for N2/Ar
ratios on a MAT 252 mass spectrometer. N2/Ar standard gases were calibrated
relative to air with assumed dry mole fractions of 9.34e-3 for Ar and 0.78084
for N2.
 
University of Washington \\- Water samples were collected through CO2-flushed
tubing into evacuated flasks until half-full. The water was equilibrated with
the headspace and then removed. Neon samples were determined following the
method described in (Hamme, R.C., and S.R. Emerson 2004). A calibrated aliquot
of 22Ne was added to the sample flasks before sampling. Gas samples were
purified though a trap in liquid nitrogen to remove water vapor and carbon
dioxide and then through an activated charcoal trap in liquid nitrogen to
remove argon and heavier gases. Samples were then measured for Ne isotopes on
a quadrupole mass spectrometer. Absolute Ne concentrations were determined by
Ne isotope dilution with the added 22Ne. The spike aliquot was calibrated
relative to air with assumed dry mole fractions of 1.818e-5 for Ne. Ar
concentration and N2/Ar ratios were determined by two different methods.
Samples collected in 2001 and earlier were determined following the method
described in (Emerson et al. 1999). Gas samples were purified though a trap in
liquid nitrogen to remove water vapor and carbon dioxide. Samples were then
measured for N2/Ar and O2/Ar ratios on a MAT 251 mass spectrometer. For the
samples collected near Bermuda in 2001, the O2/Ar ratio measurements were
combined with absolute O2 concentrations determined by Winkler titration to
yield Ar concentrations. More recent Ar concentration and N2/Ar ratio
measurements were determined following the method described in (Emerson, S.,
T. Ito, and R.C. Hamme 2012). Gas samples were purified though a trap in
liquid nitrogen to remove water vapor and carbon dioxide and then a calibrated
aliquot of 36Ar was added. Samples were then measured for Ar isotopes and
N2/Ar ratios on a Delta X/L isotope ratio mass spectrometer. Absolute Ar
concentrations were determined by Ar isotope dilution with the added 36Ar. Ar
and N2/Ar gas standards were calibrated relative to air with assumed dry mole
fractions of 9.34e-3 for Ar and 0.78084 for N2. Through rigorous method inter-
comparison and repeated laboratory comparison of oxygen concentration
determined by isotope dilution and Winkler titration, Ar concentration samples
analyzed by this 36Ar isotope dilution method have been found to be 0.7% too
low. Accordingly, the Ar concentration and Ar saturation anomaly data from
this method have all be increased by 0.7% in this database. Kr/Ar samples were
determined following a method similar to that described in (Hamme, R.C., and
J.P. Severinghaus 2007). Gas samples were purified through a -90\\u00b0C trap
to remove water vapor and exposed to a hot getter to remove all but the noble
gases. Compressed nitrogen was added to bring the pressure back up. Samples
were then measured for Kr/Ar ratios on a MAT 253 isotope ratio mass
spectrometer. Noble gas standards were calibrated relative to air with assumed
dry mole fractions of 9.34e-3 for Ar, and 1.141e-6 for Kr.
 
Quality control \\- Samples measured at University of Victoria, Scripps
Institution of Oceanography, and University of Washington were nearly all
collected in duplicate. For these samples in this database, only data where
both duplicates were analyzed successfully and where their standard deviation
was less than three times the pooled standard deviation are included. Noble
gas duplicates were required to be within 0.93% of each other for Ne, within
0.28% for Ar, and within 0.35% for Kr. Similarly N2/Ar duplicates were
required to be within 0.17% of each other. Both duplicates are present in the
database. The exception to this is the N2/Ar data collected in 2007 in the
Labrador Sea and analyzed at Scripps Institution of Oceanography. These
samples were not collected in duplicate but are present in the database. For
samples collected at the Bermuda Atlantic Time-series Study and in the
Southern Ocean that were analyzed at Woods Hole Oceanographic Institution, we
binned the data by depth for each cruise and removed samples that were outside
three times the standard deviation of samples within each depth bin. For the
2010-2011 Atlantic GEOTRACES transect samples that were analyzed at Woods Hole
Oceanographic Institution, we simply removed data where the Ne saturation
anomaly was less than -10% or larger than 5%. For the 2013 Pacific GEOTRACES
transect samples that were analyzed at Woods Hole Oceanographic Institution,
we removed Xe measurements analyzed by peak height manometry, retaining only
those analyzed by isotope dilution.
 
NaN = missing data.";
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    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "Dr Donald  L. Rice";
    String awards_0_program_manager_nid "51467";
    String awards_10_award_nid "743870";
    String awards_10_award_number "OCE-9819181";
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    String awards_12_award_number "OCE-0221247";
    String awards_12_data_url "https://www.nsf.gov/awardsearch/showAward?AWD_ID=0221247";
    String awards_12_funder_name "NSF Division of Ocean Sciences";
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    String awards_12_program_manager "Dr Donald  L. Rice";
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    String awards_13_award_nid "743875";
    String awards_13_award_number "OCE-0242139";
    String awards_13_data_url "https://www.nsf.gov/awardsearch/showAward?AWD_ID=0242139";
    String awards_13_funder_name "NSF Division of Ocean Sciences";
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    String awards_13_program_manager "Dr Donald  L. Rice";
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    String awards_14_award_number "OCE-0647979";
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    String awards_14_funder_name "NSF Division of Ocean Sciences";
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    String awards_15_funding_source_nid "355";
    String awards_15_program_manager "Dr Eric  C. Itsweire";
    String awards_15_program_manager_nid "50415";
    String awards_1_award_nid "55126";
    String awards_1_award_number "OCE-0926659";
    String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward?AWD_ID=0926659";
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    String awards_2_award_nid "55213";
    String awards_2_award_number "328290-2006";
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    String awards_2_funder_name "National Sciences and Engineering Research Council of Canada";
    String awards_2_funding_acronym "NSERC";
    String awards_2_funding_source_nid "398";
    String awards_3_award_nid "502594";
    String awards_3_award_number "OCE-1130870";
    String awards_3_data_url "http://www.nsf.gov/awardsearch/showAward?AWD_ID=1130870&HistoricalAwards=false";
    String awards_3_funder_name "NSF Division of Ocean Sciences";
    String awards_3_funding_acronym "NSF OCE";
    String awards_3_funding_source_nid "355";
    String awards_3_program_manager "Dr Henrietta N Edmonds";
    String awards_3_program_manager_nid "51517";
    String awards_4_award_nid "663603";
    String awards_4_award_number "OCE-1232991";
    String awards_4_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1232991";
    String awards_4_funder_name "NSF Division of Ocean Sciences";
    String awards_4_funding_acronym "NSF OCE";
    String awards_4_funding_source_nid "355";
    String awards_4_program_manager "Dr Henrietta N Edmonds";
    String awards_4_program_manager_nid "51517";
    String awards_5_award_nid "719784";
    String awards_5_award_number "OCE-1029299";
    String awards_5_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1029299";
    String awards_5_funder_name "NSF Division of Ocean Sciences";
    String awards_5_funding_acronym "NSF OCE";
    String awards_5_funding_source_nid "355";
    String awards_5_program_manager "Dr Donald  L. Rice";
    String awards_5_program_manager_nid "51467";
    String awards_6_award_nid "719806";
    String awards_6_award_number "329290-2012";
    String awards_6_data_url "http://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=507834";
    String awards_6_funder_name "National Sciences and Engineering Research Council of Canada";
    String awards_6_funding_acronym "NSERC";
    String awards_6_funding_source_nid "398";
    String awards_7_award_nid "719807";
    String awards_7_award_number "433848-2012";
    String awards_7_data_url "http://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=512032";
    String awards_7_funder_name "National Sciences and Engineering Research Council of Canada";
    String awards_7_funding_acronym "NSERC";
    String awards_7_funding_source_nid "398";
    String awards_8_award_nid "719809";
    String awards_8_award_number "433898-2012";
    String awards_8_data_url "http://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=512038";
    String awards_8_funder_name "National Sciences and Engineering Research Council of Canada";
    String awards_8_funding_acronym "NSERC";
    String awards_8_funding_source_nid "398";
    String awards_9_award_nid "743868";
    String awards_9_award_number "OCE-9617487";
    String awards_9_data_url "https://www.nsf.gov/awardsearch/showAward?AWD_ID=9617487";
    String awards_9_funder_name "NSF Division of Ocean Sciences";
    String awards_9_funding_acronym "NSF OCE";
    String awards_9_funding_source_nid "355";
    String awards_9_program_manager "Dr Donald  L. Rice";
    String awards_9_program_manager_nid "51467";
    String cdm_data_type "Other";
    String comment 
"Global Noble Gases 
  PI: Roberta C. Hamme  
  Co-PIs: Steven R. Emerson, William J. Jenkins, David P. Nicholson 
  Version: 1.0";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.2d  13 Jun 2019";
    String date_created "2018-08-21T16:27:05Z";
    String date_modified "2019-01-08T20:47:42Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.744563";
    Float64 Easternmost_Easting 178.9985;
    Float64 geospatial_lat_max 78.9988;
    Float64 geospatial_lat_min -68.1081;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 178.9985;
    Float64 geospatial_lon_min -159.9952;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 5840.5;
    Float64 geospatial_vertical_min 0.8;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2019-10-16T04:34:11Z (local files)
2019-10-16T04:34:11Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_743867.das";
    String infoUrl "https://www.bco-dmo.org/dataset/743867";
    String institution "BCO-DMO";
    String instruments_0_acronym "IR Mass Spec";
    String instruments_0_dataset_instrument_nid "744097";
    String instruments_0_description "The Isotope-ratio Mass Spectrometer is a particular type of mass spectrometer used to measure the relative abundance of isotopes in a given sample (e.g. VG Prism II Isotope Ratio Mass-Spectrometer).";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB16/";
    String instruments_0_instrument_name "Isotope-ratio Mass Spectrometer";
    String instruments_0_instrument_nid "469";
    String instruments_0_supplied_name "MAT 253 isotope ratio mass spectrometer";
    String instruments_1_acronym "IR Mass Spec";
    String instruments_1_dataset_instrument_nid "744099";
    String instruments_1_description "The Isotope-ratio Mass Spectrometer is a particular type of mass spectrometer used to measure the relative abundance of isotopes in a given sample (e.g. VG Prism II Isotope Ratio Mass-Spectrometer).";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB16/";
    String instruments_1_instrument_name "Isotope-ratio Mass Spectrometer";
    String instruments_1_instrument_nid "469";
    String instruments_1_supplied_name "MAT 252 isotope ratio mass spectrometer";
    String instruments_2_acronym "IR Mass Spec";
    String instruments_2_dataset_instrument_nid "744100";
    String instruments_2_description "The Isotope-ratio Mass Spectrometer is a particular type of mass spectrometer used to measure the relative abundance of isotopes in a given sample (e.g. VG Prism II Isotope Ratio Mass-Spectrometer).";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB16/";
    String instruments_2_instrument_name "Isotope-ratio Mass Spectrometer";
    String instruments_2_instrument_nid "469";
    String instruments_2_supplied_name "MAT 251 mass spectrometer";
    String instruments_3_acronym "IR Mass Spec";
    String instruments_3_dataset_instrument_nid "744101";
    String instruments_3_description "The Isotope-ratio Mass Spectrometer is a particular type of mass spectrometer used to measure the relative abundance of isotopes in a given sample (e.g. VG Prism II Isotope Ratio Mass-Spectrometer).";
    String instruments_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB16/";
    String instruments_3_instrument_name "Isotope-ratio Mass Spectrometer";
    String instruments_3_instrument_nid "469";
    String instruments_3_supplied_name "Delta X/L isotope ratio mass spectrometer";
    String instruments_4_acronym "Mass Spec";
    String instruments_4_dataset_instrument_nid "744098";
    String instruments_4_description "General term for instruments used to measure the mass-to-charge ratio of ions; generally used to find the composition of a sample by generating a mass spectrum representing the masses of sample components.";
    String instruments_4_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB16/";
    String instruments_4_instrument_name "Mass Spectrometer";
    String instruments_4_instrument_nid "685";
    String instruments_4_supplied_name "quadrupole mass spectrometer";
    String keywords "analysis, analysis_lab, ar2, Ar_conc, Ar_conc2, Ar_conc_secondary, Ar_conc_secondary2, arsat, arsat2, Arsat_secondary, Arsat_secondary2, bco, bco-dmo, biological, cast, chemical, conc, conc2, cruise, cruise_name, ctdsal, ctdtemp, data, dataset, day, density, depth, dmo, earth, Earth Science > Oceans > Salinity/Density > Density, erddap, event, He_conc, He_conc2, hesat, hesat2, identifier, Kr_Ar, Kr_Ar2, Kr_Arsat, Kr_Arsat2, Kr_conc, Kr_conc2, krsat, krsat2, lab, latitude, longitude, management, month, N2_Ar, N2_Ar2, N2_Ar_secondary, N2_Ar_secondary2, N2_Arsat, N2_Arsat2, N2Arsat_secondary, N2Arsat_secondary2, name, Ne_Ar, Ne_Ar2, Ne_Arsat, Ne_Arsat2, Ne_conc, Ne_conc2, nesat, nesat2, niskin, ocean, oceanography, oceans, office, physical, physical oceanography, potential, potential_temp, preliminary, press, salinity, science, sea, sea_water_sigma_theta, seawater, secondary, secondary2, secondary_analysis_lab, sequence, sigma, sigma_theta, station, temperature, theta, time, water, Xe_conc, Xe_conc2, xesat, xesat2, year";
    String keywords_vocabulary "GCMD Science Keywords";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    String metadata_source "https://www.bco-dmo.org/api/dataset/743867";
    Float64 Northernmost_Northing 78.9988;
    String param_mapping "{'743867': {'latitude': 'flag - latitude', 'depth': 'master - depth', 'longitude': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/743867/parameters";
    String people_0_affiliation "University of Victoria";
    String people_0_affiliation_acronym "UVic";
    String people_0_person_name "Roberta C. Hamme";
    String people_0_person_nid "51066";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of Washington";
    String people_1_affiliation_acronym "UW";
    String people_1_person_name "Steven R. Emerson";
    String people_1_person_nid "50698";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Woods Hole Oceanographic Institution";
    String people_2_affiliation_acronym "WHOI";
    String people_2_person_name "William J. Jenkins";
    String people_2_person_nid "50745";
    String people_2_role "Co-Principal Investigator";
    String people_2_role_type "originator";
    String people_3_affiliation "University of Washington";
    String people_3_affiliation_acronym "UW";
    String people_3_person_name "David P. Nicholson";
    String people_3_person_nid "664588";
    String people_3_role "Co-Principal Investigator";
    String people_3_role_type "originator";
    String people_4_affiliation "University of Victoria";
    String people_4_affiliation_acronym "UVic";
    String people_4_person_name "Roberta C. Hamme";
    String people_4_person_nid "51066";
    String people_4_role "Contact";
    String people_4_role_type "related";
    String people_5_affiliation "Woods Hole Oceanographic Institution";
    String people_5_affiliation_acronym "WHOI BCO-DMO";
    String people_5_person_name "Shannon Rauch";
    String people_5_person_nid "51498";
    String people_5_role "BCO-DMO Data Manager";
    String people_5_role_type "related";
    String project "Carbon Dioxide Dynamics in Mode Water of the North Atlantic Ocean , U.S. GEOTRACES East Pacific Zonal Transect, Measurement of Helium Isotopes, Tritium, Noble Gases, and Radiocarbon, The Marine Dissolved N2/Ar Ratio, A Tracer for Deep Ocean Denitrification?, Characterizing the Formation, Nature, and Export of Weddell Sea Bottom Water using Noble Gases and Transient Tracers, Measuring Diapycnal Mixing in the Upper Ocean therMocline using Noble Gas Supersaturation, GEOTRACES Atlantic Section: Measurement of Helium Isotopes and Tritium, Tracers of Biological Productivity and Gas Exchange, Is There an Ocean Primary Production Paradox(OP3)?, The Biological Carbon Pump in the Subtropical North Pacific Ocean: Mechanisms of Nutrient Supply, Net Biological Oxygen Production at the Japanese JGOFS Time-Series Station, Gas Tracers of Net Biological Oxygen Production in the Subtropical Pacific Ocean";
    String projects_0_acronym "CarboMODE";
    String projects_0_description 
"from the NSF proposal abstract
The formation of mode waters, like Eighteen Degree Water (EDW) in the North Atlantic Ocean, is important for driving ocean circulation, ventilating and transferring biogeochemical properties to the ocean interior. Recent studies suggest that EDW plays an important role in setting the nutrient reservoir of the subtropical gyre [Jenkins and Doney, 2003; Doney and Jenkins, 2004; Palter et al., 2005], with significant implications for nutrient and carbon dynamics, and productivity in the subtropical gyre of the North Atlantic. In addition, EDW has a potentially important role in the ocean uptake and decadal variability of atmospheric CO2 [Bates et al., 2002].
In this study, researchers at the Bermuda Biological Station for Research (BBSR) and the Woods Hole Oceanographic Institution (WHOI) hope to achieve a better quantitative and mechanistic understanding of the CO2 dynamics in EDW. The work leverages the 2006-2007 field program and improved understanding about the physics of EDW that an NSF sponsored field project, CLImate MOde water Dynamics Experiment (CLIMODE) will gain. The main question posed in CarboMODE is \"What is the oceanic uptake and fate of CO2 in EDW in the North Atlantic Ocean?\" From this general question, more specific questions are raised, including: (1) What is the air-sea CO2 flux during wintertime EDW formation? (2) What are the relative contributions from vertical/lateral mixing, advection/stirring, air-sea CO2 gas exchange and biological depletion of CO2 due to net community production during EDW formation that influence the DIC properties of EDW? (3) What is the dissolved inorganic carbon (DIC) content of EDW upon subduction (injection) into the subtropical gyre and what is the overall flux? (4) How does the formation of EDW impact the subsurface inorganic carbon reservoir and air-sea CO2 fluxes in the subtropical gyre of the North Atlantic Ocean? (5) What is the fate of inorganic carbon in EDW as it advects away from the region of formation and how does subsurface remineralization contribute to the DIC content of EDW?
In addressing these questions, the investigators propose will collect inorganic carbon data in 2007 as part of the CLIMODE project. Their contribution to the CLIMODE (and CarboMODE) project will be measurements of DIC, Total Alkalinity (TA) and underway pCO2 (i.e., seawater and air pCO2). Although focused on physics, the observational and modeling program framed by CLIMODE's questions and hypotheses fortuitously provide a timely and unique opportunity to address questions raised about CO2 dynamics (and related issues concerning nutrient and dissolved oxygen dynamics). Synthesis and modeling of several different datasets, including the 2007 CLIMODE field surveys of EDW, CO2 data collected from a 2006 CLIMODE cruise, a 4 day northward extension of the BATS Bermuda-Puerto Rico annual transect, and surface seawater pCO2 (and DIC and alkalinity) data collected twice a week in the region of EDW formation from the Volunteer Observing Ship (VOS) MV Oleander (funded by NOAA COSP), form the nucleus for addressing relevant CarboMODE questions.";
    String projects_0_end_date "2010-03";
    String projects_0_geolocation "North Atlantic";
    String projects_0_name "Carbon Dioxide Dynamics in Mode Water of the North Atlantic Ocean";
    String projects_0_project_nid "2077";
    String projects_0_start_date "2007-04";
    String projects_10_acronym "Net Bio O2 Prod JGOFS";
    String projects_10_description 
"NSF Award Abstract:
OCE-9819181
Primary production in the ocean is important not only for the functioning of the marine ecosystem but also for its pivotal role in regulating sea-air exchange of carbon dioxide, the most important atmospheric greenhouse gas. In this study, the principal investigator will use an indirect method to determine the net annual oxygen production in the northwest Pacific Ocean by measuring eleven profiles of O2, N2, and Ar concentrations in the upper ocean at the Japanese Joint Global Ocean Flux Study (JGOFS) time-series station. The time-series station is located in what is probably the most biologically productive region of the North Pacific, and the oxygen flux estimates are expected to provide the first good estimates of the regional primary production. The PI will be taking advantage of a unique opportunity to participate in Japanese JGOFS cruises in this region between 1998 and 2000.";
    String projects_10_end_date "2001-02";
    String projects_10_name "Net Biological Oxygen Production at the Japanese JGOFS Time-Series Station";
    String projects_10_project_nid "743958";
    String projects_10_start_date "1999-03";
    String projects_11_acronym "Gas Tracers O2 Prod Subtropical Pacific";
    String projects_11_description 
"NSF Award Abstract:
9617487 Emerson Organic carbon export from the euphotic zone of the ocean regulates the CO2 content of the atmosphere and controls the redox balance in ocean chemistry on millenial time scales. One of the fundamental goals of oceanography is to evaluate the organic carbon flux and determine the controlling mechanisms so that system can be modeled well enough to predict responses to changes in forcing. Recent estimates of carbon export by a variety of methods at the U.S. JGOFS time-series stations indicate that the subtropical oceans are responsible for 25-50 percent of the global ocean new production. Progress in estimating the rate of new carbon export from the euphotic zone in the subtropical north Pacific Ocean now require knowledge of the mechanisms(s) controlling the supply rate of nutrients. Suggestions of diapycnal mixing, horizontal transport of dissolved organic matter, and various biological processes are currently being advanced. The implications of the different mechanisms regarding the coupling of the biological pump and ocean circulation are obvious and hold extremely important consequences for our understanding of the response of the ocean's \"biological pump\" to physical forcing. This study is designed to test the hypothesis that the mechanism supplying nutrients to the euphotic zone in the subtropical north Pacific is diapycnal transport. Focus will be on two main problems: (1) the role of intermittent transport in supplying nutrients necessary to create the shallow oxygen maximum, and (2) the utility of inert gases as tracers of diapycnal transport in the upper ocean. A fully instrumented deep-sea mooring will soon be deployed at the Hawaii Ocean Time-series (HOT) and can be used to make continuous measurements of oxygen and total gas pressure on the mooring to determine whether formation of the shallow O2 maximum is correlated to short-term intermittent supply of nutrients from below. ***";
    String projects_11_end_date "2000-01";
    String projects_11_name "Gas Tracers of Net Biological Oxygen Production in the Subtropical Pacific Ocean";
    String projects_11_project_nid "743962";
    String projects_11_start_date "1997-02";
    String projects_1_acronym "U.S. GEOTRACES EPZT";
    String projects_1_description 
"From the NSF Award Abstract
The mission of the International GEOTRACES Program (www.geotraces.org), of which the U.S. chemical oceanography research community is a founding member, is \"to identify processes and quantify fluxes that control the distributions of key trace elements and isotopes in the ocean, and to establish the sensitivity of these distributions to changing environmental conditions\" (GEOTRACES Science Plan, 2006). In the United States, ocean chemists are currently in the process of organizing a zonal transect in the eastern tropical South Pacific (ETSP) from Peru to Tahiti as the second cruise of the U.S.GEOTRACES Program. This Pacific section includes a large area characterized by high rates of primary production and particle export in the eastern boundary associated with the Peru Upwelling, a large oxygen minimum zone that is a major global sink for fixed nitrogen, and a large hydrothermal plume arising from the East Pacific Rise. This particular section was selected as a result of open planning workshops in 2007 and 2008, with a final recommendation made by the U.S.GEOTRACES Steering Committee in 2009. It is the first part of a two-stage plan that will include a meridional section of the Pacific from Tahiti to Alaska as a subsequent expedition.
This award provides funding for management of the U.S.GEOTRACES Pacific campaign to a team of scientists from the University of Southern California, Old Dominion University, and the Woods Hole Oceanographic Institution. The three co-leaders will provide mission leadership, essential support services, and management structure for acquiring the trace elements and isotopes samples listed as core parameters in the International GEOTRACES Science Plan, plus hydrographic and nutrient data needed by participating investigators. With this support from NSF, the management team will (1) plan and coordinate the 52-day Pacific research cruise described above; (2) obtain representative samples for a wide variety of trace metals of interest using conventional CTD/rosette and GEOTRACES Sampling Systems; (3) acquire conventional JGOFS/WOCE-quality hydrographic data (CTD, transmissometer, fluorometer, oxygen sensor, etc) along with discrete samples for salinity, dissolved oxygen (to 1 uM detection limits), plant pigments, redox tracers such as ammonium and nitrite, and dissolved nutrients at micro- and nanomolar levels; (4) ensure that proper QA/QC protocols are followed and reported, as well as fulfilling all GEOTRACES Intercalibration protocols; (5) prepare and deliver all hydrographic-type data to the GEOTRACES Data Center (and US data centers); and (6) coordinate cruise communications between all participating investigators, including preparation of a hydrographic report/publication.
Broader Impacts: The project is part of an international collaborative program that has forged strong partnerships in the intercalibration and implementation phases that are unprecedented in chemical oceanography. The science product of these collective missions will enhance our ability to understand how to interpret the chemical composition of the ocean, and interpret how climate change will affect ocean chemistry. Partnerships include contributions to the infrastructure of developing nations with overlapping interests in the study area, in this case Peru. There is a strong educational component to the program, with many Ph.D. students carrying out thesis research within the program.
Figure 1. The 2013 GEOTRACES EPZT Cruise Track. [click on the image to view a larger version]";
    String projects_1_end_date "2015-06";
    String projects_1_geolocation "Eastern Tropical Pacific - Transect from Peru to Tahiti";
    String projects_1_name "U.S. GEOTRACES East Pacific Zonal Transect";
    String projects_1_project_nid "499723";
    String projects_1_project_website "http://www.geotraces.org/";
    String projects_1_start_date "2012-06";
    String projects_2_acronym "EPZT Noble Gases He Tritium";
    String projects_2_description 
"The biogeochemical cycling of trace elements and isotopes (TEIs) in the marine environment is an important research area within the context of global change that motivates the International GEOTRACES program. Some trace elements are known to play potentially important roles as micronutrients in biological cycling, particularly in regard to enzymatic and catalytic processes in the marine environment. Radioisotopes, transient tracers, and noble gases are valuable tracers of these and related processes, and of the ocean?s interaction with the atmosphere and the solid earth, which in turn play a role in shaping many trace element distributions within the ocean.
According to the GEOTRACES Science Plan, the guiding mission of the GEOTRACES program is \"to identify processes and quantify fluxes that control the distributions of key trace elements and isotopes in the ocean\". The key observational strategy for GEOTRACES is an internationally-coordinated global-scale ocean survey of key TEIs. The second US GEOTRACES section, set for the Eastern South Pacific in 2013, is aimed at the characterization of key processes in an oxygen minimum zone (OMZ), as well as a major abyssal hydrothermal plume extending westward from the East Pacific Rise.
To help achieve these goals, with support from this grant, a research team at the Woods Hole Oceanographic Institution will collaborate with other GEOTRACES investigators on the Eastern South Pacific expedition to measure a suite of tracers useful for interpreting the rest of the synoptic TEI data. Specifically, the team will make measurements of the noble gases, helium isotopes, tritium, and radiocarbon include in order to: (1) quantify ventilation, circulation, and diapycnal mixing in the OMZ to enable estimation of fluxes and transformation rates of key TEIs; (2) determine upwelling rates in the oxygen minimum zone (OMZ) over a range of timescales to constrain the fluxes of biogeochemically important properties; (3) estimate hydrothermal fluxes of key TEIs using 3He as a flux gauge, and also use 3He as a measure of downstream dilution in the hydrothermal plume; (4) use radiocarbon to estimate abyssal remineralization rates for key TEIs; and (5) probe for evidence of off-axis contribution of hydrothermal processes to TEI distribution. The collective effort will allow marine geochemists to understand mechanistically and quantitatively how a variety of physical, chemical, and biological processes join to determine the distribtuion of TEIs in the ocean.
It has been argued that anthropogenic influence on the global cycles of many elements is emerging as significant. As outlined in the International GEOTRACES Science Plan, the broader impacts of this activity include both an important \"baseline snapshot\" of the biogeochemical state of the oceanic environment, and a quantitative improvement in the characterization and understanding of important processes in the marine environment. Both of these build a foundation for improved models and quantitative predictions of the oceanic response and role in global change and climate, particularly with anthropogenic forcing. For example, recent evidence of \"ocean deoxygenation\" has profound implications for marine biologic response. In particular, the evolving state of marine OMZs represents an important biogeochemical \"climate canary\". A key benefit of diagnosing trace metal dynamics and response to changing redox conditions is the improvement in prognostic capabilities of coupled ocean-atmosphere biogeochemical models for global change.";
    String projects_2_end_date "2016-12";
    String projects_2_geolocation "Oxygen minium zone; East Pacific Rise";
    String projects_2_name "Measurement of Helium Isotopes, Tritium, Noble Gases, and Radiocarbon";
    String projects_2_project_nid "663604";
    String projects_2_start_date "2013-01";
    String projects_3_acronym "N2:Ar Deep Tracer";
    String projects_3_description 
"The role of nitrate in the ocean carbon cycle and its relatively short residence time make it crucial to understand the marine nitrogen cycle; however, there is currently insufficient experimental evidence to accurately determine present day fluxes. Denitrification and nitrogen fixation are the main sink and source for dissolved inorganic nitrogen in the sea.
In this study a research team at the University of Washington will collaborate with colleagues at the University of Victoria to study changes in the N2/Ar ratio in seawater caused by denitrification. Previous research has demonstrated the utility of this tracer in the oxygen minimum zones of the Pacific and Indian Ocean, but they will investigate observed changes in the \"background\" distribution of the ratio. The investigators already have unpublished data that indicate the N2/Ar ratio increases by about 0.5 % from the Atlantic to Pacific Oceans in waters below 1000 meters. If this increase is assumed to be caused by denitrification in deep ocean sediments it amounts to roughly 80 Tg/yr of denitrification. This is a significant portion of estimated global denitrification (between 200 and 400 Tg/yr) and within the range of the largely untested predictions of deep-ocean sediment denitrification using global sediment diagenesis models. Presently it is not possible to unequivocally attribute the observed deep water column N2/Ar increase to denitrification because it could also be caused by deep-water formation processes in the Antarctic.
The investigators will separate the fraction of the N2/Ar ratio increase due to the physical processes of atmosphere or ice-water interaction from that due to denitrification by measuring other noble gas ratios (primarily Ne/Ar and Kr/Ar) that change only in response to ocean surface cooling and bubble processes. They will measure deep water-column profiles of N2/Ar, Ne/Ar and Kr/Ar in strategically-located sites where there are ships of opportunity: the Labrador Sea, the North Atlantic at the Bermuda time-series site, the Drake Passage, the Indian Ocean south of Madagascar, the subtropical North Pacific at the Hawaii Ocean time-series site, and the subarctic North Pacific at Station P. Preliminary measurements of all of the gas ratios have been made, and extensive testing has been done to identify sources of contamination in the sampling methods. This proposal involves a two-laboratory collaboration to make it possible to sample a large number of ocean sites, minimize atmospheric contamination by rapid sample analysis, and create maximum accuracy through laboratory intercalibration.
Broader Impacts: This project will promote international ocean science collaboration between the U.S.and Canada. It will support the research of an assistant professor to apply analytical methods that she has helped develop to an important problem in oceanography. A PhD candidate at the University of Washington will be trained in the area of chemical oceanography using analytical methods of gas ratio and isotope ratio mass spectrometry.";
    String projects_3_end_date "2013-07";
    String projects_3_geolocation "Global oceans";
    String projects_3_name "The Marine Dissolved N2/Ar Ratio, A Tracer for Deep Ocean Denitrification?";
    String projects_3_project_nid "719785";
    String projects_3_start_date "2010-08";
    String projects_4_acronym "Weddell Sea Tracers";
    String projects_4_description 
"NSF Award Abstract:
Intellectual Merit: It is commonly accepted that since at least the last glacial maximum, the substantial millennial-timescale changes in global climate have been caused by, or at least associated with abrupt changes in the oceanic Meridional Overturning Circulation (MOC). There is the lingering suspicion that perhaps the ultimate trigger of the climate transients may lie in the southern hemisphere. Dense waters formed by buoyancy modification on Antarctic shelf regions leave the shelves and sink to comprise the major water mass complex known as Antarctic Bottom Water (AABW). AABW, the coldest and densest water to play a role in the MOC, in turn enters all of the major ocean basins and thereby closes the southern end of the MOC loop. The Weddell Sea features prominently in the production of AABW, where interaction between seawater and the floating ice-shelves produces a unique pre-cursor to AABW by a combination of processes, including) strong heat extraction at ice-edge polynyas, sea-ice formation and export, melting of glacial ice at the grounding line, and formation and deposition of sub-marine sea-ice. These processes not only produce oceanographically and climatically significant injections of fresh water into the AABW pre-cursor, but are hypothesized to have significant impact on its dissolved noble gas composition. We propose to use high precision measurements of the latter as a diagnostic tool of the magnitude of these processes.
The investigator will participate in a British research cruise ANDREX which is a section connecting the CLIVAR (CLImate VARiability and predictability Program) repeat line I6S with the Antarctic Peninsula in early 2009. The cruise follows the northern rim of the Weddell Sea gyre, and is ideally situated to study the exchange of water masses across the Antarctic Circumpolar Current. A combination of noble gases, transient tracers (tritium and radiocarbon) along with CFCs, stable isotopes, 3He, and traditional hydrographic measurements will be used to place constraints on an oceanographically and climatically important region.
Broader Impacts: This project involves the development and use of novel oceanographic tracers and the application of more traditional tracers in new ways to characterize water mass transformation processes that are of fundamental importance in the operation of the global climate system. The new insights into these processes will provide valuable guidance in the design and construction of the next generation of coupled ocean-atmosphere climate models, which will be of strategic importance in facing the broad range of economic, policy, and societal issues created by climate change. The data set produced in this work will be submitted to the appropriate data centers/repositories to be made available to modelers and climate scientists to guide future research efforts and to evaluate or test existing and future climate models.";
    String projects_4_end_date "2013-08";
    String projects_4_name "Characterizing the Formation, Nature, and Export of Weddell Sea Bottom Water using Noble Gases and Transient Tracers";
    String projects_4_project_nid "743899";
    String projects_4_start_date "2008-09";
    String projects_5_acronym "Measuring Diapycnal Mixing";
    String projects_5_description 
"NSF Award Abstract:
In this project, researchers at the University of Washington School of Oceanography will develop a new method of constraining the rate of diapycnal (cross-isopycnal) mixing in the ocean using the natural distributions of dissolved noble gases. They will apply this method to determine the diapycnal mixing rate in the ventilated thermocline of the subtropical oceans where there is long-standing uncertainty about the physical mechanisms supplying nutrients to the euphotic zone. Noble gases are not affected by biology, so their distribution in the ocean is determined purely by physical processes. Because the equilibrium concentrations of these gases are non-linear functions of temperature, mixing between waters equilibrated with the atmosphere at different temperatures induces a supersaturation in the gases. Advances in analytical methodology have recently made it possible to measure this mixing signal, and a theoretical basis for understanding it has also just been developed. The theory indicates that noble gas supersaturation accumulates over the time since the water parcel left the surface and that it is most sensitive to diapycnal mixing in the ventilated thermocline of the ocean. Thus, this tracer records the effect of diapycnal mixing over time scales of decades and compliments purposeful tracer release experiments that last months to a year and whole-ocean analyses of thermocline mixing that represent hundreds of years.
The project will combined analytical and theoretical research. The research team will measure the concentrations of Ne, Ar, Kr and Xe in transects through three sections of the world's ventilated thermocline. Two meridional sections through the central North Pacific and eastern South Pacific and a zonal transect across the southern North Atlantic cross contrasting regions where we expect the noble gas tracers to reveal different degrees of supersaturation due to diapycnal mixing. The theoretical/modeling aspect of the proposal focuses on using a series of ocean global circulation model runs to help separate the different physical processes causing noble gas supersaturation. The model will then be used to determine the effect of the diapycnal mixing rates deduced from the inert gas tracers on the transport of nutrients to the euphotic zone in the subtropical oceans. Using this interdisciplinary approach the team will evaluate the utility of noble supersaturation as a tracer of diapycnal mixing in the ocean thermocline and advance our understanding of a classic problem in oceanography.
The project is expected to have a number of broader impacts. By developing a new method of quantifying diapycnal mixing rates in the ocean's thermocline, this project should help to solve the many issues that depend on this fundamental quantity, from determining biological productivity and its controls to understanding the driving forces behind the overturning circulation. Better constraints over mixing rates and wide dissemination of the observational dataset for other data/model comparisons will lead to improved predictions for anthropogenic CO2 uptake by the ocean and for changes in biological productivity caused by global warming, both topics of clear interest to society. The project will also promote education by involving a graduate student that will be jointly advised by the principle investigators and will enhance international scientific collaboration by establishing joint field and analytical research with Japanese and Canadian colleagues.";
    String projects_5_end_date "2011-02";
    String projects_5_name "Measuring Diapycnal Mixing in the Upper Ocean therMocline using Noble Gas Supersaturation";
    String projects_5_project_nid "743903";
    String projects_5_start_date "2007-03";
    String projects_6_acronym "NAT He and Tritium";
    String projects_6_description 
"NSF Award Abstract:
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
The guiding mission of the GEOTRACES program is to identify processes and quantify fluxes that control the distributions of key trace elements and isotopes in the ocean. The key observational strategy for GEOTRACES is an internationally-coordinated global-scale ocean survey of key trace elements and isotopes (TEIs), and the first U.S. section as part of that survey is in the North Atlantic. Knowing rates and fluxes is a vital step in the development of mechanistic and predictive models of ocean biogeochemical cycles of TEIs, particularly within the framework of global change (both past and future). Much of what we have learned about large scale oceanic rates and fluxes has been inferred from the observation and modeling of tracer distributions, both radioactive and transient. Measurement of appropriate transient tracers alongside of core TEIs would be an effective strategy for achieving GEOTRACES goals.
In this project, researchers at the Woods Hole Oceanographic Institution will make helium isotope and tritium measurements to provide useful biogeochemical rate information for the more centrally important TEI measurements made on the first U.S. GEOTRACES global survey section. The primary contributions that tritium and 3He measurements can make to the program include: (1) Quantifying transit timescales and TEI dilution in the MOC: 3H and 3He are useful tracers for determining deep western boundary current tracer transport rates and interior mixing dilution scales, an important issue for many TEIs; (2) A shallow water chronometer: Using the tritium-3He clock, the time elapsed since fluid parcels have been subducted on timescales ranging from 6 months to several decades can be determined; (3) A TEI thermocline reflux gauge: 3He is a unique \"nutrient-like\" transient tracer that can be used as a \"flux gauge\" to determine the rates at which thermocline-remineralized TEIs are returned to the upper ocean; and (4) Gauging TEI hydrothermal dilution scales: Volcanic 3He injected during hydrothermal activity is a powerful conservative tracer of dilution in these plumes, allowing diagnosis of nonconservative behavior in some TEIs, and permitting flux estimates associated with hydrothermal activity on basin and global scale.
Broader Impacts: The proposed work is in support of the GEOTRACES program, and as such contributes to the broader societal goals and intellectual objectives espoused by that program. The primary issues related to this are pertinent to understanding the carbon cycle and predicting/mitigating climate change, as well as the marine food web and anthropogenic impacts on the oceans.";
    String projects_6_end_date "2013-09";
    String projects_6_name "GEOTRACES Atlantic Section: Measurement of Helium Isotopes and Tritium";
    String projects_6_project_nid "743907";
    String projects_6_start_date "2010-01";
    String projects_7_acronym "Tracers of Bio Prod and Gas Exchange";
    String projects_7_description 
"NSF Award Abstract:
OCE-0242139
The export of carbon from the surface of the ocean is one of the processes controlling the partial pressure of carbon dioxide (pCO2) of the atmosphere, which greatly influences the climate of the Earth. Changes in atmospheric pCO2 over glacial time scales are often interpreted as a response to changes in the ocean's biological carbon pump. Models of the carbon pump are limited by our understanding of mechanisms that control it in different areas of the ocean. Satellite color images hold great promise for determining the biological pump globally, but only if the images can be ground truthed by field measurements. To date this calibration has been achieved in only four places in the ocean: the long-term time series locations and parts of the Equator.
In this project, researchers at the University of Washington will develop experimental methods of improving our knowledge of the ocean.s biological carbon pump. The research program is twofold. First they will deploy four oxygen sensors and a GTD on the new mooring at HOT to measure a profile of O2 in the euphotic zone and the surface concentrations of N2. They believe that this will be sufficient to determine the net biological oxygen production. Two methods will be tested for calibrating the oxygen sensors in situ. This research will develop methods to determine the oxygen mass balance (and hence biological carbon pump) on moorings at other locations in the ocean.
The second, and much smaller, aspect of the project builds on the research team's analytical ability to determine N2, Ar and Ne in seawater. They will conduct a field program to study the concentrations of these gases as a function of wind speed on several short cruises in the Drake Passage of the Southern Ocean. The goal is to develop a correlation between bubble flux and wind speed. This knowledge could be used to characterize the bubble process in locations where it is not possible to measure these gases and to improve estimates of the biologically produced oxygen flux from the ocean using climatological surface ocean oxygen concentrations.
Broader impacts of this proposal include the benefits to society that will result from understanding the marine biological pump well enough to incorporate it into ocean-atmosphere models that will be used to predict future climate. The proposal also promotes education of a graduate student who will work on the project.";
    String projects_7_end_date "2007-02";
    String projects_7_name "Tracers of Biological Productivity and Gas Exchange";
    String projects_7_project_nid "743944";
    String projects_7_start_date "2003-03";
    String projects_8_acronym "OP3";
    String projects_8_description 
"NSF Award Abstract:
OCE-0221247
Primary production and remineralization in oligotrophic ocean waters like those around Bermuda are phenomena of central importance in the ocean carbon-cycle and figure prominently in climate change impact modeling. Geochemical constraints on primary production at Bermuda, characterized by annual and longer time-scales and based on three fundamentally different systems, lead to quantitatively consistent estimates of new, net community and export production. This agreement between the three types of primary production would at first seem to be expected on such time-scales, but leads to the basic \"Redfield Paradox \": nutrients advected or mixed upward into the euphotic zone must carry with them an associated oxygen debt (AOU) and dissolved inorganic carbon sufficient to negate largely the observed seasonal photosynthetic oxygen buildup and carbon drawdown. An exhaustive consideration of various explanations and scenarios that can be offered fail to explain the observations -- a dilemma here referred to as the \"Ocean Primary Production Paradox (OP3)\".
A team of researchers at the Woods Hole Oceanographic Institution will re-examine the OP3 by simultaneously and definitively measuring all three geochemical systems over a period of three to four years. These three systems are, specifically, euphotic zone oxygen production, aphotic zone oxygen consumption, and nutrient flux-gauge determinations. The euphotic zone oxygen system will be constrained by the time-series measurement of the full suite of noble gases (He-Xe) plus O2 and N2,with emphasis on precision measurements of O2 and Ar (to 0.1%),the latter as a biogenic analog of oxygen. The other gases will be used to more completely constrain and refine the air-sea gas exchange and upper ocean model.
Aside from addressing fundamental problem in ocean biogeochemistry, this work is expected to have considerably broader impact in the field of ocean geochemistry by providing the oceanographic community with new sampling technology (the noble gas sampler) that can be used in a broad variety of biogeochemical problems. The design and expertise will be made freely available to those who request it.";
    String projects_8_end_date "2007-08";
    String projects_8_name "Is There an Ocean Primary Production Paradox(OP3)?";
    String projects_8_project_nid "743948";
    String projects_8_start_date "2002-09";
    String projects_9_acronym "C Pump in Subtropical N Pacific";
    String projects_9_description 
"NSF Award Abstract:
OCE-9906922
The subtropical gyres occupy a large fraction of the world ocean and until recently, the common view was that these vast nutrient-depleted regions support only a small amount of primary productivity. However, this view is changing and it appears that production in these areas is important. Thus it is important to understand the mechanism by which nutrients are supplied to these areas. To this end, this project will seek to improve estimates of the mechanisms of nutrient transport to the euphotic zone, and also better constrain the magnitude of the carbon pump. The three main elements of this proposal are improvment in the continuous measurements of oxygen and N2 on the HOT mooring, ship-of-opportunity measurements of DOP (dissolved organic phosphorus) and DON (dissolved organic nitrogen), and measurements of neon to clarify the mechanism of bubble-induced gas exchange in the O2 budget. The goal is to determine the quantitative importance of intermittment diapycnal and surface transport of phosphorus, and to improve mass balance estimates of net biological oxygen production.";
    String projects_9_end_date "2003-12";
    String projects_9_name "The Biological Carbon Pump in the Subtropical North Pacific Ocean: Mechanisms of Nutrient Supply";
    String projects_9_project_nid "743952";
    String projects_9_start_date "2000-01";
    String publisher_name "Shannon Rauch";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing -68.1081;
    String standard_name_vocabulary "CF Standard Name Table v29";
    String summary "Inert gases dissolved in the ocean are powerful tracers of the impact of physical processes on gases, particularly air-sea gas exchange (by both diffusive and bubble-meditated processes), temperature change, atmospheric pressure variation, mixing between different water masses, and ice processes. We have compiled a global ocean database of dissolved neon, argon, and krypton measurements, supplemented by helium, xenon, and nitrogen/argon (N2/Ar) ratios in some locations. Samples were collected on board multiple research cruises spanning the period 1999 through 2016 and analyzed by mass spectrometry at four different shore-based laboratories (University of Victoria, Woods Hole Oceanographic Institution, University of Washington, and Scripps Institution of Oceanography).";
    String title "A compilation of dissolved noble gas and N2/Ar ratio measurements collected from 1999-2016 in locations spanning the globe";
    String version "1";
    Float64 Westernmost_Easting -159.9952;
    String xml_source "osprey2erddap.update_xml() v1.5-beta";
  }
}

 

Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its selection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.


 
ERDDAP, Version 1.82
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