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Dataset Title:  Global reconstruction of surface oceanic N2O disequilibrium and its associated
flux
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_810032)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Files | Make a graph
 
Variable ?   Optional
Constraint #1 ?
Optional
Constraint #2 ?
   Minimum ?
 
   Maximum ?
 
 cruise (unitless) ?          "27th Chinese Natio..."    "laurier"
 date (unitless) ?          "1971-06-09"    "2018-06-04"
 year (unitless) ?          1971    2018
 month (unitless) ?          1    12
 day (unitless) ?          1    31
 latitude (degrees_north) ?          -77.177    88.3943333333333
  < slider >
 longitude (degrees_east) ?          0.0    359.999995521472
  < slider >
 depth (m) ?          0.0    9.973
  < slider >
 n2o_ppb (ppb) ?          140.097262188757    30401.7581479621
 n2o_nM (nmol/L) ?          3.41    842.972937199327
 dn2o_ppb (ppb) ?          -178.519816514739    30076.2397223307
 atmPressure (atm) ?          0.940551788914571    1.02529054735944
 temperature (degrees Celsius) ?          -2.2    31.896
 salinity (g/kg) ?          0.0    42.2815780639648
 
Server-side Functions ?
 distinct() ?
? ("Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.")

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  cruise {
    String bcodmo_name "Cruise Name";
    String description "Cruise name";
    String long_name "Cruise";
    String units "unitless";
  }
  date {
    String bcodmo_name "date";
    String description "Date; format: YYYY-MM-DD";
    String long_name "Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String source_name "date";
    String time_precision "1970-01-01";
    String units "unitless";
  }
  year {
    Int16 _FillValue 32767;
    Int16 actual_range 1971, 2018;
    String bcodmo_name "year";
    String description "Measurement year; format: YYYY";
    String long_name "Year";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
    String units "unitless";
  }
  month {
    Byte _FillValue 127;
    Byte actual_range 1, 12;
    String bcodmo_name "month";
    String description "Measurement month; format: MM";
    String long_name "Month";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/MNTHXXXX/";
    String units "unitless";
  }
  day {
    Byte _FillValue 127;
    Byte actual_range 1, 31;
    String bcodmo_name "day";
    String description "Measurement day; format: DD";
    String long_name "Day";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DAYXXXXX/";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range -77.177, 88.3943333333333;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Measurement latitude";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 359.999995521472;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Measurement longitude";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String standard_name "longitude";
    String units "degrees_east";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 9.973;
    String axis "Z";
    String bcodmo_name "depth";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "Measurement depth";
    String ioos_category "Location";
    String long_name "Depth";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  n2o_ppb {
    Float64 _FillValue NaN;
    Float64 actual_range 140.097262188757, 30401.7581479621;
    String bcodmo_name "Nitrous Oxide";
    String description "Ocean n2o mixing ratio";
    String long_name "N2o Ppb";
    String units "ppb";
  }
  n2o_nM {
    Float64 _FillValue NaN;
    Float64 actual_range 3.41, 842.972937199327;
    String bcodmo_name "Nitrous Oxide";
    String description "Ocean n2o mixing ratio";
    String long_name "N2o N M";
    String units "nmol/L";
  }
  dn2o_ppb {
    Float64 _FillValue NaN;
    Float64 actual_range -178.519816514739, 30076.2397223307;
    String bcodmo_name "Nitrous Oxide";
    String description "Estimated n2o disequilibrium";
    String long_name "Dn2o Ppb";
    String units "ppb";
  }
  atmPressure {
    Float64 _FillValue NaN;
    Float64 actual_range 0.940551788914571, 1.02529054735944;
    String bcodmo_name "press_bar";
    String description "Sea level pressure estimated at observed time and location";
    String long_name "Atm Pressure";
    String units "atm";
  }
  temperature {
    Float64 _FillValue NaN;
    Float64 actual_range -2.2, 31.896;
    String bcodmo_name "temperature";
    String description "Co-measured temperature";
    String long_name "Temperature";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius";
  }
  salinity {
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 42.2815780639648;
    String bcodmo_name "sal";
    Float64 colorBarMaximum 37.0;
    Float64 colorBarMinimum 32.0;
    String description "Co-measured salinity (or estimated from climatology if absent)";
    String long_name "Sea Water Practical Salinity";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PSALST01/";
    String units "g/kg";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"We compiled surface (0m-12m depth) marine N2O concentrations and partial
pressures measurements from a variety of sources. The core of the data is
sourced from the MEMENTO database (Kock\\u00a0& Bange, 2015). We complement
MEMENTO with additional published N2O measurements from the literature, and
unpublished N2O measurements from 16 additional cruises (see Supplemental File
\\\"SuppCruiseTable_dec11.xlsx\\\"), including 11 cruises from the Global Ocean
Ship-Based Hydrographic Investigations Program (GO-SHIP). We do not perform
any further quality control of the N2O data from published sources besides
that performed by the individual contributors and the MEMENTO database
administrators (Kock\\u00a0& Bange, 2015). A description of the quality control
performed on new unpublished N2O data is reported as footnotes to the
annotations labeled as qc1**, qc2**, or qc3** (see
\\\"SuppCruiseTable_dec11.xlsx\\\"). We convert each marine N2O measurement to
XwN2O (the N2O mixing ratio in seawater, in units of ppb) using, when needed,
the N2O solubility coefficient (Weiss & Price, 1980). The coefficient is
calculated using co-measured temperature, and salinity, as well as sea level
pressure from the ERA5 reanalysis (Copernicus Climate Change Service, 2017),
at the time (month and year), and location of the measurement. If the
measurement time is not available in the ERA5 reanalysis prediction, we
instead use the climatological atmospheric pressure at sea level, calculated
from the monthly predictions for the years from 1979 through 2018. We then
calculated N2O disequilibrium as DN2O = XwN2O \\u2212 XaN2O, where XaN2O is the
atmospheric N2O mixing ratio estimated by linear interpolation of NOAA\\u2019s
flask measurement dataset (Hall\\u00a0et al., 2007) at the time and latitude of
each marine N2O measurement.;
 
To convert sparse observations to a global climatology, we trained 100
ensembles of regressions trees (Random Forests) to predict DN2O based on its
relationship to well-sampled physical and biogeochemical predictors. We note
that, while the prediction of N2O disequilibrium is done in mixing ratio units
(ppb), the results are reported in the more commonly used pressure units
(natm): pN2O = XN2O . P, where P is the climatological atmospheric pressure at
sea level in atm, predicted by ERA5, included as part of the relevant data
file for easy conversion. (see Data File: dn2o-mapped-Yang2020.nc).\\u00a0
 
We calculate the N2O air-sea flux using two wind-speed dependent
parameterizations: an updated version of a commonly-used quadratic formulation
(Wanninkhof, 1992; Wanninkhof, 2014) and a recent formulation that explicitly
accounts for the effect of bubble-mediated fluxes (Liang et al., 2013). We
apply each parameterization to two high-resolution wind products (Copernicus
Climate Change Service, 2017;\\u00a0Wentz et al., 2015), yielding four
permutations of the piston velocity. In total, we obtain an ensemble of 400
global N2O air-sea flux estimates, from which we calculate a mean and
uncertainty range (see Data File: n2oFlux-Yang2020.nc).
 
Sampling and analytical prodcedures:  
 The data is compiled from multiple sources, published and unpublished.\\u00a0
Refer to the associated Supplemental File
\\\"SuppCruiseTable_dec11.xlsx\\\"\\u00a0for a detailed description of sampling and
analytical methods associated with new data and references associated with
published data. \\\"qc\\\" refers to \\\"qualtiy control and methods\\\"; see related
references and descriptions in the Supplemental File.
 
GOSHIP (qc1): N2O was measured using shipboard gas chromatography-electron
capture detection (GC-ECD) using analytical techniques modified from those
described in Bullister and Wisegarver (2008). N2O was purged from 200 mL
seawater samples using N2 carrier gas and trapped onto a trap that included
MS5A held at -60\\u00b0C. The trap was subsequently heated to 175\\u00b0C to
release N2O, which was further separated and purified via two precolumns
before being quantified using electron capture detection. (The carrier gas for
the N2O analyses was a 95%Ar/5% CH4 mix) The analytical system was calibrated
frequently using internal standards of known N2O compositions or standards
from Working Group no. 143 of the Scientific Committee on Oceanic Research
(SCOR) (Wilson et al. 2018). Concentrations of N2O in seawater samples and gas
standards are reported relative to the SIO98 calibration scale.
 
SPOT (qc3): Dissolved N2O concentrations were measured using a headspace
equilibration method modified from Laperriere et al. (2019). A 30-mL ultra-
high purity N2 headspace was introduced into 160 mL seawater samples using a
30-mL syringe with a second empty 30-mL syringe inserted into the septum to
collect displaced sample water. Each headspace was overpressured with 10 mL of
ultra high purity N2 to minimize atmospheric contamination. Samples were
analyzed on an SRI 8610 Greenhouse Gas Monitoring Gas Chromatograph (GC)
equipped with an electron capture detector (ECD), dual HayeSep D packed
columns, and a 1-mL sample loop (SRI Instruments, Torrance, California, USA).
Ultra-high purity N2 gas was used as the carrier with the sample loop kept at
60 \\u00b0C and the column oven kept at 100 \\u00b0C. Two certified standards,
0.1 ppm and 1 ppm N2O (Matheson Tri-Gas) were used for daily calibration using
a linear calibration scheme.
 
Others (qc2): N2O concentrations were measured with a GV IsoPrime Continuous
Flow Isotope-Ratio Mass Spectrometer (CF-IRMS) as described in Bourbonnais et
al. (2017). Briefly, seawater was pumped from sample bottles and completely
extracted using a gas-extractor continuously sparged with He. N2O was then
concentrated and purified in a purge-trap system. CO2 and H2O were removed
with chemical and cryogenic traps. N2O was cryofocused with liquid N2 traps
and passed through a gas chromatography (GC) column before IRMS analysis. N2O
concentrations were calculated from relative peak heights between the samples
and seawater standards of known N2O concentration equilibrated with the
atmosphere at 5C and 20C. Equilibrium surface N2O concentrations were
calculated based on the global mean atmospheric N2O dry mole fraction at the
time of the cruise. The data were inter calibrated with samples also measured
using purge-trap gas extraction systems coupled with either a GC-Electron
Capture Detector (ECD) or a GC-quadrupole mass spectrometer (Fenwick et al.,
2017) when available (e.g., P18 GO-SHIP, ArcticNet 2017 expedition) and
yielded comparable N2O concentrations (generally less than 5% difference).
 
The data is compiled from multiple sources, published and unpublished. Refer
to the Supplemental File, \\\"SuppCruiseTable_dec11.xlsx\\\", for a detailed
description of sampling and analytical methods associated with new data and
references associated with published data.";
    String awards_0_award_nid "809664";
    String awards_0_award_number "OCE-1847687";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1847687";
    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 "Elizabeth Canuel";
    String awards_0_program_manager_nid "778642";
    String cdm_data_type "Other";
    String comment 
"Surface N2O Compilation 
   orig file name: surfocean-n2o-compilation.csv 
  PI: Daniele Bianchi (UCLA) 
  Co-PI: Simon Yang (UCLA) 
  Version date: 27 April 2020 
 ** NOTE: This is a large dataset that may take some time to fully load in the browser.";
    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 2.3  19 Dec 2019";
    String dataset_current_state "Final and no updates";
    String date_created "2020-04-27T16:58:48Z";
    String date_modified "2020-05-01T19:16:38Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.26008/1912/bco-dmo.810032.1";
    Float64 Easternmost_Easting 359.999995521472;
    Float64 geospatial_lat_max 88.3943333333333;
    Float64 geospatial_lat_min -77.177;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 359.999995521472;
    Float64 geospatial_lon_min 0.0;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 9.973;
    Float64 geospatial_vertical_min 0.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2022-01-23T17:38:37Z (local files)
2022-01-23T17:38:37Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_810032.html";
    String infoUrl "https://www.bco-dmo.org/dataset/810032";
    String institution "BCO-DMO";
    String instruments_0_acronym "IR Mass Spec";
    String instruments_0_dataset_instrument_description "Others (see qc2): GV IsoPrime Continuous Flow Isotope-Ratio Mass Spectrometer (CF-IRMS)";
    String instruments_0_dataset_instrument_nid "810055";
    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 "GV IsoPrime Continuous Flow Isotope-Ratio Mass Spectrometer";
    String instruments_1_acronym "Gas Chromatograph";
    String instruments_1_dataset_instrument_description 
"GOSHIP: Shipboard gas chromatography-electron capture detection (GC-ECD)
SPOT: SRI 8610 Greenhouse Gas Monitoring Gas Chromatograph (GC) equipped with an electron capture detector (ECD), dual HayeSep D packed columns, and a 1-mL sample loop (SRI Instruments, Torrance, California, USA).";
    String instruments_1_dataset_instrument_nid "810054";
    String instruments_1_description "Instrument separating gases, volatile substances, or substances dissolved in a volatile solvent by transporting an inert gas through a column packed with a sorbent to a detector for assay. (from SeaDataNet, BODC)";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB02/";
    String instruments_1_instrument_name "Gas Chromatograph";
    String instruments_1_instrument_nid "661";
    String instruments_1_supplied_name "Shipboard gas chromatography-electron capture detection";
    String keywords "atm, atmPressure, bco, bco-dmo, biological, chemical, cruise, data, dataset, date, day, density, depth, dmo, dn2o, dn2o_ppb, earth, Earth Science > Oceans > Salinity/Density > Salinity, erddap, latitude, longitude, management, month, n2o, n2o_nM, n2o_ppb, ocean, oceanography, oceans, office, ppb, practical, preliminary, pressure, salinity, science, sea, sea_water_practical_salinity, seawater, temperature, time, water, year";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "https://www.bco-dmo.org/dataset/810032/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/810032";
    Float64 Northernmost_Northing 88.3943333333333;
    String param_mapping "{'810032': {'latitude': 'flag - latitude', 'depth': 'flag - depth', 'longitude': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/810032/parameters";
    String people_0_affiliation "University of California-Los Angeles";
    String people_0_affiliation_acronym "UCLA";
    String people_0_person_name "Daniele Bianchi";
    String people_0_person_nid "809667";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of California-Los Angeles";
    String people_1_affiliation_acronym "UCLA";
    String people_1_person_name "Simon Yang";
    String people_1_person_nid "810042";
    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 BCO-DMO";
    String people_2_person_name "Shannon Rauch";
    String people_2_person_nid "51498";
    String people_2_role "BCO-DMO Data Manager";
    String people_2_role_type "related";
    String project "Multiple Scales of Nitrogen Cycle in the Ocean";
    String projects_0_acronym "Multiple Scales of Nitrogen Cycle in the Ocean";
    String projects_0_description 
"NSF Award Abstract:
The nitrogen cycle in the ocean is key to ocean productivity, carbon storage, and emissions of nitrous oxide, a potent greenhouse gas, to the atmosphere. The chemical processes that connect nitrogen species in the ocean are sensitive to the amount of oxygen dissolved in seawater. These reactions become more intense within oxygen minimum zones, areas of the ocean with little or no dissolved oxygen. Oxygen minimum zones are affected by currents that range in scale from hundreds to less than few kilometers. These currents create microhabitats where nitrogen cycling and nitrous oxide emissions are higher. This project investigates the interaction between small-scale ocean circulation, oxygen availability, and the nitrogen cycle. It uses a series of increasingly finer-scale numerical simulations of the Pacific Ocean, where two of the largest oxygen minimum zones are found. These simulations provide information about nitrogen transformations and nitrous oxide emissions on timescales from less than one year to several decades, and spatial scales from a few kilometers to the basin scale. This research will increase our ability to simulate and predict ocean responses to natural and human disturbances, with implications for society. The educational component of the project establishes a series of ocean-going chemical oceanography activities for approximately 100 undergraduate students at the University of California at Los Angeles each year. The field trips involve half-day cruises in the Santa Monica Bay, where students sample a variety of biogeochemical properties. Observations collected during the field trips will be used as a resource in classroom activities and student research projects. The field trips and educational materials offer opportunities to explore cutting-edge questions in ocean biogeochemistry, increase student interest in ocean sciences and access to research, and enhance student learning and self-efficacy, ultimately promoting retention in oceanography and STEM.
Oxygen minimum zones host major nitrogen transformations, including denitrification, anammox, and nitrous oxide production, which are essential for biogeochemistry and climate. These reactions are strongly partitioned along oxygen gradients in the suboxic range, making them sensitive to ventilation and chemical heterogeneity driven by variable ocean currents. However, the nature of this sensitivity is poorly understood. The objective of this project is to test the hypothesis that physical circulation at scales from tens of kilometers (mesoscale) to less than one kilometer (submesoscale) is critical in shaping these nitrogen cycle transformations. To test the hypothesis and investigate its implications, we will optimize a new model of the nitrogen cycle against a range of recent observations, and implement it in a realistic three-dimensional hydrodynamic-biogeochemical model. We will adopt a nesting strategy to downscale a Pacific-wide historical simulation to a series of regional domains at resolutions down to few kilometers or less, resolving the oxygen minimum zone boundaries and their fine-scale variability. By analyzing these model solutions, we will: (1) constrain the sensitivity of the microbial nitrogen cycle to oxygen, ventilation, and chemical heterogeneity; (2) in light of this sensitivity, quantify the role of mesoscale and submesoscale processes in shaping nitrogen transformations and transport across oxygen minimum zone boundaries; and (3) investigate the response of the nitrogen cycle to climate variability, in particular fixed-nitrogen losses and nitrous oxide emissions to the atmosphere.";
    String projects_0_end_date "2024-06";
    String projects_0_geolocation "Global";
    String projects_0_name "CAREER: Multiple Scales of Nitrogen Cycle in Oxygen Minimum Zones";
    String projects_0_project_nid "809665";
    String projects_0_start_date "2019-07";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing -77.177;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Global reconstruction of surface oceanic N2O disequilibrium and its associated flux. The dataset consists of (1) a global compilation of observed nitrous oxide pressure, concentration, mixing ratio measurements and their associated disequilibrium in the surface ocean; (2) the globally mapped N2O disequilibrium predicted by a supervised learning algorithm, and (3) the reconstructed ocean to atmosphere N2O flux.";
    String title "Global reconstruction of surface oceanic N2O disequilibrium and its associated flux";
    String version "1";
    Float64 Westernmost_Easting 0.0;
    String xml_source "osprey2erddap.update_xml() v1.5";
  }
}

 

Using tabledap to Request Data and Graphs from Tabular Datasets

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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.

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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.


 
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