Contributors | Affiliation | Role |
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Bianchi, Daniele | University of California-Los Angeles (UCLA) | Principal Investigator |
Yang, Simon | University of California-Los Angeles (UCLA) | Co-Principal Investigator |
Rauch, Shannon | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Global reconstruction of surface oceanic N2O disequilibrium and its associated flux. The dataset includes 3 files:
(1) surfocean-n2o-compilation.csv: available as a .csv/.tsv file via the "Get Data" button on the BCO-DMO landing page. A global compilation of observed surface ocean nitrous oxide pressure, concentration, mixing ratio measurements and their implied nitrous oxide disequilibrium used to train the supervised learning algorithm. Also attached as a .mat file (surfocean-n2o-compilation.mat) under "Data Files".
(2) dn2o-mapped-Yang2020.nc: available under "Data Files". Predicted N2O disequilibrium.
(3) n2oFlux-Yang2020.nc: available under "Data Files". The predicted ocean to atmosphere N2O flux.
This dataset contains a compilation of data from multiple sources. A list of all datasets and the associated information, including cruise name, is included in the associated Supplemental File, "SuppCruiseTable_dec11.xlsx".
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 & 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 We complement MEMENTO with additional published N2O measurements from the literature, and unpublished N2O measurements from 16 additional cruises (see Supplemental File "SuppCruiseTable_dec11.xlsx"), including 11 cruises from the Global Ocean Ship-Based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 & 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 A description of the quality control performed on new unpublished N2O data is reported as footnotes to the annotations labeled as qc1**, qc2**, or qc3** (see "SuppCruiseTable_dec11.xlsx").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 − XaN2O, where XaN2O is the atmospheric N2O mixing ratio estimated by linear interpolation of NOAA’s flask measurement dataset (Hall et 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).
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; Wentz 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. Refer to the associated Supplemental File "SuppCruiseTable_dec11.xlsx" for 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°C. The trap was subsequently heated to 175°C 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 °C and the column oven kept at 100 °C. 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.
Data Processing:
The N2O disequilibrium at the location of each measurement is estimated following: _DXN2O = XN2O_ocean − XN2O_atm. Here, XN2O_ocean is the ocean-side N2O measurement converted to mixing ratios, and XN2O_ atm is the atmospheric N2O mixing ratio estimated by interpolating the National Oceanic and Atmospheric Administration atmospheric N2O flask dataset at the latitude and time of the oceanic measurements.
BCO-DMO Processing:
added "date" column as YYYY-MM-DD.
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dn2o-mapped-Yang2020.nc - Predicted N2O disequilibrium filename: dn2o-mapped-Yang2020.nc (NetCDF, 170.65 MB) MD5:88d77b3149edd5cad2d39781ec4cc8f7 Predicted N2O disequilibrium. The N2O disequilibrium at the location of each measurement is estimated following: _DXN2O = XN2O_ocean − XN2O_atm . Here, XN2O_ocean is the ocean-side N2O measurement converted to mixing ratios, and XN2O_atm is the atmospheric N2O mixing ratio estimated by interpolating the National Oceanic and Atmospheric Administration atmospheric N2O flask dataset at the latitude and time of the oceanic measurements. 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.
File contents:
latitude_2d = enter-cell latitude; degrees north
longitude_2d = center-cell longitude; degrees east
cellArea_m2 = Cell area in m^2
n2oFlux_EnsMean_g-pm2-pyr = dn2o_EnsMean_natm; natm
n2oFlux_EnsStd_g-pm2-pyr = dn2o_EnsStd_natm; natm
n2oFluxSeas_g-pm2-pyr = dn2o_griddedobs_mean_natm; natm
atm-press = Sea Level Pressure -- ERA5 climatological average; atm
biomes_masks = ocean biomes masks (1:Tropical ocean, 2:Coastal upwelling systems, 3:Polar ocean, 4: Mid-latitudes, 5: Deep mixed layer systems, 6: Subtropical gyres)
coastal_upwellSys_masks = coastal upwelling systems masks (1:Peru, 2:Benguela, 3:Costa-Rica, 4:Chile, 5:California current, 6: Canary, 7:Arabian sea , 8: Bay of Bengal)
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n2oDataYang2020PNAS.mat - Surface N2O Compilation filename: n2oDataYang2020PNAS.mat (MATLAB Data (.mat), 9.90 MB) MD5:c8ddb77538e77dbb6486ff3a9c887cf9 Surface N2O Compilation in .mat format |
n2oFlux-Yang2020.nc - Predicted ocean to atmosphere N2O flux filename: n2oFlux-Yang2020.nc (NetCDF, 357.95 MB) MD5:35620fa745a5a6a5c823cab32efbf3df We calculate the N2O air-sea flux using two wind-speed dependent parameterizations: an updated version of a commonly-used quadratic formulation, and a recent formulation that explicitly accounts for the effect of bubble-mediated . We apply each parameterization to two high-resolution wind products 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.
File contents:
latitude_2d = center-cell latitude; degrees north
longitude_2d =center-cell longitude; degrees east
cellArea_m2 =Cell area in m^2
n2oFlux_EnsMean_g-pm2-pyr =n2o flux -- ensemble mean prediction; g m^(-2) y^(-1)
n2oFlux_EnsStd_g-pm2-pyr =n2o flux -- ensemble standard-deviation; g m^(-2) y^(-1)
n2oFluxSeas_g-pm2-pyr = n2o flux seasonality (sum of components); g m^(-2) y^(-1)
n2oFluxSeas_fromWind_g-pm2-pyr = wind component of the n2o flux; g m^(-2) y^(-1)
n2oFluxSeas_fromXn2o_g-pm2-pyr = dn2o component of the n2o flux; g m^(-2) y^(-1)
n2oFluxSeas_fromIcePress_g-pm2-pyr = sea-ice and atmospheric pressure component of the n2o flux; g m^(-2) y^(-1)
n2oFluxSeas_fromCovar_g-pm2-pyr = covariations component of the n2o flux; g m^(-2) y^(-1)
biomes_masks =ocean biomes masks (1:Tropical ocean, 2:Coastal upwelling systems, 3:Polar ocean, 4: Mid-latitudes, 5: Deep mixed layer systems, 6: Subtropical gyres)
coastal_upwellSys_masks = coastal upwelling systems masks (1:Peru, 2:Benguela, 3:Costa-Rica, 4:Chile, 5:California current, 6: Canary, 7:Arabian sea , 8: Bay of Bengal)
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surface_n2o_compilation.csv (Comma Separated Values (.csv), 19.12 MB) MD5:0abcef36779cfe164efa21b29617defb Primary data file for dataset ID 810032 |
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SuppCruiseTable_dec11.xlsx - Supplemental table of cruise information and methods used in the N2O global compilation dataset filename: SuppCruiseTable_dec11.xlsx (Octet Stream, 24.25 KB) MD5:5b3bddfe412003ab70d18838105d6002 N2O data included in the compilation listed by cruise name, the associated year, number of observations, source and if relevant, a reference. See tag description qc1, qc2, and qc3 at the end of the table for a detailed description of the methods associated with new data. |
Parameter | Description | Units |
cruise | Cruise name | unitless |
date | Date; format: YYYY-MM-DD | unitless |
year | Measurement year; format: YYYY | unitless |
month | Measurement month; format: MM | unitless |
day | Measurement day; format: DD | unitless |
latitude | Measurement latitude | degrees North |
longitude | Measurement longitude | degrees East |
depth | Measurement depth | meters (positive down) |
n2o_ppb | Ocean n2o mixing ratio | ppb |
n2o_nM | Ocean n2o mixing ratio | nmol/L |
dn2o_ppb | Estimated n2o disequilibrium | ppb |
atmPressure | Sea level pressure estimated at observed time and location | atm |
temperature | Co-measured temperature | degrees Celsius |
salinity | Co-measured salinity (or estimated from climatology if absent) | g/kg |
Dataset-specific Instrument Name | Shipboard gas chromatography-electron capture detection |
Generic Instrument Name | Gas Chromatograph |
Dataset-specific 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). |
Generic Instrument 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) |
Dataset-specific Instrument Name | GV IsoPrime Continuous Flow Isotope-Ratio Mass Spectrometer |
Generic Instrument Name | Isotope-ratio Mass Spectrometer |
Dataset-specific Description | Others (see qc2): GV IsoPrime Continuous Flow Isotope-Ratio Mass Spectrometer (CF-IRMS) |
Generic Instrument 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). |
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.
Funding Source | Award |
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NSF Division of Ocean Sciences (NSF OCE) |