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| Dataset Title: | [Global model nitrate d15N] - Estimated nitrate d15N modeled using an ensemble of artificial neural networks (EANNs) (Collaborative research: Combining models and observations to constrain the marine iron cycle) 
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| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_768655) |
| Range: | longitude = 0.5 to 359.5°E, latitude = -79.5 to 83.5°N, depth = 0.0 to 5500.0m |
| Information: | Summary
| License
| FGDC
| ISO 19115
| Metadata
| Background
| Data Access Form
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Zoom: ![[The graph you specified. Please be patient.]](https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_768655.png?longitude,latitude,d15N&.draw=markers&.marker=5%7C5&.color=0x000000&.colorBar=%7C%7C%7C%7C%7C&.bgColor=0xffccccff)
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Attributes {
s {
latitude {
String _CoordinateAxisType "Lat";
Float64 _FillValue NaN;
Float64 actual_range -79.5, 83.5;
String axis "Y";
String bcodmo_name "latitude";
Float64 colorBarMaximum 90.0;
Float64 colorBarMinimum -90.0;
String description "Latitude in degrees north";
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 0.5, 359.5;
String axis "X";
String bcodmo_name "longitude";
Float64 colorBarMaximum 180.0;
Float64 colorBarMinimum -180.0;
String description "Longitude in degrees East";
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, 5500.0;
String axis "Z";
String bcodmo_name "depth";
Float64 colorBarMaximum 8000.0;
Float64 colorBarMinimum -8000.0;
String colorBarPalette "TopographyDepth";
String description "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";
}
d15N {
Float32 _FillValue NaN;
Float32 actual_range 0.98889, 26.663;
String bcodmo_name "dN15_NO3";
String description "modeled nitrate d15N";
String long_name "D15 N";
String units "per mil";
}
d15N_stdev {
Float32 _FillValue NaN;
Float32 actual_range 0.036923, 15.627;
String bcodmo_name "dN15_NO3";
String description "standard deviation";
String long_name "D15 N Stdev";
String units "per mil";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
String acquisition_description
"For complete methodology, refer to Rafter et al. (2019). In summary:
Data Compilation:\\u00a0Nitrate d15N observations were compiled from studies
dating from 1975 to 2018. This global ocean nitrate d15N database was
interpolated using an ensemble of artificial neural networks (EANNs). For the
compiled observed global ocean nitrate d15N data, see the related dataset:
[https://www.bco-dmo.org/dataset/768627](\\\\\"https://www.bco-
dmo.org/dataset/768627\\\\\")
Building the neural network model: We utilize an ensemble of artificial neural
networks (EANNs) to interpolate our global ocean nitrate d15N database,
producing complete 3D maps of the data. By utilizing an artificial neural
network (ANN), a machine learning approach that effectively identifies
nonlinear relationships between a target variable (the isotopic dataset) and a
set of input features (other available ocean datasets), we can fill holes in
our data sampling coverage of nitrate d15N.
Binning target variables (Step 1): We binned the nitrate d15N observations to
the World Ocean Atlas 2009 (WOA09) grid with a 1-degree spatial resolution and
33 vertical depth layers (0-5500 m). When binning vertically, we use the depth
layer whose value is closest to the observation's sampling depth (e.g. the
first depth layer has a value of 0 m, the second of 10 m, and the third of 20
m, so all nitrate isotopic data sampled between 0-5 m fall in the 0 m bin;
between 5-15 m they fall in the 10 m bin, etc.). An observation with a
sampling depth that lies right at the midpoint between depth layers is binned
to the shallower layer. If more than one raw data point falls in a grid cell
we take the average of all those points as the value for that grid cell.
Certain whole ship tracks of nitrate d15N data were withheld from binning to
be used as an independent validation set.
Obtaining input features (Step 2): Our input dataset contains a set of
climatological values for physical and biogeochemical ocean parameters that
form a non-linear relationship with the target data. We have six input
features including objectively analyzed annual-mean fields for temperature,
salinity, nitrate, oxygen, and phosphate taken from the WOA09
([https://www.nodc.noaa.gov/OC5/WOA09/woa09data.html](\\\\\"https://www.nodc.noaa.gov/OC5/WOA09/woa09data.html\\\\\"))
at 1-degree resolution. Additionally, daily chlorophyll data from Modis Aqua
for the period Jan-1-2003 through Dec-31-2012 is averaged and binned to the
WOA09 grid (as described in Step 1) to produce an annual climatological field
of chlorophyll values, which we then log transform to reduce their dynamic
range.
The choice of these specific input features was dictated by our desire to
achieve the best possible R2 value on our internal validation sets (Step 4).
Additional inputs besides those we included, such as latitude, longitude,
silicate, euphotic depth, or sampling depth either did not improve the R2
value on the validation dataset or degraded it, indicating that they are not
essential parameters for characterizing this system globally. By opting to use
the set of input features that yielded the best results for the global oceans,
we potentially overlooked combinations of inputs that perform better at
regional scales. However, given the scarcity of d15N data in some regions, it
is not possible to ascribe the impact of a specific combination of input
features versus the impact of available d15N data, which may not be
representative of the region's climatological state, to the relative model
performance in these regions.
Training the ANN (Step 3): The architecture of our ANN consists of a single
hidden layer, containing 25 nodes, that connects the biological and physical
input features (discussed in Step 2) to the target nitrate isotopic variable
(as discussed in Step 1). The role of the hidden layer is to transform input
features into new features contained in the nodes. These are given to the
output layer to estimate the target variable, introducing nonlinearities via
an activation function. The number of nodes in this hidden layer, as well as
the number of input features, determines the number of adjustable weights (the
free parameters) in the network. For complete information, refer to Rafter et
al. (2019).
Validating the ANN (Step 4):To ensure good generalization of the trained ANN,
we randomly withhold 10% of the d15N data to be used as an internal validation
set for each network. This is data that the network never sees, meaning it
does not factor into the cost function, so it works as a test of the ANN's
ability to generalize. This internal validation set acts as a gatekeeper to
prevent poor models from being accepted into the ensemble of trained networks
(see Step 5). A second, independent or 'external' validation set, composed of
complete ship transects from the high and low latitude ocean were omitted from
binning in Step 1 and used to establish the performance of the entire
ensemble. Our rationale for using complete ship transects is the following. If
we randomly chose 10% of observations to perform an external validation, this
dataset will be from the same cruises as the wider data. In other words,
despite being randomly selected, the validating observational dataset will be
highly correlated geographically. Contrast this with validating the EANN
results with observations from whole research cruises in unique geographic
regions\\u2014areas where the model has not \\\"learned\\\" anything about nitrate.
We therefore argue that these observations from whole ship tracks therefore
provide a more difficult test of the model.
Forming the Ensemble (Step 5):\\u00a0The ensemble is formed by repeating Steps
3 to 4 (using a different random 10% validation set) until we obtain 25
trained networks for the nitrate d15N dataset. A network is admitted into the
ensemble if it yields an R\\u00b2 value greater than 0.81 on the validation
dataset.\\u00a0For complete information, refer to Rafter et al. (2019).";
String awards_0_award_nid "766422";
String awards_0_award_number "OCE-1658392";
String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1658392";
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 Simone Metz";
String awards_0_program_manager_nid "51479";
String cdm_data_type "Other";
String comment
"Global modeled nitrate d15N
PI: Patrick Rafter (UC Irvine)
Co-PIs: Aaron Bagnell (UCSB), Dario Marconi (Princeton), & Timothy DeVries (UCSB)
Version date: 28-May-2019";
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 date_created "2019-05-28T17:50:26Z";
String date_modified "2019-06-17T20:08:40Z";
String defaultDataQuery "&time<now";
String doi "10.1575/1912/bco-dmo.768655.1";
Float64 Easternmost_Easting 359.5;
Float64 geospatial_lat_max 83.5;
Float64 geospatial_lat_min -79.5;
String geospatial_lat_units "degrees_north";
Float64 geospatial_lon_max 359.5;
Float64 geospatial_lon_min 0.5;
String geospatial_lon_units "degrees_east";
Float64 geospatial_vertical_max 5500.0;
Float64 geospatial_vertical_min 0.0;
String geospatial_vertical_positive "down";
String geospatial_vertical_units "m";
String history
"2024-11-08T05:43:06Z (local files)
2024-11-08T05:43:06Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_768655.das";
String infoUrl "https://www.bco-dmo.org/dataset/768655";
String institution "BCO-DMO";
String keywords "bco, bco-dmo, biological, chemical, d15, d15N, d15N_stdev, data, dataset, depth, deviation, dmo, erddap, latitude, longitude, management, oceanography, office, preliminary, standard, standard deviation, stdev";
String license "https://www.bco-dmo.org/dataset/768655/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/768655";
Float64 Northernmost_Northing 83.5;
String param_mapping "{'768655': {'latitude': 'flag - latitude', 'depth': 'flag - depth', 'longitude': 'flag - longitude'}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/768655/parameters";
String people_0_affiliation "University of California-Irvine";
String people_0_affiliation_acronym "UC Irvine";
String people_0_person_name "Patrick Rafter";
String people_0_person_nid "615040";
String people_0_role "Principal Investigator";
String people_0_role_type "originator";
String people_1_affiliation "University of California-Santa Barbara";
String people_1_affiliation_acronym "UCSB";
String people_1_person_name "Aaron Bagnell";
String people_1_person_nid "768632";
String people_1_role "Co-Principal Investigator";
String people_1_role_type "originator";
String people_2_affiliation "University of California-Santa Barbara";
String people_2_affiliation_acronym "UCSB";
String people_2_person_name "Timothy DeVries";
String people_2_person_nid "766426";
String people_2_role "Co-Principal Investigator";
String people_2_role_type "originator";
String people_3_affiliation "Princeton University";
String people_3_person_name "Dario Marconi";
String people_3_person_nid "768634";
String people_3_role "Co-Principal Investigator";
String people_3_role_type "originator";
String people_4_affiliation "University of California-Irvine";
String people_4_affiliation_acronym "UC Irvine";
String people_4_person_name "Patrick Rafter";
String people_4_person_nid "615040";
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 "Fe Cycle Models and Observations";
String projects_0_acronym "Fe Cycle Models and Observations";
String projects_0_description
"NSF Award Abstract:
Tiny marine organisms called phytoplankton play a critical role in Earth's climate, by absorbing carbon dioxide from the atmosphere. In order to grow, these phytoplankton require nutrients that are dissolved in seawater. One of the rarest and most important of these nutrients is iron. Even though it is a critical life-sustaining nutrient, oceanographers still do not know much about how iron gets into the ocean, or how it is removed from seawater. In the past few years, scientists have made many thousands of measurements of the amount of dissolved iron in seawater, in environments ranging from the deep sea, to the Arctic, to the tropical oceans. They found that the amount of iron in seawater varies dramatically from place to place. Can this data tell us about how iron gets into the ocean, and how it is ultimately removed? Yes. In this project, scientists working on making measurements of iron in seawater will come together with scientists who are working on computer models of iron inputs and removal in the ocean. The goal is to work together to create a program that allows our computer models to \"learn\" from the data, much like an Artificial Intelligence program. This program will develop a \"best estimate\" of where and how much iron is coming into the ocean, how long it stays in the ocean, and ultimately how it gets removed. This will lead to a better understanding of how climate change will impact the delivery of iron to the ocean, and how phytoplankton will respond to climate change. With better climate models, society can make more informed decisions about how to respond to climate change. The study will also benefit a future generation of scientists, by training graduate students in a unique collaboration between scientists making seawater measurements, and those using computer models to interpret those measurements. Finally, the project aims to increase the participation of minority and low-income students in STEM (Science, Technology, Engineering, and Mathematics) research, through targeted outreach programs.
Iron (Fe) is an important micronutrient for marine phytoplankton that limits primary productivity over much of the ocean; however, the major fluxes in the marine Fe cycle remain poorly quantified. Ocean models that attempt to synthesize our understanding of Fe biogeochemistry predict widely different Fe inputs to the ocean, and are often unable to capture first-order features of the Fe distribution. The proposed work aims to resolve these problems using data assimilation (inverse) methods to \"teach\" the widely used Biogeochemical Elemental Cycling (BEC) model how to better represent Fe sources, sinks, and cycling processes. This will be achieved by implementing BEC in the efficient Ocean Circulation Inverse Model and expanding it to simulate the cycling of additional tracers that constrain unique aspects of the Fe cycle, including aluminum, thorium, helium and Fe isotopes. In this framework, the inverse model can rapidly explore alternative representations of Fe-cycling processes, guided by new high-quality observations made possible in large part by the GEOTRACES program. The work will be the most concerted effort to date to synthesize these rich datasets into a realistic and mechanistic model of the marine Fe cycle. In addition, it will lead to a stronger consensus on the magnitude of fluxes in the marine Fe budget, and their relative importance in controlling Fe limitation of marine ecosystems, which are areas of active debate. It will guide future observational efforts, by identifying factors that are still poorly constrained, or regions of the ocean where new data will dramatically reduce remaining uncertainties and allow new robust predictions of Fe cycling under future climate change scenarios to be made, ultimately improving climate change predictions. A broader impact of this work on the scientific community will be the development of a fast, portable, and flexible global model of trace element cycling, designed to allow non-modelers to test hypotheses and visualize the effects of different processes on trace metal distributions. The research will also support the training of graduate students, and outreach to low-income and minority students in local school districts.";
String projects_0_end_date "2020-06";
String projects_0_name "Collaborative research: Combining models and observations to constrain the marine iron cycle";
String projects_0_project_nid "766423";
String projects_0_start_date "2017-07";
String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
String publisher_type "institution";
String sourceUrl "(local files)";
Float64 Southernmost_Northing -79.5;
String standard_name_vocabulary "CF Standard Name Table v55";
String summary "We utilize an ensemble of artificial neural networks (EANNs) to interpolate our global ocean nitrate d15N database, producing complete 3D maps of the data. By utilizing an artificial neural network (ANN), a machine learning approach that effectively identifies nonlinear relationships between a target variable (the isotopic dataset) and a set of input features (other available ocean datasets), we can fill holes in our data sampling coverage of nitrate d15N.";
String title "[Global model nitrate d15N] - Estimated nitrate d15N modeled using an ensemble of artificial neural networks (EANNs) (Collaborative research: Combining models and observations to constrain the marine iron cycle)";
String version "1";
Float64 Westernmost_Easting 0.5;
String xml_source "osprey2erddap.update_xml() v1.3";
}
}
Data Access Protocol (DAP) and its
selection constraints.
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.