ERDDAP > info > bcodmo_dataset_768655

Grid DAP Data	Sub- set	Table DAP Data	Make A Graph	W M S	Source Data Files	Acces- sible	Title	Sum- mary	FGDC, ISO, Metadata	Back- ground Info	RSS	E mail	Institution	Dataset ID
		data	graph		files	public	[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)		F   I   M	background			BCO-DMO	bcodmo_dataset_768655

The Dataset's Variables and Attributes

Row Type	Variable Name	Attribute Name	Data Type	Value
attribute	NC_GLOBAL	access_formats	String	.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson
attribute	NC_GLOBAL	acquisition_description	String	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).
attribute	NC_GLOBAL	awards_0_award_nid	String	766422
attribute	NC_GLOBAL	awards_0_award_number	String	OCE-1658392
attribute	NC_GLOBAL	awards_0_data_url	String	http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1658392
attribute	NC_GLOBAL	awards_0_funder_name	String	NSF Division of Ocean Sciences
attribute	NC_GLOBAL	awards_0_funding_acronym	String	NSF OCE
attribute	NC_GLOBAL	awards_0_funding_source_nid	String	355
attribute	NC_GLOBAL	awards_0_program_manager	String	Dr Simone Metz
attribute	NC_GLOBAL	awards_0_program_manager_nid	String	51479
attribute	NC_GLOBAL	cdm_data_type	String	Other
attribute	NC_GLOBAL	comment	String	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
attribute	NC_GLOBAL	Conventions	String	COARDS, CF-1.6, ACDD-1.3
attribute	NC_GLOBAL	creator_email	String	info at bco-dmo.org
attribute	NC_GLOBAL	creator_name	String	BCO-DMO
attribute	NC_GLOBAL	creator_type	String	institution
attribute	NC_GLOBAL	creator_url	String	https://www.bco-dmo.org/
attribute	NC_GLOBAL	data_source	String	extract_data_as_tsv version 2.3 19 Dec 2019
attribute	NC_GLOBAL	date_created	String	2019-05-28T17:50:26Z
attribute	NC_GLOBAL	date_modified	String	2019-06-17T20:08:40Z
attribute	NC_GLOBAL	defaultDataQuery	String	&time<now
attribute	NC_GLOBAL	doi	String	10.1575/1912/bco-dmo.768655.1
attribute	NC_GLOBAL	Easternmost_Easting	double	359.5
attribute	NC_GLOBAL	geospatial_lat_max	double	83.5
attribute	NC_GLOBAL	geospatial_lat_min	double	-79.5
attribute	NC_GLOBAL	geospatial_lat_units	String	degrees_north
attribute	NC_GLOBAL	geospatial_lon_max	double	359.5
attribute	NC_GLOBAL	geospatial_lon_min	double	0.5
attribute	NC_GLOBAL	geospatial_lon_units	String	degrees_east
attribute	NC_GLOBAL	geospatial_vertical_max	double	5500.0
attribute	NC_GLOBAL	geospatial_vertical_min	double	0.0
attribute	NC_GLOBAL	geospatial_vertical_positive	String	down
attribute	NC_GLOBAL	geospatial_vertical_units	String	m
attribute	NC_GLOBAL	infoUrl	String	https://www.bco-dmo.org/dataset/768655
attribute	NC_GLOBAL	institution	String	BCO-DMO
attribute	NC_GLOBAL	keywords	String	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
attribute	NC_GLOBAL	license	String	https://www.bco-dmo.org/dataset/768655/license
attribute	NC_GLOBAL	metadata_source	String	https://www.bco-dmo.org/api/dataset/768655
attribute	NC_GLOBAL	Northernmost_Northing	double	83.5
attribute	NC_GLOBAL	param_mapping	String	{'768655': {'latitude': 'flag - latitude', 'depth': 'flag - depth', 'longitude': 'flag - longitude'}}
attribute	NC_GLOBAL	parameter_source	String	https://www.bco-dmo.org/mapserver/dataset/768655/parameters
attribute	NC_GLOBAL	people_0_affiliation	String	University of California-Irvine
attribute	NC_GLOBAL	people_0_affiliation_acronym	String	UC Irvine
attribute	NC_GLOBAL	people_0_person_name	String	Patrick Rafter
attribute	NC_GLOBAL	people_0_person_nid	String	615040
attribute	NC_GLOBAL	people_0_role	String	Principal Investigator
attribute	NC_GLOBAL	people_0_role_type	String	originator
attribute	NC_GLOBAL	people_1_affiliation	String	University of California-Santa Barbara
attribute	NC_GLOBAL	people_1_affiliation_acronym	String	UCSB
attribute	NC_GLOBAL	people_1_person_name	String	Aaron Bagnell
attribute	NC_GLOBAL	people_1_person_nid	String	768632
attribute	NC_GLOBAL	people_1_role	String	Co-Principal Investigator
attribute	NC_GLOBAL	people_1_role_type	String	originator
attribute	NC_GLOBAL	people_2_affiliation	String	University of California-Santa Barbara
attribute	NC_GLOBAL	people_2_affiliation_acronym	String	UCSB
attribute	NC_GLOBAL	people_2_person_name	String	Timothy DeVries
attribute	NC_GLOBAL	people_2_person_nid	String	766426
attribute	NC_GLOBAL	people_2_role	String	Co-Principal Investigator
attribute	NC_GLOBAL	people_2_role_type	String	originator
attribute	NC_GLOBAL	people_3_affiliation	String	Princeton University
attribute	NC_GLOBAL	people_3_person_name	String	Dario Marconi
attribute	NC_GLOBAL	people_3_person_nid	String	768634
attribute	NC_GLOBAL	people_3_role	String	Co-Principal Investigator
attribute	NC_GLOBAL	people_3_role_type	String	originator
attribute	NC_GLOBAL	people_4_affiliation	String	University of California-Irvine
attribute	NC_GLOBAL	people_4_affiliation_acronym	String	UC Irvine
attribute	NC_GLOBAL	people_4_person_name	String	Patrick Rafter
attribute	NC_GLOBAL	people_4_person_nid	String	615040
attribute	NC_GLOBAL	people_4_role	String	Contact
attribute	NC_GLOBAL	people_4_role_type	String	related
attribute	NC_GLOBAL	people_5_affiliation	String	Woods Hole Oceanographic Institution
attribute	NC_GLOBAL	people_5_affiliation_acronym	String	WHOI BCO-DMO
attribute	NC_GLOBAL	people_5_person_name	String	Shannon Rauch
attribute	NC_GLOBAL	people_5_person_nid	String	51498
attribute	NC_GLOBAL	people_5_role	String	BCO-DMO Data Manager
attribute	NC_GLOBAL	people_5_role_type	String	related
attribute	NC_GLOBAL	project	String	Fe Cycle Models and Observations
attribute	NC_GLOBAL	projects_0_acronym	String	Fe Cycle Models and Observations
attribute	NC_GLOBAL	projects_0_description	String	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.
attribute	NC_GLOBAL	projects_0_end_date	String	2020-06
attribute	NC_GLOBAL	projects_0_name	String	Collaborative research: Combining models and observations to constrain the marine iron cycle
attribute	NC_GLOBAL	projects_0_project_nid	String	766423
attribute	NC_GLOBAL	projects_0_start_date	String	2017-07
attribute	NC_GLOBAL	publisher_name	String	Biological and Chemical Oceanographic Data Management Office (BCO-DMO)
attribute	NC_GLOBAL	publisher_type	String	institution
attribute	NC_GLOBAL	sourceUrl	String	(local files)
attribute	NC_GLOBAL	Southernmost_Northing	double	-79.5
attribute	NC_GLOBAL	standard_name_vocabulary	String	CF Standard Name Table v55
attribute	NC_GLOBAL	summary	String	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.
attribute	NC_GLOBAL	title	String	[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)
attribute	NC_GLOBAL	version	String	1
attribute	NC_GLOBAL	Westernmost_Easting	double	0.5
attribute	NC_GLOBAL	xml_source	String	osprey2erddap.update_xml() v1.3
variable	latitude		double
attribute	latitude	_CoordinateAxisType	String	Lat
attribute	latitude	_FillValue	double	NaN
attribute	latitude	actual_range	double	-79.5, 83.5
attribute	latitude	axis	String	Y
attribute	latitude	bcodmo_name	String	latitude
attribute	latitude	colorBarMaximum	double	90.0
attribute	latitude	colorBarMinimum	double	-90.0
attribute	latitude	description	String	Latitude in degrees north
attribute	latitude	ioos_category	String	Location
attribute	latitude	long_name	String	Latitude
attribute	latitude	nerc_identifier	String	https://vocab.nerc.ac.uk/collection/P09/current/LATX/
attribute	latitude	standard_name	String	latitude
attribute	latitude	units	String	degrees_north
variable	longitude		double
attribute	longitude	_CoordinateAxisType	String	Lon
attribute	longitude	_FillValue	double	NaN
attribute	longitude	actual_range	double	0.5, 359.5
attribute	longitude	axis	String	X
attribute	longitude	bcodmo_name	String	longitude
attribute	longitude	colorBarMaximum	double	180.0
attribute	longitude	colorBarMinimum	double	-180.0
attribute	longitude	description	String	Longitude in degrees East
attribute	longitude	ioos_category	String	Location
attribute	longitude	long_name	String	Longitude
attribute	longitude	nerc_identifier	String	https://vocab.nerc.ac.uk/collection/P09/current/LONX/
attribute	longitude	standard_name	String	longitude
attribute	longitude	units	String	degrees_east
variable	depth		double
attribute	depth	_CoordinateAxisType	String	Height
attribute	depth	_CoordinateZisPositive	String	down
attribute	depth	_FillValue	double	NaN
attribute	depth	actual_range	double	0.0, 5500.0
attribute	depth	axis	String	Z
attribute	depth	bcodmo_name	String	depth
attribute	depth	colorBarMaximum	double	8000.0
attribute	depth	colorBarMinimum	double	-8000.0
attribute	depth	colorBarPalette	String	TopographyDepth
attribute	depth	description	String	Depth
attribute	depth	ioos_category	String	Location
attribute	depth	long_name	String	Depth
attribute	depth	nerc_identifier	String	https://vocab.nerc.ac.uk/collection/P09/current/DEPH/
attribute	depth	positive	String	down
attribute	depth	standard_name	String	depth
attribute	depth	units	String	m
variable	d15N		float
attribute	d15N	_FillValue	float	NaN
attribute	d15N	actual_range	float	0.98889, 26.663
attribute	d15N	bcodmo_name	String	dN15_NO3
attribute	d15N	description	String	modeled nitrate d15N
attribute	d15N	long_name	String	D15 N
attribute	d15N	units	String	per mil
variable	d15N_stdev		float
attribute	d15N_stdev	_FillValue	float	NaN
attribute	d15N_stdev	actual_range	float	0.036923, 15.627
attribute	d15N_stdev	bcodmo_name	String	dN15_NO3
attribute	d15N_stdev	description	String	standard deviation
attribute	d15N_stdev	long_name	String	D15 N Stdev
attribute	d15N_stdev	units	String	per mil

The information in the table above is also available in other file formats (.csv, .htmlTable, .itx, .json, .jsonlCSV1, .jsonlCSV, .jsonlKVP, .mat, .nc, .nccsv, .tsv, .xhtml) via a RESTful web service.