Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Related Publications
• Parameters
• Instruments
• Deployments
• Project Information
• Program Information
• Funding

Sampling and analytical methodology: 

Sample collection and filtering: Water column samples were collected by the BAIT sampling team from 12 trace metal clean modified 6-L Niskin-X samplers (General Oceanics Inc.) deployed on a clean CTD rosette system (Sedwick et al. 2005). The BAIT sampling team filtered the samples through 0.2 µm Supor Acropak filter cartridges (Pall) inside a clean-air laboratory van.

Filtered seawater samples for iron speciation (organic complexation) were collected in 500 mL acid-cleaned Nalgene narrow-mouth fluorinated high-density polyethylene (FPE) bottles that had been filled with Milli-Q for at least two weeks after acid cleaning and rinsed three times with filtered seawater prior to sample collection (Buck et al. 2012). All filtered seawater samples for dissolved iron speciation were frozen at -20 ºC shipboard and shipped to the University of South Florida for laboratory-based analyses post cruise.

Dissolved iron speciation (organic complexation) analyses:

Dissolved iron speciation (organic complexation) was analyzed using competitive ligand exchange- adsorptive cathodic stripping voltammetry (CLE-AdCSV) with salicylaldoxime as the added competing ligand (Buck et al. 2015), modified from the original Rue and Bruland (1995) method. A borate-ammonium buffer was added to all vials, for a final pH of 8.2 (NBS scale; Buck et al. 2007, 2012). Dissolved iron additions of 0 to 10 nM were used in the titrations, for a total of 15 points in each titration. CLE-AdCSV analyses were performed on Bioanalytical Systems (BASi) Controlled Growth Mercury Electrodes (static mercury drop setting, size 14) with Epsilon e2 (BASi) electrochemical analyzers. Deposition time of 120 s was applied to the analyses. There are no reference samples available for iron speciation measurements of iron in seawater, though this method has been shown to compare well with other labs and techniques in GEOTRACES intercalibration exercises (Buck et al. 2012, 2016).

Science Data Processing Notes:

CLE-AdCSV data was interpreted using a freely available interpretation program, ProMCC (Omanovic et al. 2015; https://sites.google.com/site/daromasoft/home/promcc), which employs matrix based optimization of titration data fitting to obtain ligand concentrations and conditional stability constants for each sample. Uncertainties in the fitted parameter are presented as the 95% confidence interval provided by the interpretation program. When replicate titrations were conducted, results are presented as averages of the replicates.

Dissolved iron (DFe) concentrations determined by inductively coupled plasma-mass spectrometry (ICP-MS) by Bettina Sohst in project PI Peter Sedwick’s lab at Old Dominion University were used in the ProMCC interpretation program to calculate ligand concentrations and conditional stability constants from the sample titrations. The DFe values used for this dataset are provided in the speciation submission for reference, please see Sedwick’s dataset for the complete dissolved iron data and metadata.

The detection limit for the electroactive Fe-SA complex has been found to be 0.01 nM (Buck et al. 2007), calculated as three times the standard deviation of measured peak height from a 0.05 nM Fe addition to UV-oxidized and chelexed seawater with a 900 s deposition time. For a 90 s deposition time, the detection limit for Fe-SA is ~0.1 nM (Buck et al. 2018).

Data flag notes:

The standard Ocean Data View qualifying flags were used within the flag columns of the dataset (reference all flags at https://www.bodc.ac.uk/resources/delivery_formats/odv_format/):

1: Good Value: Good quality data value that is not part of any identified malfunction and has been verified as consistent with real phenomena during the quality control process. [Used when data were shown to be reproducible]

2: Probably Good Value: Data value that is probably consistent with real phenomena but this is unconfirmed or data value forming part of a malfunction that is considered too small to affect the overall quality of the data object of which it is a part. [Used when the reported value reflects analysis of a single or unverified replicate]

3: Probably Bad Value: Data value recognized as unusual during quality control that forms part of a feature that is probably inconsistent with real phenomena.  [Not used]

4: Bad Value: An obviously erroneous data value. [Not used]

5: Changed Value: Data value adjusted during quality control. [Not used]

6: Value Below Detection Limit: The level of the measured phenomenon was too small to be quantified by the technique employed to measure it. The accompanying value is the detection limit for the technique or zero if that value is unknown.  [Not used]

7: Value in Excess: The level of the measured phenomenon was too large to be quantified by the technique employed to measure it. The accompanying value is the measurement limit for the technique.  [Not used]

8: Interpolated Value: This value has been derived by interpoloation from other values in the data object. [Not used]

9: Missing Value: The data value is missing. Any accompanying value will be a magic number representing absent data. [Any missing data reflect samples that were not analyzed or were analyzed but results are not (or not yet) reported; “nd” = “no data” used in these cases]

A: Value Phenomenon Uncertain: There is uncertainty in the description of the measured phenomenon associated with the value such as chemical species or biological entity. 

BCO-DMO Processing Notes

• Spaces within column headers were replaced with underscores
• Special characters removed from column name (% changed to Percent)

NSF and NERC Award Abstract:
Iron is an essential nutrient for the growth of phytoplankton in the oceans. As such, iron plays key roles in regulating marine primary production and the cycling of carbon. It is thus important that models of ocean biology and chemistry consider iron, in order to explore past, present and future variations in marine productivity and the role of the ocean in the global carbon cycle. In this joint project involving researchers in the U.S. and the U.K., supported by both NSF and the Natural Environment Research Council (U.K.), field data from the Bermuda Atlantic Time-series Study (BATS) region will be combined with an established, state-of-the-art ocean biogeochemical model. By leveraging the known seasonal-scale physical, chemical and biological changes in the BATS region, the oceanographic context provided by the BATS core data, and an existing model of the regional physical circulation, the proposed study will yield process-related information that is of general applicability to the open ocean. In particular, the proposed research will focus on understanding the atmospheric input, biological uptake, regeneration and scavenging removal of dissolved iron in the oceanic water column, which have emerged as major uncertainties in the ocean iron cycle. The project will include significant educational and training contributions at the K-12, undergraduate, graduate and postdoctoral levels, as well as public outreach efforts that aim to explain the research and its importance.

The ability of ocean models to simulate iron remains crude, owing to an insufficient understanding of the mechanisms that drive variability in dissolved iron, particularly the involvement of iron-binding ligands, colloids and particles in the surface input, biological uptake, regeneration and scavenging of dissolved iron in the upper ocean. Basin-scale data produced by the GEOTRACES program provide an important resource for testing and improving models and, by extension, our mechanistic understanding of the ocean iron cycle. However such data provide only quasi-synoptic 'snapshots', which limits their utility in isolating and identifying the processes that control dissolved iron in the upper ocean. The proposed research aims to provide mechanistic insight into these governing processes by combining time-series data from the BATS region with numerical modeling experiments. Specifically, seasonally resolved data on the vertical (upper 2,000 meters) and lateral (tens of kilometers) distributions of particulate, dissolved, colloidal, soluble and ligand-bound iron species will be obtained from the chemical analysis of water column samples collected during five cruises, spanning a full annual cycle, shared with the monthly BATS program cruises. These data, along with ancillary data from the BATS program, will be used to test and inform numerical modeling experiments, and thus derive an improved understanding of the mechanisms that control the distribution and dynamics of dissolved iron in the oceanic water column.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This is a project jointly funded by the National Science Foundation’s Directorate for Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK). 

GEOTRACES is a SCOR sponsored program; and funding for program infrastructure development is provided by the U.S. National Science Foundation.

GEOTRACES gained momentum following a special symposium, S02: Biogeochemical cycling of trace elements and isotopes in the ocean and applications to constrain contemporary marine processes (GEOSECS II), at a 2003 Goldschmidt meeting convened in Japan. The GEOSECS II acronym referred to the Geochemical Ocean Section Studies To determine full water column distributions of selected trace elements and isotopes, including their concentration, chemical speciation, and physical form, along a sufficient number of sections in each ocean basin to establish the principal relationships between these distributions and with more traditional hydrographic parameters;

* To evaluate the sources, sinks, and internal cycling of these species and thereby characterize more completely the physical, chemical and biological processes regulating their distributions, and the sensitivity of these processes to global change; and

* To understand the processes that control the concentrations of geochemical species used for proxies of the past environment, both in the water column and in the substrates that reflect the water column.

GEOTRACES will be global in scope, consisting of ocean sections complemented by regional process studies. Sections and process studies will combine fieldwork, laboratory experiments and modelling. Beyond realizing the scientific objectives identified above, a natural outcome of this work will be to build a community of marine scientists who understand the processes regulating trace element cycles sufficiently well to exploit this knowledge reliably in future interdisciplinary studies.

Expand "Projects" below for information about and data resulting from individual US GEOTRACES research projects.