Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
• Data Files
• Supplemental Files
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Project Information
• Funding

This time-series data synthesis pilot product includes data from 12 fixed ship-based time-series programs with a focus on biogeochemical essential ocean variables. Data used in this synthesis product were made possible with funding through the following:

• EU Horizon 2020 through the EuroSea Innovation Action (grant agreement 862626)
• EU Horizon 2020 iAtlantic programme (grant agreement 818123) 
• European Union’s Horizon 2020 research and innovation program (grant agreement 820989; COMFORT).
• WASCAL MRP-CCMS project from the German Federal Ministry of Education and Research (BMBF; grant agreement no. 01LG1805A).
• National Science Foundation (OCE-1259043, OCE-175651, and RISE-2028291).
• Norwegian Environment Agency under grant agreement nos. 14078029, 15078033, 16078007, 17018007, and 21087110.
• Grant-in-Aid for Scientific Research (20H04349) from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) KAKENHI.
• Mediterranean Ocean Observing System for the Environment program (MOOSE) coordinated by CNRS-INSU and the Research Infrastructure ILICO (CNRS-IFREMER).
• The European projects CARBOOCEAN, CARBOCHANGE, SESAME, PERSEUS and COMFORT
• The Spanish Ministry of Science through the grants CTM2005/01091-MAR and CTM2008-05680-C02-01 and the Junta de Andalucía through the TECADE project (PY20_00293)
• Centro Nacional Instituto Español de Oceanografía (IEO-CSIC)

Oceanographic data from twelve fixed ship-based time-series programs were synthesized into a pilot product with focus on biogeochemical essential ocean variables (BGC-EOV).  Measurements of dissolved oxygen, dissolved inorganic nutrients, inorganic carbon (pH, TALK, DIC, pCO2), particulate matter, and DOC were compiled from the time series programs listed below.  

Methods, Sampling, and Instruments are dependent on individual time-series programs, and often vary within a single time series program from cruise-to-cruise.

Instruments are listed in the section below, with detailed metadata available at ODIS (https://oceaninfohub.org/odis/). 
Additional details may be found by viewing the related datasets and publications sections below. 

Time-Series Programs
Listed according to ocean/sea location, details include time series program; PIs; start/end dates (of dataset); measurement frequency; location; depth
Pacific
ALOHA: Angelicque White; 1988-2019; monthly; 22.8°N 158.0°W, Subtropical eastern North Pacific (4750m)
K2: Masahide Wakita; 1999-2020; 1-3 cruises yr-1; 47.0°N 160.0°E, Subarctic western North Pacific (6000m) 
KNOT: Masahide Wakita; 1997-2020; 1-3 cruises yr-1; 44.0°N 155.0°E, Subarctic western North Pacific (6000m) 
Munida: Kim Currie; 1998-2019; 6 cruises yr-1; 45.8°S 171.5°E, Southwest Pacific (1000m)
Atlantic
CVOO: Björn Fiedler; 2006-2019; 1-3 cruises yr-1; 17.6°N 24.3°W, Eastern tropical North Atlantic (3600m)
GIFT: Emma Huertas; 2005-2015; seasonal; 35.9°N 6.0°W / 35.9°N 5.7°W / 36.0°N 5.3°W, Strait of Gibraltar (315– 842m)
RADCOR: Marta Álvarez; 2013 - 2020; monthly; 43.4°N 8.4°E, Eastern Atlantic along NW Galician coast (15-80 m)
Nordic Seas
Irminger Sea time-series: Sólveig Rósa; seasonal; 1983 - 2019; 64.3°N 28.0°W, Irminger Sea (1000m)
Iceland Sea time-series: Sólveig Rósa; seasonal; 1983 - 2019; 68.0°N 12.7°W, Iceland Sea (1850m)
OWSM: Ingunn Skjelvan; 2001 - 2021; 4-12 cruises yr-1; 66.0°N 2.0°E, Norwegian Sea (2100m)
Marginal Seas
CARIACO: Frank Muller-Karger; 1995 - 2017; monthly; 10.5°N 64.7°W, Cariaco Basin of the Caribbean Sea (1300m)
DYFAMED: Laurent Coppola; 1991 - 2017; monthly; 42.3°N 7.5°E, Mediterranean/Ligurian Sea (2400m)

-----------------------------------
Glossary

• ALOHA = A Long-term Oligotrophic Habitat Assessment
• BP = Best Practices
• CARIACO = CArbon Retention In A Colored Ocean 
• CARINA = CARbon IN the Atlantic
• CVOO = Cape Verde Ocean Observatory
• DOC = Dissolved Organic Carbon
• DYFAMED = DYnamique des Flux Atmospheriques en MEDiterranee (atmospheric flux dynamics in the Mediterranean)
• EOV = Essential Ocean Variables
• GLODAP = GLobal Ocean Data Analysis Project
• GIFT = GIbraltar Fixed Time-series
• HOT = Hawaii Ocean Time-series
• K2 = time series station in the North Pacific Ocean near both the Kamchatka peninsula and Kunashiri Island
• KNOT = Kyodo North Pacific Ocean Time-Series
• ODIS = Ocean Data and Information System
• OIH = Ocean Info Hub
• OWSM = Ocean Weather Station M
• PC = Particulate Carbon
• PN = Particulate Nitrogen
• POC = Particulate Organic Carbon
• PON = Particulate Organic Nitrogen
• QC = Quality Control
• RADIALES = Estudio de las series históricas de datos oceanográficos (RADIALES is one of the longest multidisciplinary programs in operation in the northern and northwestern coast of Spain)
• RADCOR = RADIALES A Coruña (the A Coruña section is part of the broader RADIALES time series program)
• SOP = Standard Operating Procedures
• WOCE = World Ocean Circulation Experiment

The data from the 12 participating time-series programs were retrieved from data centers or directly obtained from the responsible principal investigator. In the latter case, merging, formatting, additional quality-control (QC), and archiving of existing data were carried out. Only bottle data for BGC EOVs (Biogeochemical Essential Ocean Variables) that had been measured by at least two of the participating programs were included in the pilot project, along with accompanying ancillary pressure, salinity, and temperature data. The product  was created by standardizing data format, units, header names, primary QC flags, times, locations, and fill values and subsequently merging the individual datasets of each time-series program into one file. Only data that received a WOCE quality flag 2 were included in the product. Existing data were altered as little as possible without interpolation or calculation of “missing” variables. Similarly, original station-, cast- and bottle numbers were kept or created artificially if non-existent to ensure consistency. The headers, units, and flags of the individual time-series datasets were standardized to conform with the WOCE exchange bottle data format (Swift and Diggs, 2008). 

The standardization process entailed unit conversions, most frequently from micromoles per liter (µmol L-1; nutrients and dissolved organic carbon (DOC)) or from micrograms per kilogram (µg kg-1; particulate matter) to micromoles per kilogram of seawater (µmol kg-1). The default procedure to convert from volumetric to gravimetric units was to use seawater density at in-situ salinity, reported laboratory temperature (otherwise assuming 20°C as laboratory conditions), and pressure of 1 atm (following recommendations from Liqing et al., 2022). For some time-series datasets, the combined concentration of nitrate and nitrite was reported. If explicit nitrite concentrations were provided, these were subtracted to obtain the nitrate values. If not, the combined concentration was renamed to nitrate assuming that the relative nitrite amount is negligible. For the HOT program specifically, low-level, high-sensitivity measurements of macronutrients (phosphate and nitrate) were available but not included in the pilot product. Particulate organic matter was derived by subtracting the particulate inorganic matter from the total particulate matter, if available. For particulate carbon (PC) and particulate nitrogen (PN), the factors 1/12.01 and 1/14.01 (inverse standard atomic masses) were used, respectively, for the unit conversion to micromoles per kilogram. If neither temperature nor pressure was provided, all corresponding data entries were excluded from the product. The potential density anomaly is the only calculated variable. Missing and excluded values were set to -999. 

The information on the applied methods of each time-series program, was evaluated against, ideally, published Best Practices (BPs), and otherwise known standard operating procedures (SOPs). “SOP Flags” were assigned accordingly to each cruise of a time-series program (Lange et al., 2023). Precision (duplicate measurements) and accuracy (deviation from reference material) estimates, as provided by each time-series program’s primary quality-assurance procedure, were assigned to the bottle data. The temporal resolution of these estimates varies from estimates given for each cruise, i.e. on a cruise-to-cruise basis, to estimates given for longer time periods (covering multiple cruises) without recorded changes in applied methodology.

When additional quality control was necessary, various methods, packages, and tools were used: CANYON-B is a Bayesian neural network mapping that accurately reproduces GLODAPv2 bottle data and the biogeochemical relations contained therein (Bittig et al., 2018); AtlantOS Ocean Data QC software is an interactive tool for quality control of hydrographic cruise data (Velo et al., 2021); the work of Tanhua et al. (2010) provided rigorous quality control procedures to assure the highest possible quality and consistency. The QC was used to identify outliers and obvious errors, as well as to quantify systematic differences in reported values using crossover analysis (Lauvset & Tanhua, 2015).

Problems, Issues, Notes:
1st QC flags W bottle flags
2 = No problems noted
9 = encompasses WOCE flags 3, 4, 5, 7, 8, and 9 (samples not drawn, questionable or bad data) 

"SOP flags" (Standard Operating Procedures)
1 = Methods meet required and desired Standard Operating Procedures
2 = Methods meet required Standard Operating Procedures
3 = Methods do not meet Standard Operating Procedures

- Imported data from source file "spots.csv" into the BCO-DMO data system; kept missing data identifier of -999
- Ensured parameter/field/column names conformed with BCO-DMO naming conventions
- Rounded values to precision indicated by the PI
- Created glossary of metadata terms
- Converted Supplemental 'Product Variables' file to CSV and added NERC term URL links

NSF Award Abstract:
This project will support coordination efforts that bring together participants in large- and small group formats to foster the necessary dialog to develop Findable, Accessible, Interoperable, and Reusable (FAIR) data solutions and practices. The project will include a Consensus Building Workshop and METS Data Working Group to develop reference implementations of a data model for adoption by the METS community; formation of regional METS user networks and a Broadening Users Workshop to identify the needs of a broader range of data end users and associated data interfaces and tools to meet those needs; and a Data Hackathon to build capacity to ingest, analyze, and integrate METS data with other disciplinary and cross-disciplinary data to accelerate scientific discovery.

This project will develop community consensus for a FAIR METS data model. The METS RCN will leverage the wealth of oceanographic coordination and community building experience and staff capacity of the Ocean Carbon and Biogeochemistry (OCB) Project Office and the infrastructure, expertise, and extensive METS data handling experience of the Biological and Chemical Oceanography Data Management Office (BCO-DMO), along with an RCN Steering Committee that comprises expertise in the fields of oceanography, data science, earth system models, statistics, and data synthesis.

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.

Project description
An integrated observation system for sustainable ocean management
Our oceans are a vital source of wealth, but ocean monitoring systems are inadequate and lower management capacity. Scientists agree that oceans need an observation system coordinated at an international level. The EU-funded EuroSea project aims to coordinate a wide range of European actors towards integrating national systems for an international observation system. The project will advance a system that will collect ocean information data important for blue growth and sustainable ocean management. It will advance technology readiness levels (TRLs) of crucial components required for ocean observation systems and improve international coordination of ocean monitoring to ensure ocean health and optimal resource utilisation.

Objective
Although the Ocean is a fundamental part of the global system providing a wealth of resources, there are fundamental gaps in ocean observing and forecasting systems, limiting the capacity in Europe to sustainably manage the ocean and its resources. Ocean observing is “big science” and cannot be solved by individual nations; it is necessary to ensure high-level integration for coordinated observations of the ocean that can be sustained in the long term. EuroSea brings together key European actors of ocean observation and forecasting with key end users of ocean observations, responding to the Future of the Seas and Oceans Flagship Initiative. Our vision is a truly interdisciplinary ocean observing system that delivers the essential ocean information needed for the wellbeing, blue growth and sustainable management of the ocean. EuroSea will strengthen the European and Global Ocean Observing System (EOOS and GOOS) and support its partners. EuroSea will increase the technology readiness levels (TRL) of critical components of ocean observations systems and tools, and in particular the TRL of the integrated ocean observing system. EuroSea will improve: European and international coordination; design of the observing system adapted to European needs; in situ observing networks; data delivery; integration of remote and in-situ data; and forecasting capability. EuroSea will work towards integrating individual observing elements to an integrated observing system, and will connect end-users with the operators of the observing system and information providers. EuroSea will demonstrate the utility of the European Ocean Observing System through three demonstration activities focused on operational services, ocean health and climate, where a dialogue between actors in the ocean observing system will guide the development of the services, including market replication and innovation supporting the development of the blue economy.

Project DOI: 10.3030/862626