Vertical profiles of environmental parameters measured from physical, optical and imaging sensors during station TARA_027 of the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set includes properties of seawater, particulate matter and dissolved matter from physical, optical and imaging sensors mounted on a vertical sampling system (Rosette) used during the 2009-2013 tara Oceans Expedition. It comprised 2 pairs of conductivity and temperature sensors (SEABIRD components), and a complete set of WEtLabs optical sensors, including chrorophyll and CDOM fluorometers, a 25 cm transmissiometer, and a one-wavelength backscatter meter. In addition, a SATLANTIC ISUS nitrate sensor and a Hydroptic Underwater Vision Profiler (UVP) were mounted on the rosette. In the Arctic Ocean and Arctic Seas (2013), a second oxygen sensor (SBE43) and a four frequency Aquascat acoustic profiler were added. The system was powered on specific Li-Ion batteries and data were self-recorded at 24HZ. Sensors have all been factory calibrated before, during and after the four year program. Oxygen was validated using climatologies (WOA09). Nitrate and Fluorescence data were adjusted with discrete measurements from Niskin bottles mounted on the Rosette, and optical darks were performed monthly on board. A total of 839 quality checked vertical profiles were made during the tara Oceans expedition 2009-2013.

Short-term temporal and spatial variations in physical, optical and biogeochemical parameters in June of 2013 in Alfacs Bay (NW Mediterranean)

With the aim of analyzing the complex physical and biogeochemical interactions at high temporal and spatial resolution in the complex estuarine waters of Alfacs Bay, a beam attenuation-based approach was used as optical proxy of different biogeochemical variables. Thus, the dataset contains the attenuation proxies as well as laboratory results from the analysis of water samples, which were used to validate our approach. In addition, the major physical forcing in the Bay was also measured.

A compilation of global bio-optical in situ data for ocean-colour satellite applications

A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately.