Demonstration of a transnational cooperation for harmonized chlorophyll a monitoring in the North East Atlantic Ocean

Comunicación presentada al EUROGOOS 2023, Galway, Ireland 3-5 October 2023The concentration of chlorophyll a (Chla), a proxy for phytoplankton biomass, is used as indicator for several criteria of three Marine Strategy Framework Directive (MSFD) descriptors (D1C6, the biodiversity of pelagic habitats; D4, food webs; and D5, eutrophication). Satellite Earth observation utilises algorithms that link the satellite observations of waterleaving radiance and the in-water Chla. Among the main sources of variability around this regression to define algorithms are the uncertainties in the in situ measurements due to the lack of consistency in the approaches employed in monitoring programs and research cruises. For example, global analyses based on measurements of Chla by high-performance liquid chromatography (HPLC), considered the reference technique for Chla, are usually derived from studies of independent investigators, so methodological differences between laboratories can introduce significant uncertainties. In addition, since HPLC is a relatively expensive and expertise-demanding technique, Chla concentration have been customarily determined in long-term oceanographic time-series programs by alternative techniques, such as spectrofluorometry (e.g., in RADIALES (Spain)) and fluorometry (e.g., in Plymouth Station L4, Western Channel Observatory (UK)). However, the agreement in the results obtained with these techniques has only been compared in a few ancient studies. The cooperation among Member States required by the MSFD for methodological harmonization has triggered a transnational collaboration involving some partners of the Interreg Atlantic Area project iFADO (Innovation in the framework of the Atlantic deep ocean) for a joint monitorization of Chla in the North East Atlantic Ocean (NEA) region. In situ data have been obtained in 21 research cruises and sampling sites, from coastal to offshore environments, by using standardized sampling and analytical methods. We will report on the results obtained from this operational demonstration and how this collaborative transnational initiative allowed us: i) to intercalibrate the methods currently used for the analysis of discrete samples (HPLC, spectrofluorometry, fluorometry) and assess them in terms of accuracy, costs and effectiveness; ii) to calibrate continuous measurements obtained with optical sensors and remote sensing results with HPLC data; iv) to extend in situ observations temporally and spatially through remote sensing for MSFD assessments; iii) to contribute to the integration of data of different accuracy, spatial scale and resolution in databases and to their dissemination in data hubs according to FAIR principles. This work will provide detailed guidelines for in situ sampling, analysis, and data quality control for Chla monitoring and will contribute harmonized data for the next MSFD assessment cycles for the target descriptors

Processing Images from the Zwicky Transient Facility

The Zwicky Transient Facility is a new robotic-observing program, in which a newly engineered 600-MP digital camera with a pioneeringly large field of view, 47~square degrees, will be installed into the 48-inch Samuel Oschin Telescope at the Palomar Observatory. The camera will generate $\sim 1$~petabyte of raw image data over three years of operations. In parallel related work, new hardware and software systems are being developed to process these data in real time and build a long-term archive for the processed products. The first public release of archived products is planned for early 2019, which will include processed images and astronomical-source catalogs of the northern sky in the $g$ and $r$ bands. Source catalogs based on two different methods will be generated for the archive: aperture photometry and point-spread-function fitting.Comment: 6 pages, 4 figures, submitted to RTSRE Proceedings (www.rtsre.org

Tests of the Accelerating Universe with Near-Infrared Observations of a High-Redshift Type Ia Supernova

We have measured the rest-frame B,V, and I-band light curves of a high-redshift type Ia supernova (SN Ia), SN 1999Q (z=0.46), using HST and ground-based near-infrared detectors. A goal of this study is the measurement of the color excess, E_{B-I}, which is a sensitive indicator of interstellar or intergalactic dust which could affect recent cosmological measurements from high-redshift SNe Ia. Our observations disfavor a 30% opacity of SN Ia visual light by dust as an alternative to an accelerating Universe. This statement applies to both Galactic-type dust (rejected at the 3.4 sigma confidence level) and greyer dust (grain size > 0.1 microns; rejected at the 2.3 to 2.6 sigma confidence level) as proposed by Aguirre (1999). The rest-frame $I$-band light cur ve shows the secondary maximum a month after B maximum typical of nearby SNe Ia of normal luminosi ty, providing no indication of evolution as a function of redshift out to z~0.5. A n expanded set of similar observations could improve the constraints on any contribution of extragalactic dust to the dimming of high-redshift SNe Ia.Comment: Accepted to the Astrophysical Journal, 12 pages, 2 figure

Providing comprehensive and consistent access to astronomical observatory archive data: the NASA archive model

Since the turn of the millennium a constant concern of astronomical archives have begun providing data to the public through standardized protocols unifying data from disparate physical sources and wavebands across the electromagnetic spectrum into an astronomical virtual observatory (VO). In October 2014, NASA began support for the NASA Astronomical Virtual Observatories (NAVO) program to coordinate the efforts of NASA astronomy archives in providing data to users through implementation of protocols agreed within the International Virtual Observatory Alliance (IVOA). A major goal of the NAVO collaboration has been to step back from a piecemeal implementation of IVOA standards and define what the appropriate presence for the US and NASA astronomy archives in the VO should be. This includes evaluating what optional capabilities in the standards need to be supported, the specific versions of standards that should be used, and returning feedback to the IVOA, to support modifications as needed. We discuss a standard archive model developed by the NAVO for data archive presence in the virtual observatory built upon a consistent framework of standards defined by the IVOA. Our standard model provides for discovery of resources through the VO registries, access to observation and object data, downloads of image and spectral data and general access to archival datasets. It defines specific protocol versions, minimum capabilities, and all dependencies. The model will evolve as the capabilities of the virtual observatory and needs of the community change

Assessing phytoplankton community composition in the Atlantic Ocean from in situ and satellite observations

The Atlantic Meridional Transect (AMT) program (www.amt-uk.org) provides the perfect opportunity to observe the phytoplankton community size structure over a long latitudinal transect 50oN to 50oS, thereby covering the most important latitude-related basin-scale environmental gradients of the Atlantic Ocean. This work presents cell abundance data of phytoplankton taxa recently collected during cruises AMT28 and 29 (in 2018 and 2019, respectively) using flow cytometer and microscope observations, as well as the pigment composition of the community, to assess the abundance and spatial distribution of taxonomic groups across the Atlantic. The community size structure showed a clear consistency between cruises at large spatial scale, with a dominance of picoplanktonic Cyanobacteria in oceanic gyres, an increase in all groups in the equatorial upwelling region, and high biomass of microplankton size class at higher latitudes. Phytoplankton carbon biomass for oceanographic provinces, ranged from median values of 10 to 47 mg Carbon m-3, for the oligotrophic gyres, and South Atlantic (45°S-50oS), respectively. Satellite images of total chlorophyll a (as a proxy for phytoplankton biomass) as well as the relative contribution of the three phytoplankton size classes were produced for both cruises, and despite the small number of matchups, statistically agreed well with in situ size classes estimated as carbon biomass, constituting the first attempt in the literature to match satellite size classes with in situ data derived from cell abundance. The comparison of community structure between recent cruises (2019, 2018, 2015) and earlier ones (1995-1998) indicates a decrease in the number of diatom-bloom forming species, and an increase in Dinoflagellates, whereas nitrogen-fixing Trichodesmium abundance in tropical Atlantic remains constant. Within the recent period, a relative increase in the median values of picoplankton fraction was seen in SATL region, counterbalanced by a decrease in both nano- and microplankton fractions. Additionally, this study includes a database of species identified by microscopy, which had been interrupted for 20 years, providing a basis for long-term series of phytoplankton species

An ocean-colour time series for use in climate studies: the experience of the ocean-colour climate change initiate (OC-CCI)

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea viewingWide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel

Multiple Sources of Contamination in Samples from Patients Reported to Have XMRV Infection

Xenotropic murine leukemia virus (MLV)-related retrovirus (XMRV) was reported to be associated with prostate cancer by Urisman, et al. in 2006 and chronic fatigue syndrome (CFS) by Lombardi, et al. in 2009. To investigate this association, we independently evaluated plasma samples from 4 patients with CFS reported by Lombardi, et al. to have XMRV infection and from 5 healthy controls reported to be XMRV uninfected. We also analyzed viral sequences obtained from supernatants of cell cultures found to contain XMRV after coculture with 9 clinical samples from 8 patients. A qPCR assay capable of distinguishing XMRV from endogenous MLVs showed that the viral sequences detected in the CFS patient plasma behaved like endogenous MLVs and not XMRV. Single-genome sequences (N = 89) from CFS patient plasma were indistinguishable from endogenous MLVs found in the mouse genome that are distinct from XMRV. By contrast, XMRV sequences were detected by qPCR in 2 of the 5 plasma samples from healthy controls (sequencing of the qPCR product confirmed XMRV not MLV). Single-genome sequences (N = 234) from the 9 culture supernatants reportedly positive for XMRV were indistinguishable from XMRV sequences obtained from 22Rv1 and XMRV-contaminated 293T cell-lines. These results indicate that MLV DNA detected in the plasma samples from CFS patients evaluated in this study was from contaminating mouse genomic DNA and that XMRV detected in plasma samples from healthy controls and in cultures of patient samples was due to cross-contamination with XMRV (virus or nucleic acid)

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

A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022)

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

A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022)