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  • 1
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    Photosynthetically available radiation (PAR) on the seafloor calculated from 21 years of ocean colour satellite data (2020)
    PANGAEA
    Details
    Publication Date: 2023-01-30
    Description: Monthly satellite data (SeaWiFS, MODIS, MERIS, VIIRS) over a 21-year period (1998-2018; from the Globcolour project; http://globcolour.org) are used to calculate the Photosynthetically Available Radiation (PAR) reaching the seafloor in the coastal zone (0 to 200 m depth). Depths are from the 2019 General Bathymetric Chart of the Oceans (GEBCO; https://www.gebco.net) gridded bathymetry data (1/240 degree resolution). * Longitude, latitude, depth and pixel area can be found in the NETCDF file CoastalLight_geo.nc. * Optical parameters (PAR at surface, attenuation coefficient for PAR, and PAR on sea floor)can be found in the following NETCDF files: - Monthly climatologies, mean values: January (01) to December (12): CoastalLight_01.nc, CoastalLight_02.nc, ..., CoastalLight_12.nc - Monthly climatologies, minimum values: January (01) to December (12): CoastalLight_min_01.nc, CoastalLight_min_02.nc, ..., CoastalLight_min_12.nc - Monthly climatologies, maximum values: January (01) to December (12): CoastalLight_max_01.nc, CoastalLight_max_02.nc, ..., CoastalLight_max_12.nc - Monthly climatologies, standard deviation values: January (01) to December (12): CoastalLight_sd_01.nc, CoastalLight_sd_02.nc, ..., CoastalLight_sd_12.nc - Climatology over the whole 21 year period, mean values: CoastalLight_00.nc - Climatology over the whole 21 year period, minimum values: CoastalLight_min_00.nc - Climatology over the whole 21 year period, maximum values: CoastalLight_max_00.nc - Climatology over the whole 21 year period, standard deviation values: CoastalLight_sd_00.nc
    Keywords: coastal zone; File content; File format; File name; File size; irradiance; ocean colour; satellite; underwater light; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 265 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 2
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    Global data sets of Chlorophyll a concentration for diatoms, coccolithophores (haptophytes) and cyanobacteria obtained from in situ observations and satellite retrievals (2017)
    PANGAEA
    In:  Supplement to: Losa, Svetlana N; Soppa, Mariana A; Dinter, Tilman; Wolanin, Aleksandra; Brewin, Robert J W; Bricaud, Annick; Oelker, Julia; Peeken, Ilka; Gentili, Bernard; Rozanov, Vladimir V; Bracher, Astrid (2017): Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT). Frontiers in Marine Science, 4(203), 22 pp, https://doi.org/10.3389/fmars.2017.00203
    Details
    Publication Date: 2024-02-14
    Description: We derive the chlorophyll a concentration (Chla)for three main phytoplankton functional types (PFTs)-- diatoms, coccolithophores and cyanobacteria- by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm (Hirata et al. 2011) applied to the Ocean Colour Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm (Bracher et al. 2009, Sadeghi et al. 2012) is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 ? March 2012 and evaluated against in situ HPLC pigment data and satellite information on phytoplankton size classes (PSC) (Brewin et al. 2010, Brewin et al. 2015) and the size fraction (Sf) by Ciotti and Bricaud (2006. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi- and hyperspectral instruments are highlighted.
    Keywords: AC3; Arctic Amplification
    Type: Dataset
    Format: application/zip, 3 datasets
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    DATA PANGAEA
  • 3
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    Optical properties in surface water during Thalassa cruise PROSOPE (2013)
    PANGAEA
    Details
    Publication Date: 2024-02-01
    Keywords: Absorption and attenuation meter AC-9; Absorption coefficient, 412 nm; Absorption coefficient, 440 nm; Absorption coefficient, 488 nm; Absorption coefficient, 510 nm; Absorption coefficient, 532 nm; Absorption coefficient, 555 nm; Absorption coefficient, 630 nm; Absorption coefficient, 676 nm; Absorption coefficient, 715 nm; Biogeochemical Processes in the Oceans and Fluxes; CTD/Rosette; CTD-RO; Date/Time of event; DEPTH, water; Direction; DYF1; DYF2; DYF3; DYF4; DYF5; Event label; JGOFS; Joint Global Ocean Flux Study; Latitude of event; Longitude of event; MIO1; MIO2; MIO3; MIO4; MIO5; Optical beam attenuation coefficient, 412 nm; Optical beam attenuation coefficient, 440 nm; Optical beam attenuation coefficient, 488 nm; Optical beam attenuation coefficient, 510 nm; Optical beam attenuation coefficient, 532 nm; Optical beam attenuation coefficient, 555 nm; Optical beam attenuation coefficient, 630 nm; Optical beam attenuation coefficient, 676 nm; Optical beam attenuation coefficient, 715 nm; PROOF; PROSOPE; PROSOPE_pro002; PROSOPE_pro003; PROSOPE_pro004; PROSOPE_pro005; PROSOPE_pro006; PROSOPE_pro007; PROSOPE_pro008; PROSOPE_pro010; PROSOPE_pro011; PROSOPE_pro013; PROSOPE_pro014; PROSOPE_pro019; PROSOPE_pro022; PROSOPE_pro023; PROSOPE_pro025; PROSOPE_pro026; PROSOPE_pro027; PROSOPE_pro028; PROSOPE_pro029; PROSOPE_pro030; PROSOPE_pro031; PROSOPE_pro032; PROSOPE_pro033; PROSOPE_pro035; PROSOPE_pro037; PROSOPE_pro038; PROSOPE_pro039; PROSOPE_pro041; PROSOPE_pro043; PROSOPE_pro044; PROSOPE_pro045; PROSOPE_pro046; PROSOPE_pro047; PROSOPE_pro048; PROSOPE_pro049; PROSOPE_pro050; PROSOPE_pro051; PROSOPE_pro052; PROSOPE_pro053; PROSOPE_pro054; PROSOPE_pro055; PROSOPE_pro056; PROSOPE_pro057; PROSOPE_pro058; PROSOPE_pro060; PROSOPE_pro061; PROSOPE_pro064; PROSOPE_pro067; PROSOPE_pro068; PROSOPE_pro069; PROSOPE_pro070; PROSOPE_pro071; PROSOPE_pro072; PROSOPE_pro073; PROSOPE_pro074; PROSOPE_pro075; PROSOPE_pro077; PROSOPE_pro078; PROSOPE_pro079; PROSOPE_pro080; PROSOPE_pro081; PROSOPE_pro082; PROSOPE_pro083; PROSOPE_pro084; PROSOPE_pro085; PROSOPE_pro086; PROSOPE_pro087; PROSOPE_pro088; PROSOPE_pro090; PROSOPE_pro092; PROSOPE_pro093; PROSOPE_pro094; PROSOPE_pro095; PROSOPE_pro096; PROSOPE_pro097; PROSOPE_pro098; PROSOPE_pro099; PROSOPE_pro100; PROSOPE_pro101; PROSOPE_pro102; PROSOPE_pro103; PROSOPE_pro104; PROSOPE_pro105; St#1; St#2; St#4; St#5; St#6; St#7; St#8; St#9; Thalassa; UPW1; UPW2
    Type: Dataset
    Format: text/tab-separated-values, 1103216 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 4
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    FjordLight: PAR data for Arctic fjords (2023)
    PANGAEA
    Details
    Publication Date: 2024-05-06
    Description: Fjords play a pivotal role in the Arctic, for both the natural world and human societies. It is therefore of the utmost importance that the key parametres of these systems be monitored and quantified in order to better manage them. The collection of temperature and salinity data for many Arctic fjords is well documented, but data for other values, like light availability, are scarce. It is to address this shortcoming that this dataset has been created. Herein one may find data for the surface PAR, Kd, and bottom PAR of seven well studied and representative fjords throughout the European Arctic. The three PAR variables were created via remotely sensed estimates of surface irradiance and turbidity. This output was then mapped to the highest resolution bathymetry data available (~ 50 - 200 m grid) to create a high resolution output. All of the PAR variables are available as overall global averages, monthly climatologies, and annual averages for all months when light is present (March - October). The bottom PAR data are also available as monthly averages per year, making these data the majority of the volume of the dataset. An R package 'FjordLight' has been developed to aid in the access and handling of these data, and their applicability is demonstrated in an ESSD publication.
    Keywords: Arctic; Arctic Biodiversity & Livelihoods; Binary Object; Biological sample; BIOS; Disko_Bay; EXP; Experiment; extinction coefficient; FACE-IT; fjord; Isfjorden_Svalbard; Kongsfjorden; Kongsfjorden, Spitsbergen, Arctic; Northeast Greenland; Nuup_Kangerlua; PAR; Porsangerfjorden; Storfjorden; West Greenland Margin; Young_Sound; YS
    Type: Dataset
    Format: text/tab-separated-values, 7 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 5
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    FjordLight: addendum to PAR data for Arctic fjords (2024)
    PANGAEA
    Details
    Publication Date: 2024-05-19
    Description: Fjords in the Arctic are key ecosystems and of great importance to both natural systems and human societies. The in situ monitoring of these systems is however quite difficult and some variables, such as light availability, are more difficult to collect than others. To address this knowledge gap the 'FjordLight' dataset was published on PANGAEA (Gentili et al., 2023). This dataset provides monthly values for bottom PAR (PAR_B) across seven key Arctic fjords, as well as annual averages and monthly climatology values for PAR_B, surface PAR (PAR(0-)), and the extinction coefficient of photosynthetically available radiation (K_PAR). Thanks to the rapid interest from the community for this dataset, we are now expanding upon it with an addendum: a collection of three additional files for each of the seven sites. Of particular importance to the study of photo-biological processes in the water column is K_PAR; however, the original FjordLight dataset did not include these monthly values because the size of these combined data would make the file large for many users' systems. In the present addendum, all monthly K_PAR data are included. These values are based on estimates of surface turbidity and irradiance derived from remotely sensed observations. Also included in this addendum are one file each for the standard deviation of the monthly climatologies and the annual climatologies for the three PAR variables (e.g. PAR(0-), K_PAR, PAR_B). Note that as these files are in addition to the original FjordLight dataset, they do not contain the same list of global variables and other metadata. All variables in the NetCDF files in this addendum are internally documented. The FjordLight R package that may be utilised to access and work with the FjordLight data has been updated and expanded for reading new files (Gentili et al., 2023; https://cran.r-project.org/web/packages/FjordLight/index.html).
    Keywords: Arctic; Arctic Biodiversity & Livelihoods; Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); Binary Object (Media Type); extinction coefficient; FACE-IT; fjord; PAR
    Type: Dataset
    Format: text/tab-separated-values, 21 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 6
    Electronic Resource
    Electronic Resource
    Application of objective analysis to metallic traces in the atmospheric aerosol (1993)
    Springer
    Journal of atmospheric chemistry 16 (1993), S. 277-284 
    Details
    ISSN: 1573-0662
    Keywords: Correlation analysis ; singular value decomposition ; metallic traces ; atmospheric aerosol
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology , Geosciences
    Notes: Abstract An objective correlation analysis based on the concept of singular-value decomposition of a matrix is proposed here for the field of metallic traces in the atmospheric aerosol. On the basis of an atmospheric sampling at Cap Ferrat (southeastern coast of France), the method is applied to the airborne concentrations of Al, Pb, Cd, Cu, and Zn. The data matrix is decomposed in two series of singular vectors. These vectors are orthogonal and classified in decreasing importance, according to the percentage of the total variance that they explain. Such a method is easy to apply and, if compared with a standard correlation analysis, it exhibits such advantages as (i) atypical points can be objectively discarded in order to improve the description of the general characteristics of the data set; (ii) all the elements are simultaneously taken into account by the analysis, which permits the enhancement of the features of the data set involving one or several metals; (iii) the importance of these independent features in the variability of the data set is measured.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00696900
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    Paper (German National Licenses)
    Fulltext
  • 7
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    Assessing the Variability in the Relationship Between the Particulate Backscattering Coefficient and the Chlorophyll a Concentration From a Global Biogeochemical-Argo Database (2018)
    American Geophysical Union
    Details
    Publication Date: 2018-02-01
    Print ISSN: 2169-9275
    Electronic ISSN: 2169-9291
    Topics: Geosciences , Physics
    Published by American Geophysical Union
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  • 8
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    Application of objective analysis to metallic traces in the atmospheric aerosol (1993)
    Springer
    Details
    Publication Date: 1993-04-01
    Print ISSN: 0167-7764
    Electronic ISSN: 1573-0662
    Topics: Chemistry and Pharmacology , Geosciences
    Published by Springer
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  • 9
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    Global distribution of photosynthetically available radiation on the seafloor (2020)
    Copernicus
    Details
    Publication Date: 2020-07-31
    Description: A 21 year (1998–2018) continuous monthly data set of the global distribution of light (photosynthetically available radiation, PAR, or irradiance) reaching the seabed is presented. This product uses ocean color and bathymetric data to estimate benthic irradiance, offering critical improvements on a previous data set. The time series is 4 times longer (21 versus 5 years), the spatial resolution is better (pixel size of 4.6 versus 9.3 km at the Equator), and the bathymetric resolution is also better (pixel size of 0.46 versus 3.7 km at the Equator). The paper describes the theoretical and methodological bases and data processing. This new product is used to estimate the surface area of the seafloor where (1) light does not limit the distribution of photosynthetic benthic organisms and (2) net community production is positive. The complete data set is provided as 14 netCDF files available on PANGAEA (Gentili and Gattuso, 2020a, https://doi.org/10.1594/PANGAEA.910898). The R package CoastalLight, available on GitHub (https://github.com/jpgattuso/CoastalLight.git, last access: 29 July 2020), allows us (1) to download geographical and optical data from PANGAEA and (2) to calculate the surface area that receives more than a given threshold of irradiance in three regions (nonpolar, Arctic, and Antarctic). Such surface areas can also be calculated for any subregion after downloading data from a remotely and freely accessible server.
    Print ISSN: 1866-3508
    Electronic ISSN: 1866-3516
    Topics: Geosciences
    Published by Copernicus
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  • 10
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    Decrease of lead concentrations in the Western Mediterranean atmosphere during the last 20 years (2008)
    Elsevier
    Details
    Publication Date: 2008-02-01
    Print ISSN: 1352-2310
    Electronic ISSN: 1873-2844
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Geosciences , Physics
    Published by Elsevier
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