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  • 1
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    Unknown
    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 Limitation 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
    Location Call Number Limitation Availability
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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 Limitation 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 Limitation Availability
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    DATA PANGAEA
  • 5
    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
    Location Call Number Limitation Availability
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    Paper (German National Licenses)
    Fulltext
  • 6
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    An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a-based models (2015)
    In:  EPIC3Journal of Geophysical Research: Oceans, pp. n/a-n/a, ISSN: 21699275
    Details
    Publication Date: 2015-09-15
    Description: We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters. This article is protected by copyright. All rights reserved.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev , info:eu-repo/semantics/article
    Location Call Number Limitation Availability
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  • 7
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    Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT) (2017)
    Frontiers
    In:  EPIC3Frontiers in Marine Science, Frontiers, 4(203), pp. 1-22, ISSN: 2296-7745
    Details
    Publication Date: 2017-07-17
    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 satellite measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm applied to the Ocean Color Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm 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 PFT Chla data obtained from in situ marker pigment data and the NASA Ocean Biogeochemical Model simulations and satellite information on phytoplankton size. 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.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 8
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    Corrigendum: Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT) (2017)
    Frontiers
    In:  EPIC3Frontiers in Marine Science, Frontiers, 4(258), pp. 1-1, ISSN: 2296-7745
    Details
    Publication Date: 2017-08-29
    Description: A corrigendum on Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT) by Losa, S. N., Soppa, M. A., Dinter, T., Wolanin, A., Brewin, R. J. W., Bricaud, A., et al. (2017). Front. Mar. Sci. 4:203. doi: 10.3389/fmars.2017.00203. In the original article, we neglected, but would like to acknowledge the North-German Supercomputing Alliance (HLRN) for providing HPC resources that have contributed to the research results reported in this paper. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev
    Format: application/pdf
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  • 9
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    Assessing the uncertainties of model estimates of primary productivity in the tropical Pacific Ocean (2009)
    Details
    Publication Date: 2022-05-25
    Description: Author Posting. © Elsevier B.V., 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 76 (2009): 113-133, doi:10.1016/j.jmarsys.2008.05.010.
    Description: Depth-integrated primary productivity (PP) estimates obtained from satellite ocean color based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BOGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of ~1000 14C measurements spanning more than a decade (1983- 1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PP, specifically yielding too few low PP (〈 0.2 gC m-2d-2) values; more than half of the total root-mean-squared model-data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomass-normalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s, which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140°W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison six years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill.
    Description: This research was supported by a grant from the National Aeronautics and Space Agency Ocean Biology and Biogeochemistry program (NNG06GA03G), as well as by numerous other grants to the various participating investigators
    Keywords: Primary production ; Modeling ; Remote sensing ; Satellite ocean color ; Statistical analysis ; Tropical Pacific Ocean (15°N to 15°S and 125°E to 95°W)
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
    Format: application/pdf
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