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  • File content; File name; File size; SOCAT; Surface Ocean CO2 Atlas Project; Uniform resource locator/link to file  (2)
  • Food web dynamic  (1)
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  • 1
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    PANGAEA
    In:  Supplement to: Jones, Steve D; Le Quéré, Corinne; Rödenbeck, Christian; Manning, Andrew C; Olsen, Are (2015): A statistical gap-filling method to interpolate global monthly surface ocean carbon dioxide data. Journal of Advances in Modeling Earth Systems, 7(4), 1554-1575, https://doi.org/10.1002/2014MS000416
    Publication Date: 2023-06-10
    Description: We have developed a statistical gap-filling method adapted to the specific coverage and properties of observed fugacity of surface ocean CO2 (fCO2). We have used this method to interpolate the Surface Ocean CO2 Atlas (SOCAT) v2 database on a 2.5°×2.5° global grid (south of 70°N) for 1985-2011 at monthly resolution. The method combines a spatial interpolation based on a 'radius of influence' to determine nearby similar fCO2 values with temporal harmonic and cubic spline curve-fitting, and also fits long term trends and seasonal cycles. Interannual variability is established using deviations of observations from the fitted trends and seasonal cycles. An uncertainty is computed for all interpolated values based on the spatial and temporal range of the interpolation. Tests of the method using model data show that it performs as well as or better than previous regional interpolation methods, but in addition it provides a near-global and interannual coverage.
    Keywords: File content; File name; File size; SOCAT; Surface Ocean CO2 Atlas Project; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 8 data points
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  • 2
    Publication Date: 2023-06-10
    Description: We have developed a statistical gap-filling method adapted to the specific coverage and properties of observed fugacity of surface ocean CO2 (fCO2). We have used this method to interpolate the Surface Ocean CO2 Atlas (SOCAT) v2 database on a 2.5°×2.5° global grid for 1985-2017 at monthly resolution. The method combines a spatial interpolation based on a 'radius of influence' to determine nearby similar fCO2 values with temporal harmonic and cubic spline curve-fitting, and also fits long term trends and seasonal cycles. Interannual variability is established using deviations of observations from the fitted trends and seasonal cycles. An uncertainty is computed for all interpolated values based on the spatial and temporal range of the interpolation. Tests of the method using model data show that it performs as well as or better than previous regional interpolation methods, but in addition it provides a near-global and interannual coverage.
    Keywords: File content; File name; File size; SOCAT; Surface Ocean CO2 Atlas Project; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 8 data points
    Location Call Number Expected Availability
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  • 3
    Publication Date: 2022-05-25
    Description: © The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Modelling 261-262 (2013): 43–57, doi:10.1016/j.ecolmodel.2013.04.006.
    Description: Dynamic Green Ocean Models (DGOMs) include different sets of Plankton Functional Types (PFTs) and equations, thus different interactions and food webs. Using four DGOMs (CCSM-BEC, PISCES, NEMURO and PlankTOM5) we explore how predator–prey interactions influence food web dynamics. Using each model's equations and biomass output, interaction strengths (direct and specific) were calculated and the role of zooplankton in modeled food webs examined. In CCSM-BEC the single size-class adaptive zooplankton preys on different phytoplankton groups according to prey availability and food preferences, resulting in a strong top-down control. In PISCES the micro- and meso-zooplankton groups compete for food resources, grazing phytoplankton depending on their availability in a mixture of bottom-up and top-down control. In NEMURO macrozooplankton controls the biomass of other zooplankton PFTs and defines the structure of the food web with a strong top-down control within the zooplankton. In PlankTOM5, competition and predation between micro- and meso-zooplankton along with strong preferences for nanophytoplankton and diatoms, respectively, leads to their mutual exclusion with a mixture of bottom-up and top-down control of the plankton community composition. In each model, the grazing pressure of the zooplankton PFTs and the way it is exerted on their preys may result in the food web dynamics and structure of the model to diverge from the one that was intended when designing the model. Our approach shows that the food web dynamics, in particular the strength of the predator–prey interactions, are driven by the choice of parameters and more specifically the food preferences. Consequently, our findings stress the importance of equation and parameter choice as they define interactions between PFTs and overall food web dynamics (competition, bottom-up or top-down effects). Also, the differences in the simulated food-webs between different models highlight the gap of knowledge for zooplankton rates and predator–prey interactions. In particular, concerted effort is needed to identify the key growth and loss parameters and interactions and quantify them with targeted laboratory experiments in order to bring our understanding of zooplankton at a similar level to phytoplankton.
    Description: This work was supported with funding from Palmer LTER (NSF OPP-0823101) and C-MORE (NSF EF-0424599).
    Keywords: Dynamic Green Ocean Model ; Plankton Functional Types ; Zooplankton ; Food web dynamic ; Predator–prey interactions
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
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