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Scholarly Works (3 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2016)

Iron is a key micronutrient for marine biogeochemistry, limiting growth and nitrogen fixation in over a third of the ocean. However, the impacts of iron-binding ligands and iron source processes on dissolved iron distributions is not well known. The goal of this dissertation is to better understand the cycling of iron-binding ligands and the controls on their distributions and how that impacts dissolved iron. This dissertation also seeks to understand how individual iron sources influence iron distributions and biogeochemistry. To accomplish this, a new prognostic ligand tracer and iron-ligand speciation chemistry are incorporated into the Community Earth System Model (CESM). The CESM is now able to simulate realistic distributions of iron-binding ligands. The results show that with relatively few ligand sources and sinks the model was able to match observations of ligands, and that inclusion of a dynamic ligand tracer improves simulation of dissolved iron. To better understand the influence iron sources have on dissolved iron concentrations and biogeochemistry, sensitivity experiments for each source are conducted with the CESM. The results show that atmospheric dust and sedimentary iron inputs have the largest impact on dissolved iron concentrations and biogeochemistry. Hydrothermal vent inputs are important for deep ocean iron, and their inclusion in global biogeochemical ocean models would allow for more realistic iron simulation.

This dissertation work also reevaluates the parameters governing the temperature influence on community phytoplankton growth rates. A dataset of in situ community phytoplankton growth rates was compiled and parameter values for the Q10 and Arrhenius models were optimized for use in global biogeochemical and ecosystem models. The results show and optimized Q10 value of 1.47 and an activation energy of 0.277 eV. Both the Q10 and Arrhenius models do equally well for estimating the temperature influence on community phytoplankton growth rates against the dataset. Evaluation of global biogeochemical and ecosystem models against our dataset will allow for further constraints on phytoplankton ecology and associated biogeochemitry.

    Cover page: A Modeling Study of the Biogoechemical Cycling of Iron, Ligands, and Phytoplankton in the Ocean
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2016)
    A large database of field estimates of phytoplankton community growth rates in natural populations was compiled and analyzed to determine the apparent temperature effect on phytoplankton community growth rate. We conducted an ordinary least squares regression to optimize the parameters in two commonly used growth-temperature relations (Arrhenius and Q10 models). Both equations fit the observational data equally with the optimized parameter values. The optimum apparent Q10 value was 1.47 ± 0.08 (95% confidence interval, CI). Microzooplankton grazing rates closely matched the temperature trends for phytoplankton growth. This likely reflects a dynamic adjustment of biomass and grazing rates by the microzooplankton to match their available food source, illustrating tight coupling of phytoplankton growth and microzooplankton grazing rates. The field-measured temperature effect and growth rates were compared with estimates from the satellite Carbon-based Productivity Model (CbPM) and three Earth System Models (ESMs), with model output extracted at the same month and sampling locations as the observations. The optimized, apparent Q10 value calculated for the CbPM was 1.51, with overestimation of growth rates. The apparent Q10 value in the Community Earth System Model (V1.0) was 1.65, with modest underestimation of growth rates. The GFDL-ESM2M and GFDL-ESM2G models produced apparent Q10 values of 1.52 and 1.39, respectively. Models with an apparent Q10 that is significantly greater than ~1.5 will overestimate the phytoplankton community growth response to the ongoing climate warming and will have spatial biases in estimated growth rates for the current era.
      Cover page: Temperature influence on phytoplankton community growth ratesCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2016)
      Numerical models of ocean biogeochemistry are relied upon to make projections about the impact of climate change on marine resources and test hypotheses regarding the drivers of past changes in climate and ecosystems. In large areas of the ocean, iron availability regulates the functioning of marine ecosystems and hence the ocean carbon cycle. Accordingly, our ability to quantify the drivers and impacts of fluctuations in ocean ecosystems and carbon cycling in space and time relies on first achieving an appropriate representation of the modern marine iron cycle in models. When the iron distributions from 13 global ocean biogeochemistry models are compared against the latest oceanic sections from the GEOTRACES program, we find that all models struggle to reproduce many aspects of the observed spatial patterns. Models that reflect the emerging evidence for multiple iron sources or subtleties of its internal cycling perform much better in capturing observed features than their simpler contemporaries, particularly in the ocean interior. We show that the substantial uncertainty in the input fluxes of iron results in a very wide range of residence times across models, which has implications for the response of ecosystems and global carbon cycling to perturbations. Given this large uncertainty, iron fertilization experiments based on any single current generation model should be interpreted with caution. Improvements to how such models represent iron scavenging and also biological cycling are needed to raise confidence in their projections of global biogeochemical change in the ocean.
        Cover page: How well do global ocean biogeochemistry models simulate dissolved iron distributions?
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