Modeling organic iron-binding ligands in a three-dimensional biogeochemical ocean model

Abstract

a b s t r a c tMost dissolved iron in the ocean is bound to organic molecules with strong conditional stability constants, known as ligands that are found at concentrations ranging from 0.2 to more than 10 nmol L −1 . In this work we report the first mechanistic description of ligand dynamics in two three-dimensional models of ocean biogeochemistry and circulation. The model for ligands is based on the concept that ligands are produced both from organic matter remineralization and phytoplankton processes, and that they are lost through bacterial and photochemical degradation, as well as aggregation and to some extent in the process of phytoplankton uptake of ligand-bound iron. A comparison with a compilation of in-situ measurements shows that the model is able to reproduce some largescale features of the observations, such as a decrease in ligand concentrations along the conveyor belt circulation in the deep ocean, lower surface and subsurface values in the Southern Ocean, or higher values in the mesopelagic than in the abyssal ocean. Modeling ligands prognostically (as opposed to assuming a uniform ligand concentration) leads to a more nutrient-like profile of iron that is more in accordance with data. It however, also leads to higher surface concentrations of dissolved iron and negative excess ligand L ⁎ in some ocean regions. This is probably an indication that with more realistic and higher ligand concentrations near the surface, as opposed to the traditionally chosen low uniform concentration, iron modelers will have to re-evaluate their assumption of low scavenging rates for iron. Given their sensitivity to environmental conditions, spatio-temporal variations in ligand concentrations have the potential to impact primary production via changes in iron limitation.

Toward a Regional Classification to Provide a More Inclusive Examination of the Ocean Biogeochemistry of Iron-Binding Ligands

“…Based on the observation that 34% of bacterial EPS was carbon (Hassler et al, 2011a) and a median ligand contribution of 18 µmol L g −1 EPS (Hansell, 2013;Norman et al, 2015), the production rate of ligands from EPS of 2.4 nmol d −1 L −1 , which is close to concentrations and production rates of ligands determined previously Gledhill and Buck, 2012;Völker and Tagliabue, 2015). This production rate suggests that EPS represent an important group of ligands accounting for most of iron binding ligands and ∼14% HS-like substances in the SubAntarctic seasonal bloom occurring between Australia and New Zealand .…”

“…The use of proxies for iron binding ligands, such as bulk DOC concentrations, is an approach that was employed initially but was unable to predict Fe solubility and its global distribution (Völker and Tagliabue, 2015). Therefore, iron biogeochemical models are developing by considering organic ligands production and loss terms to reconcile model outputs with observations (Völker and Tagliabue, 2015).…”

“…Therefore, iron biogeochemical models are developing by considering organic ligands production and loss terms to reconcile model outputs with observations (Völker and Tagliabue, 2015). The identification of which fraction of the DOC pool (englobing its sources production pathways, solubility, molecular weight, or other chemical characteristics) most influences Fe biogeochemistry is one of the major analytical obstacles in the field of marine biogeochemistry (Benner, 2011).…”

Molecular determination of marine iron ligands by mass spectrometry

“…Other models have tied ligand concentration to dissolved organic carbon concentrations [Tagliabue and Völker, 2011], although this does not accurately reflect the distribution of ligands in the marine environment [Boyd and Tagliabue, 2015]. More recently, models have implemented prognostic ligands based on remineralization rates or apparent oxygen utilization [Misumi et al, 2013;Völker and Tagliabue, 2014]. To more accurately account for faster decay of labile ligands, ligand degradation rates have been tied to ligand concentration [Völker and Tagliabue, 2014].…”

“…More recently, models have implemented prognostic ligands based on remineralization rates or apparent oxygen utilization [Misumi et al, 2013;Völker and Tagliabue, 2014]. To more accurately account for faster decay of labile ligands, ligand degradation rates have been tied to ligand concentration [Völker and Tagliabue, 2014]. All of these models represent ligands as a single compound class.…”