How well do global ocean biogeochemistry models simulate dissolved iron distributions?

Abstract

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.

The devil's in the disequilibrium: sensitivity of ocean carbon storage to climate state and iron fertilization in a general circulation model

“…It is worth noting that Parekh et al (2006) found ∆DIC dis /∆DIC soft of 2 in response to iron fertilization, using a modern ocean circulation, as analyzed by Ito and Follows (2013). We also caution that the quantitative values of DIC soft and DIC dis resulting from the altered iron flux 5 should be taken with a grain of salt, given the very large uncertainty in models of iron cycling (Tagliabue et al, 2016).…”

“…This includes the Modular Ocean Model version 5, a sea ice module, static land and ice sheets, and a module of Biogeochemistry with Light, Iron, Nutrients and Gases (BLINGv1.5) 5 (Galbraith et al, 2010). Unlike BLINGv0, BLINGv1.5 allows for variable stoichiometry and calculates the mass balance of phytoplankton in order to prevent unrealistic bloom magnitudes at high latitudes, reducing the magnitude of disequilibrium O 2 , which was very high in BLINGv0 (Duteil et al, 2013;Tagliabue et al, 2016).…”

An investigation of basin-scale controls on upper ocean export and remineralization

“…Because trace metals are necessary for many cellular functions and their abundance or scarcity can influence the BCP, it is important to quantify the surface export of these elements and understand the drivers of trace metal cycling in the upper ocean. Efforts to include metals in global biogeochemical models have illustrated our lack of understanding of the controls on trace metal distributions in the global ocean (Tagliabue et al, 2016;van Hulten et al, 2017) and the surface export estimates contained within Chapter 3 are a first step to improving these models.…”

How well can we quantify dust deposition to the ocean?

“…Operationally defined solubility of aerosol-derived iron spans approximately two orders of magnitude [87]. This large range, and the uncertainty about the environmental factors that regulate it, presents a challenge to the successful modelling of iron supply to marine ecosystems [9] even if dust deposition can be established reliably.…”

“…Ocean productivity, in turn, is a major component of the global carbon cycle [8], so there is a recognized need to include dust supply in global biogeochemical models [9].…”