In and out. Driven by winds, the Southern Ocean's currents (blue globe) transport CO2 (red) northward. Credit: T. Ito et al., Nature 463 (2010)

story by Helen Hill

In this article we spotlight work by Taka Ito, Molly Woloszyn and Matt Mazloff who have been using MITgcm to study anthropogenic CO₂ transport in the Southern Ocean.

Key to their study is a high-resolution (1/6^o x 1/6^o) estimate of the Southern Ocean circulation, consistent with modern observations, deriving from an MITgcm adjoint calculation, developed as part of the ECCO-GODAE project.  In addition MITgcm’s offline model enabled a ready coupling of the high resolution circulation estimate to a carbon cycle model.

Focussing on intra-annual ACO₂ variability, the team found a clear correlation between the pattern of carbon uptake and oceanic vertical exchange in strong support of the wind-driven circulation as a primary regulator of Southern Ocean ACO₂ transport.

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Ecological Control of Subtropical Nutrient Concentrations

Figure 1: Multiple-Resource Experiment. (top) Emergent biogeographical provinces, defined by most dominant species, reminiscent of Longhurst (1995). (bottom) Biogeography of four major functional groups: (i) Diatom-analogs (red), (ii) other large phytoplankton (orange), (iii)Prochlorococcus-analogs (green), and (iv) other small phytoplankton (yellow-green).

In this article we spotlight recent work by Darwin Project team members Stephanie Dutkiewicz, Mick Follows and Jason Bragg, who have been examining the utility of resource control theory to interpret the relationships between organisms and resources in a global coupled physical-biogeochemistry-ecosystem model built around MITgcm.

The team find that in regions of low seasonality, resource competition theory (Tilman, ‘77)  not only anticipates the competitive outcome amongst organisms but also provides a quantitative diagnostic of ecological control of nutrient concentrations. DFB’s sensitivity experiments clearly indicate control on the ambient nutrient by phytoplankton physiology as predicted by the theory. Read the rest of this entry »

Modeling the Great Lakes

This month we focus on the work of Galen McKinley and Val Bennington at the University of Wisconsin, Madison.

With a view to developing a quantitative understanding of the role such bodies of water may play in the terrestrial carbon cycle, Galen and Val have been using the MITgcm to put together a comprehensive,  up-to-date description of the general circulation and temporal variability of Lake Superior. Figure 1 shows the summer-time mean circulation from their model.

Figure 1: Summer-time mean circulation from a simulation of Lake Superior using MITgcm.

Because of its large size (the lake is of order 500km long by 250km wide, with depths to 300 m) as well as difficulties associated with  fieldwork (particularly in winter), Lake Superior’s  general  circulation is rather poorly known. Believing the terrestrial ecosystem around the lake to be a substantial sink of CO₂ from the atmosphere, but not knowing to what degree carbon is being transferred to the lake and fluxed back to the atmosphere, Bennington and McKinley set out to build a holistic model of the system; step one being to build an up-to-date general circulation model. Read the rest of this entry »

Population Competition in Action

As the Darwin project begins to bear fruit we focus here on the work of recent doctoral graduate Fanny Monteiro who together with Mick Follows and Stephanie Dutkiewicz have been using the MITgcm to probe the behaviour of self-assembling phytoplankton communities within a global ocean circulation (Follows et al. 2007). Figure 1 summarises the core ideas behind the teams work: Within this, many tens of phytoplankton “types” are initialized, each one having a randomly assigned sensitivity to light, temperature and nutrient requirements. In Fanny’s work, she additionally initialised this model with populations of “diazotrophs” – organisms that can fix their own nitrogen, but with the trade-off of correspondingly slow growth and high iron to phosphorus requirements.

Figure 1. Illustration of the key components in the self-assembling phytoplankton community model. After some years of interaction, the fittest "types" persist and occupy distinct habitats.

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