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Ecological Control of Subtropical Nutrient Concentrations
Jan 31st, 2010

Ecological Control of Subtropical Nutrient Concentrations

story by Helen Hill and Stephanie Dutkiewicz.

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) <i>Prochlorococcus</i>-analogs (green), and (iv) other small phytoplankton (yellow-green).

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 »

Planet-in-a-Bottle
Oct 31st, 2009

Planet-in-a-Bottle

story by Helen Hill

 

Here we look at work by Sai Ravela, John Marshall, Chris Hill, Andrew Wong and Scott Stransky in which they use MITgcm to provide the virtual analogue for a fluid lab experiment in the physical laboratory, as part of an effort to demonstrate how to achieve real-time model-data synthesis, using measurements from a robotically controlled automated sensor system.

Figure 1. The components of the system: The laboratory observatory consists of a physical system: a rotating table on which a tank, camera and control system for illumination are mounted. The computational part consists of a measurement system for velocimetry, a numerical model (MITgcm), and an assimilation system.

Figure 1. The components of the system: The laboratory observatory consists of a physical system: a rotating table on which a tank, camera and control system for illumination are mounted. The computational part consists of a measurement system for velocimetry, a numerical model (MITgcm), and an assimilation system.

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Tidal Mixing Over Rough Topography
Jul 31st, 2009

Tidal Mixing Over Rough Topography

story by Helen Hill

This month we focus on the work of recent doctoral graduate Maxim Nikurashin (now working at GFDL) who, in a collaboration with long time user Sonya Legg,  has  been using a non-hydrostatic, 2-D version of the MITgcm to explore tidal mixing over rough topography (Fig. 1). The MITgcm’s elegant non-hydrostatic and topography-representing capabilities  make it an ideal choice for this kind of high-resolution process study.

Snapshot of wave zonal velocity (ms-1) deviation from the barotropic tide in the control simulation.

Figure 1: Snapshot of wave zonal velocity (ms-1) deviation from the barotropic tide from the control simulation in Nikurashin and Legg's high-resolution 2-D, MITgcm study of tidal mixing over rough topography.

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