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 Frozen Seas

Martin Losch of the Alfred-Wegener-Institute, Bremerhaven, Germany, in collaboration with Jean Michel Campin (MIT), Patrick Heimbach (MIT), Chris Hill (MIT) and Dimitris Menemenlis (JPL) have been extending the reach of the MITgcm in to the Polar oceans, with the development of a dynamic-thermodynamic sea-ice model and its adjoint. Figure 1 shows their sea-ice thickness distribution results for the Arctic and Antarctic in opposing seasons.

Figure 1. Arctic and Antarctic results from an eddy-permitting, MITgcm, global ocean and sea-ice simulation: Sea ice thickness distribution (color, in meters) averaged over the years 1992-2002. The ice-edge (estimated as the 15% isoline of ice concentration) retrieved from passive microwave satellite data is shown as a white contour for comparison. The top row shows the results for the Arctic Ocean and the bottom row for the Antarctic Oceans; the left column shows distributions for March and the right column for September.

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Multi Scale Superparameterization in Ocean Modeling

Multi scale approaches allow explicit modeling of the many different phenomena that are present in the real ocean. Borrowing an idea from meteorology (that of the embedded Cloud Resolving Model), Jean Michel Campin and colleagues have been using the MITgcm to exploit a multi scale superparameterization approach to increase efficiency in modeling oceanic deep convection (ODC). Figure 1 compares cross-domain temperature sections from  three runs after 2.5 days of simulation. The bottom left hand panel (from the run taking a multi scale  superparameterization approach) shares much more in character with the field deriving from the fully resolved model (top left) than that from the balanced model with only a simple convective adjustment algorithm (bottom right) yet for only a modest increase in computational cost.

Figure 1. Temperature sections after 60 hours from (top left) the fully resolved model, (bottom left) the multi-scale simulation and (bottom right) the balanced model with a simple convective adjustment algorithm. The multi-scale simulation image is derived from a composite of 2-D embedded elements.

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Adjoint Advances

Two MITgcm adjoint activities are (i) the development of an open-source, extensible automatic differentiation tool, OpenAD and (ii) the configuration of an ~18km resolution global ocean and sea-ice experiment as part of the ECCO2 project.

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