A significant challenge in understanding the changing earth
system is to quantify and model the role of ocean ecosystems in the
global carbon cycle. Researchers within CMI are developing tools to allow biogeochemistry to be modeled within
the framework of eddy-resolving
models of the ocean circulation to better understand the interplay
of the processes involved. Much of the current work is being carried
out under aegis of the Darwin
Project.
A marine ecosystem model with self-assembling community
structure
Modeled phytoplankton types on cubed sphere:
Dominant phytoplankton types during 1994-1998 from a
high-resolution ocean and ecosystem model. Colors
represent the most dominant type of phytoplankton at a
given location. Animations: Oliver Jahn, Chris Hill,
Stephanie Dutkiewicz, and Mick Follows (MIT) and the
ECCO2 Project (MIT/NASA)
The structure of microbial
communities in the surface ocean is known to regulate
important biogeochemical pathways, including the
efficiency of export of organic carbon to the deep
ocean. Although there is extraordinary diversity in the
oceans, the biomass of local microbial communities at
any location is typically dominated by only a small number of strains. Their relative fitness and ecosystem
community structure are regulated by a variety of factors, including
physical conditions, dispersal predation, competition for resources
and variability of environment. CMI researchers have developed a
technique for initializing model simulations with many tens of functional
phytoplankton types (exhibiting an assortment of growth parameters)
to create biogeochemical-ecosystems that can be embedded in
circulation state estimates, provided by, for example, the
ECCO-GODAE
consortium, to investigate how ocean circulation can lead to
biogeography.
Dutkiewicz, S., M. J. Follows, and J. G. Bragg (2009), Modeling
the coupling of ocean ecology and biogeochemistry, Global Biogeochem.
Cycles, 23, GB4017, doi:10.1029/2008GB003405.
pdf
Follows, M.J., S. Dutkiewicz, S. Grant and S.W. Chisholm (2007)
Emergent biogeography of microbial communities in a model ocean.
Science, 315, 1843-1846, [DOI: 10.1126/science.1138544]:
Link to abstract and
link to full text.
Marine iron-cycle parameterization
Schematic representation of the iron cycle model used in MITgcm
- Image credit M. Follows.
Another important aspect of the ocean biogeochemistry model is the development
of parameterizations of key biogeochemical processes. Modeling the iron cycle is
an important example. It is now clear that the
supply of iron to the euphotic zone plays a key role in modulating
biological productivity of the oceans, yet until recently the important
processes and mechanisms which control the distribution and transport of iron
within the ocean had not been well understood. Researchers in CMI worked to
develop an iron-cycle model that could capture the transport and biogeochemical
cycling of iron in an ocean model and its subsequent control on export
production and macronutrient distributions.
publications:
Parekh, P., M.J. Follows, and E.A. Boyle (2004a)
Modeling the global ocean iron cycle. Global Biogeochem.
Cycles., 18, GB1002, doi:10.1029/2003GB002061
pdf
Adjoint modeling
CMI continues to develop adjoints of MITgcm's growing suite of biogeochemical
models with the goal of systematically and efficiently exploring the
sensitivities of each to boundary conditions and parameter choices.
In this
method, an objective ("cost") function, J, is identified with some
measure of the model state which can be evaluated through a forward integration.
Subsequently a "backward" integration of the adjoint model returns the gradient,
or sensitivity, of the cost function, dJ/dX, with respect to a given set
of control variables, X. In such a way the adjoint can be used to
elucidate how a macroscopic quantity such as, say, global production, depends
on the concentration of a given nutrient in a particular region.
Due to the efficiency of the adjoint method the
residence times for all possible sources in the model,
say, can be evaluated simultaneously at a
small fraction of the computational cost than if each source were treated
independently in a perturbation experiment.
In this study, MITgcm and its adjoint is
used to infer the mean residence time of a
carbon-like tracer with respect to interior
sources in the ocean model. Shown are the
residence times for "carbon" emitted from sources at three model depths - image
source: S. Dutkiewicz
publications:
Dutkiewicz, S., P. Heimbach, M.J. Follows and J.C. Marshall
(2006) Controls on ocean productivity and air-sea carbon flux: an
adjoint model sensitivity study. Geophys. Res. Lett., 33(2), L02603,
10.1029/2005GL024987
pdf
Carbonate chemistry system
Schematic representation of the carbonate
chemistry system coded in MITgcm - Image source M.
Follows
In order to determine the air-sea fluxes of CO2 in numerical
models it is necessary to solve for the local equilibrium partitioning
of the carbonate system. Typically this involves solution of a high
order polynomial at each surface grid point using a method such as Newton-Raphson
iteration. CMI researchers have developed an efficient scheme for solving this system which requires no iteration.
The method has several advantages over the iterative schemes typically used: it
is more robust, the code is cleaner and more compact and the scheme is
computationally more efficient. The compact and efficient algorithm and code are
also ideal for the adjoint model approach since each is easily differentiated, requires no iterative procedure.
publications:
Follows, M.J., S. Dutkiewicz, and T. Ito (2006) On the solution
of the carbonate system in ocean biogeochemistry models. Ocean
Modeling, 12, 290-301.
pdf
Tracer transport
Modeling biogeochemical variables introduces different requirements
for tracer transport schemes. For example, to prevent numerical instabilities when representing nutrient limited biological processes
it is necessary to use positive definite advection schemes in the advection
of nutrient species. CMI's biogeochemical modeling team work with MITgcm
developers to optimize the fidelity of the schemes they use. Some of the schemes used in our models are described
here.
A selection of recent biogeochemical studies using MITgcm
