544
ANDREAS OSCHLIES
vide an optimal description of the physical environment for the marine
ecosystem models to be run at these sites. Covering much longer time
scales and more extreme climate states, even paleo information can help
to constrain marine ecosystem models. By combining these very different time and space scales and the information provided by the different
observational data sets with numerical models in an data assimilative
approach, we will hopefully gain a better mechanistic understanding of
marine ecosystem dynamics and their effects on biogeochemical cycles
as well as their sensitivity to a changing climate.
Acknowledg ments
I thank Eric Chassignet and Jacques Verron for allowing me to participate in this very enjoyable Summer School. This work is a contribution
to the MERSEA Project. Partial support of the European Commission
under contract SIP3-CT-2003-502885 is gratefully acknowledged.
References
Anderson, L. A., and J. L. Sarmiento, 1995: Global ocean phosphate and oxygen
simulations. Global Biogeochem. Cycles, 9, 621–636.
Athias, V., P. Mazzega, and C. Jeandel, 2000: Selecting a global optimization method
to estimate the oceanic particle cycling rate constants. J. Mar. Res., 58, 675–707.
Aumont, O., E. Maier-Reimer, S. Blain, and P. Monfray, An ecosystem model of the
global ocean including Fe, Si, P co-limitations. Global Biogeochem. Cycles, 17(2),
doi:10.1029/2001GB001745, 2003.
Bacastow, R., and E. Maier–Reimer, 1990: Ocean–circulation model of the carbon
cycle. Climate Dynamics, 4, 95–125.
Bennett, A. F., 1989: The Kalman smoother for a linear quasi-geostrophic model of
ocean circulation. Dyn. Atmos. Oceans, 13, 219–267.
Bertino, L., G. Evensen, and H. Wackernagel, 2003: Sequential data assimilation techniques in oceanography. Int. Stat. Rev., 71, 223–242.
Carmillet, V., J.-M. Brankart, P. Brasseur, H. Drange, G. Evensen, and J. Verron,
2001: A singular evolutive extended Kalman filter to assimilate ocean color data
in a coupled physical-biochemical model of the North Atlantic ocean. Ocean Modelling, 3, 167–192.
Chai, F., R. T. Barber, and S. T. Lindley, 1996: Origin and maintenance of high
nutrient condition in the equatorial Pacific. Deep-Sea Res. II, 43, 1031–1064.
Dring, M. J., and D. H. Jewson, 1982: What does
14 C uptake by phytoplankton really
measure? A theoretical modelling approach. Proc. R. Soc. Lond., B 214, 351–368.
Ebenh¨ oh, W., J. G. Baretta, and J. W. Baretta, 1997: The primary production module
in the marine ecosystem model ERSEM II with emphasis on the light forcing.
J. Sea Res., 38, 173–193.
Evans, G. T., 2003: Defining misfit between biogeochemical models and data sets.
J. Mar. Syst., 40–41, 49–54.
Evensen, G., 1992: Using the extended Kalman filter with a multilayer quasi-geostrophic
ocean model. J. Geophys. Res., 97(C11), 17,905–17,924.
e
ANDREAS OSCHLIES
vide an optimal description of the physical environment for the marine
ecosystem models to be run at these sites. Covering much longer time
scales and more extreme climate states, even paleo information can help
to constrain marine ecosystem models. By combining these very different time and space scales and the information provided by the different
observational data sets with numerical models in an data assimilative
approach, we will hopefully gain a better mechanistic understanding of
marine ecosystem dynamics and their effects on biogeochemical cycles
as well as their sensitivity to a changing climate.
Acknowledg ments
I thank Eric Chassignet and Jacques Verron for allowing me to participate in this very enjoyable Summer School. This work is a contribution
to the MERSEA Project. Partial support of the European Commission
under contract SIP3-CT-2003-502885 is gratefully acknowledged.
References
Anderson, L. A., and J. L. Sarmiento, 1995: Global ocean phosphate and oxygen
simulations. Global Biogeochem. Cycles, 9, 621–636.
Athias, V., P. Mazzega, and C. Jeandel, 2000: Selecting a global optimization method
to estimate the oceanic particle cycling rate constants. J. Mar. Res., 58, 675–707.
Aumont, O., E. Maier-Reimer, S. Blain, and P. Monfray, An ecosystem model of the
global ocean including Fe, Si, P co-limitations. Global Biogeochem. Cycles, 17(2),
doi:10.1029/2001GB001745, 2003.
Bacastow, R., and E. Maier–Reimer, 1990: Ocean–circulation model of the carbon
cycle. Climate Dynamics, 4, 95–125.
Bennett, A. F., 1989: The Kalman smoother for a linear quasi-geostrophic model of
ocean circulation. Dyn. Atmos. Oceans, 13, 219–267.
Bertino, L., G. Evensen, and H. Wackernagel, 2003: Sequential data assimilation techniques in oceanography. Int. Stat. Rev., 71, 223–242.
Carmillet, V., J.-M. Brankart, P. Brasseur, H. Drange, G. Evensen, and J. Verron,
2001: A singular evolutive extended Kalman filter to assimilate ocean color data
in a coupled physical-biochemical model of the North Atlantic ocean. Ocean Modelling, 3, 167–192.
Chai, F., R. T. Barber, and S. T. Lindley, 1996: Origin and maintenance of high
nutrient condition in the equatorial Pacific. Deep-Sea Res. II, 43, 1031–1064.
Dring, M. J., and D. H. Jewson, 1982: What does
14 C uptake by phytoplankton really
measure? A theoretical modelling approach. Proc. R. Soc. Lond., B 214, 351–368.
Ebenh¨ oh, W., J. G. Baretta, and J. W. Baretta, 1997: The primary production module
in the marine ecosystem model ERSEM II with emphasis on the light forcing.
J. Sea Res., 38, 173–193.
Evans, G. T., 2003: Defining misfit between biogeochemical models and data sets.
J. Mar. Syst., 40–41, 49–54.
Evensen, G., 1992: Using the extended Kalman filter with a multilayer quasi-geostrophic
ocean model. J. Geophys. Res., 97(C11), 17,905–17,924.
e