Atmos. Chem. Phys. Discuss., 3, 5185-5204, 2003
© Author(s) 2003. This work is licensed under the
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Review Status
This discussion paper has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP.
Optimizing CO2 observing networks in the presence of model error: results from TransCom 3
P. J. Rayner
CSIRO Atmospheric Research, Melbourne, Australia

Abstract. We use a genetic algorithm to construct optimal observing networks of atmospheric CO2 concentration for inverse determination of net sources. Optimal networks are those that produce a minimum in average posterior uncertainty plus a term representing the divergence among source estimates for different transport models. The addition of this last term modifies the choice of observing sites, leading to larger networks than would be chosen under the traditional estimated variance metric. Model-model differences behave like sub-grid heterogeneity and optimal networks try to average over some of this. The optimization does not, however, necessarily reject apparently difficult sites to model. Although the results are so conditioned on the experimental set-up that the specific networks chosen are unlikely to be the best choices in the real world, the counter-intuitive behaviour of the optimization suggests the model error contribution should be taken into account when designing observing networks. Finally we compare the flux and total uncertainty estimates from the optimal network with those from the TransCom 3 control case. The comparison suggests that the TransCom 3 control case is robust.

Citation: Rayner, P. J.: Optimizing CO2 observing networks in the presence of model error: results from TransCom 3, Atmos. Chem. Phys. Discuss., 3, 5185-5204, doi:10.5194/acpd-3-5185-2003, 2003.
Search ACPD
Discussion Paper
    Final Revised Paper