HR: 1340h
AN: B53B-1174    [Abstracts]
TI: Development of Regional CO2 Inversion for Terrestrial Sources/Sinks Using Genetic Algorithm
AU: * Oda, T
EM: oda@ge.see.eng.osaka-u.ac.jp
AF: Graduate School of Engineering, Osaka University, Japan, 2-1 Yamadaoka, Suita, Osaka, 565-0871, Japan
AU: Machimura, T
EM: mach@see.eng.osaka-u.ac.jp
AF: Graduate School of Engineering, Osaka University, Japan, 2-1 Yamadaoka, Suita, Osaka, 565-0871, Japan
AB: Global CO₂ inversion is a well-developed technique for deducing a reasonable surface CO₂ flux combination that is consistent with atmospheric CO₂ observation. It provides a global wide distribution of CO₂ sources/sinks, however, the obtained CO₂ source/sink information is coarse for the use of local level policy making against global warming. Thus, finer spatial resolution information of source/sink is preferable. When CO₂ inversion technique is applied to regional study, it is more difficult to relate surface flux to concentration properly because of difficulty in modeling of regional transport. And, especially in regional study, the concentration variation occurs in shorter time scale, which is usually removed as "noise" should be utilized because it may contain spatiotemporal information of regionally distributed sources/sinks. In this study, a regional CO₂ inversion that employs Genetic Algorithm (GA) for the inverse method is proposed. It enables us to utilize the concentration variations as signals to detect regionally distributed CO₂ sources and sinks. Our framework consists of atmospheric CO₂ observation, modeling of atmospheric transport and an optimization technique. In practical, unknown parameters corresponding to fluxes are optimized by matching the modeled concentration with the observed concentration. The flux estimates are calculated using the optimized parameters. The hourly atmospheric CO₂ concentration was measured using an infra-red gas analyzer at an observation point located in the target domain and the modeled concentration at the observation point was calculated using our regional transport model driven by hourly meteorological fields from MM5. Genetic Algorithm (GA) is a universal optimization technique that searches within the solution space globally while avoiding local optima. It generates candidate solutions (i.e. combinations of parameters) that result in smaller misfit between the observed and modeled concentration. A surface flux estimate of August 2005 over a region of 126 km x 126 km was obtained using 10-day complete CO₂ dataset (August 18-27; 240 hour). The study area is located in the centre of the main island in Japan and we assumed it contained four flux classes including open water, urban area, cropland and forest according to land use data. The surface fluxes of vegetation were prescribed using the modeled short wave radiation while the fluxes of urban and open water were assumed to be constant. Since the obtained flux estimates in our framework have variability, several estimation runs are required. For ensuring our flux estimation, we studied required number of estimation runs using reduced version of our transport model. In reduced model, the spatial resolution was degraded and the time period for assimilating the observation was shortened for the simplification and reduction of computational time of our estimation. In addition to the above, the footprint of the observation was studied.
DE: 0315 Biosphere/atmosphere interactions (0426, 1610)
DE: 0322 Constituent sources and sinks
DE: 0414 Biogeochemical cycles, processes, and modeling (0412, 0793, 1615, 4805, 4912)
DE: 0428 Carbon cycling (4806)
SC: Biogeosciences [B]
MN: 2007 Fall Meeting