HR: 08:15h
AN: A11F-02    [Abstracts]
TI: Parameter Optimization Using the Adjoint of a Biospheric Model
AU: * Pak, B C
EM: bernard.pak@csiro.au
AF: CSIRO Atmospheric Research, Private Bag #1, Aspendale, VIC 3195 Australia
AB: Regional budgets are best tackled by a combination of top-down and bottom-up estimation. In such approaches, process-based models are simultaneously constrained by local and integrated observations. This paper presents the local part of such an approach. We use an adjoint model generated from a biospheric model. The adjoint model, once generated, is a powerful tool in data assimilation. Our intention is to couple it to the adjoint of an atmospheric model in the next step to provide an interactive land-surface flux at much finer resolution. Running the adjoint of such a coupled model can solve the inversion problem efficiently. On top of the improved flux estimates and uncertainties, it would also optimize the parameters in the biospheric model for individual regions/biomes to shine light on the underlying processes. Here, we present the results from the adjoint of the CSIRO Biospheric Model. Using the eddy covariance measurements from Tumbarumba (an evergreen forest of eucalyptus trees) and Harvard Forest (deciduous), the modelled latent heat, sensible heat and carbon fluxes are compared to observations. The parameters in the biospheric model are optimized via the gradient descent method guided by a cost function based on the least squares of the misfits. The optimized parameters for both annual and seasonal datasets already show large discrepancies in the prescribed leaf area index which has been a product from remote sensing instruments. There is also a general insensitivity to parameters for soil and plant respirations; however, they are best optimized by the CO2 concentration data which will be introduced in the next step when coupled to an atmospheric model.
DE: 3210 Modeling
DE: 3260 Inverse theory
DE: 1615 Biogeochemical processes (4805)
DE: 0315 Biosphere/atmosphere interactions
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting