Abstract ID: 713

Optimising the ORCHIDEE model using eddy covariance data at tropical forest sites in the Amazon

Eddy covariance measurements at the Santarém (km 67) site revealed seasonal carbon flux patterns which could not be simulated by existing state-of-the-art global ecosystem models (Saleska et al., 2003). An unexpected high carbon uptake was measured during dry season. In contrast, carbon release was observed in the wet season. There are several possible underlying mechanisms of this phenomenon: (1) increased soil respiration in the wet season, (2) increased photosynthesis during the dry season due to deep rooting and increased radiation. We optimise the ORCHIDEE model using eddy covariance data in order to mimic the seasonal response of carbon fluxes to dry/wet conditions in tropical forests and to identify the underlying mechanisms. ORCHIDEE is a state-of-the-art global vegetation model. Here the model is run locally at eddy flux sites. It calculates the carbon and water cycle for different soil and vegetation pools. ORCHIDEE is built on the concept of plant functional types (PFT). ORCHIDEE parameters are associated with uncertainty and may vary between and within PFTs in a way currently not captured by the model. Recently developed assimilation techniques allow for the objective use of eddy covariance data to improve our knowledge on these parameters in a statistically coherent approach. ORCHIS (Orchidee Inversion System) is used to assimilate flux data. ORCHIS uses a Bayesian optimisation approach that minimizes a cost function. The parameters can be optimized at different time scales (annually, monthly,... ). This work is focused on several tropical forest sites with a different behaviour of the seasonal C flux response on wet and dry conditions. Several key processes are optimised: phenology, soil water stress, heterotrophic respiration, stomatal conductance and photosynthesis. We also test the coherence between optimised parameters of different sites within the tropical forest PFT and compare the model response to climate variations between sites.

Session:  Carbon - The LBA Model-Intercomparison Project.

Presentation Type:  Oral