HR: 09:40h
AN: B41F-07    [Abstracts]
TI: Carbon Cycle Steady State Assumption Impacts in Biogeochemical Modeling Inverse Parameter Retrieval and Implications for Regional Net Ecosystem Fluxes Estimates
AU: * Carvalhais, N
EM: ncarvalhais@fct.unl.pt
AF: Departamento de Cíncias e Engenharia do Ambiente Faculdade de Cíncias e Tecnologia Universidade Nova de Lisboa, Quinta da Torre - Monte de Caparica, Caparica, 2829 - 516, Portugal
AU: Reichstein, M
EM: markus.reichstein@bgc-jena.mpg.de
AU: Seixas, J
EM: mjs@fct.unl.pt
AF: Departamento de Cíncias e Engenharia do Ambiente Faculdade de Cíncias e Tecnologia Universidade Nova de Lisboa, Quinta da Torre - Monte de Caparica, Caparica, 2829 - 516, Portugal
AU: Collatz, G J
EM: jcollatz@ltpmail.gsfc.nasa.gov
AU: Pereira, J S
EM: jspereira@isa.utl.pt
AU: Berbigier, P
EM: berbigie@bordeaux.inra.fr
AU: Carrara, A
EM: arnaud@ceam.es
AU: Granier, A
EM: agranier@nancy.inra.fr
AU: Montagnani, L
EM: leonar@inwind.it
AU: Papale, D
EM: darpap@unitus.it
AU: Rambal, S
EM: serge.rambal@cefe.cnrs.fr
AU: Sanz, M J
EM: mjose@ceam.es
AU: Valentini, R
EM: rik@unitus.it
AB: We analyze the impacts of the steady state assumption on inverse model parameter retrieval from biogeochemical models. An inverse model parameterization study using eddy covariance CO₂ flux data was performed with the Carnegie Ames Stanford Approach (CASA) model under conditions of strict and relaxed carbon-cycle steady state assumption (CCSSA), in order to evaluate both the robustness of the model’s structure for the simulation of net ecosystem carbon fluxes and the assessment of the CCSSA effects on simulations and parameter estimation. Net ecosystem production (NEP) measurements from several eddy- covariance sites were compared with NEP estimates from the CASA model driven by local weather stations climate inputs as well as by remotely sensed fraction of photosynthetically active radiation absorbed by vegetation (fAPAR) and leaf area index (LAI). The parameters considered for optimization are directly related to above and belowground modeled responses to temperature and water availability, as well as a parameter (η) that relaxed the CCSSA in the model, allowing for site level simulations to be initialized either as net sinks or sources. A robust relationship was observed between NEP observations and predictions for most of the sites through the range of temporal scales considered (daily, weekly, biweekly and monthly), supporting the conclusion that the model structure is able to capture the main processes explaining NEP variability. Overall, relaxing CCSSA increased model efficiency and decreased normalized average error. Inter site variability was a major source of variance in model performance differences between fix (CCSSA_f) and relaxed (CCSSA_r) CCSSA conditions. These differences were correlated with mean annual NEP observations. A set of ancillary parameters were tested as alternatives to η, yielding statistically significant lower model performance results. η was found to be a key parameter in the optimization exercise, generating the highest model efficiency losses when removed from the initial parameter set. Differences between ε* and Q₁₀ estimates under CCSSA_r and CCSSA_f conditions were correlated with η, suggesting parameter compensation effects in steady state approaches. Parameter errors were significantly lower under CCSSA_r. Overall, the importance of model structure evaluation in data assimilation approaches is thus emphasized. Furthermore, using the parameter estimates and their covariances we compute and analyze spatially distributed carbon balance estimates for parts of Europe and associated uncertainties. Uncertainties introduced by wrong model assumptions (e.g. CCSSA) clearly exceed the purely statistical uncertainties that come out of conventional data- assimilation schemes.
DE: 0414 Biogeochemical cycles, processes, and modeling (0412, 0793, 1615, 4805, 4912)
DE: 0428 Carbon cycling (4806)
DE: 0466 Modeling
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting