ABSTRACT Title of Document: CARBON CYCLE DATA ASSIMILATION USING A COUPLED ATMOSPHERE- VEGETATION MODEL AND THE LOCAL ENSEMBLE TRANSFORM KALMAN FILTER

We develop and test new methodologies to best estimate CO2 fluxes on the Earth’s surface by assimilating observations of atmospheric CO2 concentration, using the Local Ensemble Transform Kalman Filter. We perform Observing System Simulation Experiments and assimilate simultaneously atmospheric observations and atmospheric carbon observations, but no surface fluxes of carbon. For the experiments, we modified an atmospheric general circulation model to transport atmospheric CO2 and coupled this model with a dynamical terrestrial carbon model and a simple physical land model. The state vector of the model prognostic variables was augmented by the diagnosed carbon fluxes CF, so that the carbon fluxes were updated by the background error covariance with other variables. We designed three types of analysis systems: a C-univariate system where CF errors are coupled only with CO2, a multivariate system where all the error covariances are coupled, and a one-way multivariate analysis where the wind is included in the carbon error covariance, but there is no feedback on the winds. With perfect model experiments, the one-wa