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[1] We developed a modeling system which combines a mesoscale meteorological model, the Weather Research and Forecasting (WRF) model, with a diagnostic biospheric model, the Vegetation Photosynthesis and Respiration (VPRM). The WRF-VPRM modeling system was designed to realistically simulate high-resolution atmospheric CO 2 concentration fields. In the system, WRF takes into account anthropogenic and biospheric CO 2 fluxes and realistic initial and boundary conditions for CO 2 from a global model. The system uses several ''tagged'' tracers for CO 2 fields from different sources. VPRM uses meteorological fields from WRF and high-resolution satellite indices to simulate biospheric CO 2 fluxes with realistic spatiotemporal patterns. Here we present results from the application of the model for interpretation of measurements made within the CarboEurope Regional Experiment Strategy (CERES). Simulated fields of meteorological variables and CO 2 were compared against ground-based and airborne observations. In particular, the characterization by aircraft measurements turned out to be crucial for the model evaluation. The comparison revealed that the model is able to capture the main observed features in the CO 2 distribution reasonably well. The simulations showed that daytime CO 2 measurements made at coastal stations can be strongly affected by land breeze and subsequent sea breeze transport of CO 2 respired from the vegetation during the previous night, which can lead to wrong estimates when such data are used in inverse studies. The results also show that WRF-VPRM is an effective modeling tool for addressing the near-field variability of CO 2 fluxes and concentrations for observing stations around the globe.Citation: Ahmadov, R., C. Gerbig, R. Kretschmer, S. Koerner, B. Neininger, A. J. Dolman, and C. Sarrat (2007), Mesoscale covariance of transport and CO 2 fluxes: Evidence from observations and simulations using the WRF-VPRM coupled atmospherebiosphere model,

Abstract.We investigate the ability of a mesoscale model to reconstruct CO 2 fluxes at regional scale. Formally, we estimate the reduction of error for a CO 2 flux inversion at 8 km resolution in the South West of France, during four days of the CarboEurope Regional Experiment Strategy (CERES) in spring 2005. Measurements from two towers and two airplanes are available for this campaign. The lagrangian particle dispersion model LPDM was coupled to the nonhydrostatic model Meso-NH and integrated in a matrix inversion framework. Impacts of aircraft and tower measurements are quantified separately and together. We find that the configuration with both towers and aircraft is able to significantly reduce uncertainties on the 4-day averaged CO 2 fluxes over about half of the 300×300 km 2 domain. Most of this reduction comes from the tower measurements, even though the impact of aircraft measurements remains noticeable. Imperfect knowledge of boundary conditions does not significantly impact the error reduction for surface fluxes. We test alternative strategies to improve the impact of aircraft measurements and find that most information comes from measurements inside the boundary layer. We find that there would be a large improvement in error reduction if we could improve our ability to model nocturnal concentrations at tower sites.

Models and observational strategies of carbon exchange need to take into account synoptic and mesoscale transport for correct interpretation of the relation between surface fluxes and atmospheric concentration gradients.A dequate quantification of the geographical distribution of sources and sinks of C02 is still a major task with considerable implications for both our understanding of the global climate and the possible opportunities to mitigate climate change. Atmospheric measurements of C02 mixing ratios at a number of locations around the globe have helped significantly to quantify the source-sink distribu-AFFILIATIONS: DOLMAN, TOLK, AND

Abstract.We study the characteristics of a statistical ensemble of mesoscale simulations in order to estimate the model error in the simulation of CO 2 concentrations. The ensemble consists of ten members and the reference simulation using the operationnal short range forecast PEARP, perturbed using the Singular Vector technique. We then used this ensemble of simulations as the initial and boundary conditions for the meso scale model (Méso-NH) simulations, which uses CO 2 fluxes from the ISBA-A-gs land surface model. The final ensemble represents the model dependence to the boundary conditions, conserving the physical properties of the dynamical schemes, but excluding the intrinsic error of the model. First, the variance of our ensemble is estimated over the domain, with associated spatial and temporal correlations. Second, we extract the signal from noisy horizontal correlations, due to the limited size ensemble, using diffusion equation modelling. The computational cost of such ensemble limits the number of members (simulations) especially when running online the carbon flux and the atmospheric models. In the theory, 50 to 100 members would be required to explore the overall sensitivity of the ensemble. The present diffusion model allows us to extract a significant part of the noisy error, and makes this study feasable with a limited number of simulations. Finally, we compute the diagonal and non-diagonal terms of the observation error covariance matrix and introduced it into our CO 2 flux matrix inversion for 18 days of the 2005 intensive campaign CERES over the South West of France. Variances are based on model-data mismatch to ensure we treat model bias as well as ensemble dispersion, whereas spatial and temporal covariances are estimated with our method.The horizontal structure of the ensemble variance maniCorrespondence to: T. Lauvaux (thomas.lauvaux@lsce.ipsl.fr) fests the discontinuities of the mesoscale structures during the day, but remains locally driven during the night. On the vertical, surface layer variance shows large correlations with the upper levels in the boundary layer (> 0.6), dropping to 0.4 with the lower levels of the free troposphere. Large temporal correlations were found during the afternoon (> 0.5 for several hours), reduced during the night. The diffusion equation model extracted relevant error covariance signals horizontally, with reduced correlations over mountain areas and during the night over the continent. The posterior error reduction on the inverted CO 2 fluxes accounting for the model error correlations illustrates the predominance of the temporal over the spatial correlations when using tower-based CO 2 concentration observations.