Structure of the transport uncertainty in mesoscale inversions of CO₂ sources and sinks using ensemble model simulations

Abstract

Abstract. We study the characteristics of a statistical ensemble of mesoscale simulations in order to estimate the model error in the simulation of CO2 concentrations. The ensemble consists of ten members and the reference simulation using the operationnal short range forecast PEARP, perturbed by Singular Vector (SV) technic. We then used this ensemble of simulations as the initial and boundary conditions for the meso scale model simulations, here the atmospheric transport model Méso-NH, transporting CO2 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. 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. Finally, we compute the diagonal and non-diagonal terms of the observation error covariance matrix and introduced it into our CO2 flux matrix inversion over 18 days of the 2005 intensive campaign CERES over the South West of France. On the horizontal plane, variance of the ensemble follows the discontinuities of the mesoscale structures during the day, but remain locally driven during the night. On the vertical, surface layer variance shows large correlations with the upper levels in the boundary layer (>0.6), down to 0.4 with the low free troposphere. Large temporal correlations were found during the afternoon (>0.5 for several hours), reduced during the night. Diffusion equation model extracted relevant error covariance signals on the horizontal space, and shows reduced correlations over mountain area and during the night over the continent. The posterior error reduction on the inverted CO2 fluxes accounting for the model error correlations illustrates finally the predominance of the temporal over the spatial correlations when using tower-based CO2 concentration observations.