Development of CarbonTracker Europe-CH₄ – Part 1: system set-up and sensitivity analyses

Abstract. CarbonTracker Europe-CH₄ (CTE-CH₄) inverse model versions 1.0 and 1.1 are presented. The model optimizes global surface methane emissions from biosphere and anthropogenic sources using an ensemble Kalman filter (EnKF) based optimization method, using the TM5 chemistry transport model as an observation operator, and assimilating global in-situ atmospheric methane mole fraction observations. In this study, we examine sensitivity of our CH₄ emission estimates on the ensemble size, covariance matrix, prior estimates, observations to be assimilated, assimilation window length, convection scheme in TM5, and model structure in the emission estimates by performing CTE-CH₄ with several set-ups. The analyses show that the model is sensitive to most of the parameters and inputs that were examined. Firstly, using a large enough ensemble size stabilises the results. Secondly, using an informative covariance matrix reduces uncertainty estimates. Thirdly, agreement with discrete observations became better when assimilating continuous observations. Finally, the posterior emissions were found sensitive to the choice of prior estimates, convection scheme and model structure, particularly to their spatial distribution. The distribution of posterior mole fractions derived from posterior emissions is consistent with the observations to the extent prescribed in the various covariance estimates, indicating a satisfactory performance of our system.