Comment on acp-2022-510

Abstract

We present an analysis of atmospheric transport impact on estimating CO2 fluxes using two atmospheric inversion systems (CarboScope Regional (CSR) and LUMIA) over Europe for 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT and FLEXPART were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO2 was assessed by applying the global transport models TM3 and TM5. CO2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems show a relatively large spread for the annual domain wide flux ranging between -0.72 and 0.20 PgC yr-1 with a mean estimate of -0.29 PgC. The largest discrepancies resulted from varying the mesoscale transport model, which amounted to a difference of 0.51 (PgC yr-1), in comparison with 0.23 and 0.10 (PgC yr-1) that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions lead to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO2 estimates, but the largest impact seems to come from the models themselves. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical priors, and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.