ACs on RC 1 and RC2

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> We present an analysis of atmospheric transport impact on estimating CO<span class="inline-formula">₂</span> fluxes using two atmospheric inversion systems (CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm (LUMIA)) over Europe in 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO<span class="inline-formula">₂</span> estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO<span class="inline-formula">₂</span> was assessed by using two different estimates from the global inversion systems CarboScope (TM3) and TM5-4DVAR. CO<span class="inline-formula">₂</span> 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 fluxes, ranging between <span class="inline-formula">−</span>0.72 and 0.20 PgC yr<span class="inline-formula">^−1</span>, which is larger than the a priori uncertainty of 0.47 PgC yr<span class="inline-formula">^−1</span>. The discrepancies in annual budget are primarily caused by differences in the mesoscale transport model (0.51 PgC yr<span class="inline-formula">^−1</span>), in comparison with 0.23 and 0.10 PgC yr<span class="inline-formula">^−1</span> 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 led 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 CO<span class="inline-formula">₂</span> estimates, but the largest impact seems to come from the transport model schemes. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical prior fluxes 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.