Atmospheric inversions are used to constrain emissions of trace gases using atmospheric mole-fraction measurements. The four-dimensional variational (4DVAR) inversion approach allows optimization of emissions at a higher temporal and spatial resolution than ensemble or analytical approaches but provides limited opportunities for scalable parallelization because it is an iterative optimization method. Multidecadal variational inversions are needed to optimally extract information from the long measurement records of long-lived atmospheric trace gases like carbon dioxide and methane. However, the wall time needed – up to months – complicates these multidecadal inversions. The physical parallelization (PP) method introduced by Chevallier (2013) addresses this problem for carbon dioxide inversions by splitting the period of the chemical transport model into blocks and running them in parallel. Here, we present a new implementation of the PP method which is suitable for methane inversions accounting for the chemical sink of methane. The performance of the PP method is tested in an 11-year inversion using a TM5-4DVAR inversion setup that assimilates surface observations to optimize methane emissions at grid scale. Our PP implementation improves the wall time performance by a factor of 5 and shows excellent agreement with a full serial inversion in an identical configuration (global mean emissions difference =0.06 % with an interannual variation correlation R=0.99; regional mean emission difference <5 % and interannual variation R>0.94). The wall time improvement of the PP method increases with the size of the inversion period. The PP method is planned to be used in future releases of the Copernicus Atmosphere Monitoring Service (CAMS) multidecadal methane reanalysis.
