Sensitivity of the recent methane budget to LMDz sub-grid-scale physical parameterizations

With the densification of surface observing networks and the development of remote sensing of greenhouse gases from space, estimations of methane (CH4) sources and sinks by inverse modeling are gaining additional constraining data but facing new challenges. The chemical transport model (CTM) linking the flux space to methane mixing ratio space must be able to represent these different types of atmospheric constraints for providing consistent flux estimations. Here we quantify the impact of sub-grid-scale physical parameterization errors on the global methane budget inferred by inverse modeling. We use the same inversion setup but different physical parameterizations within one CTM. Two different schemes for vertical diffusion, two others for deep convection, and one additional for thermals in the planetary boundary layer (PBL) are tested. Different atmospheric methane data sets are used as constraints (surface observations or satellite retrievals). At the global scale, methane emissions differ, on average, from 4.1 Tg CH4 per year due to the use of different sub-grid-scale parameterizations. Inversions using satellite total-column mixing ratios retrieved by GOSAT are less impacted, at the global scale, by errors in physical parameterizations. Focusing on large-scale atmospheric transport, we show that inversions using the deep convection scheme of Emanuel (1991) derive smaller interhemispheric gradients in methane emissions, indicating a slower interhemispheric exchange. At regional scale, the use of different sub-grid-scale parameterizations induces uncertainties ranging from 1.2 % (2.7 %) to 9.4 % (14.2 %) of methane emissions when using only surface measurements from a background (or an extended) surface network. Moreover, spatial distribution of methane emissions at regional scale can be very different, depending on both the physical parameterizations used for the modeling of the atmospheric transport and the observation data sets used to constrain the inverse system. When using only satellite data from GOSAT, we show that the small biases found in inversions using a coarser version of the transport model are actually masking a poor representation of the stratosphere–troposphere methane gradient in the model. Improving the stratosphere–troposphere gradient reveals a larger bias in GOSAT CH4 satellite data, which largely amplifies inconsistencies between the surface and satellite inversions. A simple bias correction is proposed. The results of this work provide the level of confidence one can have for recent methane inversions relative to physical parameterizations included in CTMs.