With the densification of surface observing networks and the development ofremote sensing of greenhouse gases from space, estimations of methane(CH) sources and sinks by inverse modeling are gaining additionalconstraining data but facing new challenges. The chemical transport model (CTM)linking the flux space to methane mixing ratio space must be able torepresent these different types of atmospheric constraints for providingconsistent flux estimations.Here we quantify the impact of sub-grid-scale physical parameterizationerrors on the global methane budget inferred by inverse modeling. We use thesame inversion setup but different physical parameterizations within oneCTM. Two different schemes for vertical diffusion, twoothers for deep convection, and one additional for thermals in the planetaryboundary layer (PBL) are tested. Different atmospheric methane data sets are used asconstraints (surface observations or satellite retrievals).At the global scale, methane emissions differ, on average, from 4.1 Tg CH per year due to the use of different sub-grid-scaleparameterizations. Inversions using satellite total-column mixing ratiosretrieved by GOSAT are less impacted, at the global scale, byerrors in physical parameterizations. Focusing on large-scale atmospherictransport, we show that inversions using the deep convection scheme ofEmanuel (1991) derive smaller interhemispheric gradients in methaneemissions, indicating a slower interhemispheric exchange. At regional scale,the use of different sub-grid-scale parameterizations induces uncertaintiesranging from 1.2 % (2.7 %) to 9.4 % (14.2 %) of methane emissionswhen using only surface measurements from a background (or anextended) surface network. Moreover, spatial distribution of methaneemissions at regional scale can be very different, depending on both thephysical parameterizations used for the modeling of the atmospherictransport and the observation data sets used to constrain the inversesystem. When usingonly satellite data from GOSAT, we show that the small biases found ininversions using a coarser version of the transport model are actuallymasking a poor representation of the stratosphere–troposphere methanegradient in the model. Improving the stratosphere–troposphere gradientreveals a larger bias in GOSAT CH satellite data, which largelyamplifies inconsistencies between the surface and satellite inversions. A simplebias correction is proposed. The results of this work provide the level ofconfidence one can have for recent methane inversions relative to physicalparameterizations included in CTMs.
展开▼
