Characterizing model errors in chemical transport modelling of methane: Using GOSAT XCH<sub>4</sub> data with weak constraint four-dimensional variational data assimilation

Abstract

<p><strong>Abstract.</strong> We examined biases in the global GEOS-Chem chemical transport model for the period of February–May 2010 using weak constraint (WC) four-dimensional variational (4D-Var) data assimilation and dry-air mole fractions of CH<sub>4</sub> (XCH<sub>4</sub>) from the Greenhouse gases Observing SATellite (GOSAT). The ability of the observations and the WC 4D-Var method to mitigate model errors in CH<sub>4</sub> concentrations was first investigated in a set of observing system simulation experiments (OSSEs). We then assimilated the GOSAT XCH<sub>4</sub> retrievals and found that they were capable of differentiating the vertical distribution of model errors and of removing a significant portion of biases in the modelled CH<sub>4</sub> state. In the WC 4D-Var assimilation, corrections were added to the modeled CH<sub>4</sub> state at each model time step to account for model errors and improve the model fit to the assimilated observations. Compared to the conventional strong constraint (SC) 4D-Var assimilation, the WC method was able to significantly improve the model fit to independent observations. Examination of the WC state corrections suggested that a significant source of the model errors was associated with discrepancies in the model CH<sub>4</sub> in the stratosphere. The WC state corrections also suggested that the model vertical transport in the troposphere at mid- and high-latitudes is too weak. The problem was traced back to biases in the uplift of CH<sub>4</sub> over the source regions in eastern China and North America. In the tropics, the WC assimilation pointed to the possibility of biased CH<sub>4</sub> outflow from the African continent to the Atlantic in the mid-troposphere. The WC assimilation in this region would greatly benefit from glint observations over the ocean to provide additional constraints on the vertical structure of the model errors in the tropics. We also compared the WC assimilation at the 4° × 5° and 2° × 2.5° horizontal resolutions and found that the WC corrections to mitigate the model errors were significantly larger at 4° × 5° than at 2° × 2.5° resolution, indicating the presence of resolution-dependent model errors. Our results illustrate the potential utility of the WC 4D-Var approach for characterizing model errors. However, a major limitation of this approach is the need to better characterize the specified model error covariance in the assimilation scheme.</p>

The Carbon Cycle of Southeast Australia During 2019–2020: Drought, Fires, and Subsequent Recovery

“…This suggests that modeled vertical motions are too weak and do not fully capture the vertical structure of biomass burning species produced by strong pyroconvective motions. Such systematic errors are challenging to address, but one possible avenue of future study would be to utilize weak constraint 4D-Var (Stanevich et al, 2019), which would allow for optimizing both surface fluxes and the atmospheric state.…”

Reply on RC2

“…Previous GEOS-Chem-based inversions at 4°´5° horizontal resolution had excessive stratospheric methane poleward of 60 o in winter-spring due to the inability to reproduce the polar vortex dynamical barrier, and this needed to be corrected in the inversion [Turner et al, 2015;. The polar vortex dynamics are much better captured at 2°´2.5° resolution [Stanevich et al, 2019;, and we do not use satellite data poleward of 60 o in our inversion. There is therefore no need for stratospheric bias correction.…”

“…This is generally done by Bayesian optimization of a posterior emission estimate given the observations and a prior estimate [Jacob et al, 2016]. Most inverse analyses use a variational solution to the Bayesian optimization problem, which enables inference of emissions at any resolution but does not readily provide error statistics [Meirink et al, 2008;Monteil et al, 2013;Wecht et al, 2014;Stanevich et al, 2019]. Analytical solution has the advantage of including posterior error statistics and hence information content as part of the solution [Brasseur and Jacob, 2017].…”