Abstract
Abstract. Posterior fluxes obtained from inverse modelling are difficult to verify because there is no dense network of flux measurements available to evaluate estimates against. Here we present a new diagnostic to evaluate structures in posterior fluxes. First, we simulate the change in atmospheric CO2 fields between posterior and prior fluxes, referred to as the posterior atmospheric adjustments due to updated fluxes (PAAFs). Second, we calculate the uncertainty in atmospheric CO2 fields due solely to uncertainty in the meteorological fields, referred to as the posterior atmospheric adjustments due to imperfect meteorology (PAAMs). We argue that PAAF can only be considered robust if it exceeds PAAM, that is, the changes in atmospheric CO2 between the posterior and prior fluxes should at least exceed atmospheric CO2 changes arising from imperfect meteorology. This diagnostic is applied to two CO2 flux inversions: one which assimilates observations from the in situ CO2 network and the other which assimilates observations from the Greenhouse Gases Observing SATellite (GOSAT). On the global scale, PAAF in the troposphere reflects northern extratropical fluxes, whereas stratospheric adjustments primarily reflect tropical fluxes. In general, larger spatiotemporal variations in PAAF are obtained for the GOSAT inversion than for the in situ inversion. Zonal standard deviations of the PAAF exceed the PAAM through most of the year when GOSAT observations are used, but the minimum value is exceeded only in boreal summer when in situ observations are used. Zonal spatial structures in GOSAT-based PAAF exceed PAAM throughout the year in the tropics and through most of the year in the northern extratropics, suggesting GOSAT flux inversions can constrain zonal asymmetries in fluxes. However, we cannot discount the possibility that these structures are influenced by biases in GOSAT retrievals. Verification of such spatial structures will require a dense network of independent observations. Because PAAF depends on the choice of prior fluxes, the comparison with PAAM is system dependent and thus can be used to monitor a given assimilation system's behaviour.
Highlights
Flux inversion systems have become useful tools for understanding the global carbon budget, as evidenced by their presence in Intergovernmental Panel on Climate Change (IPCC) reports (Ciais et al, 2013)
Assimilation process yields updates to prior fluxes, or “flux increments”, but here we consider the tracer field increment. This increment is denoted the posterior atmospheric adjustment (PAA) and refers to the change in concentrations obtained from a model integration using posterior fluxes, initial states and wind fields relative to those from another integration using prior fluxes, initial states and wind fields
We show that there are many components to the PAA and consider two of these: posterior atmospheric adjustments due to fluxes (PAAF) and those due to meteorological uncertainty (PAAM)
Summary
Flux inversion systems have become useful tools for understanding the global carbon budget, as evidenced by their presence in Intergovernmental Panel on Climate Change (IPCC) reports (Ciais et al, 2013). Polavarapu et al.: A comparison of posterior atmospheric CO2 adjustments tation was that should space-based measurements of column-integrated CO2 offer better spatial coverage, but the column amount should be less sensitive to modelling errors associated with the planetary boundary layer (PBL) and its representativeness should better correspond to that of coarse model grids (Keppel-Aleks et al, 2011). This occurs because mainly long-range fluxes are seen in column data, whereas both local and long-range flux signals are seen by surface in situ observations (Keppel-Aleks et al, 2011). The question is how to use the different types of observations to their strengths within a given data assimilation system
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