Error correlation between CO<sub>2</sub> and CO as constraint for CO<sub>2</sub> flux inversions using satellite data

Abstract

Abstract. Inverse modeling of CO 2 satellite observations to better quantify carbon surface fluxes requires a chemical transport model (CTM) to relate the fluxes to the observed column concentrations. CTM transport error is a major source of uncertainty. We show that its effect can be reduced by using CO satellite observations as additional constraint in a joint CO 2 -CO inversion. CO is measured from space with high precision, is strongly correlated with CO 2 , and is more sensitive than CO 2 to CTM transport errors on synoptic and smaller scales. Exploiting this constraint requires statistics for the CTM transport error correlation between CO 2 and CO, which is significantly different from the correlation between the concentrations themselves. We estimate the error correlation globally and for different seasons by a paired-model method (comparing GEOS-Chem CTM simulations of CO 2 and CO columns using different assimilated meteorological data sets for the same meteorological year) and a paired-forecast method (comparing 48-vs. 24-h GEOS-5 CTM forecasts of CO 2 and CO columns for the same forecast time). We find strong error correlations (r 2 >0.5) between CO 2 and CO columns over much of the extra-tropical Northern Hemisphere throughout the year, and strong consistency between different methods to estimate the error correlation. Application of the averaging kernels used in the retrieval for thermal IR CO measurements weakens the correlation coefficients by 15% on average (mostly due to Correspondence to: H. Wang (hwang@cfa.harvard.edu) variability in the averaging kernels) but preserves the largescale correlation structure. We present a simple inverse modeling application to demonstrate that CO 2 -CO error correlations can indeed significantly reduce uncertainty on surface carbon fluxes in a joint CO 2 -CO inversion vs. a CO 2 -only inversion.

Characterization of Tropospheric Emission Spectrometer (TES) CO<sub>2</sub> for carbon cycle science

“…On the other hand, the large random error in the individual measurements could be an issue for inverse modeling. For example, Wang et al (2009) showed that a joint inversion analysis of CO and CO 2 , exploiting the correlations in the model errors for CO and CO 2 would provide more constraints on the CO 2 fluxes than using only CO 2 . But a requirement of joint inversion approach, as noted by Wang et al (2009), is that the measurement error must be smaller than the model error.…”

“…For example, Wang et al (2009) showed that a joint inversion analysis of CO and CO 2 , exploiting the correlations in the model errors for CO and CO 2 would provide more constraints on the CO 2 fluxes than using only CO 2 . But a requirement of joint inversion approach, as noted by Wang et al (2009), is that the measurement error must be smaller than the model error. A more detailed analysis is clearly needed to better assess the potential impact of the spatial and temporal averaging of the data on the inferred flux estimates.…”

CH<sub>4</sub> and CO distributions over tropical fires during October 2006 as observed by the Aura TES satellite instrument and modeled by GEOS-Chem

“…However, this analysis suggests that CO might be a useful transport tracer for placing constraints on total methane emissions as demonstrated for CO 2 fluxes (e.g., Palmer et al, 2006;Wang et al, 2009). Alternatively, observations in the change of the slope of the CH 4 /CO distribution over "short" time periods might provide constraints on biomass burning over this region if we assume that non-fire emissions remain approximately constant over the "short" time period.…”

Satellite observations of atmospheric methane and their value for quantifying methane emissions

“…The operational retrieval for TROPOMI is described by , who find that the precision is almost always better than 1 % and that over 90 % of cloud-free scenes can be successfully retrieved. Observations of methane-CO corre-lations from joint 2.3 µm retrievals may provide useful additional information for inferring methane sources (Xiao et al, 2004;Wang et al, 2009;Worden et al, 2013).…”

“…TROPOMI will provide data for both methane and CO from common SWIR retrievals. Beyond constraining the combustion source of methane, the CO observations could be valuable for decreasing model transport errors in joint methane-CO inversions (Wang et al, 2009). …”