The impacts of fossil fuel emission uncertainties and accounting for 3-D chemical CO₂ production on inverse natural carbon flux estimates from satellite and in situ data

The inversion system used here has been described and evaluated in detail in the Wang et al (2018) paper. In brief, it involves a batch Bayesian synthesis inversion technique (which gives an exact solution to the flux optimization problem, subject to prior constraints) based on that used in the TransCom 3 (TC3) global CO₂ inversion intercomparisons (Gurney et al 2002, Baker et al 2006) and that of Butler et al (2010). Advances over the previous methods include higher spatial and temporal resolution for the flux optimization—108 land and ocean regions in total (figure S1a (available online at stacks.iop.org/ERL/15/085002/mmedia)) and 8-day intervals, and the use of individual flask-air observations and daily averages for continuous observations rather than monthly averages. The Parameterized Chemistry and Transport Model (PCTM) (Kawa et al 2004), with meteorology from the NASA Global Modeling and Assimilation Office (GMAO) MERRA reanalysis (Rienecker et al 2011), was run at a resolution of 2° latitude × 2.5° longitude and 56 levels up to 0.4 hPa, and hourly temporal resolution. Prior constraints include gridded, 3-hourly net ecosystem production (NEP) and fire carbon fluxes estimated by the Carnegie-Ames-Stanford-Approach (CASA) biogeochemical model coupled to version 3 of the Global Fire Emissions Database (GFED3) (Randerson et al 1996, van der Werf et al 2006, 2010; with updates described in Ott et al 2015), and gridded, monthly, climatological, measurement-based air-sea CO₂ fluxes from Takahashi et al (2009). The prescribed FFCO₂ emissions in the Wang et al (2018) study were from the 1° × 1°, monthly- and interannually-varying Carbon Dioxide Information Analysis Center (CDIAC) inventory (Andres et al 2012), but in the present study, we use emissions from ODIAC (described below in section 2.2) as the baseline and present results using CDIAC only in sensitivity analysis.

In both the previous and present study, we assimilated in situ atmospheric CO₂ observations from 87 flask and continuous measurement sites in the NOAA ESRL (Dlugokencky et al 2013, Andrews et al 2009) and Japan Meteorological Agency (JMA; Tsutsumi et al 2006) networks (figure S1a), and the ACOS B3.4 filtered and bias-corrected retrieval of column-average CO₂ dry air mole fractions (XCO₂) from GOSAT-measured near infrared radiances (figure S1b; O'Dell et al 2012; Osterman et al 2013). And as in the previous study, our inversions span the period March 2009-September 2010 (with the focus starting from June 2009), which is sufficiently long for assessing the impacts of FFCO₂ uncertainties and the chemical pump on global inversions.

Inversion system components specific to the present study are described in the following sub-sections.

ODIAC is a global, gridded FFCO₂ data product with 1 × 1 km, monthly resolution over land and 1° × 1°, annual resolution for international bunkers from year 2000 onward (Oda et al 2018); the data product is commonly used in flux inversions (e.g. Takagi et al 2011, Maksyutov et al 2013, Lauvaux et al 2016, Crowell et al 2019). It shares country-level estimates with CDIAC, another commonly used data set, but distributes emissions within countries differently and includes gridded international bunker emissions. Rather than distributing emissions based on population density as in CDIAC, ODIAC applies information such as power plant profiles (emissions intensity and geographical location) and satellite nighttime light observations to different fuel types. The resulting emission distribution is in better agreement with the US bottom-up inventory developed by Gurney et al (2009) than is CDIAC (Oda and Maksyutov 2011). Global total emissions in the ODIAC version used are 8.70 and 9.13 Pg C for 2009 and 2010.

Shipping and aviation total emissions are derived from CDIAC and distributed using ship and flight track data (Oda et al 2018). Global total emissions are 0.17 and 0.12 Pg C yr⁻¹ for shipping and aviation in 2009 and 0.18 and 0.13 Pg C yr⁻¹ in 2010. For the present study, a simple vertical distribution for the aviation emissions is implemented. The emissions are partitioned into three layers—surface-4 km (27%), 4–10 km (34%), and 10–13 km (39%)—based on the altitude distribution from the AERO2k 2002 aviation inventory (Eyers et al 2005).

In the present study, we use the 2017 version of ODIAC (ODIAC2017, 2000–2016, Oda and Maksyutov 2015), and degraded the resolution to 2° × 2.5° for use in our version of PCTM. Figure 1 shows maps comparing ODIAC and CDIAC emissions. Sizable differences due to the spatial modeling approaches can be seen in many areas of high emissions, such as the eastern U.S. and East Asia (figure 1(c)), although negative and positive differences tend to compensate each other within each of these regions, given the shared country-level data of ODIAC and CDIAC.

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We applied period-specific 3-D CO loss rates archived from a state-of-the-art NASA GEOS Chemistry and Climate Model (GEOSCCM; Oman et al 2013, Nielsen et al 2017) simulation in a forward PCTM run to simulate the distribution of CO₂ originating from oxidation of reduced carbon compounds. Since CO is an intermediate product in most oxidation pathways for carbon compounds and the only significant product of its oxidation is CO₂ (Folberth et al 2005), its rate of loss through reaction with hydroxyl (OH) radicals is approximately equal to the rate of production of CO₂. The GEOS simulation uses the comprehensive Global Modeling Initiative (GMI) stratospheric-tropospheric chemical mechanism, which includes O₃-NO_x-hydrocarbon interactions (Duncan et al 2007, Strahan et al 2007), and is nudged to meteorology from the latest reanalysis (MERRA-2; Gelaro et al 2017). From now on, we refer to the GEOS simulation as 'MERRA2-GMI.' Additional details on MERRA2-GMI are provided in the Supplementary Material. The MERRA2-GMI latitude-altitude distribution of CO loss rate (averaged over longitudes) for a selected month can be seen in figure 2(a), and a longitude-latitude cross-section at ∼5 km altitude is shown in figure 2(b). CO oxidation (and thus CO₂ production) is greatest where OH oxidant is most abundant, i.e. in the tropics, and where CO concentrations are highest, e.g. downwind of biomass burning regions. The global total chemical CO₂ production is 1.15 Pg C yr⁻¹, similar to that of previous studies (table 1).

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To accurately simulate the impact of the chemical pump, it is necessary to also subtract amounts of CO₂ at the surface actually emitted in the form of fossil and biospheric CO, CH₄, and non-methane VOCs (NMVOCs). Suntharalingam et al (2005) provide a thorough explanation of the purpose of the surface correction. Although errors in surface CO₂ fluxes can be corrected to a certain extent by the inversion, applying the chemical pump surface correction helps to minimize bias in the prior estimate, and thus strengthen the validity of a fundamental assumption of Bayesian inversion (i.e. unbiased, Gaussian errors). Some other inversion analyses have neglected this prior correction while accounting for 3-D CO₂ production (Baker 2001, Chevallier et al 2017), relying on the inversion to make the necessary adjustments in surface fluxes; Baker (2001) then adds non-CO₂ emissions on to regional carbon budgets a posteriori. For the fossil fuel CO and NMVOC and biospheric NMVOC sources, we adopt the emissions fields from MERRA2-GMI, which promotes consistency between the surface correction and the chemical CO₂ production. Details on these and the other components of the surface correction are provided in the Supplementary Material. Global, annual totals are shown in table 1.

One important difference in this surface correction compared to those of previous studies is a much larger relative contribution from biospheric NMVOCs, i.e. 47% of the total vs. 15% and 19% (table 1), which places more of the surface correction at lower latitudes. A possible reason for the difference is that isoprene emissions are likely overestimated in GMI compared to that of, e.g. Guenther et al (1999, 2000). Another difference is the smaller contribution from CH₄ due to our exclusion of ruminants and landfills, which also de-weights higher latitudes. Yet another difference is that our fossil fuel correction is smaller overall and weighted more towards developing countries than that of Nassar et al (2010), which was a globally uniform percentage of FFCO₂ emissions.

The atmospheric concentrations of CO₂ attributable to CO₂ chemical production and to the surface correction simulated by PCTM after a year of production/subtraction and transport are shown in figure 3 for illustrative purposes (similar to figures 3 and 4 of Suntharalingam et al 2005). Figures 3(a) and (b) show the impact for the model surface layer, and figures 3(d) and (e) are for the atmospheric column average. Figure 3(c)/(f) show the net effect of chemical production and surface correction in the surface layer/column. As expected, the surface correction has a stronger effect on surface concentrations than on the column average, and is less dominant over chemical production in the column average as compared to the surface concentrations.

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Inversions using either ODIAC FFCO₂ emissions, excluding the international bunkers, or CDIAC FFCO₂ emissions produce similar natural flux estimates in general, at least at the large spatial and temporal scales (e.g. sub-continental and seasonal) that are most relevant for the global carbon budget. Differences in estimated fluxes when GOSAT data are used in the inversions are within 0.25 Pg C yr⁻¹ (in absolute value) at the scale of large, aggregated regions (e.g. northern land, tropical oceans) and seasons; twelve-month means are shown in figure 4. Inversions using in situ data exhibit some noticeably larger differences, even at the scale of these large-aggregate regions and twelve-month means; for example, ODIAC results in a 0.30 Pg C yr⁻¹ larger inferred source for southern land than does CDIAC, and a 0.28 Pg C yr⁻¹ weaker sink for northern oceans (figure 4). For individual seasons, differences for the in situ inversions are as large as 1 Pg C yr⁻¹ (not shown). However, the differences are probably mostly noise rather than real differences in inferred natural fluxes at these large scales; Wang et al (2018) found the in situ inversion to be much noisier than the GOSAT inversion, with large temporal fluctuations, fewer degrees of freedom for signal, and more extensive flux error correlations, reflecting insufficient constraints on the flux estimation provided by the relatively sparse in situ observations. In the FFCO₂ sensitivity results here, fluctuations in the differences between ODIAC- and CDIAC-based in situ inversions can be seen from season to season, as well as compensation between neighboring TC3 regions, e.g. ODIAC-CDIAC values of 0.52 and −0.33 Pg C yr⁻¹ for Temperate Asia and Tropical Asia in DJF 2009–2010 and −2.37 and 1.47 Pg C yr⁻¹ for Temperate Asia and Boreal Asia in JJA 2010, likely reflecting negative error correlations. The GOSAT inversions do not exhibit similar fluctuations and compensation. An explanation for the noisy impact of FFCO₂ in particular is that the ODIAC and CDIAC emissions are distributed differently relative to the surface observation sites, some of which are located close to areas of high FFCO₂ emissions. Given that the sensitivity to surface fluxes, or 'footprint,' of surface observations can be rather localized and can vary greatly with meteorological conditions, a shift in prescribed FFCO₂ emissions could strongly and variably affect the fluxes inferred by certain sites, and for a sparse observation network, the impact could be substantial even when aggregated to large spatial scales. The GOSAT column observations, in contrast, are influenced by broader areas of surface fluxes, and the GOSAT data set provides better coverage than the in situ data set over many regions. Thus, the GOSAT inversion is not as sensitive to detailed spatial patterns of FFCO₂ emissions.

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We also examined the impacts on inversions of accounting for international bunker FFCO₂ emissions and vertically distributing international aviation emissions. Figure 4 shows in situ and GOSAT aggregated posterior fluxes when international bunkers are included, either placed entirely at the surface or distributed over flight altitudes, next to results based on only land-based ODIAC FFCO₂. The differences are small, especially between the inversions with 2-D vs. 3-D bunkers. The impact of bunkers is most noticeable in the north (as that is where there is the most maritime and air traffic), where inclusion of bunkers results in inferred fluxes that are more negative by up to 0.16 Pg C yr⁻¹ over land and 0.07 Pg C yr⁻¹ over ocean regions. (Global mass balance requires that larger FFCO₂ emissions be balanced by larger natural sinks.) The impact of vertically distributing aviation emissions is nearly imperceptible, with the largest impact being a decrease in the net source of 0.03 Pg C yr⁻¹ over tropical land in the in situ inversion.

We examine the posterior fit of the inversions to observations to assess whether that could provide an objective rationale for accounting for bunker emissions in CO₂ inversions. Results are presented and discussed in detail in the supplementary material. We find that the posterior fit differs little between the cases with and without bunkers for either the in situ or GOSAT inversions.

Results for inversions accounting for atmospheric chemical CO₂ production and the surface correction are shown alongside those for inversions without the chemical pump in figure 5. (All of the inversions are based on ODIAC land-based and vertically-distributed bunker emissions.) Notable features include larger net carbon sources over tropical land and southern land when the chemical pump is included, larger net sinks or smaller net sources over ocean regions, and overall shifts in the global sink from the tropics to the north and, for the GOSAT inversions, from land to ocean. The effects can generally be explained by mass balance considerations—e.g. CO₂ production downwind of continental reduced carbon emissions necessitates more CO₂ uptake over ocean regions to fit observations, and surface corrections that are especially large over tropical and southern land necessitate more CO₂ emissions over those regions. The more negative oceanic flux is consistent with what was found in the previous inversion study by Suntharalingam et al (2005). However, the increased source over tropical land (and lack of flux adjustment over northern land) is different from the decreased tropical land source and decreased northern land sink of Suntharalingam et al (2005) and Jacobson et al (2007). Our analysis suggests this is due to differences in our surface corrections, as discussed in detail later in this section.

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The GOSAT inversion is more sensitive to the chemical pump than the in situ inversion in general, exhibiting relatively large twelve-month mean impacts of 0.28, 0.53, and −0.47 Pg C yr⁻¹ over tropical land, global land, and global oceans; these are changes of 12%, 78%, and 15% relative to the posterior net sources/sinks. Note, however, that the impacts all lie within or close to the 1σ uncertainty ranges of the flux estimates. For comparison, the inversions of Suntharalingam et al (2005) (based on in situ observations) exhibited impacts of 0.10 and −0.09 Pg C yr⁻¹ over global land and global oceans (averaged over multiple models). The differences between the in situ and GOSAT inversions are consistent with differences between the data sets in horizontal and vertical sampling. Some insight can be gained by examining the impact of the chemical pump on atmospheric CO₂ concentrations averaged over the locations and times of the in situ and GOSAT observations (table 2). The numbers, though of small magnitude, exhibit particular patterns. For example, the GOSAT sampling exhibits values that are more positive (or less negative) for ocean overall and in some of the zones, i.e. 0°–30°N and 30°–60°N, than the in situ sampling does. This reflects the greater sensitivity of the column observations to chemical CO₂ production, which occurs over a range of altitudes above the surface downwind of continents (figures 2(a) and (b)), and can explain the larger negative flux adjustments over oceans in the GOSAT inversion in response to the chemical pump. Also, the GOSAT combined land-ocean impacts are weighted towards the land values in all zones except for 60°–30°S, reflecting a much larger number of land nadir than ocean glint observations (figure S1b), whereas the in situ combined impacts are weighted towards land only outside of the tropics, reflecting the dearth of land sites in the tropics (figure S1a). This could explain the lack of flux adjustments over tropical and southern land in the in situ inversions in response to the land-based surface correction.

We examine the posterior fit of the inversions to observations for the cases with and without the chemical pump also. (See the Supplementary Material for details.) We find that the posterior fit generally differs little between the cases.

Since our surface corrections differ in important ways from those assumed in previous studies, we also examine results of an alternative set of inversions using surface corrections that are more similar to those of previous studies. Specifically, the global, annual magnitude of the correction for biospheric NMVOCs is the same as that of Suntharalingam et al (2005) and Nassar et al (2010), rather than much larger as with our baseline correction (table 1). In addition, the fossil fuel correction is based on the uniform 4.89% scaling of Nassar et al (2010), which makes it much larger over developed countries (located mostly in the north) and smaller over developing countries and possibly more similar to that of Suntharalingam et al (2005), whose earlier study period may have occurred before emissions controls greatly reduced the proportion of incomplete combustion products in developed countries. These alternative surface corrections result in a chemical pump impact on atmospheric CO₂ with a north-south interhemispheric difference of −0.23 ppm sampled at in situ sites, which is quite different from the −0.07 ppm of our baseline experiment and more similar to the −0.20 ppm of Suntharalingam et al (2005) (though the networks of in situ sites are not exactly the same). Accounting for this version of the chemical pump shifts a portion of the global CO₂ sink from the north to the tropics and south, as in previous studies (figure S3). Unchanged from our baseline inversions is the overall shift in the sink from land to oceans in the GOSAT inversion. Thus, regional flux shifts are sensitive to the surface correction, and differences in the correction appear to explain the contrasting latitudinal shifts in our analysis and previous studies.