Modeling global atmospheric CO<sub>2</sub> with improved emission inventories and CO<sub>2</sub> production from the oxidation of other carbon species

Abstract

Abstract. The use of global three-dimensional (3-D) models with satellite observations of CO 2 in inverse modeling studies is an area of growing importance for understanding Earth's carbon cycle. Here we use the GEOS-Chem model (version 8-02-01) CO 2 mode with multiple modifications in order to assess their impact on CO 2 forward simulations. Modifications include CO 2 surface emissions from shipping (∼ 0.19 Pg C yr −1 ), 3-D spatially-distributed emissions from aviation (∼0.16 Pg C yr −1 ), and 3-D chemical production of CO 2 (∼1.05 Pg C yr −1 ). Although CO 2 chemical production from the oxidation of CO, CH 4 and other carbon gases is recognized as an important contribution to global CO 2 , it is typically accounted for by conversion from its precursors at the surface rather than in the free troposphere. We base our model 3-D spatial distribution of CO 2 chemical production on monthly-averaged loss rates of CO (a key precursor and intermediate in the oxidation of organic carbon) and apply an associated surface correction for inventories that have counted emissions of CO 2 precursors as CO 2 . We also explore the benefit of assimilating satellite observations of CO into GEOS-Chem to obtain an observation-based estimate of the CO 2 chemical source. The CO assimilationCorrespondence to: R. Nassar (ray.nassar@ec.gc.ca) corrects for an underestimate of atmospheric CO abundances in the model, resulting in increases of as much as 24% in the chemical source during May-June 2006, and increasing the global annual estimate of CO 2 chemical production from 1.05 to 1.18 Pg C. Comparisons of model CO 2 with measurements are carried out in order to investigate the spatial and temporal distributions that result when these new sources are added. Inclusion of CO 2 emissions from shipping and aviation are shown to increase the global CO 2 latitudinal gradient by just over 0.10 ppm (∼3%), while the inclusion of CO 2 chemical production (and the surface correction) is shown to decrease the latitudinal gradient by about 0.40 ppm (∼10%) with a complex spatial structure generally resulting in decreased CO 2 over land and increased CO 2 over the oceans. Since these CO 2 emissions are omitted or misrepresented in most inverse modeling work to date, their implementation in forward simulations should lead to improved inverse modeling estimates of terrestrial biospheric fluxes.

Profiling tropospheric CO<sub>2</sub> using Aura TES and TCCON instruments

“…The original CO 2 simulation in GEOS-Chem was described by Suntharalingam et al (2004) and was updated by Nassar et al (2010). The "bottom-up" inventories used to force GEOS-Chem are drawn from Nassar et al (2010) and from the Carbon Monitoring System Flux Pilot Project (http://carbon.nasa.gov) and are available at http://cmsflux.…”

Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model

“…This model is a global 30 3-D chemical transport model driven by assimilated meteorology from the Goddard Earth Observing System (GEOS-5) of the NASA Global Modelling and Assimilation Office (GMAO). Nassar et al (2010) described an update of the atmospheric CO 2 simulation in GEOS-Chem. In this study, the model has a horizontal resolution of 4…”

“…The GEOS-Chem model has often been used to simulate atmospheric CO 2 (e.g. Suntharalingam et al (2004); Nassar et al (2010)). This model is a global 30 3-D chemical transport model driven by assimilated meteorology from the Goddard Earth Observing System (GEOS-5) of the NASA Global Modelling and Assimilation Office (GMAO).…”

“…Given the great heterogeneity of vegetation and soils, the coverage and accuracy of the flux measurements are not sufficient for obtaining large-scale flux estimates with high confidence (Jung et al, 2009;Beer et al, 2010). As a result, considerable efforts have been made to develop terrestrial ecosystem models (TEMs) (whether simple regression or processoriented) in order to quantify the magnitude, geographical distribution, and evolution of sources and sinks of carbon at regional 10 and global scales (Potter et al, 1993;McGuire et al, 2001;Sitch et al, 2003;Thornton et al, 2005;Krinner et al, 2005;Reichstein et al, 2005;Badawy et al, 2013;Arora and Boer, 2005;Melton and Arora, 2016). However, systematic errors and uncertainties in the models can result from driving or forcing data (Jung et al, 2007;Clein et al, 2007;Zhao et al, 2006;Garnaud et al, 2014;Dalmonech et al, 2015;Anav et al, 2015;Wei et al, 2014), process formulation (also called model structure) (Sitch et al, 2015), model parameter specification, and initial conditions (Carvalhais et al, 2008(Carvalhais et al, , 2010Melton et al, 15 2015; Zhu and Zhuang, 2015), leading to differing estimates of CO 2 fluxes from different models (McGuire et al, 2001;Piao et al, 2013;Sitch et al, 2015).…”

“…The model is represented by three living vegetation pools (leaves, stems, and roots) and two dead carbon pools (soil organic matter and litter). The terrestrial ecosystem processes are calculated for nine PFTs: Needleleaf evergreen, 10 Needleleaf deciduous, broadleaf evergreen, broadleaf cold deciduous, broadleaf drought/dry deciduous, crops (C 3 and C 4 ) and grasses (C 3 and C 4 ). When coupled, CTEM provides time-varying vegetation structure attributes to CLASS and the calculated variables for the nine PFTs are averaged (weighted by the fractional coverage of each PFT) to obtain the four PFTs in CLASS that share similar functionality.…”