Variational regional inverse modeling of reactive species emissions with PYVAR-CHIMERE-v2019

Abstract

Abstract. Up-to-date and accurate emission inventories for air pollutants are essential for understanding their role in the formation of tropospheric ozone and particulate matter at various temporal scales, for anticipating pollution peaks and for identifying the key drivers that could help mitigate their concentrations. This paper describes the Bayesian variational inverse system PYVAR-CHIMERE, which is now adapted to the inversion of reactive species. Complementarily with bottom-up inventories, this system aims at updating and improving the knowledge on the high spatiotemporal variability of emissions of air pollutants and their precursors. The system is designed to use any type of observations, such as satellite observations or surface station measurements. The potential of PYVAR-CHIMERE is illustrated with inversions of both carbon monoxide (CO) and nitrogen oxides (NOx) emissions in Europe, using the MOPITT and OMI satellite observations, respectively. In these cases, local increments on CO emissions can reach more than +50 %, with increases located mainly over central and eastern Europe, except in the south of Poland, and decreases located over Spain and Portugal. The illustrative cases for NOx emissions also lead to large local increments (> 50 %), for example over industrial areas (e.g., over the Po Valley) and over the Netherlands. The good behavior of the inversion is shown through statistics on the concentrations: the mean bias, RMSE, standard deviation, and correlation between the simulated and observed concentrations. For CO, the mean bias is reduced by about 27 % when using the posterior emissions, the RMSE and the standard deviation are reduced by about 50 %, and the correlation is strongly improved (0.74 when using the posterior emissions against 0.02); for NOx, the mean bias is reduced by about 24 % and the RMSE and the standard deviation are reduced by about 7 %, but the correlation is not improved. We reported strong non-linear relationships between NOx emissions and satellite NO2 columns, now requiring a fully comprehensive scientific study.

Complementing XCO2 imagery with ground-based CO2 and 14CO2 measurements to monitor CO2 emissions from fossil fuels on a regional to local scale

“…The ability to track large-scale budgets of FF emissions over longer time periods relies on complementary observations of FF emission tracers. These tracers, such as the 14 CO 2 measurements considered here, may help filter a relatively low FF signal from the biogenic signal, which is generally much larger over long distances (Pinty et al, 2017;Palmer et al, 2006;Fortems-Cheiney et al, 2021;Sadiq et al, 2021). CO 2 and 14 CO 2 ground-based networks could also reinforce the constraint on the FF CO 2 emission estimates during the few hours before the satellite overpass.…”

Complementing XCO<sub>2</sub> imagery with ground-based CO<sub>2</sub> and <sup>14</sup>CO<sub>2</sub> measurements to monitor CO<sub>2</sub> emissions from fossil fuels on a regional to local scale

“…The ability to track large-scale budgets of FF emissions over longer time periods relies on complementary observations of FF emission tracers. These tracers, such as the 14 CO 2 measurements considered here, may help filter a relatively low FF signal from the biogenic signal which is generally much larger over long distances (Pinty et al, 2017;Fortems-Cheiney et al, 2021). CO 2 and 14 CO 2 ground-based networks could also reinforce the constraint on the FF CO 2 emission estimates during the few hours before the satellite overpass.…”

Supplementary material to "The consolidated European synthesis of CO2 emissions and removals for EU27 and UK: 1990–2020"

“…DGVMs, bookkeeping models, see Table 2). TD approaches include both high spatial resolution regional inversions (CarboScopeReg, EUROCOM (Monteil et al, 2020), inversions based on the CIF-CHIMERE system (Berchet et al, 2021) and LUMIA) and coarser spatial resolution global inversions (GCP 2021: Friedlingstein et al, 2022. Most of the inversions were carried out for CO2 land emissions, with only a single inversion for CO2 fossil emissions (CIF-CHIMERE).…”

“…A methodology for reconciling LULUCF country estimates from the FAOSTAT datasets with the NGHGIs is presented in Grassi et al (2022a) and Grassi et al (in prep) for the global scale. 2022a)); 2) for sector-specific models, in particular for cropland and grassland, improving treatment of the contribution of soil organic carbon dynamics to the budget; 3) for TD estimates, using the recently developed Community Inversion Framework (Berchet et al, 2021) to better assess the different sources of uncertainties from the inversion set-ups (model transport, prior fluxes, observation networks), 4) standardize methods to compare datasets with and without interannual variability, and 5) develop a clear way to report key system boundary, data, or definitional issues, as it often necessary to have deep understanding of each estimate to know how to do a like-for-like comparison.…”

“…The systematic uncertainties are still poorly characterized, including uncertainties due to atmospheric transport modeling, dependence on the prior fluxes, and the weighting between the prior and observation uncertainties. To improve knowledge of the systematic uncertainties, dedicated studies with controlled comparisons between inversions using different atmospheric transport models (such as planned with the Community Inversion Framework, Berchet et al, 2021) are still needed. Furthermore, the posterior uncertainty and uncertainty reductions between inversions depend on internal parameterizations, e.g., the weighting of prior and observation uncertainties.…”