The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies

Abstract

Abstract. Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface greenhouse gas (GHG) fluxes. In the past decade, the research community has developed various inversion softwares, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry-transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in-situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here a Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source python-based tool to estimate the fluxes of various GHGs both at global and regional scales. It will allow running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow a comprehensively assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and demonstrate how it operates in a simple academic case.

Atmospheric composition in the European Arctic and 30 years of the Zeppelin Observatory, Ny-Ålesund

“…Top-down CH 4 and N 2 O estimates from these sources have been assessed with atmospheric inversion frameworks (e.g. FLEXINVERT; Thompson and Stohl, 2014) and the Community Inversion Framework (CIF; Berchet et al, 2021). Such inversions are based on combining observations with an atmospheric transport model (e.g.…”

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

“…The transport operator of CO 2 and 14 CO 2 in the atmosphere, H transp , relies on the CHIMERE transport model, driven here by the Community Inversion Framework (CIF, Berchet et al, 2021). The domain and the horizontal grid for the CHIMERE configuration used here are represented in Fig.…”

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 transport operator of CO 2 and 14 CO 2 in the atmosphere, H transp , relies on the CHIMERE transport model, driven here by the Community Inversion Framework (CIF, Berchet et al, 2021). The domain and the horizontal grid for the CHIMERE configuration used here are represented in Figure 1.…”