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 fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, 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 and reactive species 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 the 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 and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.

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).…”

“…In order to provide a quasi-independent estimate of fossil emissions assimilating satellite observations of the atmosphere, the CIF-CHIMERE model was used to produce a fossil fuel CO2 emission estimate for the year 2017. CIF-CHIMERE is a coupling between the variational mode of the Community Inversion Framework (CIF) platform developed in the VERIFY project (Berchet et al, 2021), the CHIMERE chemistry transport model (Menut et al, 2013) and the adjoint of this model (Fortems-Cheiney et al, 2021a). To overcome the lack of CO2 observation networks suitable for the monitoring of fossil fuel CO2 emissions at national scale, this inversion is based on the assimilation of satellite NO2 data, as NO2 is co-emitted with CO2 during fossil fuel combustion.…”

Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate <i>δ</i><sup>13</sup>C(CH<sub>4</sub>) and CH<sub>4</sub>: a case study with model LMDz-SACS

“…Data availability. The code files of the CIF version used in the present paper are registered under the following link: https://doi.org/10.5281/zenodo.6304912 (Berchet et al, 2022). Prior anthropogenic fluxes (EDGARv4.3.2) can be downloaded from the EDGAR website (https://edgar.jrc.ec.europa.eu/dataset_ ghg432, last access: 23 February 2022).…”

“…The isotopic multi-constraint system was implemented in the CIF. The CIF has been designed to allow comparison of different approaches, models, and inversion systems used in the inversion community (Berchet et al, 2021). Different atmospheric transport models, regional and global and Eulerian and Lagrangian, are implemented within the CIF.…”

“…Computational times and memory costs do not scale with the number of measurements and the number of variables to control, contrary to the analytical and ensemble methods, which can hardly accommodate very large observational datasets and control vectors at the same time. Thus, the variational formulation is preferred to the others when optimizing emissions and sinks at the pixel scale using large volumes of observational data, although its main limitation is the numerical cost for accessing posterior uncertainties when there is nonlinearity in the inversion problem (Berchet et al, 2021).…”