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.
Highlights
IntroductionThe role of greenhouse gases (GHGs) in climate change has motivated an exceptional effort over the last couple of decades to densify the observations of GHGs around the world (Ciais et al, 2014): from the ground (e.g., with the European Integrated Carbon Observation System, ICOS; https: //www.icos-cp.eu/, last access: 23 August 2021), from mobile platforms (e.g., from aircraft or balloons equipped with Aircore sampling; Filges et al, 2016; Karion et al, 2010) and from space (e.g., Crisp et al, 2018; Janssens-Maenhout et al, 2020), despite occasional budgetary difficulties (Houweling et al, 2012)
The role of greenhouse gases (GHGs) in climate change has motivated an exceptional effort over the last couple of decades to densify the observations of GHGs around the world (Ciais et al, 2014): from the ground, from mobile platforms and from space (e.g., Crisp et al, 2018; Janssens-Maenhout et al, 2020), despite occasional budgetary difficulties (Houweling et al, 2012)
Research groups have developed various atmospheric inversion systems based on different techniques and atmospheric transport models, targeting specific trace gases or types of observations, as well as at various spatial and temporal scales, according to the particular scientific objectives of the study
Summary
The role of greenhouse gases (GHGs) in climate change has motivated an exceptional effort over the last couple of decades to densify the observations of GHGs around the world (Ciais et al, 2014): from the ground (e.g., with the European Integrated Carbon Observation System, ICOS; https: //www.icos-cp.eu/, last access: 23 August 2021), from mobile platforms (e.g., from aircraft or balloons equipped with Aircore sampling; Filges et al, 2016; Karion et al, 2010) and from space (e.g., Crisp et al, 2018; Janssens-Maenhout et al, 2020), despite occasional budgetary difficulties (Houweling et al, 2012) These observations quantify the effect of exchange between the surface and the atmosphere on GHG concentrations (e.g., Ramonet et al, 2020) and can be used to determine the surface fluxes of GHGs through the inversion of atmospheric chemistry and transport (e.g., Peylin et al, 2013; Houweling et al, 2017). Intercomparisons provide an assessment of the systematic uncertainty on final flux estimates induced by the variety of options and choices in different inversion systems
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