Preprints
https://doi.org/10.5194/gmd-2019-227
https://doi.org/10.5194/gmd-2019-227

Submitted as: model description paper 11 Sep 2019

Submitted as: model description paper | 11 Sep 2019

Review status: this preprint is currently under review for the journal GMD.

Regional CO2 inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0

Guillaume Monteil and Marko Scholze Guillaume Monteil and Marko Scholze
  • Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

Abstract. Atmospheric inversions are commonly used for estimating large-scale (continental to regional) net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. Recently, there has been an increasing demand from stakeholders for robust estimates of greenhouse gases at country-scale (or higher) resolution, in particular in the framework of the Paris agreement. This increase in resolution is in theory enabled by the growing availability of observations from surface in-situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the process-based (bottom-up) modelling community (vegetation models, fossil fuel emission inventories). This, however, calls for new developments in the inverse modelling systems, mainly in terms of diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency,

We have developed the Lund University Modular Inversion Algorithm (LUMIA) as a tool to address some of these new developments. LUMIA is meant to be a platform for inverse modelling developments at Lund University. It aims at being a flexible, yet simple and easy to maintain set of tools that modellers can combine to build inverse modelling experiments. It is in particular designed to be transport model agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. Here, we briefly describe the motivations for developing LUMIA as well as the underlying development principles, current status and future prospects. We present a first LUMIA inversion setup for a regional CO2 inversions over Europe, based on a new coupling between the Lagrangian FLEXPART (high resolution foreground transport) and the global coarse resolution TM5 transport models, using in-situ data from surface and tall tower observation sites.

Guillaume Monteil and Marko Scholze

 
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Guillaume Monteil and Marko Scholze

Guillaume Monteil and Marko Scholze

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Short summary
LUMIA is a python library for atmospheric inversions, originally developed at Lund University for performing regional atmospheric CO2 inversions. The inversions rely on a coupling the regional transport model FLEXPART and the global transport model TM5. The paper presents the modelling setup and some first results, and introduces the LUMIA python package as a toolbox for inversions, beyond the use-case presented in the paper.