Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.154 IF 5.154
  • IF 5-year value: 5.697 IF 5-year
    5.697
  • CiteScore value: 5.56 CiteScore
    5.56
  • SNIP value: 1.761 SNIP 1.761
  • IPP value: 5.30 IPP 5.30
  • SJR value: 3.164 SJR 3.164
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 59 Scimago H
    index 59
  • h5-index value: 49 h5-index 49
Discussion papers
https://doi.org/10.5194/gmd-2019-227
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-227
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 11 Sep 2019

Submitted as: model description paper | 11 Sep 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (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
Interactive discussion
Status: open (extended)
Status: open (extended)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Guillaume Monteil and Marko Scholze
Guillaume Monteil and Marko Scholze
Viewed  
Total article views: 231 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
169 58 4 231 22 5 4
  • HTML: 169
  • PDF: 58
  • XML: 4
  • Total: 231
  • Supplement: 22
  • BibTeX: 5
  • EndNote: 4
Views and downloads (calculated since 11 Sep 2019)
Cumulative views and downloads (calculated since 11 Sep 2019)
Viewed (geographical distribution)  
Total article views: 152 (including HTML, PDF, and XML) Thereof 152 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 15 Nov 2019
Publications Copernicus
Download
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.
LUMIA is a python library for atmospheric inversions, originally developed at Lund University...
Citation