Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2016-213
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Model evaluation paper
08 Nov 2016
Review status
This discussion paper has been under review for the journal Geoscientific Model Development (GMD). The revised manuscript was not accepted.
Description and evaluation of REFIST v1.0: a regional greenhouse gas flux inversion system in Canada
Elton Chan1, Douglas Chan1, Misa Ishizawa2, Felix Vogel3, Jerome Brioude4, Andy Delcloo5, Yuehua Wu6, and Baisuo Jin7 1Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada
2Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan
3Laboratoire des Sciences du Climat et de l'Environnement, Chaire BridGES, Gif-sur-Yvette, France
4Laboratoire de l'Atmosphere et des Cyclones, UMR8105, CNRS-Meteo France-Universite La Reunion, La Reunion, France
5Royal Meteorological Institute of Belgium, Uccle, Belgium
6Mathematics and Statistics, York University, Toronto, Ontario, Canada
7Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China
Abstract. A regional greenhouse gas flux inversion system (REFIST v1.0) is described. This paper provides a comprehensive evaluation of REFIST for three provinces in Canada that include Alberta (AB), Saskatchewan (SK) and Ontario (ON). Using year 2009 fossil fuel CO2 CarbonTracker model results as the target, the synthetic data experiment analyses examined the impacts of the errors from the Bayesian optimisation method, inversion time span, prior flux distribution, region definition and the atmospheric transport model, as well as their interactions. The posterior fluxes were estimated by two different optimisation methods, the Markov chain Monte Carlo (MCMC) simulation and cost function minimization (CFM) methods. Increasing the number of sub-regions (unknowns) beyond "optimality" can produce unstable and unrealistic fluxes for some sub-regions, and does not yield significantly different flux estimates overall. The two optimisation methods can provide comparable, stable and realistic flux results when the transport model error is small (prior R2~0.8 with synthetic observations), but both methods present difficulty when the transport model error is large (prior R2~0.3). Stable and realistic sub-regional and monthly flux estimates for the western region of AB+SK can be obtained, but not for the eastern region of ON without excluding a poorly simulated station. This indicates a real observation-based inversion will likely work for the western region for tracers with similar temporal and spatial emission characteristics to fossil fuel CO2 [e.g. wintertime CH4 in Canada]. However, improvements are needed with the current inversion setup before a real inversion is performed for the eastern region.

Citation: Chan, E., Chan, D., Ishizawa, M., Vogel, F., Brioude, J., Delcloo, A., Wu, Y., and Jin, B.: Description and evaluation of REFIST v1.0: a regional greenhouse gas flux inversion system in Canada, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-213, 2016.
Elton Chan et al.
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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RC1: 'Referee comments', Anonymous Referee #1, 03 Jan 2017 Printer-friendly Version 
AC1: 'Response to Referee #1', Elton Chan, 10 Feb 2017 Printer-friendly Version Supplement 
 
RC2: 'review', Anonymous Referee #2, 13 Jan 2017 Printer-friendly Version 
AC2: 'Response to Referee #2', Elton Chan, 10 Feb 2017 Printer-friendly Version Supplement 
Elton Chan et al.

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Short summary
The main objective of this study is to examine the impacts of errors introduced by different components in our newly developed inversion system on flux estimates with a series of controlled experiments. It is very critical for any inversion system to be fully evaluated prior to applying to real observations. As demonstrated, the results can be very sensitive to the model setup and region. It is not reasonable to expect realistic results can always be obtained using the same approach.
The main objective of this study is to examine the impacts of errors introduced by different...
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