Abstract
A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Meteorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes.
In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr(-1) at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr(-1) in North America to 7 Tg yr(-1) in Boreal Eurasia (from 23 to 48 %, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems.
Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes.
Original language | English |
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Pages (from-to) | 9917-9937 |
Number of pages | 21 |
Journal | Atmospheric chemistry and physics |
Volume | 13 |
Issue number | 19 |
DOIs | |
Publication status | Published - 2013 |
Funding
This work is supported by DGA (Direction Generale de l'Armement) and by CEA (Centre a l'Energie Atomique et aux Energies Alternatives). The research leading to the IFS results has received funding from the European Community's Seventh Framework Programme (FP7 THEME [SPA.2011.1.5-02]) under grant agreement n. 283576 in the context of the MACC-II project (Monitoring Atmospheric Composition and Climate -Interim Implementation). The contribution by the LLNL authors was prepared under Contract DE-AC52-07NA27344, with different parts supported by the IMPACTS project funded by the US DOE (BER) and project (07-ERD-064) funded by the LDRD program at LLNL. The TRANSCOM community is to be thanked for sustained efforts on transport model studies since 1993. We thank two anonymous reviewers for critical evaluation and providing very helpful comments and suggestions for improving this article.
Keywords
- GENERAL-CIRCULATION MODEL
- ATMOSPHERIC TRANSPORT
- TRACER TRANSPORT
- CO2 INVERSIONS
- BOUNDARY-LAYER
- VERTICAL PROFILES
- DATA ASSIMILATION
- CLIMATE-CHANGE
- GROWTH-RATE
- PART I