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Title: Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling

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 Météorologie 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. Here 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 -1more » 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.« less
Authors:
 [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [2] ;  [3] ;  [4] ;  [3] ;  [5] ;  [5] ;  [6] ;  [4] ;  [7] ;  [8] ;  [9] ;  [10] ;  [8] ;  [5]
  1. Laboratoire des Sciences du Climat et de l' Environnement (LSCE), Gif sur Yvette (France)
  2. European Centre for Medium-Range Weather Forecasts, Reading, Berkshire (United Kingdom)
  3. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Atmospheric, Earth, and Energy Division
  4. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States). Goddard Earth Sciences and Technology Center
  5. Univ. of Leeds (United Kingdom). Inst. for Climate and Atmospheric Science, School of Earth and Environment
  6. SRON Netherlands Inst. for Space Research, Utrecht (Netherlands); 7Institute for Marine and Atmospheric Research Utrecht (IMAU), Utrecht (Netherlands)
  7. SRON Netherlands Inst. for Space Research, Utrecht (Netherlands); 7Institute for Marine and Atmospheric Research Utrecht (IMAU), Utrecht (Netherlands); Wageningen Univ. and Research Centre, Wageningen (Netherlands)
  8. Research Inst. for Global Change/JAMSTEC, Yokohama (Japan)
  9. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Center for Global Change Science
  10. Univ. of Bristol (United Kingdom). School of Chemistry; Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Center for Global Change Science
Publication Date:
Report Number(s):
LLNL-JRNL-639932
Journal ID: ISSN 1680-7324
Grant/Contract Number:
AC52-07NA27344
Type:
Accepted Manuscript
Journal Name:
Atmospheric Chemistry and Physics (Online)
Additional Journal Information:
Journal Name: Atmospheric Chemistry and Physics (Online); Journal Volume: 13; Journal Issue: 19; Journal ID: ISSN 1680-7324
Publisher:
European Geosciences Union
Research Org:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org:
USDOE Laboratory Directed Research and Development (LDRD) Program
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES
OSTI Identifier:
1390020

Locatelli, R., Bousquet, P., Chevallier, F., Fortems-Cheney, A., Szopa, S., Saunois, M., Agusti-Panareda, A., Bergmann, D., Bian, H., Cameron-Smith, P., Chipperfield, M. P., Gloor, E., Houweling, S., Kawa, S. R., Krol, M., Patra, P. K., Prinn, R. G., Rigby, M., Saito, R., and Wilson, C.. Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling. United States: N. p., Web. doi:10.5194/acp-13-9917-2013.
Locatelli, R., Bousquet, P., Chevallier, F., Fortems-Cheney, A., Szopa, S., Saunois, M., Agusti-Panareda, A., Bergmann, D., Bian, H., Cameron-Smith, P., Chipperfield, M. P., Gloor, E., Houweling, S., Kawa, S. R., Krol, M., Patra, P. K., Prinn, R. G., Rigby, M., Saito, R., & Wilson, C.. Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling. United States. doi:10.5194/acp-13-9917-2013.
Locatelli, R., Bousquet, P., Chevallier, F., Fortems-Cheney, A., Szopa, S., Saunois, M., Agusti-Panareda, A., Bergmann, D., Bian, H., Cameron-Smith, P., Chipperfield, M. P., Gloor, E., Houweling, S., Kawa, S. R., Krol, M., Patra, P. K., Prinn, R. G., Rigby, M., Saito, R., and Wilson, C.. 2013. "Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling". United States. doi:10.5194/acp-13-9917-2013. https://www.osti.gov/servlets/purl/1390020.
@article{osti_1390020,
title = {Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling},
author = {Locatelli, R. and Bousquet, P. and Chevallier, F. and Fortems-Cheney, A. and Szopa, S. and Saunois, M. and Agusti-Panareda, A. and Bergmann, D. and Bian, H. and Cameron-Smith, P. and Chipperfield, M. P. and Gloor, E. and Houweling, S. and Kawa, S. R. and Krol, M. and Patra, P. K. and Prinn, R. G. and Rigby, M. and Saito, R. and Wilson, C.},
abstractNote = {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 Météorologie 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. Here 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.},
doi = {10.5194/acp-13-9917-2013},
journal = {Atmospheric Chemistry and Physics (Online)},
number = 19,
volume = 13,
place = {United States},
year = {2013},
month = {10}
}