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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 01 Apr 2019

Submitted as: research article | 01 Apr 2019

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Atmospheric Chemistry and Physics (ACP).

Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period

Yuanhong Zhao1, Marielle Saunois1, Philippe Bousquet1, Xin Lin1,a, Michaela I. Hegglin2, Josep G. Canadell3, Robert B. Jackson4, Didier A. Hauglustaine1, Sophie Szopa1, Ann R. Stavert5, Nathan Luke Abraham6,7, Alex T. Archibald6,7, Slimane Bekki8, Makoto Deushi9, Patrick Jöckel10, Béatrice Josse11, Douglas Kinnison12, Ole Kirner13, Virginie Marécal11, Fiona M. O'Connor14, David A. Plummer15, Laura E. Revell16,17, Eugene Rozanov16,18, Andrea Stenke16, Sarah Strode19,20, Simone Tilmes21, Edward J. Dlugokencky22, and Bo Zheng1 Yuanhong Zhao et al.
  • 1Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, France
  • 2Department of Meteorology, University of Reading, Earley Gate, Reading RG6 6BB, United Kingdom
  • 3Global Carbon Project, CSIRO Oceans and Atmosphere, Canberra, Australian Capital Territory 2601, Australia
  • 4Earth System Science Department, Woods Institute for the Environment, and Precourt Institute for Energy, Stanford University, Stanford, CA 94305, USA
  • 5CSIRO Oceans and Atmosphere, Aspendale, Victoria, 3195, Australia
  • 6Department of Chemistry, University of Cambridge, CB2 1EW, UK
  • 7NCAS-Climate, University of Cambridge, CB2 1EW, UK
  • 8LATMOS, Université Pierre et Marie Curie, 4 Place Jussieu Tour 45, couloir 45–46, 3e étage Boite102, 75252, Paris Cedex 05, France
  • 9Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki, 305-0052, Japan
  • 10Deutsches Zentrum für Luft-und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 11Centre National de Recherches Météorologiques, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • 12Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, 3090 Center Green Drive, Boulder, CO, 80301, USA
  • 13Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 14Met Office Hadley Centre, Exeter, EX1 3PB, UK
  • 15Climate Research Branch, Environment and Climate Change Canada, Montréal, Canada
  • 16Institute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, Switzerland
  • 17School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand
  • 18Physikalisch-Meteorologisches Observatorium Davos World Radiation Centre, Dorfstrasse 33, 7260 Davos Dorf
  • 19NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 20Universities Space Research Association (USRA), GESTAR, Columbia, MD, USA
  • 21National Center for Atmospheric Research, Boulder, CO, USA
  • 22Global Monitoring Division, NOAA Earth System Research Laboratory, Boulder, CO, USA
  • anow at: Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI 48109, USA

Abstract. The modeling study presented here aims to estimate how uncertainties in global hydroxyl radical (OH) distributions, variability, and trends may contribute to resolve discrepancies between simulated and observed methane (CH4) changes since 2000. A multi-model ensemble of 14 OH fields were analysed and were aggregated into 64 scenarios to force the offline atmospheric chemistry transport model LMDz with a standard CH4 emission scenario over the period 2000–2016. The multi-model simulated global volume-weighted tropospheric mean OH concentration ([OH]) averaged over 2000–2010 ranges between 8.7 × 105 and 12.8 × 105 molec cm−3. The inter-model differences in tropospheric OH burden and vertical distributions are mainly determined by the differences in the nitrogen oxide (NO) distributions, while the spatial discrepancies between OH fields are mostly due to differences in natural emissions and VOC chemistry. From 2000 to 2010, most simulated OH fields show an increase of 0.1–0.3 × 105 molec cm−3 in the tropospheric mean [OH], with year-to-year variations much smaller than during the historical period 1960–2000. Once ingested into the LMDz model, these OH changes translated into a 5 to 15 ppbv reduction in CH4 mixing ratio in 2010, which represent 7 %–20 % of the model simulated CH4 increase due to surface emissions. Between 2010 and 2016, the ensemble of simulations showed that OH changes could lead to a CH4 mixing ratio uncertainty of > ±30 ppbv. Over the full 2000–2016 time period, using a common state-of-the-art but non-optimized emission scenario, the impact of [OH] changes tested here can explain up to 54 % of the gap between model simulations and observations. This result emphasizes the importance of better representing OH abundance and variations in CH4 forward simulations and emission optimizations performed by atmospheric inversions.

Yuanhong Zhao et al.
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Status: final response (author comments only)
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Yuanhong Zhao et al.
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Publications Copernicus
Short summary
The role of hydroxyl radical changes in methane trend remains debated, hindering our understanding of methane cycle. This study quantifies how the uncertainties in hydroxyl radical may influences methane abundance in the atmosphere based on the inter-model comparison of hydroxyl radical fields and model simulations of CH4 abundance with different hydroxyl radical scenarios during 2000–2016. We show that hydroxyl radical changes could contribute up to 54 % of model-simulated methane biases.
The role of hydroxyl radical changes in methane trend remains debated, hindering our...