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

Yuanhong Zhao 1 Marielle Saunois 1, 2 Philippe Bousquet 1, 2 Xin Lin 1, 3 Antoine Berchet 1, 2 Michaela Hegglin 4 Josep G. Canadell 5 Robert B. Jackson 6, 7, 8 Didier Hauglustaine 1, 9 Sophie Szopa 1, 10 Ann R. Stavert 11 Nathan Luke Abraham 12, 13 Alex T. Archibald 12, 13 Slimane Bekki 14 Makoto Deushi 15 Patrick Jöckel 16 Béatrice Josse 17 Douglas Kinnison 18 Ole Kirner 19 Virginie Marécal 17 Fiona M. O'Connor 20 David Plummer 21 Laura Revell 22, 23 Eugene Rozanov 24, 22 Andrea Stenke 22 Sarah Strode 25, 26 Simone Tilmes 27 Edward J. Dlugokencky 28 Bo Zheng 1
1 LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette]
2 SATINV - Modélisation INVerse pour les mesures atmosphériques et SATellitaires
LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] : DRF/LSCE
3 CLaSP - Department of Climate and Space Sciences and Engineering
4 Department of Meteorology [Reading]
5 GCP - Global Carbon Project
CSIRO-MAR - CSIRO Marine and Atmospheric Research
6 Stanford Woods Institute for the Environment
7 Precourt Institute of Energy
8 ESS - Department of Earth System Science [Stanford]
9 MERMAID - Modelling the Earth Response to Multiple Anthropogenic Interactions and Dynamics
LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] : DRF/LSCE
10 CLIM - Modélisation du climat
LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] : DRF/LSCE
11 CSIRO Marine and Atmospheric Research [Aspendale]
12 Department of Chemistry [Cambridge, UK]
13 NCAS-Climate [Cambridge]
Department of Chemistry [Cambridge, UK]
14 STRATO - LATMOS
LATMOS - Laboratoire Atmosphères, Milieux, Observations Spatiales
15 MRI - Meteorological Research Institute [Tsukuba]
16 IPA - DLR Institut für Physik der Atmosphäre
17 CNRM - Centre national de recherches météorologiques
18 ACOML - Atmospheric Chemistry Observations and Modeling Laboratory
19 SCC - Steinbuch Centre for Computing [Karlsruhe]
20 MOHC - Met Office Hadley Centre for Climate Change
21 Environment and Climate Change Canada
22 IAC - Institute for Atmospheric and Climate Science [Zürich]
23 School of Physical Chemical Sciences [Christchurch]
24 PMOD/WRC - Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center
25 USRA - Universities Space Research Association
26 GSFC - NASA Goddard Space Flight Center
27 NCAR - National Center for Atmospheric Research [Boulder]
28 GML - ESRL Global Monitoring Laboratory [Boulder]
Abstract : The modeling study presented here aims to estimate how uncertainties in global hydroxyl radical (OH) distributions, variability, and trends may contribute to resolving discrepancies between simulated and observed methane (CH4) changes since 2000. A multi-model ensemble of 14 OH fields was analyzed and aggregated into 64 scenarios to force the offline atmospheric chemistry transport model LMDz (Laboratoire de Meteorologie Dynamique) 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 volatile organic compound (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 the CH4 mixing ratio in 2010, which represents 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 nonoptimized 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.
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Yuanhong Zhao, Marielle Saunois, Philippe Bousquet, Xin Lin, Antoine Berchet, et al.. Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period. Atmospheric Chemistry and Physics, European Geosciences Union, 2019, 19 (21), pp.13701-13723. ⟨10.5194/acp-19-13701-2019⟩. ⟨insu-02088245⟩

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