Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period
<p>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 (<span class="inline-formula">CH<sub>4</sub&g...
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2019-11-01
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Y. Zhao M. Saunois P. Bousquet X. Lin X. Lin A. Berchet M. I. Hegglin J. G. Canadell R. B. Jackson D. A. Hauglustaine S. Szopa A. R. Stavert N. L. Abraham N. L. Abraham A. T. Archibald A. T. Archibald S. Bekki M. Deushi P. Jöckel B. Josse D. Kinnison O. Kirner V. Marécal F. M. O'Connor D. A. Plummer L. E. Revell L. E. Revell E. Rozanov E. Rozanov A. Stenke S. Strode S. Strode S. Tilmes E. J. Dlugokencky B. Zheng |
spellingShingle |
Y. Zhao M. Saunois P. Bousquet X. Lin X. Lin A. Berchet M. I. Hegglin J. G. Canadell R. B. Jackson D. A. Hauglustaine S. Szopa A. R. Stavert N. L. Abraham N. L. Abraham A. T. Archibald A. T. Archibald S. Bekki M. Deushi P. Jöckel B. Josse D. Kinnison O. Kirner V. Marécal F. M. O'Connor D. A. Plummer L. E. Revell L. E. Revell E. Rozanov E. Rozanov A. Stenke S. Strode S. Strode S. Tilmes E. J. Dlugokencky B. Zheng Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period Atmospheric Chemistry and Physics |
author_facet |
Y. Zhao M. Saunois P. Bousquet X. Lin X. Lin A. Berchet M. I. Hegglin J. G. Canadell R. B. Jackson D. A. Hauglustaine S. Szopa A. R. Stavert N. L. Abraham N. L. Abraham A. T. Archibald A. T. Archibald S. Bekki M. Deushi P. Jöckel B. Josse D. Kinnison O. Kirner V. Marécal F. M. O'Connor D. A. Plummer L. E. Revell L. E. Revell E. Rozanov E. Rozanov A. Stenke S. Strode S. Strode S. Tilmes E. J. Dlugokencky B. Zheng |
author_sort |
Y. Zhao |
title |
Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period |
title_short |
Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period |
title_full |
Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period |
title_fullStr |
Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period |
title_full_unstemmed |
Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period |
title_sort |
inter-model comparison of global hydroxyl radical (oh) distributions and their impact on atmospheric methane over the 2000–2016 period |
publisher |
Copernicus Publications |
series |
Atmospheric Chemistry and Physics |
issn |
1680-7316 1680-7324 |
publishDate |
2019-11-01 |
description |
<p>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
(<span class="inline-formula">CH<sub>4</sub></span>) 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 <span class="inline-formula">CH<sub>4</sub></span> 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 <span class="inline-formula">8.7×10<sup>5</sup></span> and <span class="inline-formula">12.8×10<sup>5</sup></span> molec cm<span class="inline-formula"><sup>−3</sup></span>. 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–<span class="inline-formula">0.3×10<sup>5</sup></span> molec cm<span class="inline-formula"><sup>−3</sup></span> 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 <span class="inline-formula">CH<sub>4</sub></span> mixing ratio in 2010, which
represents 7 %–20 % of the model-simulated <span class="inline-formula">CH<sub>4</sub></span> increase due to
surface emissions. Between 2010 and 2016, the ensemble of simulations showed
that OH changes could lead to a <span class="inline-formula">CH<sub>4</sub></span> mixing ratio uncertainty of
<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M11" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>></mo><mo>±</mo><mn mathvariant="normal">30</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="32pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="4a73e472ab7e050d281fe67c354e0ae1"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-13701-2019-ie00001.svg" width="32pt" height="10pt" src="acp-19-13701-2019-ie00001.png"/></svg:svg></span></span> 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 <span class="inline-formula">CH<sub>4</sub></span> forward
simulations and emission optimizations performed by atmospheric inversions.</p> |
url |
https://www.atmos-chem-phys.net/19/13701/2019/acp-19-13701-2019.pdf |
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doaj-ec8dc07f1ffb47e3b6b64bbd13f56b672020-11-25T01:54:57ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242019-11-0119137011372310.5194/acp-19-13701-2019Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 periodY. Zhao0M. Saunois1P. Bousquet2X. Lin3X. Lin4A. Berchet5M. I. Hegglin6J. G. Canadell7R. B. Jackson8D. A. Hauglustaine9S. Szopa10A. R. Stavert11N. L. Abraham12N. L. Abraham13A. T. Archibald14A. T. Archibald15S. Bekki16M. Deushi17P. Jöckel18B. Josse19D. Kinnison20O. Kirner21V. Marécal22F. M. O'Connor23D. A. Plummer24L. E. Revell25L. E. Revell26E. Rozanov27E. Rozanov28A. Stenke29S. Strode30S. Strode31S. Tilmes32E. J. Dlugokencky33B. Zheng34Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceLaboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceLaboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceLaboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, Francenow at: Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI 48109, USALaboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceDepartment of Meteorology, University of Reading, Reading, UKGlobal Carbon Project, CSIRO Oceans and Atmosphere, Canberra, Australian Capital Territory 2601, AustraliaEarth System Science Department, Woods Institute for the Environment, and Precourt Institute for Energy, Stanford University, Stanford, CA 94305, USALaboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceLaboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceCSIRO Oceans and Atmosphere, Aspendale, Victoria, 3195, AustraliaDepartment of Chemistry, University of Cambridge, CB2 1EW, Cambridge, UKNCAS-Climate, University of Cambridge, CB2 1EW, Cambridge, UKDepartment of Chemistry, University of Cambridge, CB2 1EW, Cambridge, UKNCAS-Climate, University of Cambridge, CB2 1EW, Cambridge, UKLATMOS, Université Pierre et Marie Curie, 4 Place Jussieu Tour 45, couloir 45–46, 3e étage Boite 102, 75252, Paris CEDEX 05, FranceMeteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki, 305-0052, JapanDeutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, GermanyCentre National de Recherches Météorologiques, Université de Toulouse, Météo-France, CNRS, Toulouse, FranceAtmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, 3090 Center Green Drive, Boulder, CO 80301, USASteinbuch Centre for Computing, Karlsruhe Institute of Technology, Karlsruhe, GermanyCentre National de Recherches Météorologiques, Université de Toulouse, Météo-France, CNRS, Toulouse, FranceMet Office Hadley Centre, Exeter, EX1 3PB, UKClimate Research Branch, Environment and Climate Change Canada, Montreal, CanadaInstitute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, SwitzerlandSchool of Physical and Chemical Sciences, University of Canterbury, Christchurch, New ZealandInstitute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, SwitzerlandPhysikalisch-Meteorologisches Observatorium Davos World Radiation Centre, Dorfstrasse 33, 7260 Davos Dorf, SwitzerlandInstitute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, SwitzerlandNASA Goddard Space Flight Center, Greenbelt, MD, USAUniversities Space Research Association (USRA), GESTAR, Columbia, MD, USAAtmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, 3090 Center Green Drive, Boulder, CO 80301, USAGlobal Monitoring Division, NOAA Earth System Research Laboratory, Boulder, CO, USALaboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, France<p>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 (<span class="inline-formula">CH<sub>4</sub></span>) 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 <span class="inline-formula">CH<sub>4</sub></span> 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 <span class="inline-formula">8.7×10<sup>5</sup></span> and <span class="inline-formula">12.8×10<sup>5</sup></span> molec cm<span class="inline-formula"><sup>−3</sup></span>. 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–<span class="inline-formula">0.3×10<sup>5</sup></span> molec cm<span class="inline-formula"><sup>−3</sup></span> 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 <span class="inline-formula">CH<sub>4</sub></span> mixing ratio in 2010, which represents 7 %–20 % of the model-simulated <span class="inline-formula">CH<sub>4</sub></span> increase due to surface emissions. Between 2010 and 2016, the ensemble of simulations showed that OH changes could lead to a <span class="inline-formula">CH<sub>4</sub></span> mixing ratio uncertainty of <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M11" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>></mo><mo>±</mo><mn mathvariant="normal">30</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="32pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="4a73e472ab7e050d281fe67c354e0ae1"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-13701-2019-ie00001.svg" width="32pt" height="10pt" src="acp-19-13701-2019-ie00001.png"/></svg:svg></span></span> 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 <span class="inline-formula">CH<sub>4</sub></span> forward simulations and emission optimizations performed by atmospheric inversions.</p>https://www.atmos-chem-phys.net/19/13701/2019/acp-19-13701-2019.pdf |