Articles | Volume 17, issue 21
https://doi.org/10.5194/acp-17-13283-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-17-13283-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
09 Nov 2017
Research article |
| 09 Nov 2017
Contributions of the troposphere and stratosphere to CH4 model biases
Zhiting Wang
CORRESPONDING AUTHOR
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Thorsten Warneke
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Nicholas M. Deutscher
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, New South Wales, Australia
Justus Notholt
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Ute Karstens
Max Planck Institute for Biogeochemistry, Jena, Germany
Marielle Saunois
Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRSUVSQ), Université Paris-Saclay, 91 191 Gif Sur Yvette, France
Matthias Schneider
Karlsruhe Institute of Technology, IMK-ASF, Karlsruhe, Germany
Ralf Sussmann
Karlsruhe Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, Germany
Harjinder Sembhi
Earth Observation Science, Department of physics and Astronomy, University of Leicester, Leicester, UK
David W. T. Griffith
Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, New South Wales, Australia
Dave F. Pollard
National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
Rigel Kivi
Finnish Meteorological Institute Arctic Research Center, FMI-ARC, Sodankylä, Finland
Christof Petri
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Voltaire A. Velazco
Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, New South Wales, Australia
Michel Ramonet
Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRSUVSQ), Université Paris-Saclay, 91 191 Gif Sur Yvette, France
Huilin Chen
Center for Isotope Research (CIO), University of Groningen, Groningen, the Netherlands
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA
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Cited
8 citations as recorded by crossref.
- Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations Y. Zhang et al. 10.5194/acp-21-3643-2021
- Influences of Uncertainties in the STT Flux on Modeled Tropospheric Methane Z. Wang 10.1029/2023JD039107
- A statistical model to predict and analyze air surface temperature based on remotely sensed observations H. Lim et al. 10.1007/s11356-021-16321-z
- Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part I: Physical Background and Coupled Modeling Aspects R. Ménard et al. 10.3390/atmos11020150
- Characterizing model errors in chemical transport modeling of methane: using GOSAT XCH<sub>4</sub> data with weak-constraint four-dimensional variational data assimilation I. Stanevich et al. 10.5194/acp-21-9545-2021
- A decade of GOSAT Proxy satellite CH<sub>4</sub> observations R. Parker et al. 10.5194/essd-12-3383-2020
- Retrieval of atmospheric CH<sub>4</sub> vertical information from ground-based FTS near-infrared spectra M. Zhou et al. 10.5194/amt-12-6125-2019
- Variability and quasi-decadal changes in the methane budget over the period 2000–2012 M. Saunois et al. 10.5194/acp-17-11135-2017
7 citations as recorded by crossref.
- Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations Y. Zhang et al. 10.5194/acp-21-3643-2021
- Influences of Uncertainties in the STT Flux on Modeled Tropospheric Methane Z. Wang 10.1029/2023JD039107
- A statistical model to predict and analyze air surface temperature based on remotely sensed observations H. Lim et al. 10.1007/s11356-021-16321-z
- Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part I: Physical Background and Coupled Modeling Aspects R. Ménard et al. 10.3390/atmos11020150
- Characterizing model errors in chemical transport modeling of methane: using GOSAT XCH<sub>4</sub> data with weak-constraint four-dimensional variational data assimilation I. Stanevich et al. 10.5194/acp-21-9545-2021
- A decade of GOSAT Proxy satellite CH<sub>4</sub> observations R. Parker et al. 10.5194/essd-12-3383-2020
- Retrieval of atmospheric CH<sub>4</sub> vertical information from ground-based FTS near-infrared spectra M. Zhou et al. 10.5194/amt-12-6125-2019
1 citations as recorded by crossref.
Latest update: 20 Nov 2024
Short summary
In this paper we separate the biases of atmospheric methane models into stratospheric and tropospheric parts. It is observed in other studies that simulated total columns of atmospheric methane present a latitudinal bias compared to measurements. The latitudinal gradients are considered to be from the stratosphere. However, our results show that the latitudinal biases could come from the troposphere in two of three models evaluated in this study.
In this paper we separate the biases of atmospheric methane models into stratospheric and...
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