A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1

Nicely, Julie M.; Duncan, Bryan N.; Hanisco, Thomas F.; Wolfe, Glenn M.; Salawitch, Ross J.; Deushi, Makoto; Haslerud, Amund S.; Jöckel, Patrick; Josse, Béatrice; Kinnison, Douglas E.; Klekociuk, Andrew; Manyin, Michael E.; Marécal, Virginie; Morgenstern, Olaf; Murray, Lee T.; Myhre, Gunnar; Oman, Luke D.; Pitari, Giovanni; Pozzer, Andrea; Quaglia, Ilaria; Revell, Laura E.; Rozanov, Eugene; Stenke, Andrea; Stone, Kane; Strahan, Susan; Tilmes, Simone; Tost, Holger; Westervelt, Daniel M.; Zeng, Guang

doi:https://doi.org/10.5194/acp-20-1341-2020

Articles | Volume 20, issue 3

Atmos. Chem. Phys., 20, 1341–1361, 2020

https://doi.org/10.5194/acp-20-1341-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Chemistry–Climate Modelling Initiative (CCMI) (ACP/AMT/ESSD/GMD...

Atmos. Chem. Phys., 20, 1341–1361, 2020

https://doi.org/10.5194/acp-20-1341-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 3

Research article 05 Feb 2020

Research article | 05 Feb 2020

A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1

Julie M. Nicely et al.

Download

Final revised paper (published on 05 Feb 2020)
Supplement to the final revised paper
Preprint (discussion started on 05 Sep 2019)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

RC1: 'Review Nicely et al.', Peer Johannes Nowack, 02 Oct 2019
RC2: 'Review comments', Anonymous Referee #2, 03 Oct 2019
SC1: 'Comments: A Machine Learning Examination of Hydroxyl Radical Differences Among Model Simulations for CCMI-1 by Nicely et al., 2019', Karl Seltzer, 07 Oct 2019
RC3: 'Comments on Nicely et al.', Leif Denby, 10 Nov 2019
AC1: 'Response to reviewers', Julie Nicely, 21 Dec 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Julie Nicely on behalf of the Authors (21 Dec 2019) Author's response Manuscript

ED: Publish subject to technical corrections (10 Jan 2020) by Paul Young

Short summary

Differences in methane lifetime among global models are large and poorly understood. We use a neural network method and simulations from the Chemistry Climate Model Initiative to quantify the factors influencing methane lifetime spread among models and variations over time. UV photolysis, tropospheric ozone, and nitrogen oxides drive large model differences, while the same factors plus specific humidity contribute to a decreasing trend in methane lifetime between 1980 and 2015.