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.

Supplement

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

Data sets

Full Results Accompanying A Machine Learning Examination of Hydroxyl Radical Differences Among Model Simulations for CCMI-1 J. M. Nicely, B. N. Duncan, T. F. Hanisco, G. M. Wolfe, R. J. Salawitch, M. Deushi, A. S. Haslerud, P. Jöckel, B. Josse, D. E. Kinnison, A. Klekociuk, M. E. Manyin, V. Marécal, O. Morgenstern, L. T. Murray, G. Myhre, L. D. Oman, G. Pitari, A. Pozzer, I. Quaglia, L. E. Revell, E. Rozanov, A. Stenke, K. Stone, S. Strahan, S. Tilmes, H. Tost, D. M. Westervelt, and G. Zeng https://doi.org/10.13016/vvbp-p6o8

CCMI-1 Data Archive CEDA Archive http://data.ceda.ac.uk/badc/wcrp-ccmi/data/CCMI-1/output

Climate Data at the National Center for Atmospheric Research Climate Data Gateway at NCAR https://www.earthsystemgrid.org/

BADC Data Access IGAC/SPARC http://blogs.reading.ac.uk/ccmi/badc-data-access/

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.