Articles | Volume 20, issue 3
Atmos. Chem. Phys., 20, 1341–1361, 2020

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

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

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.

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

CCMI-1 Data Archive CEDA Archive

Climate Data at the National Center for Atmospheric Research Climate Data Gateway at NCAR


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.
Final-revised paper