Publication Abstracts

Nicely et al. 2020

Nicely, J.M., 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, 2020: A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1. Atmos. Chem. Phys., 20, 1341-1361, doi:10.5194/acp-20-1341-2020.

The hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH4), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting methane lifetime (τCH4), both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of τCH4 differences among 10 models are the flux of UV light to the troposphere (indicated by the photolysis frequency JO1D), the mixing ratio of tropospheric ozone (O3), the abundance of nitrogen oxides (NOx≡NO+NO2), and details of the various chemical mechanisms that drive OH. Water vapour, carbon monoxide (CO), the ratio of NO:NOx, and formaldehyde (HCHO) explain moderate differences in τCH4, while isoprene, methane, the photolysis frequency of NO2 by visible light (JNO2), overhead ozone column, and temperature account for little to no model variation in τCH4. We also apply the NNs to analysis of temporal trends in OH from 1980 to 2015. All models that participated in the specified dynamics historical simulation for CCMI demonstrate a decline in τCH4 during the analysed timeframe. The significant contributors to this trend, in order of importance, are tropospheric O3, JO1D, NOx, and H2O, with CO also causing substantial interannual variability in OH burden. Finally, the identified trends in τCH4 are compared to calculated trends in the tropospheric mean OH concentration from previous work, based on analysis of observations. The comparison reveals a robust result for the effect of rising water vapour on OH and τCH4, imparting an increasing and decreasing trend of about 0.5% decade-1, respectively. The responses due to NOx, ozone column, and temperature are also in reasonably good agreement between the two studies.

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BibTeX Citation

  author={Nicely, J. M. and Duncan, B. N. and Hanisco, T. F. and Wolfe, G. M. and Salawitch, R. J. and Deushi, M. and Haslerud, A. S. and Jöckel, P. and Josse, B. and Kinnison, D. E. and Klekociuk, A. and Manyin, M. E. and Marécal, V. and Morgenstern, O. and Murray, L. T. and Myhre, G. and Oman, L. D. and Pitari, G. and Pozzer, A. and Quaglia, I. and Revell, L. E. and Rozanov, E. and Stenke, A. and Stone, K. and Strahan, S. and Tilmes, S. and Tost, H. and Westervelt, D. M. and Zeng, G.},
  title={A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1},
  journal={Atmos. Chem. Phys.},

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RIS Citation

ID  - ni04100o
AU  - Nicely, J. M.
AU  - Duncan, B. N.
AU  - Hanisco, T. F.
AU  - Wolfe, G. M.
AU  - Salawitch, R. J.
AU  - Deushi, M.
AU  - Haslerud, A. S.
AU  - Jöckel, P.
AU  - Josse, B.
AU  - Kinnison, D. E.
AU  - Klekociuk, A.
AU  - Manyin, M. E.
AU  - Marécal, V.
AU  - Morgenstern, O.
AU  - Murray, L. T.
AU  - Myhre, G.
AU  - Oman, L. D.
AU  - Pitari, G.
AU  - Pozzer, A.
AU  - Quaglia, I.
AU  - Revell, L. E.
AU  - Rozanov, E.
AU  - Stenke, A.
AU  - Stone, K.
AU  - Strahan, S.
AU  - Tilmes, S.
AU  - Tost, H.
AU  - Westervelt, D. M.
AU  - Zeng, G.
PY  - 2020
TI  - A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1
JA  - Atmos. Chem. Phys.
VL  - 20
SP  - 1341
EP  - 1361
DO  - 10.5194/acp-20-1341-2020
ER  -

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