Articles | Volume 22, issue 11
https://doi.org/10.5194/acp-22-7523-2022
https://doi.org/10.5194/acp-22-7523-2022
Research article
 | 
10 Jun 2022
Research article |  | 10 Jun 2022

The roles of the Quasi-Biennial Oscillation and El Niño for entry stratospheric water vapor in observations and coupled chemistry–ocean CCMI and CMIP6 models

Shlomi Ziskin Ziv, Chaim I. Garfinkel, Sean Davis, and Antara Banerjee

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Influence of the El Niño–Southern Oscillation on entry stratospheric water vapor in coupled chemistry–ocean CCMI and CMIP6 models
Chaim I. Garfinkel, Ohad Harari, Shlomi Ziskin Ziv, Jian Rao, Olaf Morgenstern, Guang Zeng, Simone Tilmes, Douglas Kinnison, Fiona M. O'Connor, Neal Butchart, Makoto Deushi, Patrick Jöckel, Andrea Pozzer, and Sean Davis
Atmos. Chem. Phys., 21, 3725–3740, https://doi.org/10.5194/acp-21-3725-2021,https://doi.org/10.5194/acp-21-3725-2021, 2021
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Cited articles

Avery, M. A., Davis, S. M., Rosenlof, K. H., Ye, H., and Dessler, A. E.: Large anomalies in lower stratospheric water vapour and ice during the 2015–2016 El Niño, Nat. Geosci., 10, 405–409, 2017. a
Banerjee, A., Chiodo, G., Previdi, M., Ponater, M., Conley, A. J., and Polvani, L. M.: Stratospheric water vapor: an important climate feedback, Clim. Dynam., 53, 1697–1710, 2019. a, b
Boser, B. E., Guyon, I. M., and Vapnik, V. N.: A training algorithm for optimal margin classifiers, in: Proceedings of the fifth annual workshop on Computational learning theory (COLT '92), Association for Computing Machinery, New York, NY, USA, 144–152, https://doi.org/10.1145/130385.130401, 1992. a
Breiman, L.: Random forests, Machine Learning, 45, 5–32, 2001. a
Brinkop, S., Dameris, M., Jöckel, P., Garny, H., Lossow, S., and Stiller, G.: The millennium water vapour drop in chemistry–climate model simulations, Atmos. Chem. Phys., 16, 8125–8140, https://doi.org/10.5194/acp-16-8125-2016, 2016. a
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Stratospheric water vapor is important for Earth's overall greenhouse effect and for ozone chemistry; however the factors governing its variability on interannual timescales are not fully known, and previous modeling studies have indicated that models struggle to capture this interannual variability. We demonstrate that nonlinear interactions are important for determining overall water vapor concentrations and also that models have improved in their ability to capture these connections.
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