Articles | Volume 2, issue 3
Research article
12 Jul 2021
Research article |  | 12 Jul 2021

An unsupervised learning approach to identifying blocking events: the case of European summer

Carl Thomas, Apostolos Voulgarakis, Gerald Lim, Joanna Haigh, and Peer Nowack

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Cited articles

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Barnes, E. A. and Polvani, L. M.: CMIP5 Projections of Arctic Amplification, of the North American/North Atlantic Circulation, and of Their Relationship, J. Climate, 28, 5254–5271,, 2015. a
Barnes, E. A. and Screen, J. A.: The impact of Arctic warming on the midlatitude jet-stream: Can it? Has it? Will it?, WIREs Clim. Change, 6, 277–286,, 2015. a
Barnes, E. A., Dunn-Sigouin, E., Masato, G., and Woollings, T.: Exploring recent trends in Northern Hemisphere blocking, Geophys. Res. Lett., 41, 638–644,, 2014. a, b
Barriopedro, D., García-Herrera, R., and Trigo, R.: Application of blocking diagnosis methods to General Circulation Models. Part I: A novel detection scheme, Clim. Dynam., 35, 1373–1391,, 2010. a, b, c, d
Short summary
Atmospheric blocking events are complex large-scale weather patterns which block the path of the jet stream. They are associated with heat waves in summer and cold snaps in winter. Blocking is poorly understood, and the effect of climate change is not clear. Here, we present a new method to study blocking using unsupervised machine learning. We show that this method performs better than previous methods used. These results show the potential for unsupervised learning in atmospheric science.