Articles | Volume 2, issue 3
https://doi.org/10.5194/wcd-2-581-2021
https://doi.org/10.5194/wcd-2-581-2021
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

Related authors

Tropospheric ozone trends and attributions over East and Southeast Asia in 1995–2019: an integrated assessment using statistical methods, machine learning models, and multiple chemical transport models
Xiao Lu, Yiming Liu, Jiayin Su, Xiang Weng, Tabish Ansari, Yuqiang Zhang, Guowen He, Yuqi Zhu, Haolin Wang, Ganquan Zeng, Jingyu Li, Cheng He, Shuai Li, Teerachai Amnuaylojaroen, Tim Butler, Qi Fan, Shaojia Fan, Grant L. Forster, Meng Gao, Jianlin Hu, Yugo Kanaya, Mohd Talib Latif, Keding Lu, Philippe Nédélec, Peer Nowack, Bastien Sauvage, Xiaobin Xu, Lin Zhang, Ke Li, Ja-Ho Koo, and Tatsuya Nagashima
Atmos. Chem. Phys., 25, 7991–8028, https://doi.org/10.5194/acp-25-7991-2025,https://doi.org/10.5194/acp-25-7991-2025, 2025
Short summary
Improving historical trends in the INFERNO fire model using the Human Development Index
Joao C. M. Teixeira, Chantelle Burton, Douglas I. Kelley, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
EGUsphere, https://doi.org/10.5194/egusphere-2025-3066,https://doi.org/10.5194/egusphere-2025-3066, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Constraining uncertainty in projected precipitation over land with causal discovery
Kevin Debeire, Lisa Bock, Peer Nowack, Jakob Runge, and Veronika Eyring
Earth Syst. Dynam., 16, 607–630, https://doi.org/10.5194/esd-16-607-2025,https://doi.org/10.5194/esd-16-607-2025, 2025
Short summary
Estimating future wildfire burnt area over Greece using the JULES-INFERNO model
Anastasios Rovithakis, Eleanor Burke, Chantelle Burton, Matthew Kasoar, Manolis G. Grillakis, Konstantinos D. Seiradakis, and Apostolos Voulgarakis
EGUsphere, https://doi.org/10.5194/egusphere-2025-274,https://doi.org/10.5194/egusphere-2025-274, 2025
Short summary
Opinion: Why all emergent constraints are wrong but some are useful – a machine learning perspective
Peer Nowack and Duncan Watson-Parris
Atmos. Chem. Phys., 25, 2365–2384, https://doi.org/10.5194/acp-25-2365-2025,https://doi.org/10.5194/acp-25-2365-2025, 2025
Short summary

Related subject area

Dynamical processes in midlatitudes
Extreme weather anomalies and surface signatures associated with merged Atlantic–African jets during northern winter
Sohan Suresan, Nili Harnik, and Rodrigo Caballero
Weather Clim. Dynam., 6, 789–806, https://doi.org/10.5194/wcd-6-789-2025,https://doi.org/10.5194/wcd-6-789-2025, 2025
Short summary
Long vs. short: understanding the dynamics of persistent summer hot spells in Europe
Duncan Pappert, Alexandre Tuel, Dim Coumou, Mathieu Vrac, and Olivia Martius
Weather Clim. Dynam., 6, 769–788, https://doi.org/10.5194/wcd-6-769-2025,https://doi.org/10.5194/wcd-6-769-2025, 2025
Short summary
Environments and lifting mechanisms of cold-frontal convective cells during the warm season in Germany
George Pacey, Stephan Pfahl, and Lisa Schielicke
Weather Clim. Dynam., 6, 695–713, https://doi.org/10.5194/wcd-6-695-2025,https://doi.org/10.5194/wcd-6-695-2025, 2025
Short summary
Seasonal to decadal variability and persistence properties of the Euro-Atlantic jet streams characterized by complementary approaches
Hugo Banderier, Alexandre Tuel, Tim Woollings, and Olivia Martius
Weather Clim. Dynam., 6, 715–739, https://doi.org/10.5194/wcd-6-715-2025,https://doi.org/10.5194/wcd-6-715-2025, 2025
Short summary
A pan-European analysis of large-scale drivers of severe convective outbreaks
Monika Feldmann, Daniela I. V. Domeisen, and Olivia Martius
EGUsphere, https://doi.org/10.5194/egusphere-2025-2296,https://doi.org/10.5194/egusphere-2025-2296, 2025
Short summary

Cited articles

Barnes, E. A.: Revisiting the evidence linking Arctic amplification to extreme weather in midlatitudes, Geophys. Res. Lett., 40, 4734–4739, https://doi.org/10.1002/grl.50880, 2013. a
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, https://doi.org/10.1175/JCLI-D-14-00589.1, 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, https://doi.org/10.1002/wcc.337, 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, https://doi.org/10.1002/2013GL058745, 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, https://doi.org/10.1007/s00382-010-0767-5, 2010. a, b, c, d
Download
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.
Share