14 Jan 2021

14 Jan 2021

Review status: a revised version of this preprint is currently under review for the journal WCD.

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

Carl Thomas1, Apostolos Voulgarakis1,2, Gerald Lim3, Joanna Haigh1,4, and Peer Nowack1,4,5,6 Carl Thomas et al.
  • 1Department of Physics, Imperial College London, South Kensington Campus, London, SW7 2BW, UK
  • 2School of Environmental Engineering, Technical University of Crete, Chania, Crete, 73100, Greece
  • 3Centre for Climate Research Singapore, 36 Kim Chuan Road, 537054, Singapore
  • 4Grantham Institute, Imperial College London, SW7 2AZ, UK
  • 5Climatic Research Unit, School of Environmental Sciences, Norwich, NR4 7TJ, UK
  • 6Data Science Institute, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK

Abstract. Atmospheric blocking events are mid-latitude weather patterns, which obstruct the usual path of the polar jet streams. They are often associated with heat waves in summer and cold snaps in winter. Despite being central features of mid-latitude synoptic-scale weather, there is no well-defined historical dataset of blocking events. Various blocking indices (BIs) have thus been suggested for automatically identifying blocking events and are frequently used to study their occurrence historically as well as in climate model simulations. However, BIs can show significant regional and seasonal differences and therefore several indices are typically applied in parallel to test scientific robustness. Here, we introduce a new blocking index using self-organizing maps (SOMs), an unsupervised machine learning approach, and compare its detection skill to some of the most widely applied BIs. To enable this intercomparison, we first create a new ground truth time series classification of European blocking based on expert judgement.We then demonstrate that our method (SOM-BI) has several key advantages over previous BIs because it exploits all of the spatial information provided in the input data and avoids the need for arbitrary thresholds. Using ERA5 reanalysis data (1979–2019), we find that the SOM-BI identifies blocking events with a higher precision and recall than other BIs. We present case studies of the 2003 and 2019 European heat waves and highlight that well-defined groups of SOM nodes can be an effective tool to reliably and accurately diagnose such weather events. We further compare the skill at detecting historic blocking events by applying our new SOM-BI to several meteorological variables that are associated with the study of blocking, including geopotential height, sea level pressure and four variables related to potential vorticity. The 500 hPa geopotential height anomaly field is the variable that most effectively supports the identification of blocking events with our new approach. Finally, we evaluate the SOM-BI performance on around 100 years of climate model data from a pre-industrial simulation with the new UK Earth System Model (UK-ESM1). For the model data, all blocking detection methods have lower skill than for the ERA5 reanalysis, but SOM-BI performs noticeably better than the conventional indices. SOM-BI performs well using at least 20 years of training data, which suggests that observational records are sufficiently long to train our new method effectively. Overall, our results demonstrate the significant potential for unsupervised learning to complement the study of blocking events in both reanalysis and climate modelling contexts.

Carl Thomas et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wcd-2021-1', Anonymous Referee #1, 26 Feb 2021
    • AC1: 'Reply on RC1', Carl Thomas, 06 Apr 2021
  • RC2: 'Comment on wcd-2021-1', Anonymous Referee #2, 04 Mar 2021
    • AC2: 'Reply on RC2', Carl Thomas, 06 Apr 2021

Carl Thomas et al.

Data sets

Ground Truth Dataset for European summer blocking events 1979-2019 Carl Thomas

Model code and software

Scripts Carl Thomas, Gerald Lim, and Peer Nowack

Carl Thomas et al.


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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.