Abstract
<p>Atmospheric blocking events are mid-latitude weather patterns, which obstruct the usual path of the polar jet stream. Several blocking indices (BIs) have been developed to study blocking patterns and their associated trends, but these show significant seasonal and regional differences. Despite being central features of mid-latitude synoptic-scale weather, there is no well-defined historical dataset of blocking events. 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 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 a case study of the 2003 European heat wave and highlight that well-defined groups of SOM nodes can be an effective tool to reliably and accurately diagnose such weather events. This contrasts with the way SOMs are commonly used, where an individual SOM node can be wrongly assumed to represent a weather pattern. We also evaluate the SOM-BI performance on about 100 years of climate model data from a preindustrial 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 significantly better than the conventional indices. This shows that our method can be effectively applied to climate models to develop our understanding of how climate change will affect regional blocking characteristics. Overall, our results demonstrate the significant potential for unsupervised learning to complement the study of blocking events in both reanalysis and climate modelling contexts.</p>
Highlights
IntroductionAtmospheric blocking events are large-scale mid-latitude anticyclones that can persist for several days, which obstruct the typical westerly flow pattern (Rex, 1950)
We find that through comparison with three blocking indices (BIs) used in a recent inter-comparison study (Pinheiro et al, 2019), the self-organizing maps (SOMs)-BI method has an improved skill at detecting regional blocking events
The 2019 heat wave was concurrent with persistent hot air that originated in North Africa, which was sustained by an omega block centred on western Europe (Mitchell et al, 2019)
Summary
Atmospheric blocking events are large-scale mid-latitude anticyclones that can persist for several days, which obstruct the typical westerly flow pattern (Rex, 1950). Blocking systems are often associated with regional extreme weather events, heat waves in summer and cold snaps in winter. C. Thomas et al.: An unsupervised learning approach to identifying blocking events. The 2003 summer heat wave and 2009/10 winter cold events in Europe were both associated with atmospheric blocking (Black et al, 2004; Cattiaux et al, 2010). There is a large seasonal, interannual and decadal variability in the occurrence of blocking (Kennedy et al, 2016; Brunner et al, 2017), which compounds the problem of separating externally forced changes from internal variability (Barnes et al, 2014; Shepherd, 2014). The influence of climate change on blocking remains an open question (Francis and Vavrus, 2012; Barnes, 2013; Hassanzadeh et al, 2014; Barnes and Polvani, 2015; Barnes and Screen, 2015; Francis and Vavrus, 2015; Coumou et al, 2018; Mann et al, 2018)
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