| Summary: | © 2018 Royal Meteorological Society Persistence in time series of daily weather pattern (WP) classifications can provide useful information such as on the memory of broad-scale atmospheric circulation. Despite this, research of WP persistence has lagged behind that exploring their frequencies of occurrence. We develop two methods for identifying persistence in a 167-year time series of WPs defined over the North Atlantic–European domain. The first is an empirical counting technique used to find periods of persistence among sets of WPs, with the definition of persistence more relaxed than just consecutive occurrences. We then condition this method on the driest WPs to see if persistence can be used to identify historical drought. The second method uses a Markov model to assess if WP transition probabilities change when conditioned on information up to 20 days prior, without the need for estimating the large number of parameters usually required for high-order Markov chains. Results are compared with a benchmark ensemble of synthetic time series generated using first-order transition probabilities. We show that there were multi-month periods when small sets of WPs dominated, and some of these periods coincided with notable meteorological events, including droughts and storms, such as the mid-1990s drought in northern England and the Burn's Day Storm over southern Scotland in 1990. Some WPs also behave as “attractors,” showing increased probability of reoccurrence despite other WPs occurring in-between. However, we find no link between the persistence statistics of each WP and their flow characteristics, except for those featuring an easterly flow over the United Kingdom, which are among the most persistent. The benchmark simulation ensemble is unable to reproduce many of the key persistence statistics of the observations, confirming that the persistence is a physical phenomenon. Finally, we discuss the potential processes underpinning WP persistence, such as the effects of large-scale circulation patterns and land-surface feedbacks.
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