It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
Sudden stratospheric warmings (SSWs) have been linked to surface temperature anomalies, but how these connect to changes in the likelihood of specific weather extremes and their associated weather patterns remains uncertain. While, on average, it is true that cold surface temperatures follow SSW events, particularly in Northern Europe, there is considerable event-to-event variability. Over the British Isles and Central Europe, only around 45% of SSWs are followed by a colder than average period and a negative phase of the North Atlantic Oscillation, cautioning against an over-generalised approach to surface anomalies associated with SSWs. Focussing on more hazardous weather, which in winter is associated with cold extremes, we use reanalysis data to consider how SSWs impact temperature-related hazards; namely the frequency of snowy days, frost days and spells of extreme cold weather in 12 major European cities. In general, SSWs are associated with an increased risk of snow across most of western Europe, and that this is particularly significant in milder, more maritime locations such as London where in reanalysis, snowfall days are 40% more likely after an SSW. However, there is considerable variation in surface temperature anomalies between SSW events; the third of SSWs with the warmest surface anomalies are statistically more likely to have a decreased risk of snow, frost and persistent cold spells compared with non-SSW time periods. These warmer events are associated with a different temperature anomaly pattern, which is consistent in both reanalysis data and large ensemble CMIP6 models. We further show that these warm surface temperature anomaly SSWs are becoming more frequent, a trend which is consistent with background global warming. The varied surface anomalies associated with SSWs highlights the need to study their impacts in a probabilistic sense, and motivates further work to enable better prediction of the impacts of a given event.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details

1 School of Geographical Sciences, University of Bristol , Bristol, United Kingdom; Cabot Institute for the Environment, University of Bristol , Bristol, United Kingdom
2 Global Systems Institute and Department of Mathematics, University of Exeter , Exeter, United Kingdom
3 Centre for Space, Atmospheric and Oceanic Science, University of Bath , Bath, United Kingdom