Attribution of the impact of the February 2018 sudden stratospheric warming on mortality in the Nordics and United Kingdom

Abstract. Sudden stratospheric warming (SSW) events can trigger extended periods of cold surface weather in Europe, with potential consequences for public health. While previous work has established statistical links between SSWs and increased winter mortality, quantitative attribution of deaths to specific SSW events remains limited, particularly across different regions and data resolutions. This study presents a framework that combines exposure-response curves with stratospherically nudged ensemble forecasts to robustly attribute excess mortality to the February 2018 SSW event and its associated cold surface anomalies. We analyse mortality in various UK regions as well as three Nordic countries using a combination of daily, weekly and monthly aggregated mortality datasets. Exposure-response curves are derived using both distributed lag nonlinear models (DLNMs) and a simpler binning-based approach, allowing evaluation across varying temporal resolutions and data constraints. We find that while the Nordic countries experienced the strongest post-SSW temperature anomalies, the highest attributable mortality risk impacts occurred in the UK. This is explained by the steepness of the cold branch of the exposure-response relationship in southern UK regions, likely reflecting lower population-level adaptation to cold weather. Our results suggest that approximately 750 deaths in England and Wales and 250 in the Nordic countries can be attributed to the 2018 SSW. We show that even with coarser temporal resolution data, the binning-based approach yields consistent mortality estimates, supporting its use in data-limited settings. The regional variation in exposure-response characteristics further highlights the need to consider both meteorological hazard magnitude and societal vulnerability. Beyond mortality, the framework is applicable to other societal impacts of extreme weather, providing a flexible and interpretable tool for retrospective attribution and climate risk assessment.