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

Sudden stratospheric warming (SSW) events take place well above the Earth's surface, but can nonetheless 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 counterfactual attribution 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 attributable mortality risk impacts are mostly found 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 ensemble-based attribution framework suggests that several hundred expected deaths in England and Wales and a combined total of a few hundred in the Nordics can be attributed to the 2018 SSW, with substantial uncertainty arising from both the estimated exposure-response relationships and the spread between forecast systems. Comparing DLNM and binning approaches across daily, weekly and monthly input data, we find that the resulting estimates agree in sign and order of magnitude, which supports the use of coarser data where higher resolution is unavailable while making the associated structural uncertainty explicit. 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.