| 11 Nov 2025
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.
The study at hand seeks to attribute the mortality impacts over the UK and the Nordics of the sudden stratospheric warming event in February 2018. To do so, the authors combine state-of-the-art methods from weather prediction modelling with an epidemiological impact analysis. The approach of attributing impacts of single weather events is novel and might help preparing societies better for these consequences. I further want to congratulate the authors on a very clearly written paper that is well structure and thus easy to follow.
My major concerns are about the epidemiological analysis and its application, the use of monthly data for estimating temperature-mortality relationships, and the application of the different temperature datasets. My review focus is in the health impact analysis, as I'm no expert on stratospheric processes. I’ve also included some minor comments, that caught my eye whilst reading the draft.
To my understanding the authors first fit a model that expresses the expected mortality as a function of (daily mean, I assume) temperature. However, in environmental epidemiology, temperature attributable mortality is calculated differently (see Gasparrini, 2014). Attributable fraction of mortality is calculated as
AF = (RR(T) − 1) / RR(T)
where AF stands for attributable fraction and the RR for relative risk from the DLNMs. Temperature attributable numbers of mortality are then subsequently calculated as
AN = n * AF
Where n denotes the number of cases (daily deaths, on that day). To be more precise, you could also use the (forward looking) running mean of n, with the number of lag days (same number as lags within the DLNM) as duration.
Directly estimating cold-attributable mortality from the relative risk, as I suspect the authors are doing here, would thus lead to a strong overestimation and is technically incorrect.
Several recent studies use a two-stage approach (i.e. Sera, 2022) two improve the estimate of the coefficient by pooling the spline parameters of the DLNM. This would likely improve your counterintuitive results of the RR curves for some locations as displayed in Figure 7 (i.e. where the curves bend downward again at very cold temperatures). There are several studies with open code which you can use for mimicking the methods (i.e. Vicedo-Cabrera, 2021).
The authors state that applying DLNMs to weekly data is not trivial. However, this is routinely being done (i.e. Ballerster et al. (2023)).
While I acknowledge that there is an advantage of using several statistical approaches to estimate temperature-related mortality – I’d suggest restricting the analysis to the standard DLNMs here, as this should not be an epidemiological methods development study. Also, I don’t think that it is appropriate to use monthly data for estimating temperature-mortality relationships, as the short term effect of individual days are important. Otherwise, I’d suggest to quantitatively compare models based on their AIC score, and not qualitatively as currently done.
Again, I’d suggest to sticking to standard epidemiological methods, as otherwise you should show a clear model intercomparison (local vs. district/country vs. country level results, daily vs. weekly vs. monthly data, model assumption, model parameterization, etc.). The argument of using country level and monthly data for Nordic countries is not very convincing to me, as there certainly are daily mortality counts somewhere (at least there is published literature on it).
I see two concerns with the temperature data – I don’t think it’s adequate to use a country level mean as input to the epidemiological analysis, especially for Finland, where a vast majority of the population is concentrated around Helsinki. I’d suggest using a population weighted temperature time series. Same could be done on a county-level. But then again, I don’t think that a country wide assessment is suitable in the first place.
The spatial resolution column in Table 3 is not very helpful (for me at least). Could you maybe express everything in degrees (as being done for the CESM2)?
It is quite important that the temperature distribution of the model data corresponds to the distribution with which the temperature-mortality relationships are calculated, otherwise you might include a model-bias in your impact calculation. This can be done i.e. using quantile mapping as a bias-correction method.
Minor comments
Gasparrini, A., & Leone, M. (2014). Attributable risk from distributed lag models. BMC medical research methodology, 14(1), 55.
Sera, F., & Gasparrini, A. (2022). Extended two-stage designs for environmental research. Environmental health, 21(1), 41.
Ballester, J., Quijal-Zamorano, M., Méndez Turrubiates, R.F. et al. Heat-related mortality in Europe during the summer of 2022. Nat Med 29, 1857–1866 (2023). https://doi.org/10.1038/s41591-023-02419-z