气候变化领域集成服务门户

The space-time structure of flood losses is arguably related to large-scale climatic patterns; however, this interconnection is not always well understood. Here we show that the North Atlantic Oscillation (NAO) correlates with the occurrence of catastrophic floods across Europe and the associated economic losses. The analysis reveals that in Northern Europe the majority of historic winter floods occurred during a positive NAO state, whereas the majority of summer floods occurred during a negative state. Analogous, but stronger, patterns can be observed in individual Atlantic countries. Through the application of a state-of-the-art catastrophe model, we find that there is a statistically significant relationship between the NAO and flood losses. Critically, we observe that the average flood loss during opposite NAO states can differ by up to 50%. These results can inform financial preparedness and disaster fund allocation, as stakeholders, such as governments and insurance companies, can distribute resources more effectively.

Plain Language Summary Increasing flood exposure and alarming climate change scenarios are responsible for growing concerns about economic losses from future floods. While it is well known that large-scale climatic patterns control meteorological events, it is not always clear whether this connection can be extended to the occurrence of flood events and the associated losses. In this study, based on the analysis of recent flood databases and the application of a catastrophe model, we show that there is indeed a significant relationship between the North Atlantic Oscillation (NAO) and flood losses. The use of a model allows to quantify the impact of the NAO on flood losses and to show that average flood losses vary significantly across opposite NAO states. The study shows that the NAO index constitutes a meaningful predictor for long-term average losses. In particular, based on recent development in NAO predictability, we argue that temporal variability of flood risk, due to climatic oscillations, can be predicted in advance, allowing for mitigation measures to take place sooner and enhance resiliency to flood hazard.