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Climate scientists are often asked if climate change has affected an occurred extreme event, for example a severe flood. And if so, how big was the effect? Determining whether climate change directly influences specific extreme events, given the inherent natural variability within our climate system, requires a nuanced examination. In other words, distinguishing natural variability from the impacts of climate change is not trivial. This challenge has spurred the emergence of attribution science as a separate branch of climate research.

Attribution science aims to assess the increased intensity or likelihood of extreme events within our current climate compared to pre-industrial conditions. Timely attribution assessments that are made shortly after extreme events occur provide relevant information to the public, fostering greater citizens' awareness and understanding of climate change impacts. These assessments may include information about, for example, how much heavier a rainfall event was or how much hotter a heatwave was due to human-caused climate change.

Effective communication regarding climate change impacts on extreme events relies on observational weather data, climate model simulations, and diverse methods and tools. Attribution studies entail the use of large computer simulations based on general circulation models (GCMs) or regional climate models (RCMs) to build factual (with climate change) and counterfactual (without climate change) scenarios for assessing the differences in the likelihood of extreme events across the two. Currently, climate services across Europe are actively developing attribution services to facilitate such communication. For example, Finnish Meteorological Institute (FMI) researchers in Finland have developed a method for assessing the effect of climate change on monthly mean temperatures. Using this method, it was discovered that climate change made the record-warm September of 2023 1.4 degrees warmer and 9.4 times more likely (Rantanen et al., 2024) in Helsinki. These kinds of scientific, peer-reviewed methods will serve as a basis for regular communication about monthly mean temperature deviations induced by climate change.

As attribution science progresses and provides tangible evidence linking extreme events to climate change, it will lead to a better understanding of the consequences of climate change. This will hopefully drive concrete adaptation efforts and strengthen the impetus for reducing greenhouse gas emissions. Accurate quantification of climate change impacts on specific extreme weather events will help in quantifying the socio-economic impacts of climate change in terms of the losses and damages related to those events, further fostering adaptive action. For example, an assessment of the percentage increase in the intensity of a heavy precipitation event will help in dimensioning of the drainage systems in cities.

The PIISA project envisions that by 2030, at least 50% of losses attributable to climate change in Europe will be covered by insurance, with a notable reduction in the adaptation gap, the latter facilitated by the successful development and implementation of novel insurance solutions. This vision underscores the importance of close collaboration of different scientific disciplines and different actors in the society with attribution science.

Specifically, climate change attribution can contribute to:
• Understanding climate risk factors: Attribution science can provide insights into the climate hazards and risks faced by different sectors such as agriculture, forestry, and cities and citizens’ well-being. By identifying how climate change is linked to floods, droughts, forest fires, biotic risks, and storms, attribution science can inform the design of insurance products tailored to these risks.
• Quantifying climate change impacts: It can quantify the extent to which climate change contributes to increased losses and damages associated with extreme weather events, allowing insurance companies to accurately assess and price climate change related risks, and thereby develop effective insurance portfolios and risk-sharing solutions.
• Enhancing adaptation: It can guide adaptation strategies by highlighting the causal links between climate change and specific hazards. This knowledge can assist PIISA in developing adaptive measures that address the evolving nature of hazard risks. This is crucial as climate change impacts are expected to intensify in the future, implying that the variation in hazard risks is likely to be explained more by climate change rather than climate variability.
• Data: Leveraging climate model data and observational records used in attribution studies can facilitate risk assessments within insurance frameworks. While observational weather data is required for parametric insurance, climate model simulations can be used in the context of forecast based insurance solutions for longer-term contracts.
• Policy and regulation: It can inform policymaking by providing scientific evidence on the impacts of climate change. This can help create conducive regulatory environments for the implementation of insurance solutions and related adaptation measures for sharing and reducing climate risks.

To ensure the successful achievement of these objectives, PIISA scientists are closely connected with the latest developments in attribution science. For example, partners at CMCC are involved in the European project CLINT, in which new AI-based techniques are being developed for attribution studies. Additionally, partners at FMI are part of the Nordic Networking Group which promotes collaboration between Nordic meteorological institutes for addressing attribution-related challenges.

Authors

• Anna Luomaranta, Finnish Meteorological Institute (FMI)
• Sheetal Saklani, Barcelona Supercomputing Center (BSC)
• Leone Cavicchia, Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)

References

Rantanen, M., Räisänen, J., & Merikanto, J. (2024). A method for estimating the effect of climate change on monthly mean temperatures: September 2023 and other recent record-warm months in Helsinki, Finland.

Atmospheric Science Letters, e1216.