Hazard and Impact Synthesis and Attribution for Phase I use case

The overarching goal of COMPASS is to develop a harmonised, yet flexible, methodological framework for climate and impact attribution of various complex extremes that includes compound, sequences, and cascading hazard events. This is an important step towards operationalizing event attribution, further supporting
contextual understanding of the interplay between climate change and extreme event impacts, and supporting locally relevant responses.

The COMPASS Use Cases (UC) are central to the approach as they provide the development test beds through which the framework, including harmonized datasets, modeling workflows, and attribution experiments are evaluated in real world test cases. The first round of UCs was selected to span a range of event types as well as
geographical contexts to ensure that the methodological framework was applicable across these different contexts. In support of the UCs, guidance on suitable datasets for exposure and vulnerability modeling was developed, including new economic and population exposure datasets, as detailed in D2.1 on the best available
methods and datasets for impact attribution.

As the methodological framework and associated datasets were being developed in parallel with the first UCs, the UCs have followed the general principles of the framework, the actual implementation varied based on existing institutional modeling infrastructure and experience, and available local level exposure and impacts
data. This has been valuable in exploring the advantages and disadvantages of different implementation approaches and is detailed in the associated D1.1 Guidelines for compound extremes modeling in current and future climates.

Two different attribution analysis methods have been implemented, probabilistic or risk based, and storyline-based methods. Each approach has advantages and disadvantages, particularly when communicating the results with stakeholders. While probabilistic approaches provide statistical estimates of the shifting probability of an event, or shifting intensity of events, as a result of climate change, storyline methods help to understand how a particular event may have unfolded without the influence of climate change and also allow consideration of other non-climate factors in contributing to or mitigating impacts.

The purpose of this report is to provide an in-depth summary of the implementation of each use case with a primary focus on the modeling framework implementation, strengths, and challenges emerging. While each UC description includes details of the actual event and reported impacts, these details are provided to help the
reader interpret the modeling in context rather than to provide definitive event descriptions which already exist and are referenced. Strengths of the applied datasets and methods are identified while limitations and challenges are also noted in order to inform further advances in both datasets and modeling frameworks.
A concluding synthesis section provides an over-arching assessment of the implementation of the use cases and points towards possible avenues for advancement for the second set of use cases within COMPASS.