The attribution to climate change, and improved forecasting of extreme and slow-onset climate- and weather-related events and their impacts

Project results are expected to contribute to all of the following expected outcomes:

• Advanced understanding of the causality between anthropogenic climate change and the frequency and intensity of climate and weather extremes (including temperature extremes, heavy precipitation and pluvial floods, river floods, droughts, storms, as well as compound events), and their risks and impacts, including cascading impacts, on human systems and ecosystems;
• Improved methodologies and tools of attribution of extreme climate- and weather-related events, and their impacts, to anthropogenic climate change;
• Enhancement of existing or creation of new pilot global databases of extreme events, impacts and their attribution;
• Advanced knowledge of how attribution science and forecasting can be operationalised for a range of policy purposes, including informing and improving preparedness, civil protection and humanitarian planning for future extreme and slow-onset events, post-disaster reconstruction, resilience and adaptation plans.

Anthropogenic climate change influences the intensity and likelihood of extreme weather events – the latest IPCC report warns that anthropogenic climate is already affecting weather and climate extremes across the globe and with every additional increment of global warming, changes in extremes will continue to become larger.

Attribution science tries to answer the question of what the role of anthropogenic climate change relative to other drivers (natural and non-climate anthropogenic factors), is for a given extreme climate or weather event. It is relatively nascent, and while it is fast advancing, numerous gaps remain, including on compound and cascading events, the interplay between slow and fast onset events, the appropriate statistical methods and the proper consideration of various degrees of vulnerabilities and exposure.

Some tail events, risks and associated impacts are inherently poorly represented in current simulation records. The latest advances in numerical modelling, AI and Machine Learning, counter-factual datasets using large ensembles and digital twins, for example, could increase the sample size of simulated rare – including compound and cascading - events and offer opportunities to explore the decision-making and estimated impact space (e.g., in relation to water, air pollution, ecosystem status, land use – and their combination). Propagating uncertainties along the causality chain is an important aspect to address in this context.

Actions should address all of the following aspects:

• Advance attribution science through a combination of observations, models, attribution methodologies applied to the physical climate conditions (fast and slow-onset event attribution for a more accurate estimation of how the likelihood and intensity of the hazards have been altered by anthropogenic climate change) and impacts (identifying how the interplay between anthropogenic climate change and local implemented responses affects residual impacts);
• Advance the understanding of the interplay between natural variability and anthropogenic climate change both in the recent past (since the instrumental data is available) and in the near- and mid- term future (2025-2060), as well as the interplay between climate and non-climate drivers of impacts, and socially differentiated vulnerability patterns;
• Advance methodologies to collect diverse in-situ and remote sensing observations to develop or contribute to robust extreme event and impact databases;
• In the context of attribution, focus on extreme and slow-onset events and their interactions (including cascading and compound events) and impacts (on human systems and ecosystems), locally implemented responses and their limits (response capacities), with due consideration of vulnerable regions;
• Deliver enhanced methods to separate the effects of climate trends (including in extreme events) from trends in exposure and vulnerability, both in observed datasets and in model scenarios;
• Investigate how different model enhancements (e.g., finer resolution, increased complexity) impact the realism and accuracy of the modelled climate and weather extremes. Strive to investigate inter-model differences and their implications for extreme event attribution and contribute to multi-model and intercomparison approaches (e.g., Inter-Sectoral Impact Model Intercomparison Project, ISIMIP), including with downscaling and bias correction of global models for better simulation of extreme events;
• Building on latest advances in attribution studies, improve forecasting of extreme climate- and weather-related events and their impacts, and contribute to the evolution of climate services;
• Improve the knowledge of how to operationalise the attribution science and forecasting for informing future planning including in some of the areas relevant for advancing disaster preparedness and prevention capacity building, humanitarian aid operations, and adaptation plans (e.g., early warning systems, disaster risk reduction including with nature-based solutions, emergency relief) via co-design and co-production with operational actors, including citizens and civil society globally and with due consideration of associated challenges in the Global South;
• The results should serve as a basis to ensure policies and actions that follow from the attribution studies can integrate climate justice.

When dealing with models, actions should promote the highest standards of transparency and openness, as much as possible going well beyond documentation and extending to aspects such as assumptions, protocols, code, and data that is managed in compliance with the FAIR principles^[1].

All projects funded under this topic are strongly encouraged to connect, coordinate, and participate in networking and joint activities together, as appropriate. Collaboration with Destination Earth is encouraged. Clustering activities with other relevant ongoing projects (in and out of Horizon Europe) should be envisaged for cross-projects cooperation and results from relevant past and ongoing projects, including XAIDA^[2] and CLINT^[3], should be considered.

This topic requires the effective contribution of social sciences and humanities (SSH) disciplines and the involvement of SSH experts, institutions as well as the inclusion of relevant SSH expertise, in order to produce meaningful and significant effects enhancing the societal impact of the related research activities. Citizen Science and other innovative and participatory forms of research could be appropriate for this action.

International cooperation is encouraged, in particular with the Global South^[4] in the context of scientific capacity building, disaster risk reduction and strengthening of climate resilience.

[1] FAIR (Findable, Accessible, Interoperable, Reusable).

[2] https://cordis.europa.eu/project/id/101003469

[3] https://cordis.europa.eu/project/id/101003876

[4] In absence of a single formal definition of the Global South, the list of low- to middle-income countries automatically eligible for Horizon Europe funding should be used for this purpose – see the Horizon Europe List of Participating Countries on EU Funding and Tenders Portal for up-to-date information