CLOSER pursues five interconnected objectives. The first is to co-create scientific innovations and decision-making tools together with the people who need them most — emergency responders, climate services, adaptation planners, and policymakers.

The second objective is to build a new global database of climate-related disasters, integrating for the first time harmonised information on climate hazards, exposure, vulnerability and impacts.

The third objective is to model the full complexity of climate disaster impacts on human and natural systems.

The fourth objective is to design a Protocol for Expanded Attribution and Forecast — a practical, step-by-step methodology that scientists and practitioners can use to attribute the impacts of climate disasters to their multiple causes, whether past, ongoing or future.

The fifth objective is to analyse how past and present policy decisions have shaped vulnerability and disaster impacts, with a particular focus on inequalities. By understanding which policies have increased or reduced risk — and for whom — CLOSER will provide actionable guidance for more just and effective climate governance.

Together, these five objectives produce three key outputs that will serve scientists, decision-makers and society long after the project ends: a Knowledge Base of Climate-related Disasters, a Protocol for Expanded Attribution and Forecast, and Decision-Making Frameworks for Climate-related Disasters.

CMCC role
CMCC plays a central role in CLOSER, contributing expertise in two of the project’s most technically advanced areas: artificial intelligence applied to climate impact assessment, and the attribution of climate disaster impacts to their multiple causes.

In the area of AI-based impact modelling, CMCC leads the development of new artificial intelligence methods to rapidly assess the impacts of extreme weather events, from the local scale of individual Case Applications up to the continental scale. These AI models are trained to recognise the complex relationships between climate drivers — such as temperature, precipitation and sea level — and their real-world consequences on agriculture, infrastructure, health and ecosystems. They are then used to explore how impacts could unfold under different future climate and socio-economic scenarios, providing decision-makers with a forward-looking picture of climate risk.

CMCC also brings specific expertise in tropical cyclones and coastal storms. This expertise is particularly relevant for the Caribbean Case Application, one of the six real disaster cases at the heart of CLOSER.

In the area of impact attribution, CMCC leads the application of the project’s attribution Protocol to the impacts of the Case Application events. This means going beyond identifying what caused an extreme weather event, to quantifying how much of the resulting damage — to crops, buildings, human health, ecosystems — can be attributed to climate change, and how much to other factors such as land-use change, population growth or policy decisions.

Finally, CMCC is responsible for the preparation of the project’s ethics reports on the use of artificial intelligence, ensuring that all AI methods developed in CLOSER meet the highest standards of transparency, fairness and accountability as required by European guidelines.

Activities
Development of AI-based models to assess the systemic impacts of extreme weather events, from the local scale of the six Case Applications up to the continental scale, including future climate storylines exploring how different combinations of climatic and non-climatic drivers shape impacts under different scenarios

Modelling of tropical cyclones and coastal storms, with particular focus on the Caribbean Case Application

Contribution to the global mapping of climate hazards and exposure, with a focus on floods, wildfires and coastal storms

Application of the attribution Protocol to the impacts of the Case Application events, quantifying the contribution of both climatic and non-climatic drivers to observed and projected damages, including a real-time attribution test for an event occurring during the project

Finalisation and validation of the Protocol for Expanded Attribution and Forecast (KOUT2), one of the three key scientific outputs of CLOSER, together with the preparation of ethics reports on the use of artificial intelligence in the project

Expected results
A set of AI-based models and future climate storylines for assessing the systemic impacts of extreme weather events, from local to continental scale, capable of tracing how climate and non-climatic drivers combine to produce damages across agriculture, infrastructure, health and ecosystems

Attribution reports for each of the six Case Applications, quantifying the contribution of both climatic and non-climatic drivers — such as land-use change, water management and socio-economic conditions — to observed and projected disaster impacts, including a real-time attribution demonstration

The validated final Protocol for Expanded Attribution and Forecast (KOUT2), a practical and openly available methodology for scientists and practitioners worldwide to attribute the impacts of climate disasters to their multiple causes, across past, ongoing and future events

Partners
RCCC – Stichting International Red Cross Red Crescent Centre on Climate Change and Disaster Preparedness
UUP – Uppsala Universitet
CNRS-IPSL – Centre National de la Recherche Scientifique CNRS
CEA – Commissariat à l’Énergie Atomique et aux Énergies Alternatives
ENPC – École Nationale des Ponts et Chaussées
UNESCO-ICTP – United Nations Educational, Scientific and Cultural Organization
UCL – University College London
CAG – Climate Analytics gGmbH
CMCC – Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici
CRS – Stowarzyszenie Centrum Rozwiazan Systemowych
UBERN – Universität Bern
UC3M – Universidad Carlos III de Madrid
UEDIN – The University of Edinburgh
ULEIP – Universität Leipzig
IITR – Indian Institute of Technology Roorkee