CLINT – CLImate INTelligence: Extreme events detection, attribution and adaptation design using machine learning

Weather and climate extremes pose challenges for adaptation and mitigation policies as well as disaster risk management, emphasizing the value of Climate Services in supporting strategic decision-making. Today Climate Services can benefit from an unprecedented availability of data, in particular from the Copernicus Climate Change Service, and from recent advances in Artificial Intelligence (AI) to exploit the full potential of these data. The main objective of CLINT is the development of an AI framework composed of Machine Learning (ML) techniques and algorithms to process big climate datasets for improving Climate Science in the detection, causation and attribution of Extreme Events (EE), including tropical cyclones, heatwaves and warm nights, and extreme droughts, along with compound events and concurrent extremes. Specifically, the framework will support (1) the detection of spatial and temporal patterns, and evolutions of climatological fields associated with Extreme Events, (2) the validation of the physically based nature of causality discovered by ML algorithms, and (3) the attribution of past and future Extreme Events to emissions of greenhouse gases and other anthropogenic forcing. The framework will also cover the quantification of the Extreme Events impacts on a variety of socio-economic sectors under historical, forecasted and projected climate conditions by developing innovative and sectorial AI-enhanced Climate Services. These will be demonstrated across different spatial scales, from the pan European scale to support EU policies addressing the Water-Energy-Food Nexus to the local scale in three types of Climate Change Hotspots. Finally, these services will be operationalized into Web Processing Services, according to most advanced open data and software standards by Climate Services Information Systems, and into a Demonstrator to facilitate the uptake of project results by public and private entities for research and Climate Services development.

Duration
48 months from 01/07/2021 to 30/06/2025
Funded by
  • European Commission

Coordinating organization
  • POLIMI - Politecnico di Milano

CMCC Scientific Leader
CMCC Project manager
Division

General aims

General objectives

CLINT has the following five specific objectives and key deliverables:

Objective 1: A thorough analysis of how ML and AI can overcome the present limitations of C3S datasets for detection and attribution of EE under current and future climate.

Objective 2: To develop an AI-enhanced Climate Science to:

– support the detection of spatial and temporal patterns, and evolutions of climatological fields associated with EE;

– validate the physically based nature of causality discovered by ML;

– support the attribution of single EE and observed trends in EE to man-made climate change and improve the quantification of future changes in EE.

Objective 3: To build AI-enhanced CS at European continental scale, based on the improved characterization of EE impacts across the WEF Nexus for estimating the vulnerability of EU climate change related policies.

Objective 4: To demonstrate the potential of AI-enhanced CS in informing risk-aware decision-making and in supporting early warning systems at the local scale as key elements for adaptive capacity in different Climate Change Hotspots.

Objective 5: To put these services into operation via the Climate Services Information Systems supporting both the access to existing Copernicus climate data and the efficient processing of information through the CLINT AI-framework, as well as the development of CS prototypes and a demonstrator to be commercially exploited after the duration of the project.

CMCC role

CMCC leads the WP3 on Extreme Events detection.
CMCC is the deputy coordinator of CLINT.

Activities

CMCC will lead WP3 on Extreme Events detection, with a special focus on two tasks related to tropical cyclones and heatwaves in strong collaboration with WP2 devoted to the development of ML tools. CMCC will also participate to all of the other WPs, to support causation (WP4), attribution (WP5) and the preparation of Climate Services (WP6, WP7). Finally, CMCC will also contribute to WP8 in the design of Web Processing Services (WPS) and to dissemination activities within WP9. As deputy coordinator CMCC will support POLIMI for the whole project management.

Expected results

CMCC strategic exploitation interests within CLINT are:

  • Use the AI-Enhanced EE detection indices for future CMCC research on EE.
  • Propose the new AI-Enhanced EE indices to the Climate community (i.e. CMIP) as improved standard diagnostics.
  • Extend the CLINT collaboration between Climate and AI communities through shared academic programs.

Partners:

  • CMCC
  • HELMHOLTZ-ZENTRUM HEREON GMBH (HZG)
  • AGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS (CSIC)
  • SVERIGES METEOROLOGISKA OCH HYDROLOGISKA INSTITUT (SMHI)
  • HKV LIJN IN WATER BV (HKV)
  • E3-MODELLING AE (E3M)
  • THE CLIMATE DATA FACTORY (TCDF)
  • DEUTSCHES KLIMARECHENZENTRUM GMBH (DKRZ)
  • STICHTING IHE DELFT INSTITUTE FOR WATER EDUCATION (IHE)
  • EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS (ECMWF)
  • UNIVERSIDAD DE ALCALA (UAH)
  • JUSTUS-LIEBIG-UNIVERSITAET GIESSEN (JLU)
  • OPEN GEOSPATIAL CONSORTIOM EUROPE (OGC)
  • UNIVERSIDAD COMPLUTENSE DE MADRID (UCM)

 

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