Periodic Reporting for period 1 - CLINT (CLImate INTelligence: Extreme events detection, attribution and adaptation design using machine learning)
Berichtszeitraum: 2021-07-01 bis 2022-12-31
Climate Services are an essential component of adaptation and mitigation strategies as well as disaster risk management because extreme events, including tropical cyclones, heatwaves and warm nights, extreme droughts, along with compound events and concurrent extremes, are expected to increase in both frequency and intensity in many regions of the world over the next decades.
The implementation of effective local and regional adaptation strategies in line with the Paris Agreement and Sustainable Development Goals (e.g. climate action, clean water and sanitation, sustainable cities and communities, life on land, affordable and clean energy) is however challenged by the fact that extreme events are expected to be regionally more complex than that expected from thermodynamic changes alone. On the other hand, climate services can benefit from an unprecedented availability of data, in particular from the Copernicus Climate Change Service, and recent advances in Artificial Intelligence and Machine Learning offer a unique opportunity to exploit the full potential of these data with the aim of providing easily accessible, timely, and de-cision-relevant information to policy makers and end-users.
The main objective of CLINT is the development of an Artificial Intelligence framework composed of Machine Learning techniques and algorithms to process big climate datasets for improving Climate Science in the detection, causation, and attribution of Extreme Events. The CLINT AI framework will also cover the quantification of the extreme impacts on a variety of socio-economic sectors under historical, forecasted, and projected climate conditions, and across different spatial scales (from European to local), ultimately developing innovative and sectorial AI-enhanced Climate Services. Finally, these services will be operationalised into Web Processing Services, according to the 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.
The implementation of effective local and regional adaptation strategies in line with the Paris Agreement and Sustainable Development Goals (e.g. climate action, clean water and sanitation, sustainable cities and communities, life on land, affordable and clean energy) is however challenged by the fact that extreme events are expected to be regionally more complex than that expected from thermodynamic changes alone. On the other hand, climate services can benefit from an unprecedented availability of data, in particular from the Copernicus Climate Change Service, and recent advances in Artificial Intelligence and Machine Learning offer a unique opportunity to exploit the full potential of these data with the aim of providing easily accessible, timely, and de-cision-relevant information to policy makers and end-users.
The main objective of CLINT is the development of an Artificial Intelligence framework composed of Machine Learning techniques and algorithms to process big climate datasets for improving Climate Science in the detection, causation, and attribution of Extreme Events. The CLINT AI framework will also cover the quantification of the extreme impacts on a variety of socio-economic sectors under historical, forecasted, and projected climate conditions, and across different spatial scales (from European to local), ultimately developing innovative and sectorial AI-enhanced Climate Services. Finally, these services will be operationalised into Web Processing Services, according to the 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.
In the first 18 months of the CLINT project, the activities have been characterised by the preliminary work necessary to harmonize the ambitious agenda of the project. This required the establishment of a common language among partners with different professional backgrounds, and efficient coordination to enable the smooth functioning of the project. The CLINT partners worked hard on their challenging tasks by exploring state-of-the-art literature and existing datasets, such as the ones offered by the Copernicus Climate Change Service, and by interacting with climate services end-users to understand strengths, weaknesses, opportunities and threats of existing products. In parallel, the development of Artificial Intelligence and Machine Learning algorithms is supporting the detection of unforeseen relationships between extreme events and large-scale climatological fields, the quantification of their causal interdependencies and their physical meaning, and the isolation of human fingerprints in the relevant processes associated to extreme events. Once consolidated, the results of these analyses will support the generation of sub-seasonal to seasonal forecasts of extreme events. Moreover, the first CLINT Artificial Intelligence prototype(s) of the enhanced Climate Services are being implemented (one is already operational and running, another one initiated, and other two are in planning).
As highlights, the following peer-reviewed articles on CLINT research advances have been already published in top-level journals:
• Ascenso G., Cavicchia L., Scoccimarro E., Castelletti A., Optimisation-based refinement of genesis indices for tropical cyclones. Environmental Research Communications, 2023. https://doi.org/10.1088/2515-7620/acb52a2023.
• Gómez-Orellana, A. M., D. Guijo, J. Pérez-Aracil, P. A. Gutierrez, S. Salcedo-Sanz, C. Hervás-Martínez. One month in advance prediction of air temperature from Reanalysis data with eXplainable Artificial Intelligence techniques. Atmospheric Research, 2023. https://doi.org/10.1016/j.atmosres.2023.106608.
• Peláez-Rodríguez, C., Pérez-Aracil, J., Fister, D., Prieto-Godino, L., Deo, R.C. Salcedo-Sanz, S. A hierarchical classification/regression algorithm for improving extreme wind speed events prediction. Renewable Energy 201, 157-178, 2022. https://doi.org/10.1016/j.renene.2022.11.042
As highlights, the following peer-reviewed articles on CLINT research advances have been already published in top-level journals:
• Ascenso G., Cavicchia L., Scoccimarro E., Castelletti A., Optimisation-based refinement of genesis indices for tropical cyclones. Environmental Research Communications, 2023. https://doi.org/10.1088/2515-7620/acb52a2023.
• Gómez-Orellana, A. M., D. Guijo, J. Pérez-Aracil, P. A. Gutierrez, S. Salcedo-Sanz, C. Hervás-Martínez. One month in advance prediction of air temperature from Reanalysis data with eXplainable Artificial Intelligence techniques. Atmospheric Research, 2023. https://doi.org/10.1016/j.atmosres.2023.106608.
• Peláez-Rodríguez, C., Pérez-Aracil, J., Fister, D., Prieto-Godino, L., Deo, R.C. Salcedo-Sanz, S. A hierarchical classification/regression algorithm for improving extreme wind speed events prediction. Renewable Energy 201, 157-178, 2022. https://doi.org/10.1016/j.renene.2022.11.042
CLINT is designed to develop an Artificial Intelligence framework for evolving Climate Science and Services by improving the understanding and predictability of extreme events and by quantifying their impacts on various targeted climate-related sectors under both historical and projected climate conditions and across different spatial scales, from the whole European to the local scale in different Climate Change Hotspots in the Netherlands deltas, Iberian Peninsula, Southern Africa, and the Italian Alps. Specifically, CLINT will (1) enhance the adaptive capacity in climate-sensitive sectors, from pan-European to local scale, by advancing the detection, attribution and quantification of future changes in extreme events based on a suite of machine learning techniques coupled with a physical understanding of the key processes; (2) reduce vulnerability to climate change, by developing prototypes of AI-enhanced CS to support EU policies, such as EU floods directive, the new Green Deal, the Climate Adaptation strategy, the Common Agricultural Policy; (3) enhance actions on climate change adaptation informed by continuous and consistent information on extreme events over different time horizons, ranging from sub-seasonal forecasts to climate projections, (4) advance the current scientific knowledge on detection, attribution, and causation of cli-mate extreme events by strengthening the link with the Copernicus climate-related services and fostering effective knowledge transfer between researchers/scientists and service providers; and finally (5) inform CS and decision-making by engaging with local users in the Climate Change Hotspots to put forward AI-enhanced modelling chains for local impact-based predictions and projections of Extreme Events.