Related Content: Climate Impact

CLINT – Climate Intelligence

The main objective of CLINT is the development of an Artificial Intelligence framework (Climate Intelligence) 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, including tropical cyclones, heatwaves and warm nights, and extreme droughts, along with compound events and concurrent extremes (see Box 1). The CLINT AI framework will also cover the quantification of the EE impacts on a variety of socio-economic sectors under historical, forecasted and projected climate conditions, and across different spatial scales, from the whole European to the local scale, ultimately developing innovative and sectorial AI-enhanced Climate Services. 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.

Climate Resilience Pilot

The objective of this pilot is to accelerate our collective readiness for accessing, fusing and analyzing data from the climate change modeling community with earth observation and social science data to contribute to the global push for achieving climate resilience. Our goal is to develop a reliable foundation for decision ready data services for climate change adaptation actions. For this purpose, OGC members engaged in this pilot will develop a series of demonstrators that show the integration and combined exploitation of climate data, EO data, and data from the social sciences.

CLIMOS

CLIMOS aims to assist mitigation of climate- and climate change-induced emergence, transmission and spread of vector-borne and zoonotic pathogens based on Eco-health and One Health approaches. This will be achieved by quantifying climate and environmental-related drivers of sand fly vector populations and the sand fly-borne diseases (SFBDs) across Europe. The project will provide an Early Warning System (EWS) and decision support frameworks for more accurate climate and health modelling, prognosis of infection risk and range expansions, and adaptation options. Socio-economic analysis and risk assessments will inform decision
support providing social and cost-benefit evaluations. Towards these goals, an open access interactive CLIMOS online platform will be developed containing data on vector and SFBD species geography and up-to-date monitoring, climate, environment, and mathematical algorithms. The accompanying educational platform will enable evidence-based mitigation decision-making by social, environmental and financial stakeholders, public bodies and policy makers.