BlueAdapt brings together an interdisciplinary team of biologists, climate scientists, economists, epidemiologists and public health experts to investigate and quantify the future health risks associated with selected coastal pathogens. 

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This contract presents a continuation, a temporal extension, and an enhancement of the current C3S Windstorm Service. Leveraging the current Service structure, contractorss will temporally extend the detection and tracking of Pan-European potentially harmful windstorms associated with extratropical cyclones along the whole available period provided by the ECMWF ERA5 reanalysis dataset (1940-present).

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This contract is the second phase of Quality Assurance for Datasets in the Climate Data Store and cover the last 2 years of the work plan. The activities cover the operational phase of the Evaluation and Quality Control (EQC) framework using the new Content Integration Manager (CIM) as a tool for the creation, management and publication of EQC content.

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The IRIDE program is an innovative project undertaken by the Italian government in collaboration with the European Space Agency (ESA) to leverage resources from the National Recovery and Resilience Plan (PNRR). Phase 2 of IRIDE Lot 1 started in October 2024 following the successful implementation of the IRIDE Precursor Phase. The main purpose of the project is to deliver an operational portfolio of geospatial services and develop digital tools for End and Pilot users within the Thematic Services S1-Coastal and Marine Monitoring, S2- Air Quality, S5- Hydro-Meteorological-Climate, S6- WaterManagement. The operational services allow mapping, monitoring and forecasting of various characteristics of coastal areas (including geomorphological, land use, flooding, habitats etc.) as well as operational model validation, operational air quality monitoring and forecast, pollutant emissions monitoring and assessment, re-analysis of air quality at national scale, hydro-meterological mapping and monitoring atmospheric structure, greenhouse gases and others essential climate variables monitoring, lightening monitoring, flood forecasting and sediment management, etc.

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Recognizing the central role played by snow, ice and permafrost in the global climate system, the LIQUIDICE project joins expert cryospheric observers and modelers to: i) comprehensively re-assess the past and future century-plus of climate-induced high impact changes to the Greenland ice sheet and climate vulnerable locations across the Alps, Norway, High Mountain Asia (HMA) and Svalbard, including permafrost areas and their ecosystems; ii) develop new, expanded and harmonized data from satellite Earth Observation (EO) and ground stations; iii) use these data to improve and test a hierarchy of ice sheet and glacier models with Earth System Models (ESMs); iv) through these steps, yield new process understanding, and ultimately v) inform water resource, hydropower, and socio-economic strategies through clear and transparent communication of results and uncertainties. The project’s strengths lie in new multidisciplinary collaborations across 18 research institutions, from eight European countries (Poland, Italy, Denmark, Germany, Spain, Sweden, Norway, United Kingdom) and India, encompassing expertise in field observations, satellite EO techniques, ESM development and application, and socio-economic analysis. Key deliverables include a) FAIR-principled new multi-decade data catalogues of multi-regional snow water equivalent and a 44-year EO-derived albedo record; b) assessments of impact of model resolution and degree of coupling on results; c) refined past and future glacier, ice cap and Greenland ice sheet freshwater fluxes to oceans and global sea level rise with indirect constraint on Antarctica; d) new hydrological simulations for HMA; e) a new framework for a Water Discharge Impact Assessments; f) socio-economic integrated risk and adaptation assessments;

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Natural hazards, such as extreme weather events, are exacerbated by climate change. As a result, emergency responses are becoming more protracted, expensive, frequent, and stretching limited available resources. This is especially apparent in rapidly warming regions. MedEWSa addresses these challenges by providing novel solutions to ensure timely, precise, and actionable impact and finance forecasting, and early warning systems (EWS) that support the rapid deployment of first responders to vulnerable areas. 

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ObsSea4Clim brings together key European actors within ocean observing science, climate assessment, Earth System modelling, data sharing and standards, with users of oceanographic products and services to deliver an improved observation framework based on Essential Ocean & Climate Variables (EOV/ECVs).

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The SCEWERO project will be developed by a consortium of 5 organizations from 4 countries: Babeș-Bolyai University (UBB), a research institution located in Romania as a widening country and acting as coordinator, three top-class leading partners, Fondazione Centro Euro-Mediterraneo Sui Cambiamenti Climatici (IT), Universiteit Antwerpen (BE), and Justus-Liebig-Universität Giessen (DE), and a private partner (SME), Indeco Soft (RO) aiming to improve the excellence capacity in research, to raise the scientific reputation, research profile and attractiveness through networking, and strengthening research management capacity and administrative skills of the UBB team.

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The project will build the WeatherGenerator – the world’s best generative Foundation Model of the Earth system – that will serve as a new Digital Twin for Destination Earth. The WeatherGenerator will be based on representation learning and create a general and versatile tool that models the dynamics of the Earth system based on a large variety of Earth system data. The WeatherGenerator will be task-independent and will improve results for a wide range of machine learning applications when compared to task specific machine learning tools. It will also be more resilient for climate applications when the underlying data distributions are changing, and it will lead to a significant reduction in computational costs and faster turnaround times. To achieve this, the project will: (1) Collect and use the most important datasets of Earth system science including data from Digital Twins of Destination Earth, selected observations, analysis and reanalysis datasets, and output of conventional Earth system models. (2) Build the WeatherGenerator as a novel representation learning- based machine learning tool that exploits the full potential of Europe’s largest supercomputers. (3) Engage with the wider community via services and apply the WeatherGenerator for 22 selected applications that can be integrated into the Destination Earth framework. The applications include global and local predictions, local downscaling, data assimilation, model post-processing, and impact applications in the domains of renewable energy, water, health and food. The project consortium that will build the WeatherGenerator consists of experts in machine learning, supercomputing and Earth system sciences, and includes