Description
ECMWF, as one of the Entrusted Entities for the Destination Earth (DestinE) initiative, entered into contracts for the provision of services aimed at implementing DestinE ML-based demonstrators. Contracts are expected to implement ML Demonstrators on different topics, addressing needs in different domains. To this end, contracts were planned to be placed in different Lots: Lot 1: Supporting water resilience in Europe; Lot 2: Demonstrating DestinE in the domain of food security; and Lot 3: Combining heterogeneous climate projections for climate adaptation. As result of this ITT, ECMWF awarded two contracts under lot 1 and two contracts under lot 3 to the two highest ranking tenderers of each due to exceptional value, and no contracts under Lot 3 due to unsuitable tenders.
The objective of the first contract awarded under Lot 1: DestinE ML Demonstrator for Water Resilience combines Large Language Models (LLMs) and Graph Neural Networks (GNNs) with physics-based hydrological and water quality models. This hybrid system could enable users to interact with complex simulations using natural language, configure “what-if” scenarios, and receive tailored, interpretable outputs-instantly and intuitively. The "what-if" scenarios-such as changes in land use, nutrient loads, pollutant emissions, or infrastructure-will be translated into adjustments of model schematizations, model parameters, nutrient loads, and pollutant emissions. These adjustments will be used to simulate the effects on water availability, water quality (e.g., nitrogen, phosphorus, PFAS), water temperature, and/or sediment dynamics. The simulations will utilize multi-model climate change scenarios available from DestinE, enabling adaptive water resilience planning. The DT climate storyline simulations of present and future climate will be used to develop storylines to show the need and options for better water resilience. The objective of the second contract awarded under Lot 1: The contract covers the development and demonstration of an ML application that provides probabilistic forecasts of freshwater temperature extremes in the Scheldt river (Belgium) under climate change, with a focus on supporting climate-resilient energy systems and industrial cooling operations. It exploits Climate DT simulations and implements a “storylines-framework” to present plausible future heatwave scenarios and their impact on freshwater temperatures.; Tenders under this Lot were asked to suggest how to target the implementation of a Demonstrator that supports a clearly defined stakeholder group in their tasks linked to agricultural market information systems, food security mechanisms, or resilience planning. However, Lot 2 produced no satisfactory results.; The first contract under Lot 3 focuses on combining information from the high-resolution climate simulations in the DestinE Climate DT with global CMIP6 and regional EURO-CORDEX climate projections, using artificial intelligence (AI). The Climate DT provides globally consistent, high-resolution data that explicitly resolves key climate processes for three models, reducing model uncertainty and bias, while CMIP and EURO-CORDEX ensembles suffer from biases and coarse resolution but can contribute long, multi-model time series that quantify structural uncertainty for climate services. Using the FRAIMHiRes AI downscaling method, trained on Climate DT simulations, the contract will try to transfer the potential added value of high-resolution DT data to (potentially longer) coarser-resolution DT ensembles by creating a “ClimateDT FineScaler”. The resulting downscaled ensemble will be evaluated for its statistical robustness, added value over original projections and CMIP/CORDEX simulations, and suitability for climate impact and adaptation studies. The second contract under Lot 3 develops and demonstrates a machine-learning application for high-resolution regional dynamical downscaling of global and regional climate scenarios (using the Anemoi framework), integrated within -and trained on data from- the Destination Earth Climate DT. By combining Climate DT simulations with CMIP6, EURO-CORDEX and national climate scenarios, the contract aims to increase the size of European climate ensembles, improve uncertainty quantification, and deliver high-resolution climate information for adaptation planning across Europe.
Source officielle
Source : TED - Tenders Electronic Daily (Publications Office of the EU)
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Attributions récentes
- Services de recherche et développement et services de conseil connexes - C3S2_520_bis Évaluation et contrôle qualité des jeux de données du Climate Data Store
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- Valeur estimée: Fixé à 1 000 000 €
- Date d'attribution: Attribué le 19 janvier 2026
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- Valeur estimée: Fixé à 1 000 000 €
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