ClimAtion

Climate-Smart Optimal Aircraft Operations for Next Generation Aviation

ClimAtion is a research project that aims to tackle one of the most urgent global challenges of our time: mitigating aviation’s contribution to climate change.

While CO₂ emissions have historically been the focus, non-CO₂ effects—particularly contrails and aviation-induced cloudiness—are increasingly recognized as significant drivers of global warming. The project’s primary motivation stems from the need to reduce overall greenhouse gas impacts in alignment with international targets, notably capping global temperature rise at 1.5°C–2.0°C above pre-industrial levels.

By bringing together expertise in artificial intelligence, optimization, and climate science, ClimAtion aspires to develop a new generation of neural architectures that incorporate theoretical insights from nonlinear programming (NLP) into deep learning. This innovation will enable improved data-driven approaches for assessing the non-CO₂ impacts of aviation, as well as for designing multi-scale “climate optimal” solutions at both the trajectory and network levels considering uncertainties.

Project blocks

BLOCK	LEADING UNIVERSITY	DESCRIPTION
B1: Aviation-Induced Climate Models	Universidad Carlos III de Madrid	AI-driven contrail detection from satellite imagery and ground-based cameras, as well as the development of contrail models based on advection-diffusion processes.
B2: Optimal Flight Planning	Universidad de Sevilla	Stochastic assessments of how different flight trajectories impact climate, and to designing robust flight-planning algorithms that minimize these effects.
B3: Reliable Tactical Aircraft Trajectory Optimization	Universidad Rey Juan Carlos	Data-driven surrogate modeling, active learning for reliability analyses, and sensitivity studies to refine trajectory optimization strategies.
B4: Network Scale	Coordinated by UC3M with participation from all subprojects	Network-wide optimization, large-scale simulations, the development of comprehensive indicators, and the creation of a climate-service framework to guide decision-making.

Through these coordinated efforts, ClimAtion seeks to deliver new scientific knowledge, algorithmic methodologies, and practical solutions. The project will produce open-access software libraries and publish in top-tier journals, thereby contributing to open-science principles. Additionally, ClimAtion features a dedicated visualization service to evaluate the climate impact of different air traffic management strategies and provide actionable insights for policymakers, regulators, and industry stakeholders.

Ultimately, by addressing both CO₂ and non-CO₂ contributions to aviation-induced warming, ClimAtion strives to facilitate more sustainable flight operations, supporting the EU’s objectives of reducing aviation’s climate footprint by 2030 and achieving carbon neutrality by 2050. This holistic approach—encompassing contrail modeling, advanced AI methods, and large-scale analyses—places ClimAtion at the forefront of European efforts to reconcile the continued growth of air travel with pressing environmental imperatives.

General structure of ClimAtion

Publications

• A multi-physics Eulerian framework for long-term contrail evolution. Amin Jafarimoghaddam, Manuel Soler. Atmoshperic Chemistry and Physics. EGUsphere [Under Review Preprint] https://doi.org/10.5194/egusphere-2025-4155, 2025.
• Robust Evaluation of Neural Networks Trained on the OpenContrails Dataset. Irene Ortiz, Javier García-Heras, Amin Jafarimoghaddam, Manuel Soler. IEEE Transactions on Geoscience and Remote Sensing, 2025.  DOI: 10.1109/ TGRS.2025.3629628