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Published Date : 10/10/2025
AI4 Climate leverages advanced AI and machine learning techniques to enhance climate modeling, providing more accurate and locally relevant climate projections.
AI4 Climate leverages advanced AI and machine learning techniques to enhance climate modeling, providing more accurate and locally relevant climate projections.
AI4 Climate is a pioneering initiative that explores and applies cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) techniques to advance climate science. Funded by the UK Government’s Department for Science, Innovation and Technology (DSIT) through the International Science Partnerships Fund (ISPF), AI4 Climate operates within the Met Office’s National Capability AI (NCAI) Programme.
The primary goal of AI4 Climate is to integrate AI/ML methods with traditional physics-based approaches to enhance climate modeling and projection. The programme aims to deliver more accurate, locally-relevant climate predictions and projections, faster and more cost-effectively, to support timely decision-making. It also seeks to support rapid responses to climate-related questions and enable global collaboration through open science and shared innovation.
Research Areas
AI Climate Downscaling
To support faster and more locally relevant climate projections, this work package applies AI techniques to emulate high-resolution regional climate simulations. These simulations are crucial for understanding climate risks and informing adaptation strategies but are often limited by computational costs. AI4 Climate addresses this by learning relationships between global and local climate patterns, enabling broader and more efficient downscaling. The team is developing benchmark datasets and evaluation metrics for the UK and international regions, alongside researching an open-access AI model adaptable to diverse climates, advancing the goal of scalable, cost-effective climate information.
Data-Driven Climate Modelling
In pursuit of rapid and flexible climate insights, this work package explores models trained directly on observational and simulation data. These data-driven approaches offer low-cost alternatives to traditional climate models, especially valuable in resource-constrained settings. Applications include seasonal-to-decadal prediction and emulation of Earth system components. The work includes exploring the development of deployment frameworks, adapting existing models for climate applications, and co-developing capabilities with international partners, supporting AI4 Climate’s mission to accelerate climate science through innovative AI/ML integration.
Hybrid Physical Model / AI Approach
This work package exemplifies the fusion of AI with physics-based modelling, replacing uncertain components of traditional models with ML. By training AI systems on high-resolution simulations, the team aims to develop hybrid models that retain trusted physical dynamics while improving accuracy and efficiency. These models feedback within the simulation itself, offering a transformative approach to climate modelling. The work supports AI4 Climate’s vision by enhancing model fidelity and enabling faster responses to climate questions, while building capacity in partner institutions.
Dataset Creation and Curation
Central to enabling AI/ML innovation is the availability of high-quality training data. This work package delivers curated, sustainable datasets for training, testing, and validating ML-based climate models. It includes global 10km Numerical Weather Prediction forecasts, ensemble datasets, and tools for cataloguing and code refactoring. These resources underpin multiple AI4 Climate objectives, including downscaling, hybrid modelling, and urban-scale prediction, ensuring that data infrastructure supports scalable and reproducible climate science.
K-Scale Simulations for AI Training
This work, led by the University of Leeds, generates kilometre-scale simulations over large domains to improve the representation of fine-scale climate processes. Using advanced modelling frameworks like the Unified Model and LFRic, the team produces high-resolution datasets that serve as training foundations for AI models. The simulations support hybrid modelling strategies and contribute to international collaborations, reinforcing AI4 Climate’s aim to accelerate predictive capability and reduce long-term computational costs.
Urban Scale Modelling
To address climate challenges in cities, this work package develops ML-based downscaling techniques that translate coarse-resolution model outputs into detailed urban-scale climate information. The team is building pipelines for prediction and evaluation, leveraging new observational networks and collaborating with partners in cities like London, Paris, Delhi, and Singapore. The project also aims to deliver a common software framework and benchmarking tools, with a focus on spatial transferability and global applicability. This work directly supports AI4 Climate’s goals of rapid, locally relevant projections and international collaboration, particularly in Official Development Assistance (ODA) regions.
Technical Development of Evaluation Tools
To ensure trust and transparency in AI-driven climate science, this work package modernizes legacy workflows into a unified Climate Model Evaluation Workflow (CMEW). Built around the Earth System Model Evaluation Tool (ESMValTool), the framework supports diagnostics tailored to emerging ML models and integrates third-party observations via a generic Application Programming Interface (API). By delivering open-source tools and scalable workflows, the project empowers scientists to assess model performance and internal consistency, advancing AI4 Climate’s commitment to open science and responsible innovation.
Evaluation Tools
In collaboration with the University of Reading, this work develops interoperable tools to evaluate both physics-based and ML climate models. The focus is on building portable Python-based workflows and extending ESMValTool to support new metrics and data formats. These tools help operational teams and researchers assess model accuracy and coherence, supporting AI4 Climate’s objectives of reproducibility, innovation, and global impact.
Global Impact
Aligned with ISPF objectives, AI4 Climate:
- Supports international partnerships by co-developing AI tools with global research institutions and ODA partners
- Advances sustainable development through accessible, low-cost climate modelling tools
- Enables transformative technology by applying AI/ML to climate science
- Strengthens UK leadership in science, technology, and innovation
- Promotes open science and responsible data sharing
- Contributes to a resilient planet by improving understanding of extreme weather and climate risks
- Builds tomorrow’s talent by developing AI/ML skills across the UK and partner countries
Partners
AI4 Climate collaborates with institutions including:
- University of Leeds
- University of Reading
- University of Bristol
- Centre National de Recherches Météorologiques (CNRM) France
- University of the Witwatersrand, South Africa
- The Allen Institute, USA
Frequently Asked Questions (FAQS):
Frequently Asked Questions (FAQS):
Q: What is AI4 Climate?
A: AI4 Climate is an initiative that applies advanced AI and machine learning techniques to enhance climate science, aiming to deliver more accurate and locally relevant climate projections.
Q: Who funds AI4 Climate?
A: AI4 Climate is funded by the UK Government’s Department for Science, Innovation and Technology (DSIT) through the International Science Partnerships Fund (ISPF).
Q: What are the main goals of AI4 Climate?
A: The main goals of AI4 Climate include delivering more accurate and locally relevant climate predictions, supporting rapid responses to climate-related questions, and enabling global collaboration through open science and shared innovation.
Q: What are some key research areas of AI4 Climate?
A: Key research areas include AI climate downscaling, data-driven climate modelling, hybrid physical model/AI approach, dataset creation and curation, K-scale simulations for AI training, urban scale modelling, and technical development of evaluation tools.
Q: How does AI4 Climate contribute to global impact?
A: AI4 Climate supports international partnerships, advances sustainable development, enables transformative technology, strengthens UK leadership in science, promotes open science, and contributes to a resilient planet.
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