The rapid growth of weather and climate datasets is increasing the pressure on data centres and hinders scientific analysis and data distribution. For example, kilometre-scale weather and climate models can generate 20 gigabytes of data per second when run operationally, making it generally infeasible to store all output unless advanced compression is applied. 

To address this challenge, novel lossy compression techniques, including recently so-called neural compressors which learn smaller representations of climate data, have been proposed with compression factors beyond 100x. However, if applied without care, lossy compression can remove valuable information from a dataset for often unknown downstream applications. It is therefore important to validate that the compression process does not alter scientific conclusions drawn from the data. Whether the compression error is tolerable is often easier to assess for domain experts and rarely well defined. 

Here, we address this challenge by presenting a benchmark suite for lossy compression of climate data (atmosphere, ocean, and land). We are defining data sets that can be used to train neural compressors as well as corresponding evaluation methods. Compressors have to pass a set of tests for each data set while compressing into the smallest file size possible at a reasonable (de)compression speed. To ensure evaluation on a diverse set of inputs, the benchmark covers climate variables following various statistical distributions at medium to very high resolution in time (hourly to yearly) and space (~1 km to 150 km). Evaluation tests are for single and multi-variable compression of gridded data with stable or changing statistics, random data access or large archives, in medium to very large datasets.

To provide references towards what compression levels can be achieved with current state of the art lossy compressors, we also evaluate a set of baseline compressors (SZ3, ZFP, Real Information) on our benchmark tasks. The benchmark is a quality check for new compressors towards a standardization of climate data compression, aiming to make compressors with high compression factors safe to use and widely supported.

How to cite: Reichelt, T., Tyree, J., Kloewer, M., Dueben, P., Lawrence, B., Hammerling, D., Baker, A., Faghih-Naini, S., and Stier, P.: ClimateBenchPress: A Benchmark for Compression of Climate Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15912, https://doi.org/10.5194/egusphere-egu25-15912, 2025.

Urban heat islands (UHI) exacerbate health and environmental challenges, disproportionately affecting vulnerable populations. This study identifies high-risk areas for UHI effects in the Americas, including their metropolitan regions, using a suitability analysis model. It highlights the interplay between urban expansion, social vulnerability, and climate stress, emphasizing the urgency of addressing these issues in rapidly urbanizing contexts.

High-resolution satellite imagery and geospatial data were used to build the model. Key criteria included population density (dasymetric layers from WorldPop), relative wealth index, land surface temperature (LST) from MODIS, land cover from MODIS, PM2.5 and NO2 concentrations (Sentinel-5P), and road network layers derived for the analysis. Each criterion was reclassified, transformed to a common scale, and weighted equally to ensure consistency and comparability.

The suitability index was generated using raster algebra (weighted sum), producing a continuous map where higher values indicate greater susceptibility to heat stress and lower socioeconomic status. The analysis revealed spatial patterns that highlight areas with high potential impacts due to UHI characteristics.

The suitability index serves as a tool for identifying priority areas for targeted interventions and climate mitigation actions. This integrative approach highlights the need for sustainable urban development policies that reduce socio-environmental disparities and promote resilience in vulnerable communities

How to cite: Rocha, T.: Identifying Urban Heat Island Risk Areas with Vulnerable Populations: A Suitability Analysis Approach to support Health Interventions in the Americas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-742, https://doi.org/10.5194/egusphere-egu25-742, 2025.

With the increasing availability and demand of remote sensing data from Earth observation satellites, the accuracy of weather prediction models can be improved substantially. Satellite products, such as Land-Surface Temperature (LST), however, suffer from missing data, either caused by clouds that cover the ground, by missing spatial coverage of the mission, or by outages of the sensors. Such spatial data gaps in LST products impose strict limitations when aiming to process the data further with, e.g., numerical weather prediction models assuming spatial continuity with gapless input data. We therefore propose a gap-filling algorithm based on a masked autoencoder that only receives a small percentage from a 32x32 LST snapshot and learns to reconstruct the missing patches. We use the spatial domain defined by the Land Atmosphere Feedback Initiative (LAFI) over central Europe and operate on geostationary LST data from the Copernicus Global Land Service in June 2023 at 5 km resolution. Our approach indicates considerable potential when filling spatial gaps in LST products, however, we emphasize one critical aspect. The LST estimates below clouds cannot be expected to be realistic and would require a sophisticated atmospheric correction. To mitigate this limitation, we aim to incorporate microwave data in future that penetrates clouds and therefore could help to estimate LST below clouds. In its current formulation, our algorithm can be used to fill gaps in LST products as if there were no clouds. We will show the potential and limitations of the autoencoder-based gap-filling algorithm for several showcases across Europe. 

How to cite: Karlbauer, M., Hellwig, F. M., Jagdhuber, T., and Butz, M. V.: Spatial Gap Filling in a Geostationary Land-Surface Temperature Product with a Masked Autoencoder, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17080, https://doi.org/10.5194/egusphere-egu25-17080, 2025.

Systematic model errors significantly limit the predictability horizon and practical utility of the current state-of-the-art forecasting systems. Even though accounting for these systematic model errors is increasingly viewed as a fundamental challenge in the field of numerical weather prediction, estimation and correction of the predictable component of the model error has received relatively little attention. Modern implementations of weak-constraint 4D-Var are an exception here and a promising avenue within the variational data assimilation framework, showing encouraging results. Weak-constraint 4D-Var can be viewed as an online hybrid data assimilation and machine learning approach which gradually learns about model errors from partial and imperfect observations, allowing to improve the state estimation. We propose a natural extension of this approach by applying deep learning techniques to further develop the concept of online model error estimation and correction.

In this talk, we will present recent progress in developing a hybrid model for the ECMWF Integrated Forecasting System (IFS). This system augments the state-of-the-art physics-based model with a statistical model implemented via a neural network, providing flow dependent model error corrections. While the statistical model can be pre-trained offline, we demonstrate that by extending the 4D-Var control vector to include the parameters of the neural network, i.e. the model of model error, we can further improve its predictive capability. We will discuss the impact of applying the flow dependent model error corrections in the medium range forecasts on the forecast quality.

How to cite: Chrust, M., Farchi, A., Bonavita, M., Bocquet, M., and Laloyaux, P.: Development of an offline and online hybrid model for the Integrated Forecasting System, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11988, https://doi.org/10.5194/egusphere-egu25-11988, 2025.

The Children’s Climate Risk Index (CCRI) was first released in 2021, providing a comprehensive, global view of children’s exposure and vulnerability to the impacts of climate change. The CCRI is a composite index that aims to rank countries where children are exposed to climate and environmental hazards. The CCRI 2.0 builds on the previous index by integrating two pillars; Pillar 1 focusing on climate hazards and Pillar 2 on inherent vulnerabilities to WASH, health, education and other relevant dimensions. 

We highlight our contributions to CCRI 2.0, using a cluster methodology for quantifying children’s exposure to climate risks including riverine and coastal flooding, tropical storms, heatwaves, and drought. Using unsupervised learning, we allow for a data-driven approach to provide an interpretation of the ranking of children’s exposure to climate risks on a global scale between countries, as well as at the sub-national and local levels. It complements the previous method of constructing the synthesized index, which involved calculating the simple average of multiple indicators. We further discuss our techniques in tackling the challenges of multisource data processing, analysis, and visualization of geospatial data for user insight.

How to cite: Kim, D. and Doerksen, K.: Quantifying Children’s Exposure to Climate Risks using Unsupervised Learning with Multi-Source Geospatial Datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17875, https://doi.org/10.5194/egusphere-egu25-17875, 2025.

Machine learning models have emerged as powerful tools for simulating Earth system processes. Following their successful application in capturing atmospheric evolution for medium-range weather forecasts, attention has increasingly shifted towards other components of the Earth system, such as the marine and land environments. This interest is further driven by the potential to enhance forecasting capabilities beyond the medium range. Machine learning frameworks offer remarkable flexibility in integrating these model components to achieve a coherent Earth system representation. At one end of the spectrum, model components can be trained jointly within a unified framework optimised using a shared loss function. At the other end, components may be developed independently and coupled by exchanging physically relevant information at multiple interfaces, mirroring the traditional coupling strategies employed in numerical models. In this presentation, we will examine the advantages and challenges of these approaches, with a particular emphasis on coupling the atmospheric, land, and marine components within the deterministic AIFS model, the machine learning-based forecast system developed at ECMWF. Furthermore, we will compare the coupling strategies of data-driven models with those of traditional numerical models, highlighting their strengths and limitations.

How to cite: Zampieri, L., Cook, H., Furner, R., Hahner, S., Pinault, F., Raoult, B., Raoult, N., Santa Cruz, M., and Chantry, M.: Coupling approaches for data-driven Earth system models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4499, https://doi.org/10.5194/egusphere-egu25-4499, 2025.

How to cite: Purohit, K., Sinha, M., Panda, A., Banerjee, S., and S Nanjundiah, R.: Enhancing the Skill of Medium Range Forecasts with a Machine Learning Based Multi-Model Super-Ensemble (MMSE), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5909, https://doi.org/10.5194/egusphere-egu25-5909, 2025.

Artificial Intelligence-based Numerical Weather Prediction (AI-NWP) models have recently emerged as powerful tools for weather forecasting, offering computational efficiency and high accuracy. This study explores the extreme weather events simulated by the Artificial Intelligence Forecasting System (AIFS), initialised with conditions derived from kilometer-scale storyline experiments using the IFS-FESOM model ─ where the atmospheric circulation is constrained to observations. We present two case studies: the 2023 South Asian humid heatwave and the 2024 Storm Boris. These two events are reproduced in the present climate, but also simulated if they were to unfold in pre-industrial and +2K future climates, effectively creating AI-driven storylines. The methodology we employ offers a complementary framework, where the use of AI-driven ensembles provides a scalable and rapid way to assess the potential uncertainty and variability associated with such events, by enabling us to explore a broader range of plausible outcomes at very low computational costs. By combining the strengths of physics-based modelling with the efficiency and flexibility of AI-driven simulations, this dual approach offers a pathway to operationalise ensemble-based extreme weather storylines.

How to cite: John, A., Rackow, T., Koldunov, N., Beyer, S., Sanchez Benitez, A., Gößling, H., Athanase, M., and Jung, T.: Exploring AI-Driven Event-based Storylines, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8169, https://doi.org/10.5194/egusphere-egu25-8169, 2025.

Numerical Weather Prediction (NWP) models, which integrate physical equations forward in time, are the traditional tools for simulating atmospheric processes and forecasting weather in modern meteorology. With recent advancements in deep learning, Neural Weather Models (NeWMs) have emerged as competent medium-range NWP emulators with reported performances that compare favorably to state-of-the-art NWP models. However, they are commonly trained on reanalysis with limited spatial resolution (e.g., 0.25° horizontal grid spacing) and thus smooth out key features associated with a number of weather phenomena. For example, tropical cyclones—among the most impactful weather events due to their devastating effects on human activities—are challenging to forecast, as extrema like wind gusts, which serve as proxies for tropical cyclone intensity, are smoothed in deterministic forecasts at 0.25° resolution. To address this, we use our best global observational estimate of wind gusts and minimum sea level pressure to train models that post-process NeWM outputs and enable accurate and reliable forecasts of TC intensity. We present a tracking-independent post-processing algorithm and show that even naïve, linear models extract useful information from NeWM model outputs beyond what is present in the initial conditions used to roll out NeWM predictions. We explore how the NeWM’s spatial context may further improve the forecast through masking and convolutional architectures. Our post-processing framework thus presents a step towards democratization of tropical cyclone intensity forecasting, given the reduction in computational requirements for producing global weather forecasts with NeWMs compared to traditional NWP approaches and the algorithmic simplicity of the tracking-independent approach.

How to cite: Gomez, M., Beucler, T., Berne, A., and Poulain--Auzéau, L.: Post-Processing Neural Weather Model Outputs for Tropical Cyclone Intensity Forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15669, https://doi.org/10.5194/egusphere-egu25-15669, 2025.

Sampling from climate models to generate ensembles of predictions is computationally expensive (Hawkins et al., 2015). Climate model ensembles are used to understand probabilities of climatic events and identify internal variability in climate models. In the short term, model uncertainty and inter-annual variability dominate uncertainty in climate predictions (Smith et al., 2019). A typical approach to address these uncertainties is to use large ensembles of non-learned, physical numerical global circulation models (GCM) (Eade et al., 2014). These ensembles allow for statistical analysis of distributions and determination of internal variability in the climate model.

Our approach demonstrates that we can efficiently learn to emulate a GCM. We use ensembles generated by the IPSL submission to the Decadal Climate Prediction Project (DCPP). The dataset ranges from 1960-2016 and produces 10-member, 10-year forecast ensembles for each year. On this dataset, we train a modified version of ArchesWeatherGen, a Swin Transformer based on PanguWeather that can be used in a generative way using flow matching (Couairon et al. 2024). The model was modified to predict additional climatic variables (e.g. air temperature, specific humidity, ocean potential temperature at depth, sea surface temperature, sea level pressure) at a monthly temporal resolution. Once trained, the model probabilistically generates ensemble members rapidly which can be auto-regressively rolled out. We show that they are physically reliable via evaluation methods that assess physical processes derived from the variables represented in the machine learning model, such as by evaluating it on El Niño/La Niña events. This model demonstrates that machine learning can enhance climate models by expanding ensemble sizes to improve our understanding of climatic processes. We aim to output physically realizable month-to-month trajectories to estimate future climate and its uncertainties across various domains, including land, ocean, and atmospheric processes.

Couairon, G., Singh, R., Charantonis, A., Lessig, C., & Monteleoni, C. (2024). ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting. arXiv preprint arXiv:2412.12971.

Eade, Rosie, Doug Smith, Adam Scaife, Emily Wallace, Nick Dunstone, Leon Hermanson, et Niall Robinson. « Do Seasonal-to-Decadal Climate Predictions Underestimate the Predictability of the Real World? » Geophysical Research Letters 41, no 15 (2014): 5620‑28. https://doi.org/10.1002/2014GL061146.

Hawkins, Ed, Robin S. Smith, Jonathan M. Gregory, et David A. Stainforth. « Irreducible Uncertainty in Near-Term Climate Projections ». Climate Dynamics 46, no 11 (1 juin 2016): 3807‑19. https://doi.org/10.1007/s00382-015-2806-8.

Smith, D. M., R. Eade, A. A. Scaife, L.-P. Caron, G. Danabasoglu, T. M. DelSole, T. Delworth, et al. « Robust Skill of Decadal Climate Predictions ». Npj Climate and Atmospheric Science 2, no 1 (17 mai 2019): 1‑10. https://doi.org/10.1038/s41612-019-0071-y.

How to cite: Clyne, G., Couairon, G., Mignot, J., Gastineau, G., Charantonis, A., and Monteleoni, C.: ArchesClimate: Ensemble Generation for Decadal Prediction using Flow Matching, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18447, https://doi.org/10.5194/egusphere-egu25-18447, 2025.

High-resolution machine learning faces the challenge of balancing local computation with large physical context windows. GPU memory limitations and the slow training process when distributing the model across multiple GPUs further complicate this task. 

We present a transformer model for high-resolution climate-related tasks that uses neural operators within a multi-grid architecture. This approach allows resolution independence, large physical context windows, and the handling of discontinuities such as coastlines.

The model is spatially flexible, supporting both regional and global training schemes. It is also independent of the number of input variables, allowing training to be scaled to large numbers of input variables.

We demonstrate the ability of the model to scale, both spatially and in terms of variables. The model forms the foundation of an approach to learn from the rich and diverse climate data available, enabling high-resolution downscaling, infilling, and predictions.

How to cite: Witte, M., Meuer, J., and Christopher, K.: A Spatial Multi-Grid Neural Operator-Transformer Mdoel for High-Resolution Climate Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19942, https://doi.org/10.5194/egusphere-egu25-19942, 2025.

How to cite: Walt, E., Bruinsma, W., Schmeits, M., Gavves, E., and Coumou, D.:  Xaurora: Advancing subseasonal-to-seasonal forecasting by fine-tuning foundation weather models with spectral consistency , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9956, https://doi.org/10.5194/egusphere-egu25-9956, 2025.

The Climate Change Adaptation Digital Twin (Climate DT) is part of the Destination Earth (DestinE) initiative, developing Digital Twins of Earth to increase resilience against environmental changes. More specifically, Climate DT provides capabilities supporting climate change adaptation at regional and national levels at multi-decadal time scales. We present here an overview of Climate DT, highlighting the added value for the users and discussing the transition of the system towards the operations.  

The development of Climate DT has started in Phase 1 of DestinE, during which the first prototype of the new climate information system has been developed. A key innovation of Phase 1 was the introduction of a generic state vector (GSV), which is evolved by the Earth system models (ESMs) and streamed to applications from climate adaptation impact sectors.  This has created a basis for a pioneering climate information system that enables (i) provision of global climate information at an unprecedented granularity, (ii) scaling the system across a number applications that have access to all the data they need, (iii) user-centric approach with new ways of co-design and opportunities for enhancing interactivity. In Phase 2, which started in May 2024, our focus is on operationalizing Climate DT to deliver high-quality climate and impact-sector information regularly while incorporating new interactive features.

The operational model of the Climate DT is built around three storm- and eddy resolving ESMs; ICON, IFS-NEMO and IFS-FESOM. The operational framework utilizes a DevOps-like cycle, including three set-ups: d-suite for development, e-suite for testing the operational set-up and o-suite for operating the system. The o-suite simulations will provide data covering both past (1990-2020) and future periods (2020-2050) with a 5 km global grid. Additionally, capabilities for special simulations are developed, including story-line simulations for future periods of extremes as well as what-if scenario simulations enabling a new level of interactivity.

The added value of Climate DT to users is demonstrated through four impact sector applications. These applications operate on the streamed GSV as part of the operational framework, and they are improved in co-design with key users. The impact sector applications cover societally relevant climate change adaptation domains, including wind energy management, disaster risk management (with regards to wildfires and floods), as well as agriculture and water management. Climate DT output, including high-resolution climate simulations, storyline simulations, user-relevant indicators and impact assessments are made available to users via DestinE Service Portal.

How to cite: Kontkanen, J., Acosta, M., Bretonnière, P.-A., Castrillo, M., Davini, P., Doblas-Reyes, F., Früh, B., von Hardenberg, J., Jung, T., Järvinen, H., Klocke, D., Koldunov, N., Manninen, P., Milinski, S., Mäkelä, J., Naraynappa, D., Polade, S., Sandu, I., Sievi-Korte, O., and Thober, S.: Climate Adaptation Digital Twin – building an operational climate information system to support decision-making, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15811, https://doi.org/10.5194/egusphere-egu25-15811, 2025.

Our presentation aims to describe the development and operationalisation of the Destination Earth (DestinE) Extremes Digital Twin (DT), including the On-Demand component, a system designed to improve the prediction and management of extreme weather events in Europe. The system leverages high-resolution weather models using information from Extreme Detection (EDF) and Triggering (DTF) Frameworks, as well as ECMWF ensemble, incorporating impact-specific models for hydrology, air quality, renewable energy, and more. A key component is a configuration lookup table prioritising end-user needs and available resources. The system incorporates various masking techniques (ACCORD models configurations, geographical, capacity, event type) to refine forecasts. The presentation describes the system's architecture, data sources, and workflow, emphasising the integration of multiple models and data sources, and the use of cutting-edge technologies such as GPUs and machine learning for enhanced forecasting and efficient resource utilisation. Pilot regions are used for testing and operationalisation, with a phased approach planned for broader deployment. The project addresses challenges in forecasting accuracy, communication of uncertainty, and the integration of forecasts into decision-making processes across various sectors.

How to cite: Randriamampianina, R. and the DestinE Extremes Digital Twin Team: Digital twin for weather-induced extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5110, https://doi.org/10.5194/egusphere-egu25-5110, 2025.

How to cite: Montazeri, S., Stoicescu, M., Hinojo Comellas, O., Puechmaille, D., and Schick, M.: MLOps on DestinE Data Lake – Towards Reproducible AI on Edge Services, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10817, https://doi.org/10.5194/egusphere-egu25-10817, 2025.

Destination Earth (DestinE) is a flagship initiative led by the European Commission, implemented by EUMETSAT, ESA and ECMWF. It aims to create highly detailed Digital Twins (DTs) of the Earth, enabling precise simulations for a variety of uses. Currently, the initiative focuses on two primary Digital Twins:  the Weather Extremes Digital Twin (ExtremeDT) and the Climate Change Adaptation Digital Twin (ClimateDT). Over the coming years, the scope of DTs is set to expand, necessitating improved access to data and streamlined methods for working with it. This is where the Destination Earth Data Lake (DEDL) plays a pivotal role, offering comprehensive data discovery, access, and processing services tailored to the needs of DestinE users.

The DEDL operates on two key levels: ‘Data Discovery and Access’ and ‘Edge Services’. DEDL Discovery and Data Access services is provided by Harmonized Data Access (HDA) tool which provides a single, federated entry point to the services and data, including resources from existing datasets and complementary sources such as in-situ and socio-economic data. Notably, it also provides access to the unique datasets generated by DestinE’s DT’s. The services rely on use of the SpatioTemporal Asset Catalogs (STAC) standard which means:

• The search in the dataset is done according to the STAC protocol;
• The Federated Catalog search proxy component converts STAC queries into queries adapted to the underlying catalog and returns the results to the user in STAC format.

The cloud computing service is powered by the ISLET infrastructure, a distributed Infrastructure as a Service (IaaS) built on OpenStack. It allows users to manage virtual machines, s3 storage, and run advanced computations via a graphical user interface or command-line interface. A standout feature of ISLET is its proximity to data sources, operating near High-Performance Computing (HPC) facilities. This is achieved through data bridges, enabling efficient processing of large datasets, including those from Digital Twins, in conjunction with HPC systems.

The STACK environment supports application development using JupyterHub and DASK, with Python, and R languages. Users can create DASK clusters on selected infrastructure (sites) to process data directly where it resides, removing the need for extensive local setup and optimization.

Hook Services is a set of pre-defined workflows which could be used by users as a ready-to-use processors like: Sentinel-2: MAJA Atmospheric Correction; Sentinel-1: Terrain-corrected backscatter. It also enables workflow functions to generate on-demand higher-level products, such as temporal composites.

DEDL is a transformative initiative that revolutionizes how Earth Observation data is managed and utilized. By integrating innovative infrastructure (ISLET), data services (HDA), reliable processors (Hook Services), and user-friendly development tools (STACK), DEDL enables unprecedented levels of data harmonization, federation, and processing. Moreover, the DEDL plays a crucial role in empowering DestinE users by providing them with seamless access to vast datasets and advanced computational tools. It simplifies the process of data exploration, integration, and analysis, enabling researchers, policymakers, and developers to focus on innovation and decision-making rather than technical barriers. This cutting-edge system enhances climate research capabilities and supports sustainable development efforts on a scale previously unattainable.

How to cite: Grzybowski, P., Ziółkowski, M., Lambare, A., Reimer, C., and Schick, M.: How Destination Earth Data Lake support Destination Earth users, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12349, https://doi.org/10.5194/egusphere-egu25-12349, 2025.

How to cite: Delmas, C., Lambaré, A., and Le Carvennec, A.: Destination Earth Data Lake user story on Danube Delta water reservoir , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19238, https://doi.org/10.5194/egusphere-egu25-19238, 2025.

Digital twins are an exciting and rapidly developing research area, with the potential to provide a step change in the way we understand our evolving environment and its impact on sensitive systems.

The TWInning Capability for the Natural Environment (TWINE) programme is being co-delivered by the Met Office and Natural Environment Research Council (NERC) to explore the potential of this technology for transforming environmental science and across priority areas including climate change adaptation and mitigation, biodiversity and ecosystems, and natural hazards and their mitigation.

Through the TWINE programme, NERC and the Met Office have funded six digital twin pilot projects across a range of applications, including harmful algal blooms, flooding and coastal overtopping, optimising data collected by ocean gliders and aircraft, and multi-objective land-use decisions.

We will introduce the TWINE programme, giving a brief overview of the projects which are advancing our understanding of how we can harness the potential of digital twinning technology. These include cross-sector challenges such as: risk to natural resources, Net Zero targets, and addressing the science-to-policy lag.

How to cite: Mendes, J., Pope, E., Jones, Z., Cottrell, A., Eastman, M., Wiggs, J., Findley, H., Heard, A., Hallett, P., Vanvyve, E., Vandaele, R., Williams, H., Miltiadou, M., Gibson, F., Dale, K., Angus-Smyth, A., Gardner, S., and Tailby, S.: TWINE: TWInning capability for the Natural Environment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20187, https://doi.org/10.5194/egusphere-egu25-20187, 2025.