This presentation discusses the definition of randomness, the sources of randomness in the physical system (the Universe) as well as in the formal mathematical system. I discuss how randomness, through nonlinearity and chaos, the second law of thermodynamics, the quantum mechanical character of small scales, and stochasticity, is an intrinsic property of nature. I then move to our mathematical system and show that even in this formal system we cannot do away with randomness, and that the randomness in the physical world is consistent with the origins of randomness suggested from the study of mathematical systems. Many examples are presented ranging from pure mathematical processes, to natural processes, to social processes, and to life in general, which clearly demonstrate how the combination of rules and randomness produces and explains the world we live in. Finally, a possible explanation is provided as to why rules and randomness cannot exist by themselves but instead, they have to coexist.

How to cite: Tsonis, A.: On the Origins of Randomness, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1503, https://doi.org/10.5194/egusphere-egu26-1503, 2026.

Climate models, in the form of Earth System Models (ESMs), are central to both climate science and climate-informed decision-making. Technically, they are complex, nonautonomous, and chaotic mathematical and computational representations of the Earth’s interacting “spheres”, including the atmosphere, oceans, cryosphere and biosphere. In practice, they are widely used to produce conditional projections of future climate under prescribed forcing and emissions scenarios - and remain the only tools capable of doing so within a physically consistent framework. However, despite their indispensable role, the interpretation of ESM outputs is fundamentally constrained by multiple sources of uncertainty, normally grouped as internal climate variability, uncertainty in future forcing scenarios, and uncertainty arising from model formulation.

While internal variability and scenario uncertainty have traditionally received most attention, attempts to partition uncertainty (e.g. [1]) have shown that it is actually the latter, model uncertainty, which is responsible for most uncertainty in climate projections. But despite its relevance, model uncertainty is frequently treated only implicitly, commonly subsumed under the broad label of “structural uncertainty”, with limited clarity regarding its definition or impact. This raises several unresolved questions of direct relevance to both modelling and decision-making efforts: what constitutes structural uncertainty in contemporary ESMs, how does it propagate through ensembles and projections, and how does it affect downstream socio-economic impact assessments and climate risk analyses? 

In this presentation, I will discuss these questions by reviewing recent advances in the characterisation and interpretation of model uncertainty (including results from my collaborators and myself, e.g. [2-4]) and examining their implications for the use of climate projections in scientific inference and decision-making. I conclude by identifying key conceptual and methodological gaps that must be addressed to improve confidence in climate information under persistent structural uncertainty.

With many and wholehearted thanks to all my collaborators, mentors, friends, colleagues, students and funders who made the journey to this award possible.

References:

[1] Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E. M., Brunner, L., Knutti, R., and Hawkins, E. (2020): Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6. Earth System Dynamics, 11, 491–508. https://doi.org/10.5194/esd-11-491-2020

[2] de Melo Viríssimo, F., Stainforth, D. A., and Bröcker, J. (2024): The evolution of a non-autonomous chaotic system under non-periodic forcing: A climate change example. Chaos, 34, 013136. https://doi.org/10.1063/5.0180870

[3] Martin, A. P., Bahamondes Dominguez, A., Baker, C. A., Baumas, C. M. J., Bisson, K. M., Cavan, E., Freilich, M., Galbraith, E., Galí, M., Henson, S., Kvale, K. F., Lemmen, C., Luo, J. Y., McMonagle, H., de Melo Viríssimo, F., Möller, K. O., Richon, C., Suresh, I., Wilson, J. D., Woodstock, M. S., and Yool, A. (2024): When to add a new process to a model – and when not: A marine biogeochemical perspective. Ecological Modelling, 498, 110870. https://doi.org/10.1016/j.ecolmodel.2024.110870

[4] de Melo Viríssimo, F., and Stainforth, D. A. (2025): Micro- and Macroparametric Uncertainty in Climate Change Prediction: A Large Ensemble Perspective. Bulletin of the American Meteorological Society, 106, E1319–E1341. https://doi.org/10.1175/BAMS-D-24-0064.1

How to cite: de Melo Viríssimo, F.: Is it possible to have confidence in climate information with structurally uncertain models?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21589, https://doi.org/10.5194/egusphere-egu26-21589, 2026.

In arid and semi-arid regions, pressure on groundwater resources has reached critical levels. Long-term over-pumping has depleted many aquifers, and climate change is intensifying this process. Rising temperatures increase evaporation from rivers and reservoirs, reducing the amount of surface water available for infiltration and natural recharge. Under these conditions, the use of surface water during periods of availability and its storage underground represents a key mechanism of managed aquifer recharge, effectively avoiding evaporation losses.

In this study, a practical framework is developed and tested to identify feasible ways to transfer accumulated surface water toward stressed aquifers. Rather than relying on complex ranking approaches, the locations of existing water infrastructure specifically wells and traditional khettara systems are used as reference points. These features indicate where aquifers are accessible and provide realistic spatial anchors for planning recharge at the regional scale.

The method combines satellite imagery to map surface water, geographic information systems (GIS) to identify cost-effective transfer pathways across the landscape, and multi-objective optimization to evaluate trade-offs between competing objectives. Feasibility is assessed through a cost function that accounts for terrain slope, elevation differences, transfer distance, pumping energy requirements, infrastructure costs, and potential water treatment needs.

The approach is applied to the Draa Oued Noun Basin in southern Morocco, a region strongly affected by water scarcity, high evaporation rates, and declining groundwater levels. Several surface water sources are examined, and feasible conveyance routes toward aquifers supplying key wells and khettara systems are identified.

The results show substantial variations in cost between water sources. Available water volume, transfer distance, and especially elevation lift emerge as the main cost drivers. Trade-off analysis helps identify the most cost-effective projects under limited budgets. The results also highlight opportunities for cost reduction: where gravity-driven transfer is possible, costs are significantly lower, and where pumping is required, solar energy offers a viable option for reducing long-term operational expenses.

Overall, this work provides a spatially explicit and realistic basis for planning artificial groundwater recharge, while respecting economic constraints and supporting sustainable groundwater management in highly water-stressed regions.

How to cite: Fri, R., Scozzari, A., Haida, S., Kili, M., Chao, J., Mridekh, A., and El Mansouri, B.: A Multi-Objective Cost Minimization Framework for Managed Aquifer Recharge Integrating Pareto Optimization and Least-Cost Path Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5340, https://doi.org/10.5194/egusphere-egu26-5340, 2026.

Modern Earth system models increasingly hit I/O limits—not only in performance, but also in reproducibility, maintainability, and developer productivity. As data volumes and workflows evolve, tightly coupled, file-centric I/O approaches can become hard to scale and hard to extend.

We present the design and lessons learned from introducing an asynchronous, modular I/O server concept in the Modular Earth Submodel System (MESSy). I/O operations were decoupled from the Fortran-based scientific core and implemented as separate Python services, where the communication between the two components was implemented using the Yet Another Coupler (YAC) library. This architecture was chosen to improve flexibility and long-term maintainability, while enabling heterogeneous workflows and evolving storage backends.

Using MESSy as a case study, we discuss practical decision criteria for selecting an I/O concept in large models (e.g., scaling behavior, accessibility for developers, testing and CI strategies, and reproducibility).  We conclude with lessons learned from bridging Fortran and Python communities and from lowering entry barriers for user-developers in a large modeling system.

How to cite: Mitic, A., Jöckel, P., Kerkweg, A., Hartung, K., Kern, B., and Hanke, M.: Choosing an I/O approach for Earth system models: lessons learned from a modular I/O server for MESSy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11154, https://doi.org/10.5194/egusphere-egu26-11154, 2026.

Modern digital technologies and geoinformatics have experienced rapid growth, offering powerful tools to bridge the gap between scientific communities and society in landscape assessment and mapping. This research details the application of a crowdsourcing scheme that utilizes a dedicated mobile application to facilitate direct public participation in quantifying perceptions of urban landscapes and architecture. Initially developed as an educational tool, the methodology has been tested by university students across Italy, Greece, and France, providing a foundational phase for assessing landscape quality and urban typologies. Building upon these educational pilot studies, the work explores the evolution of this methodology into a broader, multicultural citizen science initiative designed to improve the quality and quantity of available landscape perception data.

A significant technical advancement in this research involves the integration of automated image analysis to process the novel data generated by participants from any location. The photographic material was examined using stochastic image analysis based on climacograms, in which images are treated as two-dimensional grayscale intensity fields and analyzed across multiple spatial scales. The method enables the comparison of image patterns based on the visual complexity of the uploaded photographs. A primary challenge addressed was the algorithm's performance when processing real-world, non-curated smartphone images. The analysis began an assessment on how the methodology handles environmental noise, such as sky, trees, and unconventional capture angles, which are inherent to bottom-up crowdsourcing schemes.

The early results indicate that the method can reveal group-level tendencies associated with differing architectural characteristics, particularly in relation to visual complexity, while not supporting reliable classification at the level of individual image. In detail, the findings indicate a trend towards two categorizations: firstly, between modernist-type movements, characterized by minimal elements, and secondly between eclectic or decorative movements, which exhibited higher measured complexity; however, this this behaviour was not observed universally on all analyzed movements The stochastic analysis also indicated theoretical overlaps between certain movements, such as Postmodernism and Eclecticism, based on shared decorative patterns. While the results highlight that environmental factors can influence the analysis of individual photographs, the method utilized presents potential for distinguishing movement trends with logical consistency even from unfiltered data.

Scientifically, this yield of quantitative data sets the groundwork for improved research in the humanities and culture, showing a strong correlation with established landscape quality indices. Socially, the project provides a scalable model for participatory mapping that fosters critical thinking about urban quality, creating new conditions for communication between universities and the broader public. Overall, the presented work reports on the early-stage results of this methodological exploration and aims to evaluate the combined use of participatory mobile data collection and exploratory image-based analysis for landscape and architectural studies, while identifying key challenges related to data quality, interpretation, and future methodological refinement.

How to cite: Kopelia, S., Tepetidis, N., Tzortzi, J. N., Sargentis, G.-F., and Ioannidis, R.: Integrating Participatory Perception-Mapping Data and Stochastic Image Analysis for Urban Landscape Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13270, https://doi.org/10.5194/egusphere-egu26-13270, 2026.

Accurate monitoring of land cover is essential for sustainable environmental management and urban planning in arid regions. However, rapid changes in land use often make it difficult to distinguish between different surface types, such as urban areas and bare soil, using standard satellite data alone. This research examines land-use changes in the Bahr Qarun district of Fayoum, Egypt, during 2019, 2021, and 2023. The study used Sentinel-2 and Landsat OLI 8 satellite images taken each April to ensure data consistency. We applied the Maximum Likelihood (ML) method to classify Sentinel-2 images. They used 30 training samples for each land category to guide the process. The results achieved a Kappa coefficient above 75%, indicating a reliable level of accuracy. We measured vegetation using the Normalized Difference Vegetation Index (NDVI) and urban areas using the Normalized Difference Built-up Index (NDBI). A comparative analysis revealed that NDVI results were closely aligned with those obtained from supervised classification, reflecting its strong capability in accurately identifying vegetated areas. In contrast, NDBI exhibited a tendency to overestimate urban extent, primarily due to spectral confusion between built-up surfaces and bare soil within individual pixels. The study concludes that NDVI is an effective tool for mapping the green cover in this area.

Keywords: Land Cover Change, Sentinel-2, Landsat OLI 8, Supervised Classification,  Spectral Indices (NDVI & NDBI), Bahr Qarun, Egypt.

How to cite: Elsehsah, A., Negm, A., Ashour, E., and Elsahabi, M.: Monitoring Land Cover Dynamics in Bahr Qarun District, Egypt, via Remote Sensing Data , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13783, https://doi.org/10.5194/egusphere-egu26-13783, 2026.

Satellite-Derived Bathymetry (SDB) offers a cost-effective alternative to traditional shipborne surveys for mapping large coastal areas. This technique utilizes optical remote sensing data from multispectral sensors to estimate water depth. The fundamental principle relies on the behavior of light as it travels through the water column; as depth increases, light intensity decreases due to absorption and scattering. Different wavelengths penetrate to varying degrees, with blue light reaching the greatest depths while red light is absorbed quickly. By analyzing these spectral features, researchers can calculate underwater topography. Currently, SDB techniques are categorized into two primary groups: physically based (analytical) models, which simulate light propagation without needing local in-situ depth calibration, and statistical (empirical) models, which correlate satellite data with known depth measurements from nautical charts, ship-based acoustic surveys or airborne LiDAR.

While both approaches provide extensive spatial coverage at a lower cost, they are generally limited to clear, shallow waters, typically reaching depths of less than 20 meters. Analytical models are highly accurate but complex and data-intensive, whereas empirical models are more accessible but rely heavily on the quality of reference data. Recent advancements in machine learning have significantly improved the automation and performance of these empirical methods. This study evaluates the core concepts, advantages, and limitations of various SDB approaches, with a focus on Landsat-8 and Sentinel-2 data. Furthermore, the research details essential processes for empirical model calibration, validation, and detecting model bias. The findings emphasize that rigorous evaluation and bias correction are critical for ensuring the reliability of depth data in diverse coastal environments.

Keywords: Satellite-Derived Bathymetry, Remote Sensing, Empirical Models, Stumpf Algorithm, Coastal Waters, Model Bias Detection and Correction.

How to cite: Abdalla, M. H., Elhalawany, H., Abdelrahman, S. M., Negm, A., and Scozzari, A.: Monitoring Shallow Water Depths: A Review of Satellite-Derived Bathymetry Methods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13852, https://doi.org/10.5194/egusphere-egu26-13852, 2026.

Significant heterogeneity in metadata schemas, vocabularies, and ontologies hinders the discovery, reuse, and integration of European environmental data infrastructures across national and disciplinary boundaries. Recent initiatives have identified semantic interoperability as a vital enabler of FAIR data flows between infrastructures, paving the way for sophisticated, AI-driven, large-scale analyses.

Powered by OntoPortal technology, EarthPortal is a specialised catalogue of semantic resources (ontologies, thesauri and controlled vocabularies) for Earth and environmental sciences. It provides navigation, multi-ontology searching, mapping management, text annotation and recommendation services via web interfaces and REST APIs. These support data catalogues and repositories in an interoperable way.

EOSC LUMEN builds an interoperable discovery ecosystem across multiple domains (including Earth System Science, Social Sciences and Humanities, and Mathematics) to enable cross-platform search and meaningful reuse across communities. Rather than focusing only on metadata aggregation, LUMEN targets the practical enablers of interoperability that make resources discoverable and machine-actionable across infrastructures.

LUMIS (LUMEN Infrastructure for Semantics) is the shared semantic layer of LUMEN. It supports the end-to-end lifecycle of semantic artefacts (ontologies and controlled vocabularies, including SKOS resources) from scoping and requirements to implementation, publication and long-term maintenance. LUMIS focuses on governance, provenance, versioning and quality checks, while adopting an integration-first strategy: it connects and orchestrates established community tools (deployed services and/or API-based components) into coherent workflows, so that semantic resources can be created, aligned, validated and delivered in reusable forms for discovery platforms.

Integrating EarthPortal into LUMIS links a domain-specific semantic catalogue to a cross-domain discovery ecosystem. This enables repositories to annotate metadata using EarthPortal resources, while making use of LUMIS’s lifecycle-driven workflows and FAIR-aligned governance and quality checks.

In this presentation, we will demonstrate how integrating EarthPortal into the LUMIS platform supports more consistent semantic interoperability and FAIR-aligned practices across European Earth System Science infrastructures. We will showcase practical data workflows to enhance interdisciplinary research.

How to cite: Homo, J., Pierkot, C., Darty, K., and Allem, H.: Operationalising Semantic Interoperability for Cross-domain Discovery with LUMIS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19784, https://doi.org/10.5194/egusphere-egu26-19784, 2026.

Modern Earth system research relies on integrating heterogeneous datasets such as reanalysis, satellite observations, in situ measurements, climate model ensembles, and reforecasts, yet these data are often stored in fragmented, inconsistent, and difficult to reuse forms. This limits reproducibility, slows modelling workflows, and constrains the development of operational digital twins for water and climate risk management.

This contribution presents a scalable, FAIR aligned data lake architecture implemented on the EVE high performance computing environment. The system transforms a large, unstructured source pool of more than two million files into a curated, duplication free, metadata rich repository designed for hydrological modelling, machine learning, and climate analytics. The architecture follows a four stage lifecycle: raw, curated, database ready, and ancillary GIS layers, reflecting data governance practices used by major climate centres.

A reproducible ingestion workflow classifies, deduplicates, and standardizes datasets from ERA5, ERA5 Land, MERRA 2, PRISM, E OBS, GPM IMERG, CMIP6, ISIMIP3, ECMWF reforecasts, MODIS, CHIRPS, GFED, GRDC, GSIM, and other sources. A Python based metadata extractor, built on CF convention standards, automatically captures variables, units, dimensions, spatial resolution, temporal coverage, coordinate reference systems, and checksums. Metadata are stored both as dataset level JSON and as a global inventory, enabling transparent provenance tracking and rapid dataset discovery.

The curated data hub is implemented under /data/db/earth_system and organized by scientific domain, temporal resolution, spatial extent, and processing stage. The system supports SLURM based workflows, HPC native processing, and cloud optimized formats such as Zarr.

This work demonstrates how a single researcher can design and operationalize a modern, HPC native data infrastructure that accelerates hydro climate research and forms the backbone of an emerging Digital Hydro Twin. The approach is transferable to institutions seeking to modernize their data ecosystems and improve reproducibility in environmental modelling.

How to cite: Amin, B., Zscheischler, J., Samaniego, L., Peng, J., García-García, A., and Harzendorf, T.: A Scalable, FAIR‑Aligned Data Lake Architecture for Earth System Modelling: From Heterogeneous Raw Archives to Curated, Metadata‑Rich, Analysis‑Ready Climate Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20391, https://doi.org/10.5194/egusphere-egu26-20391, 2026.

The Upper Senegal River Basin is a strategic water resource system supporting agriculture, hydropower generation, and essential ecosystem services in West Africa. However, a comprehensive understanding of its hydrological dynamics remains constrained by the limited availability of in situ hydroclimatic observations. This study applies the Soil and Water Assessment Tool (SWAT) to simulate hydrological processes in the basin, with a particular emphasis on the influence of precipitation data sources on model performance and uncertainty. Hydrological simulations were conducted at six representative gauging stations (Bakel, Kayes, Gourbassy, Oualia, Bafing Makana, and Daka Saidou) over the period 1983–2021, using a combination of ground-based observations, satellite precipitation products, and reanalysis datasets (ERA5, MERRA-2, PERSIANN, and CHIRPS). Model calibration demonstrated satisfactory performance, with Nash–Sutcliffe Efficiency (NSE) values reaching up to 0.74 at upstream stations, while reduced performance was observed downstream. Validation results showed a moderate decline in model efficiency, highlighting the sensitivity of SWAT outputs to precipitation inputs and data uncertainty. The comparative analysis of precipitation datasets reveals substantial variability in simulated streamflow and water balance components, underscoring the importance of precipitation data selection in data-scarce regions. These findings highlight the need for robust, multi-source hydroclimatic data integration to improve hydrological modelling reliability and support informed water resource management decisions.

Keywords: Upper Senegal River, SWAT, Hydrological modelling, Precipitation uncertainty; Satellite rainfall; Reanalysis data.

How to cite: Boussabou, S. M., Taia, S., El Mansouri, B., Kebd, A., Mohamedou Idriss, A., Legsabi, H., and Erraioui, L.: Hydrological Modelling of the Upper Senegal River Basin Using SWAT: Assessing the Impact of Multi-Source Precipitation Data on Model Performance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21793, https://doi.org/10.5194/egusphere-egu26-21793, 2026.

The establishment of the European Open Science Cloud (EOSC) places renewed emphasis on the role of national e-infrastructures in enabling standards-based, interoperable, and reusable research workflows in the EU. Within the context of Ireland’s EOSC Node, there is particular interest in demonstrating how European-scale open-data services can be digested by national research clouds, transformed into analysis-ready assets, and made available for both open research and applied industry use-cases. Earth Observation (EO) provides a strong test case, given the volume and complexity of the data involved, and its growing role in scalable environments that support operational decision-making.

This pilot project, QuarryLink, presents a Phase-1 study focused on building a reproducible EO data ingestion workflow that connects the Copernicus Data Space Ecosystem with the HEAnet Research Cloud, operating on the SURF Research Cloud platform. Through a real-world quarry case-study in the Dublin region (Ireland), the work demonstrates how EOSC-aligned principles, including auditable machine-readable workflows, can be applied from the outset of the EO research process. We will demonstrate how precise spatial boundaries can be defined and validated; how modern OAuth-based authentication mechanisms can be integrated into research cloud workflows; and how Sentinel-2 Level-2A products can be programmatically discovered, retrieved, and prepared for downstream analysis using current Copernicus services.

By executing the ingestion workflow on the HEAnet Research Cloud using open-source geospatial tooling, the pilot aims to establish an analytics-ready foundation for working with Sentinel-2 data in a reproducible research cloud environment. The resulting data products are structured to support downstream analysis, with compute resources accessed dynamically through the HEAnet Research Cloud workspace as required. Building on this foundation, Phase 2 will focus on developing time-series analyses, EO data cubes, and derived environmental indicators to support both research-driven investigation and applied monitoring scenarios in European quarry environments.

More broadly, the pilot seeks to illustrate how EOSC-aligned integration across data ingestion and compute layers can support open research practices while enabling scalable, real-world EO-enabled industrial applications, providing a practical pathway for national EOSC Nodes to translate open data into shareable analytics and societal impact.

How to cite: Neff, F., Sabatino, R., Arreba, A., and Sweeney, J.: An EOSC Node Ireland Pilot Study: Bridging European and National e-Infrastructures for Reproducible Sentinel-2 Data Ingestion in Quarry Applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21965, https://doi.org/10.5194/egusphere-egu26-21965, 2026.

Groundwater in arid regions is highly sensitive to human activity, especially when untreated wastewater interacts with shallow aquifers. This study evaluates the hydrogeochemical response of the Kima aquifer in Aswan, Egypt, following the Kima Drain Covering Project. The research uses an integrated framework of field measurements, geospatial analysis, and multi-criteria decision-making. The team analyzed groundwater samples from 2020 and 2025. They tested eleven physicochemical parameters and six irrigation indices. Spatial interpolation through Inverse Distance Weighting (IDW) helped map temporal variations and identify contamination hotspots. To classify water suitability, the study standardized values according to WHO and Egyptian guidelines. The Analytical Hierarchy Process (AHP) was used to determine the importance of various drinking and irrigation indicators. Finally, a Weighted Linear Combination (WLC) generated composite Groundwater Quality Index (GWQI) maps. The results show a significant improvement in groundwater quality after the drain was covered. Levels of TDS, chloride, sulfate, sodium, and magnesium decreased substantially across the area. The ionic balance shifted toward a more favorable calcium-magnesium-bicarbonate facies. Irrigation indices also improved, with most parameters falling into safe or excellent ranges. The 2025 GWQI map reveals a transition from "good–permissible" to "excellent–safe" zones. This confirms that eliminating direct seepage from the drain had a positive environmental impact. This integrated AHP–GIS–IDW approach is an effective tool for monitoring groundwater changes. It provides a robust decision-support system for managing water resources in arid urban environments.

How to cite: Khairy, M., S. Nour-Eldeen, A., Hossen, H., Abd-Elaty, I., and Negm, A.: Monitoring Groundwater Quality and Improvement in the Kima Area, Aswan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22084, https://doi.org/10.5194/egusphere-egu26-22084, 2026.

Hyperspectral image (HSI) classification often struggles with feature interference across different scales and the inherent challenges of data imbalance and sample scarcity. While deep learning models have significantly advanced the field, traditional single-branch architectures often suffer from scale-related noise, where features from different receptive fields interfere with one another. To address this, we propose the Multibranch Adaptive Feature Fusion Network (MBAFFN). Our approach utilizes three parallel branches to independently extract scale-specific features, effectively decoupling the multiscale information to prevent interference. This architecture is enhanced by two specialized modules: Global Detail Attention (GDA) for capturing broad contextual dependencies and Distance Suppression Attention (DSA) for refining local pixel-level discrimination. Furthermore, a pixel-wise adaptive fusion mechanism is introduced to dynamically weigh and integrate these features, prioritizing the most relevant scales for final classification. The performance of MBAFFN was validated on four benchmark datasets: Indian Pines (IP), Pavia University (PU), Longkou (LK), and Hanchuan (HC). Compared to current state-of-the-art methods, our model improved Overall Accuracy (OA) by 0.91%, 1.71%, 0.86%, and 3.16% on the IP, PU, LK, and HC datasets, respectively. The significant improvement on the HC and PU datasets underscores the model’s robustness in scenarios with limited training samples and complex class distributions. These results, supported by detailed ablation studies, demonstrate that adaptive fusion and scale-specific branching are effective strategies for mitigating feature interference in hyperspectral analysis.

How to cite: Li, C. and Du, B.: Multibranch Adaptive Feature Fusion for Hyperspectral Image Classification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3080, https://doi.org/10.5194/egusphere-egu26-3080, 2026.

Characterizing the thermal state of urban surfaces is fundamental for mitigating the impacts of the Surface Urban Heat Island (SUHI) effect. This study presents an intensive in-situ thermal infrared monitoring campaign in the high-density urban core of Athens, Greece. Utilizing a calibrated handheld TIR sensor (7.5–14 μm), surface temperatures were recorded across strategic locations in the center of Athens during hot weather conditions. The methodology emphasizes the critical role of material-specific parameterization, where thermographic data were post-processed to account for emissivity (ε) variations and surface temperature, ensuring high-fidelity measurements.

Experimental results reveal extreme thermal stress, with maximum surface temperatures reaching 56.0°C on conventional paving materials, while the mean ambient air temperature was close to 35.0°C during peak solar hours (13:00–18:00LT). Spatial analysis and visualization of the results were performed using QGIS, correlating thermal signatures with urban geometry, shading conditions, and vegetation density. The aim of this study was to highlight the significant cooling potential of specific urban materials and nature-based solutions.

How to cite: Gkountaras, O., Georgakis, C., Velissaridis, T., and Assimakopoulos, M. N.: In-situ Thermal Infrared Monitoring in an Urban Area: A Case Study of Micro-scale Thermal Transitions during Hot Weather Conditions in Athens, Greece., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3363, https://doi.org/10.5194/egusphere-egu26-3363, 2026.

Landslides claim thousands of lives and cause billions in economic losses annually, with impacts disproportionately concentrated in developing regions across Asia, Africa, and Latin America. Paradoxically, the current trajectory of artificial intelligence in geohazard detection—characterized by billion-parameter foundation models requiring substantial computational infrastructure—risks widening, rather than closing, the gap between technological capability and operational deployment where it is needed most. We argue that this paradigm requires fundamental reconsideration, proposing domain adaptation on strategically curated geological datasets as a more equitable and effective path toward globally accessible landslide detection systems.

Foundation models like the Segment Anything Model (SAM), pre-trained on over one billion masks, demand computational resources—312 million parameters, 1,376 GFLOPs per inference, specialized GPU infrastructure—that remain inaccessible to disaster management agencies in resource-constrained regions. Beyond these practical constraints, we contend that the apparent generalization capabilities of such models reflect pattern coverage in training data rather than emergent understanding transferable to geological contexts. The SA-1B dataset, despite its scale, was not curated to systematically represent landslide morphological diversity, creating coverage gaps for rare failure types, unusual triggering mechanisms, and underrepresented terrain configurations precisely where robust detection is operationally critical.

Given these limitations, we propose that effective generalization for geological applications emerges not from architectural scale but from strategic coverage of domain-relevant pattern space. We developed and tested GeoNeXt, a lightweight architecture that exploits the hierarchical transferability of geological features through targeted domain adaptation. Low-level representations (edges, spectral gradients) transfer universally across sensors and terrain; mid-level patterns (drainage networks, slope morphology) require adaptation to local expressions; and high-level configurations (failure geometries, trigger signatures) demand targeted training. Our results showed that this approach outperformed SAM-based methods across three independent benchmarks while requiring 10× fewer parameters (32.2M versus 312.5M) and a 62% reduction in computational cost. Zero-shot transferability to geographically distinct test sites (74–78% F1 score) emerged from the training dataset's systematic morphological diversity rather than parameter count. Inference at 10.6 frames per second on standard hardware, versus 3.0 frames per second for foundation model alternatives, transforms theoretical capability into deployable technology for resource-constrained environments. These findings suggest that strategic domain adaptation, rather than architectural scale, offers the most viable path toward operational landslide detection in vulnerable regions.

How to cite: Uribe-Ventura, R., Viveen, W., Pineda-Ancco, F., and Beltrán-Castañon, C.: Democratizing landslide detection for vulnerable regions beyond resource-intensive foundation models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3619, https://doi.org/10.5194/egusphere-egu26-3619, 2026.

Numerical modeling is a fundamental tool for understanding physically driven processes in geosciences. In multiparametric settings, the Finite Element Method is widely used because it can accommodate irregular geometries and complex boundary conditions. However, this advantage critically depends on the quality of the computational mesh, which must faithfully represent geological features such as faults, stratigraphic interfaces, and wells. In practice, mesh generation remains a major bottleneck, requiring specialized expertise and significant manual effort. We present Geo2Gmsh, an automated, lightweight workflow built on Gmsh (Geuzaine & Remacle, 2009), that generates geological meshes directly from simple text‐based descriptions of topological elements, including surfaces, lines, and points. These elements correspond to geologically meaningful features, allowing users to define faults, horizons, wells, and domain boundaries in a transparent, reproducible, and solver‐independent way. The workflow is demonstrated using two contrasting case studies: (1) Ringvent, an active sill‐driven hydrothermal system in the Guaymas Basin, and (2) the Eastern Llanos Basin, a foreland basin in eastern Colombia. To evaluate solver compatibility, we solved the heat equation in SfePy (https://sfepy.org/doc-devel/index.html) using the Eastern Llanos Basin model as the computational domain. Although the simulation is illustrative and not calibrated to observations, it confirms that meshes produced by Geo2Gmsh can be readily incorporated into numerical solvers. By explicitly embedding wells, faults, and geological interfaces in the mesh, Geo2Gmsh enables boundary conditions to be applied directly to physically meaningful features and allows model outputs to be extracted along them, simplifying both model setup and post‐processing. Meshes can be exported in standard formats (e.g., VTK, MSH, and Exodus via meshio), ensuring broad interoperability. Overall, Geo2Gmsh provides a lightweight, scalable, and reproducible workflow that dramatically lowers the technical barrier to geological mesh generation. This contribution establishes a practical foundation for reproducible, open-source numerical modeling in geosciences, facilitating the integration of geological knowledge into high-fidelity computational simulations.

How to cite: Buitrago, H., Contreras, J., and Neumann, F.: Geo2Gmsh: A Scalable Workflow for Automated Mesh Generation of Geological Models Using Gmsh, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6022, https://doi.org/10.5194/egusphere-egu26-6022, 2026.

Many geoscientific datasets, such as those produced by climate and weather models, are stored in the NetCDF file format.  These datasets are typically very large and often strain institutional data storage resources. While lossy compression methods for scientific data have become more studied and adopted in recent years, most advanced lossy approaches do not work easily and/or transparently with NetCDF files. For example, they may require a file format conversion or they may not work correctly with “missing values” or “fill values” that are often present in model outputs.  While lossy quantization approaches such at BitRound and Granular BitRound have built-in support by NetCDF and are quite easy to use, such approaches are generally not able to reduce the data size as much as more advanced compressors (for a fixed error metric), like SPERR, ZFP, or SZ3.

We are particularly interested in reducing the data size of the CONUS404 dataset.  CONUS404 is a publicly available unique high-resolution hydro-climate dataset produced by Weather Research and Forecasting (WRF) Model simulations that cover the CONtiguous United States (CONUS) for 40 years at 4-km resolution (a collaboration between NSF National Center for Atmospheric Research the U.S. Geological Survey Water Mission Area). 

Here, we investigate one advanced lossy compressor, SPERR [1], together with its plugin for NetCDF files, H5Z-SPERR [2], in a Python-based workflow to compress and analyze CONUS404 data.  SPERR is attractive due to its support for quality control in terms of both maximum point-wise error (PWE) and peak signal-to-noise ratio (PSNR), enabling easy experimenting of storage-quality tradeoffs. Further, given a target quality metric, previous work has shown that SPERR likely produces the smallest compressed file size compared to other advanced compressors. It leverages the HDF5 dynamic plugin mechanism to enable users to stay in the NetCDF ecosystem with minimal to no change to existing analysis workflows, whenever a typical NetCDF file is able to be read. And, importantly for our work, the SPERR plugin supports efficient masking of “missing values,” which are common to climate and weather model output.  The support for missing values enables compression on many variables which are not naturally handled by other advanced compressors that rely on HDF5 plugins. Further, because H5Z-SPERR directly handles missing values, they can be stored in a much more compact format (and are restored during decompression), further improving compression efficiency. (Note that built-in NetCDF quantization approaches can work with missing values.) 

Our experimentation demonstrates the benefit of enabling advanced lossy (de)compression in the NetCDF ecosystem: adoption friction is kept at the minimum with little change to workflows, while storage requirements are greatly reduced.

[1] https://github.com/NCAR/SPERR

[2] https://github.com/NCAR/H5Z-SPERR

How to cite: Li, S., Baker, A., and Xue, L.: Application of advanced lossy compression in the NetCDF ecosystem for CONUS404 data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6232, https://doi.org/10.5194/egusphere-egu26-6232, 2026.

Investigations have been carried out into the initiation of the Pangu weather model, initiating the model with both ERA5 data (on which it was trained) and with the Met Office’s Global UM model data. There are many consistent local biases at ground level between these two sets of initial conditions. The geographically local biases are not dissipated by the Pangu model with timestep but instead remain geographically fixed and gradually decrease with lead time. Whilst the Pangu model initiated with UM initial conditions remains further from the ERA5 truth than the ERA5-initiated Pangu model at all timesteps, it initially moves towards the ERA5 truth with timestep, as the geographically static differences in initiation decrease, before moving further away from the ERA5 truth as differences in large-scale systems begin to dominate.

Also investigated was the difference between the Pangu model 24-hour timesteps and 6-hour timesteps; it was found that the 6-hour timesteps were better able to reduce the geographically static initial differences than the 24-hour timesteps.

If time permits, a similar analysis will be made of the FastNet and GraphCast models.

How to cite: Buttery, H.: Investigations into the Reaction of the Pangu ML Weather Model to Different Initial Conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7344, https://doi.org/10.5194/egusphere-egu26-7344, 2026.

Solar phenomena such as flares, coronal mass ejections (CMEs), and solar energetic particles (SEPs) are actively monitored and assessed for space weather hazards. In recent years, machine learning has demonstrated considerable success in solar flare forecasting. Accurate SEP forecasting remains challenging in space weather monitoring due to the complexity of SEP event origins and propagation. We introduce SEPNET, an innovative multi-task neural network that integrates forecasting of solar flares and CME summary statistics into the SEP prediction model, leveraging their shared dependence on space-weather HMI active region patches (SHARP) magnetic field parameters. SEPNET incorporates long short-term memory and transformer architectures to capture contextual dependencies. The performance of SEPNET is evaluated on the state-of-the-art SEPVAL SEP dataset and compared with classical machine learning methods and current state-of-the-art pre-eruptive SEP prediction models. The results show that SEPNET achieves higher detection rates and skill scores while being suitable for real-time space weather alert operations.

How to cite: Chen, Y., Yu, Y., Zhao, L., Whitman, K., Manchester, W., and Gombosi, T.: SEPNET: a multi-task deep learning framework for SEP forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11945, https://doi.org/10.5194/egusphere-egu26-11945, 2026.

Quantifying soil properties such as Soil Organic Carbon (SOC), texture, and Calcium Carbonate (CaCO₃) is essential for assessing soil health and ensuring food security. While Visible, Near Infrared, and Short Wave Infrared (VSWIR) remote sensing is a standard operational tool, the Longwave Infrared (LWIR, 8-14 μm) offer complementary information on mineralogy and moisture that are still not yet fully explored for this specific application. This study investigates the synergy between VSWIR and LWIR data that will be available with future hyperspectral satellite missions. Among them, the European Space Agency's Copernicus Expansion missions that will add to the EO capacity the Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM) mission. Alongside are the NASA's Surface Biology and Geology (SBG and SBG-TIR) missions.

The research focuses on Jolanda di Savoia (Italy), an agricultural landscape resulting from land reclamation projects in the late 19th century. Ground truth data were collected during a field campaign on June 22, 2023, providing 59 topsoil samples further analysed for SOC, texture, and CaCO₃. Field campaign was coincident with an airborne survey carried out with the LWIR Hyperspectral Thermal Emission Spectrometer (HyTES) sensor. HyTES captured data across 256 spectral bands from 7.5 to 11.5 μm, providing a pixel size of approximately 2.3 meters.

To evaluate the multi-frequency potential, we developed a workflow combining a soil composite from PRISMA (VSWIR) satellite time-series with simulated SBG-TIR (LWIR) data. The SBG-TIR simulation chain included as input a surface emissivity map derived from the airborne HyTES survey. To cover the LWIR wide spectral range (up to 12 µm), the emissivity spectrum was extended using an autoencoder neural network procedure trained on the ECOSTRESS Soil Spectral Library. Top-Of-Atmosphere (TOA) radiance was then simulated using the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-14) model, incorporating the optical depth and cloud/aerosol optical properties coefficients specific to SBG-TIR. Furthermore, these simulated data were atmospherically corrected to produce the target satellite emissivity products according to the TES algorithm.

Soil properties prediction models were developed using supervised machine learning algorithms. We benchmarked two scenarios: 1) the proposed combined approach using PRISMA and the simulated SBG-TIR L2 emissivity product; and 2) a VSWIR-only approach using PRISMA. A quantitative assessment by 10-fold cross-validation using common literature metrics (R², RMSE, RPD) highlighted the benefits of the multi-sensor approach. For SOC retrieval, the standalone VSWIR (PRISMA) model yielded an R² of 0.55 (RPD = 1.5), while the synergistic integration of PRISMA with simulated SBG-TIR data improved the retrieval accuracy, reaching an R² of 0.65 and increasing the RPD to 1.69. This work indicates that, on the agricultural test site of Jolanda di Savoia, the combined use of SVWIR and LWIR spectral range slightly improves the SOC retrieval. Further validation across diverse agricultural scenarios will be essential to test the real advantage of combining next-generation imaging spectroscopy missions.

How to cite: Rossi, F., Casa, R., Marrone, L., Mirzaei, S., Pascucci, S., and Pignatti, S.: Evaluating the combined potential of VSWIR and Thermal Infrared data for soil characterisation., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13611, https://doi.org/10.5194/egusphere-egu26-13611, 2026.

Accurate high resolution wind field prediction is essential for wind resource as-
sessment, renewable energy planning, and regional weather analysis. Although
Numerical Weather Prediction (NWP) models such as the Weather Research
and Forecasting (WRF) model provide physically consistent wind forecasts, their
outputs often suffer from systematic biases arising from uncertainties in surface
characteristics, simplified physical parameterizations, and resolution limitations.
Furthermore, increasing model resolution to the kilometer scale significantly
raises computational cost. To address these challenges, this study presents a
machine learning–based framework for bias correction of WRF-simulated wind
fields over the Southern Tamil Nadu region, with particular focus on the Mup-
pandal wind farm area.
An extensive validation of WRF configurations was first performed using mul-
tiple physics scheme combinations and domain setups, evaluated against ERA5
reanalysis data. The optimal configuration was identified and used to gener-
ate three years (2023–2025) of wind simulations at 3 km × 3 km resolution.
Significant biases were observed in the raw WRF outputs, motivating the appli-
cation of an Artificial Neural Network (ANN) based bias correction approach.
A Random Forest algorithm was employed for feature selection, followed by
Principal Component Analysis (PCA) to reduce dimensionality while retaining
95% of the variance. A feedforward neural network with multiple hidden layers
was trained to correct the U10 and V10 wind components, with the hyperbolic
tangent activation function yielding the best performance. The bias-corrected
wind fields exhibited substantial improvement in mean and extremes, achieving low error metrics and
strong correlation with ERA5 data.
The results demonstrate that combining physically based NWP simulations with
machine learning driven bias correction provides an accurate and computation-
ally efficient approach for generating high-resolution wind fields. This hybrid
framework offers significant potential for wind energy assessment and localized
meteorological applications in data-sparse regions.

How to cite: Pm, V. and Chakravarthy, B.: Bias Correction of Numerical Weather PredictionWind Fields in Southern Tamil Nadu RegionUsing Machine Learning Techniques, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16232, https://doi.org/10.5194/egusphere-egu26-16232, 2026.

Early laboratory experiments of shear flow by Thorpe (Thorpe, 2002) provided evidence of Kelvin-Helmholtz Instability (KHI) billow interactions either due to misaligned adjacent billow cores or varying phases along the adjacent billow axes. Similar evidence has been found in the observations of tropospheric clouds, airglow, and Polar Mesospheric Clouds (PMC) imagery data in the mesosphere. Initial High-Resolution Direct Numerical Simulations (DNS) studies performed at Reynolds Number of 5000 (Fritts et al., 2021a, Fritts et al., 2021b) have demonstrated the that misaligned KH billow cores exhibit strong and complex vortex interactions inducing ‘Tubes and Knots’ (T&K) structures (Thorpe, 2002). These T&K structures were observed to accelerate transition to small-scale turbulence in contrast to previously known notable transitional mechanisms such as secondary KHI and convective instabilities emerging in individual KH billows. Also, the KHI T&K dynamics evidently yield intense turbulence dissipation rates contrasting that of secondary KHI and convective instabilities in billow cores.

More recent high-resolution imaging of OH airglow (Hecht et al., 2021) provide concrete evidence of KHI billows with wavelength ranging between 7-10 km modulated by atmospheric Gravity Waves (GWs) of dominant horizontal wavelengths ∼ 30km and oriented orthogonal to KH billow axes and propagate along the billow cores which result in apparent T&K dynamics rapidly driving KH billow breakdown. Similar evidence has been found in recent PMC imaging. This is the central theme of the idealized DNS discussed in this talk.

We conducted DNS studies to demonstrate the turbulence energetics of KHI billow interactions when subject to modulations due to monochromatic atmospheric gravity waves of small perturbation amplitudes and intrinsic frequency of N/5 (where N is the background Brunt-Vaisala Frequency). Preliminary analyses of our DNS results indicate that GW modes with modest amplitudes promote KHI billow misalignments resulting in complex multi-scale T&K dynamics fixed at specific GW phases. An increase in the GW amplitude resulted in noticeable reduction of KHI billow wavelengths further promoting KH billow misalignments. The resulting turbulence is expected to consist of broader scale ranges of intense turbulence dissipation rate and diffusivity.

References
[Fritts et al., 2021a] Fritts, D. C., Wang, L., Lund, T. S., and Thorpe, S. A. (2021a). Multi-Scale Dynamics of Kelvin-Helmholtz Instabilities . Part 1 : Secondary Instabilities and the Dynamics of Tubes and Knots. pages 1–27.

[Fritts et al., 2021b] Fritts, D. C., Wang, L., Thorpe, S. A., and Lund, T. S. (2021b). Multi-Scale Dynamics of Kelvin-Helmholtz Instabilities . Part 2 : Energy Dissipation Rates , Evolutions , and Statistics. pages 1–39.

[Hecht et al., 2021] Hecht, J. H., Fritts, D. C., Gelinas, L. J., Rudy, R. J., Walterscheid, R. L., and Liu, A. Z. (2021). Kelvin-Helmholtz Billow Interactions and Instabilities in the Mesosphere Over the Andes Lidar Observatory: 1. Observations. Journal of Geophysical Research: Atmospheres, 126(1):e2020JD033414. Publisher: John Wiley & Sons, Ltd.

[Thorpe, 2002] Thorpe, S. A. (2002). The axial coherence of Kelvin–Helmholtz billows. Quarterly Journal of the Royal Meteorological Society, 128(583):1529–1542. Publisher: John Wiley & Sons, Ltd.

How to cite: Doddi, A., Fritts, D., and Lund, T.: Rapid Turbulence Evolution Resulting from Stable Shear layer and Atmospheric Gravity Wave Interactions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4129, https://doi.org/10.5194/egusphere-egu26-4129, 2026.

Socio-economic drought represents the stage at which water stress translates into tangible disruptions to livelihoods, infrastructure, and economic systems, often preceding severe physical water shortages. In India, pronounced climatic variability combined with extreme physiographic heterogeneity leads to strong spatial contrasts in socio-economic vulnerability to drought. Despite this, most drought assessments in the country remain dominated by hydro-meteorological indicators, with limited integration of socio-economic exposure, sensitivity, and adaptive capacity.
This study develops a spatially explicit socio-economic drought risk assessment framework for India by integrating multi-dimensional climatic, environmental, and socio-economic indicators within a Geographic Information System (GIS). Thirteen indicators capturing water availability, agricultural productivity, infrastructure, population pressure, economic activity, and social deprivation are compiled from multi-source datasets and harmonized to a common spatial resolution. The indicators include available soil water, agricultural yield, livestock density, road density, population density, biomass, electricity consumption, Gross Domestic Product (GDP), global surface water availability, digital elevation model, groundwater availability, land use/land cover, and relative deprivation. Indicator weights are objectively derived using the Analytic Hierarchy Process (AHP), with consistency of expert judgments ensured through the consistency ratio criterion (CR < 0.1). A GIS-based weighted overlay approach is then employed to generate a composite socio-economic drought risk index, which is classified into four risk categories to identify spatial patterns and hotspots.
The resulting risk map reveals pronounced regional disparities, highlighting drought-prone agrarian and socio-economically marginalized regions as areas of elevated risk. The proposed framework offers a transferable and scalable decision-support tool for integrating socio-economic dimensions into drought monitoring and preparedness. By explicitly linking water stress to livelihood and infrastructure vulnerability, the study provides actionable insights for risk-informed planning, targeted mitigation, and long-term drought resilience in India.

How to cite: Beerkur, A. K. and Palagiri, H.: A Multi-Criteria GIS Framework for Socio-Economic Drought Risk Assessment across India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4184, https://doi.org/10.5194/egusphere-egu26-4184, 2026.

Scour, defined as the erosion or removal of sediment from around bridge piers due to flowing water, remains one of the primary causes of hydraulic structure failures worldwide. Local scour around bridge piers poses a serious threat to bridge stability, particularly during high-flow events, as the development of downflow, horseshoe vortices, and wake vortices at the pier base leads to intense sediment removal and foundation instability. To address this challenge, the present study investigates the hydrodynamic behaviour and scour reduction performance of tapered submerged vanes installed upstream of a cylindrical bridge pier as an effective countermeasure against local scour. A combined numerical and experimental approach was adopted to evaluate the influence of tapered submerged vanes on flow structure and scour characteristics. Numerical simulations were carried out using FLOW-3D Hydro to analyse the three-dimensional flow field around the pier–vane system under steady clear-water conditions. The simulations focused on assessing velocity distribution, near-bed shear stress, vortex dynamics, and secondary flow patterns generated by the tapered vanes. Particular attention was given to the formation of leading-edge vortices (LEVs) and their role in modifying erosive flow structures near the pier foundation. Based on the numerical insights, a series of physical model experiments were conducted in a laboratory flume to quantify the scour reduction achieved by the tapered vanes. The experiments aimed to optimize the longitudinal and transverse placement of the vanes relative to the pier. The vanes were installed at a fixed longitudinal distance upstream of the pier, while transverse spacing was systematically varied to examine its effect on sediment transport and scour depth. Bed elevation profiles and maximum scour depths were measured after equilibrium scour conditions were attained. The results demonstrate that tapered submerged vanes significantly alter the near-bed flow field by generating localized leading-edge vortices that effectively deflect high-energy flow away from the pier base. This flow redirection weakens the horseshoe vortex and reduces near-bed shear stress in the vicinity of the pier. Among the tested configurations, the vane arrangement with a longitudinal spacing of 1.5D and transverse spacing of 2D exhibited the best performance, resulting in a 56% reduction in maximum scour depth compared to the no-vane case. Additionally, localized sediment deposition was observed upstream and downstream of the pier, indicating favourable redistribution of sediment induced by the vane-generated secondary currents. By integrating numerical modelling with experimental validation, this study provides valuable insights into the flow mechanisms and optimal placement strategies of tapered submerged vanes. The findings highlight their potential as a practical, efficient, and sustainable solution for mitigating local scour around bridge piers in alluvial channels.

Keywords: Scour, Submerged Vane, Horseshoe Vortices, Wake Vortices, Leading-Edge Vortex (LEV)

How to cite: Karmishtha, K., Behera, R. K., and Singhal, G. D.: Performance of Tapered Submerged Vanes in Mitigating Local Scour Around Bridge Piers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4200, https://doi.org/10.5194/egusphere-egu26-4200, 2026.

The Piano Key Weir (PKW) has earned recognition for its adaptability for large discharges across weir types of varying heights and with small footprints. Therefore, it has the potential to be a substitute for linear weirs (space being a factor), Ouamane and Lempérière, (2006). Even with the above-mentioned advantages of PKWs, other geometries leave much to be desired. The rectangular PKW and the trapezoidal PKW illustrate a most common inefficiency example. Standard literature describes construction and operational shortfalls such as flowing separation at the inlet key, varying discharge and uneven velocities along the crest, vortex shedding and formation at the key intersections, dead zones in the inlet-outlet, zones of intensified energy dissipation, and lowering weir versatility at high flows. These challenges are combined to mean loss of efficiency in weir discharge capability. In response to these challenges, the present study introduces the Modified Piano Key Weir (MPKW) to assess its performance using 3D computational hydraulic modeling. The Volume of Fluid (VOF) methodology for free surface tracking and the Reynolds-Averaged Navier Stokes (RANS) for turbulence closure modeling characterize pressure gradients, flow accelerations in the several dimensions, and eddies. A systematic numerical investigation was conducted to compare the discharge efficiency of RPKW, TPKW, and MPKW across a range of steady inflow discharges: 0.030, 0.060, 0.090, 0.120, and 0.160 m³·s⁻¹. The MPKW demonstrated consistently superior discharge efficiency over both RPKW and TPKW for all tested cases, without requiring an increase in structural footprint or crest length. The highest relative improvement was observed at 0.060 m³·s⁻¹, which was therefore selected as a representative discharge for in-depth flow diagnostics. Discharge at 0.060 m³·s⁻¹ was applied to determine vorticity structures, turbulent kinetic energy (TKE), and energy dissipation to better understand the flow mechanisms that explain the efficiency of the weir. The MPKW design, with refined geometry and improved inlet–outlet design, rounded key transitions, and adjustable wall skew, was successful in mitigating flow separation at the key inlets and reducing the large-scale vortex formation at the key junctions. The modified sidewall skewed the internal recirculation, and as a consequence, TKE in the stagnation zones was less, and recirculation was more along the crests of the weir, thereby nullifying turbulent structures. While the breakdown of turbulence resulted in localized energy dissipation, the stabilization of the approach flow was improved because the process converted rotational energy of large eddies with a low energy loss to rapidly decaying eddies which do not sustain and produce a recycling of energy. Thus, less energy was concentrated in the vortex cells at the key junctions, the loss due to flow contraction was less, and the nappe cohesion over the crests was improved. MPKW, relative to other configurations, was characterized by a lower level of turbulence and vorticity at the junctions, a greater effective utilization of the crest, and improved pressure recovery. The results confirm MPKW as a hydraulically efficient and economically feasible solution for both new installations and retrofit applications under head or footprint constraints.

How to cite: Kumar, A., Padhi, E., and Mishra, S. K.: CFD-Based Comparative Analysis of Conventional and Modified Piano Key Weirs for Improved Discharge Efficiency, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4951, https://doi.org/10.5194/egusphere-egu26-4951, 2026.

Groundwater is the second largest reserve of fresh water and is an important resource that supports agriculture, industrial and domestic water supplies. Groundwater is facing unsustainable impacts by human activities over the years in different forms. The situation is aggravated by climate change which aggravates groundwater stress through variable precipitation leading to reduced recharge. Thus, highlighting the importance of assessing aquifer potential for sustainable groundwater management. The analysis was carried out in the Manjra and Maner sub-basins, of Godavari river basin, India where data-driven assessments remain limited. In this regard, the present research employs a Multi-Criteria Decision Analysis (MCDA) framework that integrates Geographic Information Systems (GIS) and the Analytical Hierarchy Process (AHP) to define groundwater potential zones (GWPZ) in the Manjra and Maner sub-basins. In a GIS environment, eight thematic layers—geology, land use/land cover, lineament density, drainage density, rainfall, soil, and slope—were examined. These factors were weighted using AHP, and combined using weighted overlay analysis. Area under the Curve (AUC), Receiver Operating Characteristic (ROC) analysis, and groundwater inventory data were used to validate the final GWPZ map. Five classifications of groundwater potential were identified for the research area: very low, low, moderate, high, and very high. The research region's predominance of moderate (45%) to high potential (28%) zones suggests that groundwater availability is generally fair to good. Priority locations for sustainable groundwater development and management are indicated by the high and very high potential zones.

How to cite: Belluti Nanjundagowda, P. and Vema, V. K.: A Geospatial and AHP-Based Approach for Delineating Groundwater Potential Zones in Vulnerable Groundwater Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5098, https://doi.org/10.5194/egusphere-egu26-5098, 2026.

Deep rock masses have complex internal structures, which results in significant discreteness and blocky structures. With the increase in the depth of engineering construction and energy extraction, the unique pendulum-type waves emerge in deep blocky rock masses under the action of dynamic loads from mining and blasting, and they are characterized by low frequency, low velocity, large displacement amplitude and high kinetic energy, distinguishing them fundamentally from conventional seismic waves. Pendulum-type waves can induce alternating stress states of relative compression and separation within blocky rock masses, and may lead to rockburst disasters and even engineering-induced seismicity, thus posing great challenges to the safety of underground engineering such as tunnel construction and mining. In this paper, experimental research is conducted on the mechanical behaviors and typical characteristics of pendulum-type waves of multi-fractured blocky rock masses under static and dynamic loads. Firstly, the strength, deformation and failure mode were analysized based on uniaxial compression tests. The weak structural layers will significantly reduce the uniaxial compressive strength and enhance the ultimate deformation capacity of rock masses. Fractured rock masses have significant nonlinear deformation and may develop macroscopic fractures (vertical splitting failure, with the failure mode transitioning from brittle failure to ductile failure) at the stress level significantly lower than their uniaxial compressive strengths. Subsequently, based on the dynamic impact tests, the dynamic response, overall displacement, wave velocity and the mechanism of anomalously low friction were investigated, and the typical characteristics of pendulum-type waves, including the low frequency (177 Hz and 153 Hz in this experiment, which are much lower than the natural frequency of the rock itself), low velocity (about 900 m/s in this experiment, which is significantly lower than those of P-waves and S-waves), large displacement amplitude (it is more than two orders of magnitude larger than the deformation of an intact rock under an identical load) and high kinetic energy (The total kinetic energy accounts for 40% and 28% of the total energy in this experiment, which has its particularity and cannot be ignored) were quantitatively described. This study holds significant research importance for understanding the nonlinear waves in deep fractured rock masses and their dynamic behaviors, as well as for preventing and controlling engineering disasters in deep rock masses.

How to cite: Jiang, K., Qi, C., and Hu, X.: Research on the mechanical behaviors of multi-fractured blocky rock masses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5765, https://doi.org/10.5194/egusphere-egu26-5765, 2026.

Predictability of river bank erosion in sinuous alluvial channels requires a combined study of the planform processes, hydraulics processes, sediment transportation, and the geotechnical properties of riverbanks. The research paper provides a detailed analysis of the evolution of channels within the Nabadwip-Kalna stretch of the Bhagirathi-Hooghly River (1990-2020). This analysis combines the synthesis of remote sensing, on-field surveys, lab experiments, and numerical model analysis into a multidimensional analysis. GIS was used through the Digital Shoreline Analysis System (DSAS) to measure changes on the bank-lines using historical satellite images of the same period of time. A two-dimensional migration coefficient (MC) model was used to model spatial-temporal changes in channel centrelines, and an RVR Meander was used to develop a model that takes into consideration depth-averaged flow velocity and reach-averaged hydraulic parameters. The characterisation of cross-sectional bathymetry and near-bank hydraulics was based on ADCP. The results of the geotechnical analysis showed that stratified streambanks showed critical shear stresses of 7.1-7.7 kPa, internal frictional angles of soils less than 30°–34°, and were predominantly affected by either cantilever collapse or piping as a result of varying maximum heights of streams between 5.7 and 6.8 metres. Bank stability through both BSTEM and BEHI was assessed, whereas sediment forecasting combined with SWAT to predict overbank flow and a Genetic Algorithm (GA) to estimate the total load. DSAS analysis on bank-line displacement revealed different erosion patterns within 170 transects, showing different RMSE of 0.090 to 0.162 in predicting zone boundaries. The MC method was able to model the 24-year centreline migration patterns, recording changes in the centreline-geometry parameters. Analysis of five cross-sections instrumented found instability and a factor-of-safety ratio of 0.81-0.95, resulting in 4.07-5.85m/yr and 4.35-7.15 km²/yr, respectively, lateral retreat and the eroded areas. Mean collapse rates were 0.125 to 0.198 m/yr, and the failure angle was 81°–87°. The maximum bank-failure mass was 41.24 kg (seasonal maximum), and the calibrated toe-scour mass was 0.28 kg. The GA model was tried using ten parameterisations and demonstrated the best prediction ability with the coefficient set at ten, where R² = 0.96 and mean relative error (MRE) = 42% gave significantly better performance than the traditional regression analysis (R² = 0.87 and MRE = 40%). There were also considerable changes in the area behind sandbar dynamics, that is, Nandai-Hatsimla increased by 11.87 ha in 1990 to 19.05 ha in 2020; Media by 39.7 ha to 57.68 ha; Char Krishnabati by 82.52 ha to 81.07 ha. Land-use/land-cover (LULC) predictions for 2040 indicated settlement expansion from 13.61% (2020) to 20.19%, with validation accuracy (RMSE = 0.253) confirming model reliability. This combined model shows that the combination of remotely sensed, field, laboratory, and model data provides quantitatively sound estimations of fluvial risks and forms the basis of evidence-based management of high-suspended riverine areas. The modular design can be applied to monsoon-dominated alluvial basins throughout the globe, which will promote adaptive land-use planning and long-term infrastructure development in the vulnerable riparian societies.

How to cite: Ghosh, A.: Unveiling integrated geo-hydraulic assessment of river meandering, bank erosion and sandbar dynamics in Alluvial channels, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7917, https://doi.org/10.5194/egusphere-egu26-7917, 2026.

Acoustic emission (AE) monitoring is widely applied to track damage development in brittle rock, although relating recorded signals to specific fracture mechanisms can remain uncertain, particularly when comparing thermal and mechanical loadings. This contribution presents a preliminary assessment of AE waveform characteristics measured during two heating-only experiments and two uniaxial compressive strength (UCS) experiments performed on 100 mm diameter basalt specimens containing a central axial circular hole. This geometry provides a consistent configuration that promotes stress redistribution and damage localisation around an opening, allowing fracture processes to be compared within a common specimen form.

Full AE waveforms were acquired throughout each test using broadband piezoelectric sensors coupled to the specimen surface, with pre-amplification and digital acquisition. Event features were extracted in the time and frequency domains, including rise angle, duration, hit counts, average frequency, peak frequency, peak amplitude, and amplitude distributions. Feature-space comparisons were then used to evaluate whether thermally and mechanically induced microfracturing exhibit separable signal characteristics.

The thermal experiments were associated with a single dominant fracture initiating along the shortest ligament between the aperture boundary and the nearest specimen edge. In contrast, UCS loading produced a more complex fracture network consistent with mixed tensile and shear microfracturing. Rise angle versus hits per duration plots indicated that thermal events occupied a more restricted region, whereas UCS events displayed a broader spread, which may reflect greater variability in source processes during complex damage evolution. Frequency-based comparisons further highlighted the differences: thermally induced events clustered mainly within a lower-frequency band (approximately 100-300 kHz), while the UCS tests exhibited an additional higher-frequency population (approximately 400-600 kHz), alongside the lower-frequency component. Amplitude distributions were also differed, with thermal events tending toward a narrower amplitude range relative to the wider distribution observed under UCS loading. Collectively, these observations suggest that the combined time-domain, frequency-domain, and amplitude-based AE features support mechanism-informed discrimination between thermally driven tensile fracture and mechanically driven complex fracture networks providing a basis for subsequent statistical or learning-based classification in coupled thermomechanical experiments

How to cite: De Alwis, A., Serati, M., Dyskin, A., Pasternak, E., Martin, D., and Williams, D.: Waveform signatures of acoustic emission from thermally and mechanically induced microfracture in centrally apertured basalt, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8596, https://doi.org/10.5194/egusphere-egu26-8596, 2026.

Partial blockage in open channels and urban drainage systems is a common issue arising from debris accumulation, sediment deposition, and inadequate maintenance, often resulting in reduced flow capacity and increased flood risk. Despite its practical relevance, the hydraulic effects of partial blockage on flow behaviour are not well quantified through controlled experimental studies. This work aims to investigate the influence of partial blockage on flow characteristics in open channels and explore its implications for urban stormwater drainage systems.Laboratory experiments are carried out in a rectangular open-channel flume under steady flow conditions. Velocity measurements are obtained at multiple depths for unblocked conditions and for different partial blockage configurations. Blockages of varying size and location are introduced manually to represent realistic obstructions commonly observed in urban drains. The changes in velocity distribution, water depth, and flow-carrying capacity due to partial blockage are analysed to understand the hydraulic response of the system.

Based on these observations, relationships between blockage extent and hydraulic performance are developed to identify critical blockage conditions.The study framework is applied to urban stormwater drainage networks using SWMM modelling to extend the experimental findings to real-world applications. Blockage scenarios are simulated in selected channels to assess their impact on system performance and flooding behaviour.

The outcomes of this study provide experimental insight into blockage-induced hydraulic effects and highlight the importance of considering partial blockage in urban drainage analysis. The combined experimental and modelling approach offers a practical basis for improving flood risk assessment and maintenance planning in urban stormwater systems.

How to cite: Mishra, A. K., Kumar, H., and Vinnarasi, R.: Assessment of Partial Blockage in Urban Drains for Flood Risk Reduction , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8813, https://doi.org/10.5194/egusphere-egu26-8813, 2026.

Rising flooding, which is exacerbated by both climate change and human behavior, demands proper identification of vulnerable zones. Conventional hydrological analysis can neglect geographical variability. In this study, a combined geospatial and decision-making process is used to determine the levels of vulnerability and risk of flooding in the Koshi River Basin in the state of Bihar.  The research work has developed a susceptible, vulnerable and risk map by integrating GIS, Remote Sensing and AHP. Weightings of eleven physical and hydrological factors and five socio-economic indicators were carried out in a systematic manner using a multi-criteria decision-making framework that allowed appropriate consideration of their relative contributions to flooding. Flood susceptibility, vulnerability and risk maps were created using the GIS environment's Weighted Overlay technique. According to the analysis, population density (41.6%) and literacy rate (24%) are controlling factors for flood vulnerability in the basin, whereas rainfall (23.9%), elevation (14.7%) and drainage density are the main elements that influence flood susceptibility. The Koshi basin is largely covered by the low and moderate classes of flood susceptibility, whereas a very minor amount (0.03%) comes under the high susceptibility classes, according to results from flood susceptibility maps. A significant section (42.87%) of the basin has moderate flood susceptibility due to a combination of exposure and socioeconomic characteristics, according to the results of the flood vulnerability analysis. According to the flood risk results, a significant amount of the basin (84.18%) has moderate flood risk, while a tiny portion has high flood risk in the low-lying, heavily inhabited areas close to the basin's riverbanks.  ROC-AUC for model validation yielded an accuracy of 66.3% and proved that the proposed GIS-AHP model was a reliable. Conclusion from this study underscore an integrating role in both physical and socio-economic considerations with prospects of enhancement through climate scenarios in flood mitigation and planning/early warning maps.

How to cite: Chaudhary, P. and Padhi, E.: Flood Hazard Analysis and Risk Assessment of Koshi River, Bihar (India) using Remote Sensing, GIS and AHP Techniques , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8975, https://doi.org/10.5194/egusphere-egu26-8975, 2026.

Block elbowing, the process in which rotating blocks push neighbouring blocks apart, influences both geological deformation and the stability of mining excavations in blocky rock masses. A clearer understanding of elbowing is essential for improving rock mass modelling and maintaining the safety of engineering structures. To this end, we analyse a chain of stiff blocks connected by springs, with one or two end active (driving) blocks – the blocks whose rotation is externally induced. All other - passive blocks - have translational and rotational degrees of freedom. The results show that block rotation is sequential (starting from driving blocks) producing a rotational wave with strongly configuration-dependent rotational patterns.

Opposite to a single driving block system, a double-driving block system exhibits more complex behaviour, as the active blocks may rotate in the same direction (Case I) or in opposite directions (Case II). In Case I passive blocks can exhibit anticlockwise rotation that is opposite to the clockwise rotating driving blocks, while in Case II all passive blocks do not rotate at all.

Further deformation patterns arise from block geometry, introduced by varying block corner rounding to represent spheroidal weathering. The results reveal a transition from reversible to irreversible passive block kinematics. Reversible responses include either clockwise rotation followed by full recovery or no rotation. The boundary between these types of block behaviour is defined by a linear relationship between the active-passive and passive-passive contact friction coefficients, with the intercept related to block corner rounding. In contrast, irreversible kinematics characterised by residual rotation emerge only for highly rounded blocks. This irreversible behaviour is restricted to short block chains and disappears in chains of five blocks suggesting a critical size of the Cosserat like zone with independent rotational degrees of freedom. This study provides new insights for modelling the stability and long-term evolution of blocky rock masses.

How to cite: Zhang, M., Dyskin, A., and Pasternak, E.: Non-linear rotational waves and complex rotation patterns in a chain of blocks with elbowing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8985, https://doi.org/10.5194/egusphere-egu26-8985, 2026.

The subject of this study is the process of hydraulic stimulation of a tectonic fault, leading to induced seismicity. We consider a scenario in which fluid injected near ​​an existing fault, causing a localized change in pore pressure and a reduction in effective stresses. This, in turn, initiates slippage of the fault segments and the formation of a slip zone, the size and slip velocity of which determine the magnitude of the resulting seismic events. The goal of this study was to develop a relatively simple model for estimating the potential magnitude of induced seismic events based on a limited set of governing parameters. The primary objectives of the study were to identify the key factors that have the greatest impact on the characteristics of the slip zone and to determine how fluid injection parameters (rate and injected fluid volume) affect earthquake magnitude by changing slip dynamics. The model obtained is based on the results of a series of numerical experiments analyzing the hydromechanical behavior of the fault under various injection conditions. The modeling was performed using a two-parameter rate-and-state friction law, which, unlike a single-parameter model, allows for a wider range of slip regimes to be simulated and accurately describes the transition from stable slip to dynamic failure.

The functional relationships were established between the initial system parameters and the key obtained slip characteristics. It was shown that the final slip zone length is almost linearly related to the length of the initial unstable zone, and the maximum slip velocity increases exponentially with increasing pore pressure rate. At the same time, in the area of high loading rates, the saturation of the sliding velocity is observed at a characteristic level, which leads to a limitation of the possible magnitudes of earthquakes induced by fluid injection.

How to cite: Turuntaev, S., Baryshnikov, N., and Riga, V.: Estimation of potential magnitudes of induced seismic events based on direct numerical simulation of fluid injection near an active tectonic fault., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11339, https://doi.org/10.5194/egusphere-egu26-11339, 2026.

Efficient water use in agriculture is crucial for sustainable water resource management, especially in areas experiencing increasing water scarcity. A critical yet often oversimplified component of irrigation planning is the estimation of water storage soil profile depth, commonly assumed to be 1-1.5 m as the root-zone depth based on practitioner experience rather than validated soil-water dynamics. Such assumptions introduce uncertainty and limit the reliability of irrigation scheduling decisions. This study presents a novel framework for estimating soil profile depth to store maximum water by integrating Richards’ equation, geotechnical soil column concepts, and the Soil Conservation Service Curve Number (SCS-CN) technique to derive an optimal soil profile depth that maximizes storage capacity based on measurable hydraulic and retention soil properties. By linking the water storage soil column depth with the SCS-CN parameter, for practical field applications such as irrigation scheduling and planning. The proposed framework improves model reliability and interpretability by replacing fixed-depth assumptions with soil-specific storage behaviour, thereby reducing uncertainty in irrigation water estimation. It enables consistent evaluation of field capacity, average soil moisture content, and maximum storage potential across soil types, leading to improved irrigation efficiency. By emphasizing physically constrained model selection, data-informed parameterization, and transparent decision-making metrics, this work enhances the reliability of hydrologic modeling and supports robust irrigation management under water-scarce conditions.
Keywords:  Water storage soil profile depth, Richards’ equation, Irrigation water management, Data-informed parameterization, SCS-Curve Number method.

How to cite: Sharma, D., Mishra, S. K., and Pandey, R. P.: From Empirical Assumptions to Data-Informed Decisions: A Reliable Water Storage Soil Depth Estimation Method, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13831, https://doi.org/10.5194/egusphere-egu26-13831, 2026.

The energy cascade rate (ε) depicts the energy transfer in a turbulent system. In incompressible magneto-hydrodynamic (MHD)  turbulence, ε is linked to the third-order structure function (Yaglom vector) via the Yaglom/Politano–Pouquet law in the inertial range. In this study, we compare three estimators of ε in anisotropic MHD turbulence: (1) the lag polyhedral derivative ensemble (LPDE) technique that reconstructs the divergence of the Yaglom vector via tetrahedral linear gradients; (2) a directional-averaged third-order estimator that evaluates the Yaglom vector along a finite number of lag directions and averages over solid angle; and (3) the Yaglom vector on 60 degree with respect to the mean magnetic field direction.  To ensure a fair comparison in more realistic MHD turbulence, we emulate a multipoint virtual mission within anisotropic three-dimensional MHD simulations with a guide field B₀ along the z-axis. This work illuminates the reliable regime for LPDE and directional-averaging methods, and also tests whether 60 degree Yaglom vector is an accurate estimate of ε, providing practical guidance in both simulation and observational turbulence analysis.

How to cite: Gao, Z., Yang, Y., Jiang, B., and Pecora, F.: Anisotropic energy transfer rate quantified by LPDE and directional averaging methods in MHD turbulence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15102, https://doi.org/10.5194/egusphere-egu26-15102, 2026.

Digital elevation models (DEMs) play a fundamental role in hydrological modeling by controlling watershed delineation, stream networks and runoff generation processes. This study assess the impact of global DEM product provided by Shuttle Radar Topography Mission SRTM and the Indian national CartoDEM developed by ISRO-Bhuvan (Indian Space Research Organisation-Bhuvan) on streamflow simulation using the Soil and Water Assessment Tool (SWAT) in the Ong River watershed (4650 sq. km), India. The study area is characterized by forest and cropland. Both DEMs, resampled to 30m resolution, were used as inputs to SWAT, along with meteorological data (IMD), land use/land cover data (Sentinel-2), and soil data (FAO). Streamflow data was sourced from Global Flood Awareness System discharge data (GloFAS). Model calibration (2011-2017) and validation (2018-2020) were performed using SWAT-CUP with the SUFI2 algorithm. Model performance was evaluated using Willmott's index of agreement, Nash-Sutcliffe Efficiency (NSE), R², PBIAS, and RSR. Results showed that both DEMs performed satisfactorily, with CartoDEM exhibiting slightly better performance (higher NSE and R², lower PBIAS and RSR) during both calibration and validation periods. Sensitivity analysis revealed that the runoff curve number was the most sensitive parameter, highlighting the impact of DEM selection on surface runoff simulation. The study concluded that CartoDEM is a preferable choice for hydrological modeling in similar catchments, though further research on stream accuracy and catchment delineation in diverse topographies can be explored.

How to cite: Prashant, P., Kumar Mishra, S., and Kumar Lohani, A.: Assessing the Impact of Digital Elevation Model Selection on Hydrological Predictions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16403, https://doi.org/10.5194/egusphere-egu26-16403, 2026.

Accurate prediction of sediment mobility in open channel flows is essential for effective river engineering and sediment management. This study examines the combined influence of flow depth and sediment grain size on near bed hydraulics and sediment mobility using high-resolution Acoustic Doppler Velocimeter (ADV) measurements in a controlled laboratory flume. Experiments were conducted over uniform sand beds with median grain sizes of d₅₀ = 0.321 mm and d₅₀ = 0.81 mm under four different flow depths (12cm, 15cm,18cm,21cm) and a range of flow velocities. Three dimensional velocity components were measured at multiple vertical locations throughout the flow depth, while water surface elevations were continuously monitored. Depth resolved ADV data were used to compute mean streamwise velocity, Reynolds shear stress, friction velocity, and turbulent kinetic energy for each sediment size and flow depth. Sediment mobility was assessed using the Shields parameter, estimated from ADV-derived bed shear stress, and compared with the critical Shields parameter at multiple velocity points for each depth. The results indicate that coarser sediment beds exhibit increased near-bed turbulence intensity and higher friction velocity across all flow depths, while yielding lower Shields parameter values relative to finer sediment beds. Comparisons across the four flow depths reveal that sediment mobility transitions from stable to mobile conditions depending on the combined effects of flow depth, sediment size, and local velocity magnitude. At lower velocities, Shields parameter values remain below the critical threshold, indicating stable bed conditions, whereas higher velocities at the same depth result in Shields values exceeding the critical limit, signifying active sediment motion. Depth wise velocity and turbulence profiles demonstrate that both flow depth and sediment roughness significantly modify near-bed hydraulic structure and bed shear stress distribution. The findings highlight the importance of accounting for depth-dependent flow structure and sediment characteristics when evaluating sediment mobility. This study provides a robust experimental framework for identifying stable and mobile sediment regimes and estimating sediment transport potential using high-resolution ADV measurements without direct sediment transport observations.

How to cite: Banothu, J. and Devi, K.: Effects of Flow Depth and Sediment Size on Near Bed Hydraulics and Sediment Mobility in Open Channel Flow, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18007, https://doi.org/10.5194/egusphere-egu26-18007, 2026.

Urban flooding poses a growing challenge for rapidly urbanizing cities, where climate change–driven increases in extreme rainfall, expanding impervious surfaces, and limited drainage capacity collectively exacerbate the frequency and severity of surface water inundation. In this context, understanding urban surface flood resilience, defined as the capacity of stormwater drainage systems to withstand, convey, and recover from intense rainfall events, remains essential for effective flood risk management and climate adaptation planning. The present study investigates urban surface flood resilience in Janakpur Sub-Metropolitan City, Nepal, a fast-growing urban center increasingly exposed to pluvial flooding. The study develops an integrated modelling framework using a 3-way coupled MIKE+ hydrodynamic model, integrated with intense spatial analysis using GIS, to evaluate the performance of the existing stormwater drainage system under extreme rainfall conditions. The model represents the urban drainage network and surface flow processes using drainage infrastructure data obtained from field surveys, terrain information derived from a high-resolution digital elevation model, and delineated urban catchments. To characterize rainfall extremes, the analysis employs long-term observed hourly rainfall records spanning 25 years to generate design storm events corresponding to multiple return periods. The modelling framework simulates system response for a representative extreme rainfall event and quantifies inundation dynamics across the urban landscape. The results shows that the coupled approach effectively captures critical flood hazard characteristics, including inundation depth, flow velocity, and the depth–velocity product, allowing for the spatial identification of highly vulnerable catchments and drainage bottlenecks. The findings provide actionable insights into the limitations of existing stormwater infrastructure and support the development of targeted adaptation strategies aimed at enhancing urban surface flood and drainage resilience. Overall, the study underscores the value of integrated hydrodynamic modelling for resolving location-specific flood behaviour and strengthening urban flood resilience assessments under evolving climatic and urbanization pressures.

How to cite: Singh, P., Deopa, R., and Mohanty, M. P.: Assessing urban surface flood resilience using hydrodynamic modelling under extreme rainfall conditions in urban catchment of Nepal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18481, https://doi.org/10.5194/egusphere-egu26-18481, 2026.

We present a one-layer global energy balance climate model with highly parameterized radiation, convection, and large-scale atmosphere/ocean macroturbulence. Planetary heat content is parameterized by a 2D in latitude-longitude layer characterized by a temperature field and a uniform constant heat capacity. Radiation is parameterized by mean-annual zonal average top-of-atmosphere solar irradiance. Radiative heating and cooling are parameterized by a uniform constant albedo and Stefan-Boltzmann emission with uniform constant emissivity. Convection is parameterized by a temperature threshold for convection which restricts the layer from warming beyond the threshold, effectively cooling the layer. Macroturbulence is parameterized by 2D barotropic turbulence forced at small scales and damped by Rayleigh friction. Energy conservation is maintained by balancing the convective cooling of the layer with the turbulent kinetic energy forcing, resulting in tropical forcing, while the frictional loss of kinetic energy is balanced by frictional heating of the layer. The parameterized energy transforming processes are characterized by timescales, which, for Earth-like planets, are ordered as t_radiation > t_{macroturbulence} > t_convection.

We investigate the model’s equilibrium climate state in terms of the meridional heat transport (MHT), the resulting zonally averaged temperature profile, and their fluctuations by simulating the system over many radiation times. For Earth-like parameters, despite the model’s extremely simplified dynamics, our simulations reveal a MHT profile comparable to the observed, annually averaged MHT on Earth, featuring a maximum in the mid-latitudes of approximately 5PW, a form of Bjerknes compensation. 

How to cite: van Kan, A., Weiss, J., and Knobloch, E.: Global climate dynamics in a highly parameterized radiative-convective-macroturbulent energy balance model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18820, https://doi.org/10.5194/egusphere-egu26-18820, 2026.

High-resolution climate projections are essential for climate impact, vulnerability, and adaptation studies, particularly over regions with strong spatial heterogeneity such as Brazil. Although CMIP6 global climate models (GCMs) provide valuable information on future climate change, their coarse spatial resolutions, typically ranging from 100 to 200 km, limit their direct application at regional and local scales. Statistical downscaling techniques offer computationally efficient alternatives to dynamical downscaling, but their relative performance and added value remain insufficiently assessed over Brazil.

In this study, we compare two statistical downscaling approaches applied to a subset of CMIP6 models previously evaluated by Bazanella et al. (2024) – 10.1007/s00382-023-06979-1 – and identified as skillful in representing Brazilian climate: (i) a bilinear interpolation method followed by percentile-to-percentile bias correction, and  (ii) machine learning–based downscaling approaches. The original GCM outputs are interpolated to a common high-resolution grid of 10 km × 10 km using bilinear weights, providing a physically consistent reference framework. In parallel, ML-based models are trained using historical GCM predictors and high-resolution reference climate datasets to learn nonlinear relationships and generate high-resolution climate fields.

The performance of both approaches is evaluated for the historical period in terms of mean climatology, spatial patterns, and variability. Future projections under the SSP2-4.5 and SSP5-8.5 scenarios are then analyzed to assess regional climate change signals and associated uncertainties. Results assess the extent to which ML-based downscaling provides added value relative to bilinear interpolation, particularly for variables with strong spatial heterogeneity, such as precipitation and temperature extremes, while also evaluating the ability of the approach to preserve the large-scale climate signals projected by the driving CMIP6 models. This comparative analysis provides insights into the applicability, robustness, and limitations of statistical and ML-based downscaling methods for regional climate assessments over Brazil.

How to cite: Jatobá Santos, D., Goracci, G., Alves Martins, M., and Schneider, R.: From bilinear interpolation to machine learning: a comparative assessment of statistical downscaling methods for CMIP6 projections over Brazil, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19471, https://doi.org/10.5194/egusphere-egu26-19471, 2026.

Accurate estimation of evapotranspiration (ET) is critical for various applications in hydrology and agricultural water management. However, direct observations of ET, specially its spatial variation, in time-consuming and cumbersome, thus necessitating the need to use of indirect methods for its estimation. In this study, stomatal conductance data is used in conjunction with bio-physical parameters of wheat crops for deriving the spatially varied estimates of ET (ET_SC) for different irrigation treatments using the Penman-Monteith equation. For this, five treatments, including drip (DI) and flood (FI) irrigated treatments were used in the study, namely fully irrigated (DI)), 50% MAD (maximum allowable deficit) (DI), 50% MAD (FI), farmer fields replication (FI) and rain-fed treatment.

The ET_SC estimates are also compared to the ET estimates derived using a method based on field water balance (ET_WB). When compared with the ET_WB values, the ET_SC estimates compared well particularly for the irrigated treatments. The average root mean square error (RMSE) of ET_SC estimates in comparison to ET_WB values are 0.11, 0.2, 0.23 and 0.26 mm/day for fully irrigated, 50% MAD (FI), 50% MAD (DI) and farmers field replication treatments, respectively. The corresponding RMSE value (0.47 mm/day) for the rain-fed treatment are found significantly higher than the irrigated treatments indicating the limitation of the approach in high water stress conditions. The differences between ET_SC andET_WB values also increase significantly during the end-season stage when the wheat crop is close to maturity. Overall, the results demonstrate the robustness of the proposed approach in estimating the spatial variation of ET using the Penman-Monteith method in conjunction with the on-field field stomatal conductance observations.

How to cite: Upreti, H. and Yadav, M.: Evaluation of Penman-Monteith estimates of evapotranspiration derived using field-collected stomatal conductance observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20031, https://doi.org/10.5194/egusphere-egu26-20031, 2026.

Understanding the spatial distribution and intensity of climate-related hazards is essential for effective risk assessment and adaptation planning.  This study presents a comprehensive analysis of climate hazard indices applied across all IPCC reference regions, using all the available CMIP5-driven regional climate model (RCM) simulations at 25 km resolution over the CORDEX domains, together with Euro-CORDEX simulations at 12 km resolution. The objective is to identify climate hazard hot spots through the formulation of a composite hazard index. 

A subset of hazard indicators representing key climate extremes is selected. Temperature- and heat-stress–related hazards are characterized using TX90p (extreme maximum temperature), TN90p (extreme minimum temperature), and the NOAA Extended Heat Index (HI). Heavy precipitation and drought-related hazards are represented by RX1DAY (maximum 1-day precipitation), P99 (99th percentile of precipitation), and CDD (consecutive dry days).

The composite index integrates both the frequency and intensity of extremes and is computed at both regional and grid-point levels. A normalization approach is used to ensure comparability across regions with diverse climatic characteristics. Results reveal pronounced spatial heterogeneity in hazard intensity, highlighting regions where multiple hazards converge and amplify overall risk. This framework enables systematic identification of global and regional climate hot spots, offering insights into areas that may face heightened climate stress under current and projected conditions. By providing a consistent, region-wide assessment of hazard exposure, this study aims to support comparative climate risk analyses and inform policy-relevant decision-making for climate adaptation and resilience strategies at multiple scales.

How to cite: Zazulie, N., Raffaele, F., and Coppola, E.: Global Hot Spots of Climate Extremes from Composite Hazard Indices, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21173, https://doi.org/10.5194/egusphere-egu26-21173, 2026.

Reliable river discharge simulation generally relies on observed streamflow data for model calibration; however, such observations are often uncertain or unavailable in data-scarce regions, limiting the applicability of conventional hydrological models. This study presents a hybrid modeling framework that uses soil moisture as an alternative calibration variable to improve discharge simulations in the absence of reliable streamflow observations. The framework couples a two-layer version of the daily lumped MISDc (Modello Idrologico Semi-Distribuito in continuo) hydrological model with a Feedforward Neural Network (FFNN), which is employed to enhance parameter calibration by exploiting soil moisture dynamics. The proposed approach is evaluated across three contrasting basins: Tahanaout in semi-arid Morocco, and Colorso (Italy) and Bibeschbach (Luxembourg) in temperate climates. Both in situ and ERA5-Land soil moisture datasets are used as calibration inputs. Model performance is assessed using multiple hydrological metrics, including Mean Absolute Error (MAE), Kling-Gupta Efficiency (KGE), and the correlation coefficient (R). Results show that the hybrid MISDc-FFNN framework substantially improves river discharge simulations compared to the traditional model. Across all basins, MAE is reduced by up to 61%, KGE increases by more than 200%, and R improves by up to 87%, with consistent performance gains observed for both observed and reanalysis-based soil moisture. These findings demonstrate the potential of soil moisture driven calibration strategies to enhance hydrological modeling in data-scarce environments, offering a viable pathway for improved water resources assessment and flood risk management where discharge observations are limited or unreliable.

Keywords: Soil moisture; river discharge simulation; hydrological modeling; machine learning; ERA5-Land; data-scarce regions; feedforward neural network

How to cite: Ait Naceur, K., El Khalki, E. M., Brocca, L., Hadri, A., Jaffar, O., Rachdane, M., Simonneaux, V., Saidi, M. E. M., and Chehbouni, A.: Soil Moisture Based Calibration of a Hybrid Hydrological-Neural Network Model in Data Scarce Basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21830, https://doi.org/10.5194/egusphere-egu26-21830, 2026.

Present-day practices of bridge piers design often employ group arrangements of piers in various configurations to modify flow dynamics and mitigate subsequent scour formation around the piers. These group arrangement configurations may vary in aspects of spacing ratio, number of piers, and orientations to alter the flow-structure interaction, and hence the scour development. Investigating the turbulent flow behaviour around various common group arrangements has been a topic of interest for researchers for a few years now. This study presents an experimental investigation aimed at comparing the equilibrium scour depth caused by various four-pier group arrangements. To assess the impact of spacing, the face-to-face distance between piers (G) was taken to values of D, 2D, and 3D, where D refers to the diameter of the circular pier. The scour patterns reveal that the maximum scour depth occurred when spacing G was equal to D. The equilibrium scour depth decreased with an increase in the pier spacing to 2D and 3D, corresponding to an approximate flow intensity of 0.9. The scour contours exhibit the impact of neighbouring piers and how it differs with an increase in pier spacing. Instantaneous velocity data were collected to derive the flow characteristics in the flow field. Velocity vectors depict the influence of different configurations on the flow pattern. The study provides an insight into the spacing effects on equilibrium scour, which can be useful in the design of pier group arrangements.

How to cite: Sahu, C.: Spacing Effect on the Equilibrium Scour and Flow Pattern around Four-Pier group in Different Configurations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22153, https://doi.org/10.5194/egusphere-egu26-22153, 2026.

How to cite: Thurler Rondon da Fonseca, J. F., Schertzer, D., da Silva Rocha Paz, I., and Tchiguirinskaia, I.: Analysis of Vector-Field Multifractal Cascades, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23043, https://doi.org/10.5194/egusphere-egu26-23043, 2026.

The 3D-Var method for data assimilation estimates atmospheric states by minimizing a cost function that measures the mismatch between model forecasts and observations, weighted by their error covariances. Standard implementations employ preconditioned conjugate-gradient (CG) solvers. CG performs well for quadratic cost functions under Gaussian error assumptions, but in nonlinear or non-Gaussian settings, the overall minimization process may converge to suboptimal local minima. These conditions are characteristic of aviation turbulence assimilation, where measurements are spatially and temporally sparse, exhibit heterogeneous uncertainty, and involve nonlinear relationships between observed quantities and model states.

This study develops a turbulence reanalysis by assimilating Eddy Dissipation Rate forecasts from the COSMO time-lagged ensemble with turbulence observations derived from Mode-S EHS radar, as well as AMDAR and AIREP reports. To address the limitations of CG-based optimization in this nonlinear, non-Gaussian setting, we implement a hybrid metaheuristic framework combining Simulated Annealing, Particle Swarm Optimization, and Differential Evolution with local Quasi-Newton methods. The algorithm dynamically exchanges information between exploration and exploitation phases to avoid premature convergence to suboptimal solutions.

We benchmark the hybrid metaheuristic 3D-Var against the conventional CG approach, evaluating convergence characteristics, computational efficiency, and accuracy of analysis. Results will demonstrate whether the hybrid approach can improve solution stability and quality in nonlinear, non-Gaussian data assimilation problems.

How to cite: Zakrzewski, G. and Mańdziuk, J.: Hybrid metaheuristic optimization for variational data assimilation in turbulence reanalysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-512, https://doi.org/10.5194/egusphere-egu26-512, 2026.

A non-autonomous system can undergo a rapid change of state in response to a small or slow change in forcing, due to the presence of nonlinear processes that give rise to critical transitions or tipping points. Such transitions are thought to exist in various subsystems (tipping elements) of the Earth’s climate system. The Atlantic Meridional Overturning Circulation (AMOC) is considered a particular tipping element where models of varying complexity have shown the potential for bi-stability and tipping. Quasipotentials are a useful mathematical tool for understanding the ‘potential’ of such a system, where the potential cannot be calculated analytically, or may not exist. Quasipotentials can be used to calculate useful features such as minimum action paths and transition times, based on a purely stochastically forced system. In this work, we utilise an Ordered Line Integral Method (OLIM) of Cameron et.al. (2017) to estimate quasipotentials for a 2-dimensional AMOC box model with anisotropic noise estimated from complex model output. We also examine how the quasipotential depends on the anisotropy of the noise, calculate minimum action paths between stable states for these various scenarios, and how the quasipotential changes as an external forcing is increased. We also extend this model and the OLIM to 3-dimensions and explore different statistical features.

How to cite: Chapman, R., Ashwin, P., and Wood, R.: Quasipotential analysis of tipping points for a box model of the Atlantic Meridional Overturning Circulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1540, https://doi.org/10.5194/egusphere-egu26-1540, 2026.

In SIAM J. Applied Dynamical Systems, 12 (2013), pp. 2068-2092, Widiasih proposed and analyzed a deterministic one-dimensional Budyko-Sellers energy-balance model with a moving ice line. In the present paper, we extend this model to a stochastic setting and study it within the framework of stochastic slow-fast systems. In the limit of a small parameter, we derive the effective ice-line dynamics as a solution to a stochastic differential equation. This stochastic formulation enables the investigation of coexisting (metastable) climate states, transition dynamics between them, stationary distributions, bifurcations, and the system’s sensitivity to perturbations. This talk is based on the joint work with M. Ritsch, SIAM J. Applied Dynamical Systems, 23(3), pp. 2061-2098.

How to cite: Pavlyukevich, I.: Stochastic Energy-Balance Model With A Moving Ice Line, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1795, https://doi.org/10.5194/egusphere-egu26-1795, 2026.

We investigate the lifetime of dynamical regimes under the impact of noise motivated by models of the atmosphere. One may expect that the inclusion of noise tends to make the system leave prescribed regions of the state space faster. However, for relevant systems with complexities ranging from phenomenological toy models to models of atmospheric dynamics, this intuition has proven misleading. As long as the noise is sufficiently small, the noisy system stays in regimes of interest on average longer than its deterministic counterpart, an effect we call "stochastic inertia''. This phenomenon has been observed through extensive numerical simulations for different noise levels. We propose a numerical technique for testing the occurrence of stochastic inertia, constructing, for any fixed noise level, a Markov chain on the set of points given by a  sufficiently long trajectory of the system without noise. The method is shown to correctly predict the presence of stochastic inertia in simple systems, and its utility is demonstrated on a paradigm model of atmospheric dynamics.

How to cite: Schoeller, H., Chemnitz, R., Koltai, P., Engel, M., and Pfahl, S.: Regime persistence through noise - A data-driven approach using deterministic trajectories, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2772, https://doi.org/10.5194/egusphere-egu26-2772, 2026.

A refined 4D-Var assimilation system within DestinE allows us to assimilate the Meteosat-10/SEVIRI clear-sky radiances over Europe, as well as globally at a spatial scale of 75 km instead of the previous 125 km in the ECMWF Integrated Forecasting System (IFS). Higher resolution observations can potentially improve the analysis and therefore the prediction of extreme weather events over Europe, as well as globally. The effects of using higher resolution observations have been investigated with a detailed set of experiments and the impact on wind, temperature, and humidity has been evaluated. A broad range of experiments indicate that exploiting the higher spatial density clear-sky radiances leads to an improvement of humidity sensitive fields in short-range forecasts with the IFS as independently measured for example by instruments on low-Earth-orbiting satellites (IASI, CrIS, SSMIS, or ATMS). Due to a reduced representativeness error, these changes further lead to improvements in longer range forecasts as these errors would propagate upscale nonlinearly. Our experiments show an upscale propagation of initially very localised increments in the analysis fields of vertical wind, as well as humidity above the Pacific or the North Atlantic. Over the first 25 days of cycling, these incremental improvements from the 4D-Var system lead to an improvement in forecast scores of the IFS. Such a configuration with globally denser radiances will go into the next IFS Cycle 50r1. In the DestinE 4 km analysis, spatial error correlations are significantly reduced, e.g., for Meteosat-10/SEVIRI above Europe, highlighting the potential of high resolution data assimilation, as a reduction in spatially correlated errors leads to more accurate inital conditions, and globally improved forecasts up to 5 days ahead.

For the chosen configuration with spatially denser observations every 75 km globally at the sub- mesoscale, we focus on assimilating geostationary satellite observations at sub-hourly timescales every 10 minutes. For that purpose, we assimilate the pre-processed GOES-16-18/ABI observations by NOAA, as well as HIMAWARI-9/AHI by the Japanese Meteorological Agency (JMA), every 10 min, 20 min and 30 min. Exploring how to best assimilate relatively small spatial and temporal scales for these geostationary satellites, will allow us to approach a higher resolution for the whole MTG/FCI satellite series above Europe. Thereby, single cycle experiments with a 4 km global analysis reveal the impact of wind tracing in 4D-Var. In combination with the spatially and temporally denser observations, we further discuss the impact of diabatic heating on the role of establishing a meridional circulation that significantly improves wind, temperature and humidity over the southern oceans.

How to cite: Schröttle, J., Lupu, C., and Burrows, C.: On the benefits of assimilating clear-sky radiances every 75 km globally at sub-hourly time scales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3556, https://doi.org/10.5194/egusphere-egu26-3556, 2026.

El Niño–Southern Oscillation (ENSO) is a prominent driver of global climate variability, with significant impacts on ecosystems and societies. While existing empirical–dynamical forecasting methods, such as Linear Inverse Models (LIMs), are limited in capturing ENSO’s inherent nonlinearity, Koopman operator theory offers a framework for analyzing such complex dynamics. Recent advancements in Koopman-based methods, such as DMD-based approaches, have enabled exploration of nonlinear ENSO-related modes. However, they suffer from challenges in robustness and interpretability. Specifically, k-EDMD algorithms tend to produce a large number of modes, complicating their physical relevance and reliability. In this study, we address these limitations by employing Colbrook’s Residual DMD framework as a tool to classify and prioritize modes based on their residuals. Together with the application of pseudospectrum theory, this approach enables us to systematically identify robust and physically meaningful modes, distinguishing them from less reliable counterparts. Furthermore, leveraging the property that eigenfunctions of Koopman operators can generate higher-order harmonics through powers and multiplications, we introduce a methodology to detect fundamental modes and their associated harmonics. Applying this framework to tropical Pacific SST data, we demonstrate that k-EDMD, together with ResDMD, is capable of isolating fundamental modes of tropical SST dynamics. These modes not only provide insights into the system’s physical evolution but also prove highly effective in reproducing the Niño3.4 index and in generating forecasts that outperform state-of-the-art LIM-based predictions. By systematically identifying, interpreting, and exploiting these modes, we establish a pathway to overcome the limitations of conventional Koopman-based methods, thereby enhancing their applicability for studying and forecasting complex climatic systems like ENSO. This study underscores the potential of ResDMD to refine mode selection in Koopman spectral analysis, paving the way for robust, physically interpretable, and predictively powerful insights into tropical SST variability.

How to cite: Lorenzo Sánchez, P., Colbrook, M., and Navarra, A.: Residual Ordering of Koopman Spectra for the Identification of Tropical Fundamental Modes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4078, https://doi.org/10.5194/egusphere-egu26-4078, 2026.

Data-driven emulation of chaotic dynamics in the Earth system is a central challenge in modern climate science. Low-dimensional systems such as the Lorenz-63 model, derived in the context of atmospheric convection, are commonly used to benchmark system-agnostic methods for learning dynamics from data. Here we show that learning from noise-free observations in such systems can be achieved up to machine precision: using ordinary least squares regression on high-degree polynomial features with 512-bit arithmetic, our system-agnostic method matches the accuracy of standard numerical ODE solvers using the systems' governing equations. For the Lorenz-63 system, we obtain valid prediction times of 36 Lyapunov times, and even up to 105 Lyapunov times with favorable precision configurations, dramatically outperforming prior work, which reaches 13 Lyapunov times at most. We further validate our results on Thomas' Cyclically Symmetric Attractor, a non-polynomial chaotic system that is considerably more complex than the Lorenz-63 model, and show that similar results extend to higher dimensions using the spatiotemporally chaotic Lorenz-96 model. Our findings suggest that learning low-dimensional chaotic systems from noise-free data is a solved problem.

How to cite: Schötz, C. and Boers, N.: Machine-Precision Prediction of Low-Dimensional Chaotic Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4443, https://doi.org/10.5194/egusphere-egu26-4443, 2026.

In classical low‑dimensional chaotic systems, small initial‑condition errors grow exponentially on average in the tangent‑linear regime, with a rate set by the leading Lyapunov exponent, before entering a nonlinear regime in which the growth follows a quadratic law and saturates at a finite error amplitude. In systems with coupled temporal and spatial scales, the growth of initial‑condition errors is scale‑dependent and is most appropriately described by a power‑law behavior. We demonstrate how the parameters of the power law are linked to the intrinsic properties of individual scales and to the coupling between them. In systems where the model does not perfectly represent reality due to the omission of small temporal and spatial scales, the mean growth of model error (in the absence of initial‑condition error) can be approximated by a quadratic law with an additional parameter characterizing model error. To describe this process, we extend Orrell’s definition of drift by interpreting its generation at each time step, within our hypothesis, as an effective initial‑condition error that evolves according to classical chaotic growth. Based on this hypothesis, we explain the values of the parameters governing the model‑error growth law. The interpretations of the parameters and the underlying hypotheses are tested using multiscale atmospheric Lorenz systems. 

How to cite: Bednar, H. and Kantz, H.: Analysis of Forecast Error Growth in Atmospheric Multiscale Lorenz Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4975, https://doi.org/10.5194/egusphere-egu26-4975, 2026.

Occurrence of cold spells in different North American regions has been related to concurrent wet and windy extremes in Western Europe. This link is driven by an anomalous state of the North Atlantic storm track. Two dynamical pathways have been defined as potential origins of the Pan-Atlantic compound extremes. The first pathway is linked to a Rossby wave train propagating from the Pacific toward the Atlantic, associated with a pronounced Alaskan ridge. The second pathway is characterized by the presence of a high west of Greenland, that favors simultaneously a southward displacement of a trough over eastern USA and an upper level trough over South western Europe. This study investigates the predictability of flow associated with cold spells over north America from a dynamical systems perspective, with a focus on the underlying diversity of atmospheric states and wave processes.

We start by assessing the intrinsic predictability of these two pathways using the ERA5 reanalysis and dynamical systems indicators. These indicators can be used as proxies for the predictability of each pathway. We also examine the predictability of those two pathways across different climatological periods. We further explore how variations in Rossby wave behavior and stratospheric anomalies modulate the predictability of these cold spells. We complement this analysis using the ECMWF ensemble reforecasts at different lead times, and computing skill scores for the two pathways. This help to provide new insights into the dynamical precursors and sources of predictability for compound cold and windy extremes across the North Atlantic sector.

How to cite: Krouma, M. and Messori, G.: Assessment of the predictability of cold-wet-windy Pan Atlantic compound extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5109, https://doi.org/10.5194/egusphere-egu26-5109, 2026.

Ocean convection is an essential component of the climate system. In the Labrador Sea of the North Atlantic, convection can be particularly deep and intense, forming the downward branch of the Atlantic Meridional Overturning Circulation (AMOC). Increased freshwater input to the Labrador Sea resulting from melting Greenland ice caps puts convection at risk of shutting down. This could weaken the AMOC and would have wide impacts on global climate. Here, we represent ocean convection in a two-box model with seasonal forcing. The model may exhibit various convective regimes, including where convection is permanently shut down. Despite its simplicity, the model reproduces the observed variability well. We explore the possible climate regimes of the two-box model by fitting its parameters to a variety of observation-based datasets, including the Arctic Subpolar gyre sTate Estimate (ASTE), gridded Argo data and CMEMS reanalysis. We construct bifurcation diagrams showing the proximity of the system to a deep convective shutdown. Results suggest that in the Labrador Sea this shutdown is not as close as suggested in previous literature. Our approach allows a deeper understanding of the dynamics of a deep convective shutdown and provides improved estimates of deep convective stability.

How to cite: Lewin, S., Oltmanns, M., Wilson, C., Berloff, P., and Shepherd, T.: A dynamical systems analysis of deep ocean convection with applications to the subpolar North Atlantic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5179, https://doi.org/10.5194/egusphere-egu26-5179, 2026.

Heat poses a critical risk to human health around the world. Recent work has investigated how anthropogenic climate change can modulate atmospheric circulation patterns, finding that circulation patterns increasing in frequency are associated with high temperatures in Europe. Here, we investigate the role of these changes in the dynamics of the atmosphere for European heat-related mortality. Specifically, we identify circulation patterns whose occurrence has become either more or less frequent over past decades. We couple this with an epidemiological framework, which uses an advanced regression model to compute associations between temperature and mortality. This association accounts for lags extending up to three weeks, and is fit for each subnational region within our dataset, which covers almost all of Europe. This allows us to estimate the heat-related mortality burden associated with circulation patterns that have changed in frequency. We find that dynamical changes have reinforced the thermodynamic warming trend, and are associated with increased heat-related mortality in northern and central continental Europe. Furthermore, dynamical changes appear to have played an important role for the extreme temperatures of the European summer of 2003, and the associated heat-related mortality. We thus highlight the importance of considering the role of changes in atmospheric circulation patterns when investigating the role of climate change for heat events and their impacts. Furthermore, we argue that heat action plans should consider the possibility of record-shattering heat events, where dynamical changes contributing to anomalously high temperatures could coincide with the peak of the seasonal temperature cycle, as seen in 2003. 

How to cite: Holmberg, E., Ballester, J., Faranda, D., Méndez Turrubiates, R., and Messori, G.: Changes in atmospheric circulation patterns are associated with increased European heat-related mortality, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5296, https://doi.org/10.5194/egusphere-egu26-5296, 2026.

This study introduces a new methodology for diagnosing atmospheric circulation associated with surface extremes in modal space. The approach is conceptually similar to spherical harmonics analysis but employs Hough harmonics as basis functions. These harmonics arise from the linearised primitive equations and form an orthogonal basis. Projection onto this basis yields complex Hough expansion coefficients that describe the amplitudes and phases of the modal contributions to the global three-dimensional fields. Each Hough coefficient is indexed by zonal wavenumber, meridional mode, and vertical structure function. The orthogonality of the modes allows a decomposition of the total energy into the energy of the zonal mean flow and the energies of different wave components.

The method is applied to global reanalysis datasets and to a subset of CMIP5 climate model simulations from 1980 onwards. Reconstructed circulation fields, obtained by inverse projection onto wind and geopotential using scale-selective filtering, indicate that Eurasian heatwaves (EHWs) are primarily driven by large-scale anticyclonic systems. This agrees with previous dynamical studies and supports the physical interpretability of the diagnostic. Probability distribution functions of Rossby wave energies are computed separately for the zonal mean, for planetary-scale, and for synoptic-scale zonal wavenumbers, focusing on barotropic structures in the troposphere. The corresponding energy time series are well described by chi-square distributions, and the skewness indicates about a 50% reduction in the effective degrees of freedom of planetary-scale circulation during EHWs.

This reduction is not observed in the CMIP5 simulations, which points to systematic model deficiencies. The models reproduce present-day surface EHW characteristics and associated Rossby wave patterns reasonably well, but struggle to reproduce day-to-day circulation variability observed in reanalyses. This limitation reduces confidence in projections of future changes in heatwaves and their related large-scale circulation. The results suggest that metrics describing intrinsic variability should be included as complementary to existing ones when evaluating simulations of heatwaves and associated circulation.

Overall, the diagnostic provides a holistic dynamical view of the variability spectrum of Rossby waves linked to surface extremes. It enables scale-selective filtering of variability in physical space and reveals statistical properties in modal space, offering a useful tool for model assessment and for studying complex atmospheric dynamics.

How to cite: Strigunova, I.: Modal-space statistics of Rossby waves during Eurasian heatwaves: implications for circulation dynamics in reanalyses and climate models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5650, https://doi.org/10.5194/egusphere-egu26-5650, 2026.

Connecting the different levels of the hierarchy of mathematical and conceptual complexity at which climate models operate, and comparing the assumptions that apply at each level, and the results produced, has led to much progress in climate science.  A particularly notable success was Klaus Hasselmann’s use of Brownian motion to inspire his linear Markovian stochastic energy balance model (EBM) and its successors . Another informative, but lateral, connection and comparison is that between either studying climate through the lens of stochastic physical models and doing so via statistical methods. This presentation showcases how comparing these approaches can sometimes surprise us.

It has been asserted that because the Hasselmann stochastic EBM has a mean-reverting term due to feedbacks, this property must also be detected in global mean temperature time series by statistical models such as the well-known Box-Jenkins ARIMA family. Conversely its absence has been taken as an indication of fundamental difficulties with anthropogenic driving. By fitting Hasselmann models, with and without anthropogenic driving, to an ARIMA model with automatically selected parameters I will show that in this instance the absence of a prominent autoregressive term can have quite the opposite meaning and  instead be a clear indication of strong driving. I will present results of our ensemble study which is examining the ability of automatic fitting to correctly infer ARIMA parameters on EBMs with realistic values of heat capacity and other system variables. Progress in extending the study to fractional EBMs and to ARFIMA models will be discussed.

How to cite: Watkins, N. W. and Stainforth, D.: What do we learn from looking at the Hasselmann model through 2 lenses ? Stochastics meets statistics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5791, https://doi.org/10.5194/egusphere-egu26-5791, 2026.

Sea ice is a multiscale composite displaying complex structure on length scales ranging over many orders of magnitude. Finding the effective properties relevant to large-scale dynamics and thermodynamics is a central challenge in modeling and predicting sea ice behavior, similar to finding macroscopic behavior from microscopic laws in statistical mechanics. Integral representations for the homogenized properties of composites, where the microstructural geometry is encoded into the spectrum of a random operator, have opened up new theoretical and computational approaches to sea ice modeling. We’ll give an overview of how they’re being used to study sea ice electromagnetics, thermal transport, wave-ice interactions, and advection diffusion processes at the floe scale. They also allow us to connect sea ice to random matrix theory, uncertainty quantification, and exotic materials such as twisted bilayer graphene.

How to cite: Golden, K.: Multiscale homogenization and random matrix theory for sea ice, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6058, https://doi.org/10.5194/egusphere-egu26-6058, 2026.

This study introduces a novel sequential data assimilation method that uses conditional denoising score matching (CDSM). The CDSM leverages iterative refinement of noisy samples guided by conditional score functions to achieve real-time state estimation by incorporating observational constraints at each time step. Unlike traditional methods, such as variational assimilation and Kalman ffltering, which rely on Gaussian assumptions and can be computationally expensive because of iterations or ensembles, CDSM is based on stochastic differential equations (SDEs). It does not require explicit noise addition or manipulation of probability density functions, thus simplifying the assimilation process and enhancing the computational efficiency. Here, error growth and reduction were modeled using noise addition and denoising processes based on SDEs. This transforms the data assimilation problem into a denoising problem based on conditional score matching. Our approach integrates dynamic models, performs data assimilation through Langevin dynamics at the observation times, and uses the analyzed states for subsequent integration. The noise addition process is embedded in the score model training using neural networks and is not explicitly used in the assimilation process. The results from twin experiments using the Lorenz ‘63 model demonstrate that the CDSM achieves a performance comparable to that of traditional methods in nonlinear systems. This method is robust and flexible with low requirements for training data quality. This is particularly suitable for scenarios in which the observation intervals are much larger than the model integration steps. The CDSM shows great potential for application inlarge-scale numerical and data-driven models.

How to cite: Shen, Z.: A Novel Sequential Data Assimilation by Conditional Denoising Score Matching, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6704, https://doi.org/10.5194/egusphere-egu26-6704, 2026.

How to cite: Brolly, M.: Trajectory-based probabilistic learning is essential for representing unresolved dynamics in chaotic systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6847, https://doi.org/10.5194/egusphere-egu26-6847, 2026.

The 2022 launch of the SWOT satellite (Surface Water and Ocean Topography) enabled sea surface height observations at unprecedented high resolution of approximately 2 km. These measurements are used to generate sea surface current maps by applying the geostrophic balance equation. At these fine scales, intense small-scale eddies become visible. These eddies exhibit strong ageostrophic behavior driven by non-linear advection, with Rossby numbers larger than 1. Theoretical work indicates that geostrophic current estimates can overestimate or underestimate actual current velocities by approximately a factor of 2 for ageostrophic cyclones and anticyclones respectively. This makes solving the gradient-wind equation essential for accurate representation. Earlier efforts to address this challenge employed explicit iterative finite difference schemes, which are known to lose stability when Rossby numbers exceed 1. We present a novel approach using a semi-implicit finite difference method. Our method is first tested against analytical solutions in a simplified framework, then validated using a 1 km resolution primitive equation ocean model. We demonstrate the method's application to SWOT observations of an intense oceanic submesoscale cyclone.

How to cite: Collin, J., Volorio-Galéa, A., and Rivière, P.: Stable gradient-wind balance at high Rossby numbers: A semi-implicit method applied to high-resolution Ocean satellite data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7147, https://doi.org/10.5194/egusphere-egu26-7147, 2026.

One of the main issues faced by climate models is the presence of biases due to uncertainties in model parameters. Here, we set out to constrain such parameter values by reducing the mismatch between a climate model's equilibrium state and ground-truth observations through the minimisation of a cost function, using Bayesian optimisation. We illustrate this method on the parametrisation of the ocean vertical diffusivity $\kappa$, first as a proof-of-concept in a conceptual ocean model, then in VEROS, a global ocean model of intermediate complexity. In the first case, we can artificially introduce an error in $\kappa$ and show that Bayesian optimisation allows us to retrieve its true value. In the case of VEROS, we aim at improving the model's description of the Atlantic Meridional Overturning Circulation (AMOC), so we can compare the simulated AMOC strength to the measured mean AMOC strength over the past two decades.

However, the equilibrium state of a model depends on the model parameters. Since we are modifying these parameters at each Bayesian iteration, the equilibrium state of the model needs to be recomputed every time in order to be compared to observations. In climate models, equilibria are usually computed through spin-ups, or trajectories of typically several thousands of years. But this method is extremely costly and does not guarantee that all model variables have converged to the equilibrium, since they evolve on a large range of time scales. On the other hand, Anderson Acceleration (AA) is an iterative method designed to solve fixed-point equations for any dynamical system much more efficiently than using direct integration. Indeed, AA determines at each iteration an educated guess of the position of the equilibrium by combining previous iterates. Here, we combine AA and Bayesian optimisation to re-compute the model's equilibrium at every Bayesian iteration. We show that we are able to constrain the distribution of $\kappa$ values to minimise the distance to observations.

But this process still requires running the model a large number of times at each Bayesian iteration, which remains computationally costly. To reduce the computational burden even further, we train a deep machine learning (ML) scheme to reconstruct the entire state vector of the model from a few significant fields, such as temperature and salinity, that most contribute to the large-scale dynamics of the system. This ML scheme therefore acts as an emulator of the climate model, which does not need to perfectly reproduce all processes, but mostly the model's equilibria. AA is then applied to these few fields only, while the full model state is reconstructed by the ML scheme at each AA iteration.

How to cite: Jacques-Dumas, V., Dijkstra, H. A., and Vedel, J.: Accelerated Bayesian Optimisation for bias correction in an Intermediate Complexity Climate Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7720, https://doi.org/10.5194/egusphere-egu26-7720, 2026.

Multilayer ocean models (see e.g. Audusse et al., ESAIM: Mathematical Modelling and Numerical Analysis 2011) are popular approximations to the 3D Euler and Navier-Stokes equations. Computational cost obviously increases with the number of layers, which is often chosen to be around 50 in ocean simulations. The barotropic-baroclinic splitting is an important strategy used in numerical ocean models to reduce this computational cost (see e.g. Killworth et al., Journal of Physical Oceanography 1991).

In the present contribution, we focus on the numerical analysis of the barotropic-baroclinic splitting in the context of finite volume schemes. We reformulate the splitting strategy within the nonlinear multilayer framework using terrain-following coordinates, and present it as an exact operator splitting. The barotropic step captures the evolution of free surface and depth averaged velocity with a well-balanced one-layer shallow water model. The baroclinic step incorporates vertical exchanges between layers and adjusts velocities around their mean vertical value.

Our scheme is numerically robust, i.e. no filters or corrections are needed. The numerical solution inherently observes a discrete maximum principle for the tracer and hence guarantees non-negative tracer concentrations. In the language of applied mathematics, we prove a discrete entropy inequality. In the language of geophysics, this guarantees dissipation of kinetic and potential, and therefore of total energy. This is the key stability property for the class of finite volume schemes under consideration. Last, but not least, the gain in terms of computational cost is large, especially in low Froude simulations.

Currently, this work addresses the constant density case; however, ongoing work extends the barotropic-baroclinic splitting to variable density scenarios and models situations such as coastal upwelling. The paper is submitted for publication (Aguillon, Hörnschemeyer, Sainte-Marie, International Journal for Numerical Methods in Fluids, January 2026).

How to cite: Hörnschemeyer, S., Aguillon, N., and Sainte-Marie, J.: Barotropic-Baroclinic Splitting for Multilayer Shallow Water Models with Exchanges, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9418, https://doi.org/10.5194/egusphere-egu26-9418, 2026.

It is hardly questioned today that climate can be described in theory by an ensemble of trajectories differing in their initial conditions, which is then translated to numerical ensembles in climate models. It is also widely accepted that any evolution observed within a few decades after initialization is not relevant to climate. Evolution at a later stage, instead, is then used to characterize climate and its change, under the implicit assumption that slower processes do not considerably contribute to differences between ensemble members, letting internal variability of climate be identified with these differences. However, a justification for this practice is as yet lacking. In particular, a definition of climate in support of this practice is outstanding, including the identification of the kind of time scales at play through providing an argumentation for their relevance. Our study aims at filling this gap. After pointing out that the most important criterion for a definition of climate is the uniqueness of the probability measure on which the definition relies, we first recall the naive proposal to represent such a probability measure by the distribution of ensemble members that has, loosely speaking, converged to the natural probability measure of the so-called snapshot or pullback attractor of the dynamics. We then consider the time scales of convergence and refine the proposal by taking a probability measure that is conditional on the (possibly time-evolving) state of modes characterized by convergence time scales longer than the horizon of a particular study. We design an ensemble simulation initialization scheme for studying convergence time scales and uniqueness of ensembles in Earth system models.

How to cite: Drótos, G. and Bódai, T.: Can we define climate by means of an ensemble? A tale of time scales of convergence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9626, https://doi.org/10.5194/egusphere-egu26-9626, 2026.

One of the challenges in weather forecasting is the understanding of the nonlinear interactions between the fast and slow dynamics in the atmosphere. This is related to both numerical problems, such as the choice of a stable time step, and modelling and understanding the dynamics of atmospheric phenomena, such as the Madden-Julian Oscillation. Using a Rotating Shallow Water model on the sphere, in which both fast (inertia-gravity) and slow (Rossby-Haurwitz) waves occur, the nonlinear interactions in reduced models containing three, four and five waves were analysed using Hough harmonics spectral decomposition. Considering a Galerkin expansion as a solution of the nonlinear system, equations for the dynamics of each mode were derived, along with necessary conditions in the zonal and meridional structure of the modes for three interacting waves. In this talk, we will show results of three, four and five wave system interaction, discussing the energy transfers between Rossby-Haurwitz and gravity waves. We will particularly illustrate how we can observe relevant slow oscillations emerging from fast wave dynamics in realistic parameter ranges.

How to cite: Peixoto, P., Dourado, M., Raphaldini, B., and Teruya, A.: Nonlinear wave interactions in Rotating Shallow Water Equations on the Sphere: Theory and multi-wave applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10391, https://doi.org/10.5194/egusphere-egu26-10391, 2026.

The mesoscale atmospheric energy spectrum has puzzled scientists for decades, sitting between classical turbulence and wave theories. Using year-long ECMWF operational analyses of high resolution and a spherical coarse-graining framework (Flowsieve), we present the first consistent global maps of local mesoscale kinetic energy fluxes. At 200~hPa, we identify a striking band of upscale transfer aligned with the ITCZ, while storm tracks and orography leave distinct dynamical imprints at both 200 and 600~hPa. By decomposing divergent and rotational components, we show that divergent energy dominates in the tropics and stratosphere, while rotational energy dominates in the extratropical troposphere. Conditioning spectra on this balance reveals contrasting regimes: a Nastrom–Gage-like spectrum under divergent dominance, and a spectrum reminiscent of the classical dual cascade of textbook two-dimensional turbulence under rotational dominance at 600~hPa. These results demonstrate that mesoscale energy transfer is shaped by a patchwork of mechanisms, reconciling long-standing debates and providing new inspiration for parametrisations and predictability in weather and climate models.

How to cite: Christensen, H., Kouhen, S., Storer, B., Aluie, H., and Marshall, D.: Local kinetic energy fluxes in the atmospheric mesoscales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10479, https://doi.org/10.5194/egusphere-egu26-10479, 2026.

RTTOV is the operational fast radiative transfer model used as the forward operator in data assimilation systems at major NWP centres, including Météo-France and ECMWF. Its accuracy plays a crucial role in the evaluation and representation of observation errors. For instance, any limitations of its transmittance model can introduce systematic biases in the simulated brightness temperatures. These biases may propagate through the assimilation system, affecting both the retrieved atmospheric fields and the performance of the bias correction scheme.

Estimating and attributing biases in fast RT simulations remains challenging due to the complex and interacting error sources. In this study, we present a new ANOVA-style methodology to diagnose and separate these sources of biases using reference line-by-line models, satellite observations, and 1D-Var retrievals. We focus on three main contributors: spectroscopy, transmittance parametrisation, and uncertainties in atmospheric profiles. By analysing spectral biases across channels, gas absorption bands, and atmospheric regimes (e.g., dry, humid, tropical, polar), we identify dominant error sources and their impact on temperature and humidity retrievals.

Recent improvements in RTTOV coefficients and spectroscopy are also evaluated, demonstrating their impact on forward simulations for IASI (and prospectively FORUM) and on retrieved profiles. By isolating key error sources, this work strengthens the link between fast forward model development, bias correction schemes and retrieval accuracy.

How to cite: Volonnino, V., Lalande, J.-M., and Vidot, J.: A Diagnostic Framework for Spectral Biases in Fast Radiative Transfer Models: An ANOVA-based Uncertainty Decomposition of RTTOV, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10906, https://doi.org/10.5194/egusphere-egu26-10906, 2026.

How to cite: Brožová, A., Šmídl, V., Tichý, O., and Evangeliou, N.: Nonlinear Atmospheric Inversion with Interpretable Bias Correction via Gaussian Process Prior, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11347, https://doi.org/10.5194/egusphere-egu26-11347, 2026.

We consider a prior-based dimension reduction Kalman filter for state estimation in high-dimensional settings. The method extend ideas from prior-based dimension reduction in static inverse problems by projecting covariance equations to lower-dimensional space using a global reduction operator. In contrast to reduced rank Kalman filters the dimension reduction is defined entirery a priori. Here, it is constructed using standard wavelet transforms, yielding a stable and portable framework that does not depend on empirical parameter estimation to form the projection. 

The Kalman filter update step equations are projected onto a global wavelet basis, thereby avoiding explicit construction of covariance matrices in the full state space. This makes classical Kalman filtering tractable for large spatio-temporal systems otherwise computationally inaccessible. Combined with PyTorch implementation exploiting GPU acceleration, the approach leads to a drastic reduction in computational cost, while preserving the consistent filter and enabling Gaussian uncertainty quantification.

We demonstrate the method on two high-dimensional application, highlightning the wavelet representation's natural adaptation to different data patterns and structures. The first example concerns sparsely observed oceanographic data, where the reduced filter reconstructs the full state from limited measurements with uncertainty estimates with state model derived from modelled ocean current. The second focuses on satellite-derived cloud product with state dynamics provided by neural network estimates and the observations exhibit heterrogeneous quality and frequent gaps.

Overall, we demonstrate how reduced-basis Kalman filtering with a priori selected wavelet subspaces provides a general and computationally viable framework for nonstationary Gaussian inverse problems. The approach combines scalable data assimilation, uncertainty quantification, and the integration of data-driven dynamics in high-dimensional geophysical applications.

How to cite: Himanka, T. and Laine, M.: Dimension reduction Kalman filtering: examples from high-dimensional dynamical systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11361, https://doi.org/10.5194/egusphere-egu26-11361, 2026.

Typical cloud physics systems at small scales are often formulated as coupled discrete–continuous problems, comprising discrete, stochastically evolving hydrometeors and continuous, field-like thermodynamic variables. For modeling purposes, the inherent stochastic and particle-based nature of these systems is frequently simplified into more tractable mathematical frameworks, such as moment-based schemes. However, such approximations often fail to adequately capture the full impact of stochastic effects and the structure of distribution tails – features that can significantly influence system behavior. Although these effects can be resolved at small scales through numerical simulations of Master equations and related methods, approaches to upscale such descriptions to large-scale systems have remained elusive.
In this work, we introduce a novel mathematical framework that translates general coupled discrete–continuous problems into a path integral formulation, and consequently into an approximate field theory. This approach circumvents the need for computationally expensive numerical simulations and enables direct analytical computation of distribution moments. As a result, parameter spaces of models can be efficiently explored via analytical means, facilitating their application to significantly larger spatial and temporal scales.
We illustrate the efficacy of our method using a simple model system and explore its applicability to typical atmospheric situations.

How to cite: Sparmann, T., Sorinca, A.-A., te Vrugt, M., Pruessner, G., Garcia Millan, R., and Spichtinger, P.: A path-integral approach to coupled discrete-continuous problems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11431, https://doi.org/10.5194/egusphere-egu26-11431, 2026.

The problem of forced response estimation from a single realization was addressed in the recent ForceSMIP project [Wills et al. 2025], which compiles many state-of-the-art statistical methods, including both methods supervised by large Earth System Model (ESM) ensembles and methods that use only a single target climate realization. Single-realization estimation is frequently approached using various linear filtering techniques, in particular Linear Inverse Models (LIMs) and Dynamic Mode Decomposition (DMD) [Penland et al. 1995 and Schmid 2010]. Standard LIM and DMD do not explicitly account for external forcing. DMD with control (DMDc) naturally extends these methods to incorporate essential external forcing information as a control variable [Proctor et al. 2016].

We investigate how these forcing inputs can be incorporated into the DMDc model to estimate forced responses. This results in three variants of DMDc for forced response estimation. One variant was already used in Tier 1 of the ForceSMIP project, while the other two have yet to be tested. We evaluate all three methods using near-surface air temperature (tas) and sea-level pressure (psl) from four Earth system models (CanESM5, MIROC6, MPI-ESM, and MPI-ESM1-2-LR) using data from MMLEA v2 [Maher et al. 2025]. Specifically, we analyze their ability to recover forced responses and characterize the DMDc variants across these Earth system models and variables.

References:

    Maher, Nicola, et al. "The Updated Multi-Model Large Ensemble Archive and the Climate Variability Diagnostics Package: New Tools for the Study of Climate Variability and Change." Geoscientific Model Development 18.18 (2025): 6341-6365.

    Penland, Cécile, and Prashant D. Sardeshmukh. "The Optimal Growth of Tropical Sea Surface Temperature Anomalies." Journal of Climate 8.8 (1995): 1999-2024.

    Proctor, Joshua L., Steven L. Brunton, and J. Nathan Kutz. "Dynamic Mode Decomposition with Control." SIAM Journal on Applied Dynamical Systems 15.1 (2016): 142-161.

    Schmid, Peter J. "Dynamic Mode Decomposition of Numerical and Experimental Data." Journal of Fluid Mechanics 656 (2010): 5-28.

    Wills, Robert CJ, et al. "Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)." Authorea Preprints (2025).

How to cite: Mankovich, N., Gavrilov, A., and Camps-Valls, G.: Dynamic Mode Decomposition with Control for Forced Response Estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11895, https://doi.org/10.5194/egusphere-egu26-11895, 2026.

The Atlantic meridional overturning circulation, the Greenland and Antarctic ice sheets have been identified as parts of the climate system that can potentially react nonlinearly to climate change albeit on very different time scales. While critical thresholds remain difficult to quantify from existing observations for all of these subsystems, they certainly do not stand on their own. In fact, the AMOC and polar ice sheets form an intricate network of multiscale systems, with interactions that can be stabilizing or destabilizing, the latter opening the possibility of cascading tipping events.

The interaction between Greenland ice sheet and AMOC on the larger scale shows the possibility of a collapse of the AMOC once a critical amount or rate of freshwater has entered the North Atlantic. This interaction also involves smaller scales, because the Greenland meltwater needs to reach the deep-water formation regions in the North Atlantic subpolar gyre, exhibiting substantial variability in the critical regions. Moreover, the Greenland ice sheet acts on slower time scales than the AMOC, such that these two systems can form an ‘accelerating cascade’. Specfically, when tipping of the ice is underway, the ‘coupling’, i.e. the freshwater flux into the North Atlantic is at maximum. These properties have consequences for the possibility of early warning predictions; in accelerating cascades early warning signs can break down due to lack of extrapolation.

On the other hand, West Antarctic Ice Sheet melting may be able to to stabilize the AMOC. Here, we investigate through a hierarchy of models of the AMOC and idealized forms of polar ice sheet collapse, the origin and relevance of stabilization and destabilization effects. In both deterministic and stochastic conceptual models, we find that rate- and noise-induced effects have substantial impact on the AMOC stability. Moreover, rate-induced effects can stabilize the AMOC depending on the relative timing of the peak meltwalter fluxes from both ice sheets.

How to cite: von der Heydt, A.: How stable is the Atlantic meridional ocean circulation when interacting with polar ice sheets?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12246, https://doi.org/10.5194/egusphere-egu26-12246, 2026.

Air pollution estimates represent key inputs in computer models for assessing air quality. They are also important in the evaluation of pollution control policies. 

In the last decade, neural networks have demonstrated exceptional ability to model complex spatiotemporal data. Meanwhile, advances in our ability to observe the earth's atmosphere using satellites have enabled the collection of high-resolution atmospheric composition data in near real-time. These developments open up opportunities to combine the predictive power of neural networks with satellite observations to deliver rapid and accurate estimates of pollutant emissions in near real-time.

Chemical weather prediction models offer insights into the forward relationship between emissions and atmospheric composition, and some studies are already suggesting that neural networks might be able to estimate with reasonable predictive skills the chemical concentrations obtained from these physics-based models. While the forward mapping is well-defined, the inverse mapping—from atmospheric composition to emissions— is not. Our objective is ultimately to exploit neural networks to predict emissions from atmospheric composition. This presents challenges, as we will show in our presentation.

We present preliminary results from our study in training a variational autoencoder, with data from a chemical weather prediction model, to invert Spanish NOx emissions. We demonstrate a workflow in which we jointly train two neural network models: one for inverse modelling of emissions and a second to regularise the predictions of the inverse model.  

How to cite: Petticrew, J., Petetin, H., Mas Magre, I., Guevara Vilardell, M., Jorba, O., and Pérez García-Pando, C.: Towards inverse estimation of Spanish NOx emissions with TROPOMI observations using a variational autoencoder, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12391, https://doi.org/10.5194/egusphere-egu26-12391, 2026.

Bayesian inverse problems in Earth sciences often ask for inversion techniques capable of handling high-dimensional nonlinear forward models, and prior information that is neither Gaussian nor analytically representable. This contribution focuses on the methodological developments underlying our application of cross entropy based importance sampling for Bayesian updating (CEBU) to Antarctic tidal grounding line migration based upon Sentinel-1 line of sight offsets. In particular, we highlight how the algorithm is extended to incorporate empirical, hence, nonparametric priors, how its sequential structure enables detailed convergence diagnostics, and how its evidence estimate can support filtering and model selection.

The grounding line marks the transition from grounded ice to floating ice shelf in Antarctica’s marine-terminating glaciers. The underlying elastic beam model simulating the bending of the ice in response to tidal deflection is, among others, based on an ice thickness parameter. Its prior shall be defined by the values from a dataset of a previous study. This prior exhibits non‑Gaussian structure and parameter dependencies that cannot be captured by standard parametric assumptions. Hence, we extend the CEBU framework by introducing an isoprobabilistic transform that maps the empirical ensemble into the standard normal space in which the update is performed. The extension allows CEBU to operate directly on empirical prior information, thereby embedding physical knowledge into the Bayesian update in a fully nonparametric manner.

After the initial transformation to standard normal space, CEBU proceeds through a sequence of tempered intermediate distributions that gradually introduce the likelihood. This sequential structure provides a transparent view of convergence behavior: we introduce the Kolmogorov–Smirnov distance between each intermediate importance sampling density and the prior as a measure of information gain and respective parameter importance. This quantity provides a nonparametric and interpretable metric of which components of the parameter vector are most informed by our observations and which remain dominated by prior uncertainty. The difference of information gained per step determines the respective importance of a parameter at a particular tempering step. Hence, by the distance metric introduced, CEBU intrinsically provides a convergence curriculum used to attain the posterior distribution.

After convergence, CEBU yields a Bayesian model evidence estimate. It quantifies the conceptual fit of the data observed and the model used. Accordingly, this evidence can be used for filtering the results, e.g., if the observation data of a particular inverse problem is too noisy, i.e., does not follow the measurement error model. Further, that quantity may be used for Bayesian model selection, offering a principled mechanism for evaluating competing forward models or prior assumptions. For example, that setting can be used to decide between multiple empirical priors, and thus between competing studies.

From a computational perspective, all forward evaluations and likelihood computations are embarrassingly parallelizable. That makes the approach well suited for large‑scale inference tasks on modern high performance clusters and cloud infrastructures.

How to cite: Engel, M., Ramanath, S., Krieger, L., Wuite, J., Floricioiu, D., and Körner, M.: Extending Cross Entropy Based Importance Sampling for Bayesian Updating (CEBU) with Empirical Priors and Kolmogorov-Smirnov Based Convergence Diagnostics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12966, https://doi.org/10.5194/egusphere-egu26-12966, 2026.

Complex systems such as the climate are often described in terms of linear modes of variability, but these modes cannot capture the intrinsically nonlinear organization of the dynamics. We introduce a framework for extracting topological modes of variability (TMVs) directly from observational, laboratory or simulation data. 

TMVs were introduced in the context of the templex framework [Charó et al., 2022; 2025], which represents a dynamical system through a combination of its topological structure and the way the flow in phase space moves across it. In this framework, TMVs correspond to flow patterns that are organized around special regions of an attractor, called joining loci, where different pathways merge.

Here we show how these joining loci — and the TMVs organized around them — can be recovered directly from data, without explicitly constructing a cell complex. We use dynamical indicators of local dimension and stability [Lucarini et al., 2016; Faranda et al., 2017] to locate the regions of the attractor where joining loci are expected, and we then extract the corresponding cycles from a directed graph built on a clustering of the data. By retaining only the robust transitions in this graph, we obtain a set of persistent TMVs.

We apply this approach to the El Niño–Southern Oscillation (ENSO) using Niño-3.4 sea-surface temperature anomalies from NOAA’s Oceanic Niño Index (ONI), providing new insight into ENSO variability and predictability.

How to cite: Charó, G. D., Faranda, D., Ghil, M., and Sciamarella, D.: Extracting persistent topological modes of variability in complex dynamics from data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13629, https://doi.org/10.5194/egusphere-egu26-13629, 2026.

Arctic sea ice is one of the most sensitive components of the Earth's climate system and acts as a bellwether for changes in it. The ice cover grows, shrinks, and moves because of its interactions with the atmosphere and the underlying ocean. One of the principal challenges associated with modelling the atmosphere-ice-ocean interactions is the lack of definitive knowledge of the rheological properties of the ice cover at large scales. A systematic study of sea ice dynamics since the 1960s has led to the development of many rheological models, but the predictions from these models are not entirely consistent with observations.

In this work, I will consider the motion of sea ice at three different scales: (i) floe-scale or `microscopic'; (ii) mesoscopic; and (iii) continuum. Starting from the dynamics at the scale of an individual ice floe I will obtain the continuum equations by coarse graining. This approach is similar to the one used to obtain the Navier-Stokes equation from the Boltzmann equation, and allows for the determination of shear viscosity of the ice cover as an explicit function of ice concentration and mean thickness. I will compare results from the theory with observations and idealised simulations and also discuss a more general approach that accounts for phase change and mechanical deformation of ice floes.

How to cite: Toppaladoddi, S.: Sea ice motion on multiple scales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13703, https://doi.org/10.5194/egusphere-egu26-13703, 2026.

A signal-to-noise "paradox" was first described in the context of ensemble forecasts on seasonal timescales. It refers to a situation in which the correlation between the ensemble mean and the actual verification is larger than the correlation between the ensemble mean and individual ensemble members. A noted problem of the signal-to-noise paradox remains that the signal-to-noise ratio itself, or equivalently the ratio of predictable components (RPC), which are used to diagnose the signal-to-noise paradox, has poorly understood statistical properties, rendering reliable identification of the signal-to-noise paradox difficult.

In this contribution, a generalised concept of the RPC is discussed based on proper scoring rules. This definition is the natural generalisation of the classical RPC, yet it allows one to define and analyse the signal-to-noise properties of any type of forecast that is amenable to scoring, thus drastically widening the applicability of these concepts. The methodology is illustrated for ensemble forecasts, scored using the continuous ranked probability score (CRPS), and for probability forecasts of a binary event, scored using the logarithmic score. Numerical examples demonstrate that the classical and new RPC statistic agree regarding which data sets exhibit anomalous signal-to-noise ratios, but exhibit different variance, indicating different statistical properties.

How to cite: Broecker, J. and Bach, E.: A generalisation of the signal-to-noise ratio using proper scoring rules, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14157, https://doi.org/10.5194/egusphere-egu26-14157, 2026.

The Atlantic Meridional Overturning Circulation (AMOC) is a key tipping element of the climate system and can be viewed as a multistable, stochastic dynamical system subject to both external forcing and internal variability. While most modelling studies emphasize deterministic thresholds for AMOC collapse, the role of internal variability in shaping the timing, probability, and nature of transitions remains poorly constrained.

This motivates a shift toward probabilistic prediction of AMOC tipping. Transition probabilities can be estimated using direct Monte Carlo sampling with large ensembles; however, this approach is severely limited in climate applications, as simulations are computationally expensive and statistical precision improves only slowly with increasing ensemble size. Rare-event algorithms provide an efficient alternative. In particular, the Giardina–Kurchan–Tailleur–Lecomte (GKTL) and Trajectory-Adaptive Multilevel Splitting (TAMS) methods enable targeted sampling of low-probability transitions at substantially reduced computational cost.

Using the intermediate-complexity PlaSIM–LSG model, we estimate AMOC transition probabilities by comparing direct Monte Carlo sampling with GKTL and TAMS. In a 600 ppm CO₂ case study, TAMS delivers the most precise probability estimates per unit cost, outperforming both Monte Carlo and GKTL and emerging as the most reliable approach for probability estimation.

We further apply TAMS to assess the transition probability to a weak AMOC state under three SSP scenarios, revealing a strong dependence on the forcing pathway. Under the high-emissions scenario SSP5–8.5, the probability of entering the AMOC-weak state remains below 1% by 2100, increases to about 20% by 2150, and reaches roughly 95% by 2200. In contrast, lower-emission scenarios (SSP4–6.0 and SSP2–4.5) maintain substantially lower probabilities throughout. These results are consistent with recent multi-model projections, suggesting that AMOC collapse is very unlikely in the 21st century but becomes plausible in the 22nd century under sustained high forcing. Additional freshwater input from Greenland ice-sheet melt would likely further increase these probabilities and advance the transition.

Overall, when direct sampling fails to capture rare transitions, rare-event methods enable both improved probability estimation and deeper insight into the underlying physical mechanisms. GKTL is well suited for exploring multistability and multiple transitions, while TAMS provides a rigorous framework for quantifying transition probabilities. Together, these approaches help bridge the gap between theoretical concepts of multistability and their practical investigation in complex climate models.

How to cite: Cini, M., Jacques-Dumas, V., Zappa, G., Ragone, F., and Dijkstra, H. A.: Comparing Rare-Event Algorithms and Direct Sampling for Estimating the Probability of CO₂-Driven AMOC Tipping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14650, https://doi.org/10.5194/egusphere-egu26-14650, 2026.

Over the past decade there have been unprecedented events of record low sea ice concentration in the Antarctic region. Previous work has attributed these anomalous sea ice loss events to persistent anomalies in various atmospheric drivers such as the Southern Annular Mode (SAM), the Pacific South American (PSA) patterns, and the Amundsen Sea Low (ASL). The majority of such studies employ methodologies that either assume stationarity or use averages over uniform fixed periods (e.g. months). In this study we show how a machine learning method applied to multiscale climate data can extract drivers across subsystems without predefining patterns or time periods. Specifically, we employ a nonstationary data-clustering framework to coupled sea ice and atmosphere reanalysis data to extract persistent coherent events across both systems. We use time-varying Markov transition matrices to extract the dominant states over a sliding time window and identify persistence as an uninterrupted period of a dominant state for at least ten days.

Analysing three years consisting of anomalously low sea ice events, we find that our approach identifies a variety of atmospheric drivers for these events without preconditioning. The dominant drivers vary in spatial extent and duration, as opposed to many stationary methods which require an a priori selection of scales. Here each event’s spatial and temporal boundaries are determined by the optimal model itself. This nonstationary analysis is thus particularly valuable for characterizing multiscale interactions and addressing dynamics across coupled climate subsystems.

How to cite: Quinn, C., Axelsen, A., O'Kane, T., and Bassom, A.: Data-driven identification of atmospheric drivers of anomalous Antarctic sea ice loss, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15666, https://doi.org/10.5194/egusphere-egu26-15666, 2026.

Accurate subsurface parameter estimation remains challenging due to the inherent nonlinearity and non-uniqueness of geophysical inverse problems. In this study, we present an integrated Bayesian–Gauss–Newton inversion framework for Electrical Resistivity Tomography (ERT) aimed at achieving robust model parameter estimation and uncertainty quantification. The Bayesian component provides a probabilistic description of the inverse problem, enabling the incorporation of prior geological information and the assessment of posterior parameter distributions. Bayesian optimization is employed to efficiently explore the high-dimensional model space and obtain a geologically consistent initial model. Subsequently, a Gauss–Newton optimization scheme is applied to refine this solution and obtain the maximum a posteriori estimate with improved convergence characteristics. The combined approach leverages the global search capability of Bayesian optimization and the computational efficiency of the Gauss–Newton method, resulting in enhanced resolution of sharp resistivity contrasts and reduced ambiguity in subsurface models. Applications to both synthetic and field ERT datasets demonstrate that the proposed methodology improves data fitting, stabilizes inversion results, and provides a comprehensive measure of model uncertainty. The results highlight the potential of the Bayesian–Gauss–Newton framework as a reliable and efficient inversion strategy for ERT-based subsurface characterization, particularly in complex environments affected by strong resistivity contrasts and saline intrusion.

How to cite: Sarkar, K. and Singh, A.: A Bayesian–Gauss–Newton Inversion Framework for Electrical Resistivity Tomography with Improved Parameter Estimation and Uncertainty Quantification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15971, https://doi.org/10.5194/egusphere-egu26-15971, 2026.

The albedo contrast between sea ice and open ocean introduces a strong positive feedback in the surface energy balance of polar regions. Classical low-order models show that this feedback robustly produces multiple equilibria: the system can exist in either a cold, ice-covered state or a warm, ice-free state with the same external forcing.  The resulting hysteresis implies that polar regions will lose sea ice abruptly and irreversibly as external forcing increases. However, this tipping-point behavior is not observed in full-complexity climate models: in experiments where global radiative forcing is gradually ramped up until sea ice disappears, ice loss is indeed found to be relatively abrupt; but when the forcing is subsequently ramped down, sea ice reappears at the same rate, showing no sign of hysteresis or irreversibility. How do we reconcile this discrepancy between simple and complex models?

Here, I show that this reconciliation can be achieved by introducing atmospheric weather noise into the simple model. The polar ocean is modelled as a collection of points subject to local stochastic forcing, introduced as an additive white noise in the  energy balance model. This leads to a Fokker-Planck equation describing the probability distribution function (PDF) of ice thickness over the ocean basin, including a zero-thickness (ice free) class. For realistic values of noise amplitude estimated from reanalysis data, the PDF is bimodal when the global forcing supports multiple equilibria of the energy balance equation, with modes centered on the corresponding ice-free and ice covered equilibria. When global forcing is ramped up or down over long (~1000 year) timescales, the PDF evolves reversibly, showing relatively abrupt but reversible loss/recovery of sea ice. However, if the ramping timescale is shorter (~100 years), some residual irreversibility is still present. In conclusion, taking stochastic atmospheric fluctuations into account provides a promising avenue for resolving a long-standing problem in climate science.

How to cite: Caballero, R.: Atmospheric noise removes sea-ice tipping points in a simple stochastic model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17941, https://doi.org/10.5194/egusphere-egu26-17941, 2026.

The transport of Antarctic Bottom Water (AABW) supplies the densest layers of the abyssal ocean circulation, which accounts for up to 40% of the ocean's volume and plays a vital role in Earth's climate. Due to its recently ventilated nature, AABW carries heat and carbon from the surface to the deep ocean, allowing these elements to be isolated for centuries, while also gathering oxygen and delivering it to the ocean's depths. AABW forms when dense, cold waters from the continental shelves descend along the Antarctic slope. The physical conditions necessary for sinking are created by ice formation and freezing winds in this region.

This implies that, as temperatures rise and ice melts due to climate change, the circulation could diminish. Model projections also suggest this, identifying meltwater forcing as a potential primary factor in the reduction of AABW transport. However, the variability of AABW remains poorly constrained by observations. Its origin on the Antarctic continental shelf and slope presents limited opportunities for in situ measurements, and satellite observations are hindered, especially in winter, due to sea ice cover. Further north, AABW spreads approximately 2 km below the surface, making it difficult to monitor directly by satellites, with in situ measurements remaining scarce.

Here, we explore the plausibility of inferring AABW circulation from available satellite measurements of the ocean's surface properties, via machine learning techniques. Our work is focused on implementing a Deep Neural Network (DNN) with high skill and potential for reconstructing the circulation's strength. Different architectures are trained and tested on the ACCESS-OM2-01 model, and a cross-training with other ocean models is investigated, as well as the use of real satellite measurements and change-point detection techniques.
These studies offer a valuable means to overcome current limitations on Southern Ocean and abyssal circulation research, making it more accessible, sustainable, and consistent.

How to cite: Ferrotti, A., Naveira Garabato, A., Silvano, A., Zheng, C., and Morrison, A.: Detecting Rapid Changes and Tipping Points in the Abyssal Ocean Circulation via Deep Learning and Satellite Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19315, https://doi.org/10.5194/egusphere-egu26-19315, 2026.

Intermittent dynamics are a common feature of many Earth-system components that often interact across ample ranges of temporal and spatial scales.  Our previous work shed light on the mechanism driving intermittency and identified precursors of its onset (Barone et al., 2025). This current study moves forward, and it investigates the processes by which an intermittent component in a coupled system influences other ones that, in the absence of the coupling, would evolve quasi stationarily. In particular, we investigate a prototypical fast–slow, e.g. atmosphere-ocean, setup in which fast intermittent systems act as a unidirectional forcing on slow components characterized by a stable limit cycle.

Using a two-scale version of the Lorenz–63 model, we show that intermittent bursts in the fast dynamics induce deviations from the slow dynamics’s limit cycle, which, depending on the strength of the coupling and the timescale difference, can even fully destabilize the limit cycle and lead to a chaotic regime. We show that increasing the frequency of intermittent events does not necessarily affect the slow component response, which below a critical value retains its structural properties, highlighting the non-trivial nature of intermittent information transfer across scales. The induced transition from periodicity to chaos caused by the intermittent burst, is looked through the lens of the power spectrum decomposition (PSD) of the finite-time Lyapunov exponents, offering a unique view on the progressive loss of predictability in the slow component. The analysis is then extended to a spatially extended system based on unidirectionally coupled Kuramoto–Sivashinsky equations. As the coupling strength increases, the energy PSD of the slow and initially regular dynamics, progressively approaches that of the fast intermittent system, up to a regime in which the two become effectively indistinguishable. Remarkably, mutual information between subsystems reveals a clear latency in the slow response that increases with the degree of time-scale separation.

Our study provides a robust framework to investigate similar dynamical configurations in Earth system models, whereby a fast intermittent atmosphere induces short-living, yet impactful, changes in a slow ocean. 

A. Barone, A. Carrassi, T. Savary, J. Demaeyer, S. Vannitsem; Structural origins and real-time predictors of intermittency. Chaos 1 October 2025; 35 (10): 103119. https://doi.org/10.1063/5.0287572

How to cite: Barone, A., Carrassi, A., Demaeyer, J., and Vannitsem, S.: Breaking of stationarity by intermittency in coupled dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19518, https://doi.org/10.5194/egusphere-egu26-19518, 2026.

The climate is a nonlinear system, but it is sometimes useful to approximate it as a linear system.  Considering the climate response under steady forcing (e.g., heating tendency), a linear Markov model (a model without memory effect) should never give a response opposite to the forcing, because it implies an unstable mode.  Here, using the Lorenz-63 system, a 3-variable nonlinear system simplified from 2D convection, as testbed, we show that the climate response of a nonlinear system can be exactly opposite to the forcing, demonstrating a shortcoming of linear Markov model which cannot tolerate an opposite response.  Such opposite response arises not from numerical errors nor reduction of prognostic variables, as previously suggested.  We build a linear state-space model (SSM, a model with memory effect) and quantitatively explain how memory effect gives rise to an opposite response.  Our linear SSM can serve as a benchmark in a unified testbed, where other indirect methods to compute climate response, e.g., fluctuation-dissipation theorem (FDT), can be examined and refined.  Our linear SSM can also be applied to accurately predict response under periodic forcing.  With this, the resonant frequencies of the system can be identified.  The Lorenz-63 system may be far from real world.  Yet, the same approach can be applied to quantitatively analyze the dynamics of natural variability of the climate system, such as annular mode.

Published/submitted:

Hou, Y., Chen, J., Ma, D., & Chan, P. W. (2025). Steady-state linear response matrix of the Lorenz-63 system. J. Atmos. Sci., 82(12), 2667-2675. https://doi.org/10.1175/JAS-D-25-0016.1

Hou, Y., & Chan, P. W. (submitted). A linear state-space model of the Lorenz-63 system and its applications. https://doi.org/10.6084/m9.figshare.30271819.v1

How to cite: Chan, P. W., Hou, Y., Li, X., Wei, J., Chen, J., and Ma, D.: A Linear State-Space Model of the Lorenz-63 System and Its Applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19654, https://doi.org/10.5194/egusphere-egu26-19654, 2026.

Frontal structures, frequently observed in the vicinity of westerly jets and western boundary currents, are characterized by sharp gradients in both horizontal and vertical directions. Forecast errors associated with these fronts often exhibit non-Gaussian distributions due to biases in frontal location or magnitude stemming from sparse observation networks or misrepresented model physics. Such non-Gaussianity poses significant challenges for conventional data assimilation (DA) schemes that rely on Gaussian assumptions.
In this study, we investigate the performance of various ensemble DA methods in representing fronts using idealized simulations with a frontogenesis model (Keyser et al., 1988). The compared methods include the stochastic Ensemble Kalman Filter (EnKF), the Ensemble Adjustment Kalman Filter (EAKF), and the Nonlinear Ensemble Transform Filter (NETF). Furthermore, we propose a novel nonlinear DA approach termed the Kernelized EAKF (KEAKF). By integrating kernel ridge regression into the EAKF framework, KEAKF effectively accounts for nonlinear relationships between state variables.
To simulate realistic forecast biases, the first-guess ensembles are initialized with systematic errors in both frontal magnitude and location. DA performance is rigorously evaluated using three metrics: root mean squared error (RMSE) of temperature (state error), RMSE of the temperature gradient (magnitude error), and the modified Hausdorff distance of frontal locations (displacement error). Our results demonstrate that KEAKF outperforms all other methods across all evaluation metrics. While the EnKF shows relatively stable performance in state estimation, the EAKF is superior in capturing frontal magnitude and location. The NETF, despite its non-Gaussian formulation, shows limited performance due to particle degeneracy in this setting. Finally, we discuss the implications of these findings for maintaining dynamical balances and improving the predictability of frontal systems in more complex dynamical models.

How to cite: Nakashita, S. and Enomoto, T.: Evaluation of data assimilation methods suitable for frontal structures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21740, https://doi.org/10.5194/egusphere-egu26-21740, 2026.

PDAF is open-source software (https://pdaf.awi.de) providing a unified data assimilation framework for all data assimilation applications throughout the Earth system and beyond. PDAF is already coupled to a wide range of models, including all Earth system components, and is widely used for research and operational applications. With well-defined interfaces and modularization motivated by object-oriented programming, PDAF separates the forecast model, the observation handling, and the data assimilation algorithms. This structure ensures separation of concerns and allows domain experts to perform further developments of each component independently without interfering with each other. PDAF is further designed to make the coupling to models, online in memory or offline using disk files, particularly easy so that a new assimilation system can be built quickly. 
PDAF was recently upgraded to the new major revision 3.0. In PDAF V3, the code was modernized and restructured simplifying the procedure to add further data assimilation algorithms. New features are supported including model-agnostic incremental analysis updates, new diagnostics for observations and ensembles, and the ensemble square root filter (EnsRF) and ensemble adjustment Kalman filter (EAKF). With this, PDAF now provides the full range of algorithms from domain-localized ensemble filters and smoothers to Kalman filters with serial observation processing, particle and hybrid Kalman-nonlinear filters, and 3-dimensional variational data assimilation methods. Existing users can switch to PDAF V3 with minimal effort, while a new universal interface supporting all filters is recommended for new users. The Python-interface, pyPDAF, further allows the full implementation of an assimilation program in Python, leveraging the functionality and performance provided by PDAF. We will provide an overview of PDAF and the novelties of version 3.0.

How to cite: Nerger, L., Chen, Y., Corbin, A., and Keller, J.: The Parallel Data Assimilation Framework (PDAF) - Upgrade to Version 3, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22041, https://doi.org/10.5194/egusphere-egu26-22041, 2026.

The Southern Ocean is an important component of the Earth climate system through its role in regulating and impacting the global ocean circulation. The Southern Ocean is known to be strongly turbulent, and that eddies play a role in regulating the mean and vice-versa. It is of interest to understand and model the resulting internal variability arising from such eddy-mean interactions, from a theoretical point of view because it provides further understanding to strongly interacting fluid systems, but also in practical terms because such internal variability is present in eddy-present/rich models but not so in coarse resolution parameterised models, which has consequences for example for anthropogenic carbon uptake. Here a low-order dynamical systems model of the eddy-mean interaction is constructed/derived, bearing resemblance to nonlinear oscillator and/or predator-prey type models of storm-tracks in the atmosphere and those in plasma physics for zonal-flow/drift-wave turbulence. Oscillatory time-scales for the model are derived, and testing is done on whether the derived time-scales are present in a hierarchy of numerical ocean models ranging from layered models to a primitive equation sector model. Evidence is presented that the GEOMETRIC parameterisation for geostrophic mesoscale eddies may improve the representation of decadal variability in the Southern Ocean, potentially leading to impacts in the modelled ventilation of oxygen and anthropogenic carbon in the Southern Ocean.

How to cite: Mak, J., Lee, H. S., Maddison, J., Marshall, D., Wang, Y., and Wu, Y.: Modelling of Southern Ocean decadal variability arising from eddy-mean interactions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22978, https://doi.org/10.5194/egusphere-egu26-22978, 2026.

Reduced-complexity models (RCMs) are a critical tool in climate science. Their computational efficiency enables applications beyond the reach of more complex models, including uncertainty quantification, the integration of multiple lines of evidence via ensemble constraining, and running large scenario sets in the span of a few days. Thanks to these capabilities, RCMs played important roles in previous IPCC assessments, and are poised to play an important role in the upcoming Seventh Assessment Report (AR7). A key example is evaluating the climate response to the thousands of emissions scenarios in the peer-reviewed literature created with integrated assessment models. However, whether/which RCMs are suitable for performing such a task is contingent on their ability to faithfully emulate the behaviour of more complex models and observed climate change.

The Reduced-Complexity Model Intercomparison Project (RCMIP) was established to assess this capability, as well as to better understand inter-RCM differences (Nicholls et al., 2020; Nicholls et al., 2021). Here, we introduce the protocol for the third and latest phase, RCMIP3. This phase focuses on two priorities. First, it provides a common set of observational benchmarks to be optionally used for ensemble constraining prior to submission, with the objective of mitigating discrepancies arising from different calibration methodologies and facilitating a clearer assessment of intrinsic model differences. Second, it requests an expanded set of variables and experiments from modelling teams to enable a more thorough evaluation of the carbon cycle representation in these models – a key gap in previous RCMIP phases. Additionally, RCMIP3 includes many of the experiments in the “Assessment Fast Track" (AFT) of the Coupled Model Intercomparison Project Phase 7 (CMIP7). As a result, RCMIP3 will improve our understanding of future model differences under these experiments, in addition to providing the community with valuable early projections.

The presentation will outline the RCMIP3 protocol and highlight the types of analyses it enables, along with preliminary results. By explicitly comparing RCM outputs with both ESM simulations and observations, RCMIP3 aims to strengthen the linkage across the climate-model hierarchy as well as evaluating and showcasing the suitability of RCMs for climate assessment.

Nicholls, Z., Meinshausen, M., Lewis, J., Corradi, M.R., Dorheim, K., Gasser, T., Gieseke, R., Hope, A.P., Leach, N.J., McBride, L.A., Quilcaille, Y., Rogelj, J., Salawitch, R.J., Samset, B.H., Sandstad, M., Shiklomanov, A., Skeie, R.B., Smith, C.J., Smith, S.J., Su, X., Tsutsui, J., Vega-Westhoff, B. and Woodard, D.L. 2021. Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections. Earth’s Future. 9(6), https://doi.org/10.1029/2020EF001900.

Nicholls, Z.R.J., Meinshausen, M., Lewis, J., Gieseke, R., Dommenget, D., Dorheim, K., Fan, C.-S., Fuglestvedt, J.S., Gasser, T., Golüke, U., Goodwin, P., Hartin, C., Hope, A.P., Kriegler, E., Leach, N.J., Marchegiani, D., McBride, L.A., Quilcaille, Y., Rogelj, J., Salawitch, R.J., Samset, B.H., Sandstad, M., Shiklomanov, A.N., Skeie, R.B., Smith, C.J., Smith, S., Tanaka, K., Tsutsui, J. and Xie, Z. 2020. Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response. Geoscientific Model Development. 13(11), pp.5175–5190, https://doi.org/10.5194/gmd-13-5175-2020.

How to cite: Romero-Prieto, A., Sandstad, M., Sanderson, B. M., Nicholls, Z. R. J., Steinert, N. J., Gasser, T., Mathison, C., Kikstra, J., Aubry, T. J., Tanaka, K., Weber, K., and Smith, C.: Reduced Complexity Model Intercomparison Project Phase 3: protocol and preliminary results, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2310, https://doi.org/10.5194/egusphere-egu26-2310, 2026.

Here we present an extended version of the forcing-driven and overshoot-aware spatial impacts emulator METEOR, which now includes functionality to emulate monthly outputs which include seasonality and natural variability, with the option to produce large distribution ensembles for a point, regional average or spatial domain.  The philosophy of METEOR entails fast training on few and widely available datasets, sufficiently fast to be run on-the-fly and removing the need to archive large datasets and allowing interactive coupling with integrated assessment frameworks to simulate impacts directly.  METEOR1.5 introduces a state dependent seasonal model and an autoregressive spatial, state-dependent noise model which can produce distributions of realisations conforming to the climatic trends and distributional properties of the emulated model    Integrated impact modules allow the direct emulation of human and ecological stressors which are computed from easily retrained emulated climates to answer regional questions. 

How to cite: Sandstad, M., Sanderson, B., Steinert, N., and Mittal, S.: METEOR 1.5 a spatial emulator for fast and relevant responses to impact questions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4264, https://doi.org/10.5194/egusphere-egu26-4264, 2026.

Climate impact assessments increasingly rely on high-resolution climate and forcing datasets, under the premise that finer detail enhances both the accuracy and the policy relevance of projections. Systematic evaluations of when and where higher resolution data improve model outcomes remain limited, and it is still unclear whether increasing spatial resolution consistently enhances climate impact model performance across application areas, regions, and forcing variables. Here we show that improvements in climate input accuracy and impact model performance are most pronounced when moving from coarse (60 km) to intermediate (10 km) resolution, while further refinement to 3 km and 1 km provides more modest and inconsistent benefits. Using the cross-sectoral model simulations from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we demonstrate that higher resolution substantially improves model skill in temperature-sensitive impact models and topographically complex regions, whereas precipitation-driven and low-relief systems show less consistency to increase performance with resolution. For temperature, both climate inputs and model outputs improved most strongly at the 60 km → 10 km transition, with diminishing gains at finer scales. A similar result emerged for precipitation, although some models even exhibited reduced performance when resolution increased beyond 10 km. These results highlight that optimal resolution depends on sectoral and regional context, and point to the need for improving model process representation and downscaling techniques so that added spatial detail can translate into meaningful performance gains. For data providers, this implies prioritizing investments in resolutions that maximize improvements where they matter most, while for modeling groups and users, it underscores the need for explicit benchmarking of resolution choices. More broadly, this work advances the design of consistent, efficient, and policy-relevant multi-sectoral climate impact assessments by clarifying when high-resolution data meaningfully enhance outcomes.

How to cite: Malle, J., Reyer, C., and Karger, D. and the ISIMIP modellers and sector coordinators: When and where higher-resolution climate data improve impact model performance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5437, https://doi.org/10.5194/egusphere-egu26-5437, 2026.

The Atlantic Meridional Overturning Circulation (AMOC) is a crucial climate component, as its potential collapse would constitute a significant response to Earth’s changing climate. This critical transition has been the subject of numerous studies over the years, both from the aspect of climate modeling and dynamical systems theory. In the context of the latter, climate change is a process in which a complex, chaotic-like system possesses time-dependent parameters, in the form of e.g. the growing CO2 concentration. It has been known that such systems have a chaotic attractor which is also time-dependent, a so-called snapshot attractor. Such objects, and thus the systems they describe, can only be faithfully represented by a probability distribution over an ensemble of simulations, so-called parallel climate realizations.

Based on this probability distribution, we define a novel early warning indicator for crucial transitions such as an AMOC collapse. The AMOC is said to possess a multistable quasipotential landscape, and the collapse is a transition between stable states. We argue that, from the point of view of statistical physics, this is analogous to a phase transition, but in a non-adiabatic setting. As such, the variance of the distribution over the ensemble is expected to develop a local maximum around the transition point, giving rise to a potential early warning by identifying the preceding maximum of its derivative. This method is first demonstrated on a conceptual climate model, before the analysis is carried out on ensemble simulations from the ACCESS-ESM model. The analysis in the former case is simpler, while in the latter, one has to contend with the dependence of the AMOC strength on spatial coordinates, resulting in multiple early warning points for different depths and latitudes.

How to cite: Jánosi, D., Divinszki, F. T., Börner, R., and Herein, M.: Defining an early warning method for an AMOC collapse based on ensemble statistics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5816, https://doi.org/10.5194/egusphere-egu26-5816, 2026.

Offline aridity and drought indices have often implied widespread terrestrial drying under a warming environment, while Earth system models (ESMs) have projected modest changes in land-surface water fluxes. This persistent divergence has been typically attributed to missing vegetation physiological processes in offline frameworks. However, we here show that a more foundational cause is a structural inconsistency embedded in those diagnostics. Conventional potential evapotranspiration (PET) formulations can violate the assumption that precipitation (P) and atmospheric evaporative demand act as independent climatic constraints in the Budyko framework. Using open-water Penman and vegetation-responsive Penman–Monteith formulations forced by reanalysis data and ESM projections, we found that uncorrected PET strongly reflected land–atmosphere feedbacks, leading to pronounced negative P–PET correlations (-0.45 ± 0.29; mean ± s.d.). When PET was thermodynamically deflated, this dependence was largely removed (-0.02 ± 0.42), restoring consistency with the theoretical basis of Budyko-type diagnostics. This structural correction reduced inflation of the aridity index and substantially moderated projected evapotranspiration (ET) trends. Under a business-as-usual scenario, the trend of Budyko-based ET from uncorrected PET (+0.61 mm yr^-2) exceeded that of CMIP6 ensemble mean (+0.28 mm yr^-2) by more than a factor of two. CEP-deflated PET narrowed this discrepancy (+0.39 mm yr^-2), while additional physiological adjustments provided comparatively smaller improvements. We suggest that violations of structural assumptions, rather than missing physiological processes alone, can play a central role in the divergence between offline aridity diagnostics and ESM hydrological projections.

Acknowledgement: This work was jointly supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (RS-2025-16070291 & RS-2024-00416443).

How to cite: Kim, D. and Choi, M.: Why offline aridity diagnostics overestimate future drying: the role of feedback-inflated evaporative demand, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6189, https://doi.org/10.5194/egusphere-egu26-6189, 2026.

Large-scale barotropic flow in the Arctic Ocean is strongly steered by the seafloor topography, yet how this geometry constrains free modes and facilitates inter-basin interactions remains unclear. Free modes conserve potential vorticity and at high latitudes the circulation pathway is enclosed by its sloping two-basin geometry. We begin by presenting a simple two-basin model, representing the Canadian and Eurasian basin respectively, with sloping boundaries and flat bottoms to explore simplified Arctic flow behaviour. Topographic Rossby waves are analytically obtained and the two basins are linked together via a mode-matching framework. We show free modes are tightly constrained to geometry, with basin-trapped dipole wave modes only emerging in certain geometric parameters. We then extend this to a more realistic, multiple-basin Arctic Ocean model that include the Nordic seas, and demonstrate the transmission and exchange of these topographic waves across these multiple sloping basins.

How to cite: Nguyen, M. D. T. and Johnson, E.: Barotropic waves in a sloping two- and multiple-basin Arctic ocean model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6930, https://doi.org/10.5194/egusphere-egu26-6930, 2026.

The differentiability of numerical climate models exhibits  many advantages over non-differentiable models. Differentiable climate models would be able to optimize parameters and quickly solve for climate equilibrium. They can also be used to find unstable climate equilibrium states that are impossible to identify in time-forwarding models. Differentiability also enables sensitivity studies, such as the impact of initial conditions on predictions, which is the key concept in the 4-dimensional variational method. Finally, differentiable ability also integrates well with the trending data-driven artificial intelligence model, such as NeuralGCM.  

Currently, physics-based differentiable coupled climate models are still rare. Some existing ones include: ECMWF Integrated Forecasting System (ECMWF-IFS) and Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). The high scientific value of such a tool warrants development of further differentiable modelling systems.

In this work, we present jax-esm, a differentiable coupler for models written in Python with the JAX framework. JAX is a Python library developed by Google that builds on NumPy and adds automatic differentiation and just-in-time (JIT) compilation. It has been used to develop atmospheric models such as NeuralGCM and jax-gcm. In this example, we couple jax-gcm, a JAX-based atmosphere intermediate model, to a slab ocean model. We demonstrate the optimization of ocean mixed-layer depth and solving for climate equilibrium through differentiability.

How to cite: Hsu, T.-Y., Watson-Parris, D., and Feulner, G.: Jax-esm: a differentiable coupler for jax-based Earth system models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7143, https://doi.org/10.5194/egusphere-egu26-7143, 2026.

The Global Overturning Circulation (GOC) is characterized by deep water formation in the subpolar North Atlantic, which feeds the southward-flowing branch of the Atlantic Meridional Overturning Circulation (AMOC). In contrast, the North Pacific lacks deep water formation and therefore does not host an analogous Pacific Meridional Overturning Circulation (PMOC). Proxy records, however, indicate that this asymmetric pattern of deep water formation has varied in the past, suggesting that a PMOC likely existed during earlier climate states. Recent studies further show that the development of a PMOC influences the future weakening of the AMOC: climate models that develop a PMOC in response to warming exhibit a stronger decline in AMOC strength. It therefore becomes important to understand under what circumstances a PMOC is likely to develop.

Here, we extend the pycnocline model of Gnanadesikan (1999) to a two-basin configuration, consisting of a narrow basin representing the Atlantic and a wide basin representing the Pacific. By including salinity as a prognostic variable, we find that this two-basin box model may exhibit three distinct overturning states under identical, longitudinally symmetric forcing: (1) an active narrow-basin sinking state, (2) an active wide-basin sinking state, and (3) a state with active sinking in both basins. Overturning states confined to a single basin are stabilized by the salt-advection feedback, whereas the state with sinking in both basins is maintained by a meridional temperature contrast. We find that this latter state becomes the preferred equilibrium when the interhemispheric temperature contrast increases, the northern gyre transport strengthens, and the hydrological cycle weakens. Moreover, we show that this state is more sensitive to high-latitude freshwater fluxes, indicating that a transition to such a state would enhance the projected future weakening of the AMOC. We verify these findings in an uncoupled global circulation model (MITgcm) with a simplified model geometry.

How to cite: Vanderborght, E. and Dijkstra, H.: Multi-stability of the Global Overturning Circulation: A Conceptual Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7983, https://doi.org/10.5194/egusphere-egu26-7983, 2026.

Estimates of economic damages from climate change in Europe depend on temperature projections, and they are thereby subject to scenario uncertainty and model uncertainty, as well as damage function uncertainty. An additional, often implicit source of uncertainty is the projected, yet poorly constrained, weakening of the Atlantic Meridional Overturning Circulation (AMOC), which would lower European temperatures. Here, I explicitly quantify the contribution of AMOC uncertainty to total damage uncertainty, with AMOC uncertainty comprising uncertainty about future AMOC developments as well as uncertainty about the cooling pattern that would follow an AMOC weakening. I combine a newly developed pattern-scaling-type emulator of the European cooling response to AMOC weakening — calibrated for different Earth system models (ESMs) — with temperature projections from multiple ESMs and emissions scenarios, alongside several damage functions. This allows me to decompose the total uncertainty in European economic damages into different drivers and estimate the share attributable to the behaviour of the AMOC.

How to cite: Schaumann, F.: Quantifying AMOC Uncertainty in European Climate Damage Projections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8370, https://doi.org/10.5194/egusphere-egu26-8370, 2026.

The history of climate modelling is one of increasing complexity and increasing resolution, driven by and constrained by the available computational capacity. These models are widely used, directly and indirectly, to support policy and adaptation decisions across society. They are also used in academic studies across a range of disciplines to study the response of the climate system to future atmospheric greenhouse gas concentrations on multi-decadal timescales. These are extrapolatory endeavours in a non-stationary system without possibility of relevant verification.

There has been much research on individual and multi-model analyses in this context. Here I will instead discuss how the targets of our endeavours (particularly the support of societal decisions) demands a rethinking of our modelling activities. I will highlight the need to reflect on the minimum requirements for ensemble size and ensemble variety, and the role of a hierarchy of models in providing the best possible information to stakeholders across society.

These issues will be discussed in the light of a recent meeting on the foundations of climate change science attended by over 70 researchers across a variety of disciplines. The meeting was entitled “How to spend 15 billion dollars?: A workshop on how to make climate change modelling more robust and more useful to society.” It gathered expertise from disciplines as diverse as earth system modelling, integrated assessment modelling, philosophy, economics, maths, statistics and finance.

Here I will present the key messages coming out of this meeting alongside the themes presented in a recent essay on the subject, “A Model of Catastrophe”[1].

[1] Stainforth, D.A., “A Model of Catastrophe”, Aeon.co, 2025 (https://aeon.co/essays/todays-complex-climate-models-arent-equivalent-to-reality)

How to cite: Stainforth, D.: Designing Climate Change Modelling to Support Societal Decisions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8434, https://doi.org/10.5194/egusphere-egu26-8434, 2026.

Today's climate adaptation and mitigation planning tasks require rapid access to large ensembles of climate projections for a wide range of emissions scenarios, including overshoot scenarios. While Earth system models (ESMs) provide physically consistent projections, their high computational cost limits scenario exploration. Climate emulators -  statistical or machine-learning-based models trained on ESM data to generate data replicating the ESMs behaviour for a multitude of emissions scenarios - are therefore proposed to deliver these projections efficiently. Here we present the novel modular SCALES–MESH emulator framework, combining physics-based regional projections with AI downscaling capabilities. The SCALES module translates projections of global mean surface air temperature into regional surface air temperature projections aggregated over the AR6-IPCC regions, while the MESH module performs spatio-temporal downscaling to gridded fields using a conditional score-based generative model. MESH is trained on multiple datasets and evaluated against parent ESMs using spatial, temporal, and distributional diagnostics. Results show that the emulator captures regional patterns, temporal variability, and probability distributions of emulated climate variables, including during warming and cooling phases of overshoot scenarios. We further demonstrate the potential for transfer learning across ESMs, pointing toward scalable multi-model and resolution-agnostic emulation. Together, SCALES–MESH enables rapid, flexible, and physically grounded exploration of climate futures, supporting decision-relevant climate risk assessment at unprecedented scope.

How to cite: Kain, V., Schwind, N., Högner, A., Shmuel, A., Nauels, A., Nicholls, Z., Zecchetto, M., and Schleussner, C.-F.: Cascaded score-based emulation of Earth system models for impact evaluation with SCALES-MESH , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9869, https://doi.org/10.5194/egusphere-egu26-9869, 2026.

The climate and carbon cycle interact in multiple ways. An increase in carbon dioxide in the atmosphere warms the climate through the greenhouse effect, but also leads to uptake of CO2 by the land and ocean sink, a negative feedback. However, the warming associated with a CO 2 increase is also expected to suppress carbon uptake, a positive feedback. This study addresses the question: “under what circumstances could the climate–carbon cycle system become unstable?” It uses both a reduced form model of the climate–carbon cycle system as well as the complex land model JULES, combined with linear stability theory, to show that: (i) the key destabilising loop involves the increase in soil respiration with temperature; (ii) the climate–carbon system can become unstable if either the climate sensitivity to CO2 or the sensitivity of soil respiration to temperature is large, and (iii) the climate–carbon system is stabilized by land and ocean carbon sinks that increase with atmospheric CO2 , with CO2-fertilization of plant photosynthesis playing a key role. For central estimates of key parameters, the critical equilibrium climate sensitivity (ECS) that would lead to instability at current atmospheric CO2 lies between about 11K (for large CO2 fertilization) and 6K (for no CO2 fertilization). Given the apparent stability of the climate–carbon cycle, we can view these parameter combinations as implausible. The latter value is close to the highest ECS values amongst the latest Earth Systems Models. We find that the stability of the climate–carbon system increases with atmospheric CO2 , such that the glacial CO2 concentration of 190 ppmv would be unstable even for ECS greater than around 4.5 K in the absence of CO2 fertilization of land photosynthesis.

How to cite: Clarke, J., Huntingford, C., Ritchie, P., Varney, R., Williamson, M., and Cox, P.: Conditions for instability in the climate–carbon cycle system, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12377, https://doi.org/10.5194/egusphere-egu26-12377, 2026.

Traditional earth system model ensembles provide valuable information on climate extremes. However, their limited size often underrepresents rare high-impact events, restricting the ability to explore extreme outcomes and large-scale anomaly patterns. Using the climate emulator MESMER, trained on CMIP6 models, together with the risk assessment platform CLIMADA, we assess population exposure to annual maximum daily temperatures and asset exposure to annual maximum daily precipitation.

MESMER generates virtually unlimited, spatially explicit, global climate realizations for any scenario defined by emission or global-mean-temperature trajectories. This allows us to characterize the spread of potential outcomes and associated spatial patterns, identify rare high-impact realizations, compare results with standard CMIP6 ensembles, or explore custom scenarios beyond existing model experiments.

We illustrate spatial and temporal patterns of exposure for temperature and precipitation extremes, highlighting contrasting regional responses and how highly impactful outcomes can emerge from climate variability.

How to cite: Pierini, L., Kropf, C., Gudmundsson, L., Seneviratne, S. I., and Bresch, D. N.: Revealing Probabilistic Patterns of Climate Extremes and Impacts Through Emulator-Based Risk Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12408, https://doi.org/10.5194/egusphere-egu26-12408, 2026.

Parsimonious emulator models (PEMs) trained on Earth system models (ESMs) can be very useful when information  about global quantities like global mean surface temperature (GMST) and ocean heat content (OHC) are sought. Here, I use data over several millennia from ESM runs extracted from the LongRunMip repository to construct and test PEMs for GMST and net incoming radiation flux.

For the  GMST, I consider a linear impulse response in the form of a superposition of three decaying exponentials, comprising three weight coefficients and three characteristic decay times to be estimated by least square fitting to ESM runs with abrupt step function forcing. The model fit is good on all time scales, and the fitted model seems to perform even better for smoother forcing scenarios. This sugggests that the six model parameters represent essential features of each ESM.

Data for radiation flux, and its decomposition in longwave and reflected shortwave, are combined with GMST to produce Gregory plots. By fitting parabolic curves to these plots, I obtain a simple analytic expression for the evolution of the feedback parameter λt), the radiation fluxes, and the resulting increase in OHC.

From these PEMs we can easily compare the global performance of different ESMs under different forcing scenarios. For instance, a comparison of the GISS-E2-R and CESM104 models exhibit equilibrium climate sensitivities (ECSs) of 3.4  and 2.4 K, respectively. The main reason for the difference is very different albedo feedbacks in the two models. Resulting total feedback parameter  λ(t) drops from 2.1 to 1.0 Wm^-2 K^-1 in GISS-E2-R and from 1.4 to 0.6 Wm^-2 K^-1 in CESM104. The OHC grows at nearly the same rate in the two models during the first millenium, but GISS saturates earlier and at lower final OHC.

How to cite: Rypdal, K.: Parsimonious models emulating millennium-long Earth system model simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12585, https://doi.org/10.5194/egusphere-egu26-12585, 2026.

Studies of climate sensitivity and feedbacks typically employ a suite of models with similar base climates but different model physics. Such an approach is useful for uncovering how changes to physical processes affect the climate response to changes in radiative forcing, but obscures the dependence of the climate response on the initial state of the climate itself. In order to better understand this dependence, we study the response to radiative forcing of two nearly identical configurations of the Community Earth System Model (CESM) with production-grade physics and resolutions that have dramatically different climates. The first, called Aqua, is completely covered with a uniform-depth ocean except for two 10º-wide polar continents to avoid the polar singularities in the ocean model. The second, Ridge, is identical to Aqua except for the presence of a thin ridge continent connecting the two polar caps. The ridge supports gyres in the ocean and leads to a warm, ice-free climate resembling a global Pacific Ocean, with a warm pool and cold tongue in the tropical ocean connected by a Walker circulation in the atmosphere. In contrast, the mean climate of Aqua is zonally symmetric and dominated by a global cold belt in the ocean driven by vigorous equatorial upwelling. The lack of gyres leads to a deep oceanic thermocline and reduces meridional heat transport, which allows for the development of persistent sea ice at high latitudes.

These two mean climates are perturbed by increasing atmospheric CO₂ concentration at a rate of 1% per year until quadrupling. Aqua initially warms more slowly than Ridge, with the transient climate response (TCR) at doubling 23% smaller for Aqua than Ridge. After doubling, however, Aqua begins to warm faster than Ridge and Aqua’s global mean temperature surpasses Ridge’s at quadrupling. A linear feedback analysis is used to gain insight into the time-evolving responses of these two configurations to increased CO₂ concentration. At all stages, Aqua’s net top-of-the-atmosphere heating is greater than Ridge’s. At early times, this is due to high clouds replacing low clouds in Aqua’s high latitudes, but decreasing surface albedo due to sea-ice loss eventually becomes a dominant factor. Aqua’s deep thermocline supports a higher ocean heat uptake (OHU) efficiency relative to Ridge that initially offsets these positive feedbacks and results in Aqua’s lower TCR. As CO₂ concentration approaches quadrupling, the combined effects of declining OHU efficiency and a strengthening ice-albedo feedback drive Aqua’s warming to temperatures compatible to Ridge. In the century following quadrupling, Aqua warms several Kelvin more than Ridge.

These idealized systems can shed light on the fundamental aspects of Earth’s climate system—such as how the response to radiative forcing depends on the base climate—that might be obscured in more complex configurations.

How to cite: Pitt Wolfe, C., Ma, Y., Katavouta, A., Reed, K., and Williams, R.: Exploring state dependence of the climate response to radiative forcing using two idealized coupled climate models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12666, https://doi.org/10.5194/egusphere-egu26-12666, 2026.

As Earth warms, regional climate signals are accumulating. Some signals, for example, land warming more than the ocean and the Arctic warming the most, were expected and successfully predicted. Underlying this success was the application of physical laws across a climate model hierarchy under the assumption that large and small spatial scales are well separated. With additional warming, however, discrepancies between real-world signals and model predictions are accumulating, especially at regional scales. In this talk, we will highlight the emerging list of model-observation discrepancies in historical trends. We demonstrate how the climate model hierarchy can be used to understand the physical processes underlying these discrepancies. We argue that progress can be made by filling gaps in the hierarchy and making more process-informed observations.

How to cite: Shaw, T. and Kang, J.: Understanding regional discrepancies using the climate model hierarchy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13950, https://doi.org/10.5194/egusphere-egu26-13950, 2026.

The stability of the Atlantic Meridional Overturning Circulation (AMOC) under future climate change remains uncertain. While most climate models across the model hierarchy project a weakening or collapse under freshwater forcing, transient simulations under increasing CO₂ levels also commonly show a weakening or even a collapse of the AMOC. However, longer equilibrium experiments---primarily conducted with lower-complexity models due to computational costs---show more varied responses to CO₂ forcing. While most models show an initial weakening of the AMOC, some models equilibrate to a weak AMOC state only at very high CO₂ levels, while others equilibrate to a stronger-than-present AMOC. One such model is the intermediate complexity model CLIMBER-X, which (in equilibrium) shows that the AMOC strengthens until at least 16 times preindustrial CO₂ levels are reached. However, during the transient phase of increasing CO₂, the AMOC weakens. This suggests that the AMOC's transient response may differ from its equilibrium behavior. This raises the question: can the AMOC collapse under rapid and high CO₂ increase, even if a stable equilibrium state exists? 

We show that the AMOC exhibits rate-dependent tipping; when CO₂ increases fast enough and reaches sufficiently high levels, the AMOC can fully collapse. This occurs under very high forcing, starting from 7 times preindustrial CO₂ levels and a rate of 2.0% ppm/yr CO₂ increase. This collapse occurs despite the existence of a stable AMOC at equilibrium. By examining the physical processes through which the collapse occurs, we contribute to the understanding of the AMOC response in a warming climate. By also incorporating freshwater forcing, we assess the risks of rapid warming on the AMOC stability. Our results show that even models with a stable equilibrium AMOC under high CO₂ levels can experience weakening during the transient phase or even collapse. This highlights the need to assess both the rate and magnitude of CO₂ forcing when assessing the stability of the AMOC. While this effect occurs at very high CO₂ levels in CLIMBER-X, the role of the rate of CO₂ increase may become relevant at lower CO₂ levels when combined with freshwater forcing. Our findings demonstrate that the AMOC can undergo rate-dependent tipping under rapid and high CO₂increase, even if a stable AMOC exists at very high CO₂ levels.

How to cite: Terpstra, S., Falkena, S., Bastiaansen, R., and von der Heydt, A.: Rate-dependent Tipping of the AMOC under CO2 increase in an Intermediate Complexity Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14266, https://doi.org/10.5194/egusphere-egu26-14266, 2026.

Integrating physical, socio-economic, and technological perspectives is indispensable for addressing climate mitigation challenges. While directly coupling state-of-the-art Earth System Models (ESMs) and Integrated Assessment Models (IAMs) offers a way to explore feedbacks between these domains, doing so with full-complexity models remains computationally prohibitive. This is particularly true for cost-effective intertemporal optimization IAMs due to fundamental operational differences: while ESMs perform forward simulations, such IAMs optimize over time. Consequently, direct coupling would require numerous computationally intensive iterations to converge, a complication further compounded by the stochastic nature of ESMs.

To overcome the barriers to coupling ESMs and IAMs, we employ their reduced-complexity representations (i.e., emulators). We couple an IAM emulator representing 9 distinct IAMs (Xiong et al. 2025) with an ESM emulator, FaIR, representing 66 ESM configurations (Smith et al. 2024a). Using this coupled ESM-IAM emulator framework in an optimization setting, we calculate cost-effective pathways that achieve the temperature targets of the Paris Agreement with and without overshoot.

Our preliminary results indicate that the uncertainty ranges for such pathways are significantly larger than previously estimated. Our results also have implications for target setting; we show how pathways differ when IAMs optimize directly for a temperature target – a capability IAMs traditionally lack. Instead, IAMs typically rely on temperature proxies, such as carbon budgets (or their corresponding carbon price pathways), which do not necessarily provide an accurate representation of the temperature target. Furthermore, this study offers advanced insights into the dynamics of climate-economy interactions, providing a roadmap for future efforts to couple full-complexity models.

References

Xiong, W., Tanaka, K., Ciais, P., Johansson, D. J. A., & Lehtveer, M. (2025). emIAM v1.0: an emulator for integrated assessment models using marginal abatement cost curves. Geosci. Model Dev., 18(5), 1575-1612. doi:10.5194/gmd-18-1575-2025

Smith, C., Cummins, D. P., Fredriksen, H. B., Nicholls, Z., Meinshausen, M., Allen, M., . . . Partanen, A. I. (2024). fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections. Geosci. Model Dev., 17(23), 8569-8592. doi:10.5194/gmd-17-8569-2024

How to cite: Tanaka, K., Weiwei, X., Allen, M., Cain, M., Jenkins, S., Mathison, C., Patel, V., Smith, C., and Tachiiri, K.: Coupled ESM-IAM Emulator: Exploring Uncertainties in Temperature Target Pathways, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14741, https://doi.org/10.5194/egusphere-egu26-14741, 2026.

Uncertainties in conventional (GCM) climate models, defined as the structural spread among com-
peting models, have increased for the first time in the latest AR6 report despite an exponential increase
in the modern computation power. The root problem is that these models are based in the weather
regime, that is, they spend unnecessary effort in calculating irrelevant weather details. This project
aims to produce precise regional projection using the Half Order Energy Balance Equation (HEBE): a
half order fractional derivative generalization of the standard Energy Balance Equation (EBE). HEBE
has the advantage of being a direct consequence of the continuum heat equation combined with energy-
conserving surface boundary conditions. A previous paper used Fractional EBE (FEBE) to model Earth
climate projections through 2100 on a global scale, and it yields significantly smaller uncertainty com-
pared to the CMIP6 MME. This project builds on a similar methodology, enhancing climate projection
with additional regional details and upgraded precision. The current results show that the parametric
uncertainty in HEBE’s temperature response is smaller than the internal variability at most locations,
at the exceptions of the high memory deep ocean regions near Pacific. HEBE’s regional hindcast ac-
curately reproduces ERA5 2mT series’ deterministic and stochastic patterns of regional temperature.
The global hindcast is also validated by various reanalysis datasets and instrumental records. The
direct year to year relative uncertainty (ratio between 90% confidence interval and best estimate) is
stable across time and marker scenarios, with most regions projecting values below 0.5 by 2100. On a
global scale, the parametric uncertainty in HEBE’s response temperature is negligible (±0.03K by 2100
using the SSP2-4.5 marker scenario). This effectively shows that HEBE’s projection is more precise
than its competitors even without taking period averages. The exceedingly low global uncertainty was
constrained by the large amount of regional information when taking the global averages. It should be
noted that the cited parametric uncertainty does not take into account systematic biases in HEBE and
in the input datasets. The most important source should be any errors in the forcings, especially con-
cerning aerosols. HEBE aims to provide a compelling and physically grounded alternative to complex
deterministic multi-model ensembles, offering a more precise, efficient, and interpretable means of pro-
jecting regional climate changes in the coming century. This positions it as a potentially valuable tool
for policy-relevant projections and adaptation planning, thereby showing the pertinency of fractional
derivative and Bayesian framework in atmospheric sciences.

How to cite: Wang, Y., Lovejoy, S., Lebiadowski, D., and Clarke, D.: Low Uncertainty Regional Climate Projections without Irrelevant Weather Details, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15472, https://doi.org/10.5194/egusphere-egu26-15472, 2026.

Synthetic time series generation is an essential tool for robustly exploring different climate scenarios and their impacts. While sophisticated generation methods have been developed in the past, they often rely on physical and statistical assumptions and require extensive data for calibration and parameter estimation. We propose a straightforward method for time series generation based on constrained sampling of observations. This approach preserves the physical consistency between variables and maintains the short temporal structure present in the observation. We apply this procedure to generate temperature, precipitation, incoming solar radiation, and wind speed time series sampled from meteorological station observations. We obtain different sets of synthetic time series by constraining the mean temperature according to future scenarios provided by climate model projections. We show that the sampled time series preserve the multivariate dependence structure observed in both historical data and climate projections. While, by design, the method does not generate daily values beyond the observed range, it can simulate multi-day extremes that exceed those in the observational record, such as longer heatwaves. The approach is flexible and can be applied to other variables with other constraints, provided that a sufficiently long observational time series is available and the constraints are compatible with the observed data. The generation procedure may thus prove useful for studying potential future extremes and help in general downscaling tasks.

How to cite: Lehning, M., Milojevic, T., and Rivoire, P.: The future is in the past? A flexible resampling approach to generate multivariate time series, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16369, https://doi.org/10.5194/egusphere-egu26-16369, 2026.

Reservoirs are increasingly recognized as significant sources of greenhouse gas (GHG) emissions, yet their future emissions under climate change remain poorly quantified. This study evaluates the impact of climate change on net GHG emissions from Feitsui Reservoir, a major water supply reservoir in northern Taiwan, using an integrated modeling approach.

We utilized the multisite Weather Generator (multiWG) to generate future climate projections for three Shared Socioeconomic Pathways (SSP126, SSP245, SSP585) across four 20-year periods (2021-2040, 2041-2060, 2061-2080, 2081-2100), with 1995-2014 as the baseline. A Random Forest model (NSE = 0.8637) was trained to predict reservoir inflow based on temperature and precipitation data. These inflows were input into the G-RES model to calculate net GHG emissions in CO₂-equivalent units, including contributions from both CO₂ and CH₄.

Results reveal that reservoir GHG emissions will increase under all climate scenarios, with magnitude strongly dependent on emission pathways. Under the low-emission scenario (SSP126), emissions increase by 5.2-8.8% across all periods. The intermediate scenario (SSP245) shows moderate increases of 5.4-18.4%. The high-emission scenario (SSP585) demonstrates dramatic escalation, particularly in the late century (2081-2100), where emissions reach 1259.6 gCO₂e/m²/yr—a 45.8% increase. These findings underscore the critical need to consider climate impacts in reservoir management and carbon accounting frameworks.

How to cite: Yeh, F.-W. and Tung, C.-P.: Assessing Climate-Driven Greenhouse Gas Emissions from Feitsui Reservoir Using G-RES Under Multiple SSP Scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16498, https://doi.org/10.5194/egusphere-egu26-16498, 2026.

Across the globe, today’s young generations will be more frequently exposed to climate extremes over their lifetime than earlier generations. Previous work has established this finding by combining simulations of historical and projected trends in climate extremes together with data on past and future demographic changes (Thiery et al. 2021, Grant et al. 2025). However, it has so far focused on a limited set of climate extreme indicators, using climate (impact) simulations from ISIMIP2 and demographics datasets that are now outdated, and did not fully assess uncertainty across the climate impact modelling chain. 

We now build on this existing lifetime exposure framework and combine it with a chain of emulators constituted of a Simple Climate Model (SCM) and the Rapid Impact Model Emulator Extended (RIME-X, Schwind et al., submitted). RIME-X can translate the GMT distributions generated by an SCM for a given emission scenario into spatially explicit distributions of climate or climate impact indicators. It has already been used to produce projections for 40+ indicators derived from ISIMIP3 and other climate model simulations, and this list can be extended to further indicators whose evolution predominantly depends on the level of global warming and for which historical and future simulations are available.   

We also update the lifetime exposure framework to consider more recent demographic data, and package it into a GitHub repository called dem4cli (short for ‘demographics for climate’) that will be made publicly available. We use spatially explicit population reconstructions and projections from the COMPASS project, and national-level life expectancy and cohort size estimates and projections from UNWPP2024.  

This work delivers more robust calculations of lifetime exposure to changes in extremes or climate impacts, by leveraging the ability of the SCM-RIME-X emulator chain to represent both their forced response to emissions as well as the combined uncertainty arising from the GMT response to emissions, the local climate response to global warming, and interannual variability, in combination with updated demographic data. This new framework is designed to generate such policy-relevant information in a more flexible and systematic manner, as it can in theory be applied to any available emission or GMT trajectories, and extended to a broad range of climate hazards.

Thiery, W. et al. Intergenerational inequities in exposure to climate extremes. Science 374, 158–160 (2021) 

Grant, L., Vanderkelen, I., Gudmundsson, L. et al. Global emergence of unprecedented lifetime exposure to climate extremes. Nature 641, 374–379 (2025) 

Schwind et al. RIME-X v1.0: Combining Simple Climate Models, Earth System Models, and Climate Impact Models into a Unified Statistical Emulator for Regional Climate Indicators. Geoscientific Model Development (submitted) 

How to cite: Lejeune, Q., Pietroiusti, R., Laridon, A., Schwind, N., Schleussner, C.-F., and Thiery, W.: Combining emulators and demographics: Building a flexible toolkit for lifetime exposure assessments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16683, https://doi.org/10.5194/egusphere-egu26-16683, 2026.

A collapse of the Atlantic Meridional Overturning Circulation (AMOC) would have strong consequences for the global climate system.  Assessing whether the AMOC will collapse in the future is difficult since current Earth System Models (ESMs) have biases. An earlier study using an intermediate complexity Earth system model (EMIC) showed the potential effect of freshwater biases on AMOC stability.  However, the used model has a limited ocean model with respect to the used  resolution and processes represented compared to ESMs. Here, we supplement the EMIC simulations with simulations of an ocean-only model using the same resolution as is typically used in ESMs. This allows us to study the effect of ocean resolution on the physical mechanism controlling the effect of freshwater biases on AMOC stability. We find that both the intermediate complexity and the ocean-only model behave qualitatively similar. In both models freshwater biases influence AMOC stability where negative (positive) biases in the Indian Ocean tend to stabilize (destabilize) the AMOC, whereas the opposite applies to biases in the Atlantic Ocean. Based on the freshwater biases present in most ESMs, our results suggest that most ESMs have a too stable AMOC and might therefore underestimate the probability of an AMOC collapse under future emission scenarios.

How to cite: Boot, A. and Dijkstra, H.: The effect of freshwater biases on AMOC stability across the model complexity spectrum, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16747, https://doi.org/10.5194/egusphere-egu26-16747, 2026.

The shape and extent of the Greenland Ice Sheet (GIS) during the Holocene remain a matter of considerable debate, with existing studies proposing a wide range of reconstructions. In this study, we aim to combine stable water isotopic information from ice cores with outputs from isotope-enabled climate models to investigate this problem. Directly exploring the space of possible ice sheet geometries through numerical simulations is computationally prohibitive. To address this challenge, we plan to develop a Gaussian process emulator that will serve as a statistical surrogate for the full climate model. The emulator will be trained on the results of a limited number of carefully designed simulations and will be used to enable fast, probabilistic predictions of model outputs at untried inputs. The inputs will consist of GIS morphologies, parameterized using a dimension-reduction technique adapted to the spherical geometry of the ice sheet. Using predictions from the emulator, we will explore the range of ice sheet morphologies that are compatible with available ice-core isotope measurements and other complementary observational data, including those collected during recent KANG-GLAC expeditions, with the goal of ultimately reducing uncertainty in reconstructions of Holocene GIS morphology.

How to cite: Malmierca Vallet, I., Sime, L. C., Voss, J., Fasoli, D., and Hogan, K.: Using Ice Cores and Gaussian Process Emulation to Recover Changes in the Greenland Ice Sheet During the Holocene, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17291, https://doi.org/10.5194/egusphere-egu26-17291, 2026.

OSCAR is an open-source reduced-complexity Earth system model designed to probabilistically emulate the coupled climate–carbon–chemistry system with low computational cost. Following a preivously published evaluation of OSCAR v3.1 against observations and CMIP6 Earth system models, we present OSCAR v4, which incorporates a range of structural, numerical, and methodological improvements. Key developments include enhanced numerical stability, modularization of the code to allow running submodels independently, revised and streamlined modules, and recalibration using the latest AR6, CMIP6, and TRENDY datasets. Monte Carlo sampling has been improved using continuous probability distributions, and the constraining strategy now leverages Latin-hypercube sampling combined with probability integral transforms to provide more robust probabilistic ensembles compatible with observations. Alongside core model improvements, OSCAR v4 will introduce a suite of user-oriented functionalities and a full online documentation, facilitating broader adoption and reproducibility.

We illustrate the performance of OSCAR v4 through participation in the Reduced Complexity Model Intercomparison Project (RCMIP) phase 3 exercise. This benchmarking demonstrates the model’s ability to reproduce the spread of global temperature and carbon-cycle responses observed in more complex Earth system models, while providing rapid, policy-relevant probabilistic projections. Given it's level of complexity, OSCAR v4 is positioned as a versatile tool bridging comprehensive Earth system models and the simpler reduced-complexity approaches for large-scale climate assessments.

How to cite: Gasser, T., Zhu, B., Liu, X., Zhang, D., Lai, Y., and Shrivastav, G.: The compact Earth system model OSCAR v4, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18639, https://doi.org/10.5194/egusphere-egu26-18639, 2026.

How to cite: Hooke, D., Mathison, C., Burke, E., Jones, C., Gohar, L., and Wiltshire, A.: From scenarios to impacts – an emulation of regional climate impacts and their uncertainties using the CMIP7 mitigation scenarios   , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19277, https://doi.org/10.5194/egusphere-egu26-19277, 2026.

To meet Paris-aligned climate goals and minimize temperature overshoot and its impacts, rapid and deep reductions in greenhouse-gas emissions from fossil-fuel combustion are required. Climate risk projections are strongly affected by uncertainty in anthropogenic aerosol effective radiative forcing (ERF) and by the co-evolution of air-pollutant emissions under decarbonization pathways. Because running large Earth System Model (ESM) ensembles remains computationally expensive for uncertainty quantification and broad policy-scenario exploration, reduced-complexity climate emulators are needed for efficient, transparent, and observation-connected assessments.

Here we develop an aerosol extension to the simple climate model (SCM) FaIR that emulates aerosol ERF from global anomalies in aerosol optical depth (ΔAOD) relative to a pre-industrial baseline for different species. Aerosol ERF is computed using a constrained parameterization that separates aerosol–radiation and aerosol–cloud interactions, with key parameters represented probabilistically and constrained by observational and model-based lines of evidence.

To emulate ΔAOD from emissions pathways, we implement an interpretable mapping calibrated to CMIP6 ESM output. An effective linear relationship between emission and burden anomalies is fitted using a single parameter that aggregates yield and lifetime effects. In a second step, we fit an effective optical parameter linking burden perturbations to ΔAOD. This produces model-dependent parameter distributions that enable propagation of both parametric uncertainty and between-model spread. In addition, we implement an integrated-assessment-model-based relationship linking air-pollutant emissions to CO₂ emissions under different air-quality policy stringencies, interpolated into a continuous air-quality parameter suitable for exploring uncertainty and its interaction with decarbonization trajectories.

We perform Monte Carlo ensembles sampling aerosol-ERF parameters, CMIP6-calibrated aerosol–AOD mappings, air-quality policy stringency, and net-zero timing, and evaluate impact-relevant climate risk metrics including peak warming, probability of remaining below 1.5 °C, threshold crossing year, overshoot duration, and warming rates computed over multiple near-term and decadal windows. Preliminary results show strong dependence of peak temperature outcomes on net-zero timing, while threshold-based metrics and warming rates exhibit pronounced sensitivity to air-quality assumptions, consistent with a partial loss of aerosol cooling under stricter pollution controls. Overall, the results indicate non-linear interactions between decarbonization timing, air-quality stringency, and warming-rate responses. The emulator provides a scalable basis for robust climate risk screening and for coupling SCM trajectories to impact assessments.

Keywords: Climate Change, Mitigation, Aerosols, Effective Radiative Forcing, Climate Emulators, Climate Modeling, CMIP6 Calibration, Air-quality Policy, Overshoot

How to cite: Arzola Röber, T., Bruckner, T., and Quaas, J.: Accounting for Aerosols in Climate Mitigation Pathways, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19612, https://doi.org/10.5194/egusphere-egu26-19612, 2026.

Greenhouse gas emission metrics are widely used for comparing climate impacts of different gases and for guiding mitigation policy. Conventional metrics such as GWP100 perform well for representing the warming effects of long-lived gases which behave like CO₂ but poorly for short-lived climate pollutants (SLCPs). Methane (CH₄) is the most important SLCP and has been the main focus of alternative metrics. GWP* was developed to more accurately capture impact on global warming, particularly from stable and declining CH₄ emissions which are not well served by GWP100. This means that GWP* better connects emissions pathways to long-term temperature targets (Cain et al., 2022). Previous studies optimised GWP* for CH₄ for a limited range of scenarios up to 2100. However, future mitigation pathways involve a wider range of gases and transition speeds, overshoot behaviour, and long-term stabilization beyond this period. In addition, highly radiatively efficient fluorinated gases are increasingly important in mitigation strategies yet have not been demonstrated with the GWP* framework. In this study, we systematically test the performance of GWP* across an expanded set of emissions scenarios, including rapid mitigation, delayed action, and prolonged temperature overshoot pathways, and extend the analysis to multi-century time horizons with an optimisation of the flow term of GWP* (Mastropierro et al., 2025). We further develop and evaluate a generalized formulation of GWP* for fluorinated gases with diverse atmospheric lifetimes. The outcomes examine the performance of GWP* under realistic transition pathways and its representation of temperature responses for fluorinated gases. This work supports the development of more physically consistent multi-gas emission metrics for climate targets, carbon budgeting, and policy design, as it is a simple tool to calculate how much global warming is added or avoided by increasing or cutting SLCPs such as F-gases.

Cain, M., Jenkins, S., Allen, M.R., Lynch, J., Frame, D.J., Macey, A.H., Peters, G.P. Methane and the Paris Agreement temperature goals. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 380 (2022). https://doi.org/10.1098/rsta.2020.0456

Mastropierro, M., Tanaka, K., Melnikova, I. et al. Testing GWP* to quantify non-CO₂contributions in the carbon budget framework in overshoot scenarios. npj Clim Atmos Sci 8, 101 (2025). https://doi.org/10.1038/s41612-025-00980-7

How to cite: Cain, M., Patel, V., Mastropierro, M., Tanaka, K., Jenkins, S., and Allen, M.: Applying GWP* to Long-Term Climate Pathways and Fluorinated Gases, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20117, https://doi.org/10.5194/egusphere-egu26-20117, 2026.

Earth System Models of Intermediate Complexity (EMICs) allow for fast exploration of large-scale climate dynamics. These models thus enable the development and testing of large-ensemble-based techniques that would be too costly with more realistic climate models.

In this ongoing study we develop a rare event simulation setup to explore the possibility of a spontaneous collapse of the Atlantic Meridional Overturning Circulation (AMOC) in the FAMOUS model. FAMOUS is a low-resolution, coupled atmosphere-ocean general circulation model derived from the UK Met Office’s Unified Model specifically designed for efficient, long-duration and ensemble climate simulations. FAMOUS has previously been used to investigate the hysteresis of the Atlantic Meridional Overturning Circulation under freshwater hosing.

We apply a genealogical particle analysis (GPA) algorithm that is designed to probe the possibility of spontaneous AMOC transitions. The method initiates an ensemble of realisations in the "on"-state of the AMOC and clones ensemble members at regular intervals  that are showing a low AMOC.

Contrary to recent results in another EMIC, a straightforward sampling based on the AMOC indicator does not result in any spontaneous transitions to the AMOC "off"-state. To improve the selection of potentially exceedingly rare trajectories, we therefore investigate statistical methods to identify physical variables that correlate with the state of the AMOC ahead of time, to be used as selection criteria in the GPA algorithm.

How to cite: Wouters, J., Hu, G., Bröcker, J., and Smith, R.: Investigating the possibility of rare spontaneous AMOC transitions in the intermediate complexity climate model FAMOUS., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20145, https://doi.org/10.5194/egusphere-egu26-20145, 2026.

The Witwatersrand Basin on the Kaapvaal Craton hosts the world’s largest gold province, with the vast majority of gold concentrated in the 2.90–2.79 Ga Central Rand Group, whereas the slightly older 2.95–2.91 Ga West Rand Group is largely barren despite comparable sedimentary characteristics. This contrast has been attributed to intensified chemical weathering during Central Rand Group times, which promoted enhanced gold mobilisation from the Archaean hinterland. However, the climatic and environmental drivers of this weathering intensification remain poorly constrained. To address this, we investigated Mesoarchaean climate controls using the Planet Simulator (PlaSim), an Earth system model of intermediate complexity. We conducted 140 PlaSim simulations to quantify the climatic sensitivity to atmospheric greenhouse gas concentrations, continental surface area, surface albedo, and land configuration. CO₂-equivalent concentrations (3–30 %), land coverage (8–28 %), and albedo (0.15–0.30) were systematically varied across different land distributions (equatorial, polar and spread over different latitudes).

Next to the well-known effect of global warming under increased greenhouse gas concentrations, our results show that increasing continental area generally results in global cooling due to the higher albedo of land surfaces relative to oceans, particularly when land was concentrated at low latitudes. This cooling effect becomes pronounced once land exceeds approximately 13 % of Earth’s surface. At high latitudes, land has minimal climatic impact because of the low incoming radiation angle that leads to less absorption. Exceptions are noted under conditions of low greenhouse gas concentrations and low surface albedo, at which limited land growth could slightly enhance warming. Among the tested land positions, the scenario with land spread over different latitudes resulted in the highest climate sensitivity.

Overall, our results indicate that land distribution alone was unlikely to have caused global warming during the Mesoarchaean, and this climatic influence was probably dampened by a more rapid carbon cycle at that time. Instead, elevated atmospheric greenhouse gas levels emerge as the dominant driver of warming and enhanced chemical weathering. The climatic transition around ~2.9 Ga may further reflect the emergence of extensive low-albedo mafic or ultramafic surfaces and/or the latitudinal migration of the Kaapvaal Craton into a more radiatively sensitive, low-latitude zone. These combined factors likely contributed to intensified weathering, increased gold leaching, and the gold megaevent responsible for the formation of the Witwatersrand ores.

How to cite: Wasitschek, L., Frimmel, H. E., Hiby, N., and Pollinger, F.: Modelling Mesoarchaean climate: Economic implications , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20975, https://doi.org/10.5194/egusphere-egu26-20975, 2026.

The radiative balance of the early Earth was governed by other boundary conditions than today, including a fainter Sun, elevated greenhouse gas concentrations, and a smaller land surface area. Although the role of atmospheric composition in sustaining habitable surface temperatures during the Mesoarchaean has been extensively investigated, especially to solve the faint young Sun paradox, the climatic impact of land position and distribution under varying albedo remains comparatively underexplored.

We therefore assess how variations in land-surface albedo, land fraction, and land distribution could have modulated Mesoarchaean climate states. Using the Planet Simulator (PlaSim), an intermediate-complexity climate model, we conducted 195 simulations spanning CO₂-equivalent forcing levels of 3–10 % (30,000–100,000 ppm). Land-surface albedo was varied between 0.15 and 0.30, land area between 8 % and 28 %, and idealised land distributions were prescribed, including diagonal, staggered, and mid-latitude configurations. Ocean albedo was held constant at 0.144 to isolate the climatic impact of continental reflectivity.

Across all simulations, global mean temperature responds strongly and non-linearly to both land fraction and land albedo. At low land albedo (0.15) and low to intermediate CO₂-equivalent forcing (3–5 %), increasing land area produces a slight warming trend, despite minimal differences between land and ocean reflectivity. This behaviour indicates that land–ocean contrasts in surface energy partitioning and effective heat capacity can modify global climate even when shortwave albedo contrasts are small. Sensitivity increases abruptly as land albedo rises from 0.20 to 0.25. Beyond this threshold, modest increases in land area result in pronounced global cooling, consistent with a regime shift in the radiative balance. This non-linear response is most prominent at low to intermediate CO₂-equivalent forcing and becomes progressively muted at higher forcing (10 %), where greenhouse effects dampen the temperature response to surface reflectivity changes. The pattern occurs across all land configurations but is amplified when landmasses occupy equatorial to mid-latitudes, where insolation is highest and albedo exerts maximum leverage, whereas high-latitude land has a comparatively weaker effect.

These findings highlight that land surface characteristics such as albedo and distribution were critical for early Earth’s climate. Even under strongly greenhouse-forced atmospheres, surface properties significantly altered the planetary energy budget. Recognising such sensitivities is essential for reconstructing Archaean climate states and assessing the potential for climatic stability under reduced solar luminosity.

How to cite: Hiby, N., Wasitschek, L., and Frimmel, H. E.: Surface albedo as a first-order control on Mesoarchaean climate (PlaSim), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21556, https://doi.org/10.5194/egusphere-egu26-21556, 2026.

The Atlantic Meridional Overturning Circulation (AMOC) is a critical component of the global climate system and its potential for abrupt collapse represents a significant tipping point. Our project investigates whether a persistent negative phase of the North Atlantic Oscillation (NAO), a dominant mode of atmospheric variability, can induce an AMOC collapse in the absence of external perturbations within the coupled PlaSim-LSG climate model of intermediate complexity. A control simulation establishes a baseline climatology, confirming that NAO variability leads AMOC fluctuations by approximately one year. To overcome the computational limitation of simulating rare events, we implement a rare event algorithm (GKLT) that efficiently biases the model toward trajectories with negative NAO conditions over 125-year simulations. The results reveal a fundamental bistability in the system. While persistent negative NAO forcing can trigger an AMOC collapse, the outcome is probabilistic: out of six independent ensemble simulations, four evolved entirely into a collapsed state (∼ 12 Sv), one remained entirely vigorous (∼ 23 Sv) and one split into both outcomes. A cluster-based analysis traces this divergence to the early amplification of small differences in North Atlantic heat fluxes, convection and sea-ice cover. These findings show that internal atmospheric variability alone can force the AMOC across a tipping point, highlighting the role of internal climate dynamics in shaping climate transitions.

How to cite: Magagna, A., Zappa, G., Cini, M., and Corti, S.: Simulating NAO-driven AMOC collapse in the PlaSim-LSG Climate Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21970, https://doi.org/10.5194/egusphere-egu26-21970, 2026.

On 3 May 2025, a severe hailstorm affected Paris and parts of western Europe. We assess whether anthropogenic climate change contributed to its intensity using ERA5 reanalyses and an analogue-based attribution framework. The synoptic pattern featured a cut-off low and a surface cold front following several warmer-than-normal days. We identify circulation analogues to 3 May 2025 in two periods, namely a cooler “past” (1974–1999) and a warmer “present” (1999–2024). We then compare thermodynamic conditions under otherwise similar large-scale flow. Hail probability and size are estimated with two models: (i) a logistic formulation using Convective Available Potential Energy (CAPE), deep-layer wind shear, and convective precipitation, and (ii) an extended model including freezing-level height and 850 hPa temperature, tailored to European hail environments. Models are calibrated with ˆIle-de-France observations and validated independently. Present-day analogues exhibit significantly higher CAPE, a higher freezing level, and similar deep-layer shear, yielding larger hail probability and size. These results indicate that human-induced warming likely enhanced the hailstorm severity in this synoptic setting.

How to cite: Faranda, D. and Alberti, T.:   Investigating the Role of Climate Change in the 3 May 2025 Western Europe Hailstorm Using Atmospheric Analogues, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2862, https://doi.org/10.5194/egusphere-egu26-2862, 2026.

Thunderstorm activity and associated turbulence pose significant operational challenges for major airports, especially in the context of a changing climate. This study analyzes a high impact winter convective event that forced delays and cancellations at the Rome-Fiumicino airport. We investigate how the synoptic conditions of similar events have evolved over the past five decades (1974–2024) using reanalysis data and a pattern analog approach. We compare atmospheric configurations from the past (1974-1999) and recent (1999-2024), focusing on key parameters related to convection and turbulence. For similar synoptic configurations, our results show an increase in Convective Available Potential Energy (up to 20%), low-level vertical wind shear (up to 20–25%), and turbulence (up to 25-30 %) near Rome-Fiumicino airport in the more recent period, indicating a greater potential for organized convection and turbulence. The analysis of vertical atmospheric profiles reveals enhanced wind shear and turbulence especially in the lower troposphere (0-3 km), with implications for mechanical turbulence during aircraft approach and departure. At Rome-Fiumicino airport, the number of fog and thunderstorms during similar synoptic patterns is increased (from 1 to 4), average approaching visibility decreased from 10 to 7 km, stronger surface winds (from 10 to 15 km/h) are observed, with also increases in average temperatures (from 11 to 13 °C). Finally, using a multinomial logistic model we show that hazardous weather events, particularly thunderstorms and hail, are becoming more frequent for similar recent events (from 2% to 6% annual occurrence). These trends are linked to both human-driven climate change and long-term variations in large-scale modes of natural variability. 

How to cite: Alberti, T. and Faranda, D.: Was the 13 December 2024 severe thunderstorm over Rome-Fiumicino airport intensified by climate change?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2952, https://doi.org/10.5194/egusphere-egu26-2952, 2026.

The frequency and duration of hot extremes is projected to increase over the coming decades. It remains, however, unclear to what extent persistent surface temperature extremes require an anomalously persistent circulation in the upper troposphere. To shed more light on this relationship, we combine idealized model experiments with reanalysis data and assess the zonal phase speed of Rossby waves as a proxy for circulation persistence. In particular, we compare the climatological-mean phase speed spectrum to the properties of heatwave-generating Rossby wave packets.

In the idealized model without thermodynamic feedbacks, a phase speed increase in response to a localized thermal forcing reduces the frequency of heatwaves. Reanalysis data for the Southern hemisphere mid-latitudes shows a similar and significant phase speed increase from the 1980s until today. However, the observed mean phase speed increase does not apply to heatwave-generating Rossby waves and hence does not contribute to a change in heatwave frequency. The Northern hemisphere, on the other hand, does not yet show a clear phase speed trend in reanalysis. But with continued global warming, we expect an acceleration of heatwave-generating Rossby waves and a reduced upper-tropospheric forcing to persistent temperature extremes in the future.

How to cite: Wicker, W., Russo, E., and Domeisen, D.: Heatwave-generating Rossby waves and the persistence of temperature extremes in a changing climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3128, https://doi.org/10.5194/egusphere-egu26-3128, 2026.

The WASITUS project was established to build towards an operational event attribution capability for Ireland. The project’s aim is to deep dive into the effect climate change has on extreme weather events at a national level, while also providing additional support to international attribution groups such as project collaborators; World Weather Attribution. 

By focusing on smaller national scales, and investigating data products used in event attribution, attribution studies can become more accurate and offer deeper insight for local responders and policy makers. A main focus of the WASITUS project is to take advantage of the small geographical size of Ireland and work directly with end-users to better understand how event attribution can help them prepare for future changes in extreme weather. These end-users include members of the public, local representatives, and national policy makers. This directly links attribution with real-world planning and damage mitigation measures.

Focusing on the data side of event attribution, most datasets used, whether reanalysis or models, have been tested at large regional or continental scales. However, we have found that the reanalysis data for Ireland, an island nation on the western boundary of most European datasets, is not as accurate as the data over continental Europe. This is quite possibly the case for other nations globally, where a variety of geographical and observational factors may have led to reanalysis products inaccurately representing the weather and climatology. As Ireland sits on the East of the Atlantic ocean, it is prone to weather threats of marine origin. Therefore, it is important to question the data used in creating the reanalysis and model products for Ireland as changing climate trends impact Ireland in different ways to the rest of Europe. 

A particular issue found for reanalysis products is their retrospective extension to earlier decades. To combat this potential issue, we are developing a toolbox to ascertain if reanalysis products reliably characterise the temperatures experienced in a given region for the entirety of the available time-series. The toolbox also aims to identify if shorter subsets of the entire reanalysis timeseries better represent the changing climate than the full dataset. Focusing on ERA5 daily maximum and minimum temperature data over the Republic of Ireland, station observations are being statistically compared to location-specific reanalysis data. While the initial focus will be temperature in Ireland, this toolbox should be readily adaptable for use in different regions globally, as well as on different meteorological parameters, provided sufficient long-term records are available.

In future, it is hoped that other national attribution capabilities, which are being newly formed, can collaborate and aid one another in conducting analysis and report writing. National groups also allow for further research into the methods used for extreme event attribution, where a focus can be placed on improving and expanding the existing attribution capability. In addition, time and focus placed on smaller geographical regions allows for data used in attribution analysis to be thoroughly quality controlled and checked.

How to cite: Bergin, C., Barnes, C., Swan, L., Otto, F., and Thorne, P.: Validation of reanalysis products for extreme event attribution at regional and national levels, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3562, https://doi.org/10.5194/egusphere-egu26-3562, 2026.

Precipitation variability across North Africa spans a wide range of timescales and climatic regimes, from Mediterranean winter precipitation to Saharan convective systems, yet its underlying drivers remain incompletely understood. This contribution synthesizes current knowledge on the atmospheric and surface drivers of precipitation variability in North Africa, drawing on evidence from observations, reanalyses and climate simulations from the Holocene to future projections.

We review the role of large-scale circulation modes, together with synoptic-scale processes such as Rossby wave breaking, cut-off lows, and cyclogenesis, in shaping interannual variability and extreme precipitation events along the Mediterranean coast. Further south, seasonal dynamics linked to the Saharan Heat Low, moisture transport, and land–atmosphere coupling modulate the intermittency and intensity of precipitation in arid regions. Holocene evidence highlights the sensitivity of North African hydroclimate to external forcing and land-surface feedbacks, while also illustrating limits to direct analogy with anthropogenic greenhouse-gas forcing. Future projections indicate that uncertainty in precipitation change is dominated by internal variability and circulation responses, with more robust signals emerging in variability and extremes than in mean precipitation.

As precipitation variability constitutes a climate hazard in its own right, understanding its atmospheric and thermodynamic drivers is central to assessing drought–flood dynamics and their implications for water resources, ecosystems, and human systems across North Africa.

How to cite: Tanarhte, M., De Vries, A.-J., Zittis, G., Armon, M., Hochman, A., Karpasitis, A., Kaskaoutis, D., and Khodayar, S.: Atmospheric drivers and thermodynamic controls of precipitation variability in North Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3580, https://doi.org/10.5194/egusphere-egu26-3580, 2026.

Wintertime stratospheric circulation in the Northern Hemisphere is dominated by a strong and persistent westerly polar vortex. However, every one to two years, this system undergoes a strong disruption associated with a fast deceleration or even a reversal, accompanied by a massive warming of the polar stratosphere. The tropospheric impacts of these extreme events, commonly referred to as “sudden stratospheric warmings” (SSWs) are well documented, but their precursors and subsequent responses in the troposphere remain frustratingly difficult to categorize systematically. Using recent advances in dynamical systems theory applied to the atmosphere, we analyze from a general point of view, the relationship between very anomalous stratospheric states and tropospheric configurations. We find that highly anomalous geopotential configurations at 10 hPa are unequivocally associated with the occurrence of a strong stratospheric vortex deceleration. However, no distinctive tropospheric patterns can be identified either prior to or following these events. This suggests that both tropospheric precursors and responses to extreme vortex decelerations are fundamentally nonspecific and in consequence, they could be statistically indistinguishable from the background tropospheric variability.

How to cite: Gallego, D., Álvarez-Castro, C., Faranda, D., and Peña-Ortiz, C.: Can tropospheric configurations linked to the onset or aftermath of polar vortex decelerations be distinguished from climatology?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5518, https://doi.org/10.5194/egusphere-egu26-5518, 2026.

The rising frequency of extreme precipitation is a major concern linked to climate change, commonly associated with increased atmospheric water vapor due to global warming. In densely populated areas, intense rainfall has particularly severe impacts, with urbanization amplifying extreme weather through changes in land surface and local atmospheric conditions.  As attribution science increasingly informs climate policy, it is crucial to discern the extent to which shifts in extreme event probability stem from global versus local anthropogenic drivers. This study analyzes multi-decadal daily precipitation records alongside urbanization indices. In line with previous research, results show a general rise in extreme rainfall frequency, with more intense events exhibiting a larger increase. Analysis of population and urban development metrics reveals that the increase is notably smaller in rural areas, suggesting that the rise attributable to local urban development is of the same order of magnitude as that resulting from global warming. This result is shown to be associated with the urban amplification of convective updraft intensification.

How to cite: Guccione, A., Bassi, P., Desbiolles, F., Borgnino, M., D'Andrea, F., and Pasquero, C.: Extreme precipitation changes in relation to urbanization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7152, https://doi.org/10.5194/egusphere-egu26-7152, 2026.

Atmospheric variability spans interacting regimes set by rotation, stratification, and diabatic forcing. One open question is that diagnosing scale-to-scale energy transfer remains challenging because observations rarely provide complete budget closure. We analyze the June 2019 European heatwave using the Weather Research and Forecasting (WRF) model with a hybrid, scale-adaptive LES closure and five nested domains, resolving horizontal separations from O(10²) m to O(10⁶)–O(10⁷) m.

Starting from the governing equations of motion in WRF hybrid vertical coordinate, we derive and appraise generalized two-point, Scale-by-Scale (SbS) budget equations for the second-order moments of horizontal velocity increments, reflecting the kinetic energy at each scale. Whilst equations are written for all scales and any point of the considered domains, their assessment against data is performed in a plane parallel to the ground. SbS energy budget equations account for the inhomogeneity, anisotropy, and all effects present in the first principles. We complement these diagnostics with height-dependent characteristic length scales (Kolmogorov, Taylor, Ozmidov, buoyancy, Rhines and Rossby deformation).
We show results for two cases:
i) In the free troposphere, where the SbS kinetic-energy budget is dominated by the advective term (reflecting non-linear interactions and energy transfer), which is balanced by the pressure-gradient contributions. Radial integration of the advective term reproduces the third-order structure function and exhibits a sign reversal near r ∼ 10⁵ m, reflecting transitions between downscale and upscale kinetic energy transfer across mesoscale–synoptic ranges.
ii) In the lower troposphere, we investigate daytime and nocturnal conditions. First, in daytime conditions, the boundary layer exhibits a classical behavior, in which energy is transferred across scales mainly by advective, non-linear effects. Second, for stable stratification during the night, the pressure contribution increases significantly, and the advective transfer adjusts to the pressure-imposed scale dependence, as already noted in the free atmosphere.

These results provide a physically interpretable framework for diagnosing atmospheric cascades across scales and motivate extending SbS budgets to include thermodynamic variables, such as the moist potential temperature and the water vapor content. The latter would allow us to quantify the contributions of radiative and diabatic forcings over short- and long-term timescales, relevant to climate variability.

How to cite: Sayeed, K., Blervacq, C., Fossa, M., Massei, N., and Danaila, L.: Scale-by-scale two-point statistics in WRF Hybrid LES model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7744, https://doi.org/10.5194/egusphere-egu26-7744, 2026.

It is the combination of internally induced oscillations and externally forced climate change signals that we observe and feel every day as climate conditions. External forcing can change not only the mean state, but also the internal variability. One of the most important and impactful aspects of variability is the frequency and magnitude of extremes. Even though the cold extremes are expected to warm, they can still have severe impacts on society and ecosystems, which have adapted to a warmer climate. We investigate how the internal variability of winter temperature might change under stronger radiative forcing. For this purpose, we utilize two different datasets: a set of LongRunMIP simulations, analyzing near-equilibrium conditions under preindustrial and abrupt 4xCO2 forcings, and transient large ensemble simulations comparing the historical and scenario periods (the end of the 21st century under RCP8.5/SSP5-8.5 socio-economic pathways). We focus on northern middle latitudes (40 – 70 ° of latitude). In this region, the near-surface climate is largely influenced by atmospheric circulation, including various large-scale modes of variability. A change in the shape of the temperature distribution can then point to a fundamental change in climate-governing processes. It has been argued that increasing winter mean temperatures would be accompanied by a decrease in variance, as day-to-day temperature variations are induced by the occurrence of synoptic-scale weather systems, and in warmer climates, this is expected to decline. Our study provides new insights, showing that the variance shrinking is spatially heterogeneous. We further concentrate on the skewness of the temperature distribution and investigate the changes in the lengths of the cold and hot tails, which are related to the changes in variance. In many mid-latitude regions, the skewness is decreasing, and the cold tail is shrinking at a slower rate than the hot tail, implying enduring cold extremes, even in climatic states much warmer than those we are familiar with.  

How to cite: Holtanova, E., Van Loon, S., and Rugenstein, M.: Cold extremes enduring in a much warmer world, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9126, https://doi.org/10.5194/egusphere-egu26-9126, 2026.

 The expected impact of climate change on temperature extremes is an increase in both the frequency and intensity of heat waves, while cold waves are expected to become less frequent and associated with milder cold temperatures. However, cold waves cannot be ruled out, as cold temperatures similar to those experienced in the past can still occur, at least in the near future, albeit with a lower probability.

While many studies have focused on estimating hot extremes in the context of non-stationary climate change, fewer have addressed the estimation of cold extremes, which must be considered for the design of new installations. Unlike hot extremes, which will intensify over time, the coldest values that might affect existing or planned installations are expected to occur now or in the very near future.

Temperature extremes exhibit different types of non-stationarities: a seasonal cycle, the human-induced climate change trend, and interannual to decadal variability. The seasonal cycle is commonly handled by selecting the season prone to the analyzed extremes. Various methods have been proposed to account for the trend due to human-induced climate change in extreme value estimations, either by considering trends in the parameters of statistical extreme value distributions (Coles, 2001; Parey et al., 2007; Gilleland and Katz, 2016; Barbaux et al., 2025, among others) or by computing a reduced variable whose extremes can be considered stationary and then back-transformed (Parey et al., 2013, 2019; Mentachi et al., 2016). However, for cold extremes, interannual variability generally plays a more significant role.

Therefore, in this study, we propose and test an approach to infer extreme cold values representative of the current climate by combining extreme deviations from the average winter mean and variance, as observed during the coldest winters in the past, with the average conditions of current winters. The methodology will first be described then illustrated with examples.

References:

Coles S (2001) An introduction to statistical modelling of extreme values, Springer Series in Statistics. Springer, London

Parey S, Malek F, Laurent C, Dacunha-Castelle D (2007) Trends and climate evolution: statistical approach for very high temperatures in France. Clim Change 81:331–352. https://doi.org/10.1007/s10584-006-9116-4

Gilleland, E., & Katz, R. W. (2016). extRemes 2.0: An Extreme Value Analysis Package in R. Journal of Statistical Software, 72(8), 1–39. https://doi.org/10.18637/jss.v072.i08

Occitane Barbaux, Philippe Naveau, Nathalie Bertrand, Aurélien Ribes, Integrating non-stationarity and uncertainty in design life levels based on climatological time series, Weather and Climate Extremes, Volume 50, 2025,100807, ISSN 2212-0947, https://doi.org/10.1016/j.wace.2025.100807.

Parey S, Hoang TTH, Dacunha-Castelle D (2013) The importance of mean and variance in predicting changes in temperature extremes. J Geophys Res Atmos 118:8285–8296. https://doi.org/10.1002/jgrd.50629

Parey, S., Hoang, T.T.H. & Dacunha-Castelle, D. Future high-temperature extremes and stationarity. Nat Hazards 98, 1115–1134 (2019). https://doi.org/10.1007/s11069-018-3499-1

Mentaschi, L., Vousdoukas, M. I., Voukouvalas, E., Sartini, L., Feyen, L., Besio, G., & Alfieri, L. (2016). The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis. Hydrology and Earth System Sciences, 20(9), 3527–3547. https://doi.org/10.5194/hess-20-3527-2016

How to cite: Parey, S., Hoang, T. T. H., and Guisnel, B.: How to compute extreme cold levels to design power plants in the climate change context? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9245, https://doi.org/10.5194/egusphere-egu26-9245, 2026.

The risk of extreme climate events is increasing due to the compounding effects of climate change and the increasing dependence on natural resources, with impacts that cascade through ecosystems, livelihoods, and institutions long after the event itself. Climate services are therefore increasingly central to adaptation, providing information that helps anticipate hazards, guide preparedness, and support response. Yet, adaptation can often turn maladaptive when it unintentionally shifts risk to other groups, degrades ecological buffers, or locks systems into trajectories that increase their long-term vulnerability. Climate services rarely account for these unintended consequences, despite their centrality in what decisions can be taken and by whom. Against this backdrop, our contribution presents a methodological framework that integrates system thinking and system dynamics modelling to anticipate how climate services shape long-term socio-ecological outcomes of climate extremes, including the risk of maladaptation.

Our framework combines four elements. First, we use system archetypes to identify recurring maladaptive patterns relevant to extremes’ impacts, such as risk shifting across space or social groups, and “fixes” that reduce immediate losses while degrading ecological resilience. Second, these dynamics are refined through a stakeholder-led iterative process. Third, maladaptation risk and adaptation trade-offs are evaluated and described. Fourth, these dynamics are formalized in a system dynamics model to test different climate information scenarios.

Our application of this framework shows that different typologies of climate services can influence long-term impact trajectories by influencing what risks are prioritized, which measures are selected, and who is able to act. Additionally, under increasing climate variability and compounding shocks, these dynamics become more pronounced, increasing the likelihood that short-term coping undermines long-term resilience. Consequently, accessible and long-term climate services become pivotal in ensuring sustainable adaptive strategies benefitting all stakeholders.

By linking climate services to the complex, socio-ecological impact of climate extremes, this approach lays the groundwork for testing the risk of maladaptation in the development of climate services and adaptation strategies, supporting equitable and durable disaster impact reductions.

How to cite: Biella, R., Brandimarte, L., Mazzoleni, M., and Di Baldassarre, G.: Understanding Complexity to Anticipate Maladaptation: A System Dynamics Approach to Climate Extremes Adaptation with Climate Services, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11777, https://doi.org/10.5194/egusphere-egu26-11777, 2026.

The Mediterranean is a climatologically sensitive region due to its transitional position between the arid subtropics and the wetter mid-latitudes. In recent years, Mediterranean tropical-like cyclones, or Medicanes, have gained increasing attention. These rare baroclinic cyclones that evolve in their mature stage into vortices with structural characteristics similar to tropical cyclones. Although they occur only a few times per decade, Medicanes can produce severe socio-economic impacts through intense precipitation, strong winds, and coastal flooding. 

Observational and modeling studies indicate that rising sea surface temperatures may affect Medicane evolution, potentially leading to stronger storms. Understanding their dynamics is therefore important not only for climatology but also for operational sectors such as aviation, which are directly exposed to atmospheric hazards. While the surface impacts of Medicanes have been widely studied, their influence on upper-tropospheric conditions, particularly turbulence relevant to aviation, remains poorly documented. In-flight encounters with turbulent eddies represent a major aviation hazard, often resulting in injuries, aircraft damage, and economic losses to airlines. 

This study presents the first systematic investigation of aviation-scale turbulence associated with eleven Medicanes that occurred between 1996 and 2023. The analysis is based on three empirical turbulence diagnostics (TI1, TI2, and TI3), commonly used to identify synoptic-scale patterns conducive to shear-induced turbulence. These indices, derived from the ERA5 reanalysis dataset, are computed for each Medicane across the 900–200 hPa layer and as a function of radial distance from the cyclone center, with the aim of assessing how turbulence conditions within Medicanes evolve in a changing climate.

How to cite: Simone, M., Servidio, S., Miglietta, M. M., and Alberti, T.: Characterization of aviation turbulence associated with Mediterranean tropical-like cyclones (Medicanes), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12488, https://doi.org/10.5194/egusphere-egu26-12488, 2026.

Extreme flood events are among the most damaging climate-related hazards, with significant human and socio-economic impacts. Understanding the extent to which anthropogenic climate change influences both the physical characteristics and impacts of such events is important for supporting policymakers in risk management and adaptation, informing loss and damage mechanisms, and raising public awareness of the impacts of climate change. Here, we apply a circulation-analogue attribution approach to quantify the impacts of climate change on flooding, extending the use of dynamical analogues from hazard attribution to impact analysis. The framework is designed to work with limited data, making it particularly relevant for data-scarce regions, including much of the Global South.

In late April and early May 2024, extreme flooding affected large parts of the state of Rio Grande do Sul in southern Brazil, being the largest floods ever observed along several regional rivers. The event caused at least 183 fatalities and affected more than 2.3 million people, making it one of the most severe climate-related disasters in Brazil’s history. Weekly rainfall totals exceeded 300 mm across much of the state and 500 mm locally.

In this study, we assess the influence of anthropogenic climate change on the socio-economic impacts of this extreme flood event using a three-step attribution framework. First, we attribute the total event rainfall to climate change by identifying dynamical analogues—events with similar large-scale atmospheric circulation—in single-model initial-condition large ensembles under factual and counterfactual climate conditions. Second, the resulting precipitation signals are used to force a hydrological flood model to quantify climate-induced changes in flood magnitude and spatial extent. Finally, we evaluate the associated socio-economic impacts based on the climate-attributed flood signal.

How to cite: Ginesta, M., Laipelt, L., Franta, B., and Stuart-Smith, R. F.: Attribution of the Impacts of the 2024 Extreme Floods in Rio Grande do Sul, Brazil, to Climate Change , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12606, https://doi.org/10.5194/egusphere-egu26-12606, 2026.

Extreme stratospheric polar vortex events, including sudden stratospheric warmings (SSWs) and episodes of strong polar vortex, are known to influence wintertime surface weather by modulating large-scale circulation patterns. While previous studies have primarily focused on their impacts over the North Atlantic and northern Europe, the effects on Mediterranean storm activity remain less well quantified. In this study, we examine the tropospheric response to SSW events from 1979 to 2020, with a particular focus on the associated changes in cyclone activity over the Mediterranean region.

Using a composite analysis of 28 SSW events within the study period, we examine the temporal and spatial evolution of cyclone frequency, genesis density, and associated dynamical fields before and after SSW onset. Seasonal and daily climatological signals are removed to isolate anomalies directly linked to stratosphere-troposphere coupling. Our results show a clear increase in cyclone activity over North Africa and the Atlantic coast of the Iberian Peninsula, associated with increased precipitation over western and southern Europe following SSW events. This is consistent with a southward displacement of the midlatitude jet and storm track. This shift is supported by enhanced upper-level wind speeds, divergence, and potential vorticity anomalies over the region during the post-SSW 2-month period.  Despite the robust composited signal, substantial inter-event variability is observed, indicating that not all SSWs lead to an identical response. These findings highlight the importance of event-to-event differences in determining regional storm impacts.

Overall, this study demonstrates that stratospheric polar vortex disruptions can significantly modulate Mediterranean storms on subseasonal timescales, highlighting the potential value of stratospheric information for enhancing the predictability of wintertime extreme weather over southern Europe and the Mediterranean Basin.

Keywords: Sudden stratospheric warming; polar vortex; Mediterranean cyclones; jet stream; stratosphere–troposphere coupling; subseasonal variability

How to cite: Jangir, B., Álvarez-Castro, C., Peña Ortiz, C., Gallego Puyol, D., Raveh-Rubin, S., and Strobach, E.: Impact of Sudden Stratospheric Warming on the Genesis of Mediterranean Cyclones and Associated Precipitation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13596, https://doi.org/10.5194/egusphere-egu26-13596, 2026.

Using ERA5 re-analysis data, 1950 to 2024, we look at surface temperature extremes, which we define as regions of at least 0.5 million square kilometers where the monthly mean 2m temperature exceeds its 25 year climatological mean by at least 1.5 standard deviations. While heat extremes are overall a topic of intense research, we here target a facet of such extreme events that has been less examined so far: how they manifest in terms of top of atmosphere (TOA) radiative fluxes. For the short- and long-wave TOA fluxes associated with such extreme events, we find typically enhanced values. This may be expected, given that mid-latitude heat waves are often accompanied by clear skies. For the TOA net energy flux, we find typically negative values. Spatially more extended extreme events tend to be associated with stronger temperature anomalies. Individual extreme events may deviate from these general tendencies. For selected extremes, daily ERA5 re-analysis data are examined. For the period 2001 to 2024, TOA fluxes from ERA5 re-analysis are compared to CERES satellite data.

How to cite: Folini, D. and Domeisen, D.: Surface temperature extremes mirrored in top of atmosphere radiative fluxes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13622, https://doi.org/10.5194/egusphere-egu26-13622, 2026.

In recent decades, the Euro–Mediterranean region has experienced a marked increase in catastrophic summer climate extremes, including persistent record-breaking atmospheric and marine heatwaves, and destructive convective events such as long-lived mesoscale convective systems (derecho) and supercells with unparalleled hail-size. All these have provoked severe socioeconomic, ecological and human impacts. While these phenomena are often studied separately, their frequent co-occurrence suggests the influence of common large-scale circulation drivers, which remain actively debated.  

Building on recent work linking North Atlantic freshwater anomalies to downstream atmospheric circulation responses, this ongoing study explores whether part of the recent European summer climate signal may be influenced by remote hemispheric-scale forcing associated with Greenland Ice Sheet mass loss, which has also coincidentally accelerated in recent decades due to anthropogenic influences. This linkage was not initially targeted but emerged unexpectedly from exploratory diagnostics motivated by broader investigations of North Atlantic variability. Preliminary results indicate that periods of enhanced summer Greenland melt tend to coincide with subsequent anomalous spring–summer circulation patterns over the Euro-Atlantic sector that favour persistent ridging and blocking-like conditions over the Euro-Mediterranean region. Such circulation states are consistent with environments conducive to prolonged heat stress, the development of marine heatwaves, and subsequent severe convective outbreaks.

Initial comparisons with global climate models from CMIP6 suggest that this potential pathway is poorly represented, possibly due to limitations in simulating localized freshwater forcing and its coupled atmosphere–ocean effects, which indicates that current projections of future climate may be underestimating these impacts. Our findings would point out Greenland melting as a previously unreported major driver of spring-summer large-scale circulation changes. Incorporating these processes could then be essential for forecasts systems and long-term projections, likely posing a significant gap in our ability to project future risk. Ongoing work focuses on testing the robustness of this emerging signal, clarifying its relevance relative to other known drivers of European summer extremes and exploring its hemispheric-scale reach.

How to cite: González-Alemán, J. J., Oltmanns, M., González-Herrero, S., Vitard, F., Donat, M., Doblas-Reyes, F., Barriopedro, D., Riboldi, J., Calvo-Sancho, C., Jiménez-Esteve, B., Cos, P., and Wehner, M.: Emerging evidence of Greenland Ice Sheet melt influence on recent Euro-Mediterranean record-breaking heat and convective storms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14086, https://doi.org/10.5194/egusphere-egu26-14086, 2026.

Climate change adaptation (CCA) is supported by a rapidly expanding ecosystem of decision-support systems, risk and vulnerability assessments, data portals, guidance frameworks, and early-warning services. Yet selecting an appropriate tool for a specific decision context remains difficult because tool information is often fragmented, inconsistently described, and not searchable using the metadata that practitioners actually need (e.g., sector, scale, user group, methods, outputs, usability, cost, and geographic scope). Within the FutureMed COST Action, WG2 has compiled a structured inventory of Mediterranean-relevant CCA tools and developed a shared criteria systematization to describe who tools are intended to serve, what they support, and how they are applied in practice. Insights emerging from this collaborative effort highlight that availability is not the only challenge: tool–context alignment is frequently unclear, tools often operate in isolation with limited guidance for selection, and the way tools define their spatial applicability may follow administrative rather than physical boundaries. Multilingual support and pathways for incorporating local data and knowledge are uneven. These patterns motivate the need for an operational resource that makes tools legible, comparable, and easier to navigate for real-world use.

We present ADAPT-TOOLS, a live database and web platform that translates a fragmented inventory into actionable discovery through structured metadata and faceted exploration. Tools are organized using a harmonized taxonomy spanning several aspects: intended user groups (policy, local government, private sector, NGOs, academia), sector focus, tool type, political and physical target scales, temporal horizon and resolution, methodological approach, data utilization, output types, accessibility/usability, validation and reliability signals, cost and support characteristics, and geographic applicability. Users can combine filters (OR within filters, AND across filters) to rapidly narrow from broad categories to tools matching their constraints, while dedicated tool pages support transparent comparison and adoption.

Technically, the platform is implemented as a containerized stack with a relational backend and a web interface. A reproducible ingestion pipeline converts structured inventories into relational tables, enabling systematic updates and maintainable curation workflows. To support sustained evolution and community engagement, ADAPT-TOOLS includes a moderated “Suggest a Tool” workflow that collects structured submissions for review before integration, enabling continuous expansion while preserving data quality. The platform is publicly deployed at adapt-tools.org. By linking community mapping to an operational platform, ADAPT-TOOLS supports evidence-informed and more context-aware adaptation planning across the Mediterranean and beyond.

Acknowledgments

This study is based on work from COST Action CA22162 “FutureMed: A Transdisciplinary Network to Bridge Climate Science and Impacts on Society” (FutureMed), supported by COST (European Cooperation in Science and Technology).

How to cite: Tsilimigkras, A., Pagé, C., Tošić, M., Lazić, I., Savelli, E., and Koutroulis, A. and the FutureMed WG2: From Mapping to Action: ADAPT-TOOLS and What We Learn from the Mediterranean CCA Toolscape, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14429, https://doi.org/10.5194/egusphere-egu26-14429, 2026.

Earth Observation for high-impact multi-hazard science (EO4Multihazards) was a European Space Agency (ESA) project that developed methodologies for risk (hazard, vulnerability, exposure) and impact assessment with the help of Earth Observation (EO) data. We assessed cascading and compound events and developed impact chains for four case studies in Italy (upper and lower Adige river basin), the United Kingdom and Dominica, a Caribbean Small Island Developing State. This abstract presents the fifth, so-called “transferability”, case study, where developed methodologies were applied in an area with limited ground validation data, Senegal. Droughts, heatwaves, floods and fires were analysed for the regions specified by stakeholders. The risk for the population and the impact on agricultural yields were assessed in the riskchanges.org platform. The vulnerability components were shown to be the most challenging and ground-data demanding. Visit our website to explore other outputs, such as a whole Europe event database and case study geostories https://eo4multihazards.gmv.com/.

We acknowledge support from the EO4Multihazards project (Earth Observation for high-impact multi-hazards science), contract number 4000141754/23/I-DT, funded by the European Space Agency and launched as part of the joint ESA-European Commission Earth System Science Initiative.

How to cite: Prikaziuk, E., Furlanetto, J., van den Bout, B., Boscarin, G., Huesca, M., Albergo, E., Masina, M., Mauro Ferrario, D., Maraschini, M., Torresan, S., van Westen, C., Manzella, I., and Domenech, C.: EO4Multihazards: Earth Observation for high-impact multi-hazard science, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14636, https://doi.org/10.5194/egusphere-egu26-14636, 2026.

Sudden Stratospheric Warmings (SSWs) are extreme atmospheric events characterized by a rapid weakening or breakdown of the polar vortex, often followed by profound impacts on surface weather. These include abrupt temperature anomalies, shifts in large-scale circulation patterns, modulation of jet streams, and an increased likelihood of cold-air outbreaks and altered storm tracks at mid-latitudes. As a result, SSWs play a pivotal role in shaping the occurrence and intensity of extreme weather events across the Northern Hemisphere. Although low-dimensional models have proven instrumental in elucidating the fundamental wave–mean flow interactions underlying SSWs, their ability to faithfully reproduce the full complexity, variability, and predictability of real atmospheric dynamics remains limited.

In this study, developed within the framework of the VORTEX project, we introduce a novel data-driven methodology to systematically assess the realism and predictive skill of low-dimensional models in simulating SSW dynamics. Our approach is based on two complementary metrics, dimension and persistence, which quantify, respectively, the effective dynamical complexity and the temporal coherence of the system. Together, these metrics provide a robust framework to evaluate how well simplified models capture the essential features of observed stratospheric variability.

Using this methodology, we investigate the sensitivity of SSW dynamics to large-scale tropospheric forcing and stochastic variability, both of which are known to be key contributors to vortex destabilization. To this end, we propose a stochastic low-order model that couples the Holton–Mass equations, representing wave–mean flow interactions, with a Langevin formulation that accounts for the bistable nature of the polar vortex.

Our results demonstrate that both the frequency and dynamical characteristics of SSWs exhibit a pronounced sensitivity to changes in tropospheric wave forcing and noise intensity. We identify critical thresholds beyond which the probability of vortex breakdown increases sharply, offering a mechanistic interpretation of the observed intermittency and variability of SSW events. These findings provide new insight into stratosphere–troposphere coupling and highlight the potential of data-driven diagnostics to bridge the gap between conceptual models and the complexity of the real atmosphere.

How to cite: Alvarez-Castro, C., Peña-Ortiz, C., Gallego, D., and Faranda, D.: Exploring Sudden Stratospheric Warming Dynamics: A Data-Driven Analysis Using a Low-Dimensional Stochastic Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14907, https://doi.org/10.5194/egusphere-egu26-14907, 2026.

Attribution science has made substantial progress in quantifying the influence of anthropogenic climate change on extreme events, yet its application to human health outcomes remains limited and difficult to operationalize for health-sector practitioners. Methodological complexity, fragmented guidance, and challenges in interpreting and communicating results hinder the uptake of climate-health attribution evidence in public health decision-making. We present the development of a structured, accessible resource designed to support health-sector engagement with climate-health attribution and its application in public health decision-making, within the TACTIC (HealTh ImpAct ToolkIt for Climate change attribution) project funded by the Wellcome Trust. This work is designed as an accessible, practice-oriented resource that complements technical methodological materials, supporting users who wish to understand or engage with climate-health attribution studies. While primarily targeting public health professionals and health agencies, it is also intended to be useful for researchers, policy advisors, and communicators working at the climate-health interface. This work synthesizes existing evidence and emerging best practices in health impact attribution and is structured around key practical questions: when attribution is feasible for specific climate hazards and health outcomes; what data, assumptions, and methods are required; how results should be interpreted and communicated; and how uncertainty and limitations should be conveyed. Its development is informed by stakeholder engagement, community input, and applied case studies in climate-vulnerable regions, ensuring relevance across diverse geographical and resource contexts. By translating complex attribution concepts into clear, actionable guidance, this work aims to build capacity, support evidence-informed public health action, and strengthen the integration of climate-health attribution science into policy and practice.

How to cite: Corpuz, B., Ryan, S., Stuart-Smith, R., Santos Vega, M., Carrasco-Escobar, G., Marrufo, T., Chirombo, J., Shumake-Guillemot, J., Vicedo-Cabrera, A., and Lowe, R.: Bridging Climate-Health Attribution Science and Health-Sector Practice, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15294, https://doi.org/10.5194/egusphere-egu26-15294, 2026.

The Eastern Mediterranean is a well-established climate change hotspot, where intensifying hydrological extremes increasingly translate into high-impact weather conditions with cascading societal consequences. While long-term changes in mean atmospheric moisture are relatively well documented, much less is known about the evolution of extreme moisture states that act as precursors to severe precipitation, flooding, and compound hydroclimatic hazards.

In this study, we investigate the extreme behaviour of precipitable water vapour (PWV) using homogenised, high-frequency GNSS-derived observations from a dense network located in the Eastern Mediterranean transition zone. To ensure climate-quality consistency, the dataset was processed following internationally recognised standards, including IGS Repro3 strategies, covering the period 2010–2024. Moving beyond conventional trend-based analyses, we employ a non-stationary Extreme Value Theory (EVT) framework, combining Generalised Extreme Value (GEV) and Peak-Over-Threshold (POT) approaches to characterise the tails of the PWV distribution. This enables an assessment of changes in the magnitude, frequency, and persistence of rare moisture extremes under ongoing warming, independent of mean climatological shifts.

Return levels corresponding to different recurrence intervals are estimated to provide observational constraints on extreme atmospheric moisture scaling and its consistency with theoretical Clausius–Clapeyron expectations. The statistical results are further interpreted in the context of large-scale atmospheric drivers using ERA5 reanalysis data, shifting the focus from describing atmospheric states to identifying weather conditions conducive to high-impact hydroclimatic outcomes.

This contribution directly aligns with the objectives of the FutureMed COST Action (CA22162) by bridging physical climate processes, advanced statistical characterisation of extremes, and impact-relevant indicators of risk. By focusing on extreme moisture states rather than mean conditions, the study supports a shift from describing what the atmosphere is to assessing what weather conditions are likely to do in terms of hydroclimatic impacts, thereby improving the understanding and predictability of high-impact weather in the Eastern Mediterranean region.

How to cite: Zengin Kazancı, S.: Unveiling the Tails of Atmospheric Moisture Extremes in the Eastern Mediterranean: Non-Stationary GNSS-Based Evidence for High-Impact Hydroclimatic Conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16534, https://doi.org/10.5194/egusphere-egu26-16534, 2026.

Climate change is changing the statistics and the physics of extreme weather events, leading to increasing impacts from heavy precipitation, floods, droughts, heatwaves, and so on. Thus, attribution of extremes requires a process-based understanding of how large-scale forcing interacts with regional dynamics and thermodynamics. Despite significant progress at global scales, attribution of extremes at regional and local scales remains challenging, particularly in regions where small-scale processes dominate the generation of high-impact events.

The Mediterranean basin is a hotspot for climate change, characterized by land–sea interactions, complex orography, and convective activity. Extreme events in this region are often controlled by small-scale (1–10 km) processes, including atmospheric instability and convective organization. These processes are poorly represented in coarse-resolution climate models, limiting our ability to attribute observed impacts and to assess future risks.

The Mediterranean Extreme Events and Tipping elements in a changing climate on multiple spatiotemporal scales (MEET) project addresses this challenge through a process-oriented, high-resolution framework focused on Mediterranean extremes and their impacts. MEET will identify and classify historical and recent extreme events based on their impacts on key meteorological variables, such as precipitation intensity, near-surface temperature extremes, and damaging winds, and on associated societal and environmental consequences. Physics-informed decomposition techniques combined with advanced statistical methods will be applied to identify analog events across multiple spatiotemporal scales, enabling the detection of changes in event frequency, intensity, and spatial structure. A central component of MEET is the use of convection-permitting climate simulations to explicitly resolve the small-scale dynamics and thermodynamics underlying extreme events in both past and future climates. By linking high-resolution physical processes to observed impacts, MEET aims to advance the attribution of Mediterranean extreme events and to provide a physically consistent basis for improved regional risk assessment under ongoing climate change.

Acknowledgements

This research has been carried out with funding from the Italian Ministry of University and Research under the FIS-2 Call.

How to cite: Alberti, T., de Leeuw, J., Scardino, G., Siciliano, F., and Zazulie, N.: Mediterranean Extreme Events in a changing climate on multiple spatiotemporal scales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17367, https://doi.org/10.5194/egusphere-egu26-17367, 2026.

Heatwaves are among the most impactful and rapidly intensifying climate extremes in the Mediterranean region, where rising mean temperatures and the increasing frequency of extreme events interact with urban environments, exacerbating thermal stress. In densely populated cities, the Urban Heat Island (UHI) effect acts as a local amplification mechanism, transforming large-scale atmospheric heatwaves into compound extreme events with significant societal and environmental consequences. This study analyzes the spatial distribution and main controlling factors of extreme surface temperatures during three intense summer heatwaves in Thessaloniki, Greece, with the aim of linking observed geophysical extremes to urban configuration and assessing the potential of mitigation measures. For this aim, we employ LANDSAT 8–9 satellite imagery processed in QGIS to derive high spatial resolution Land Surface Temperature (LST) fields, together with key land-cover indicators such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI). These remote-sensing products are integrated with urban morphology and land-use data derived from OpenStreetMap (OSM), enabling a detailed characterization of how vegetation cover, building density, and surface materials modulate the urban thermal response under conditions of extreme atmospheric forcing. The results reveal pronounced spatial heterogeneity in LST across the metropolitan area, with persistent hotspots associated with compact historic districts, industrial zones, and highly impervious surfaces. In contrast, urban parks, coastal areas, and neighborhoods with a higher fraction of vegetation exhibit significantly lower surface temperatures, highlighting the role of land–atmosphere interactions and surface energy balance feedbacks in shaping urban-scale thermal extremes. The inverse relationship between NDVI and LST, together with the positive relationship between NDBI and LST, indicates the strong sensitivity of urban surface temperatures to land-cover composition during heatwave conditions. By framing the UHI as an intrinsic component of compound heat extremes, this work bridges observational geophysical analysis with the assessment of urban impacts. We further explore the potential of targeted mitigation strategies, such as the large-scale implementation of green roofs and high-albedo pavements, demonstrating their ability to reduce extreme surface temperatures and to moderate thermal exposure. The findings emphasize the importance of integrating physically grounded, data-driven mitigation measures into standardized urban planning frameworks in order to enhance resilience to future thermal extremes. More broadly, the study contributes to the understanding of how local-scale processes interact with large-scale climate extremes, offering transferable insights for Mediterranean and European cities increasingly exposed to heatwave risk under climate change.

How to cite: Falda, M., Adamos, G., Radenovic, T., and Laspidou, C.: Satellite-Based Analysis of Urban Heat Island Dynamics under Extreme Heatwave Conditions and Mitigation Strategies in Thessaloniki, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17874, https://doi.org/10.5194/egusphere-egu26-17874, 2026.

Extreme precipitation over Europe is often linked to large-scale atmospheric circulation anomalies, yet it remains unclear which dynamical features recur systematically across many independent events, and how their influence evolves with time and altitude. In particular, the extent to which coherent, large-scale dynamical structures act as precursors to extreme rainfall has not been quantified so far beyond traditional composite-based approaches.

Here, we introduce a lagged coupled climate-network framework to investigate the interdependency between extreme precipitation events and atmospheric circulation from a functional climate network perspective. Extreme precipitation events are identified from ERA5 precipitation data by applying a local percentile threshold to daily precipitation sums and represented as binary event series, while two-dimensional fields of additional variables in different atmospheric layers—including geopotential height, relative vorticity, and temperature at multiple pressure levels—are treated as continuous variables. Using point-biserial correlation as statistical association measure between these different types of time series, we construct lagged event–field coupled networks that explicitly distinguish positive and negative statistical associations. Network connectivity is quantified through the cross-degree, which measures how many grid points of surface extreme events are significantly linked to a given atmospheric grid point (and vice versa), thereby emphasizing the recurrence and spatial relevance of circulation features rather than their correlation strength alone.

Our analysis reveals a coherent temporal evolution and vertical structure of circulation coupling to hydrometeorological extremes at the surface. At negative lags, cross-degree patterns are dominated by mid- to upper-tropospheric geopotential height and vorticity anomalies, indicating the recurrent presence of large-scale dynamical features prior to extreme precipitation events. With increasing lag, the coupling progressively shifts toward lower tropospheric levels, suggesting a transition from large-scale circulation influences before the events to near-surface circulation imprints afterward. Spatially, regions of enhanced cross-degree exhibit a systematic west-to-east displacement with changing lag, extending from the western North Atlantic and Greenland sector toward continental Europe. This spatial progression is consistent with downstream evolution along the North Atlantic–European circulation corridor. A pronounced and recurrent signal over the British Isles emerges across multiple variables, highlighting this region as a dynamically relevant area in the large-scale circulation context of European precipitation extremes.

By explicitly quantifying where, when, and at which vertical levels circulation anomalies of the same type recur across many extreme events, our coupled network approach provides a complementary perspective to conventional correlation and composite analyses. Our results demonstrate the potential of coupled functional climate networks to identify robust, recurring circulation patterns associated with extreme precipitation, offering new insights into precursor dynamics, vertical coupling, and large-scale organization of midlatitude extremes without assuming a specific underlying mechanism.

How to cite: Bishnoi, G. and V. Donner, R.: Lagged Coupled Climate Networks for Identifying Recurrent Circulation Patterns Behind Extreme Rainfall in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18041, https://doi.org/10.5194/egusphere-egu26-18041, 2026.

How to cite: Ichikawa, Y., S. Fuckar, N., Bracegirdle, T., and Ginesta, M.: Attribution of Austral Summer Extreme Temperature Events in Antarctica Using a Circulation Analogue Method , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18062, https://doi.org/10.5194/egusphere-egu26-18062, 2026.

Climate extremes, including heatwaves, extreme precipitation, tropical cyclones, and related hazards, pose significant risks to society and ecosystems.
Recent advancements in observational techniques, numerical modeling, theoretical frameworks, and AI methods have greatly improved our understanding and prediction of these extremes. However, despite significant progress, key challenges remain unresolved, particularly in achieving a thorough understanding of the physical drivers of extreme events, improving the transparency of AI-based prediction methods, and evaluating the vulnerability and resilience of cities to their impacts. To address these challenges, we present various approaches drawn from different fields, including dynamical systems theory, explainable AI, and NLP-based methods. Given the flexible and generalizable nature of these methods, we believe they may pave the way toward more robust solutions for addressing the challenges posed by climate extremes.

How to cite: Dong, C. and Mengaldo, G.: Interdisciplinary Approaches in the Study of Climate Extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18393, https://doi.org/10.5194/egusphere-egu26-18393, 2026.

Extreme precipitation exhibits pronounced local variations associated with dynamic and thermodynamic changes on synoptic and regional scales under global warming inducing important impacts over main socioeconomic sectors such as agriculture, tourism, health and energy. Local-to-regional variations in extreme precipitation are especially marked on climate change hotspots, such as the Iberian Peninsula, reflecting the complex transition between Atlantic and Mediterranean climate influences and further hindering an accurate assessment of climate change impacts and the development of effective adaptation strategies. Therefore, it is crucial to identify variations in atmospheric dynamics as main drivers of changes in the characteristics of extreme precipitation events (EPEs) on subregional scales to better determine the areas subject to specific changes and improve our understanding of extreme weather events to enhance predictability.

In this context, this study conducted a comprehensive analysis using a precipitation regionalization approach with a high resolution (~5 km) gridded dataset for the period 1951-2021 obtaining 8 precipitation-coherent regions in the Iberian Peninsula. EPEs were characterized over each region, and their evolving atmospheric drivers were identified using an objective synoptic classification method with ERA5 data. Besides, an analysis of variations in EPEs intensity and frequency, as well as changes in the associated synoptic conditions and atmospheric water vapor distributions were assessed.

Our results revealed a generalized mean intensification of EPEs for the study period. Nevertheless, we highlight two different pathways: (i) Atlantic regions presenting a moderate (5-10 %) intensification of extreme precipitation linked to an increase of surface flows and counterposing the observed weakening or northward displacement of upper-level perturbations, and (ii) Mediterranean regions showing a marked (15-25 %) extremization of EPEs associated with vorticity intensification at 500 hPa.  Besides, these variations occur alongside an atmospheric moistening (up to 6 mm in the Ebro region) of the moistest air masses denoting the highly complex interplay between thermodynamic and dynamic factors. We emphasize the importance of regionalized approaches to enhance our comprehension on extreme precipitation over regions with complex topography and, more importantly, the corresponding implications on early warning systems and efficient climate adaptation strategies in climate change hotspots.

How to cite: Benetó, P., Valiente, J. A., and Khodayar, S.: Understanding Shifts in Extreme Precipitation and Synoptic Forces in a Regionalized Framework: The Iberian Peninsula, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18626, https://doi.org/10.5194/egusphere-egu26-18626, 2026.

The analysis of the impacts due to climate extremes, such as extreme precipitation, heatwaves, and tropical cyclones, needs to rely on multimodal data, ranging from complex geophysical fields to textual and visual data.

While recent advances in vision-language models (VLMs) have stimulated interest in multimodal-driven climate analysis, their application to natural hazard analysis is still relatively limited.

In this work, we focus on tropical cyclones, and construct a new framework, namely Visual Object Representation for Tropical Cyclone Extremes and eXtent (VORTEX), a physics-aware, visual abstraction designed to support interpretable vision-language reasoning over hazard fields for tropical cyclones.

VORTEX transforms spatiotemporal reanalysis data associated to tropical cyclones into structured, visually identifiable representations by explicitly encoding cyclone-specific physical properties, including pressure-anchored storm geometry, wind and precipitation intensity extrema, spatial asymmetry, and field-scale footprint.

Building on VORTEX, we construct ClimateFieldQA, a structured evaluation framework for diagnosing VLM reasoning over tropical cyclone hazard fields. ClimateFieldQA comprises 4,978 high-resolution reanalysis heatmaps and 243,922 instruction samples spanning spatial localization, intensity estimation, structural pattern recognition, field-scale extent reasoning, and physical impact analysis.

ClimateFieldQA is designed to expose strengths, limitations, and failure modes of VLM-based reasoning under physically constrained geoscientific settings.

Using ClimateFieldQA, we show that physics-aware visual abstractions systematically improve structure-sensitive reasoning and reduce common interpretation errors observed when VLMs operate on raw hazard fields, highlighting the methodological importance of representation design for climate impact analysis and natural hazard assessment in Earth system science.

How to cite: Xiao, L. and Mengaldo, G.: ClimateFieldQA: Evaluating Vision–Language Models on Tropical Cyclone Hazard Fields with Physics-Aware Visual Abstractions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18916, https://doi.org/10.5194/egusphere-egu26-18916, 2026.

How to cite: Pastor, F., Pardo-García, D., and Khodayar, S.: Sea surface temperature anomalies associated with Mediterranean tropical-like cyclones , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18955, https://doi.org/10.5194/egusphere-egu26-18955, 2026.

The Mediterranean region is widely recognized as a climate change hotspot, as anthropogenic warming is projected to substantially increase air temperatures by the end of the 21st century, together with longer periods of reduced rainfall. The region is likely to experience warmer and drier conditions with significant consequences for human societies, while the intensification of heatwaves is likely to trigger cascading hazards. At the same time, heavy precipitation events during hot periods may become more common, increasing the likelihood of urban flash floods, especially in densely populated metropolitan areas.

Instead of focusing only on single climate extremes,, compound extremes offer a complementary perspective for assessing future climate risks. We analyze two compound climate indices: Warm/Dry (WD) and Warm/Wet (WW) days. The analysis focuses on representative Mediterranean metropolitan areas characterized by high population density and climatic relevance.

The indices are derived from daily mean temperature and precipitation data obtained from an ensemble of CMIP6 climate model simulations. Annual and seasonal frequencies of compound extremes are evaluated for the mid-century (2041–2060) and late-century (2081–2100) periods, relative to a 1995–2014 reference period, under the SSP2-4.5 and SSP5-8.5 scenarios. Results indicate a robust increase in the frequency of Warm/Dry days across all future scenarios, suggesting that Mediterranean climates will increasingly experience concurrent warming and drying. In contrast, Warm/Wet days are scenario-dependent. These findings highlight a dual climate risk for Mediterranean cities, where more frequent prolonged hot and dry conditions coexist with a higher chance of compound heat and heavy precipitation events under high-emission scenarios.

How to cite: Polychroni, I., Hatzaki, M., Patlakas, P., and Nastos, P.: Future Warm–Dry and Warm–Wet Compound Climate Extremes in Mediterranean Metropolitan Areas under Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18974, https://doi.org/10.5194/egusphere-egu26-18974, 2026.

The late-October 2024 flooding in Valencia (eastern Spain) was triggered by an exceptional extreme precipitation event (EPE) associated with a quasi-stationary cut-off low over the western Mediterranean. In this study, we assess the meteorological exceptionality of the October 2024 event by combining a basin-scale, percentile-based catalogue of rainfall extremes with a multi-level diagnosis of thermodynamic and dynamical atmospheric drivers.

Extreme precipitation is analysed using the dense SAIH rain-gauge network covering the Júcar River Basin at hourly and 5-minute temporal resolution for the period 1990–2024. Hourly p99 precipitation thresholds are computed for each station using an autumn (September–November) rolling-hour climatology. Local exceedances above p99 are aggregated into a basin-wide “overall magnitude” index (M), which integrates intensity and spatial footprint. EPEs are identified as continuous periods with M > 0 and ranked according to duration, peak intensities at 1-, 3-, 6-, 12- and 24-hour accumulation periods, cumulative local magnitude, mean excess above threshold, and the number of affected stations. The October 2024 event is contextualised against (i) the seven most extreme autumn EPEs (>p99) over the last three decades and (ii) a broader set of extreme but non-record events (p90–p99).

To link hydrometeorological extremeness with atmospheric drivers, we analyse the 1–96 h period preceding peak precipitation using 3-hourly CERRA reanalysis fields from 1000 to 100 hPa. Diagnostics include integrated water vapour (IWV), vertical humidity and water vapour profiles over peak-impact areas, absolute vorticity, and wind shear across multiple pressure-layer pairs.

Results show that the October 2024 event ranks as the most extreme autumn EPE in the record, with an unprecedented cumulative local magnitude of 4392 mm, nearly twice that of the second-ranked event (2275 mm in October 2000). The event is characterised by exceptionally high IWV values (~40 mm) over the affected region and a rapid IWV increase of approximately 0.4 mm h⁻¹ (around 25 mm in less than 72 h) prior to peak intensity. In addition, very strong vertical wind shear exceeding 25 m s⁻¹ between the surface and 400 hPa favoured sustained convective organisation and quasi-stationarity. Together, these results point to a compound thermodynamic–dynamic anomaly rather than a purely moisture- or dynamics-driven extreme. The proposed framework provides a physically consistent, basin-relevant benchmark for diagnosing exceptional Mediterranean flood-producing precipitation events using high-resolution observations and reanalysis-based process indicators.

How to cite: Espín, D., Benetó, P., and Khodayar, S.: The exceptional October 2024 flooding in Valencia (Spain): meteorological drivers of an extreme precipitation event, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19076, https://doi.org/10.5194/egusphere-egu26-19076, 2026.

Future changes in mean climate and extremes have been extensively assessed using model simulations of the 21st century under varying levels of anthropogenic greenhouse gas (GHG) forcing. Here, we examine the long-term climate legacy of an idealized abrupt stabilization of present-day and near-future GHG concentrations, with a focus on summer heatwaves across the Northern Hemisphere. Our analysis is based on multicentennial simulations performed with the EC-Earth3 model, in which external forcing is held fixed in time. After several centuries of internal adjustment, the climate system approaches a quasi-equilibrium state characterized by a stable level of global warming that depends strongly on the timing of forcing stabilization. Crucially, far-future quasi-equilibrium conditions can differ substantially from those that would arise if the same warming levels were reached by the end of the century, reflecting the distinct roles of fast and slow components of the Earth system. A key feature of the quasi-equilibrium response is a partial recovery of the Atlantic Meridional Overturning Circulation relative to transient simulations, which influences regional climate and leads to a pronounced amplification of heatwave frequency and intensity over the North Atlantic sector. Conversely, many land areas ultimately experience less severe heatwaves than in transient scenarios, owing to the slower warming rates in the stabilization experiments. Results show that the long-term response of extremes is shaped by the magnitude of global warming, as well as the pathway and timescale over which that warming is realized, highlighting the need for equilibrium-focused experiments in future climate risk assessments.

How to cite: Corti, S., Simolo, C., Rozenberg, L., Meccia, V., and Fabiano, F.: Heatwave response in quasi-equilibrium versus transient climate scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19395, https://doi.org/10.5194/egusphere-egu26-19395, 2026.

Floods associated with extreme precipitation cause tremendous damage and losses every year, and are projected to become more frequent and more severe with climate change in most land regions. Events of much higher intensities than previously observed can cause unforeseeably large impacts due to their unprecedentedness. The changing occurrence probability of such “surprise events” is closely related to changes in the statistical distribution of extreme precipitation: while a constant scaling with temperature (such as Clausius-Clapeyron) causes a constant fractional increase for all return levels, strong increases in the variability of extreme precipitation (distribution width) lead to relatively stronger intensification of the most extreme events. The latter change is indicative of increasing high-impact surprise event probabilities. Regions where rare extremes exhibit a faster relative intensification than moderate extremes (skewed intensification) are subject to RAINE: Rareness-Amplified INtensification of Extremes. In other words, RAINE means the worst events get worse the fastest.

We present a statistical framework based on extreme value theory to diagnose RAINE in annual maximum daily precipitation (Rx1d) from observations and simulations. We focus in particular on results from the 10-member high-resolution (0.25° atmosphere/land and 0.1° ocean) CESM1 ensemble (MESACLIP, historical+RCP8.5), which has been shown to simulate Rx1d quite accurately. We identify a strong RAINE-effect for most of the global land over the 21st century under RCP8.5. We categorise the data based on region and Rx1d-causing weather phenomenon, and find that a physical scaling based on vertical updraft and relative humidity explains the RAINE pattern. Different seasons, regions and phenomena feature different relative contributions of vertical updraft and relative humidity to RAINE, which can be linked to different environmental conditions and climate change effects governing Rx1d changes. In observations, robust distributional changes are difficult to detect due to high variability of extreme precipitation. Our combined statistical and physical characterisation of RAINE can help explain and constrain uncertainties in future risks posed by unprecedented extreme precipitation.

How to cite: de Vries, I., Castruccio, F., Fu, D., and O'Gorman, P.: Rareness Amplified INtensification of Extreme rainfall (RAINE): how the worst events get worse the fastest, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19415, https://doi.org/10.5194/egusphere-egu26-19415, 2026.

Two decades of climate change attribution research have shed light on the impacts of climate change occurring worldwide. The first wave of attribution research quantified climate change impacts on the intensity and probability of extreme weather events and slow-onset changes in glaciers and sea levels. Over the past decade, impact attribution studies have extended these methods to assess the attributable impacts of extreme events on agriculture, health, economic losses and biodiversity. Concurrently, source attribution research quantified individual emitters’ contributions to climate change impacts.

The emissions of individual actors cause climate change impacts. The approximately linear relationship between cumulative CO₂ emissions and global temperature rise, combined with the fact that many climate change impacts become progressively worse with rising global temperatures, provides a conceptual basis for this claim. Steady progress towards being able to quantify individual emitters’ contributions to specific losses has brought us closer to true ‘end-to-end’ attribution. However, while studies have quantified emitters’ contributions to aggregate impacts such as regional economic losses, are there circumstances in which we might be able to attribute specific, individual losses to individual actors? This presentation will discuss the scientific possibility of achieving this objective and the legal consequences that may follow.

How to cite: Stuart-Smith, R.: Are we closing in on true ‘end-to-end’ attribution?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19524, https://doi.org/10.5194/egusphere-egu26-19524, 2026.

Predicting spatiotemporal extreme events using dynamical systems theory poses several major challenges. One of these is the phase space dimensionality of spatiotemporal systems. Extreme events are rare, while the number of variables that could potentially drive them is large. Often, a subset of the phase space is sampled, or features are engineered based on previous research on drivers, to predict spatiotemporal extreme events. On the other hand, the background attractors are often assumed to be of much smaller dimensionality than the phase space. Therefore, we propose a novel framework that approximates the background attractor of chaotic systems using an autoencoder. On this lower-dimensional attractor representation, precursor densities are created from historical analogues. Based on these precursor densities, predictions of extreme events are made. This framework proves to be efficient in predicting extreme events in a simplified turbulent flow and a climate model. Without engineering-specific predictor feature sets, this lower-dimensional representation of the attractor allows for more efficient and accurate analog prediction of extreme events in large chaotic systems.

How to cite: Schuurman, K. R., Dwight, R. P., and Doan, N. A. K.:  Predicting extreme events by identifying precursors on the chaotic attractor manifold, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20508, https://doi.org/10.5194/egusphere-egu26-20508, 2026.

Climate change has impacts on natural systems and populations, which can be analysed in attribution studies and attempted to be predicted in forward-looking analyses. Climate extremes in particular can be very impactful, be it in in terms of extreme individual climate hazards, extreme combinations of climate hazards, or less extreme climatic conditions combined with particular settings of exposure and vulnerability resulting in severe impacts. As the field of impact attribution is burgeoning, different perspectives on these complexities become apparent in different study designs, with implications for the research questions they address and the potential role they might play beyond science.

Here, we will give an overview over different climate change impact approaches, including how they each do (or don’t) consider climate extremes. Besides different attribution framings and impact modelling approaches, we will present a discussion of the climate data types typically used in impact attribution, and their implication for capturing impacts of extreme weather and climate. We will especially talk about extreme event attribution framings, and how ‘event’ can be defined in different ways from climate and impact standpoints, respectively. The differences will be illustrated using references to existing literature as well as works in progress, particularly from the field of agriculture-related impacts on food security and nutrition-related health.

How to cite: Undorf, S.: Defining events and extremes in climate change impact attribution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21133, https://doi.org/10.5194/egusphere-egu26-21133, 2026.

Human-induced climate change has increased heat stress, leading to significant losses in work productivity and subsequent economic repercussions. Not only are the climate change-related losses in work productivity due to heat unequally distributed around the globe, but the contributions of individual nations to these losses through greenhouse gas emissions are also disproportionate. Here, we present a source attribution approach that links historical national emissions to global lost working hours resulting from increased heat exposure.

Following the framework of Callahan & Mankin (2022 & 2025), we conduct the source attribution study in three steps: First, we calculate the contribution of past national emissions to the change in global mean surface temperature (GMST) using the reduced-complexity climate model Finite amplitude Impulse Response (FaIR). Second, we apply a pattern scaling technique, trained on outputs from general circulation models, to translate GMST changes into grid-level heat stress metrics, here the wet bulb globe temperature (WBGT). Third, we use the simulated GMST changes due to national emissions, the pattern scaling coefficients, and two literature-based exposure-response functions to estimate the potential loss of working hours attributable to national emissions at grid level. By integrating demographic data on population and employment, we derive estimates of total potential losses in working hours linked to specific nations' emissions. Additionally, we thoroughly assess uncertainties arising from global climate models, the FaIR model, and the exposure-response functions.

Our preliminary results highlight the different responsibilities of nations for the costs associated with increased occupational heat stress. The study thereby contributes to the growing body of literature linking individual emitters with experienced harms, providing critical insight into climate liability and national accountability for climate policy.

Callahan, C. W., & Mankin, J. S. (2022). National attribution of historical climate damages. Climatic Change, 172(3–4), 1–19. https://doi.org/10.1007/S10584-022-03387-Y/FIGURES/4

Callahan, C. W., & Mankin, J. S. (2025). Carbon majors and the scientific case for climate liability. Nature 2025 640:8060, 640(8060), 893–901. https://doi.org/10.1038/s41586-025-08751-3

How to cite: Romanovska, P., New, M., Gornott, C., Brouillet, A., and Undorf, S.: Source attribution: From national emissions to global loss in working hours due to climate-change increased heat, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21503, https://doi.org/10.5194/egusphere-egu26-21503, 2026.

Heat and drought extremes pose escalating socio-economic and ecological risks, yet the most severe combinations of these high-impact extremes possible today remain poorly understood. Using thousands of plausible ensemble-boosting current climate storylines, we reveal the risk for more intense drought compounding with far more extreme heat and fire weather than ever experienced over Europe in the recent past. The most extreme boosted heatwaves surpass historical extremes in both intensity and particularly in persistence, and also exceed levels considered extreme in a 3°C warmer world by large margins. Some of the most extreme heatwaves arise under severe soil moisture depletion, while others develop under strong surface temperature gradients in the North Atlantic and extreme heat in the nearby Mediterranean and Atlantic basins, underscoring the diversity of pathways to worst-case conditions. Furthermore, our work reveals an additional risk: worst-case heatwaves occur predominantly after another extreme heatwave. This highlights the potential for aggravated impacts due to decreased recovery times and intensified heat stress on humans, ecosystems and infrastructure made more vulnerable by the first event. Given the scale, intensity, and unprecedented successive and compounding nature of these worst-case storylines, we underscore the urgent need for well-informed adaptation strategies that sufficiently reflect these risks. 

How to cite: Suarez-Gutierrez, L., Beyerle, U., Mittermeier, M., Vautard, R., and Fischer, E. M.: Worst-Case European Heat and Drought Storylines generated using Ensemble Boosting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21564, https://doi.org/10.5194/egusphere-egu26-21564, 2026.

Global warming is projected to intensify the hydrological cycle, amplifying risks to ecosystems and society. While extreme rainfall appears to exhibit stronger sensitivity to global warming compared to mean rainfall rates, a unifying physical mechanism​ capable of explaining this systematic divergence has remained elusive. Here, we integrate theory and data from a global network of nearly 50,000 rain-gauge stations to unravel the rainfall intensity and frequency response to rising temperatures. We show that the distributions of wet-day rainfall depth exhibit self-similar shapes across diverse geographical regions and time periods. Combined with the temperature response of rainfall frequency, this consistently links mean and extreme precipitation at both local and global scales. We find that the most probable change in rainfall intensity follows Clausius-Clapeyron (CC) scaling with variations shaped by a fundamental hydrological constraint. This behavior reflects a dynamic intensification of updrafts in space and time, which produces localized heavy precipitation events enhancing atmospheric moisture depletion and hydrologic losses through runoff and percolation. The resulting reduction in evaporative fluxes slows the replenishment of atmospheric moisture, giving rise to the observed trade-off between rainfall frequency and intensity. These robust scaling laws for rainfall shifts with temperature are essential for climate projection and adaptation planning.

How to cite: Yin, J., Gao, B., and Porporato, A.: Scaling of Rainfall Intensity and Frequency with Rising Temperatures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22751, https://doi.org/10.5194/egusphere-egu26-22751, 2026.

How to cite: Sparrow, S., Perez-Fernandez, I., and Tett, S.: Attribution study of the 2023-2024 Drought on the South of Africa., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23150, https://doi.org/10.5194/egusphere-egu26-23150, 2026.

Prolonged summer droughts represent a significant and growing threat across Europe, as their persistence hinders hydrological recovery and severely impacts water resources, ecosystems, and agricultural systems under ongoing climatic warming. These extended dry periods can create soil-moisture deficits, ecological stress, and amplified heat extremes. Understanding the response of multi-year droughts to different warming levels is vital for shaping both adaptation and mitigation strategies.

In this study, we investigate the behaviour and severity of the 2018-2022 European multi-year soil moisture drought across a range of climate warming levels. We apply an innovative storyline attribution approach, which enables a physically consistent comparison of the same drought sequence under different climate conditions. Specifically, we utilise spectrally nudged AWI-CM-1-1-MR, constrained to follow observed synoptic-scale circulation from ERA5, to force the mesoscale Hydrologic Model (mHM). This modelling setup allows us to specifically isolate how anthropogenic warming modifies soil-moisture deficits, without altering the real-world atmospheric conditions that triggered the drought sequence.

Under the present-day climate conditions, the 2018-2022 drought produced a soil-moisture deficit of -44 (±11.8) km³, affecting 0.63 (±0.07) million km² (11.5% of the study area). In the absence of anthropogenic climate change (pre-industrial climate conditions), the 2018-2022 multi-year event would have shown a soil moisture surplus nearly double the magnitude of present-day losses, with drought spatial extent only about one-third of current levels. Future warming levels further exacerbate these impacts. With warming of 2 K to 4 K, the losses increase from -82 (±6.6) to -256 (±7.1) km³, while drought extent expands from approximately 16% to 43%.

Overall, our results demonstrate that rising global temperatures substantially intensify multi-year droughts by both enlarging their spatial footprint and deepening hydrological deficits. As climate warming increases the likelihood that single-year droughts transition into persistent multi-year events, the findings emphasise the urgent need for effective climate mitigation and adaptation strategies across Europe. A full version of this work is currently under review in Earth’s Future; the preprint can be accessed at https://doi.org/10.22541/au.176220208.89936181/v1 . 

How to cite: Kettaren, R., Sanchez-Benitez, A., Goessling, H., Athanase, M., Kumar, R., Samaniego, L., and Rakovec, O.: Storyline-based climate attribution reveals strong intensification of 2018-2022 multi-year droughts in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-493, https://doi.org/10.5194/egusphere-egu26-493, 2026.

Most of the world's food is produced in a handful of countries, the so-called breadbaskets of the world. Due to climate change, there is an increasing risk of crop failures, due to compounding hot and dry extremes. Furthermore, certain climate drivers – through  teleconnections – have shown to lead to simultaneous crop failures around the globe. This highlights the importance to understand which climate processes drive global crop yield variability. Here we show global crop yield failures (Maize, Soya, Wheat, Rice, and combined) are associated with La Nina-like sea surface temperature (SST) anomalies, using Archetype Analysis. The adverse crop-yield archetypes show simultaneous hot-dry-surface imprints across the world, highlighting these high risk crop failure scenarios are driven by climate extremes. Our results demonstrate the importance in understanding the climate drivers of global crop production, and highlights the deep uncertainty associated with a changing climate. The response of ENSO due to anthropogenic activities is not yet fully understood and climate models often inaccurately reproduce the observed La Nina trends. Thus the fact that our results indicate that simultaneous crop failures are linked to La Nina like SSTs, highlights the deep uncertainty we currently face regarding food security in the future. 

How to cite: Happé, T., Hamed, R., Anderson, W., Chapman, C., and Coumou, D.: Climate archetypes of simultaneous global crop failures , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-505, https://doi.org/10.5194/egusphere-egu26-505, 2026.

How to cite: Laridon, A., Thiery, W., Pietroiusti, R., Smith, C., Rogelj, J., Zhang, J., Schleussner, C.-F., Menke, I., Zekollari, H., Schuster, L., Nauels, A., Palmer, M., and Schewe, J.: Linking Emissions from Fossil Fuel Megaprojects to Lifetime Climate Extremes Across Generations and Multi-Century Committed Change , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-537, https://doi.org/10.5194/egusphere-egu26-537, 2026.

Record-breaking heat extremes imply large health risks and can disrupt critical infrastructure, because societies are often adapted only up to previously observed extremes. Understanding how new records evolve is therefore essential. The probability of record-breaking heat events depends on the regional warming rate. This rate is mainly driven by greenhouse gas-induced global warming and has increased in recent decades. The resulting annual probability of record-breaking heat extremes is additionally modified in a nonlinear way by other regional forcing changes, such as aerosols. Because aerosol concentrations have changed substantially in many regions, they can amplify or reduce the annual likelihood of exceeding previous temperature records. 

We first analyze single forcing large ensemble simulations that isolate the effects of aerosols and greenhouse gases. In Europe, decreasing aerosol concentrations have increased the regional warming rate and thereby the probability of record-breaking heat extremes by about 35% today. In contrast, in South Asia, where aerosol concentrations are increasing, we find a dampening of record-breaking probabilities of about 40%. To evaluate the effect of near-future aerosol reductions, we use simulations from the Regional Aerosol Model Intercomparison Project (RAMIP). In RAMIP, aerosol emissions are reduced from SSP3-7.0 to SSP1-2.6 either globally or only in selected regions. This allows us to analyze the regional effects of aerosol reductions as well as their remote responses. In general, aerosol reductions lead to an increased probability of record-breaking heat extremes.

Finally, we examine recent observed record-breaking events and evaluate whether their regional frequency matches the expected record breaking probabilities from model simulations. We expect that changes in aerosol concentrations contribute to changes in the annual record-breaking probability in regions with major aerosol concentration changes in recent decades, such as Europe, North America, East Asia, and South Asia. Overall, these results suggest that changes in aerosol concentrations are important for the present and near-future probability of record-breaking heat extremes.

How to cite: Kraulich, F., Pfleiderer, P., and Sippel, S.: Regional aerosol changes modulate the odds of record-breaking heat extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1535, https://doi.org/10.5194/egusphere-egu26-1535, 2026.

Climate extremes exact a heavy and differential toll on society. Reported economic losses are primarily concentrated in developed economies, whereas reported fatalities occur overwhelmingly in developing economies. Moreover, even at single locations the adverse impacts of extreme climate events are often unequally distributed across the population. Understanding such impacts holds enormous societal and economic value, and is a key step towards climate resilience and adaptation. Recent research advances include improved impact forecasting and enhanced understanding of how the interaction between human and natural systems shapes the impacts of climate extremes. Nonetheless, there are some key challenges that have hindered progress. We focus on three: Limited availability and quality of impact data, difficulties in understanding the processes leading to impacts and lack of reliable impact projections. We argue that newly released datasets and recent methodological and technical advances open a window of opportunity to address several dimensions of these challenges. Notable examples include extracting impact information from textual sources using large language models and developing impact projections using data-driven approaches. Moreover, interdisciplinary collaborations between the social and natural sciences can elucidate processes underlying past climate impacts and enable building storylines of future societal impacts. We call for building momentum in seizing these opportunities for a breakthrough in the study of impacts of climate extremes. Achieving meaningful progress will require interdisciplinary and intersectoral research, and strong collaboration across academic, policy and practitioner communities.

How to cite: Messori, G., Boyd, E., Nivre, J., and Raffetti, E.: Challenges and Opportunities for Understanding Societal Impacts of Climate Extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2212, https://doi.org/10.5194/egusphere-egu26-2212, 2026.

How hot can heatwaves get in a given region of the world? The current pace of climate change challenges the statistical methods traditionally used to answer this question. An alternative approach is to seek a physics-based upper bound to extreme surface temperatures (T_s). Recent work proposed to address this problem using the hypothesis that convective instability limits the development of heat extremes. Here, we show that under this hypothesis, the absolute upper bound for extreme T_s --- obtained in the limit of zero surface humidity --- is set by dry convection: that is, this bound is reached when the mid-troposphere and the surface are connected by a dry adiabat. Previous work suggested that this upper bound is instead set by moist convective instability and is several degrees hotter. We resolve this discrepancy by showing that moist convection only limits heatwave development when surface specific humidity is larger than a threshold, and that the moist convective upper bound cannot exceed the dry limit. Yet, numerous temperature profiles in observational and reanalysis records do exceed the dry convective limit. We show that these occur exclusively in regions with an extremely deep boundary layer and where a daytime superadiabatic layer develops near the surface. We conclude with an overview of the different upper bounds applicable in dry and moist scenarios, including the roles of processes such as entrainment and convective inhibition.

How to cite: Nicolas, Q. and Hotz, B.: Dry and moist convective upper bounds for extreme surface temperatures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2537, https://doi.org/10.5194/egusphere-egu26-2537, 2026.

Large hail and heatwaves are among the most extreme weather phenomena, posing serious risks to human health, ecosystems, and infrastructure, while also leading to significant economic losses. However, the co-occurrence of large hail and heatwaves, and the potential physical mechanisms linking these two phenomena, remain poorly understood. In this study, we investigate the climatology of large hail and the atmospheric drivers of large hail and heatwave co-occurrences across selected European regions, using an 11-year convection-permitting climate simulation with the COSMO regional climate model (2011–2021). In addition, we assess how these extremes may evolve under future climate conditions (+3°C global warming).

Results show increases in large hail frequency across Europe in a warmer climate. In central and eastern regions, the frequency rises approximately 20 %, whereas in the Alpine, Mediterranean, and Baltic regions it nearly doubles. Exceptions are France and Spain, where large-hail frequency declines by 26% and 33%, respectively. Also, there is a notable correlation between the occurrence of heatwaves and large hail across central and eastern Europe.  This relationship is less evident in southern Europe, due to large hail occurs mainly in autumn storms caused by large-scale disturbances. Additionally, large hail during heatwave days is forms in environments with higher median values of most-unstable convective available potential energy and 2 m temperature than large hail in the absence of heatwaves. A spatiotemporal analysis revealed that the days leading up to large hail events increasingly coincide with heatwave conditions. In the present climate, large hail is most often found within ~500 km of heatwave boundaries, both inside and outside them. The future climate scenario indicates a spatial shift of large hail events beyond the heatwave extent across all continental domains.

How to cite: Agayar, E., Killian, B., Thurnherr, I., and Wernli, H.: Co-occurrence of large hail and heatwaves in European regions in current and future climate scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2749, https://doi.org/10.5194/egusphere-egu26-2749, 2026.

How to cite: Carniel, C. E., Knutti, R., and Fischer, E.: Separating dynamic and thermodynamic contributions in Mediterranean extreme precipitation (in a storyline approach), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2967, https://doi.org/10.5194/egusphere-egu26-2967, 2026.

What are the worst-case heatwaves that are plausible in the present or near-future climate? Model-based experiments using ensemble boosting, a computationally efficient method to simulate unprecedented extremes, suggest that month-long heatwaves that break previous records by more than 5 K across Germany and France are possible in the near future. But how can we assess the plausibility of these heatwaves unprecedented in the observational record? We here test whether the most extreme simulated month-long heatwaves in Germany and France are consistent with current process understanding and with historical heatwaves.
We show that despite their extreme record-breaking characteristics both events cannot be ruled out as implausible. To demonstrate this, we compare these two worst-case events with historical heatwaves in the reanalysis record. To this end, we calculate standardized anomalies relative to a time-evolving climatology of relevant physical variables such as temperature, 500 hPa geopotential, surface solar radiation, and soil moisture. We focus on two different worst-case events — one in Germany and one in France — which exhibit distinct characteristics and physical drivers. The event in Germany features extreme anomalies in most physical drivers, particularly those associated with land-atmosphere feedbacks, and features three short heatwaves in quick succession. In contrast, the event in France mostly features less extreme anomalies in these drivers and consists of one less intense but very persistent heatwave caused by anomalously weak zonal flow combined with above-average southerly winds. Using a multilinear statistical model and comparing with historical analogues, we show that the characteristics and physical drivers of both events are consistent with current process understanding and with historical events.

How to cite: Roemer, F. E., Fischer, E. M., Noyelle, R., and Knutti, R.: Demonstrating the plausibility of worst-case month-long heatwave storylines in Western Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3456, https://doi.org/10.5194/egusphere-egu26-3456, 2026.

Previous studies have argued that the upper bound of temperature extremes in mid-latitude regions is reached by minimizing near-surface moisture during high low-tropospheric temperatures. Here, we revisit these theories for the Île-de-France region using the ERA5 reanalysis and show that the highest annual temperatures occur within the moist-to-expected range of the summer (June–August) near-surface humidity distribution. However, during the most extreme events, relative humidity is minimized as soil moisture approaches the wilting point and the atmospheric boundary layer deepens. Using the statistical distributions of these indicators and their temporal evolution in ERA5, we evaluate the representation of thermodynamic drivers in selected CMIP6 large ensembles. Finally, we apply a recently published revised framework of dry convective instability to estimate maximum attainable temperatures in both ERA5 and CMIP6, highlighting how climate change may modify heatwave dynamics in the Paris region.

How to cite: Mayer, M., Parey, S., Petter, C., Thao, S., and Yiou, P.: ERA5-Based Validation of Thermodynamic Extreme Heatwave Drivers of the Paris region in CMIP6 simulations., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3579, https://doi.org/10.5194/egusphere-egu26-3579, 2026.

Climate extremes, such as floods, heatwaves, and droughts, have myriad impacts across natural and social systems. However, traditional methods used for monitoring impacts tend to focus on single hazards or indicators (e.g., fatalities), address only quantitative consequences (e.g., economic losses), and frequently overlook indirect and social consequences (e.g., conflicts, mental health). Here, we show how text data can be used to measure the societal impacts of climate extremes across diverse text sources, including newspapers, social media, and Wikipedia articles.

First, we analyze over 26,000 newspaper articles on the July 2021 river floods in Germany to reveal cascading impacts across sectors like infrastructure, water quality, mental health, and tourism. Second, Twitter data from the 2022 drought in Italy is used to map public concern and perceived consequences, which align with observed socioeconomic indicators. Finally, we scale our analysis globally with Wikimpacts 1.0, a database of climate impacts extracted from 3,368 Wikipedia articles covering 2,928 events from 1034 to 2024, providing national and sub-national records of deaths, injuries, displacements, damaged buildings, and economic losses.

Together, these case studies illustrate the value of text-derived impact datasets for complementing traditional monitoring approaches. We also discuss the challenges of using such datasets, including representational biases, uneven temporal and spatial coverage, and differences in how impacts are reported. We conclude by discussing how the field can move towards shared standards and best practices, enabling more comparable and transparent use of text data for monitoring the impacts of climate extremes.

How to cite: Madruga de Brito, M., Wang, J., Sodoge, J., Li, N., and Nunes Carvalho, T. M.: Enhancing impact monitoring by using computational text analyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4070, https://doi.org/10.5194/egusphere-egu26-4070, 2026.

Global surface air temperature (GSAT) reached unprecedented heights in 2023. The record of year-to-year temperature increases was surpassed by a significant margin, especially in early boreal fall. We attribute the majority of this seasonal jump to the onset and maturing stages of the 2023 El Niño event. Using a process-based analysis of multiple observational datasets, we show that the uniqueness of the 2023 event can be largely related to the La Niña-like ocean-atmosphere background state upon which it developed.
This resulted in (1) a steep year-to-year increase of Sea Surface Temperature (SST), particularly in mean atmospheric subsidence regions, leading to extreme reduction of low-cloud-cover and giving rise to a record-breaking change in the radiative budget over the central and eastern Indo-Pacific; (2) anomalous sustained precipitation over climatological high SSTs in the Western Pacific, fueling unusual diabatic heating and an exceptionally early increase in tropical tropospheric temperature in boreal fall, ultimately influencing the GSAT jump with an additional contribution from the North Atlantic.
Our study improves the understanding of the interactions between interannual internally-driven processes and changes in mean climate background state, which a changing background is crucial to assess the evolution and modulation of anthropogenically-driven trends.

How to cite: Mex, J., Cassou, C., Jézéquel, A., Bony, S., and Deser, C.: Why was the 2023 jump in global temperature so extreme?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4263, https://doi.org/10.5194/egusphere-egu26-4263, 2026.

Humanitarian crises are the tip of the iceberg in climate change adaptation, yet their future is rarely quantified in human and economic terms. We use machine learning to simulate future estimates of people in need of humanitarian aid and required funding under the business-as-usual scenario (SSP2-RCP4.5) with warming of 2.1–2.4°C by 2100. Humanitarian needs rise to a baseline of 410±22 million people and USD₂₀₂₄ 64±8 billion annually by 2050 worldwide, increases of 28% and 30% respectively compared to the current (320 million people and USD 49 billion). A lightly optimistic simulation holds needs near the current, while a medium pessimistic simulation leads to 614±68 million people and USD₂₀₂₄ 96±19 billion by 2050, increases of 92% and 96% respectively. Our results show empirical vulnerabilities and an opportunity cost, as resources for crisis response displace funding for adaptation and mitigation. Yet, sustained investment could curb the impacts even with climate inertia.

How to cite: Jäpölä, J.-P., Berlin, A., Fabri, C., Hrast Essenfelder, A., Marzi, S., Poljanšek, K., Ronco, M., Van Passel, S., and Van Schoubroeck, S.: Future cost of climate change for humanitarian crises, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4462, https://doi.org/10.5194/egusphere-egu26-4462, 2026.

Ireland’s winters are getting wetter, with more frequent heavy precipitation events increasing flooding risk across the Island. Extreme precipitation is a key driver of flooding in northwestern Europe; however, observational records are relatively short and represent only a single realisation of the climate state. As a result, they are inadequate for sampling low-likelihood, high-impact flood-relevant extreme precipitation events and for quantifying plausible maxima of such extremes. In this study, we quantify plausible maxima for flood-relevant winter precipitation under the current climate. We apply the UNprecedented Simulated Extremes using Ensembles (UNSEEN) approach to the flood-relevant winter precipitation indices (Rx1day, Rx5day, and Rx30days), using daily winter observations, the ECMWF SEAS5 seasonal prediction systems, and the CANARI Single Model Initial-condition Large Ensemble (SMILE) over the Island of Ireland. These indices are consistently derived across observations, pooled SEAS5 winter ensembles (ensemble member x lead times), and the CANARI SMILE. Model fidelity for CANARI and ensemble independence, stability, and fidelity for pooled SEAS5 are assessed to ensure that both models realistically represent extreme precipitation. Preliminary results indicate that both SEAS5 and the CANARI sample the physically plausible Rx1day and Rx5day extremes that exceed the maximum observed in the current climate, while neither system produces UNSEEN values exceeding the observed maximum Rx30day.  The CANARI large ensemble passes the fidelity test without bias correction, whereas the SEAS5 passes the fidelity test after applying simple multiplicative mean scaling bias correction. For CANARI, plausible maxima are approximately 18.01% higher for Rx1day and 20.77% higher for Rx5day than observed maxima, while Rx30day plausible maxima are approximately 8.70% lower than the highest observed Rx30day. For SEAS5, plausible maxima exceed observations by approximately 3.05% for Rx1day and 17.68% for Rx5day, while Rx30day plausible maxima are approximately 17.74% lower than the highest observed. These results highlight the limitations of observational records in sampling extreme tails and indicate that CANARI SMILE captures a broader range of internal climate variability than the initialised SEAS5 seasonal prediction system. They also show that UNSEEN ensembles are more effective at sampling short-duration precipitation extremes (Rx1day and Rx5day) than longer-duration accumulation precipitation extremes (Rx30day). Our study highlights the value of combining the UNSEEN approach with both seasonal prediction systems and SMILEs to better understand unprecedented flood-relevant precipitation extremes in the current climate.

How to cite: Bile, M., Murphy, C., and Thorne, P.: Assessing the UNSEEN Flood-Relevant Winter Extreme Precipitation Over the Island of Ireland in the Present Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4864, https://doi.org/10.5194/egusphere-egu26-4864, 2026.

Many countries rely on international trade to ensure food security. With climate change and projected increases in the frequency and severity of extreme weather events, a significant portion of currently traded crops is vulnerable to climate extremes. While many studies have quantified the impact of extreme weather on crop production, few have linked these impacts to international trade and analyzed how future risks differ from the past. In this study, I combined crop modeling with FAOSTAT on crop and food trade data to identify the worst-case scenario in which extreme weather affects global staple crop trade. Six staple crops were included in the analysis. Probability distributions of each crop’s production were estimated for both historical and future periods under the 2020 crop distribution baseline. The worst-case scenario was determined based on the amount of traded crop affected in the past and future climates. The results provide insight into how future risks differ from historical patterns and whether international trade can continue to ensure food security under changing climate conditions.

How to cite: Su, H.: Identify the worst-case scenario where extreme weather has the greatest impact on the global staple crop trade, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5916, https://doi.org/10.5194/egusphere-egu26-5916, 2026.

Climate impact assessment studies strongly depend on fine representations of meteorological fields. Downscaling addresses the trade-off between data requirements and storage capacity, yet the faithful replication of extreme-value statistics and spatiotemporal consistency presents a persistent issue. We present an efficient generative AI model for spatiotemporal downscaling. Using coarse-resolution monthly fields as inputs, the model reconstructs sequences of daily fields with the enhanced spatial resolution. The AI-generated daily fields accurately reproduce spatial coherence, temporal persistence, and extreme-value characteristics, showing strong agreement with ground-truth daily observations. We look forward to applying this framework more effectively to future studies on the impacts of extreme events. 

How to cite: Huang, Y., Bathiany, S., Yang, S., Aich, M., Hess, P., and Boers, N.: Better serving impact assessments via AI: Reconstructing daily extremes from spatiotemporal downscaling of monthly fields, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8004, https://doi.org/10.5194/egusphere-egu26-8004, 2026.

In the beginning of 2014, exceptionally energetic swells associated with the Hercules storm (also known as “Christina”) produced one of the most devastating coastal events ever recorded in Portugal. Between January 6^th and 7^th, coastal flooding affected more than 30 municipalities along the Portuguese coastline, with offshore buoys registering maximum individual wave heights and periods of 14.91 m and 28.10 s, respectively. The storm resulted in more than 16 million euros in direct damages due to overtopping and coastal flooding, while indirect losses (considering affected businesses and populations) are estimated to have reached hundreds of millions of euros. In this study, two physical climate storylines are developed to assess the impacts of a “Hercules”-like storm, at five key-locations along the Portuguese coastline, occurring by the end of the 21^st century, under the combined influence of sea-level rise (SLR), projected changes in wave climate, and altered coastal morphology, while retaining the same statistical representativeness observed in 2014. The storyline approach enables a clear linkage to the original event and facilitates the assessment of future extreme events such as Hercules within the context of a changing climate, supporting decision-making by working backwards from specific vulnerabilities or decision points. Results indicate that the impacts of a future Hercules-like storm are projected to intensify, considering SLR and increases in high-percentile wave energy. Extreme coastal flooding is expected to affect 1.9 to 2.4 times more area than in 2014, resulting in 3.2 to 6.5 times more physically impacted buildings, particularly in densely urbanized coastal sectors. As coastal erosion is expected to reduce the natural protection of Portuguese sandy coastlines, the currently employed protection mechanisms will require robust adaptation measures, strategically defined to withstand long-return-period extreme events.

This work is supported by FCT, I.P./MCTES through national funds (PIDDAC): LA/P/0068/2020 - https://doi.org/10.54499/LA/P/0068/2020, UID/50019/2025, https://doi.org /10.54499/UID/PRR/50019/2025, UID/PRR2/50019/2025. The authors would like also to acknowledge the project “Elaboração do Plano Municipal de Ação Climática de Barcelos (PMACB).

How to cite: Lemos, G., MM Soares, P., Simões, R., Antunes, C., Bosnic, I., and Pinto, C.: A process-based physical climate storyline for the Hercules storm in Portugal: extreme coastal flooding under climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8241, https://doi.org/10.5194/egusphere-egu26-8241, 2026.

Compound hot–dry events have recently led to severe consequences globally, often triggering cascading impacts across ecological and socio-economic systems. Currently, most analyses of hot–dry extremes rely on short observational records or projections, limiting evaluation against pre-industrial variability—the climatic range to which many natural and human systems adapted over centuries. This makes it difficult to place impacts of the increased intensity and frequency of compound events in an appropriate context for examining adaptation needs.

Here we leverage a unique data coverage in the Swiss Alps to quantify changes in summer mean climate and in compound hot–dry extremes and their associated return periods from 1600 to 2099 CE. Data used include multi-century temperature and atmospheric drought reconstructions from tree rings going back to 1600 CE, instrumental station records, and local-scale climate projections for 1981-2099.

Copula-based modelling shows that summers classified as extreme in pre-industrial conditions have become common in today's climate and are expected to correspond to cold and wet conditions by the end of the century. Our analysis further shows that the hot–dry conditions witnessed in summer 2003—characterized by simultaneous positive temperature and vapor pressure deficit (VPD) anomalies of 5.3°C and 2.6 hPa relative to the pre-industrial mean, respectively—were unprecedented over at least the past 400 years and are projected to remain rare until the end of the century under RCP2.6. By contrast, they are likely to occur every 2-3 years under RCP4.5 and even to become colder and wetter than average by 2070-2099 under RCP8.5, since in the latter case, temperature and VPD anomalies are projected to exceed pre-industrial conditions by 10.4°C and 8.1 hPa in the extreme case (30-year return period).

Without countermeasures, the consequences of these changes will include, among other things, dramatic losses in agricultural production and undesirable changes in forest ecosystem dynamics. Ultimately, our analysis suggests that rapid adaptation is necessary to avoid facing more frequent extreme heat and drought conditions than those observed under pre-industrial conditions. Under RCP8.5, in particular, socio-ecological systems will need to continuously adapt within 15 years to changes in the average climate to avoid facing high-impact hot-dry compound event frequencies higher than those experienced at any time over the past 400 years. Given that adaptation in mountain regions is currently not keeping up with the realized and projected climate impacts, as pointed out in several studies, we argue that the required speed of adaptation can pose substantial challenges for alpine societies.

How to cite: Neukom, R., Arosio, T., Bottero, A., Kempel, A., Muccione, V., Rixen, C., Treydte, K., and Calanca, P.: Hot-dry compound events in the European Alps: Multi-century assessment (1600-2099 CE) indicates the need for fast adaptation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9101, https://doi.org/10.5194/egusphere-egu26-9101, 2026.

Understanding how climate change influences environmental extremes is vital for developing effective adaptation and mitigation strategies. In this study, we apply an event-based storyline approach to assess changes in accumulated precipitation associated with Storm Boris, which impacted Central Europe in September 2024. We examine both historical changes (attribution) and future projections and extend previous work by investigating the sensitivity of results to the degree of imposed dynamical constraint. Using the global CMIP6 coupled climate model AWI-CM1, we nudge simulations toward observed ERA5 winds—including the jet stream—across a range of climate backgrounds: preindustrial, present-day, and possible future states with 2, 3, and 4 °C global warming relative to preindustrial conditions. Two nudging configurations are compared: (1) a “weak constraint” configuration, in which only synoptic- and planetary-scale winds in the free troposphere are nudged, permitting some dynamical adjustment with warming; and (2) a “strong constraint” configuration, in which winds at all vertical levels and scales are imposed, thereby completely suppressing dynamical changes.

Both configurations capture the event, with stronger present-day rainfall in the strongly constrained configuration. The observed climate change between pre-industrial and present day is robust, with increases of 7% (4%) in accumulated rainfall under the weak (strong) constraint. Projections up to a 3ºC warmer climate show linear increases in the accumulated rainfall for both configurations. Beyond +3ºC, the response strongly diverges. Under weak constraint, rainfall changes at +4ºC are marginal or even mildly reduced relative to present-day, whereas the strongly constrained configuration continues to show linear increases. This divergence is linked to thermally-driven dynamical adjustments permitted under weak constraint. Whether these adjustments reflect a realistic response or methodological artifacts, and whether similar behaviour occurs in other events, remains to be explored. Our results highlight remaining uncertainties in storyline-based extreme precipitation projections, and demonstrate the importance of considering multiple possibilities.

How to cite: Sánchez Benítez, A., Athanase, M., and Goessling, H. F.: Sensitivity of Storm Boris rainfall intensification to wind nudging strength in event-based climate-change storyline simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9800, https://doi.org/10.5194/egusphere-egu26-9800, 2026.

While the performance of climate models in simulating the magnitude of global warming has been extensively assessed, their fidelity in representing the three-dimensional (3-D) structure of warming, and how this affects extreme event attribution, remains poorly understood. Pseudo-global-warming experiments implicitly assume that imposed anthropogenic warming perturbations realistically capture the observed vertical and horizontal distribution of atmospheric temperature change. However, this assumption is rarely evaluated explicitly.

We diagnose 3-D structural warming discrepancies by comparing a representative set of six CMIP6 climate models against ERA5 temperature trends over 1940–2024. We show that widely used models exhibit systematic vertical and horizontal warming biases, typically over-amplifying warming in the mid-to-upper troposphere while damping the response near the surface, particularly across Northern Hemisphere mid-latitudes. We further show that these structural biases propagate into substantially different estimates of extreme rainfall intensification.

Using an ensemble of 81 high-resolution MPAS simulations within a storyline attribution framework, we analyze the October 2024 Valencia flood-producing storm as a high-impact case study. The diagnosed anthropogenic rainfall signal is highly sensitive to the 3-D structure of the imposed warming: CMIP6-based counterfactual experiments yield weak reductions in extreme rainfall (~10%), whereas observation-constrained warming profiles produce a stronger and more significant anthropogenic contribution (~30%). This amplification arises from enhanced low-level moistening and increased convective instability, together with dynamically consistent upper-level flow strengthening. The results confirm that 3-D warming structure is a first-order control on extreme-rainfall attribution, and that persistent model-structural errors can lead to a systematic underestimation of attribution signals in mid-latitude, high-impact precipitation extremes.

How to cite: Insua Costa, D., Lemus Cánovas, M., Senande Rivera, M., M. H. Deman, V., L. Geirinhas, J., and G. Miralles, D.: Extreme rainfall attribution distorted by structural warming biases in climate models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10410, https://doi.org/10.5194/egusphere-egu26-10410, 2026.

The Alps, with their complex topography, important geographic location and varying climatic influences have become a highly vulnerable region. Especially extreme precipitation and its associated impacts - from floods to landslides - are directly amplified by this distinct local environment.

Because observational timeseries are rather short and sample only limited locations, the impact-producing extreme tail of the precipitation distribution remains largely unexplored. In addition, the non-stationarity of the climate system makes data from a past climate less useful for gaining insights into current and future conditions. Coarse resolution global climate models can be used to produce long simulations including rare extreme events, but important processes such as topographic forcing and deep convection are poorly resolved, which limits physical interpretability. A different approach is needed to produce robust and actionable climate information on the local scales required for stress testing, early warning, adaptation and risk mitigation.

We suggest expanding the method of Ensemble Boosting into the realm of high-resolution modeling. We employ a global ICON setup with 10-20 km grid spacing with a two-way nested kilometer-scale European domain. Our initial goal is to simulate the 2013 Northern Alps flooding using ERA5 initial conditions. We asses lead time sensitivities for reinitializing simulations to optimize for variability and intensity within the boosted ensemble. We expect to produce physically consistent, interpretable and realistic storylines based on a historic extreme precipitation event in the Alps. These storylines enable us to assess driving processes and test physical limits of extreme precipitation in today’s climatic conditions.

With the current focus on a specific region and event we want to exercise a proof of concept embedded in a user-oriented framework. Next steps include producing a catalogue of extremes sampling across event types with the goal to physically constrain the extreme tail of precipitation distributions to reduce uncertainty in extreme value estimation, and to estimate return periods. Further applications of our approach could also be focused on climate projections or pseudo global warming simulations to gain insights into possible extremes in future climates.

How to cite: Roither, L., Prein, A. F., Fischer, E., and Aellen, N.: Ensemble boosting of extreme precipitation in the Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10755, https://doi.org/10.5194/egusphere-egu26-10755, 2026.

This study investigates the impact of climate change on the extreme 2020 Meiyu over the middle and lower reaches of the Yangtze River (MLYR) through global variable-resolution ensemble subseasonal hindcasts. Results reveal that post-1980 climate change enhanced the 2020 extreme Meiyu rainfall over the MLYR region by approximately 17.19% at monthly scale, while simultaneously decreasing light and moderate precipitation frequency but intensifying heavy and extreme precipitation occurrences. Climate change intensified the low-pressure over northern China and southern China while weakening the Western Pacific subtropical high and the low-pressure over the Indian Peninsula. The circulation pattern results in significant shear between northeasterly and northwesterly winds in the southern MLYR region, contrasting with the high-pressure dominance in the northern MLYR region. This configuration suppressed convergence, vertical motion, and precipitation in the northern MLYR while enhancing these processes along its southern. Comparison between frequently re-initialized and subseasonal simulations further demonstrates that subseasonal simulations, by allowing full development of interactions between regional systems and large-scale circulation, more realistically represent climate change impacts on Meiyu season. In contrast, the frequently updated initial conditions in re-initialized simulations constrain such feedback processes. This study highlights the importance of utilizing global variable-resolution simulations at subseasonal-scale for climate attribution studies. Future studies would benefit from improved subseasonal forecasting capabilities to enhance attribution reliability.

How to cite: Xu, M. and Zhao, C.: Investigating Climate Change Impacts on the 2020 extreme Meiyu Through Global Variable-Resolution Ensemble Subseasonal Hindcasts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11244, https://doi.org/10.5194/egusphere-egu26-11244, 2026.

How atmospheric circulation patterns associated with extreme weather respond to climate change remains a challenging question. To explore this issue, we combine spectral nudging in a global climate model (AWI-CM1) with hindcasts, similar to ensemble boosting, in an event-based storyline framework. We examine the dynamic response to climate change of selected atmospheric blocking events associated with winter cold-air outbreaks and summer heatwaves in Eurasia. First, the large-scale circulation during the preconditioning phase of a blocking is constrained by spectral nudging toward reanalysis data, ensuring that the synoptic and planetary-scale environment is realistically and consistently reproduced in different climate backgrounds. The nudging is then switched off a few days before the blocking onset, allowing the model (including the atmospheric circulation) to evolve freely. We generate an ensemble with perturbed initial conditions to sample internal variability of the blocking development due to chaotic error growth. By applying this procedure under pre-industrial and +4 °C warmer climates compared to the present-day climate, we can separate the thermodynamic effects of climate change from the dynamical response, and quantify how a warming climate modifies both the evolution of atmospheric blocking (e.g., intensity and persistence) and the associated extreme weather impacts. We find that the climate state exerts a moderate and event-specific influence on blocking dynamics.

How to cite: Zhuo, W., Sánchez-Benítez, A., Athanase, M., Jung, T., and Gößling, H.: Exploring the changing dynamics of atmospheric blocking with a modified event-based storyline approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11790, https://doi.org/10.5194/egusphere-egu26-11790, 2026.

The increasing frequency of Mediterranean heatwaves is associated with widespread impacts on human health, agricultural productivity, and infrastructure. Previous studies have shown that large-scale circulation patterns, such as persistent ridges and atmospheric blocking, play a key role in triggering heatwaves, along with subsidence and warm-air advection. However, the intensity and persistence of these events depends not only on the advection of heat and moisture but also on the heat and moisture supplied by turbulent surface fluxes into the advected air mass. Sensible and latent heat fluxes modify air-mass temperature and humidity (and thus equivalent potential temperature) along transport pathways to the heatwave region. These flux contributions, and their relative importance for heatwave anomalies, remain uncertain.

In this study, the contribution of surface sensible and latent heat fluxes to near-surface moisture and temperature anomalies during heatwaves is quantified using a new Lagrangian framework that combines backward air-mass trajectories from the FLEXPART particle dispersion model with surface fluxes from ERA5 reanalysis data. Surface flux contributions to the moist static energy are estimated by coupling them with near-surface residence times of air parcels arriving in the heatwave region. The approach is first validated by showing that moist static energy at the heatwave location can be reproduced by the sum of the particle initial conditions (i.e., most static energy at trajectory termination points) and the surface flux contributions accumulated over the Lagrangian tracking period. Following this validation, surface flux contributions can be split into latent and sensible heat flux contributions and mapped geographically.

The method is then applied to two recent Mediterranean heatwaves to assess the relative roles of sensible and latent heat fluxes and to identify the dominant land and sea source regions. Overall, this framework provides a direct and physically consistent way to attribute the moist static energy associated with heatwaves to surface fluxes, offering new insights into the processes that build and maintain Mediterranean heatwaves.

How to cite: Deshmukh, V., Stohl, A., and Dütsch, M.: Surface flux contributions to Mediterranean heatwaves: a new Lagrangian diagnostic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12438, https://doi.org/10.5194/egusphere-egu26-12438, 2026.

Recent research has considerably advanced our ability to model extreme storm surges. Nevertheless, simulating unprecedented events remains a challenge. Current large-scale storm surge studies often rely on conventional statistical approaches to extrapolate data beyond historical records. However, these approaches entail large uncertainties and lack the capacity to physically characterise individual events. Furthermore, research on unprecedented events primarily focuses on hazard magnitude, often overlooking other dimensions relevant for risk management decisions.

This study addresses these gaps by examining unprecedented storm surges at a European scale across multiple dimensions. We follow a large-ensemble approach to generate numerous alternative pathways of reality, capturing a broader range of climate variability than the observational records. By pooling ensembles from the ECMWF SEAS5 seasonal forecast and forcing the Global Tide and Surge Model (GTSM), we obtain a 525-year dataset of unbiased, independent storm surge events. This synthetic dataset enables the identification of physically plausible events beyond those found in historical records. We evaluate the dataset against reanalysis-based storm surges to uncover and characterise unprecedented events across three dimensions: magnitude, spatial extent and temporal occurrence. Understanding these different dimensions of unprecedence provides a significant advance in our knowledge of coastal flood risk in Europe and supports improved coastal flood risk management decisions.

How to cite: Benito Lazaro, I., Ward, P. J., Aerts, J. C. J. H., Eilander, D., and Muis, S.: Unprecedented storm surges across European coastlines, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12593, https://doi.org/10.5194/egusphere-egu26-12593, 2026.

This study investigates the role of climate-change-driven sea surface temperature (SST) anomalies in intensifying extreme precipitation associated with Storm Boris. During the period 12th to 16th September 2024, Storm Boris produced extreme precipitation and subsequent flooding in Central Europe, recording over 350 mm accumulated precipitation in five days in parts of Austria. To assess the influence of climate-change-driven SSTs in the Atlantic, Mediterranean and Black Sea, we perform pseudo experiments, in which the SSTs of these water bodies are systematically reduced by 2 K. For that purpose, a model chain consisting of the Weather Research and Forecasting (WRF) model coupled to the Lagrangian particle dispersion model FLEXPART run with back-trajectory settings and a moisture source and transport diagnostic is utilized. The WRF model is further run with wind and pressure nudging over the entire simulation period and without nudging during the event in order to separate thermodynamic and dynamic responses. The moisture uptakes and losses of air parcels arriving in the Central European study region are traced backward in time for up to ten days, enabling the identification of the dominant moisture sources contributing to the observed extreme precipitation. Our analysis reveals the Eastern Europe land areas and the Mediterranean – where SSTs exhibited a strong positive anomaly compared to the long-term climatology – as primary moisture sources for Storm Boris. We further show that the decrease in available moisture by SST reduction in the Black Sea and/or the Atlantic is partially compensated by additional moisture uptake in the Mediterranean. Finally, we assess the thermodynamic sensitivity of mean precipitation to SST changes by comparing the simulated rainfall across different historical SST climatologies. The results indicate an average precipitation increase of approximately 3 % per Kelvin of SST warming for this event, emphasizing the contribution of climate-driven SST increases to the extreme precipitation observed during Storm Boris.

How to cite: Maier, P., Dütsch, M., Nadeem, I., Messmer, M., and Formayer, H.: The influence of sea surface temperatures on moisture sources of Central European Storm Boris in September 2024, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12603, https://doi.org/10.5194/egusphere-egu26-12603, 2026.

Storylines, or tales of future weather, are an increasingly popular climate communication strategy. Storyline research aims to inform about how extreme events arise and how severe they may become under different background climates, connecting scientific knowledge and lived experience. Central to this approach is a focus on plausibility rather than probability.  Such "what-if" scenarios can stress-test policy and infrastructure, guiding or strengthening adaptation efforts. This study presents a reproducible chain of methodological steps for constructing such tales through data mining, which is demonstratively applied to the EURO-CORDEX ensemble to produce a coherent and communicable extreme heat storyline for Belgium. We present the results from a first workshop with city officials and emergency coordinators, which successfully launched an ongoing dialogue between stakeholders and scientists about the broader use of storylines as an accessible tool for climate adaptation.

How to cite: Carlier, N.: Towards actionable storylines: development of a reproducible workflow, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12831, https://doi.org/10.5194/egusphere-egu26-12831, 2026.

How the frequency and intensity of extreme weather events is affected by global warming in different regions is one of the central questions of climate change research, with obvious direct implications for climate change adaptation. A standard approach of defining weather extremes is to consider the exceedance of a percentile threshold, calculated from the statistical distribution of a meteorological variable of interest in a predefined reference period. Trends can then be assessed by considering the frequency of threshold exceedances in a period that extends beyond the reference period. While this approach appears rather straightforward, it comes with several choices related to the parameter, percentile threshold, aggregation period, reference period, and boosting interval. Here aggregation period refers to the question whether, e.g., precipitation extremes are considered with a duration of 1 hour or 1 day or multiple days, and the boosting interval is the symmetric time window used to calculate percentiles for a given day of year. When checking these partly methodological choices in previous studies, e.g., those referenced in the IPCC report, it becomes evident that different studies made different choices. Since there is no obvious “best choice”, it is important to quantify the influence of these choices on the resulting trend estimates. Therefore, this study uses ERA5 reanalysis data to systematically and globally explore the trends in 2-m temperature (T2m) and precipitation (P) and their robustness with respect to the aforementioned parameters. Key results are that (i) trends vary strongly between regions, (ii) they are methodologically more robust for T2m than for P, (iii) in regions with weak P trends, the sign of the trend depends on the methodological choices. These explorative analyses with ERA5 data are complemented by synthetic data experiments, in particular to investigate the influence of the boosting window. We suggest that trend analyses of percentile threshold exceedances of any parameter in any dataset should consider these methodological sensitivities in order to communicate robust estimates.

How to cite: Wernli, H., Sternal, T., Voigt, S., Sprenger, M., and Hoefler, T.: How reliably can we estimate trends of surface weather extremes? A conceptual study using ERA5 reanalyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12895, https://doi.org/10.5194/egusphere-egu26-12895, 2026.

The formation of surface heat extremes is usually described in terms of surface processes and upper-level dynamics. However, their full vertical temperature profile contains additional essential information about the involved processes and dynamics. So far, it remains unclear whether heat extremes are associated with characteristic vertical temperature anomaly profiles and how they vary across the globe.
In this study, we globally and systematically classify vertical temperature anomaly profiles during annual maximum 2-m temperatures, so-called TXx events, using a k-means clustering approach. After a suitable normalisation and scaling of the anomaly profiles, we find three clusters, whose global distribution closely follows the polar, mid-latitude, and tropical climate zones. The three clusters capture key structural differences of heat extremes. Within the tropical cluster, positive temperature anomalies during TXx events are confined to the (often deep) boundary layer and intensify progressively in the days leading up to the event, while the upper troposphere is not deviating from its climatological mean. The mid-latitude cluster also exhibits bottom-heavy temperature anomalies, which, however, extend throughout the full troposphere, showing a strong vertical coupling during heat extremes. In the polar cluster, heat extremes are characterised by deep tropospheric warm anomalies, accompanied by the erosion of the near-surface inversion layer, resulting in a shallow layer of particularly strong temperature anomalies near the ground.
These results show that while multiple physical mechanisms can generate a heat extreme, at first order, temperature anomaly profiles during heat extremes are very similar to each other within a given climate zone. The variability between TXx events is much larger than the variability between the median profile of different grid points in the same cluster. Besides, the temperature profiles of the most extreme events are more similar to those of their cluster than the more moderate events, suggesting a typical dynamics of the most extreme heat events. 

How to cite: Hotz, B., Wernli, H., and Noyelle, R.: Global characterisation of the vertical temperature anomaly structure of heat extremes over land in ERA5, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13076, https://doi.org/10.5194/egusphere-egu26-13076, 2026.

How to cite: Priestley, M., Stephenson, D., Scaife, A., and Bannister, D.: An emergent constraint for the future frequency of European windstorms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13386, https://doi.org/10.5194/egusphere-egu26-13386, 2026.

Short-duration, high-intensity rainfall extremes associated with convective storms pose a growing risk to urban areas under a warming climate, yet their future evolution remains difficult to quantify at the global scale using existing modelling approaches. Local projections are often constrained by the lack of long high-resolution observations and by the limited ability of climate models to accurately simulate sub-daily precipitation processes at the global scale. Here, we present a globally applicable framework for projecting changes in rare, short-duration rainfall extremes using temperature as a covariate in a non-stationary extreme value framework building on the TENAX model, driven entirely by global climate model output and without reliance on local observational data. The focus on rare, short-duration extremes directly targets the class of events responsible for a disproportionate share of climate-related impacts.

The approach links changes in rainfall intensity distributions to projected shifts in wet-day temperature distributions from CMIP6 models, integrating over the full temperature distribution rather than relying on uniform scaling or mean-shift assumptions. Dew-point temperature is employed as a proxy for atmospheric moisture availability, allowing thermodynamically constrained intensification of convective rainfall extremes to be represented consistently across climates. In an initial multi-regional application, the framework projects robust intensification of hourly-scale rare rainfall events, with increases of order 10–20% by late century under intermediate emissions scenarios and substantially larger changes under high-emissions pathways. Accounting for changes in the full temperature distribution shows that the strongest intensification occurs for the rarest events, which is underestimated when intensities are scaled only by mean temperature changes.

We further extend the framework to a global scale to assess spatial patterns and key structural uncertainties in projected short-duration rainfall intensification. Results highlight that methodological choices, including the selection of temperature covariate (dew-point versus surface air temperature), can introduce differences comparable to inter-model climate uncertainty in some regions, particularly in moisture-limited and continental climates. Treating these choices explicitly as structural uncertainties provides a clearer interpretation of projection robustness across diverse hydroclimatic regimes and highlights uncertainties beyond inter-model spread alone.

Overall, this work demonstrates that temperature-covariate approaches, when carefully formulated and driven by global climate models, offer a transferable and physically grounded pathway for projecting rare, short-duration rainfall extremes worldwide. The framework enables consistent global assessments in data-scarce regions and supports climate-change impact studies and urban adaptation planning by explicitly quantifying the uncertainties that matter most for short-duration rainfall risk.

How to cite: Blagojević, J., Prein, A., Peleg, N., and Molnar, P.: Global projections of short-duration rainfall extremes using temperature-covariate models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13482, https://doi.org/10.5194/egusphere-egu26-13482, 2026.

Many of the most societally impactful weather events in Europe occur on short timescales and there is a growing demand for improved projections of how such extremes will change in the future. That is, how will global climate change over decades impact extreme weather over days? The multiscale nature of this question challenges the capabilities of current earth system models, and this is especially the case for hydrometeorological extremes. Accurately simulating the hazards posed by extreme precipitation requires faithfully resolving interactions between the large-scale circulation, synoptic dynamics, the local boundary-layer, and hydrological and land surface conditions.

This is not only a quantitative modelling challenge, but a challenge of interpretation and narrative: the dynamics of extreme precipitation are diverse across space and time, and the statistics of the highest impact events are necessarily poorly constrained. These challenges are complicated further by the evergrowing size and hetereogeneity of multi-model datasets How can we explain model biases and trends in extreme precipitation? When models project similar changes in hydrometeorological risk do they do so for the same reasons? What implications do these factors have for regional downscaling and impact modelling? Can we relate future extremes quantitatively and robustly to historical high-impact events, as often requested by societal stakeholders?

We tackle these questions through a novel flow-precursor framework, applied to observational data, large ensemble climate simulations and subseasonal weather forecasts. We decompose extreme event risk into contributions from different scales and flow conditions, using regionally specific synoptic flow precursors which are directly associated with individual high-impact extremes or classes of extreme. These precursors are algorithmically identified and can be easily computed in large datasets, allowing us to obtain a physical interpretation of changing extreme risk across Europe without obscuring regional or seasonal diversity in precipitation dynamics.

We show how climate model biases and forced changes in extreme precipitation can be explained, categorised, and visualised in a succinct way that highlights important differences in their suitability for use in downscaling, impact modelling and storyline development. We demonstrate how dynamical decomposition can extract usable climate information even from heavily biased models, and how insights from models at different scales–such as from large climate ensembles and high-resolution weather forecasts–can be quantitatively synthesised to provide new insights on future hazards and plausible worst-case scenarios. Finally, we show how the method can be used to reframe complex, probabilistic climate projections and weather forecasts in terms of individual high impact historical events, aiding scenario visualisation, and allowing stakeholders to leverage their experience and domain knowledge when preparing for future high-impact extremes.

How to cite: Oldham-Dorrington, J., Li, C., Sobolowski, S., Guillaume-Castel, R., and Lutzmann, J.: Understanding, interpreting, and communicating future extreme precipitation risk using flow precursors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13670, https://doi.org/10.5194/egusphere-egu26-13670, 2026.

Virtually all land regions have warmed over recent decades, yet heatwave trends show striking regional differences. The thermodynamic rise of hot extremes can be strongly modulated by atmospheric circulation, a phenomenon that has received increasing attention for regions such as Europe and parts of North America, where observed trends in hot extremes have been amplified and dampened, respectively. But what about other regions? How persistent are these circulation anomalies? And what are the implications for future heatwaves?

Using dedicated climate model experiments, we quantify how atmospheric internal variability has modulated historical heatwave trends globally. Building on a large ensemble framework, we interpret observed circulation contributions as placing regions on unusual warming trajectories — either well below or above the ensemble mean expectation. Regions currently displaying less warming compared to climate model simulations are effectively "lagging behind" the warming already committed to by anthropogenic forcing; those running warm are "ahead".

This warming trajectory position has profound implications for the pace of future change. Regions currently lagging behind, including much of North America, face substantially faster increases in hot extreme probability between now and the mid-century than ensemble mean projections suggest. Conversely, other regions have already experienced much of the expected probability increase. We illustrate these divergent futures through the evolving return period of what was once a 1-in-100-year hot extreme, showing how the present trajectory position determines the pace of change over the coming decades.

How to cite: Schumacher, D. L., Bauer, V., Gu, L., Pierini, L., and Seneviratne, S. I.: Behind or ahead of committed warming: what it means for future hot extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13840, https://doi.org/10.5194/egusphere-egu26-13840, 2026.

Quantifying the influence of anthropogenic global warming on extreme events requires both physical and statistical understanding. We present a framework combining two complementary conditional attribution methods: spectrally nudged storylines and flow-analogues. The storyline approach provides insights on how a specific event is shaped by the thermodynamic conditions representing past (counterfactual), present (factual) and future global warming levels (+2K, +3K, +4K). The flow-analogue method provides a statistical analysis of the recurrence of the observed event, and the future storyline-projected events based on similar dynamical patterns that lead to the event of interest. Together, this combined approach allows us to determine not only the change in likelihood of an extreme event occurring as it did in the present, but also the probability that an intensified version (storyline-projected) of it occurred in the future.

Applied to the 2018 Central European heatwave, storylines show an area-mean warming rate of 1.7 °C per degree of global warming. Through the flow-analogue method, it was evidenced that the atmospheric blocking leading to this event remains equally likely to occur regardless of global warming. Despite it, the storyline-projected intensities might become more frequent and extreme at their corresponding warming levels than the factual 2018 event was under present conditions. Specifically, the 2018 heatwave, with an intensity of 2.2 °C and a return period of 1-in-277-years today, is projected to intensify to 6.6 °C with a 1-in-26-years return period in a +4K world. This behavior revealed the importance of other physical mechanisms and interactions influencing the occurrence and intensification of heatwaves beyond the atmospheric circulation pattern and thermodynamic conditions. We conclude that this combined framework is promising for climate change attribution of individual extreme events, offering both a physical assessment of anthropogenic warming and its associated likelihood while accounting for potential shifts in atmospheric dynamics.

How to cite: León-FonFay, D., Lemburg, A., Fink, A. H., Pinto, J. G., and Feser, F.: A combined storyline-statistical approach for conditional attribution of climate extremes to global warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14325, https://doi.org/10.5194/egusphere-egu26-14325, 2026.

The Bay of Bengal is one of the most densely populated regions globally, bordered by India, Bangladesh, and Myanmar. Its coastal zones represent critical hotspots from both societal and agricultural perspectives. Major river deltas, including those of the Brahmaputra and Ganges in Bangladesh, the Mahanadi in India, and the Ayeyarwady in Myanmar, provide essential freshwater resources that sustain highly productive agricultural systems and support large local populations. However, ongoing climate change is increasingly associated with extreme weather conditions, such as elevated temperatures, prolonged droughts, and intense precipitation events.

To investigate how climate change at different time horizons and levels of warming influences these extremes, we conducted five regional climate simulations using the Weather Research and Forecasting (WRF) model at 5km horizontal spacing. One simulation represents a 30-year reference period (1981–2010). Two additional simulations cover the mid-21st century (2031–2060) under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. The remaining two simulations represent the late 21st century (2071–2100) under the same SSP2-4.5 and SSP5-8.5 emission pathways.

The results indicate a substantial increase in extreme heat across all river deltas. The number of days exceeding 40 °C is projected to double under SSP2-4.5 and to triple under SSP5-8.5 by the end of the century. Drought frequency increases markedly, with the number of drought events projected to quadruple under both scenarios. Concurrently, extreme precipitation, measured by the RX5 index, shows significant increases in the Ayeyarwady and Brahmaputra deltas. The combined effects of intensified heat stress, more frequent droughts, and increasingly severe precipitation events present major challenges for both local populations and agricultural systems, potentially increasing the risk of displacement in these vulnerable regions.

How to cite: Messmer, M., González-Rojí, S. J., and Leonard, S.: Extreme weather events in agriculturally important regions in the Bay of Bengal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14525, https://doi.org/10.5194/egusphere-egu26-14525, 2026.

Reaching net zero greenhouse gas emissions is essential to halt the current global warming trend and attempt to stabilise global temperatures. However, uncertainties remain on the sign and the magnitude of the long-term responses of the climate system following anthropogenic emissions cessation.

This study contributes to constraining this uncertainty by exploring the global and regional temperature evolution under zero CO₂ emissions conditions in the UKESM1.2 projections following the TIPMIP protocol (Jones et al., 2025). Stabilised warming levels spanning +1.5°C to +5°C above pre-industrial conditions are analysed to understand the impact of antecedent conditions on post zero-emissions trends. We find that the global average surface air temperature (GSAT) keeps increasing in all stabilised warming scenarios. The increase is more pronounced in the +3°C to +5°C scenarios, where it approaches 0.25°C per century. Most of the warming is registered in the Southern Hemisphere, particularly in the Southern Ocean, while the Northern Hemisphere experiences a slight cooling trend over land.

These regional cooling trends are more marked for the annual temperature maxima, with several regions across 45-65°N experiencing cooling of >1°C per century. The strongest cooling trends emerge in the higher warming scenarios, and we investigate their drivers in North America, where the cooling magnitude exceeds 1.5°C per century. Using a method based on constructed circulation analogues, we find that the projected cooling trend is almost completely explained by thermodynamic drivers and we reconcile this finding with the model vegetation changes. Our findings serve a double purpose. On one hand, they show the significant contribution that land-use changes can have regionally for the attenuation of annual temperature maxima, supporting the case for their careful consideration in future mitigation and adaptation strategies. On the other, they highlight how highly idealised protocols like TIPMIP could bias climate projections post emissions cessation if they do not include realistic projections of land use changes.

Bibliography

Jones, Colin, Bossert, I., Dennis, D. P., Jeffery, H., Jones, C. D., Koenigk, T., Loriani, S., Sanderson, B., Séférian, R., Wyser, K., Yang, S., Abe, M., Bathiany, S., Braconnot, P., Brovkin, V., Burger, F. A., Cadule, P., Castruccio, F. S., Danabasoglu, G., … Ziehn, T. (2025). The TIPMIP Earth system model experiment protocol: phase 1. https://doi.org/10.5194/egusphere-2025-3604.

How to cite: Rivosecchi, A., Dittus, A., Hawkins, E., Schiemann, R., and Fischer, E.: Evolution of global climate and regional hot extremes following CO2 emissions cessation., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14618, https://doi.org/10.5194/egusphere-egu26-14618, 2026.

In the aftermath of extreme weather, policy makers, contingency planners and insurers often seek to understand the likelihood of experiencing such events. The most common tool for this is extreme value analysis (EVA), but likelihood estimates based on observed or reanalysis data can be highly uncertain due to the relatively short observational record. Substantially larger samples of plausible extreme weather events can be obtained using the UNprecedented Simulated Extremes using ENsembles (UNSEEN) approach, which involves applying EVA to large forecast/hindcast ensembles. While larger sample sizes generally reduce the uncertainty associated with EVA, using seasonal or decadal forecast data introduces additional uncertainties related to model bias and model diversity. In this study, a multi-model ensemble of hindcast data from the CMIP6 Decadal Climate Prediction Project was analysed to quantify these additional uncertainties in the context of extreme temperature and rainfall across Australia. Factoring in model bias and diversity dramatically increased the uncertainty associated with estimated event likelihoods from the UNSEEN approach, to the point that it equaled or exceeded the uncertainty from an observation-based approach at most locations. Model diversity tended to be the largest source of uncertainty (60-70% of the total). Bias correction was also a significant source of uncertainty (30-40%), while the uncertainty associated with EVA was trivial. Our results suggest that an UNSEEN-based approach to estimating the likelihood of climate extremes should be understood as an approach that has different uncertainty characteristics to an observation-based approach, as opposed to less uncertainty.

How to cite: Irving, D., Stellema, A., and Risbey, J.: Quantifying the uncertainty associated with extreme weather likelihood estimates derived from large model ensembles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14625, https://doi.org/10.5194/egusphere-egu26-14625, 2026.

Climate change drives an increase in the frequency of multiple meteorological extreme event types (e.g., extreme precipitation, storms, droughts, heatwaves) by affecting thermodynamic and dynamic processes in the coupled land-atmosphere system. For example, the extended droughts during 2018-2020 in Europe, flooding triggered by extreme precipitation in Germany in 2021, as well as Valencia and central France in 2024, or prolonged heatwaves in 2003, 2015, 2018, and 2022 across continental Europe had strong adverse impacts on socio-economic systems and the environment. Given a higher frequency of extreme events, it becomes more likely that regions experience events of the same or different types in consecutive seasons, thereby challenging the regions’ short-term coping and recovery ability and long-term resilience.

While extreme events are generally well-studied, holistic analyses of typical sequences of extreme events are missing. Compound analyses commonly focus on specific combinations of events, but usually miss typical intra-annual sequences of extreme events with the potential for high impacts.

Our analysis addresses the question 1) which sequences of extremes occur most often, 2) how robust they are, and 3) their physical implications. We assess intra-annual sequences of extreme seasons on the European scale in a regional multi-member ensemble of the Canadian Regional Climate Model version 5 (CRCM5) covering the European CORDEX domain at a high spatial resolution (0.11°, 12 km). The CRCM5 was driven by four members of the Max-Planck-Institute Grand Ensemble (MPI-ESM-LR) under SSP3-7.0. Given that the four members differ only by initial conditions and thus share the same climate, this setup quadruples the sample size for finding extreme events. We selected extreme event indicators for extreme heat, droughts, extreme precipitation and wind. They cover hazards of regionally varying importance, but each of them poses considerable risks to human and natural systems in Europe. The sequences of extreme events were derived using the sequential pattern mining algorithm cSPADE.

In this contribution, we show first findings on the most prevalent sequences of seasonal events under SSP3-7.0. We map vulnerability hotspots associated with intra-annual extreme event characteristics and present physical “stories” corresponding to the sequences. Furthermore, we aim to provide the basis for understanding potential interrelations of seasonal extreme events.

How to cite: Böhnisch, A., Lee Newell, M., Meuriot, O., Soto Martin, J., Reiter, A. C., and Drews, M.: Emerging intra-annual sequences of climate extremes in Europe , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14884, https://doi.org/10.5194/egusphere-egu26-14884, 2026.

Isolated and compound climate extremes, such as droughts and heatwaves, are intensifying under global warming. Although recent studies have advanced the physical understanding and classification of compound events, their socioeconomic impacts remain poorly quantified at the global scale using disaster record databases. Building on evidence that compound drought–flood events can generate impacts substantially larger than those from isolated hazards, this study extends the inquiry by providing a global assessment of the socioeconomic impacts of compound drought–heatwave (CDH) events.

To achieve this, we use the Emergency Events Database (EM-DAT) for the period 1960–2025 and analyse reported drought and heatwave disasters at the global scale. CDH events are identified using complementary approaches, including overlapping drought and heatwave records within the same location (top-level administrative unit) and the “Associated Types” information in EM-DAT, thereby allowing assessment of sensitivity to event definition. Furthermore, EM-DAT drought events are compared with heatwave conditions derived from the ERA5 reanalysis to evaluate consistency between reported impacts and climatic co-occurrence. Socioeconomic impacts are quantified using the affected population, human fatalities, and reported damages.

Preliminary results show a clear increase in the number of reported areas affected by CDH events globally, particularly since the mid-2010s. Moreover, CDH events are consistently associated with greater impacts than single hazards. Specifically, using matching events within EM-DAT, compound events exhibit greater total damage, while fatalities during heatwaves increase by up to a factor of five when drought conditions co-occur. Furthermore, when drought impacts from EM-DAT are associated with heatwaves identified in ERA5, the damage and affected population are, respectively, two to four times higher than for isolated drought events.

Taken together, these findings provide global-scale evidence that co-occurring droughts and heatwaves substantially amplify socioeconomic impacts. This underscores the need to explicitly account for compound extremes in climate risk assessment, adaptation planning, and disaster risk reduction.

How to cite: Worou, K. and Messori, G.: Amplified socioeconomic impacts of compound drought–heatwave events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15041, https://doi.org/10.5194/egusphere-egu26-15041, 2026.

How to cite: Khadke, L., Evans, J. P., Kim, Y., Virgilio, G. D., and Kala, J.: Intensification of Short-Duration Extreme Precipitation in Greater Sydney, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15607, https://doi.org/10.5194/egusphere-egu26-15607, 2026.

On 1 October 2020, the intense extra-tropical storm Alex impacted the north-west coast of France, producing unusually strong wind gusts for the season. On 2 October, the storm triggered record-breaking rainfall over the south-eastern French Alps and north-western Italian Alps. In France, this Heavy Precipitation Event (HPE) caused severe flooding and land­slides, resulting in casualties, and over 1 billion euros in economic losses.

We used convection-permitting regional climate modeling with a spa­tial resolution of 2.5 km to investigate these observed events. Simulations were conducted over September-October 2020 on an extensive domain centered on France. Our model successfully reproduces the characteristics of both the HPE and storm Alex, including the observed sequence of events and impacts (Bador et al., 2025).

We then explored how the observed 2020 Mediterranean HPE could have been differ­ent had it occurred 2 years later, in 2022, where warmer sea surface was recorded in the western Mediterranean Sea. This storyline analysis suggested reduced precipitation impacts over the south-eastern French Alps but enhanced impacts in Italy. Additional sensitivity experiments confirmed the key role of regional sea surface temperatures (SSTs) in shaping the HPE’s intensity in the western Alps, with an eastward shift of heavy precipitation with higher Mediterranean SSTs. Our simulations consistently show that sea surface warming can further intensify the Mediterranean HPE, while cooling reduces the intensity of extreme precipitation and local impacts. In contrast, modifications to the Atlantic SSTs affecting storm Alex itself have a limited influence on the regional Mediterranean circulation and the HPE.

All simulations were performed using initial-condition large ensembles to assess the role of internal variability in shaping local extremes. We highlighted variations among ensemble members in both local rainfall extremes and in gustiness. As impact sectors increasingly rely on km-scale climate modelling to inform local climate change assessments, our results underscore the importance of the ensemble-based approaches to fully capture the range of possible outcomes for extreme events locally.

How to cite: Bador, M., Noirot, L., Caillaud, C., and Boé, J.: Cooler than observed sea surface could have reduced impacts of storm Alex and induced mediterranean heavy precipitation event in France, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16649, https://doi.org/10.5194/egusphere-egu26-16649, 2026.

Cold extremes — also referred to as cold air outbreaks, cold spells, or cold snaps — have received less attention in the scientific literature than hot extremes, largely because their frequency and intensity are projected to decrease under climate change. Nevertheless, cold extremes continue to exert substantial impacts across a wide range of sectors, including human health, agriculture, and infrastructure. Superimposed on their overall global decline is pronounced regional and seasonal variability, driven by variability in the underlying physical mechanisms, which themselves may be influenced by climate change. Here, we provide an overview of global and regional trends in cold extremes, examine their key drivers in both present and future climates, and discuss outstanding questions related to the dynamical forcing of cold extremes and their projected evolution under climate change.

How to cite: Domeisen, D., Afargan-Gerstman, H., Blackport, R., Butler, A. H., Hanna, E., Karpechko, A. Yu., Kretschmer, M., Lee, R. W., Maycock, A., Russo, E., Shen, X., and Simpson, I. R.:  Trends and Drivers of Cold Extremes in a Changing Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16825, https://doi.org/10.5194/egusphere-egu26-16825, 2026.

The increasing frequency of extreme hot events poses major societal and scientific challenges due to their adverse impacts on human and natural systems, compounded by their unpredictable nature. Climate models are essential for identifying the mechanisms that amplify extremes and for anticipating long-term changes that inform decision making, yet their accuracy is limited by internal variability, structural uncertainties, and systematic biases. Observational constraint approaches that link past and future behavior of physical observables offer a promising way to address these limitations, though they often rely on region-specific empirical relationships.

Here, we show that future changes in hot event probabilities and their uneven spread across global land areas depend critically on the historical properties of temperature distributions. In particular, historical variability controls the growth rates of probabilities, either amplifying or dampening the effects of regional background warming, with important implications for climate-change projections. Building on this insight, we develop a universal analytical framework that combines observational evidence with model output to provide more robust assessments of future changes. Results indicate that hot event probabilities may increase faster than suggested by models alone across much of the land surface. In large areas, including the Euro-Mediterranean and Southeast Asia, observation-constrained increases could exceed model-based estimates by nearly a factor of two, even at low levels of global warming. Surpassing the 2 °C warming threshold could push highly vulnerable regions, such as the Amazon and other tropical land areas, into uncharted climate conditions where extreme heat becomes routine.

These findings support more realistic evaluations of future risk and underscore the need for strengthened mitigation efforts to prevent rapid and potentially irreversible climate shifts.

How to cite: Simolo, C. and Corti, S.: Hot extremes increase faster than models suggest: evidence from observation-constrained projections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17562, https://doi.org/10.5194/egusphere-egu26-17562, 2026.

Understanding and characterizing temperature extremes is essential for assessing climate impacts and risks. Robust statistical analysis of such extremes requires large datasets, yet observational records often provide limited samples of rare events. Hindcasts, i.e., retrospective forecast model runs for past dates, are typically used to correct model biases, but their potential for extreme event analysis remains underexplored. Approaches such as UNSEEN (UNprecedented Simulated Extremes using Ensembles) have investigated the potential of seasonal hindcast ensembles to provide large samples of events that are physically plausible, particularly for assessing rare events. However, seasonal hindcasts often focus on monthly means.

In this study, we explore whether a similar approach can be applied to subseasonal hindcasts, evaluating their potential to serve as alternative realizations of extreme events at daily resolution.  We use two complementary methods to compare global temperature extremes in ECMWF subseasonal hindcast with ERA-5 reanalysis: (1) the statistical upper bound of daily 2-meter temperature, and (2) the probability of record-breaking daily 2-meter temperature. By leveraging existing subseasonal hindcast ensembles, we aim to evaluate whether these datasets can be repurposed to study temperature extremes that have not yet been observed but are plausible under current climate conditions. 

How to cite: Rivoire, P., Pyrina, M., Naveau, P., and Domeisen, D.: Heat extremes in subseasonal hindcasts: a General Extreme Value perspective, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18203, https://doi.org/10.5194/egusphere-egu26-18203, 2026.

Unclear and inconsistent terminology for high impact climate phenomena, including concepts such as tipping points, irreversibility, ‘collapse’ and ‘shutdown’, presents a substantial barrier to clear understanding of Earth system risks. These terms are frequently used in assessments of major subsystem shifts in ocean circulation, ice sheets and forest biomes, yet they are often applied without shared definitions across scientific, policy and public contexts. This inconsistency affects how scientific results are interpreted, including perceptions of how quickly changes may unfold and whether different parts of the climate system might influence one another. It also has important psychological and emotional impacts. Language that sounds dramatic or alarming may be intended to motivate action, but it can instead lead to desensitisation, message fatigue, denial or even the spread of misinformation. These reactions can weaken engagement and undermine societal preparedness for potential climate driven transitions.

Government science and policy teams, rely on clear and consistent terminology for effective decision making in situations where thresholds and impacts remain uncertain. To support this need, we – as communication specialists work extensively at the interface between science and policy - are developing an evidence-based glossary and guidance for terminology related to tipping points and other high impact climate concepts. The aim is to improve internal communication and to support clearer interpretation of scientific assessments used in national risk planning.

The project is grounded in social science and uses a mixed methods design. It began with a review of existing definitions and research on the psychological effects of climate language. We carried out semi-structured interviews and workshops with scientists and government officials, and this highlighted how linguistic ambiguity affects policy development and the evaluation of uncertain risks. Utilising ta broad cross section of Met Office staff, we carried out focus groups to explore how different definitions were perceived and understood. Participants, including those with strong scientific backgrounds, showed substantial disagreement about the meaning and implications of key terms. This indicates that confusion around terminology linked to tipping point research is not limited to public audiences but also exists within expert communities.

Insights from this analysis are guiding the co creation of a public facing glossary developed with an expert working group of twelve multidisciplinary specialists at the Met Office. Completion is planned for March 2026, alongside continued engagement with international bodies including WCRP and IPCC. By strengthening shared understanding of terms related to climate system transitions and critical thresholds, this work aims to support more coherent communication of high impact climate concepts, improve public and policy interpretation of uncertain risks and reduce unintended emotional and behavioural responses that can undermine, and distract from effective, and much needed climate action.

How to cite: Macneill, K. and Martin, L.: An Up-HILL Battle: Building consensus on terminology for high impact climate events and tipping point risks., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18736, https://doi.org/10.5194/egusphere-egu26-18736, 2026.

A stochastic weather generator (SWG) simulates realistic weather time series beyond the historical record by capturing the statistical properties of observed weather patterns. Here, we present a new spatiotemporal SWG, the MYRIAD Stochastic vIne-copula Model (MYRIAD-SIM), which simulates temperature, wind speed, and precipitation. MYRIAD-SIM captures both spatiotemporal and multivariate dependencies using conditional vine copulas. The simulated data enable new insights into compound climate and multi-hazard events by generating high-impact multivariate weather scenarios. For example, the triple storm sequence Dudley, Eunice, and Franklin, which impacted the UK and Europe in 2022, can be simulated as alternative triple-storm events, illustrating not only what happened but also what could have occurred under statistically plausible conditions, such as higher wind speeds or varying precipitation patterns. This study demonstrates how stochastic counterfactuals of historical events can support risk communication by framing hazards in a narrative, event-focused way rather than through abstract probabilities.

How to cite: Claassen, J., Jäger, W., de Ruiter, M., Koks, E., and Ward, P.: Using Stochastic Data to Simulate and Communicate Alternative Multi-Hazard Weather Extreme Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19875, https://doi.org/10.5194/egusphere-egu26-19875, 2026.

Extreme summer heat stress presents increasing public health risks across Europe. These extremes are strongly influenced by large-scale atmospheric circulation, yet the specific pathways linking circulation evolution to surface heat stress amplification remain poorly understood. Using the simplified Wet Bulb Globe Temperature (sWBGT), which accounts for both temperature and humidity effects on heat stress, we analyze extreme summer (JJA) events during 1979–2023 based on ERA5 reanalysis and a seven-class European weather regime (WR) classification. We define extreme events as regional sWBGT exceeding the 95th percentile for at least three consecutive days. Extreme sWBGT events across Europe occur predominantly during blocking regimes, with European and Scandinavian blocking playing a dominant role in many regions. We then examine how blocking evolves prior to heat stress peaks. Results show that only Scandinavia exhibits a statistically robust tendency for blocking to develop shortly before the peak, suggesting a circulation transition preceding extreme heat stress. In contrast, most other European regions experience peak heat stress under blocking conditions that are already established several days in advance, highlighting the dominant role of persistent circulation patterns. The time interval between the onset of blocking and the heat stress peak typically ranges from 3 to 7 days. These contrasting circulation pathways are closely linked to different surface amplification processes. Circulation transitions maybe associated with rapid atmospheric adjustment and surface warming, whereas persistent blocking likely promotes the accumulation of radiative forcing and progressive soil moisture depletion. Understanding how these mechanisms vary across pathways can help explain regional differences in European heat stress extremes and may improve predictions of future events.

How to cite: Zhang, Q., Kjellsson, J., and Black, E.: Circulation pathways and surface drivers of extreme summer heat stress over Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19952, https://doi.org/10.5194/egusphere-egu26-19952, 2026.

Obtaining robust and actionable information on regional precipitation change to enable adaptation planning and decision-making is a matter of great concern, since there are multiple sources of information.  Projections from large CMIP6 model ensembles (e.g., IPCC Interactive Atlas) show weak signal of climate change in total annual and seasonal precipitation over most of South America (SA), with low agreement between models. Besides, information from smaller ensembles is frequently discrepant. A dynamic framework for climate change in SA is necessary to achieve robust and actionable changes.

Even though they are weak and not robust, the precipitation changes produced over SA by large model ensembles suggest that their main driver is the ENSO increased variability in eastern Pacific, especially intensified El Niño events, produced by transient greenhouse-gas-induced warming. This is consistent with the large impact of ENSO on precipitation in SA. This dynamical framework requires that models used for climate projections in SA demonstrate good simulation not only of the climatology, but also of ENSO and its teleconnections with SA. The assessment of 31 models that provided at least three runs from the present (1979-2014) to the future climate (2065-2100), based on both criteria, selected five best-performing models. This reduced set accurately reproduces the observed seasonal impact of ENSO on precipitation in SA and produces strong and robust patterns of climate change with seasonal variation dynamically consistent with more intense future ENSO in a more El Niño-like mean state.

Since the most dramatic impacts of climate change are produced by changes in the frequency and intensity of extreme precipitation events, it is essential that robust and actionable information is also provided on changes of these events, defined as above the 90th percentile. The analysis is based on the same dynamic framework of the changes in total seasonal/monthly rainfall, since ENSO also exerts a large impact on the extreme events in SA, and the selected set of models shows good simulation of the observed seasonal/monthly impact of ENSO on the frequency and intensity of extreme events. The available information usually shows changes of annual extreme indices. We adopt a seasonal/monthly resolution, which is very useful, especially in a monsoon regime with pronounced annual precipitation cycle. The future changes in extreme events is obtained for SA with monthly temporal resolution and 1 degree spatial resolution. The patterns of change in frequency and intensity of extreme events do not coincide, as changes in frequency depend on dynamic changes, while changes in intensity also depend on thermodynamic changes that determine the precipitable water vapor. Patterns of change in the frequency of extreme events in future are similar to the patterns of El Niño impact on the frequency of extreme events in the present. Changes in the average intensity of precipitation in future extreme events are generally positive and predominate in southeastern South America, where the frequency also generally increases, maximizing impacts on densely populated areas of great importance for agricultural and energy production. The provided information contributes to increase societal preparedness to extreme precipitation in SA.

How to cite: Grimm, A. M., Fanderuff, L. G., and Saboia, J. P. J.: Robust and actionable information on climate change and extreme rainfall events in South America, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20226, https://doi.org/10.5194/egusphere-egu26-20226, 2026.

Soil moisture-precipitation feedback is an important factor in the water and energy cycles, but how important is it on the time scale of an atmospheric extreme precipitation event? We are investigating this question using the example of heavy precipitation in July 2021, which led to destructive flash floods in Western Europe.

We quantify the importance of soil moisture by running a storyline simulation. We compare the precipitation simulated in the ICON-DREAM reanalysis and in our control run to counterfactual scenarios with soils dried out to plant wilting point and soils wetted to saturation. We find that saturating the soil increases precipitation by about 10% while drying the soil decreases precipitation by about 36% comparing ensemble median values.

Moisture tracking shows that one reason is that land surfaces in the vicinity of the impacted region are relevant for fueling the heavy precipitation. We find that evaporation is not limited by water availability, which explains the non-linear response in the precipitation amounts. 

The changes in evaporation also affect the synoptic scale evolution of the event, which amplify the precipitation decrease in the dry scenario. Constraining the evolution of the event enough to produce the extreme of July 2021 was a major challenge of this study. The limited predictability of free forecasts conflicts with the need for enough lead time to allow soil moisture to impact the atmosphere in a meaningful way. We solve this problem by using data assimilation to constrain the large scale circulation of our global ICON simulations while disabling the assimilation within our region of interest.

Our work is part of the German Research Foundation (DFG) Collaborative Research Center 1502 DETECT. In DETECT we aim to answer the question of whether regional changes in land and water use impact the onset and evolution of extreme events. Our coarse approach to changes in water availability gives us an upper bound on changes we can expect as a result of human influence.

How to cite: Fohrmann, T., Szemkus, S., Heuser, O., Valmassoi, A., and Friederichs, P.: The influence of soil moisture on the extreme precipitation event in July 2021 in Western Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21160, https://doi.org/10.5194/egusphere-egu26-21160, 2026.

Antarctic sea ice has experienced small increases from 1979 to 2015, followed by an unexpectedly rapid decline reaching record-low anomalies in 2016 and 2023. The significant reduction is raising questions regarding the drivers of this decline and how the Antarctic sea ice will respond to future climate changes. Here we apply an event-based storyline approach based on a coupled global climate model (AWI-CM-1-1-MR), where the large-scale free-troposphere dynamics is constrained to ERA5 data. We focus on two multi-year sea-ice loss events, 2014–2017 and 2020–2023, to examine the response of sea ice to the observed atmospheric circulation anomalies if they occurred under different global climate backgrounds. By comparing the sea-ice response under present-day climate and projected future warm climates (+2°C, +3°C, and +4°C global mean surface warming relative to preindustrial), we separate the thermodynamic and dynamic effects of climate change and explore how the background climate state modulates the sea-ice response to wind anomalies. We find that the Antarctic sea-ice response remains surprisingly robust across this broad range of climate states, with a few exceptions where seasonal and regional deviations occur.

How to cite: Lyu, L., Sánchez-Benítez, A., Athanase, M., A. Roach, L., Jung, T., and F. Goessling, H.: Robust response of Antarctic sea ice to large-scale wind anomalies across different climate backgrounds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21435, https://doi.org/10.5194/egusphere-egu26-21435, 2026.

This study investigates how complex volcanic systems undergo major behavioral shifts, focusing on the Solfatara–Pisciarelli (SP) hydrothermal-magmatic area within the Campi Flegrei caldera (Southern Italy). The SP system is one of the most active zones of the caldera, characterized by persistent degassing, seismic swarms, strong hydrothermal circulation and long-term ground uplift. These processes arise from nonlinear interactions between magmatic inputs, fluid migration, and shallow hydrothermal pressurization, making the identification of critical transitions particularly challenging.

To address this, we developed an integrated analytical framework combining Multivariable Fractional Polynomial Analysis (MFPA) and Global Critical Point Analysis (GCPA). MFPA models nonlinear and time-lagged associations among key monitoring parameters—vertical ground deformation, seismicity, CO₂ flux, geochemical equilibrium variables, and thermal signals—while GCPA identifies the temporal moments when multiple variables collectively show systemic reorganization.

Analysis of multi-year (2018–2024) geophysical and geochemical datasets revealed that deformation is strongly associated with seismicity, equilibrium pressures of hydrothermal gases, heat flow, and CO₂ flux. Incorporating time-lagged deformation improved model accuracy and reduced unexplained variance, highlighting delayed cause–effect couplings between deformation and fluid-dynamic processes. The model confirms seismicity as the most stable explanatory parameter, consistent with sustained fracturing and fluid pressurization in the shallow system.

GCPA identified two major critical transitions:

• CP1 – 30 November 2020, dominated by thermal–chemical reorganization and increased gas-system pressurization.
• CP2 – 1 April 2023, reflecting a more open and multiparametric regime where deformation, temperature, seismicity, heat flux, and CO₂ emissions contribute comparably to system evolution.

These transitions align with independent geodetic evidence suggesting migration and reconfiguration of the shallow overpressure source beneath the SP area. The integrated MFPA–GCPA approach thus reconstructs how systemic changes propagate across geophysical and geochemical variables, providing retrospective insight into the onset and progression of unrest phases.

This framework offers several advantages over classical or non-parametric approaches: interpretability of functional relationships, explicit treatment of nonlinearities and time lags, and the ability to detect collective regime shifts rather than isolated anomalies. Although not predictive, the method provides a quantitative basis for identifying critical phases in volcanic systems and may be adapted to other densely monitored calderas. With higher-resolution and real-time data streams, it could support early indications of evolving unrest and enrich next-generation volcano-monitoring strategies.

How to cite: Vitale, A., Barone, A., Marotta, E., Vitale, D. F., Pepe, S., Peluso, R., Castaldo, R., Avino, R., Mercogliano, F., Pepe, A., Accomando, F., Avvisati, G., Belviso, P., Bellucci Sessa, E., Antonio, C., Maddalena, P., Sansivero, F., and Tizzani, P.: Critical Transitions at Campi Flegrei Resurgent Caldera: A Novel Approach to Systemic and Retrospective Signals Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-673, https://doi.org/10.5194/egusphere-egu26-673, 2026.

In the Model Uncertainty-Model Intercomparison Project (MUMIP) we compare parameterisation packages from different modelling centres using their single-column modelling (SCM) frameworks. We will showcase the dataset from an Indian Ocean experiment at a 0.2 degrees grid covering one month, with about 10 million simulations of each model. These parametrised models are compared against a convection-permitting benchmark from DYAMOND under common dynamical constraints. We will show differences and similarities in precipitation patterns and physics tendencies among four models and show how these differences can be generalised. Following earlier works, we find that at coarse grids that do not resolve convection, parameterisation packages tend to produce overconfident tendencies compared to the convection-permitting benchmark. Furthermore, we test several hypotheses on the MUMIP dataset to explain the differences. We use the data to explore the foundations of stochastic physical parametrisations. Would stochastic physics effectively overcome the overconfidence for good reasons? May the stochastic perturbations actually have a physically meaningful quantitative interpretation? Can stochastic physics be used to partially overcome truncation and grid spacing limitations?

How to cite: Groot, E., Christensen, H., Sun, X., Newman, K., Lfarh, W., Roehrig, R., Bengtsson, L., and Simonson, J.: How different are parameterisation packages really and how can we interpret stochastic perturbations?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2991, https://doi.org/10.5194/egusphere-egu26-2991, 2026.

The Atlantic Multidecadal Variability (AMV) and the North Atlantic Oscillation (NAO) are the dominant modes of oceanic and atmospheric variability in the North Atlantic, respectively, and are key sources of predictability from seasonal to decadal timescales. However, the physical processes and feedback mechanisms linking the AMV and NAO, and the role of diabatic processes in these feedbacks, remain debated. We present a data-driven dynamical modelling framework which captures coupled decadal variability in AMV, NAO, and North Atlantic precipitation. Applying equation discovery methods to observational data, we identify deterministic low-order dynamical models consisting of three coupled ordinary differential equations. These models reproduce observed North Atlantic decadal variability and show robust out-of-sample predictive skill on multi-annual to decadal lead times. The resulting model dynamics include a distinct quasi-periodic 20-year oscillation consistent with a damped oceanic mode of variability. Notably, precipitation-related terms feature prominently in the low-order models, suggesting an important role for latent heat release and freshwater fluxes in mediating ocean–atmosphere interactions. We propose new feedback mechanisms between North Atlantic sea surface temperature and the NAO, with precipitation acting as a dynamical bridge. By linearising the low-order models and computing finite-time Lyapunov exponents, we find that North Atlantic precipitation is more predictable in a positive AMV phase. We then analyse several decadal prediction ensemble experiments based on initialised hindcasts and find comparable state-dependent predictability of precipitation. Overall, these results illustrate how data-driven equation discovery can provide mechanistic hypotheses and new insight beyond conventional analyses of observations and climate model simulations.

How to cite: Nicoll, A., Christensen, H., Huntingford, C., and Smith, D.: New insights into decadal climate variability in the North Atlantic revealed by data-driven dynamical models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3111, https://doi.org/10.5194/egusphere-egu26-3111, 2026.

Ensemble forecast generate multiple predictions from a set of initial conditions, thereby producing the probability density distribution (PDF) of a variable and quantifying forecast uncertainty beyond a single deterministic forecast. However, studies focusing on the predictable lead time of ensemble forecast remain limited. In this study, orthogonal conditional nonlinear optimal perturbations (O-CNOPs) are applied to the Lorenz-96 model to investigate the predictable lead time of ensemble forecast, which is then compared with that obtained from a single deterministic forecast. Results show that the maximum predictable lead time revealed by the ensemble distribution generated with O-CNOPs is 18.5 days, 2.5 days longer than that revealed by the ensemble distribution generated with singular vectors (SVs), which is 16 days. Consistent results are obtained from the ensemble mean analysis, which reveals a longer predictable lead time for O-CNOPs (21.75 days) than for SVs (18 days). In addition, compared with ensemble forecasts generated with SVs, the ensemble forecasts generated with O-CNOPs exhibit higher deterministic forecast skill, probabilistic forecast skill, reliability, and resolution over the same forecast periods. These results collectively highlight the advantage of O-CNOPs in constructing physically consistent nonlinear ensemble distributions and provide a methodological framework for more accurate quantification of ensemble forecast lead time.

How to cite: Zhu, Y. and Duan, W.: Exploring the Predictable Lead Time of Ensemble Forecast Based on Conditional Nonlinear Optimal Perturbation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3320, https://doi.org/10.5194/egusphere-egu26-3320, 2026.

The skill of forecasting Tropical Cyclone (TC) Rapid Intensification (RI) is limited by inherent uncertainties in initial conditions and model physics. To address this, the C-NFSVs method integrates initial and model perturbations, accounting for their collective effects through the nonlinear forcing singular vector (NFSV; also known as CNOP-F) approach. In this study, we applied C-NFSVs to the Weather Research and Forecasting (WRF) model for TC ensemble forecasting across three resolutions, comparing it against O-NFSVs, which has proven superior to traditional stochastic physics schemes. Results reveal a significant resolution dependence, with the superiority of C-NFSVs maximizing at the convection-permitting scale. At this resolution, the C-NFSVs ensemble outperforms O-NFSVs for both deterministic and probabilistic metrics, and demonstrates significantly improved reliability. Notably, for the challenging prediction of RI events, C-NFSVs exhibits high discriminative skill, achieving an Area Under the ROC Curve (ROCA) of 0.80. A detailed examination of TC Hato attributes this success to capturing the evolution of the critical physical error chain, which progresses from thermodynamic priming and convective organization to the structural and dynamic response. Mechanistically, the results highlight the complementary roles of the two components: the initial component of C-NFSVs dominates the uncertainty of the dynamic structure in the early forecast stage, while the model component plays a primary role in maintaining the thermodynamic uncertainty of moisture and temperature fields throughout the forecast. This study validates the effectiveness and physical rationality of C-NFSVs in high-resolution ensembles, offering a promising strategy for enhancing the predictability of extreme weather events at convection-permitting scales.

How to cite: You, C. and Duan, W.: Enhancing Tropical Cyclone Ensemble Forecast Skill via the Collective Effect of Initial and Model Perturbations: The C-NFSVs Method, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3326, https://doi.org/10.5194/egusphere-egu26-3326, 2026.

Understanding the long-term dynamics of forest aboveground carbon (AGC) is critical for constraining the terrestrial carbon cycle. However, accurately reconstructing historical AGC spatiotemporal patterns remains a challenge due to the complex, nonlinear relationships between vegetation proxies and biomass, as well as the stochastic uncertainties inherent in multi-source satellite observations.

In this study, we propose a probabilistic deep learning framework to reconstruct harmonized, high-resolution (0.25°) global forest AGC stocks and fluxes from 1988 to 2021. By integrating multi-source optical (e.g., NDVI, LAI) and microwave (e.g., VOD) remote sensing data, our approach utilizes Probabilistic Convolutional Neural Networks (CNNs) to simultaneously estimate AGC dynamics and quantify associated predictive uncertainties (decomposing aleatoric and epistemic components). This data-driven model effectively captures the nonlinear spatial dependencies and texture features that traditional empirical methods often miss.

Our reconstruction reveals significant decadal-scale regime shifts in the global forest carbon sink. While global forests remained a net sink of 6.2 PgC over the past three decades, we identify a pronounced transition in moist tropical and boreal forests, which have shifted from carbon sinks to sources since the early 2000s. Furthermore, our analysis uncovers an intensifying negative coupling between interannual tropical AGC fluxes and atmospheric CO₂ growth rates (r=-0.63 in the last decade), suggesting a growing complexity in the climate-carbon feedback. Spatially explicit partitioning in the Amazon further indicates a dynamical shift where AGC losses are increasingly driven by indirect climate stressors in previously "untouched" forests, rather than direct deforestation alone.

In conclusion, this study elucidates the state-dependent responses of global forests to changing disturbance regimes. The probabilistic framework provides a necessary basis for distinguishing genuine regime shifts, such as the structural decline of the tropical carbon sink, from observation noise, thereby enhancing our predictive understanding of terrestrial carbon resilience in a warming climate.

How to cite: Qian, Z., Bathiany, S., Liu, T., Blaschke, L., Teo, H. C., and Boers, N.: Reconstruction of Global Forest Aboveground Carbon Dynamics with Probabilistic Deep Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4547, https://doi.org/10.5194/egusphere-egu26-4547, 2026.

The magnetic field of planets and stars is generated by the movement of conductive fluids inside these bodies. The precession and libration of these astrophysical bodies play a central role in the excitation of the internal turbulent fluid motion. In our laboratory, we have developed an experiment that allows the investigation of precession-driven inertial waves and their instability (Xu and Harlander, 2020). Wave triads play a very important role in this instability (Lagrange et al., 2011). As the Ekman number decreases, an increasing number of interacting triads arise, ultimately leading to turbulence. This process can be experimentally reproduced in the laboratory. In this experiment, precession is simulated using a slightly tilted cavity with a free fluid surface and is therefore simpler in design than a real precession experiment. 

The dynamics of fluids can be described by PDEs. However, often deeper insights can be gained from a corresponding low-dimensional dynamical system. An example is the large family of Lorenz-type models, which have led to a fundamental understanding of predictability in atmospheric dynamics (Majda et al., 1999). Also, for the problem of a precessing rotating cylinder, low-dimensional models exist. Such models are obtained from spectral discretizations of the Navier-Stokes equations and truncating the resulting hierarchy of coupled equations at low order. Truncation, however, eliminates the quadratic coupling between the resolved modes and the (unresolved) smaller scales, which can lead to unrealistic characteristics of turbulence. 

We suggest another closure to systematically derive low-order amplitude equations for rotating fluids, based on stochastic modeling of the unresolved small scales in accordance with the available experimental data. Specifically, we first remodel the small scales by an appropriate stochastic process that has a multivariate Gaussian law when conditioned on the resolved variables and, in a second step, apply a projection operator to the coupled system. In doing so, we derive closed, averaged equations for the resolved variables that retain the quadratic nonlinearities and so capture the small-scale contributions to the low-order wave dynamics. For a projection operator in the form of a conditional expectation (i.e., a projection on function space), we have recently studied necessary and sufficient conditions under which the projection operator formalism yields an approximation for nonreversible (e.g. driven) systems (Duong et al., 2025). Measuring the distance between the marginal distributions of the resolved variables for the full- and the low-order models, the accuracy of the low-order model can be measured (Hartmann et al., 2020).  

By comparing the low-order stochastic model results with data from the precession experiment, the hope is not only to capture the wave interactions correctly and develop a stochastic extension of the existing amplitude equations, but also to reduce the order of the existing model even further. 

M.H Duong, C. Hartmann, and M. Ottobre, arXiv preprint,  arXiv:2506.14939, 2025.

C. Hartmann, L. Neureither, and U. Sharma, SIAM J. Math. Anal. 52(3), 2689-2733, 2020.

R. Lagrange, P. Meunier, F. Nadal, C Eloy, J. Fluids Mech. 666, 104–145, 2011.

A.J. Majda, I. Tomofeyev, E. Vanden Eijnden, PNAS, 96(26), 14687-14691, 1999.

W. Xu, U. Harlander, Rev. Phys. Fluids., 5(9), 094801-21, 2020.

How to cite: Harlander, U. and Hartmann, C.: Low-dimensional stochastic amplitude equations for a precessing rotating cylinder, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5335, https://doi.org/10.5194/egusphere-egu26-5335, 2026.

Climate oscillations such as the El Niño–Southern Oscillation (ENSO) and the Madden–Julien Oscillation (MJO) dominate aspects of climate variability, yet they are often challenging to accurately capture in climate models. Due to their disparate underlying physical processes, any potential commonality between different climate oscillations is obscured. Common underlying dynamics is suggested by the success of relatively low-dimensional linear inverse modeling (LIM). LIMs represent climate oscillations as linear Gaussian nonequilibrium steady states (LG-NESS) defined by stochastic differential equations. Here we develop the theory of LG-NESS’s and compare with observations and models of climate oscillations.

ENSO and the MJO are often described by two-dimensional indices such as the leading SST EOFs for ENSO, or the Realtime Multivariate MJO index. The LIM algorithm parameterizes the dynamics in the index coordinate system as a two-dimensional LG-NESS specified by seven parameters. We decompose the parameter space into four parameters that define the coordinate system of the index, and three parameters that define its intrinsic dynamics. This allows us to transform all 2d LG-NESS’s to a common three-dimensional dynamical parameter space. Coordinate-invariant quantities depend only on the three dynamical parameters, while coordinate-dependent quantities can be transformed back to the original index coordinate system and depend on all seven parameters.

We parameterize ENSO and the MJO in this three-dimensional dynamical parameter space and find that, despite their distinct physical mechanisms and timescales, they lie within a narrow region of parameter space, indicating a similarity in the underlying phase-space dynamics. We compare observed and modeled dynamics with those of their parameterized LG-NESS, evaluating predictability, thermodynamic properties, and event statistics. We find this minimal three-parameter model reproduces many features of climate oscillations, revealing a deep dynamical similarity  among climate oscillations.

How to cite: Weiss, J., Benincasa, R., Du, D., and Duane, G.: Climate Oscillations and Linear Gaussian Nonequilibrium Steady-States, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8143, https://doi.org/10.5194/egusphere-egu26-8143, 2026.

Cascading extreme weather events, characterized by sequential occurrences of distinct extremes such as heatwaves, floods or droughts, pose increasing risks in a warming climate. However, existing approaches for identifying such events focus either on temporal persistence or spatial coherence alone, and are thus unable to identify the most severe events with both characteristics. Here, we propose a new approach based in dynamical systems theory that treats variables as coupled systems, with a view to enable their mechanistic understanding. We illustrate the application of the method to temperature and relative humidity data during the period 1979-2020, identifying cascading heat-drought extremes over the Mississippi, southeastern China and France. While these events are controlled by different large-scale climate modes and blocking patterns, nine of the events occurred during rapid transitions (<12 months) from El Niño to La Niña. In China, these transitional events were consistently preceded by heavy rainfall approximately two weeks earlier. Key drivers include the prolonged presence of the western north Pacific subtropical high and land-atmosphere feedbacks. Our findings uncover the speed and severity of cascading wet-dry transitions within as little as two weeks during El Niño transition years, and the need for a greater understanding of their driving mechanisms.

How to cite: Deng, Q., Slater, L., Franzke, C., Guo, Y., and Fu, Z.: Whiplash weather in ENSO Transition Years Identified by A Novel Cascading Extremes Index, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8761, https://doi.org/10.5194/egusphere-egu26-8761, 2026.

Network-based early warning signals of El Niño have been recognized for more than a decade, however, it remains unclear whether current climate models can reproduce these signals. Here, we evaluate simulations from both the pre-industrial control and historical experiments of CMIP6 models. While none of the models exhibited skill in either experiment, performance was generally better in the historical runs, suggesting that the inclusion of external forcing may improve model simulations of the early warning signals. Further analysis indicates that some models such as CESM2, FGOALS-g3, and MRI-ESM2-0 may provide potentially useful early warning information for El Niño events, but their warning signals tended to emerge later than those in reanalysis data. Using a new network-based evaluation metric to assess air-sea interactions in the tropical Pacific, we find that model performance in simulating early warning signals is generally linked to their ability to simulate these interactions. This highlights the importance of improving representations of air-sea coupling in current models. For future investigations into the physical mechanisms underlying the network-based early warning signals, CESM2, FGOALS-g3, and MRI-ESM2-0 are recommended due to their relatively better performance compared to the other models considered in this work, although the causes of their delayed signal emergence require further exploration.

How to cite: Yuan, N., Han, J., and Ludescher, J.: Evaluation of CMIP6 Models in Simulating Network-Based Early Warning Signals of El Niño, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10698, https://doi.org/10.5194/egusphere-egu26-10698, 2026.

The growing availability of multiple operational ocean data services provides unprecedented opportunities for applications such as environmental incident response, search and rescue operations, and maritime management. At the same time, despite their widespread use, most ocean datasets offer limited information regarding their performance and consistency with real-world observations.

In this presentation, I address this gap by introducing a methodology to assess uncertainty in ocean transport predictions derived from different ocean data products. Building on recent work that links transport uncertainty—understood here as deviations from ground truth—to invariant dynamical structures in the ocean [1–3], the proposed approach, discussed in [4], exploits these links to guide statistical averaging strategies. We examine how well model-predicted material transport aligns with observational evidence across different dynamical scales, including scales above the mesoscale, the mesoscale, and the submesoscale. This perspective provides a systematic pathway for quantifying the performance of different data sources and assessing their overall quality and reliability.

References:

[1] G. García-Sánchez, A. M. Mancho, A. G. Ramos, J. Coca, B. Pérez-Gómez, E. Alvarez-Fanjul, M. G. Sotillo, M. García-León, V. J. García-Garrido, S. Wiggins. Very High Resolution Tools for the Monitoring and Assessment of Environmental Hazards in Coastal Areas. Frontiers in Marine 7, 605804 (2021).

[2] G. García-Sánchez, A. M. Mancho, S. Wiggins. A bridge between invariant dynamical structures and uncertainty quantification. Commun. Nonlinear Sci. Numer. Simul. 104, 106016 (2022).

[3] G. García-Sánchez, A. M. Mancho, M. Agaoglou, S. Wiggins. New links between invariant dynamical structures and uncertainty quantification. Physica D 453 133826 (2023).

[4] G. García-Sánchez, M. Agaoglou, E.M.C Smith, A. M. Mancho. A Lagrangian uncertainty quantification approach to validate ocean model datasets. Physica D 475 134690 (2025).

Acknowledgments:

Support from PIE project Ref. 202250E001 funded by CSIC, from grant PID2021-123348OB-I00 funded by MCIN/ AEI /10.13039/501100011033/ and by FEDER A way for making Europe.

How to cite: Mancho, A. M.: Understanding Uncertainty in Ocean Transport Inferred from Multiple Data Sources, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13663, https://doi.org/10.5194/egusphere-egu26-13663, 2026.

Tropical oceanic rain clusters exhibit complex organization patterns that can reveal fundamental principles governing convective systems. In this study, we develop a simple cellular automaton (CA) model that captures essential dynamics of tropical convection, using only a minimal set of physical rules. The model focuses on how local destabilization and stabilizing feedbacks, mediated by gravity waves, shape the spatial structure of rain clusters. Specifically, the distributions of the cluster area, A, and total rain rate, R, for tropical oceanic rain clusters from the CA model are analyzed for their scaling exponent ζ_A, ζ_R, and β where ; f(s) the probability distribution of S, E(R¦a) ~ a^β, E(R¦a) the conditional expectation of R given A = a.

We find that the CA naturally exhibits critical behavior, resembling patterns seen in percolation theory. Specifically, the size distribution of rain clusters follows a scaling law whose exponent is remarkably robust and closely matches the theoretical value for 2D percolation. This suggests that rain clusters in certain tropical regions may organize in a “percolation-like” manner, where large, connected clusters emerge in a critical state. Comparisons with regional climate model simulations over the tropics (Teo et al. 2021) show that rain clusters over the Indian Ocean and tropical Atlantic behave similarly to the critical clusters in our CA, while deviations over the Pacific may result from stronger large-scale destabilization over the western Pacific warm pool and the eastern Pacific ITCZ. Although our CA reproduces key scaling relationships between cluster area and rain rate, it does not fully account for the observed ζ_A ~ 5/3 reported in observational studies (Teo et al. 2017). We propose a simplified version of the CA that may reconcile this difference through tunable criticality.

References:

Teo, C.-K., H.-N. Nuynh, T.-Y. Koh, K. K. W. Cheung, B. Legras, L.-Y. Chew, and L. Norford, 2017: The universal scaling characteristics of tropical oceanic rain clusters. J. Geophys. Res. Atmos., 122, 5582–5599, https://doi.org/10.1002/2016JD025921.

Teo, C.-K., T.-Y. Koh, K. K. W. Cheung, B. Legras, H. Nguyen, L.-Y. Chew, and L. Norford, 2021: Scaling characteristics of modeled tropical oceanic rain clusters. Quart. J. Roy. Meteorol. Soc., 147, 1055–1069, https://doi.org/10.1002/qj.3959.

How to cite: Cheung, K., Teo, C.-K., and Koh, T.-Y.: A Cellular Automata Model of Tropical Oceanic Rain Clusters with Self-organized Criticality, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15454, https://doi.org/10.5194/egusphere-egu26-15454, 2026.

While understanding systemic risk in complex systems has gained growing attention, less effort is often dedicated to understanding the system itself.  Particularly, the typology of complex systems and collapse mechanisms that are consistent across domains remains understudied. Hence, critical questions arise, such as what do we need to know about the system’s characteristics to predict systemwide collapse, identify leverage points, or design resilience interventions? What system properties allow knowledge gained from one system to be generalized to other taxonomically similar systems? What signals can be deduced from a few systems’ global parameters to determine whether a system is in a stable, unstable, or critical region of its adjacent becoming?"

Answering these questions requires determining the typology of complex systems, which enables the study of system-level behaviors independently of the specific details of individual agents. This leads to universality, facilitating the study of collapse mechanisms transferable to other typologically similar systems, thereby providing insight into systemic risk.

This presentation introduces a novel typology of complex systems based on the concept of “adjacent becoming,” drawing on works of Stuart Kaufmann, C.S. Holling, and Marten Scheffer, among others which have established the language of attractors, regime shifts, evolution, and panarchical resilience in complex systems. The System’s Adjacent Becoming (SAB) is what the system is positioned to become while appearing to be in a stable condition, i.e., potential for a critical transition in deep stability. Such a proximal transformation potential can be characterized by four interrelated components consisting of a) the system's location in phase space and proximity to the most accessible alternative attractors, b) the topography of the current boundary basin, c) the system's current momentum and energy state, and d) the prospective trajectory and regime that a transition to a given alternative attractor would induce. These four components collectively determine the SAB potential, and thus the likelihood and qualitative characteristics of an imminent regime shift.

To assess SAB, what system has, what system does, and what system could become are the critical questions.  For such an assessment, a SAB-informed typology would be the first step. Therefore, the four SAB components lead to types based on nine interconnected system variables: (1) micro-macro dynamic type; (2) state of information processing and memory capacity; (3) degree of teleonomic coherence across levels and panarchical organization; (4) degree of agent heterogeneity; (5) type and intensity of emergence; (6) functional and computational efficiency rate; (7) initial condition and presence of path dependency; (8) manifestation of critical slowing down indicators and bifurcation proximity signals and (9) the existing geometric attractor landscape. 

This SAB-informed typology is phenomenologic-mechanistic in nature, which helps to learn about the structural and dynamical signatures of critical transitions and the quality of the new becoming, offering a unified language for understanding how complex adaptive systems of any kind approach their adjacent becoming and what determines whether they persist, transform, or collapse. This framework remains theoretical with operationalization challenges; future work must advance toward measurable proxies for the nine categories to quantify SAB of real-world systems.

How to cite: Zamanifar, M. and Samaro, N.: Systemic risk in complex systems: understanding the system based on the system’s adjacent becoming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21170, https://doi.org/10.5194/egusphere-egu26-21170, 2026.

Atmospheric Rossby waves exert a strong control on the emerging pattern of summer heat and winter cold over the Arabian Peninsula, yet their regional impacts remain poorly quantified. This study uses 25 years (2000–2024) of reanalysis and observational data to assess how upper-tropospheric Rossby wave activity modulates seasonal 2 m temperature extremes over Saudi Arabia and how these responses are embedded in large-scale teleconnections linked to ENSO and Indo-Pacific variability. The analysis focuses on the evolution of warm-core structures in summer, the spatial spread of winter cold anomalies, and two recent extreme years, 2017 and 2023, that reveal the sensitivity of the Peninsula to Rossby wave regime shifts.

Results show a progressive amplification and spatial expansion of August near-surface temperatures across Saudi Arabia, with the 37–38 °C isotherms migrating northward and westward after 2010 to form a quasi-continuous warm core spanning the eastern lowlands, Rub al Khali, and central plateau. The fraction of land exceeding 39 °C in August increased from isolated spots in the early 2000s to over 20% after 2015, signifying a step-like intensification of summertime heat. Composite analyses indicate that these hot cores coincide with upper-level anticyclonic ridges and subsidence maxima, consistent with Rossby wave–induced adiabatic warming and suppressed convection.

Within this long-term warming context, 2017 stands out as a dynamical outlier. Amplified and breaking Rossby waves over the Middle East generated a quasi-stationary ridge over the Peninsula, producing exceptionally broad August heat with mean temperatures above 38 °C across central and northeastern regions. In winter 2017, enhanced wave activity drove deep trough intrusions and widespread sub‑16 °C anomalies, yielding an unusual combination of extreme summer heat and pronounced winter cooling within one year. A renewed Rossby forcing episode in 2023 accompanied one of the hottest summers on record, when the southeastern warm core intensified and spread northwestward while winter again featured strong meridional temperature gradients and broad cold coverage.

Wave activity flux diagnostics and teleconnection analyses reveal that both 2017 and 2023 extremes arose from Indo-Pacific–Eurasian Rossby wave trains. In 2017, La Niña–like conditions and a positive Indian Ocean Dipole excited a Eurasian wave train that channelled energy along the subtropical jet, reinforcing anticyclonic ridging in summer and deep winter troughs. In 2023, an ENSO phase transition under neutral IOD conditions triggered renewed Rossby dispersion from the tropical western Pacific into the Asian jet, again focusing anomalous ridging and subsidence over the Peninsula.

These results suggest that modest upstream anomalies now yield amplified regional thermal responses, implying increased dynamical gain due to background warming and altered land–atmosphere coupling. The findings point to a Rossby wave–dominated regime shift since 2017, wherein upper-level wave geometry and teleconnections increasingly control the extent of summer heat and winter cold. Saudi Arabia thus emerges as a dynamically sensitive node in the global Rossby waveguide system.

How to cite: Albert, J., Navaz, M. F., Saleem, A. A., Chaitanya Akurathi, V. S., Lateef, S., Shafeeque, M., and Alhems, L.: Seasonal Rossby Wave Dynamics Driving Winter and Summer Temperature Extremes in the Arabian Peninsula, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2241, https://doi.org/10.5194/egusphere-egu26-2241, 2026.

Atmospheric blocking, conventionally studied as a quasi-stationary phenomenon, often exhibits zonal movement under the influence of factors like the background flow and retrograding Rossby waves. However, the impact of this mobility on cold extremes remains under-investigated. This study classifies atmospheric blocking events during the winters of 1979/80–2020/21 into westward-moving, eastward-moving, and quasi-stationary types to analyze their distinct impacts on surface air temperature by region.

Our results show that westward-moving blocks occurred most frequently over the western North Pacific, whereas quasi-stationary blocks were dominant in most other regions. In terms of duration, westward-moving blocks consistently persisted longer than the other types across all regions. Notably, these long-lasting, westward-moving events were closely associated with inducing strong cold waves in downstream areas during their dissipation phase. This is attributed to the enhanced advection of cold Arctic air by blocking-induced low-level wind anomalies. These characteristics were successfully reproduced in CESM1-LENS simulations, suggesting that a better understanding of blocking mobility can contribute to improving extreme cold surge prediction.

How to cite: Kim, S.-H. and Kim, B.-M.: Characterizing Blocking Mobility and Its Role in Northern Hemisphere Cold Extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2300, https://doi.org/10.5194/egusphere-egu26-2300, 2026.

Pressure vertical velocity (ω = Dp/Dt) is commonly approximated from the geometric vertical velocity (w = Dz/Dt) as ω ≈ -ρgw, which invokes the hydrostatic relation ∂p/∂z ≈ -ρg together with the additional assumption that local pressure tendency and horizontal pressure advection term are negligible at planetary and synoptic scales. Using global nonhydrostatic simulations with the ICON model, we show that the horizontal pressure advection term can be relatively large compared with the vertical pressure advection term at planetary-to-synoptic scales in regions of strong jets such as in the winter stratosphere, contradicting the conventional assumption ω ≈ -ρgw. We further show that the horizontal and vertical pressure advection terms exhibit a predominantly out-of-phase structure and that their comparable amplitudes lead to substantial cancellation. As a consequence, ω can be suppressed or amplified at large scales relative to the -ρgw diagnostic, despite the validity of the hydrostatic balance. Scale diagnostics indicate that the large-scale enhancement of the horizontal pressure advection arises from interactions between the mean flow and eddies. From an energetic perspective, these advection terms correspond to compensating contributions of pressure-gradient work in different directions. Consequently, ω behaves more like the net pressure gradient work, rather than a direct measure of vertical motion.

How to cite: Chen, J., Vasylkevych, S., Žagar, N., and Hohenegger, C.: On the interpretation of the pressure vertical velocity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2413, https://doi.org/10.5194/egusphere-egu26-2413, 2026.

How to cite: Barnes, M. A., Reeder, M. J., and Ndarana, T.: The evolution of cyclonic and anticyclonic Rossby wave breaking morphologies and their importance in extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2487, https://doi.org/10.5194/egusphere-egu26-2487, 2026.

In December 14 to 16, 2023, East Asia experienced a severe cold wave, with record-breaking low temperatures and consequently severe natural disasters over broad areas. Results suggest that anomalous downward potential vorticity circulation (PVC) forcing across the tropopause played a critical role in triggering and amplifying this event. The results indicated that in early December, a strong positive potential vorticity substance (PVS) reservoir accompanied by an anomalous downward PVC persisted in the lower stratosphere over Siberia, whereas two distinct upper tropospheric fronts (UTFs) were located over East Asia. By December 12, as the downward PVC penetrated the tropopause into the troposphere, enhancing the northern UTF and triggering a perturbation trough at its western end. This northern trough propagated faster eastward along the UTF than its southern counterpart, and its PVS was intensified by the descending northerly flow. As the two UTFs merged on the eastern Tibetan Plateau, the northern trough was phase-locked with the southern trough, forming a deep East Asian trough with a well-developed PVS. The prominent cold descending northerly flow dominated the troposphere behind the trough, generating extremely high surface pressure and abnormal cold temperature advection below. Consequently, a severe cold wave swept over East Asia. This study improves upon previous work by directly linking tropopause PVC forcing to trough phase-locking, a previously overlooked pathway for cold wave amplification.

How to cite: Li, Y., Wu, G., Liu, Y., He, B., Mao, J., and Sheng, C.: The Influence of Tropopause Potential Vorticity Circulation Forcing on the Development of the East Asian Cold Wave in December 2023, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2492, https://doi.org/10.5194/egusphere-egu26-2492, 2026.

Forecast busts over Europe—periods of abnormally low predictive skill—are often associated with extreme weather events and linked to misrepresented upper-level dynamics, including latent heating from mesoscale convective systems (MCSs), Rossby wave breaking, and warm conveyor belt (WCB) outflow. This study investigates how explicitly resolving mesoscale processes affects the simulation of these key mechanisms in global ICON ensemble forecasts at grid spacings ranging from 40 km down to 2.5 km. As a test case, we analyze a forecast bust from ECMWF’s Integrated Forecasting System (IFS) related to the development of Storm Dennis (February 2020), the second-most intense North Atlantic winter storm of the past 150 years, and compare ICON with IFS.

We find a systematic improvement in forecast skill with finer grid spacing. Coarse-resolution simulations reproduce the forecast bust and fail to capture the correct trough–ridge pattern, while convection-permitting simulations more accurately represent upper-level potential vorticity anomalies, WCB structure, and cyclone development.

Our analysis reveals a multi-stage chain of error growth arising from several interacting factors. Large initial-condition uncertainties over the North Pacific provide a background sensitivity, but the strongest early error growth occurs over the central United States, coinciding with a period of deep convection from MCSs. Convection-permitting simulations produce stronger and more coherent MCSs, leading to enhanced negative PV injection near 250 hPa and substantially reduced Rossby wave activity errors. In contrast, coarser-resolution simulations exhibit weaker or misplaced MCSs, resulting in larger errors in the upper-tropospheric flow. These midlatitude convective differences subsequently modulate the intensity and orientation of downstream WCBs over the North Atlantic. The WCB then amplifies the pre-existing errors, linking the central-U.S. convective phase to the eventual European forecast bust.

Overall, our results demonstrate that mesoscale processes over North America—especially MCS-driven PV perturbations—play a key role in setting the predictability of the North Atlantic flow regime during Storm Dennis. Convection-permitting global simulations improve the representation of these processes and offer a physically consistent pathway toward reducing forecast busts in high-impact weather situations. To assess the robustness and generality of these findings, additional case studies are currently being analyzed.

How to cite: Rixen, M., Prein, A., Pothapakula, P., Sprenger, M., and Zeman, C.: Resolution Sensitivity of Rossby Wave Breaking and Warm Conveyor Belts in Global ICON Simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2689, https://doi.org/10.5194/egusphere-egu26-2689, 2026.

The subtropical jet dominates over specific longitudinal sectors during both winters. The major source of this zonal asymmetry is localized tropical convection. In particular, during austral winter, the wide and powerful convection over the Asian monsoon region and Maritime Continent drives a subtropical jet over the Indian Ocean, Australia and the west and central Pacific. Further downstream in the east Pacific the jet tilts poleward, gradually shifting towards eddy-driven jet characteristics, while in the Atlantic sector only an eddy-driven jet prevails.

In this study, we show that the upper tropospheric circulation pattern over the whole Southern Hemisphere during winter is similar to that in an idealized model simulation, where the only zonal asymmetry source is localized tropical convection in the summer hemisphere. A similar momentum budget is found for the observations and model simulation. The first-order momentum balance is the geostrophic balance associated with a stationary Rossby wave driven by tropical convection. The upstream part of the subtropical jet (the Indian Ocean jet) is associated with a high equatorward of it, and the downstream part (the Pacific jet) is associated with a low poleward of it. This demonstrates that the subtropical jet zonally asymmetric component is a manifestation of a stationary Rossby wave in the upper troposphere. The second-order momentum balance is associated with approximate absolute angular momentum conservation in the localized Hadley cell, as is the dominant balance in zonally symmetric models. The third-order momentum balance is between meridional advection of absolute angular momentum and zonal momentum advection. Transient eddy momentum fluxes are negligible in the maintenance of the subtropical jet zonal structure.

How to cite: Lachmy, O. and White, I.: The maintenance of a zonally asymmetric subtropical jet, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2944, https://doi.org/10.5194/egusphere-egu26-2944, 2026.

Arctic amplification has been linked to significant changes in mid-latitude weather patterns, including the increasing frequency and persistence of extreme weather events. This study investigates the influence of Barents-Kara (BK) sea-ice variability on wintertime Ural blocking and its role in Eurasian cold temperature anomalies. Using ERA5 reanalysis data, we analyse Ural blocking frequency and persistence based on two commonly used blocking indices (an absolute geopotential height reversal index and an anomaly-based index method). The relationships between BK sea ice, Ural blocking, and Eurasian surface temperature are examined within a causal network framework, accounting for ENSO as a potential common driver by including it as a covariate and by stratifying the analysis by ENSO phase. We find that Ural blocking events occur more frequently and persist longer during winters with reduced BK sea ice. Although, results are sensitive to blocking index but remain qualitatively consistent and robust across indices. Composite analyses show a characteristic warm-Arctic/cold-Eurasia temperature pattern during Ural blocking events, which is amplified during winters with low BK sea ice and La Niña conditions. To assess whether Ural blocking is influenced by specific Arctic background conditions, we further classify winters into Deep and Shallow Arctic warming regimes over the Barents-Kara region. We find that Ural blocking occurs more frequently and is more persistent under Deep Arctic warming states, leading to a stronger cold-Eurasia temperature response compared to Shallow warming regimes. By statistically quantifying the relationships between Arctic sea ice, Ural blocking, and Eurasian temperature variability, this work advances the understanding of Arctic-midlatitude interactions.

Keywords: Arctic Amplification, Ural blocking, Barents-Kara sea ice, ENSO, Blocking indices, Blocking frequency and persistence, Eurasian cold winters.

How to cite: Agyemang-Oko, E. and Kretschmer, M.: Quantifying the influence of Barents-Kara sea ice loss on Ural blocking, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3038, https://doi.org/10.5194/egusphere-egu26-3038, 2026.

The occurrence of extreme weather has recently been associated with the mechanism of Rossby wave resonance along a circumglobal jet. Resonance is possible to the extent that the jet acts as a zonal waveguide. Recently, a method was introduced to diagnose this mechanism in the framework of the linear barotropic model through numerically solving a judiciously designed model configuration. In that method, any wave activity leaving the jet region is dissipated in sponges and, hence, discarded from further consideration.

The present work goes a step further by explicitly accounting for polar and singular waveguides, which occur through wave reflection off the pole or off a critical level. In the absence of damping, these reflective boundaries generate additional resonant cavities and allow higher meridional modes to participate in the resonance. These higher meridional modes imply resonance at multiple zonal wavenumbers, in stark contrast with the earlier results. However, when a small amount of damping is included, any wave activity is strongly dissipated before these reflecting surfaces are encountered. Consequently, the impact of the polar and the singular waveguides vanishes, and the resonant behavior reduces to that from the original diagnostic. It is concluded that the impact of reflecting surfaces beyond the jet region proper is unlikely to be of practical importance for diagnosing the Rossby wave resonance along a circumglobal midlatitude jet.

How to cite: Hempel, T. and Wirth, V.: The importance of polar and singular waveguides for the occurrence of Rossby wave resonance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3695, https://doi.org/10.5194/egusphere-egu26-3695, 2026.

Severe heatwaves have become increasingly frequent over the Indian subcontinent in recent decades. This study found that the increase in extreme heatwaves is related to a significant decadal change in surface temperatures over the Indian subcontinent, and revealed that the increase in convective activity in the Philippine Sea plays a crucial role in this decadal change in surface temperature. Specifically, the surface temperature over the Indian subcontinent in spring has increased significantly by approximately 0.64 ◦C in recent years (1998–2022: post-1998) compared to the past (1959–1997: pre-1998), leading to more intense and frequent heatwaves, particularly in March and April. The difference in atmospheric changes between these two periods shows that the enhancement of convective activity over the Philippine Sea drives an anomalous elongated anticyclonic circulation over the Indian subcontinent. This circulation pattern, marked by clearer skies and increased incident solar radiation, significantly contributes to the heat extremes in the Indian subcontinent. Additionally, stationary wave model experiments demonstrate that local diabatic heating over the Philippine Sea is significantly linked to robust spring Indian heatwaves through the Matsuno–Gill response.

How to cite: Ok, J., Song, E.-J., Yang, S., Kim, B.-M., and Kim, K.-Y.: Interdecadal changes and the role of Philippine Sea convection in the intensification of Indian spring heatwaves, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4663, https://doi.org/10.5194/egusphere-egu26-4663, 2026.

Atmospheric Rossby waves are a fundamental component of large-scale circulation and low-frequency atmospheric variability. In classical theory, quasi-stationary planetary waves are characterized by infinite periods and are typically regarded as slowly varying background disturbances, which limits their ability to explain the widespread intraseasonal oscillations (ISOs) observed in the atmosphere. Given that ISOs share comparable spatial and temporal scales with planetary waves, a nonstationary Rossby waves framework provides a promising theoretical basis for interpreting their propagation characteristics.

In this study, we develop a theoretical framework for nonstationary horizontally propagating Rossby waves embedded in a prescribed background flow. We systematically derive the necessary conditions for the existence of three propagating solution branches, expressed equivalently in terms of the supremum and infimum of phase speed and wave period. Both the phase-speed and period supremum and infimum are determined by the background wind field, while the supremum and infimum of the period additionally depend on the zonal wavenumber. Two distinct regimes of admissible phase-speed and period ranges emerge, reflecting different background-flow configurations.

By combining these theoretical constraints with atmospheric reanalysis data, we diagnose the climatological supremum and infimum of nonstationary Rossby wave speriods in both the upper and lower troposphere over key tropical regions. The results reveal pronounced seasonal and regional variations in the theoretical period ranges due to differences in background circulation between tropospheric layers. In the upper troposphere, the equatorial Indian–western Pacific region does not support eastward-propagating solutions, whereas in the lower troposphere, eastward-propagating nonstationary waves with intraseasonal periods become possible under monsoonal flow conditions, consistent with monsoon ISO characteristics. During boreal winter and spring, the theoretical period supremum and infimum of lower-tropospheric nonstationary waves over the equatorial Indian–western Pacific exhibit Madden–Julian Oscillation (MJO)-like features. Over the equatorial Atlantic, vertically asymmetric background flows lead to distinct propagation characteristics between the upper and lower troposphere, consistent with observed ISO structures.

This work extends the classical theory of Rossby waves propagation by incorporating nonstationary waves and provides a unified theoretical interpretation linking nonstationary planetary waves to tropical intraseasonal variability.

How to cite: Liu, Y. and Li, J.: The theory and climatological characteristics of nonstationary horizontally Rossby waves, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4697, https://doi.org/10.5194/egusphere-egu26-4697, 2026.