In this session there will be the NP Division Outstanding ECS Award Lecture by Johannes Jakob Lohmann followed by a ceremony in memory of Vincenzo Carbone and delivery of the Lewis Fry Richardson Medal to his family or colleagues.

Cities are intricate multi-scale systems, composed of diverse sub-components such as population, energy, transport, and climate. These components interact on various time scales, from hourly to seasonal to annual and beyond. Effective urban models and digital twins, crucial for urban planning and policy-making, must account for these complex interactions as they govern the growth and functioning of cities, often giving rise to emergent large-scale phenomena. However, our ability to quantitatively describe city behaviour remains limited due to the myriad of processes, scales, and feedbacks involved.
This session invites studies focused on modelling and monitoring the dynamics of multiple sectors and city-biosphere interactions. Topics of interest include, but are not limited to:
• Demography
• Urban transport networks
• Energy consumption
• Anthropogenic emissions and Pollution
• Urban climate
• Urban hydrology
• Urban ecology

Our aim is to elucidate the complex dynamics within urban environments and explore how urban form and function can be optimised to enhance the liveability and well-being of their citizens.

Several subsystems of the Earth have been suggested to possibly react abruptly at critical levels of anthropogenic forcing. Examples of such potential Tipping Elements include the Atlantic Meridional Overturning Circulation, the polar ice sheets, tropical and boreal forests, as well as the tropical monsoon systems. Interactions between the different Tipping Elements may either have stabilizing or destabilizing effects on the other subsystems, potentially leading to cascades of abrupt transitions. The critical forcing levels at which abrupt transitions occur have recently been associated with Tipping Points.

It is paramount to determine the critical forcing levels (and the associated uncertainties) beyond which the systems in question will abruptly change their state, with potentially devastating climatic, ecological, and societal impacts. For this purpose, we need to substantially enhance our understanding of the dynamics of the Tipping Elements and their interactions, on the basis of paleoclimatic evidence, present-day observations, and models spanning the entire hierarchy of complexity. Moreover, to be able to mitigate - or prepare for - potential future transitions, early warning signals have to be identified and monitored in both observations and models.

This multidisciplinary session invites contributions that address Tipping Points in the Earth system from the different perspectives of all relevant disciplines, including

- the mathematical theory of abrupt transitions in (random) dynamical systems,
- paleoclimatic studies of past abrupt transitions,
- data-driven and process-based modelling of past and future transitions,
- methods to anticipate critical transitions from data
- the implications of abrupt transitions for climate sensitivity and response,
- ecological and socioeconomic impacts
- decision theory in the presence of uncertain Tipping Point estimates and uncertain impacts

Climate and land-use changes are reshaping ecosystems by disrupting interactions between vegetation, soils, and abiotic factors. These changes influence ecosystem stability, resource distribution, and resilience to disturbances. Vegetation pattern formation—emerging from plant-environment interactions—plays a key role in regulating water, nutrients, and soil conservation, particularly in vulnerable landscapes such as drylands, wetlands, forests, and rangelands. While some theories suggest vegetation patterns signal impending desertification, others propose they enhance resilience by localizing external stresses. Similarly, landform-soil-vegetation feedbacks contribute to ecosystem stability, influencing carbon capture, soil erosion, and landscape connectivity.

Understanding the origin and role of these patterns in ecosystem resilience against environmental stresses represents a significant endeavor that only multidisciplinary research can achieve. This session invites theoretical, empirical, and modeling studies on vegetation-soil interactions, ecogeomorphology, ecohydrology, and the implications of spatial organization for ecosystem resilience. We aim to bridge theory and observation, fostering collaboration across disciplines to better understand landscape responses to climatic and anthropogenic pressures.

This session aims at bringing together contributions from the growing interface between the Earth science, mathematical, and theoretical physical communities. Our goal is to stimulate the interaction among scientists of these and related disciplines interested in solving environmental and geoscientific challenges. Considering the urgency of the ongoing climate crisis, such challenges refer, for example, to the theoretical understanding of the climate and its subsystems as a highly nonlinear, chaotic system, the improvement of the numerical modelling via theory-informed and data-driven methods, the search for new data analysis methods, and the quantification of different types of impacts of global warming.

Specific topics include: PDEs, numerical methods, extreme events, statistical mechanics, thermodynamics, dynamical systems theory, response theory, tipping points, model reduction techniques, model uncertainty and ensemble design, non-linear waves, stochastic processes, parametrisations, data assimilation, and machine learning. We invite contributions both related to specific applications as well as more speculative and theoretical investigations. We particularly encourage early career researchers to present their interdisciplinary work in this session.

This session resulted from the merging of "NP1.1 Mathematics of Planet Earth", "NP2.1 Complexity, Nonlinearity, and Stochastic Dynamics in the Earth System", "NP5.1 Inverse problems, Predictability, and Uncertainty Quantification in the Earth System using Data Assimilation and its combination with Machine Learning" and "NP7.1 Non-linear Waves and Triggering Effects".

Abstracts are solicited related to the understanding, prediction and impacts of weather, climate and geophysical extremes, from both an applied and theoretical viewpoint.

In this session we propose to group together the traditional geophysical sciences and more mathematical/statistical and impacts-oriented approaches to the study of extremes. We aim to highlight the complementary nature of these viewpoints, with the aim of gaining a deeper understanding of extreme events. This session is a contribution to the EDIPI ITN, XAIDA and CLINT H2020 projects and to the Swedish Centre for Impacts of Climate Extremes. We welcome submissions from both project participants and the broader scientific community.

Potential topics of interest include but are not limited to the following:

· Dynamical systems theory and other theoretical perspectives on extreme events;
· Data-driven approaches to study extreme events and their impacts, incl. machine learning;
· Representation of extreme events in climate models;
· Downscaling of weather and climate extremes;
· How extremes have varied or are likely to vary under climate change;
· Attribution of extreme events;
· Early warning systems and forecasts of extreme events;
· Methodological and interdisciplinary advances for diagnosing impacts of extreme events.

The complexity of hydrological and Earth systems poses significant challenges to their prediction and understanding capabilities. The advent of machine learning (ML) provides powerful tools for modeling these complex systems. However, realizing their full potential in this field is not just about algorithms and data, but requires a cooperative interaction between domain knowledge and data-driven power. This session aims to explore the frontier of this convergence and how it facilitates a deeper process understanding of various aspects of hydrological processes and their interactions with the atmosphere and biosphere across spatial and temporal scales.

We invite researchers working in the fields of explainable AI, physics-informed ML, hybrid Earth system modeling (ESM), and AI for causal and equation discovery in hydrology and Earth system sciences to share their methodologies, findings, and insights. Submissions are welcome on topics including, but not limited to:

- Explainability and transparency in ML/AI modeling of hydrological and Earth systems;
- Process and knowledge integration in ML/AI models;
- Data assimilation and hybrid ESM approaches;
- Causal learning and inference in ML models;
- Data-driven equation discovery in hydrological and Earth systems;
- Data-driven process understanding in hydrological and Earth systems;
- Challenges, limitations, and solutions related to hybrid models and XAI.

Deep Learning has seen accelerated adoption across Hydrology and the broader Earth Sciences. This session highlights the continued integration of deep learning and its many variants into traditional and emerging hydrology-related workflows. We welcome abstracts related to novel theory development, new methodologies, or practical applications of deep learning in hydrological modeling and process understanding. This might include, but is not limited to, the following:
(1) Development of novel deep learning models or modeling workflows.
(2) Probing, exploring and improving our understanding of the (internal) states/representations of deep learning models to improve models and/or gain system insights.
(3) Understanding the reliability of deep learning, e.g., under non-stationarity and climate change.
(4) Modeling human behavior and impacts on the hydrological cycle.
(5) Deep Learning approaches for extreme event analysis, detection, and mitigation.
(6) Natural Language Processing in support of models and/or modeling workflows.
(7) Applications of Large Language Models and Large Multimodal Models (e.g. ChatGPT, Gemini, etc.) in the context of hydrology.
(8) Uncertainty estimation for and with Deep Learning.
(9) Advances towards foundational models in the context of hydrology and Earth Sciences more generally.
(10) Exploration of different training strategies, such as self-supervised learning, unsupervised learning, and reinforcement learning.

In recent years, sea ice has displayed behaviour previously unseen in the satellite record. This fast-changing sea-ice cover calls for adapting and improving our modelling approaches and mathematical techniques to simulate its behaviour and its interaction with the atmosphere and the ocean, both in terms of dynamics and thermodynamics.

Sea ice is governed by a variety of small-scale processes that affect its large-scale evolution. Modelling this nonlinear coupled multidimensional system remains a major challenge, because (1) we still lack the understanding of the physics governing sea-ice dynamics and thermodynamics, (2) observations to conduct model evaluation are scarce and (3) the numerical approximation and the simulation become more difficult and computationally expensive at higher resolution.

Recently, several new modelling approaches have been developed and refined to address these issues. These include but are not limited to new rheologies, discrete element models, advanced subgrid parameterizations, the representation of wave-ice interactions, sophisticated data assimilation schemes, often with the integration of machine learning techniques. Moreover, novel in-situ observations and the growing availability and quality of sea-ice remote-sensing data bring new opportunities for improving sea-ice models.

This session aims to bring together researchers working on the development of sea-ice models, from small to large scales and for a wide range of applications such as idealised experiments, operational predictions, or climate simulations, to discuss current advances and challenges ahead.

The Earth’s climate system exhibits complex, nonlinear interactions across a wide range of spatial and temporal scales. Gaining deeper insights into these dynamics requires reconciling knowledge gained from high-resolution Earth System Models (ESMs), simpler conceptual models, and data-driven methodologies. This session brings together efforts to bridge the gap between simulation and understanding, linking different levels of the model hierarchy with innovative data-driven and theoretical approaches.

Due to the large uncertainties in climate science, building confidence in climate projections by bringing together multiple lines of evidence is vital to facilitate mitigation and adaptation decisions.

This session invites contributions that explore the synergies between physics-based modeling and empirical methodologies to advance the understanding and predictability of atmospheric and oceanic dynamics.

Topics may include, but are not limited to:

-Transfer operators, Koopman mode decomposition, machine learning, Linear Inverse Models (LIMs), and Fourier analysis

-Identifying climate modes, extracting spatiotemporal features, analyzing climate networks, and exploring attractor properties

-Dynamical systems models, Earth System Models of Intermediate Complexity (EMICs), and simplified setups of ESMs

-Process understanding, predictability, future climate scenarios and climate storylines

-Investigation of coupled modes of climate variability (e.g., ENSO, AMV), tipping points, biogeochemical processes, and extreme events

-Nonlinear interactions and emergent phenomena

All steps in estimating future climate impacts from emission scenarios are computationally expensive: running Earth System Models, downscaling and/or bias-correcting the outputs, and running process-based impact models. Altogether, these processes can take months. The latest evolution of reduced complexity climate models, or simple climate models, can project global climate from the latest emissions scenarios for tens of thousands of physical realizations in seconds. Novel methods are being developed to leverage the outputs from simple climate models to carry out risk assessments, and quantify climate impacts beyond the global mean temperature and even climate extremes. Concurrently, the latest advances in machine learning have enabled end-to-end simulation of climate dynamics at a fraction of the computing cost of physically-based systems. Impacts may be spatially resolved, enabling policy-relevant analyses to be carried out based on emissions scenarios which have never been run through fully-coupled Earth-system models, such as Network for Greening the Financial System (NGFS) scenarios. Applications of impact emulation extend to economic and integrated assessment models of climate change. With the rise in application of machine learning for Earth system model emulation and downscaling, this session aims to bring together research on statistical, physical and hybrid emulators with a focus on climate impacts.

Theoretical and model studies show that the non-linear ocean spontaneously generates a strong, multi-scale random intrinsic variability. Equivalently, uncertainties in initial ocean states tend to grow and strongly affect the simulated variability up to multidecadal and basin scales, with or without coupling to the atmosphere. In addition, ocean simulations require both the use of subgrid-scale parameterizations that crudely mimic unresolved processes, and the calibration of the parameters associated with these parameterizations. In this context of multiple uncertainties, oceanographers are increasingly adopting ensemble simulation strategies, probabilistic analysis methods, and developing stochastic parameterizations for modeling and understanding the ocean variability in response to (or in interaction with) the atmospheric evolution.

Presentations are solicited about the conception and analysis of ocean ensemble simulations, the characterization of ocean model uncertainties, and the development of parameterizations for ocean models. The session will also cover the dynamics and structure of intrinsic ocean variability, its relationship with atmospheric variability, and the use of adequate concepts (based on e.g. dynamical systems, information, or other theories) for the investigation of oceanic variability. We welcome as well studies about the propagation of intrinsic ocean variability towards other components of the climate system, about its implications regarding ocean predictability, operational forecasts, detection and attribution of climate signals, climate simulations and projections.

The statistical characterization and modelling of precipitation are crucial in a variety of applications, such as flood forecasting, water resource assessments, evaluation of climate change impacts, infrastructure design, and hydrological modelling. This session aims to gather contributions on research, advanced applications, and future needs in the understanding and modelling of precipitation, including its variability at different scales and its sources of uncertainty.

Contributions focusing on one or more of the following issues are particularly welcome:
- Process conceptualization and approaches to modelling precipitation at different spatial and temporal scales, including model parameter identification, calibration and regionalisation, and sensitivity analyses to parameterization and scales of process representation.
- Novel studies aimed at the assessment and representation of different sources of uncertainty of precipitation, including natural climate variability and changes caused by global warming.
- Uncertainty and variability in spatially and temporally heterogeneous multi-source ground-based, remotely sensed, and model-derived precipitation products.
- Estimation of precipitation variability and uncertainty at ungauged sites.
- Modelling, forecasting and nowcasting approaches based on ensemble simulations for synthetic representation of precipitation variability and uncertainty.
- Scaling and scale invariance properties of precipitation fields in space and/or in time.
- Dynamical and statistical downscaling approaches to generate precipitation at fine spatial and temporal scales from coarse-scale information from meteorological and climate models.

Geophysical and anthropogenic systems exhibit extreme variability over a wide range of spatio-temporal scales due to non-linear interactions between various processes. To capture these interactions, as well as the underlying non-trivial symmetries, information transfer between scales, causal effects and driving dynamics, the session focuses on the most recent theoretical, methodological and applied advances. This includes, but is not limited to, scaling, (multi-) fractals, complex networks, tipping points, predictability and uncertainty analysis, data mining, information theory, new computational techniques and systems intelligence.
Join an exciting session exploring and discussing promising avenues to shed light onto fundamental theoretical aspects in order to build innovative methodologies to address the real-world challenges facing our planet, in particular to develop scientifically sound responses to mitigate risks and build resilience.

The ocean surface layer mediates the transfer of matter, energy, momentum, heat, and trace gases between the ocean, atmosphere and sea ice, and thus plays a central role in the dynamics of the climate system. This session will focus on the ocean surface layer globally, from the coasts – including the marginal sea ice zone – to the pelagic ocean, and its interactions with the overlaying low atmosphere. We will discuss in particular recent advances in the understanding of (sub-)mesoscale and internal-wave dynamics, ocean surface-interior interactions, ice-ocean interactions, particle and tracer dispersion as well as boundary-layer turbulence and surface-wave effects. We also encourage studies focusing on the coupling of physical, biological, and biogeochemical processes. Of special interest will be contributions describing the impact of ocean surface-layer processes on air-sea fluxes and atmosphere-ocean feedbacks. These include the parameterization of air-sea interactions, the impact of tropical cyclones, and the role of extreme events. Our session welcomes observational (from in-situ to remote sensing), theoretical and numerical investigations focusing on the ocean surface layer and its interactions with the atmosphere and sea ice, regardless of the temporal and spatial scales considered.

Rainfall is a “collective” phenomenon emerging from numerous drops. It reaches the ground surface with varying intensity, drop size and velocity distribution. Understanding the relation between the physics of individual drops and that of a population of drops remains an open challenge, both scientifically and for practical implications. This remains true also for solid precipitation. Hence, it is much needed to better understand small scale space-time precipitation variability, which is a key driving force of the hydrological response, especially in highly heterogeneous areas (mountains, cities). This hydrological response at the catchment scale is the result of the interplay between the space-time variability of precipitation, the catchment geomorphological / pedological / ecological characteristics and antecedent hydrological conditions. Similarly to the small scales, accurate measurement and prediction of the spate-time distribution of precipitation at hydrologically relevant scales still remains an open challenge.

This session brings together scientists and practitioners who aim to measure and understand precipitation variability from drop scale to catchment scale as well as its hydrological consequences. Contributions addressing one or several of the following topics are encouraged:
- Novel techniques for measuring liquid and solid precipitation variability at hydrologically relevant space and time scales (from drop to catchment scale), from in-situ measurements to remote sensing techniques, and from ground-based devices to spaceborne platforms. Innovative comparison metrics are welcomed;
- Drop (or particle) size distributions, small scale variability of precipitation, and their consequences for precipitation rate retrieval algorithms for radars, commercial microwave links and other remote sensors;
- Novel modelling or characterization tools of precipitation variability from drop scale to catchment scale from various approaches (e.g. scaling, (multi-)fractal, statistic, deterministic, numerical modelling);
- Novel approaches to better identify, understand and simulate the dominant microphysical processes at work in liquid and solid precipitation.
- Applications of measured and/or modelled precipitation fields in catchment hydrological models for the purpose of process understanding or predicting hydrological response.
- Rainfall simulators developed to investigate the accuracy of disdrometer measurements in assessing drop size and fall velocity.

Transport of sediments in geophysical flows occurs in mountainous, fluvial, estuarine, coastal, aeolian and other natural or man-made environments on Earth, while also shapes the surface of planets such as Mars, Titan, and Venus. Understanding the motion of sediments is still one of the most fundamental problems in hydrological and geophysical sciences. Such processes can vary across a wide range of scales - from the particle to the landscape - which can directly impact both the form (geomorphology) and, on Earth, the function (ecology and biology) of natural systems and the built infrastructure surrounding them. In particular, feedback between fluid and sediment transport as well as particle interactions including size sorting are a key processes in surface dynamics, finding a range of important applications, from hydraulic engineering and natural hazard mitigation to landscape evolution, geomorphology and river ecology.

A) particle-scale interactions and transport processes:
- mechanics of entrainment and disentrainment (fluvial and aeolian flows)
- momentum (turbulent impulses) and energy transfer between turbulent flows and particles
- upscaling and averaging techniques for stochastic transport processes
- granular flows in dry and submerged environments
- grain shape effects in granular flow and sediment transport
- interaction among grain sizes in poorly sorted mixtures, including particle segregation
- discrete element modelling of transport processes and upscaling into continuum frameworks
B) reach-scale sediment transport and geomorphic processes
- links between flow, particle transport, bedforms and stratigraphy
- derivation and solution of equations for multiphase flows (inc. fluvial and aeolian flows)
- shallow water hydro-sediment-morphodynamic processes
- highly unsteady and complex water-sediment or granular flows
- flash floods, debris flows and landslides due to extreme rainfall
C) large-scale landscape evolution, geohazards, and engineering applications
- natural and built dam failures and compound disasters
- coastal processes, e.g., long-shore and cross-shore sediment transport and the evolution of beach profile/shoreline
- reservoir operation schemes and corresponding fluvial processes
- design of hydraulic structures such as fish passages, dam spillways, also considering the impact of sediment
- dredging, maintenance and regulation for large rivers and navigational waterways

Understanding plasma energization and energy transport is a grand challenge of space plasma physics, and due to its vicinity, Geospace provides an excellent laboratory to investigate them. Strong plasma energization and energy transport occur at boundaries and boundary layers such as the foreshock, the bow shock, the magnetosheath, the magnetopause, the magnetotail current sheet, and the transition region. Fundamental plasma processes such as shock formation, magnetic reconnection, turbulence, wave-particle interactions, plasma jet braking, field-aligned currents generation and their combinations initiate and govern plasma energization and energy transport.
ESA/Cluster and NASA/MMS four-point constellations, as well as the large-scale multipoint mission NASA/THEMIS, have greatly improved our understanding of these processes at individual scales compared to earlier single-point measurements. However, such missions, as well as theory and numerical simulations, also revealed that these processes operate across multiple scales ranging from the large fluid to the smaller kinetic scales, implying that scale coupling is critical. Simultaneous in situ measurements at both large, fluid and small, kinetic scales are required to resolve scale coupling and ultimately fully understand plasma energization and energy transport processes. Such measurements are currently not yet available.
Building on previous single-scale missions, multiscale missions such as HelioSwarm and mission concepts such as MagCon and Plasma Observatory represent the next generation of space plasma physics investigations. Coordination of all of these assets and ideas is also part of a drive towards a new International Solar Terrestrial Physics program (ISTPNext), to focus on the system of systems that is heliophysics.
This session invites submissions on the topic of scale coupling in fundamental plasma processes, covering in situ observations, theory and simulations, multipoint data analysis methods and instrumentation. Submissions on coordination with ground based observations as well as on remote solar and astrophysical observations are also encouraged.

Ice sheets play an active role in the climate system by amplifying, pacing, and potentially driving global climate change over a wide range of time scales. The impact of interactions between ice sheets and climate include changes in atmospheric and ocean temperatures and circulation, global biogeochemical cycles, the global hydrological cycle, vegetation, sea level, and land-surface albedo, which in turn cause additional feedbacks in the climate system. This session will present data from climate proxies and direct measurements and modelling results that examine ice sheet interactions with other components of the climate system over several time scales, ranging from millennial to centennial and even decadal timescales to investigate climate variability. Among other topics, issues to be addressed in this session include ice sheet-climate interactions from glacial-interglacial cycles, the role of ice sheets in Cenozoic global cooling and the mid-Pleistocene transition, reconstructions of past ice sheets and sea level during warmer and colder periods than pre-industrial times, the current and future evolution of the ice sheets, and the role of ice sheets in abrupt climate change.

Over the last decade, a flurry of machine learning methods has led to novel insights throughout geophysics. As wide as the applications are the data types processed, including environmental parameters, GNSS, InSAR, infrasound, and seismic data, but also downstream structured data products such as 3D data cubes, earthquake catalogs, seismic velocity changes. Countless methods have been proposed and successfully applied, ranging from traditional techniques to recent deep learning models. At the same time, we are increasingly seeing the adoption of machine learning techniques in the wider geophysics community, driven by continuously growing data archives, accessible codes, and software. Yet, the landscape of available methods and data types is difficult to navigate, even for experienced researchers.

In this session, we want to bring together machine learning researchers and practitioners throughout the domains of geophysics. We aim to identify common challenges connecting different tasks and data types and formats, and outline best practices for the development and use of machine learning. We also want to discuss how recent trends in machine learning, such as foundation models, the shift to multimodality, or physics informed models may impact geophysical research. We welcome contributions from all fields of geophysics, covering a wide range of data types and machine learning techniques. We also encourage contributions for machine learning adjacent tasks, such as big-data management, data visualization, or software development in the field of machine learning.

In recent years, technologies based on Artificial Intelligence (AI), such as image processing, smart sensors, and intelligent inversion, have garnered significant attention from researchers in the geosciences community. These technologies offer the promise of transitioning geosciences from qualitative to quantitative analysis, unlocking new insights and capabilities previously thought unattainable.
One of the key reasons for the growing popularity of AI in geosciences is its unparalleled ability to efficiently analyze vast datasets within remarkably short timeframes. This capability empowers scientists and researchers to tackle some of the most intricate and challenging issues in fields like Geophysics, Seismology, Hydrology, Planetary Science, Remote Sensing, and Disaster Risk Reduction.
As we stand on the cusp of a new era in geosciences, the integration of artificial intelligence promises to deliver more accurate estimations, efficient predictions, and innovative solutions. By leveraging algorithms and machine learning, AI empowers geoscientists to uncover intricate patterns and relationships within complex data sources, ultimately advancing our understanding of the Earth's dynamic systems. In essence, artificial intelligence has become an indispensable tool in the pursuit of quantitative precision and deeper insights in the fascinating world of geosciences.
For this reason, aim of this session is to explore new advances and approaches of AI in Geosciences.

Recent Earth System Sciences (ESS) datasets, such as those resulting from high-resolution numerical modelling, have increased both in terms of precision and size. These datasets are central to the advancement of ESS for the benefit of all stakeholders, and public policymaking on climate change. Extracting the full value from these datasets requires novel approaches to access, process, and share data. It is apparent that datasets produced by state-of-the-art applications are becoming so large that even current high-capacity data infrastructures are incapable of storing, let alone ensuring their usability. With future investment in hardware being limited, a viable way forward is to explore the possibilities of data compression and new data space implementation.

Data compression has gained interest for making data more manageable, speeding up transfer times, and reducing resource needs without affecting the quality of scientific analyses. Reproducing recent ML and forecasting results has become essential for developing new methods in operational settings. At the same time, replicability is a major concern for ESS and downstream applications and the necessary data accuracy needs further investigation. Research on data reduction and prediction interpretability helps improve understanding of data relationships and prediction stability.

In addition, new data spaces are being developed in Europe, such as the Copernicus Data Space Ecosystem and Green Deal Data Space, as well as multiple national data spaces. These provide access to data, through streamlined access, cloud processing and online visualization generating actionable knowledge enabling more effective decision-making. Analysis ready data can easily be accessed via API transforming data access and processing scalability. Developers and users will share opportunities and challenges of designing and using data spaces for research and industry.

This session connects developers and users of ESS big data, discussing how to facilitate the sharing, integration, and compression of these datasets, focusing on:
1) Approaches and techniques to enhance shareability of high-volume ESS datasets: data compression, novel data space implementation and evolution.
2) The effect of reduced data on the quality of scientific analyses.
3) Ongoing efforts to build data spaces and connect with existing initiatives on data sharing and processing, and examples of innovative services that can be built upon data spaces.

Performing research in Earth System Science is increasingly challenged by the escalating volumes and complexity of data, requiring sophisticated workflow methodologies for efficient processing and data reuse. The complexity of computational systems, such as distributed and high-performance heterogeneous computing environments, further increases the need for advanced orchestration capabilities to perform and reproduce simulations effectively. On the same line, the emergence and integration of data-driven models, next to the traditional compute-driven ones, introduces additional challenges in terms of workflow management. This session delves into the latest advances in workflow concepts and techniques essential to address these challenges taking into account the different aspects linked with High-Performance Computing (HPC), Data Processing and Analytics, and Artificial Intelligence (AI).

In the session, we will explore the importance of the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles and provenance in ensuring data accessibility, transparency, and trustworthiness. We will also address the balance between reproducibility and security, addressing potential workflow vulnerabilities while preserving research integrity.

Attention will be given to workflows in federated infrastructures and their role in scalable data analysis. We will discuss cutting-edge techniques for modeling and data analysis, highlighting how these workflows can manage otherwise unmanageable data volumes and complexities, as well as best practices and progress from various initiatives and challenging use cases (e.g., Digital Twins of the Earth and the Ocean).

We will gain insights into FAIR Digital Objects, (meta)data standards, linked-data approaches, virtual research environments, and Open Science principles. The aim is to improve data management practices in a data-intensive world.
On these topics, we invite contributions from researchers illustrating their approach to scalable workflows as well as data and computational experts presenting current approaches offered and developed by IT infrastructure providers enabling cutting edge research in Earth System Science.

One of the big challenges in Earth system science consists in providing reliable climate predictions on sub-seasonal, seasonal, decadal and longer timescales. The resulting data have the potential to be translated into climate information leading to a better assessment of global and regional climate-related risks.
The main goals of the session is (i) to identify gaps in current climate prediction methods and (ii) to report and evaluate the latest progress in climate forecasting on subseasonal-to-decadal and longer timescales. This will include presentations and discussions of developments in the predictions for the different time horizons from dynamical ensemble and statistical/empirical forecast systems, as well as the aspects required for their application: forecast quality assessment, multi-model combination, bias adjustment, downscaling, exploration of artificial-intelligence methods, etc.
Following the new WCRP strategic plan for 2019-2029, prediction enhancements are solicited from contributions embracing climate forecasting from an Earth system science perspective. This includes the study of coupled processes between atmosphere, land, ocean, and sea-ice components, as well as the impacts of coupling and feedbacks in physical, hydrological, chemical, biological, and human dimensions. Contributions are also sought on initialization methods that optimally use observations from different Earth system components, on assessing and mitigating the impacts of model errors on skill, and on ensemble methods.
We also encourage contributions on the use of climate predictions for climate impact assessment, demonstrations of end-user value for climate risk applications and climate-change adaptation and the development of early warning systems.
A special focus will be put on the use of operational climate predictions (C3S, NMME, S2S), results from the CMIP5-CMIP6 decadal prediction experiments, and climate-prediction research and application projects.
An increasingly important aspect for climate forecast's applications is the use of most appropriate downscaling methods, based on dynamical, statistical, artificial-intelligence approaches or their combination, that are needed to generate time series and fields with an appropriate spatial or temporal resolution. This is extensively considered in the session, which therefore brings together scientists from all geoscientific disciplines working on the prediction and application problems.

This session covers climate predictions from seasonal to multi-decadal timescales and their applications. Continuing to improve such predictions is of major importance to society. The session embraces advances in our understanding of the origins of seasonal to decadal predictability and of the limitations of such predictions. This includes advances in improving forecast skill and reliability and making the most of this information by developing and evaluating new applications and climate services.
The session welcomes contributions from dynamical models, machine-learning or other statistical methods and hybrid approaches. It will investigate predictions of various climate phenomena, including extremes, from global to regional scales, and from seasonal to multi-decadal timescales (including seamless predictions). Physical processes and sources relevant to long-term predictability (e.g. ocean, cryosphere, or land) as well as predicting large-scale atmospheric circulation anomalies associated with teleconnections will be discussed. Analysis of predictions in a multi-model framework, and ensemble forecast initialization and generation will be another focus of the session. We are also interested in approaches addressing initialization shocks and drifts. The session welcomes work on innovative methods of quality assessment and verification of climate predictions. We also invite contributions on the use of seasonal-to-decadal predictions for risk assessment, adaptation and further applications.

This session explores advancements in understanding and forecasting the weather, focusing on severe weather phenomena such as moist convection and Mei-yu frontal systems. It integrates AI methods, numerical modelling with particular attention to moist-convective models with intermediate complexity, e.g. Aeolus 2.0 & mcTRSW models, and observational techniques to enhance forecasting accuracy. Key topics include AI-based weather forecasting, ensemble prediction, mesoscale models, AI-driven nowcasting, post-processing, blending, verification and remote sensing technologies. The session also delves into the dynamics of moist convection, cloud formation, precipitation patterns, and their relationship with extreme weather and climate change. A segment on Mei-yu frontal systems highlights field experiments, cloud microphysics, and model improvements for better precipitation forecasts. The session fosters interdisciplinary discussions on breakthroughs and challenges in weather science.

Modelling past climate states, and the transient evolution of Earth’s climate remains challenging. Time periods such as the Paleocene, Eocene, Pliocene, the Last Interglacial, the Last Glacial Maximum or the mid-Holocene span across a vast range of climate conditions. At times, these lie far outside the bounds of the historical period that most models are designed and tuned to reproduce, providing valuable additional constraints on model sensitivities. However, our ability to predict future climate conditions and potential pathways to them is dependent on our models' abilities to simulate a realistic range of climate variability as it occurred in Earth’s history. Thus, our geologic past is ideally suited to test and evaluate models against data, so they may be better able to simulate the present and make more reliable future climate projections.

We invite contributions on palaeoclimate-specific model development, tuning, simulations, and model-data comparison studies. Simulations may be targeted to address specific questions or follow specified protocols (as in the Paleoclimate Modelling Intercomparison Project – PMIP or the Deep Time Model Intercomparison Project – DeepMIP). They may include or juxtapose time-slice equilibrium experiments and long transient climate simulations (e.g. transient simulations covering the entire last glacial cycle as per the goal of the PalMod project). Comparisons may include different time periods (e.g., deep time, Quaternary, historical as well as future simulations), and focus on comparison of mean states, spatial gradients, circulation or modes of variability using different models, or contrast model results with reconstructions of temperature, precipitation, vegetation or circulation tracers (e.g. δ18O, δD or Pa/Th).

Presentation and discussion of results from the latest phase of PMIP4-CMIP6, and early-stage tests of new models or simulations for PMIP5/CMIP7 are particularly encouraged. However, we also solicit comparisons across time periods, between models and data, and analyses of underlying mechanisms of change as well as contributions introducing novel model or experimental designs that allow to improve future projections.

Extracting meaningful information from complex, time-dependent systems requires approaches that reduce dimensionality while preserving key dynamical structures. This session focuses on techniques to identify coherent structures, quantify variability, and infer dynamical characteristics and causal relationships in observational and modeled datasets. For instance, the Lagrangian perspective provides a powerful framework for understanding transport, mixing, and connectivity by following the evolution of tracers, particles, and dynamical features across scales. Other powerful statistical and dynamical methods — including time series analysis, reduced-order modeling, and causal inference — enhance our ability to diagnose system behavior, attribute variability and change to physical drivers, and improve predictability.

Contributions span methodological advances and applications across atmospheric, oceanic, and broader environmental systems, emphasizing cross-disciplinary insights into structure and causality in geophysical flows.

(This session brings together the themes of the 3rd edition of the Lagrangian session with themes in time-dependent system analysis)

Geophysical and astrophysical flows in stratified media exhibit stratified turbulence that gives rise to a variety of flow phenomena spanning a range of spatial scales from the Kolmogorov to planetary scales. Stratified turbulence significantly influences the flow dynamics on various temporal scales via complex nonlinear interactions, which continue to be challenging to understand, diagnose, and quantify from both theory and numerics. This understanding is fundamental to advance our knowledge of turbulent flow dynamics, and a prerequisite for improved turbulent closures and parameterizations for robust predictions of weather and climate. This session aims at bringing together the recent advancements in the field of fluid dynamics, with a focus on geophysical and astrophysical flows, as well as magneto-hydro dynamics.

Our session invites fundamental and applied contributions on stratified turbulence in fluids from theoretical, numerical, and experimental observational perspectives. The topics include, but are not limited to: two dimensional, three dimensional, isotropic, and anisotropic turbulence; energy transitions and cascades in turbulent flows; turbulent fluxes and transports; turbulent decay, mixing, and dissipation; stable boundary layer flows and intermittent turbulence; wave-vortex dynamics in various turbulent regimes; wave turbulence; clear air turbulence; turbulence in weakly and strongly stratified flows and stratified shear flows.

We particularly encourage participation from early career researchers.

This session, which is now a classic of EGU General Assemblies, was established many years ago with the fundamental contribution of Giovanni Lapenta, who sadly passed away in May 2024. This year, we conveners want to use this session to remember him through works in the many fields he contributed to during his extremely productive and versatile career: development of numerical methods for plasma simulations, nonlinear processes in space and laboratory plasma (magnetic reconnection, turbulence and shocks), particle heating and acceleration in the heliosphere, application of Machine Learning methods to space physics problems. Theoretical, observational, and numerical works, especially those highlighting the interconnection between nonlinear processes in plasmas, are welcome, along with those on new numerical methods and data analysis techniques.

Gravity flows are driven by gravity because of a density different from that of the surrounding environment, often due to temperature (e.g. katabatic winds) and/or salinity (e.g. density currents) differences, and/or the presence of particles (e.g. snow avalanches, debris-flows turbidites, pyroclastic flows). This can be observed either as a current along a slope or as an intrusion in the bulk of a stratified environment. While occurring in various planetary environments, and involving different fluids and particles, they share numerous features due to the common and similar physical processes that govern their dynamics. Yet, a universal description of their dynamics remains elusive, as specifically the feedback on the flow of various processes, such as entrainment, fluid-particle interactions,
internal waves, etc., is difficult to predict.

This session then aims to present complementary physical-based approaches, by gathering researchers from different communities, all focusing on these flows by either studying field data, improving risk assessment techniques, using analogue laboratory experiments or numerical simulations, or focusing on analytical modelling. We therefore welcome contributions including (but not limited to):
- snow avalanches, dust storms, landslides, turbidity currents
- river, volcanic and oceanic plumes
- mud, debris and pyroclastic flows
- katabatic winds, oceanic density currents
-offshore waste discharge

We particularly encourage the participation of early-career researchers and students.

Space and astrophysical plasmas are typically in a turbulent state, exhibiting strong fluctuations of various quantities over a broad range of scales. These fluctuations are non-linearly coupled and this coupling may lead to a transfer of energy (and other quantities such as cross helicity, magnetic helicity) from large to small scales and to dissipation. Turbulent processes are relevant for the heating of the solar wind and the corona, and the acceleration of energetic particles. Many aspects of the turbulence are not well understood, in particular, the injection and onset of the cascade, the cascade itself, the dissipation mechanisms. Moreover, the role of specific phenomena such as the magnetic reconnections, shock waves, solar wind expansion, plasma instabilities and their relationship with the turbulent cascade and dissipation are under debate. This session will address these questions through discussion of observational, theoretical, numerical, and laboratory work to understand these processes. This session is relevant to many space missions, e.g., Wind, Cluster, MMS, STEREO, THEMIS, Van Allen Probes, DSCOV, Solar Orbiter and the Parker Solar Probe.
This year, in particular, we welcome contributions on how future missions, such as HelioSwarm and Plasma Observatory, can advance our understanding of turbulence in space plasmas

The occurrence of pathogens and of an exponentially increasing number of contaminants in freshwater and estuary environments pose a serious problem to public health. This problem is likely to increase in the future due to more frequent and intense storm events, the intensification of agriculture, population growth and urbanization. Pathogens (e.g., pathogenic bacteria and viruses, antibiotic resistance bacteria) are introduced into surface water through the direct discharge of wastewater, by the release from animal manure or animal waste via overland flow, or, into groundwater through the transport from soil, which subsequently presents potential risks of infection when used for drinking, recreation or irrigation. Contaminants of emerging concern are released as diffuse sources from anthropogenic activities, as discharges from wastewater treatment plants (e.g., trace organic contaminants, PFAS), or occur due to microbial growth (e.g. cyanotoxins), posing a burden on human health. So far, the sources, pathways and transport mechanisms of fecal indicators, pathogens and emerging contaminants in water environments are poorly understood, and thus we lack a solid basis for quantitative risk assessment and selection of best mitigation measures. Innovative, interdisciplinary approaches are needed to advance this field of research. In particular, there is a need to better understand the dominant processes controlling fecal indicator, pathogen and contaminant fate and transport at larger scales.

This session aims to increase the understanding about the dominant processes controlling fecal indicator, pathogen and contaminant fate and transport at larger scales. Consequently, we welcome contributions that aim to close existing knowledge gaps and include both small and large-scale experiments, with the focus on
- the fate and transport of fecal indicators, pathogens, emerging contaminants including persistent and mobile organic trace substances (e.g. antibiotic resistance bacteria, cyanotoxins, PFAS) in rivers, soils, groundwater and estuaries
- Hydrological, physically based modelling approaches
- Methods for identifying the dominant processes and for transferring transport parameters of fecal indicators, pathogens and contaminants from the laboratory to the field or catchment scale
- Investigations of the implications of contamination of water resources for water safety management planning and risk assessment frameworks

Invited speaker: Prof. Gertjan Medema, KWR

The "Theory and Simulation of Solar System Plasmas" session is a forum for presenting recent results related to theoretical and numerical investigation of heliospheric plasmas. Our regions of interest are the Sun and its corona, the solar wind and planetary magnetospheres. Processes of interest are magnetic reconnection, turbulence, shock waves, plasma instabilities, plasma heating and particle acceleration. We particularly welcome studies integrating numerical modeling, theoretical investigations and in-situ measurements or remote observations from current and future space missions (MMS, Parker Solar Probe, Solar Orbiter, Bepi Colombo, ASO-S, Plasma Observatory, HelioSwarm, SMILE, SPO ...). Any modeling approach, from global to kinetic, is at home here. We particularly encourage submissions on advances in high resolution global models that reproduce mesoscale phenomena and global modeling that go beyond single fluid MHD (including global hybrid and global MHD models with embedded kinetic domains). The focus of this year's session is the interplay between global and kinetic-scale processes in heliospheric plasmas: how global drivers results into smaller scale (down to kinetic) processes, and how small scale processes in turn set constraints on global heliospheric observables.

We invite presentations on ocean surface waves, and wind-generated waves in particular, their dynamics, modelling and applications. This is a large topic of the physical oceanography in its own right, but it is also becoming clear that many large-scale geophysical processes are essentially coupled with the surface waves, and those include climate, weather, tropical cyclones, Marginal Ice Zone and other phenomena in the atmosphere and many issues of the upper-ocean mixing below the interface. This is a rapidly developing area of research and geophysical applications, and contributions on wave-coupled effects in the lower atmosphere and upper ocean are strongly encouraged

Extreme weather and climate conditions, such as recent events unprecedented in the observational record, have high-impact consequences globally. Some of these events would have been arguably nearly impossible without human-made climate change, and broke records by large margins. Furthermore, compounding hazards and cascading risks are becoming evident. Continuing warming does not only increase the frequency and intensity of events like these, or other until now unprecedented extremes, it also potentially increases the risk of crossing tipping points and triggering abrupt unprecedented impacts. To increase preparedness for high impact climate events, developing novel methods, models and process-understanding that capture these events and their impacts is paramount.

This session aims to bring together the latest research quantifying and understanding high-impact climate events in past, present and future climates. We welcome studies ranging across spatial and temporal scales, and covering compound, cascading, and connected extremes as well as worst-case scenarios and storylines, with the ultimate goal to provide actionable climate information to increase preparedness to such extreme high-impact events.

We invite work addressing high impact extreme events via, but not limited to, observations, model experiments and intercomparisons, climate projections including large ensembles and unseen events, diverse storyline approaches such as event-based or dynamical storylines, insights from paleo archives and attribution studies. We also especially welcome contributions focusing on physical understanding of high-impact events, on their ecological and socioeconomic impacts, as well as on approaches to potentially limit such impacts

The session is sponsored by the World Climate Research Programme lighthouse activity on Understanding High-Risk Events.

The concepts and tools of algebraic topology can be applied to the evolution of systems in both phase space and physical space, as well as to the interesting back-and-forth excursions between these two spaces. The way that dynamics and topology interact is at the core of the present course.

Starting with the early contributions of knot theory to nonlinear dynamics, we introduce the templex, a novel concept in algebraic topology that considers a flow in physical or phase space with no restrictions to its dimensions, drawing on both homology groups and graph theory. The templex approach is illustrated through its application to paradigmatic chaotic attractors – like the Lorenz or Rössler attractors – as well as to non-chaotic flows. Applications to kinematic and dynamic models of the ocean gyres and to idealized models of the Atlantic Meridional Overturning Circulation (AMOC) are presented, along with the topological analysis of oceanographic time series derived from altimetric velocity fields. Lagrangian ocean analysis is a key element of the course.

The extension of the templex concept to the noise-perturbed chaotic attractors of random dynamical systems theory is presented, leading to the definition of topological tipping points (TTPs). TTPs enable the study of successive bifurcations of climate models beyond those known from the classical theory of autonomous dynamical systems, as well as of those more recently added by consideration of tipping points in nonautonomous systems.

We thus propose to start a journey through the mathematical concepts and tools that characterize the topological approach to nonlinear dynamics. This approach goes beyond purely metric, i.e., non-topological, descriptions of the mechanisms that are responsible for higher and higher versions of irregular behavior, from deterministic chaos to various forms of turbulence. These novel tools provide challenging and promising inroads for understanding the effects of anthropogenic forcing on the climate system’s intrinsic variability.

Data assimilation (DA) is widely used in the study of the atmosphere, the ocean, the land surface, hydrological processes, etc. The powerful technique combines prior information from numerical model simulations with observations to provide a better estimate of the state of the system than either the data or the model alone. This short course will introduce participants to the basics of data assimilation, including the theory and its applications to various disciplines of geoscience. An interactive hands-on example of building a data assimilation system based on a simple numerical model will be given. This will prepare participants to build a data assimilation system for their own numerical models at a later stage after the course.
In summary, the short course introduces the following topics:

(1) DA theory, including basic concepts and selected methodologies.
(2) Examples of DA applications in various geoscience fields.
(3) Hands-on exercise in applying data assimilation to an example numerical model using open-source software.

This short course is aimed at people who are interested in data assimilation but do not necessarily have experience in data assimilation, in particular early career scientists (BSc, MSc, PhD students and postdocs) and people who are new to data assimilation.

Assessing the spatial heterogeneity of environmental variables is a challenging problem in real applications when numerical approaches are needed. This is made more difficult by the complexity of Natural Phenomena, which are characterized by (Chiles and Delfiner, 2012):
- being unknown: their knowledge is often incomplete, derived from limited and sparse samples;
- dimensionality: they can be represented in two- or three-dimensional domains;
- complexity: deterministic interpolators (i.e., Inverse Distance Weighted) may fail in providing exhaustive spatial distribution models, as they do not consider uncertainty;
- uniqueness: invoking a probabilistic approach, they can be assumed as a realization of a random process and described by regionalized variables.
Geostatistics provides optimal solutions to this issue, offering tools to accurately predict values and uncertainty in unknown locations while accounting for the spatial correlation of samples.

The course will address theoretical and practical methods for evaluating data heterogeneity in computational domains, exploiting the interplay between geometry processing, geostatistics, and stochastic approaches. It will be mainly split into 4 parts, as follows:
- Theoretical Overview: Introduction to Random Function Theory and Measures of Spatial Variability
- Modeling Spatial Dependence: An automatic solution to detect both isotropic and anisotropic spatial correlation structures
- The role of Unstructured Meshes: Exploration of flexible, robust, and adaptive geometric modeling, coupled with stochastic simulation algorithms
- Filling the Mesh: Developing a compact and tangible spatial model, that incorporates all alternative realizations, statistics, and uncertainty

The course will offer a comprehensive understanding of key steps to create a spatial predictive model with geostatistics. We will also promote MUSE (Modeling Uncertainty as a Support for Environments) (Miola et al., STAG2022) as an innovative and user-friendly open-source software, that implements the entire methodology. Tips on how to use MUSE will be provided, along with explanations of its structure and executable commands. Impactful examples will be used to show the effectiveness of geostatistical modeling with MUSE and the flexibility to use it in different scenarios, varying from geology to geochemistry.

The course is designed for everyone interested in geostatistics and spatial distribution models, regardless of their prior experience.

Extreme event attribution (EEA) emerged in the early 2000s to assess the impact of human-induced climate change on extreme weather events. Since then, EEA has expanded into different approaches that help us understand how climate change influences these events.

In unconditional approaches, such as the risk-based method, the oceanic and atmospheric conditions are largely left unconstrained. In contrast, conditional approaches focus on constraining the specific dynamics that lead to an event. One example is the analogues approach, where the synoptic atmospheric circulation is held relatively fixed. Both approaches can be used to assess changes in the likelihood, intensity, or both, of extreme events.

In this short course, we will examine the robustness of the analogues method for EEA, explore different strategies for defining analogues, and discuss their applications in attribution studies.

In a changing climate world, extreme weather and climate events have become more frequent and severe, and are expected to continue increasing in this century and beyond. Unprecedented extremes in temperature, heavy precipitation, droughts, storms, river floodings and related hot and dry compound events have increased over the last decades, impacting negatively broad socio-economic spheres (such as agriculture), producing several damages to infrastructure, but also putting in risk human well-being, to name but a few. The above have raised many concerns in our society and within the scientific community about our current climate but our projected future. Thus, a better understanding of the climate and the possible changes we will face, is strongly needed. . In order to give answers to those questions, and address a wide range of uncertainties, very large data volumes are needed across different spatial (from local-regional to global) and temporal scales (past, current, future), but sources are multiple (observations, satellite, models, reanalysis, etc), and their resolution may vary each other. To deal with huge amounts of information, and take advantage of their different resolution and properties, high-computational techniques within Artificial Intelligence models are explored in climate and weather research. In this short-course, a novel method using Deep Learning models to detect and characterize extreme weather and climate events will be presented. This method can be applied to several types of extreme events, but a first implementation on which we will focus in the short-course, is its ability to detect past heatwaves. Discussions will take place on the method, and also its applicability to different types of extreme events. The course will be developed in python, but we encourage the climate and weather community to join the short-course and the discussion!