This session aims at bringing together contributions from the growing interface between the Earth science, mathematical, and theoretical physics communities. Our goal is to stimulate the interaction among scientists from 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 non-linear, chaotic system, the improvement of numerical modelling via both 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: Partial differential equations, numerical methods, extreme events, statistical mechanics, thermodynamics, dynamical systems theory, large deviation theory, response theory, tipping points, model reduction techniques, waves in oceans and atmosphere, model uncertainty and ensemble design, 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.

Confirmed solicited speakers: Ulrike Feudel (Carl von Ossietzky University Oldenburg, Germany), Claire Valva (California Institute of Technology, USA)

Climate modeling is pushing the frontier towards increasingly complex, high-resolution earth system models (ESMs). At the same time, nonlinearities and emergent phenomena in the climate system are often studied by means of conceptual models, which offer qualitative understanding and permit theoretical approaches. Recent advancements in statistical and physical emulators – ranging from reduced-complexity climate models to machine learning-based techniques – are enabling rapid and computationally efficient assessments of climate trajectories, impacts, and risks.

Between these approaches, a persistent “gap between simulation and understanding” (Held 2005) challenges our ability to transfer insights from conceptual models to reality, and to distill the physical mechanisms underlying the behavior of complex (climate) models. This calls for a concerted effort to learn from the entire model hierarchy, understanding the differences and similarities across its various levels of complexity, to increase confidence in climate projections.

In this session, we invite contributions from all subfields of climate science that showcase how different modeling approaches advance our understanding of the Earth system and its components, and/or highlight inconsistencies in the model hierarchy. We also welcome studies exploring a single modeling approach, as we aim to encourage exchange between researchers working on different rungs of the model complexity ladder. Contributions may employ dynamical systems models, physics-based low-order models, explainable machine learning, Earth System Models of Intermediate Complexity (EMICs), simplified or idealized setups of ESMs (radiative-convective equilibrium, single-column models, aquaplanets, slab-ocean models, idealized geography, etc.), full ESMs or standalone models of components of the Earth system, and km-scale models.

Processes and phenomena of interest include, but are not limited to:
* Earth system response to climate forcing
* Tipping behavior and critical transitions
* (Coupled) modes of climate variability
* Extremes and predictability

This session invites contributions advancing the understanding, modelling, and prediction of extreme events in weather, climate, and other geophysical systems. It brings together researchers from the geophysical sciences and those applying mathematical, statistical, and dynamical-systems approaches.

Topics of interest include, but are not limited to:
* Variability and projected changes in extremes under climate change
* Representation of extreme events in weather and climate models
* Attribution of extreme events
* Emergent constraints on extreme-event behaviour
* Predictability of extremes across meteorological and climate timescales
* Connections between extremes in dynamical systems and observed geophysical extremes
* Theoretical and applied studies of extremes in nonlinear and chaotic systems
* Downscaling methods for extreme events
* Links between the physical dynamics of extremes and their impacts on society and ecosystems

We particularly welcome interdisciplinary contributions, novel methodologies, and studies connecting theory with observed geophysical extremes. Submissions from early-career researchers are especially encouraged.

AI models are increasingly trained on geoscientific observations and simulations. A growing body of theory investigates their representations and generalisation, but many existing results rely on assumptions that are difficult to reconcile with geoscientific data. These data reflect nonlinear, multiscale and non-stationary systems, are often incomplete, and may change across spatial scales, temporal regimes and physical conditions. Understanding how learning behaves under these conditions remains an open theoretical challenge.

This session focuses on the theory of learned representations in geoscientific AI. We seek contributions that investigate how embeddings and latent spaces encode spatial, temporal and physical structure, and how these representations depend on the data, model architecture, training dynamics and physical constraints. Relevant questions include the geometry and identifiability of latent representations, inductive biases, invariance and equivariance, stability and generalisation across scales and regimes.

The session brings together geoscience, theoretical machine learning, applied mathematics, statistics, statistical physics, dynamical systems and fluid dynamics. Contributions may develop mathematical or statistical theory, introduce diagnostics derived from theoretical principles, or test theoretical predictions using observations, simulations and idealised systems. The emphasis is on explaining model behaviour rather than reporting predictive performance alone, and on determining when learned representations are stable, transferable and physically meaningful.

Weather and climate extremes, such as recent events unprecedented in the observational record, have extensive impact globally. Some of these events would have been nearly impossible without human-made climate change, exhibiting conditions well beyond previous records due to complex, and at times unprecedented configurations of their underlying physical drivers. Furthermore, compounding hazards and cascading risks resulting from these high-impact extremes are becoming evident. Continued warming does not only increase the frequency and intensity of such extremes, it also potentially increases the risk of unseen non-linear behaviours or unprecedented impacts. To increase preparedness for high-impact climate events, developing novel methods, models and process-understanding that capture these hazards and their associated 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 across all spatial and temporal scales, and covering compound, cascading, and connected extremes as well as worst-case scenarios, with the ultimate goal to provide actionable climate information to increase societal preparedness to such extreme high-impact events.
We invite work addressing high-impact extreme events via, but not limited to, model experiments and intercomparisons, diverse storyline approaches such as event-based or dynamical storylines, climate projections including large ensembles and unseen events, 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 societal impacts.
The session is closely linked to the World Climate Research Programme lighthouse activities on Understanding High-Risk Events and Explaining and Predicting Earth System Change.

In response to anthropogenic greenhouse gas emissions and land-use change, we are transitioning towards a climate state that may feature rapid changes. This will have severe impacts on the occurrence of extreme weather events and increasing risk of crossing large-scale tipping points. Neither these transitions nor long-term climate states have been observed by instrumental measurements, making information on past climatic states increasingly important to anticipate future Earth System change. The availability of paleoenvironmental records have enormously expanded over the past decades, providing extremely rich information about physical, cryospheric, biological, and ecological processes on many spatial and temporal scales. Yet, it has been challenging so far to directly transform knowledge on past processes into a more confident evaluation of future projections for the Earth system.
Being able to reconstruct past climate evolution is a necessary step for enhancing our capacity to look into the future. Incorporating palaeodata-based information on past climate requires substantial improvements to state-of-the-art Earth System Models (ESMs). So far, ESMs are mainly calibrated and validated with respect to the instrumental records of the last ~170 years of relatively stable climate, while the Earth’s longer-term history is characterised by an interplay of gradual climate change, variability, and critical transitions between competing states, with profound impacts on climate subsystems, ecosystems, and civilisations.
Understanding the leading dynamical processes and feedbacks and in particular improving our ability to model and anticipate critical transitions in the climate and ecosystems is key to project future climate change on spatio-temporal scales relevant for societies, ecosystems, and the planet.

We invite contributions that
- advance process understanding of past climate changes and enhance ESM development through high resolution reconstructions of five key climate system parameters: atmospheric CO2-concentration, ice sheet (sea level) and sea ice extent, ocean temperatures, and terrestrial vegetation;
- explore modern approaches to incorporate palaeoclimate information into the development of ESMs of varying complexity, including rigorous model-data comparison techniques;
- make use of information from paleoenvironmental proxy data, past civilisations, ESMs, and rigorous theoretical approaches - individually or in combination.

Sitting under a tree, you feel the spark of an idea, and suddenly everything falls into place. The following days and tests confirm: you have made a magnificent discovery — so the classical story of scientific genius goes…

But science as a human activity is error-prone, and might be more adequately described as "trial and error". Handling mistakes and setbacks is therefore a key skill of scientists. Yet, we publish only those parts of our research that did work. That is also because a study may have better chances to be accepted for scientific publication if it confirms an accepted theory or reaches a positive result (publication bias). Conversely, the cases that fail in their test of a new method or idea often end up in a drawer (which is why publication bias is also sometimes called the "file drawer effect"). This is potentially a waste of time and resources within our community, as other scientists may set about testing the same idea or model setup without being aware of previous failed attempts.

Thus, we want to turn the story around, and ask you to share 1) those ideas that seemed magnificent but turned out not to be, and 2) the errors, bugs, and mistakes in your work that made the scientific road bumpy. In the spirit of open science and in an interdisciplinary setting, we want to bring the BUGS out of the drawers and into the spotlight. What ideas were torn down or did not work, and what concepts survived in the ashes or were robust despite errors?

We explicitly solicit Blunders, Unexpected Glitches, and Surprises (BUGS) from modeling and field or lab experiments and from all disciplines of the Geosciences.

In a friendly atmosphere, we will learn from each other’s mistakes, understand the impact of errors and abandoned paths on our work, give each other ideas for shared problems, and generate new insights for our science or scientific practice.

Here are some ideas for contributions that we would love to see:
- Ideas that sounded good at first, but turned out to not work.
- Results that presented themselves as great in the first place but turned out to be caused by a bug or measurement error.
- Errors and slip-ups that resulted in insights.
- Failed experiments and negative results.
- Obstacles and dead ends you found and would like to warn others about.

For inspiration, see the collection of BUGS - ranging from clay bricks to atmospheric temperature extremes - at https://meetingorganizer.copernicus.org/EGU25/session/52496

The Earth system is a complex, multiphysics system with nonlinear interactions on multiple spatial and temporal scales. Understanding constituent processes (linear, nonlinear, stochastic, etc.) on the one hand, and the complexity of individual subsystems or the full integrated system on the other, is key to being able to better model the Earth System in a predictive fashion. The renaissance of machine and deep-learning in the past decade has led to rapid progress in the development of advanced approaches in, e.g., nonlinear time series analysis, dynamical and stochastic systems theory, critical slowing down theory, complex systems theory, and these approaches, in turn show promise in facilitating further advances in modeling the Earth system.



In this context, this session seeks contributions on all aspects of complexity, nonlinearity, tipping points and stochastic dynamics of the Earth system, including the atmosphere, the hydrosphere, the cryosphere, the solid earth, etc. Communications on theoretical, experimental and modeling studies are all welcome, where the latter modeling studies can span the range of model hierarchy from idealized models to complex Earth System Models (ESM). Studies based on emerging approaches such as data driven models, Artificial Intelligence approaches, complex network methods, critical slowing down analysis, dynamical and stochastic systems theory, etc., are particularly encouraged.

Climate change is reshaping infectious-disease risks. Hydroclimatic extremes such as floods, droughts, and heatwaves can alter vector habitats, pathogen survival, water quality, sanitation, human mobility and patterns of human contact. Adaptation measures (including vector control, water management, healthcare preparedness, vaccination and behavioural responses) can reduce these risks or unintentionally create new pathways of transmission.

The session welcomes contributions on vector-borne, waterborne, foodborne, respiratory and zoonotic diseases, including research on compound hazards, unequal impacts and adaptation responses. We particularly invite diverse methodological approaches, including:

Dynamical systems approaches to modelling and understanding infectious diseases
Large-scale statistical, epidemiological and climate–health analyses;
Process-based and data-driven models;
Local, urban and neighbourhood-scale modelling;
Qualitative, participatory and mixed-methods approaches;
Surveys, behavioural studies and assessments of public perceptions;
Surveillance, early-warning and climate-informed forecasting systems;
Evaluations of adaptation, preparedness, resilience and risk-management strategies.
Veterinary medicine

The session aims to bring together researchers working across geosciences, climate science, epidemiology, microbiology, ecology, public health and disaster-risk reduction. By connecting these perspectives, we hope to improve understanding of when and where natural hazards become infectious-disease threats, and how these risks can be anticipated and managed.

Running Earth system models, downscaling outputs and driving process-based impact models are computationally expensive tasks that hinder the exploration of climate change impacts. New generations of statistical, physical, and hybrid climate change emulators reduce these bottlenecks by mimicking global, regional and extreme-event responses to generate projections of climate change and its associated impacts more efficiently. This capability transforms the assessment of climate change impacts and risks by enabling rapid policy-relevant analyses for sectors such as natural systems, agriculture, water resources, economics, energy systems and human health.

This session invites contributions on the developments and applications in climate change emulation, highlighting opportunities and challenges for robust and computationally efficient climate risk assessments. This includes simple climate models, probabilistic emulation techniques and machine learning approaches, including traditional pattern scaling, as well as benchmarking efforts that aid in interpretability and consistency between emulation methods. To foster interdisciplinary exchange, we also encourage submissions regarding approaches for climate impact projections.

The Navier-Stokes equations, initially formulated in the early 19th century, have since become the cornerstone of fluid mechanics, subsequently extending their relevance to fluid geophysics. The existence and regularity of their solutions pose a significant challenge within a substantial domain of geophysics.

Over the years, a series of partial results have been obtained, particularly in the pursuit of proving one of the four statements proposed by Charles L. Fefferman for the Millennium Clay Prize. A definitive proof of the third statement regarding the breakdown of the Navier-Stokes equations was unveiled by OpenAI on September 8th, utilising extensive IA resources. This revelation has sparked a substantial debate, encompassing various aspects such as the physical significance of the blowing-up singularity, the utilisation of intensive AI resources in disruptive research, and the connections with concepts like intermittency, cascades, multifractals and enstrophy catastrophe. It may also inspire new approaches to resolve fundamental questions of geosciences.

This PICO session seeks to provide the geophysical community with an opportunity to contribute to this ongoing discourse.

This session welcomes frontier research on foundational mathematical constructs underlying geophysically consistent system dynamic intelligence, with emphasis on rigorous, unifying formalisms that advance fundamental theoretical understanding across complex multiscale dynamics, including far-from-equilibrium behaviour, non-ergodicity, criticality, and emergence.

Mathematical contributions may encompass foundational paradigms (categorical, topological, algebraic), analytical frameworks (functional, geometric, stochastic), generalized operators (fractional, nonlocal, integro-differential, information theoretical), and unveil principled perspectives, theoretical advances and systems intelligence to shed light onto complex geophysical and multi-hazard problems.

Of interest is also how foundational mathematical structures inform and constrain modern paradigms in machine learning, explainable AI, physically informed and unified systems intelligence, enabling advances in interpretability, generalization, and robustness. Contributions are also encouraged where deep mathematical insight yields new understanding of scaling, regime behavior, extremes, and interacting hazards across the Earth system.

Collaborative dialogue is fostered among foundational mathematics, geophysical sciences and information technologies, co-evolving to shape new mathematical physics pathways in complexity science and systems intelligence.

Just as mathematical and systems intelligence advances can empower breakthroughs in the geophysical sciences, geophysical problems can inspire the development of new mathematical and systems intelligence methods and techniques that then vastly transcend the disciplinary scope that gave rise to it. Our session is therefore aimed not only at how mathematics can be developed and used to advance the geosciences, but also how new mathematics can fundamentally emerge from the challenges facing our planet.

Hydrogeomorphic processes emerge from interactions among fluid motion, sediment and granular materials, and evolving landforms. They span spatial and temporal scales from the entrainment and collective motion of individual grains to sediment-laden flows, reach-scale morphodynamics and landscape evolution. Although dynamic grain-scale mechanisms are increasingly resolved through high-resolution imaging, embedded sensing and particle-resolved simulations, translating this understanding into continuum representations and reliable field-scale forecasts remains a fundamental challenge in riverine geomorphology, hydrology, fluvial hydraulics and environmental fluid mechanics.

This session brings together field observations, physical experiments, theory, numerical modelling and data-driven approaches that advance process measurement, mechanistic understanding, scale bridging and prediction. We particularly welcome studies developing or integrating novel instrumentation and sensing, imaging and remote sensing, particle tracking, laboratory experiments, particle-resolved and discrete-element simulations, continuum modelling, data assimilation, uncertainty quantification, and model calibration, verification and validation.

Contributions may address particle entrainment and granular dynamics; grain–fluid and grain–grain interactions; stochastic sediment transport and size segregation; particle-laden, debris and granular flows; erosion, deposition and evolving boundaries; bedform development and morphodynamics; extreme or rapidly varying flows; and the representation of these processes across particle, reach and landscape scales. Applications may include rivers, hillslopes, infrastructure, environmental management, natural hazards and forecasting. Studies that connect observations to grain-scale mechanisms, upscale those mechanisms into reduced-order or continuum representations, and evaluate predictive capability across environments and scales are especially encouraged.

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.
- Machine-learning approaches for precipitation modelling, forecasting, and downscaling: Machine-learning and hybrid (physics-informed) methods for precipitation simulation, uncertainty quantification, bias correction, and spatio-temporal downscaling, including baseline comparisons, cross-climate transfer tests, and evaluations of explainability and robustness.
- 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.

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.

Natural hazards rarely occur independently. Earthquakes can trigger landslides, landslides can dam rivers and generate floods, volcanic eruptions can induce mass movements and tsunamis, wildfires can alter hydrological and sediment transport processes, and changes in glaciers and permafrost can initiate sequences of slope failure, debris flow, and flooding. In such cascades, an initial process modifies the physical state of the system and controls the initiation, magnitude, timing, or propagation of subsequent hazards. Risk evolves with these cascades, as exposure and vulnerability are reshaped by each successive event. Damaged infrastructure, depleted response capacity, displaced populations, and altered land use can amplify the consequences of later hazards, so that dynamic vulnerability becomes a first-order control on cascading risk.
Despite increasing attention to multi-hazard risk, the mechanisms governing these interactions remain poorly understood. Key questions concern how energy, mass, water, sediment, and stress are transferred between processes; how these processes interact; how antecedent conditions and event history influence subsequent hazards; and how feedbacks, nonlinearities, and differences in spatial and temporal scales control whether a disturbance attenuates or develops into a cascade. Answering these questions requires long-term observations to capture the slow preconditioning, relaxation, and recovery phases that link successive events, and allow rare but high-impact cascades to be placed in a meaningful statistical and process-based context.
This session brings together research on the physical processes governing cascading natural hazards. We welcome observational, experimental, theoretical, numerical, and data driven studies addressing topics including:
•Multiphysics modelling of triggering, coupling, amplification, and attenuation between hazard processes;
•Transfer of mass, momentum, energy, stress, water, and sediment between interacting processes;
•Reconstruction and analysis of well documented cascading events across different environments and spatial and temporal scales.
•Field observations, Earth observation, geodetic, seismological, hydrological monitoring, and other measurements used to resolve process interactions;
•Methods for identifying causal relationships and separating triggering from coincident or common forcing;
•Effects of environmental and climatic change on the physical conditions controlling hazard cascades;

Machine learning is reshaping the representation of complex physical processes in Earth system models, offering new avenues for parameterisation, emulation, and hybrid modelling. This session focuses on the use of machine learning to emulate computationally expensive or unresolved processes, accelerate physical simulations, enable data-driven discoveries, and improve representation across domains such as convection, turbulence, radiation, hydrology, sea ice, and other Earth system components. Topics include (but are not limited to):

- Subgrid-scale parameterisations via machine learning
- Emulators of physical processes, model components, or whole weather and climate models (including end-to-end learning and foundation models)
- Data-driven discoveries
- Hybrid ML-physics modelling frameworks
- Physics-informed neural networks, neural operators, and differentiable programming
- Reinforcement learning and other approaches for ensuring physical consistency, stability, and optimising model behaviour
- Calibration and parameter optimisation using ML
- Physical behaviour, encoding and analysis of ML models
- Verification and explainability (XAI) of data-driven models (including AI forecasting)
- Representation of extremes and downscaling
- Coupling of ML models with physical models
- Cross-domain applications (atmosphere, ocean, cryosphere, land)
- Impacts of architecture choices and design on physical behaviour
- Links between ML methods and fundamental physics or applied mathematics
- Novel AI methods and applications

Time series are a common type of data generated by observational and modelling efforts across Earth, environmental and space sciences. Long-term observations are particularly important for understanding gradual changes and assessing risks, yet are often difficult to sustain and fund. Their characteristics can vary substantially, from short to long records, linear to nonlinear dynamics, univariate to multivariate data, and single- to multi-scale variability. These differences call for both tailored methodologies and general approaches.

A key challenge is distinguishing random fluctuations from long-term changes in order to better understand processes within and across Earth system components. This requires knowledge of temporal variability and, often, sufficiently long observations. For example, reliable sea-level trends may require several decades of continuous measurements because of decadal variability. Likewise, the stochastic variability of geophysical time series can exhibit power-law scaling, requiring long records for robust statistical assessment.

Time series analysis encompasses a broad range of tasks, including:
- characterizing nonlinear variability in the time and/or frequency domain;
- quantifying complexity, predictability and scaling properties;
- identifying statistical interdependencies within and between time series;
- distinguishing co-variability from causal relationships;
- reducing dimensionality and identifying meaningful modes of variability; and
- developing stochastic and deterministic statistical or dynamical models.

This session invites contributions on the development and application of modern methods for analysing observational and model time series across the EGU community, including geophysical, geodynamic, oceanographic, geodetic and climate observations from terrestrial observatories and remote sensing. Contributions addressing advances in sensors, instrumentation, monitoring, analysis and interpretation, as well as comparisons of different approaches, are welcome. Studies using novel methods, including AI, for the analysis of long time series are particularly encouraged. We aim to foster interdisciplinary exchange and cross-fertilization between different EGU divisions.

The Atlantic Meridional Overturning Circulation (AMOC) plays a crucial role in shaping the dynamics of the Earth’s climate by distributing heat and nutrients across the Atlantic. It is important to understand the past, present and future changes in the dynamics of the AMOC, either gradual or abrupt, since such changes, and the possibility of its tipping, can have profound climatic and societal impacts. In this regard, big data and AI play an increasingly important role in studying AMOC dynamics based on diverse types of data. These range from geological proxies over contemporary in-situ and remote sensing observations to simulations of state-of-the-art ocean or coupled Earth system models and provide an ever increasing amount of more and more complex data on the AMOC. Advanced numerical methods and AI can help us to uncover critical aspects of the AMOC dynamics by extracting new patterns and highlighting the role of complex physical mechanisms and feedbacks, including early warnings of future regime shifts of the AMOC or some of its subcomponents like the Nordic Seas deep convection or the Northern hemisphere subpolar gyre.
In this session we welcome contributions exploring new ways of using big data and AI to elucidate AMOC dynamics. We aim to cover a broad variety of computational methods, making use of the wealth of AMOC-related observational and/or model data. These can range from statistical methods exploiting big datasets to machine learning and deep learning approaches, including neural-network emulators of the AMOC. The session is open to work on a wide range of timescales, from paleoclimate reconstruction, through current observations to future projections. Contributions may address the analysis of short-term AMOC dynamics, as well as longer-term behaviour, including tipping of the circulation and associated forecast and impact studies.

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.

Geochronological techniques such as cosmogenic nuclides, thermochronology, radiocarbon and luminescence dating continue to improve in accuracy, precision and temporal range. Developments in combining geochronological methods, data treatment and landscape evolution models have provided new insights into the timing, rates and magnitude of Earth surface processes. Integrating geochronological datasets from different techniques with tectonic, geomorphic, and landscape evolution models offers enormous potential for understanding how tectonics, climate, erosion, and sediment transport interact to shape landscapes across a wide range of spatial and temporal scales. This session includes studies ranging from erosion rates, sediment provenance, burial and transport times, geomorphic response to active deformation, cooling histories and landscape dynamics. and technical developments and novel applications of Quaternary geochronometers. We invite studies that apply novel geochronological methods, combine geochronological and geochemical techniques with numerical modeling or landscape evolution analyses, and that highlight the latest developments and open questions in the application of geochronometers in geomorphology.

The rapid development of pretrained models, including large language and foundation models, opens up many new applications in geosciences. Fine tuning or deploying existing models directly to geoscience problems gives us all the benefits of very large models, trained with vast data sets, whilst reducing the compute cost and required specialist data sets, compared to older deep learning techniques. The potential cost and energy saving, and scientific power of these models can revolutionise AI in geosciences. In this session, we look forward to seeing state-of-the-art fine tuning techniques specialized for geoscience; novel deployment of pretrained models of any sort; and lessons learnt from trial and error. We would love to see applications across the geosciences, from geophysics to education, remote sensing to hands on field applications.

Inverse Problems are encountered in many fields of geosciences. One class of inverse problems, in the context of predictability, is assimilation of observations in dynamical models of the system under study. Furthermore, objective quantification of the uncertainty during data assimilation, prediction and validation is the object of growing concern and interest. This session will be devoted to the presentation and discussion of methods for inverse problems, data assimilation and associated uncertainty quantification throughout the Earth System like in ocean and atmosphere dynamics, atmospheric chemistry, hydrology, climate science, solid earth geophysics and, more generally, in all fields of geosciences.

We encourage presentations on advanced methods, and related mathematical developments, suitable for situations in which local linear and Gaussian hypotheses are not valid and/or for situations in which significant model or observation errors are present. Specific problems arise in situations where coupling is present between different components of the Earth system, which gives rise to the so-called coupled data assimilation. Of interest are also contributions on weakly and strongly coupled data assimilation - methodology and applications, including Numerical Prediction, Environmental forecasts, Earth system monitoring, reanalysis, etc., as well as coupled covariances and the added value of observations at the interfaces of coupled models. We also welcome contributions dealing with algorithmic aspects and numerical implementation of the solution of inverse problems and quantification of the associated uncertainty, as well as novel methodologies at the crossroad between data assimilation and purely data-driven, machine-learning-type algorithms.

This session explores all work related to forecasting in geosciences using statistical methods.
Ranging from linear regression to the most advanced machine learning (ML) or artificial intelligence (AI) methods, the session welcomes all contributions developing and/or using these tools for various applications such as AI/ML-based numerical weather prediction and nowcasting, time series forecasting in geosciences, forecast blending and statistical post-processing, or downscaling.
This session aims to foster interdisciplinary discussions among geoscientists coming from meteorology, climate, hydrology, or other communities, to promote the use of statistical methods in forecasting.

Weather forecasting based on AI models is now part of our operational and research landscape. AI-based weather forecasts show improved skill with verification measures such as the root mean squared error when compared with NWP models. However, proper and in-depth assessment of strengths, weaknesses, and properties of these models is still ongoing. This session aims to gather contributions advancing the assessment of AI-based weather forecasts.

This session welcomes contributions on the following topics with applications to AI weather models:

Benchmarking activities (e.g. datasets, intercomparison projects, comparison with NWP forecasts)
Verification methodology (e.g. spatial verification methods, scoring rules, or innovative approaches)
Diagnostics of forecast realism and potential forecast artifacts
Forecasting extreme events, predictability, and other properties (e.g. fairness)
Interpretability of AI weather models, e.g. XAI methods.

Contributions covering theoretical, methodological, applied, or operational aspects are equally welcome.

Weather forecasting and its application is one of the most important subject in meteorology. This session will focus on R&D on understanding the weather, forecasting techniques and its applications, both with AI and physics based. Contributions related to nowcasting, numerical weather prediction, ensemble techniques, NWP post-processing and forecast application are very welcome.

Topics may include:
- Nowcasting and warning methods and systems, use of observations and weather analysis
- Studies on high impact weather
- Data driven and physics based numerical modelling; explainable Artificial Intelligence (xAI) in weather models
- Development of AI for ensemble prediction techniques and products
- Weather forecasting application
- Seamless prediction and application
- Statistical and AI NWP Post-processing
- Use of machine learning, data mining and other advanced analytical techniques
- ML for weather warning

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, including windows of opportunity.
The session welcomes contributions from dynamical modeling, 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 seasonal to (multi-)decadal 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.

Hydrological predictions - simulations or forecasts - are fundamentally uncertain. This has been recognized more than a century ago (see Krzysztofowicz, 2001, and references therein), and it is still true today. For a complete picture, hydrological predictions should therefore not only provide point estimates, but probabilistic statements. Such probabilistic predictions are not only an honest account of what we know (and what we do not know), they also provide practical advantages for end users and decisionmakers (Buizza, 2008). Nevertheless, and despite considerable progress, to date the majority of hydrological models still provide single-valued output. With this session, we want to establish a platform to promote the paradigm-shift towards making probabilistic predictions in hydrology the standard rather than the exception.
We welcome contributions from the following fields (but not limited to these):
- Theory and methodology for identifying and quantifying sources and pathways of uncertainty from data through models to predictions, including approaches based on probability theory and information theory
- Development of model architectures and efficient training procedures enabling fast, accurate and reliable probabilistic predictions, including physics-based, data-driven, machine-learning and hybrid approaches, stochastic parameterisations, ensemble prediction systems, and post-processing
- Probabilistic benchmarks and evaluation frameworks, including benchmark models and datasets, verification methods, scoring rules, calibration, and large-scale initiatives for assessing probabilistic hydrological predictions
- Operational implementations and real-world applications of probabilistic hydrological modelling, including flood forecasting, climate change impact assessment, and water resources management
- Development of strategies for effectively communicating probabilistic predictions to end users

References
Buizza, R. (2008), The value of probabilistic prediction. Atmosph. Sci. Lett., 9: 36-42. https://doi.org/10.1002/asl.170
Krzysztofowicz, R.: The case for probabilistic forecasting in hydrology, Journal of Hydrology, 249, 2-9, https://doi.org/10.1016/S0022-1694(01)00420-6, 2001.

Predictions of physical processes in aquifers, rivers and across compartments are strongly affected by uncertainties and errors in model structure, parameters and forcing data. Thus, reliable predictions at any scale (lab/field/catchment) require a rigorous and transparent treatment of these uncertainties, from parameter estimation to uncertainty quantification and model selection. Acknowledging uncertainties and equifinality as a fundamental part of modelling and understanding model-parameter interactions during calibration opens up otherwise-missed opportunities for scientific insight and decision support. This session is a platform for discussion of methodological advances and workflows addressing inverse problems in surface and subsurface hydrology, i.e., using available observed data to gain knowledge/ constrain uncertainty about related but unobserved quantities of interest. We invite contributions on improved concepts, approaches & computational algorithms (be they Bayesian, frequentist, optimization- or ML-based) as well as demonstrations of best practices, challenges & pitfalls, especially (but not exclusively) related to:

- parameter inference, model selection/ averaging, sensitivity and uncertainty analysis;
- representation of uncertain data and boundary conditions;
- integration of heterogeneous/multi-source data;
- identification and treatment of model-structural errors;
- distilling new model formulations (data-driven, physics-based, knowledge-guided or hybrid);
- data worth and optimal experimental design strategies toward maximum information/minimum uncertainty;
- constraint learning/ novel likelihood formulations to incorporate expert knowledge in inversion;
- other regularization strategies that help solve ill-posed problems;
- computational efficiency of solving inverse problems, including surrogate and ML-based techniques;
- Benchmarking and intercomparison efforts on synthetic or real-world, local or large-sample data-sets;
- transparent and reproducible workflows for robust predictions and visualization/communication of inference results to stakeholders;
- real-time inversion for operational forecasting;
- variations of all the above specific to low-dimensional, high-dimensional, dynamic, spatially distributed, geostatistical, linear, or non-linear inverse-problem settings.

This session focuses on recent developments and research on ocean data assimilation. Data assimilation is essential for ocean forecasting, reanalysis, and climate studies. By optimally combining numerical simulations with various observations, data assimilation provides a dynamically consistent and comprehensive estimate of the present and past ocean state. This session invites abstract submissions on developments of data assimilation for the physical ocean together with coupled components such as sea ice, marine ecosystems, land-sea interface and atmosphere for ensuring consistency with other parts of the Earth system. The session also focuses on impact of the assimilation and deployment of novel space-borne and in-situ observations such as autonomous platforms, wide-swath satellite tracks, and deep in-situ observations and biogeochemistry profilers.
Beyond state estimation, this session also welcomes contributions in parameter estimation, uncertainty quantification, and hybrid machine learning and data assimilation methods focused on the ocean.

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 (and their contribution to plastic transport)
- river, volcanic and oceanic plumes
- mud, debris and pyroclastic flows
- katabatic winds, oceanic density currents

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

The year 2026 marks the centenary of Richardson’s seminal paper on turbulent diffusion. In this pioneering work, several fundamental ideas were introduced. Richardson notably recognized the non-differentiable nature of turbulent velocity and suggested that a fractal-like process could be used to represent it, proposing a Weierstrass function as an example. Based on experimental evidence, he also proposed that turbulent diffusivity follows a scaling law with an exponent of 4/3. Fifteen years before Kolmogorov’s 1941 theory, this result is equivalent to a velocity scaling characterized by a Hurst exponent of 1/3.

To mark the centenary of this landmark paper, which led the basis of modern theory of turbulent diffusion, we propose a EGU session devoted to this topic. In particular, we aim to address ocean and atmospheric applications, from small-to-large scale processes, in light of more recent results about the presence of intermittent corrections, or recent approaches in terms of continuous-time random walk or ballistic cascade phenomenologies.

We welcome contributions addressing turbulent transport and dispersion from both Eulerian and Lagrangian perspectives, including the diffusion of chemical and biological tracers, pair dispersion, and turbulent mixing. Theoretical, experimental, numerical, and observational studies are all welcome, across a broad range of spatial and temporal scales.

We also particularly encourage contributions addressing the historical development of ideas on turbulent diffusion, from Richardson’s pioneering work to contemporary approaches in oceanic and atmospheric turbulence.

Rotation fundamentally shapes the dynamics of geophysical and astrophysical flows across a large range of scales and systems, from planetary and stellar interiors to oceans and atmospheres. Rotation gives rise to waves, coherent vortices, turbulent cascades, and large-scale mean flows. The interactions between these processes play an important role in the transport and mixing properties of the flow, and therefore the long-term evolution of planets, moons and stars.

This session welcomes theoretical, numerical, experimental, and observational studies addressing the dynamics of rotating or rotating-stratified flows. Topics include, but are not limited to, inertial, gravity, Rossby, and magnetohydrodynamic waves; wave turbulence; wave-mean flow interactions; coherent vortices and zonal flows; rotating convection; topographic effects; transport and mixing; transition to turbulence; and deep interior dynamics relevant to planetary cores, icy moons, gas giants, and stellar interiors.

This session focuses on the fundamental mechanisms governing rotating and rotating-stratified flows in natural systems, and welcomes studies that provide physical insight into these processes across geophysical and astrophysical contexts.

This session is complementary to the EGU session on 'Stratified Turbulence in Geophysical and Astrophysical Flows', with an emphasis on rotational effects and on the coupled dynamics of waves, vortices, turbulence, and mean flows.

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; regime transitions and energy cascades in turbulent flows; turbulent fluxes and transports; turbulent decay, mixing, and dissipation; stable atmospheric 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 focuses on the non-linear processes taking place in space, laboratory, and astrophysical plasmas. In many cases, these processes appear deeply linked. For instance, magnetic reconnection is an established ingredient of the turbulence cascade, and it is also responsible for the production of turbulence in reconnection outflows and for the onset of a number of instabilities especially at the depolarization fronts. Turbulent dynamos are expected to play a role in the generation and growth of magnetic fields in a number of astrophysical and heliospheric settings. Shocks can result in turbulence formation, for example, in the turbulent magnetosheath, and can be efficient particle accelerators.

The study of these processes has seen significant progress in recent years thanks to a synergistic approach based on simulations and observations. On the one hand, simulations can deliver output on a range of temporal and spatial range of scales, going from fluid to electron kinetic scales. This is partly also due to the advent of GPU facilities, which have significantly increased the capability of numerical simulations in plasma physics. On the other hand, high cadence spacecraft measurements of particles and fields and high-resolution 3D measurements of particle distribution functions are currently provided by missions such as MMS, Parker Solar Probe, and Solar Orbiter. These missions are opening new research scenarios in heliophysics and providing a consistent amount of new data to be analysed. Furthermore, other present and future missions that will give unique plasma measurements around solar system magnetospheres, such as Bepi Colombo, Juice, Comet Interceptor, Plasma Observatory and HelioSwarm, are demanding the development of new numerical tools for successful interpretation of the observations.

This session welcomes simulation, observational, and theoretical contributions relevant to studying the above-mentioned processes. We also encourage papers proposing new methods in simulation techniques and data analysis, for example, those rooted in Artificial Intelligence or those based on multi-point satellite observations.

Connect with colleagues across disciplines at the 5th Lagrangian session!

This session provides an open venue for scientists to share the latest advances in Lagrangian techniques, explore diverse applications, and build new connections.

We invite presentations on topics including, but not limited to:
- Planetary circulations and variability (fundamental processes shaping jets, gyres, waveguides, overturning circulations, transport barriers across atmosphere and ocean)
- Mesoscale eddies and coherent structures (eddy transport, wave-mean flow interactions, blocking)
- Turbulence and mixing (turbulent and convective entrainment, breaking internal waves, boundary layers)
- Numerical and computational advances (incl. data-driven techniques, GPU acceleration, graph-theoretical formulations, adaptive methods, data assimilation)
- Inverse modeling techniques (long-range transport of volcanic plumes, wildfire smoke, hazardous material, aerosols, plastics, micro-organisms, and their impacts on global composition, health, and climate)
- Field campaigns (drifters, floats, superpressure balloons, etc)

Waves in the Earth’s crust are often triggered by fractures in the process of sliding and/or propagation. Conversely, the waves can trigger fracture sliding and propagation. Analysis of wave propagation and their interaction with pre-existing or emerging fractures is central to geophysics. Recently new observations and theoretical concepts were introduced pointing out to the limitations of the traditional concepts. These are:
• Multiscale nature of wave fields and fractures in geomaterials
• Rotational mechanisms of wave and fracture propagation
• Strong rock and rock mass non-linearity (such as bilinear stress-strain curve with high modulus in compression and low in tension) and its effect on wave propagation
• Triggering effects and instability in geomaterials
• Active nature of geomaterials (e.g., seismic emission induced by stress and pressure wave propagation)
• Synchronization in fracture processes including earthquakes and volcanic activity
• Monitoring fracture processes based on recording of seismic signals and physics-based AI modelling

It is anticipated that studying these and related phenomena can lead to breakthroughs in understanding of the stress transfer and multiscale failure processes in the Earth's crust, ocean and atmosphere and facilitate developing better prediction and monitoring methods.

The session is designed as a forum for discussing these and similar topics.

Nonlinear waves transfer energy, momentum, and information across scales in the atmosphere and ocean. Rossby waves, atmospheric gravity waves, and ocean surface and internal waves interact with mean flows, turbulence, and other waves, influencing circulation, atmospheric blocking, teleconnections, ocean mixing, predictability, and extreme events. Their multiscale behaviour, nonlinear interactions, and limited observability remain challenging for conventional analysis and modelling.

This session invites contributions exploring how artificial intelligence, machine learning, and data-driven methods can improve the understanding, representation, and prediction of atmospheric and oceanic waves.

We welcome studies on Rossby-wave propagation and breaking, wave packets, wave–mean-flow interactions, blocking, teleconnections, circulation regimes, extremes, and predictability. Contributions addressing atmospheric gravity waves, ocean surface and internal waves, planetary and topographic waves, and wave–wave interactions are also encouraged.

Relevant approaches may include deep learning, neural operators, physics-informed AI, computer vision, explainable AI, reduced-order modelling, causal discovery, hybrid modelling, and machine-learning parameterizations. Applications may address wave detection, reconstruction from sparse observations, simulation acceleration, unresolved processes, prediction of wave evolution, and forecasting of wave-related extremes.
We also welcome assessments of the physical consistency, interpretability, uncertainty, and generalizability of AI models under changing climatic conditions.

Potential topics include:
* AI-based detection and tracking of waves and wave packets
* Rossby-wave breaking, blocking, and circulation regimes
* Wave–mean-flow and wave–wave interactions
* Gravity-wave detection and parameterization
* Data-driven modelling of ocean waves
* Neural operators and reduced-order models
* Physics-informed and physics-constrained AI
* AI-based simulation and prediction of wave evolution
* Waves, teleconnections, and climate variability
* Wave-related extreme and compound events
* Explainability and uncertainty quantification
* Comparisons of AI, numerical, and theoretical models

Rossby wave dynamics stands at the intersection of several open research questions, ranging from our basic understanding of mid-latitude variability, to the short- and medium-range predictability of high-impact weather events, and to the circulation changes expected from anthropogenic global warming. Rossby waves exist and propagate along vorticity gradients such as the one related to the tropopause-level jet stream, whose complex meandering often "breaks" creating nonlinear circulation features, such as atmospheric blocking.

Recent extreme weather and climate episodes, like heavy rainfall events leading to flash floods, recurrent and concurrent summer heatwaves or unforeseen winter cold spells, highlight the need to improve our understanding of jet streams and of the associated linear and non-linear, planetary and synoptic-scale Rossby wave dynamics in the atmosphere to better constrain the impacts of Rossby waves and of atmospheric blocking on extreme weather and climate events.

Abstracts are invited on a wide range of topics, with a focus on, but not limited to, the following areas:

(1) Theoretical developments in the dry and moist dynamics of Rossby waves, wave breaking, atmospheric blocking, and of jet streams acting as atmospheric Rossby waveguides. This includes the role of local and remote drivers (e.g., the tropics, Arctic, or stratosphere) in affecting Rossby wave evolution.
(2) Linkages between extreme weather/climate events and the jet stream, as well as the associated linear and non-linear Rossby wave evolution during such events, including Rossby wave breaking, cut-off formation and re-absorption, and atmospheric blocking.
(3) Application of cutting-edge methods to study the multi-scale interaction of Rossby waves from the convective scale to the large-scale dynamics, and its representation in existing weather and climate models (e.g. hierarchical and/or high-resolution modelling, machine learning/AI-based approaches).
(4) Exploring the effect of Rossby wave packets on predictability at lead times from medium range (~2 weeks) to seasonal time-scales. This includes the potential role of blocking and of teleconnections involving Rossby wave propagation.
(5) Projected future changes in planetary or synoptic-scale Rossby waves, and in their connection to extreme weather and events in a changing climate.

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.

Long spin-up times remain a major computational bottleneck in ocean and climate modelling, limiting our ability to investigate past climate states, understand model biases, and quantify parameter uncertainty. Developing more efficient spin-up methods can help overcome these limitations and support the use of past climate information to strengthen confidence in future climate projections.

This session will bring together results from the Past-to-Future Global Ocean Circulation Model Spin-Up Competition. The competition challenges participants to bring a global ocean model to equilibrium using as few computational resources as possible. Participants access the model as a time-forward black box, advancing the ocean state through a prescribed routine without modifying the underlying model or its physical configuration. This common framework enables a systematic comparison of alternative approaches.

The session will focus on three core aspects:

• presenting the methods developed by participating teams and comparing their performance against the common benchmark;
• discussing computational efficiency, convergence, and reproducibility, including the costs associated with training data where machine learning methods are used;
• exploring lessons learned and the potential for applying successful approaches more broadly in ocean and climate modelling.

We welcome competition participants and researchers interested in ocean and climate modelling, numerical analysis, scientific computing, and machine learning. The goal is to identify promising approaches, discuss remaining challenges, and encourage future collaborations on efficient model initialisation. Registration for the competition closes on 1 November 2026, with final results due on 1 March 2027.

Landscape evolution models (LEMs) have matured into powerful and widely used tools in the geomorphologist’s toolbox. Increasingly robust representations of weathering, erosion, sediment transport and hillslope processes allow LEMs to function as digital laboratories in which hypotheses about landscape dynamics can be explored across spatial and especially temporal scales that are impossible to directly observe.
LEMs have been used to investigate a wide range of questions, including the response of landscapes to climate and tectonic forcing; river incision and sediment transport; hillslope evolution and landslide dynamics; glacial and periglacial processes; soil formation and weathering; drainage-network development; interactions between geomorphic processes and ecosystems; and the propagation of disturbances through coupled Earth-surface systems. Their ability to represent interacting processes also offers opportunities to explore cascading natural hazards, from landslide–river interactions to chains of sediment, flooding and slope instability, making LEMs increasingly relevant to a key societal concern.
At the same time, the field is entering an exciting period of methodological tension. As process-based models, LEMs seek to encode physical mechanisms and study their emerging interactions explicitly, while AI and data-driven approaches offer powerful alternatives for identifying patterns and emulating processes. Rather than treating these approaches as competing paradigms, this tension raises fundamental questions about what we expect models to represent, how we evaluate them, and how process understanding can be combined with emerging computational methods.
This session welcomes contributions from researchers who use, modify, or develop landscape evolution models, as well as those whose research questions could benefit from the application of LEMs.

Earth operates as a complex, self-regulating cybernetic system driven by both internal processes and external astronomical forcing. This session explores the intricate feedback loops between the Solar System’s gravitational dynamics and Earth’s interconnected geospheres—including the atmosphere, hydrosphere, lithosphere, biosphere, pedosphere, troposphere, magnetosphere, and ionosphere. We focus specifically on how these systemic interactions govern sub-system variations, with a core emphasis on water balance components and hydrological cycles.
To decipher these non-linear, multi-scale relationships, traditional deterministic models must be augmented with data-driven methodologies. We invite contributions that advance empirical research tasks, conceptual frameworks, and system-level models. We particularly welcome studies utilizing artificial intelligence (AI), statistical learning, machine learning, and advanced data-mining techniques to analyze observational and proxy datasets. By bridging planetary dynamics, cybernetic geoscience, and modern data science, this session aims to foster a holistic understanding of Earth’s systemic balance and improve our predictive capabilities in a changing cosmic and terrestrial environment.

This session explores the need, challenges, opportunities, drawbacks, and obstacles in applying and explaining the special world of compositional data spaces.

Compositional data is a special type of non-negative data that carries relevant information in the ratios among the variables rather than in the actual data values. Considering the metric of compositional data may conflict with familiar geoscientific approaches: single chemical elements or mineralogical concentrations as proxies for environmental changes, counts of individuals as indicators of ecological states, etc.
Felix Chayes (1960) and John Aitchison (1981) began to establish a parallel universe of statistical methods beyond normality and linearity to gain sustainable insights into Compositional Data Analysis (CoDA) in the geosciences. Since then, the geoscientific community has expanded the range of methods.
However, even in the era of rapidly expanding big data methods, sustainable CoDA remains underrepresented in recent research.

We welcome contributions from all disciplines to answer the following questions: (i) How does CoDA transform our view of the earth? (ii) How can we gain new sustainable insights by applying CoDA? (iii) How can CodA increase the scalability of numeric models? (iv) How to deal with zeros and missing data? (vi) How to deal with low-count compositions? (v) How should we convincingly communicate CoDA?
This session addresses researchers from all quantitative fields to exchange ideas and foster interdisciplinary collaboration.

European seas and coastal areas are facing and are projected to face growing pressures from climate change, sea-level rise, storm surges, extreme waves, coastal flooding and erosion, marine heatwaves, changing sea-ice conditions and ecosystem degradation. Addressing these challenges requires integrated approaches that combine observations with ocean, climate, wave, biogeochemical and ecosystem models, high-resolution coastal simulations, artificial intelligence, and social and economic information.
This inter- and transdisciplinary session will explore how Ocean Digital Twins can bring these different sources of knowledge on future seasonal, decadal, and centennial ocean hazards together and turn model results into practical climate and ocean services. We invite contributions on advanced modeling; seasonal-to-decadal predictions and climate projections; model coupling and uncertainty; high-performance computing; AI-based/emulator-based predictions[AM1.1][AM1.2]; early warning; and interactive applications linked to European infrastructures such as EDITO and Destination Earth.
Applications may include future coastal flooding and erosion, compound hazards, coastal cities and ports, offshore wind and other marine renewable-energy systems, fisheries and aquaculture, marine ecosystems, biodiversity, sea ice and safe navigation. We also welcome research on adaptation pathways, nature-based solutions, vulnerability and resilience assessment, and environmental, social and economic trade-offs.
The session aims to bring together oceanographers, climate scientists, coastal engineers, ecologists, computer and data scientists, social scientists, practitioners and decision-makers. Participatory research involving public authorities, civil-protection agencies, port and energy operators, fisheries and aquaculture stakeholders, NGOs and coastal communities is also welcome.
The session is jointly organized by four Horizon Europe projects—MOIRAI, SEACLIM, EU-INTERCHANGE and RIVIERADE—which address complementary aspects of ocean and climate modelling, coastal risks, the land–river–ocean continuum, as well as, digital tools and services for climate adaptation.

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.

Nature-based Solutions (NbS) are “actions to protect, conserve, restore, sustainably use and manage natural or modified ecosystems, that address socio-economic and environmental challenges, while simultaneously providing human well-being, resilience and biodiversity benefits”. Within the framework of a global ecosystem approach, NbS must encompass ecological, societal, political, economic and cultural issues at all levels, from the individual to the collective, from local to national, from the public or private sphere.

As underlined by the IPCC and IPBES, climate change and biodiversity loss are deeply interconnected and must be addressed jointly. This session therefore focuses on how NbS can serve as adaptation strategies to climate change, while simultaneously preserving or restoring biodiversity. Considering various ecosystems (marine and coastal, urban, cropland, mountainous, forest, rivers…), NbS as climate change adaptation solutions includes the adaptation to: sea level rise (flooding and erosion), changes of the water regime (floods, droughts, water quality and availability), rise in temperatures (heat waves, forest fires, drought, energy consumption), plant stress and increase of pests (variation of yields, forest dieback), to minimize their associated social and economic negative impacts.

Therefore, this session aims to promote discussion integrating multiple disciplines related to ecosystem restoration, preservation and management, to put forward the complexity that is often hidden by simplifying hypotheses and approaches (sector-based silo approach, homogeneity of environments...).

Specific topics of interest are the followings:
- Complexity: nature of ecosystems and risk of oversimplification, interconnection between NbS and complementary areas, consideration of uncertainties
- Scales: spatial scales with the integration of NbS in their environment, and temporal scales considering sustainability over time, variability of bio-physical processes and climate change effects
- Ecosystem services: bio-geophysical processes, spatial shift between the location of NbS and the beneficiaries one, modification under climate change (tipping point), co-benefits or negative effects
- Assessment and indicators: measurement and modelling protocols, capacity to measure the complexity, resilience and stability of NbS
- Co-development with stakeholders, engaging civil society, and integrating NbS into education, aligned with IAHS HELPING Decade objectives.

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 tipping points
- methods to anticipate critical transitions from data
- tipping points in climate models across the hierarchy, including comprehensive Earth system models
- climatic, ecological and socioeconomic impacts of tipping events
- decision theory in the presence of uncertain tipping point estimates and uncertain impacts

River deltas and coastal plains are highly vital environments, offering a wide range of ecosystem services that make them hotspots of biodiversity and human settlement, and key for economic production and food security. Human activities such as river damming, resource extraction and land-use land cover change have significantly altered coastal-deltaic morphodynamics, interact with and accelerate biophysical impacts from climate change. Consequently, these low-elevation environments face increased risks of land subsidence, erosion, relative sea-level rise, flooding and salinity intrusion, all of which impact the functioning of coastal social-ecological systems. These accelerating pressures are promoting key advances in understanding the driving processes, the threats generated and the ways humans are addressing these through management. Developments have come from earth observations using remote sensing, and from modelling, increasingly supported by artificial intelligence, in addition to conceptual advances in critically analyzing the human-coastal wetland relationship from both geoscience and management perspectives, preferably interlinked. Critical research gaps persist, however, regarding all aspects of processes and geomorphic responses to these threats, and these limit our capacity to predict and manage coastal lowland and delta change. A more integrated, multi- and interdisciplinary understanding of the interacting processes and impacts is needed to manage river deltas and coastal plains in a sustainable and inclusive manner. This requires taking a system’s perspective, connecting insights from coastal geomorphology and geology, hydrology and hydrogeology, natural hazards and disaster risk science, as well as human geography and social sciences. This session provides an interdisciplinary platform that welcomes contributions that advance an integrated understanding of the emerging changes, pressures and the interconnected processes and hazards they generate; assess the implications for coping capacity and resilience; explore developments in earth observation and modelling efforts to address them; point out persisting knowledge gaps and identify opportunities for effective management and sustainable development of these coastal lowlands.

The growing scientific and policy focus on tipping points in the Earth system reflects their potential for profound physical and socio-economic consequences. If crossed, tipping points could trigger abrupt changes that are effectively irreversible on human timescales. Continued Global Warming is predicted to drive major changes across the North Atlantic, pushing key components of the climate system towards potential critical thresholds and abrupt reorganisation. Yet, the nonlinear nature of these systems means that future change is difficult to observe, model, predict, and govern. Early Warning Systems (EWS) have emerged as a potential method of translating our understanding of abrupt changes in the climate and their impacts into useful and actionable information for relevant stakeholders and decision makers.
Important questions remain around how best to inform and govern the response to North Atlantic tipping and what information can be robustly provided considering deep uncertainty.

To address these challenges, this session aims to bring together interdisciplinary perspectives to inform the co-design of EWS that can support tipping point governance. We welcome contributions from observations, modelling, impact studies, and governance. We have particular interest in modern approaches able to inform governance and response, including under deep uncertainty. Our primary focus is on North Atlantic tipping elements, including the Atlantic Meridional Overturning Circulation, Subpolar Gyre and Greenland Ice Sheet. Studies of other tipping elements and their interactions are also welcome. This session builds on themes being explored by the UK Advanced Research and Invention Agency (ARIA) Forecasting Tipping Points programme.

We welcome contributions that address a range of environmental and societal effects of nuclear war. Areas of interest include, but are not limited to:
- Radioactive contamination
-Radioactive fallout and long-range transport
-Effects on supply chains and infrastructure
-Nuclear detonations in space and EMP attacks
-Nuclear winter and climatic disruption
-Fires and blast effects
-Smoke transport and aerosol plume dynamics
-Impacts on the hydrological cycle and oceans
-Impacts on ecological systems and ecosystem services
-The use of science to contribute to reduction of nuclear threats.

Before geomaterials become “hard” rocks, they commonly pass through stages in which they behave as soft-matter systems, such as complex fluids, gels, colloids, liquid crystals, and foams. During these stages, they can be regarded as geological soft matter, which commonly occurs during lithification, crystallization and deformation. The initiation of such soft-matter behaviour is highly sensitive to the surrounding physicochemical environment, leading to the formation of striking patterns and complex structures. Understanding how these structures form and evolve remains a major challenge in the Earth sciences. Examples include soft-sediment deformation structures such as load casts and flame structures; mineral dendrites; concretions and chemical banding; crack patterns such as septaria, mud cracks and lava polygons; and dissolution–precipitation forms such as travertine dams, scallops and stalactites. Other examples include patterns associated with microbial mats, such as stromatolitic structures, together with botryoidal and spherulitic textures, as well as hydro-geomorphological patterns such as channel networks and dunes, and vegetation patterns.

The physics underlying these patterns, which evolve progressively during the soft-matter stage, commonly involves complex non-equilibrium processes governed by self-organization and self-assembly. These processes allow complex spatiotemporal patterns to emerge from simple rules, while making the underlying physicochemical mechanisms difficult to infer from the pattern alone. Exploring such physical laws and establishing a geological soft-matter paradigm would not only provide an excellent opportunity to study pattern formation itself, but also help develop analogues for understanding geological processes in Earth’s history and potentially on other planets.

In this session, we discuss the potential application of the soft-matter paradigm to geological evolution research. We invite contributions that aim to deepen our understanding of complex patterns across scales in natural and experimental systems, investigating the physicochemical processes that generate them, through field observations, experimental work, theoretical modelling, and computational simulations. Contributions involving advanced quantification, conceptual advances, quantitative model development and modelling are warmly welcome.