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 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)

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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

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.

Forecasting systems are indispensable for making informed decisions under uncertainty. Therefore, there is a need for an objective and well-understood framework for ``forecast verification'', i.e., qualitative and quantitative assessment of forecast performance.

Statistical methods compare historical forecasts with corresponding verifications, indicating whether the forecasting system behaved significantly differently (in a statistical sense) from what was expected. This requires that the forecasts have a well--defined statistical interpretation; whether a forecast represents a mean or a quantile makes a difference with regards to how we evaluate that forecast.

This short course will introduce the participants to the fundamentals of statistical forecast verification. Some necessary statistical theory will be presented, along with the concept of risk measures, which allows to provide forecasts with a precise statistical meaning. We furthermore illustrate the relation to scoring and identification functions, and discuss practical challenges with evaluating forecasts as spatial fields (as opposed to point by point). Specifically, the course will cover the following topics (more or less in that order)

(1) Forecast types, risk measures, scoring functions, and identification functions (20min)
(2) Tests and p-values (10min)
(3) How to evaluate forecasts for specific risk measures
(with hands-on part, 30min)
(3) How to evaluate forecasts of spatial fields
(with hands-on part, 30min)
(4) Open challenges (15min)

The target audience is researchers (from both academic institutions and operational centers) who are either new to forecast verification or have practical experience but want to learn more about the theory. The discussed methods are applicable not only in atmospheric forecasts but in many other fields such as parameter estimation, data assimilation, model evaluation, and machine learning.

Climate change is a major concern for public health. This was exemplified by the estimation of Vicedo et al. (2021) that 37% of heat-related deaths were attributable to climate change between 1991-2018, when considering data from 43 countries. Two key frontiers in health impact attribution are now to move from trend attribution to event attribution, and from considering heat-related mortality to a broader set of health outcomes, such as cause-specific hospital admissions or the spread of infectious diseases.

This short course will provide an overview of 3 core aspects:
• the caveats associated with moving from a hazard to a health-impact focused perspective within an attribution framework
• discuss examples of health impact assessments from heat-related mortality and infectious diseases from an epidemiological perspective
• demonstrate how to combine epidemiological modelling with attribution

We welcome all those interested in the intersection of climate and health. The content will be targeted towards an audience with experience or interest in (extreme) event attribution of hazards, and/or those with experience in epidemiology or health impacts modelling. The goal of this course is to provide participants with an understanding of some of the key challenges from both the climate and health perspectives, as well as to facilitate future collaborations between those in both the climate and health fields by providing teaching material and interactive tutorials. This short course is delivered as part of the project TACTIC, and gratefully acknowledges funding provided by the Wellcome Trust.