Time series are a very common type of data sets generated by observational and modeling efforts across all fields of Earth, environmental and space sciences. The characteristics of such time series may however vastly differ from one another between different applications – short vs. long, linear vs. nonlinear, univariate vs. multivariate, single- vs. multi-scale, etc., equally calling for both specifically tailored methodologies as well as generalist approaches. Similarly, also the specific tasks of time series analysis may span a vast body of problems, including

- dimensionality/complexity reduction and identification of statistically and/or dynamically meaningful modes of (co-)variability,
- statistical and/or dynamical modeling of time series using stochastic or deterministic time series models or empirical components derived from the data,
- characterization of variability patterns in time and/or frequency domain,
- quantification various aspects of time series complexity and predictability,
- identification and quantification of different flavors of statistical interdependencies within and between time series, and
- discrimination between mere co-variability and true causality among two or more time series.

According to this broad range of potential analysis goals, there exists a continuously expanding plethora of time series analysis concepts, many of which are only known to domain experts and have hardly found applications beyond narrow fields despite being potentially relevant for others, too.

Given the broad relevance and rather heterogeneous application of time series analysis methods across disciplines, this interdisciplinary session shall serve as a knowledge incubator fostering cross-disciplinary knowledge transfer and corresponding cross-fertilization among the different disciplines gathering at the EGU General Assembly. We equally solicit contributions on methodological developments and theoretical studies of different methodologies as well as applications and case studies highlighting the potentials as well as limitations of such techniques across all fields of Earth, environmental and space sciences and beyond.

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

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

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

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

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

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

Climate change affects ecosystems worldwide by disrupting the balance between biotic communities and the abiotic factors that sustain them. These changes in environmental conditions alter the hierarchy of ecosystem-shaping mechanisms, driving the spatial reorganisation of vegetation and resources. Vegetation pattern formation refers to the self-organisation of plant communities into distinct spatial arrangements, arising from interactions among plants and environmental factors such as resource availability and ecosystem feedback. These patterns play a crucial role in improving the management of resources such as water and soil nutrients, particularly in vulnerable regions such as arid and semi-arid landscapes. Understanding these patterns is thus vital to gaining insights into ecosystem functioning, feedback mechanisms, and how drylands will respond to ongoing climate change. However, the ecological significance of vegetation patterns in water-limited ecosystems remains unclear. For several years theoretical models suggested that vegetation patterns could serve as indicators of ongoing desertification processes, with vegetation spots preceding tipping into a desert state. More recent theoretical progress, however, has hypothesised that patterns could provide ecosystems with a route to prevent tipping by limiting the impact of external stresses to a spatially local scale. This session invites contributions that study vegetation pattern formation using a range of approaches, including mathematical modelling, data-driven and machine learning techniques, as well as ground-based or remote sensing observations. The aim is to foster dialogue and collaboration between theoretical and empirical research, facilitating a deeper integration of theory with measurement and working towards resolving existing discrepancies in the theoretical literature.

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

Specific topics include: PDEs, numerical methods, extreme events, statistical mechanics, thermodynamics, dynamical systems theory, large deviation theory, response theory, tipping points, model reduction techniques, 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.

Projections of future climate rely on 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 simple conceptual models, which offer qualitative process understanding and allow for a broad range of theoretical approaches. Simple climate models are also widely used as physics-based emulators of computationally expensive ESMs, forming the basis of many probabilistic assessments in the IPCC 6th Assessment Report.

Between these two approaches, a persistent “gap between simulation and understanding” (Held 2005, see also Balaji et al. 2022) challenges our ability to transfer insight from simple models to reality, and distill the physical mechanisms underlying the behavior of state-of-the-art ESMs. This calls for a concerted effort to learn from the entire model hierarchy, striving to understand the differences and similarities across its various levels of complexity for increased confidence in climate projections.

In this session, we invite contributions from all subfields of climate science that showcase how modeling approaches of different complexity advance our process understanding, and/or highlight inconsistencies in the model hierarchy. We also welcome studies exploring a single modeling approach, as we aim to foster exchange between researchers working on different rungs of the model hierarchy. 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.), and full ESMs.

Processes and phenomena of interest include, but are not limited to:
* Earth system response to forcing scenarios (policy-relevant, extreme, counterfactual)
* Tipping points and abrupt transitions (e.g. Dansgaard-Oeschger events)
* Coupled modes of climate variability (e.g. ENSO, AMV, MJO)
* Emergent and transient phenomena (e.g. cloud organization)
* Extreme weather events

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

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

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

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

Abstracts are solicited regarding the predictability and attribution of weather/ climate extremes and their impacts.
Weather and climate extremes often have large impacts, so it is critical that we better understand these events, improve their prediction, and gain knowledge of how and why they are changing as the planet warms. This session aims to bring together physics-based and data-driven approaches to the study of extremes and their impacts. Studies focusing on either hazards associated with extremes or directly on societal impacts (including health, insurance, energy, and other sectors) are welcome. A particular goal of this session is to explore novel approaches to the predictability of extremes, and facilitate a deeper understanding of their impacts in our changing climate. We particularly encourage submissions from early career scientists and underrepresented groups.
Topics of interest include but are not limited to:
Predictability of extremes, especially from forecasting and applied viewpoints.
Attribution of extreme events
Data-driven and AI approaches to forecasting extremes and impacts
Predictability and forecasting of the impacts of extreme events, particularly in the context of informing early warning systems
Attribution of extreme event impacts, losses etc.
Applications of attribution techniques e.g. climate litigation

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

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

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

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

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

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

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

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

The Earth 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, 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, 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, dynamical and stochastic systems theory, etc., are particularly encouraged.

The Earth's climate system is characterized by the intricate interplay of atmospheric and oceanic processes evolving at various timescales, exhibiting complex behaviors and nonlinear interactions. Gaining a deeper insight into the underlying dynamics of this system is crucial for understanding the physical origins of weather and climate variability, as well as for predicting climate trends and extreme events. However, this task poses significant challenges, as traditional theoretical approaches alone often fall short in capturing the full extent of these complexities. To address these challenges, data-driven methods have increasingly become indispensable tools in the study of oceanic and atmospheric dynamics.

Over the past few decades, the application of data-driven approaches has led to substantial advancements in our understanding of climate systems. Linear techniques such as normal modes, wave analysis, and Fourier methods, have long been employed to extract relevant spatiotemporal features and identify key climate modes. Furthermore, empirical dynamical methods, such as Linear Inverse Models (LIMs), have proven invaluable for the study and prediction of climate phenomena like the El Niño-Southern Oscillation (ENSO).

In recent years, the advent of non-linear data-driven methodologies has opened new avenues in the field. Techniques such as transfer operators, including Koopman mode decomposition, and various machine learning approaches have significantly broadened the scope of what can be achieved in the analysis and forecasting of climate dynamics. These methods offer potential to uncover complex patterns, improve climate predictability, and develop more accurate reduced-order models that capture the essence of the underlying dynamical processes, holding great potential for enhancing our understanding of complex atmospheric and oceanic climate processes.

This session aims to bring together researchers at the forefront of applying data-driven methods to study oceanic and atmospheric dynamical systems. We invite contributions that explore the application of these methodologies in various aspects of climate science, including (but not limited to) the following topics:

- Climate Predictability and Forecasting

- Spatiotemporal Feature Extraction

- Climate Mode Identification

- Climate Network Analysis

- Exploration of Climate Attractors

- Development of Reduced-order Models

- Extreme Event Analysis

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

Large-scale machine learning models, for example FourCastNet, Pangu-Weather, GraphCast and ECMWF’s AIFS, are currently transforming weather forecasting and are also reshaping the weather and climate research landscape. The first machine learning models that can be applied to climate change scenarios are also being developed. This session will bring together developers and users from research and operations, from academia as well as private enterprises, to discuss the current state of the art and future developments in the field. We welcome contributions from machine learning model developers, consortia and users of single component machine learning models, such as those focused on the atmosphere, ocean or sea ice, as well as coupled models that consider the entire Earth system. Foundation models, which learn more general representations of the Earth system, and studies on the combination of large-scale machine learning models and traditional physics-based solvers are also welcome. Of particular interest are also contributions on verification methods, out-of-distribution experiments, real or idealized case studies across different scales (e.g. air pollution, solar energy production, extratropical cyclones, ocean dynamics) as well as contributions with a focus on the physical consistency of such machine learning models. The session aims to facilitate dialogue between researchers interested in scientific discovery and developers interested in novel machine learning ideas pertinent to the domain, e.g. spatio-temporal diffusion models, variational autoencoders and novel training protocols.

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

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

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

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

Proper characterization of uncertainty remains a major research and operational challenge in Environmental Sciences and is inherent to many aspects of modelling impacting model structure development; parameter estimation; an adequate representation of the data (inputs data and data used to evaluate the models); initial and boundary conditions; and hypothesis testing. To address this challenge, methods that have proved to be very helpful include a) uncertainty analysis (UA) that seek to identify, quantify and reduce the different sources of uncertainty, as well as propagating them through a system/model, and b) the closely-related methods for sensitivity analysis (SA) that evaluate the role and significance of uncertain factors (in the functioning of systems/models).

This session invites contributions that discuss advances, both in theory and/or application, in methods for SA/UA applicable to all Earth and Environmental Systems Models (EESMs), which embraces all areas of hydrology, such as classical hydrology, subsurface hydrology and soil science.

Topics of interest include (but are not limited to):
1) Novel methods for effective characterization of sensitivity and uncertainty
2) Analyses of over-parameterised models enabled by AI/ML techniques
3) Single- versus multi-criteria SA/UA
4) Novel methods for spatial and temporal evaluation/analysis of models
5) The role of information and error on SA/UA (e.g., input/output data error, model structure error, parametric error, regionalization error in environments with no data etc.)
6) The role of SA in evaluating model consistency and reliability
7) Novel approaches and benchmarking efforts for parameter estimation
8) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, model identification and selection, model diagnostics, parallel computing, model pre-emption, model ensembles, etc.)
9) Methods for detecting and characterizing model inadequacy

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

Geophysical fields such as wind, solar power or river discharge are known to exhibit extreme variability across a wide range of space-time scales. Such behaviour significantly affects energy harvesting from all these renewable energy sources. The extreme variability and intermittency are actually intrinsic features of renewable energy that require a better understanding in a context of rapid growth and increasing share in the energy mix at global scale. Scaling laws in general are a powerful tool to better understand, analyse, and simulate the underlying extremely variable processes and their non-linear interactions.

This session will bring together scientists and practitioners who aim to better measure, understand and model the extreme variability of geophysical fields and its impact on renewable energy production. Contributions addressing one or several of the following topics are especially targeted:
- Novel high spatial and/or temporal resolution techniques for measuring geophysical fields that are used as resources for renewable energy production
- Novel modelling or characterization tools of the variability of geophysical fields ranging from mm/ms scale to regional / annual scale using various approaches (e.g. scaling, (multi-)fractal, statistic, deterministic, numerical modelling…)
- Novel approaches to better understand and characterize how extreme variability is transferred to power production.

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

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

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

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

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

The dynamics of the atmosphere in the extratropics is characterized by the coexistence of multiple fundamental processes spanning a variety of spatio-temporal scales. The interactions between the atmosphere and the oceans are central to several of these, while the interaction with sea-ice also plays a major role in high latitudes. The thermal contrast between the ocean and land surface, the different thermal inertia of the ocean and the atmosphere, and the moisture and heat exchange between the two are important for the general circulation of the atmosphere and oceans, and indicate that both a thermodynamic and a dynamic perspective are needed for understanding this topic. For example the oceanic anomalies, through air-sea interactions, affect the atmospheric dynamics already at the weather scales, and the atmosphere can quickly transfer anomalies towards remote areas, as in the case of diabatic heating along frontal zones. Atmospheric rivers originating over oceanic surfaces affect the formation of synoptic systems in the mid-latitudes and trigger climate extremes. Careful understanding of these mechanisms is crucial, especially regarding the assessment and predictability of extreme events, and the capability to discern the impacts of anthropogenic climate change on the variability of the climate system.
We welcome all contributions on the interactions between the oceanic and atmospheric circulation. These include investigations of atmosphere – ocean dynamics and thermodynamics at hemispheric and regional scales, including the role of sea-ice, and both weather and climate timescales. We also encourage submissions that address and compare different methodologies, e.g. detection of dominant patterns or weather regimes, dimensionality reduction involving traditional techniques such as PCA and EOFs, or new methods such as random forest or other AI-based algorithms. Model intercomparisons, and evaluations of past and future climate projections, are also welcome.

Soil resources are globally threatened and require our proactive adaptation to ensure sustainable and resilient land and ecosystem management practices. The complexity and variability of soils limit our capabilities to predict soil functionality and challenge the development of adequate soil management and land use strategies. In particular, soils are permanently under pressure and also highly affected by climate change and extreme weather events. It often remains unclear how environmental changes as well as management strategies influence over a broad spectrum of spatio-temporal scales various soil functions such as nutrient cycling, carbon sequestration, water quality, biodiversity and agricultural productivity.

The biogeochemical and physical modelling of soils allows unravelling the complex multi-scale dynamic interactions between biotic and abiotic soil components underlying the emergence of soil structure and functions. Soil models also deepen our understanding of soil physical and biogeochemical processes by integrating sparse data that can only be collected at limited spatial and temporal scales. However, models' outputs can only reflect the hypotheses underlying them.

Within the perspective of soil health and resilience, we would like to question and explore in this session the performance of current data-driven, theoretical, and mechanistic modelling approaches in response to extreme weather events in particular. We invite inter- and multi-disciplinary contributions, ranging for instance from models of microbiome interactions in soil pores to the modelling of agroecosystems and land-use types.

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

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

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

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

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

Recent Earth System Sciences (ESS) datasets, such as those resulting from very high resolution numerical modelling, have increased both in terms of precision and size. These datasets are central to the advancement of ESS for the benefit of all stakeholders, public policymaking on climate change and to the performance of modern applications such as Machine Learning (ML) and forecasting.

The storage and shareability of ESS datasets have become an important discussion point in the scientific community. It is apparent that datasets produced by state-of-the-art applications are becoming so large that even current high-capacity data centres and infrastructures are incapable of storing, let alone ensuring the usability and processability of such datasets. The needs of ongoing and upcoming community activities, such as various digital twin centred projects or the 7th Phase of the Coupled Model Intercomparison Project (CMIP7) already stretch the abilities of current infrastructures. With future investment in hardware being limited, a viable way forward is to explore the possibilities of data reduction and compression with the needs of stakeholders in mind. Therefore, the use of data compression has grown in interest to 1) make the data weight more manageable, 2) speed up data transfer times and resource needs and 3) without reducing the quality of scientific analyses.

Concurrently, replicability is another major concern for ESS and downstream applications. Being able to reproduce the most recent ML and forecasting results and analyses thereof has become mandatory to develop new methods and integrated workflows for operational settings. On the other hand, the data accuracy needed to produce reliable downstream products has not yet been thoroughly investigated. Therefore, research on data reduction and prediction interpretability helps to 1) understand the relationship between the datasets and the resulting prediction and 2) increase the stability of prediction.

This session discusses the latest advances in both data compression and reduction for ESS datasets, focusing on:
1) Approaches and techniques to enhance shareability of high-volume ESS datasets: data compression (lossless and lossy) or reduction approaches.
2) Understanding the effects of reduction and replicability: feature selection, feature fusion, sensitivity to data, active learning.
3) Analyses of the effect of reduced/compressed data on numerical weather prediction and/or machine learning methods.

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

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

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

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

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.

Statistical post-processing techniques for weather, climate, and hydrological forecasts are powerful approaches to compensate for effects of errors in model structure or initial conditions, and to calibrate inaccurately dispersed ensembles. These techniques are now an integral part of many forecasting suites and are used in many end-user applications such as wind energy production or flood warning systems. Many of these techniques are flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias correction up to very sophisticated machine learning and/or distribution-adjusting techniques that take into account correlations among the prognostic variables.

At the same time, a lot of efforts are put in combining multiple forecasting sources in order to get reliable and seamless forecasts on time ranges from minutes to weeks. Such blending techniques are currently developed in many meteorological centers. These forecasting systems are indispensable for societal decision making, for instance to help better prepare for adverse weather. Thus, there is a need for objective statistical framework for "forecast verification'', i.e. qualitative and quantitative assessment of forecast performance.

In this session, we invite presentations dealing with both theoretical developments in statistical post-processing and evaluation of their performances in different practical applications oriented toward environmental predictions, and new developments dealing with the problem of combining or blending different types of forecasts in order to improve reliability from very short to long time scales.

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

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

Forecasting the weather, in particular severe and extreme weather, remains an important subject in meteorology. This session highlights recent research and advancements in forecasting methods, with a strong emphasis on AI-based methods and their integration into operational and impact-oriented forecasting systems. We welcome contributions that explore applications in nowcasting, mesoscale and convection permitting modelling, ensemble prediction, and seamless approaches that optimally integrate multiple forecast sources.

Topics may include:

• Data-driven forecasting, including ensemble methods, across global, regional and local scales
• Integration of data-driven models within data assimilation algorithms
• Operational workflows: implementation, monitoring, versioning of data and models, data, training, and inference pipelines
• AI-driven nowcasting methods and systems utilizing observational data and weather analysis
• Enhancement of mesoscale and convection-permitting models through AI techniques
• Application of novel remote sensing technologies in data assimilation processes
• Utilization of ensemble prediction techniques for improved forecasting
• Development of ensemble-based products for severe/extreme weather forecasting
• Seamless deterministic and probabilistic forecast prediction in data-driven, statistical, numerical and data-blending approaches
• Post-processing techniques, statistical methods in prediction
• Impact-oriented weather forecasting
• Presentation of results from relevant international research projects of EU, WMO, and EUMETNET etc.

Climate modelling ensembles are fundamental for exploring and understanding uncertainty in future climate change projections. Uncertainty has manifold origins, including: process understanding and intermodel differences, model tuning strategies, parameterization choice and weak constraints on parameters, limited observations of initial conditions and climate model initialization strategy, and questions over the relationship between observations and model variables. These combine with unknowns such as future anthropogenic greenhouse gas emissions to give large climate prediction uncertainties which impact climate decision-making. Understanding them is also critical for advancing process understanding.

These uncertainties manifest themselves across the modelling hierarchy, from Earth system and cloud resolving models, to simple models for conceptual studies and interdisciplinary models for integrated assessment. In this context, this session invites wide-ranging and interdisciplinary contributions on the science, method, and application of long-term climate modelling ensembles, including but not restricted to:

Approaches to ensemble simulations: sampling uncertainty in large models (e.g., Earth System Models, cloud resolving models); model weighting and interpretation of small ensembles; characterising uncertainties from initial conditions & climate model initialization; data science approaches to sampling & emulation; future considerations in large ensemble simulations; uncertainty reduction in Earth system tipping elements; cross-cutting theories of ensemble design; anticipating future model evolution.

Comparing ensembles with observations: confronting model ensembles with observations including Palaeoclimate data; irreducible uncertainties in ensembles; emulators for characterizing uncertainty across scenarios; observational constraints with large forcing, slow dynamics, and internal variability; effects of model resolution on reliability; assessment and evaluation.

Ensembles and decision-making: Post-hoc emulation for uncertainty characterization; reducing generalization errors in simple models and emulators; consistent modelling hierarchies for mitigation and adaptation; irreducible errors in decision-relevant simulations; integrated assessment model ensembles; value of information from ensemble design; effects of scale on uncertainty; decision-sciences based approaches to ensemble design.

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

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

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

This session welcomes cross-cutting advances in theoretical, methodological and applied studies at the synergistic interface among physical, analytical, information-theoretic, kinematic-geometric, machine learning, artificial and systems intelligence approaches to complex system dynamics, hazards and predictability across Hydrology and broader Earth System Sciences.

Special focus is given to unveil complex system dynamics, regimes, transitions, extremes, hazards and their interactions, along with their physical understanding, predictability and uncertainty, across multiple spatiotemporal scales.

The session encourages discussion on interdisciplinary physical and data-based approaches to system dynamics across Hydrology and broader Geosciences, ranging from novel advances in stochastic, computational, information-theoretic and dynamical system analysis, to cross-cutting emerging pathways in information physics, artificial and systems intelligence with process understanding in mind.

The session further encompasses practical aspects of working with systems intelligence and emerging technological approaches for strengthening systems analytics, causal discovery, model design and evaluation, predictability and uncertainty analysis, along with geophysical automated learning, model design, prediction and decision support.

Take part in a thrilling session exploring and discussing promising avenues in system dynamics and information discovery, quantification, modelling and interpretation, where methodological ingenuity and natural process understanding come together to shed light onto fundamental theoretical aspects to build innovative methodologies to tackle real-world challenges facing our planet.

All the forecasts are connected to some level of uncertainty. When the forecast is applied to natural hazards, existing uncertainty may become critical, as significant changes in the forecasts may play a major role in the definition of risk reduction actions.

While this is pervasive across all natural hazards, significantly different approaches have been defined in the different disciplines of Earth Sciences, both in the definition of methods to quantify uncertainty, and in the selection of specific communication strategies for decision-makers or for the general public. Indeed, the need of accounting for and communicate uncertainty, coupled with the capacity of developing adequate models to this aim, strongly influenced how and at which level uncertainty has been included and communicated in forecasting models.
This session is dedicated to foster cross-discipline exchange of existing experiences as well as ongoing efforts in the quantification, communication, and use of uncertainty in decision-making along the different disciplines of Earth Sciences.

Join us for the third edition of the Lagrangian session, where researchers across disciplines showcase their work using Lagrangian tools and techniques on turbulent to planetary scales. In this session, you can expect to hear about the latest developments in Lagrangian techniques, learn about a wide range of topics and applications, and expand your professional network.

We invite presentations on topics including – but not limited to – the following:
- Large-scale circulation studies using direct Lagrangian modeling and/or age and chemical tracers (jets, gyres, overturning circulations);
- Exchanges between reservoirs and mixing studies (e.g. transport barriers and Lagrangian Coherent Structures in the stratosphere and in the ocean, stratosphere-troposphere exchange);
- Tracking long-range anthropogenic and natural influence (e.g. effects of recent volcanic eruptions and wildfire smoke plumes on the composition, chemistry, and dynamics of the atmosphere, transport of pollutants, dusts, aerosols, plastics, and fluid parcels in general, etc);
- Inverse modeling techniques for the assessment and constraint of emission sources (e.g. backtracking, including diffusion and buoyancy);
- Model and tool development, computational advances.

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

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

We particularly encourage participation from early career researchers.

Planetary convection provides many challenges, regarding the equation of state (EoS), the coefficients of transport of momentum, heat and different species, and the governing equations. The non-linear transport of momentum causes turbulence (in the restricted sense) but the non-linear transport of heat and mass causes also a range of temporal and spatial scales, chaotic mixing, enhanced transport. Compressibility (cf. EoS), planetary rotation, dynamo action are all circumstances affecting planetary convection. In addition, the interaction between planetary envelopes, at the ICB or CMB for instance, have been shown to affect convection on one or both sides of the boundaries, with or without melting and crystallization. Mathematical, numerical and experimental studies are welcome within this broad subject.

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

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

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

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

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

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

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

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

Waves in the Earth’s crust are often generated by fractures in the process of their sliding or propagation. Conversely, the waves can trigger fracture sliding or even 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
• Apparent negative stiffness associated with either rotation of non-spherical constituents or fracture propagation 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)
• Mechanics of granular material blowout by gas filtration
• Non-linear mechanics of hydraulic fracturing
• Synchronisation in fracture processes including earthquakes and volcanic activity

Complex waves are now a key problem of the physical oceanography and atmosphere physics. They are called rogue or freak waves. It may be expected that similar waves are also present in non-linear solids (e.g., granular materials), which suggests the existence of new types of seismic waves.

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.

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

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

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

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

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

Alluvial fan-river systems, which are ubiquitous at mountain fronts, are products of interactions among boulders, pebbles, grains, and the water carrying them. These cross-scale interactions among the granular materials and fluidic environment shape the macroscale self-organized and self-affined alluvial fan-river systems and control the dynamics and evolution of such systems. Theoretical reasoning, and measurements including high-resolution terrane mapping, geophysical probing, geochemical fingerprinting, state-of-art geochronology, and numerical and physical modeling, have been applied to unveil these interactions and quantify the relationship between fluxes, sizes and shapes, as well as the related constants and exponents of scaling laws. In this session, we invite contributions across these disciplines to foster our understanding of how the mechanics, physics, and chemistry at different scales regulate the alluvial fan-river system, which serves as an important habitat for species and reservoirs of nutrients and represents key sediment archives of nearby landscape evolution.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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