Taking inspiration from the Mathematics of Planet Earth 2013 initiative, 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.

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

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.

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.

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 prediction.

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

In recent years, sea ice has displayed behaviour unseen before in the observational record, both in the Arctic and the Antarctic. 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 modeling 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.

ENSO and its interactions with other tropical basins are the dominant source of interannual climate variability in the tropics and across the globe. Understanding the dynamics, predictability, and impacts of ENSO and tropical basins interactions, and anticipating their future changes are thus of vital importance for society. This session invites contributions regarding all aspects of ENSO and tropical basins interactions, including: dynamics, multi-scale interactions; decadal and paleo variability; theoretical approaches; ENSO diversity; global teleconnections; impacts on climate, society and ecosystems; seasonal forecasting and climate change projections of tropical mean state changes, ENSO and its tropical basins interactions. Studies aimed at evaluating and improving model simulations of ENSO, the tropical mean state and the tropical basins interactions basin are especially welcomed.

Geophysical processes are governed by diverse spatial and temporal scales, and often characterized by the complex fluctuations of observations. Modeling and simulation of geophysical processes becomes extremely complex because of such non-linear fluctuations, for which, the scaling characterization is extremely important.

The first part of the session expands the knowledge on scaling characterization of geophysical time series from diverse scientific domains to develop a knowledge base of complementary nature. The geophysical processes become even more complex because of the internal structural properties like intermittency and such an in-depth understanding will improve the accuracy of modeling of complex systems. This session aims to nurture the scientific development of scaling, fractals and related methodologies applicable to the time series observations from wide range of geophysical fields like hydrology, climatology, meteorology, atmospheric science, oceanography and statistical physics for their improved modeling and predictability :
- Scaling, fractal, multifractal characterization and modeling of complex geophysical data and extreme events
- In-depth understanding of the internal dynamics of geophysical data
- Understanding the fractal /scaling correlations between governing variables
- Linking network theoretical approach and scaling to find its applications across different geophysical fields

The second part of the session focuses on characterizing multi-decadal and longer Earth system dynamics, which has significant and direct impact on our society. This requires a combination of paleoclimate data and modeling given the insufficiency of short-term observational data. Our aim is to advance the understanding of climate variability across spatial and temporal scales through research focusing on:
-Characterizing multi-decadal to millennial climate dynamics through the use of proxy data and (conceptual or realistic) model simulations
-Evaluating the impact of Earth’s subsystem - such as the ocean, atmosphere, cryosphere and land-surface - in shaping long-term climate variability, and relevant feedback mechanisms
-Proxy system modeling, calibration and propagation of uncertainty with a focus on multi-decadal and longer timescales to aid reconstructions and model-data comparisons
-The attribution of climate variability to internal and/or forced dynamics

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.

Rainfall is a “collective” phenomenon emerging from numerous drops. Understanding the relation between the physics of individual drops and that of a population of drops remains an open challenge, both scientifically and at the level of practical implications. This remains true also for solid precipitation. Hence, it is much needed to better understand small scale spatio-temporal 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. Therefore, (1) accurate measurement and prediction of the spatial and temporal distribution of precipitation over a catchment and (2) the efficient and appropriate description of the catchment properties are important issues in hydrology.

This session will bring 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 especially targeted:
- 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;
- Precipitation drop (or particle) size distribution and its small scale variability, including its 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.

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 specifically tailored methodologies as well as generalist approaches. Similarly, also the specific task 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 correlation 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 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 different techniques across all fields of Earth, environmental and space sciences and beyond.

Scientific disciplines strive to explain the causes of observed phenomena. In Earth sciences, in particular in climate research, the notion of causality is discussed and understood from several different points of view. Hannart et al. (BAMS, 2016), following Judea Pearl, state that “Causal counterfactual theory provides clear semantics and sound logic for causal reasoning and may help foster research on, and clarify dissemination of, weather and climate-related event attribution.” Changing focus from explanation of single events to understanding phenomena evolving in time, represented by time series, causality can be understood in terms of improved predictability, as proposed by Norbert Wiener and formulated for time series by C.W.J. Granger. Granger causality has been further generalized for nonlinear systems using methods rooted in information theory. Extensions from bivariate to multivariate time series can also point to indirect causations. X. S. Liang and R. Kleeman derive formulas for information flows based on dynamical equations. The Wiener-Granger concept of improved predictability has been translated into computer science as compressibility changes in effect data due to knowledge of cause data. The information-theoretic formulation of Granger causality and other methods have recently been adapted for complex systems with multiple time scales and/or heavy-tailed probability distributions and extreme events. Methods for turning multivariate data into causal graphs based on Bayesian reasoning and machine learning are also intensively applied in the Earth sciences.

The session welcomes contributions discussing these diverse approaches to causality analysis in Earth sciences, with an emphasis on comparative discussions. Learning causal relationships from Earth system data is vital for understanding complex dynamics, predicting changes, and informing strategies. This session invites innovative approaches and case studies employing causal inference techniques across Earth sciences, fostering interdisciplinary discussions and encouraging the development of robust causal analysis frameworks. Topics include causal discovery methods, causal effect estimation, applications of causal inference to climate change, causal modeling, network analysis, and addressing challenges and limitations in applying causal inference to Earth system science.

The complexity of hydrological systems poses significant challenges to their prediction and understanding capabilities. The rise of machine learning (ML) provides powerful tools for modeling these intricate 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, examining how prior understanding of hydrological and land surface processes or causal representations can be incorporated into data-driven models, and conversely, how ML might enrich our causal or physical understanding of system dynamics and mechanisms.

We invite researchers working at the intersection of explainable ML/AI and hydrological or Earth system sciences to share their methods, results, and insights. Submissions are welcome on topics including, but not limited to:

- Explainability and transparency in ML/AI modeling of hydrological and Earth systems;
- Integration of hydrological processes and knowledge in ML/AI models;
- Multiscale and multiphysics representation in ML/AI models;
- Causal representation learning in hydrological and earth systems;
- Strategies for balancing model performance and interpretability;
- Leveraging insights from data science and XAI to deepen physical understanding;
- Data-driven approaches to causal analysis 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) Uncertainty estimation for and with Deep Learning.

(8) Applications of Large Language Models (e.g. ChatGPT, Bard, etc.) in the context of hydrology.

(9) Advances towards foundational models in the context of hydrology and Earth Sciences more generally.

(10) Exploration of different optimization strategies, such as self-supervised learning, unsupervised learning, and reinforcement learning.

Geomorphometry, a science of quantitative land surface analysis, gathers various mathematical, statistical and image processing techniques to quantify morphological, hydrological, ecological and other aspects of a land surface. Geomorphometry and geomorphological mapping are essential tools for understanding landscape processes and dynamics on Earth and other planetary bodies. The rapid growth of available geospatial data available for morphometric analysis and opens up considerable possibilities for morphometric analysis from mapping new landforms to understand the underlying processes. It also presents unique challenges in data processing and analysis.
The typical input to geomorphometric analysis is a square-grid representation of the land surface - a digital elevation model (DEM). Global DEMs and the increasing availability of much finer resolution LiDAR and SFM high-resolution DEMs call for new analytical methods and advanced geo-computation techniques necessary to cope with diverse application contexts. Point clouds have increasing accuracy over complex scenes, characterized by high topographic variation in three (and four) dimensions, generating a shift in geomorphologists’ work.
This session welcomes studies of advanced geo-computation methods, including high-performance and parallel computing implementations. We welcome general, technical and applied studies of geomorphometry applications and landform mapping from any discipline (geomorphology, planetary science, natural hazards, computer science, and Earth observation). Examples are:
- Use of Digital Elevation, Terrain and Surface Models and point clouds
- High-resolution LiDAR, photogrammetry and satellite data
- Automated surface analysis, machine learning, new algorithms
- Earth's and planetary morphometry, surface changes
- Collecting or derivation of geospatial data products
- Tools for extraction and analysis of geomorphometric variables
- Mapping and morphometric analysis of landforms and landscapes
- Modeling natural hazards on the Earth's surface
- Marine Geomorphometry and bathymetry
- Geomorphometry for urban areas and cultural heritage
- Professional and industrial applications of Geomorphometry
Contributions on inter-disciplinary approaches are particularly encouraged. We also welcome professional, commercial and industrial applications of terrain/surface data and geomorphometric techniques, including software packages, to bridge the gap between academic researchers and industry.

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.

Successful forecasting of timing and scale of climate- and environment-related hazards could have great impact on everyday life and overall wellbeing of many communities. It can considerably contribute to effective preparedness and mitigation in agriculture, infrastructure, health and related socio-economic areas, as well as in preservation of cultural heritage.

A significant body of empirical work has shown that geophysical variables of importance to real-time forecasting universally pass a critical threshold, amass a combination of critical conditions, and/or exhibit characteristic changes in the tipping elements at both onset and withdrawal of climate and environmental critical phenomena. The combination of physical understanding and effective parameterizations of those changes can assist in development of algorithms that are essential for risk reduction.

The session aims at discussing the concept of real-time forecasting from physical, statistical, and application points of view, with follow-up reporting of the results in a catalog of successful and unsuccessful predictions. It is mainly (but not solely) focused on approaches based on or inspired by concepts from complex systems sciences like scaling, universality, complex network analysis and physics-informed machine learning.

The session will include forecasts of different phenomena and forecasting horizons, slow or fast onset, and varied intensity and impact. We invite forecasters to submit their predictions of events at varied temporal and spatial scale, from short-term regional hazards, such as heavy rains leading to landslides, to large-scale ones, such as El Nino and continental monsoons. The main requirement is that submitted forecast should be provided in advance of the event, and the responsible forecaster commits to reporting its outcome, no matter successful or not. The reporting grounds will be a follow-up session in one of the EGU Assemblies (2025 and later, depending on the horizon of the submitted forecasts) and potentially in a special issue of the Journal CHAOS, whose time of publication will be defined by the scale of the submitted forecasts.

The session invites forecasters to present their methods and prognoses for public demonstration of research excellence in modern climatology.

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 seeks to identify, quantify and reduce the different sources of uncertainty, as well as propagating them through the 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 (Bayesian) UA methods and methods for SA applicable to all Earth and Environmental Systems Models (EESMs), which embrace 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) Novel methods for spatial and temporal evaluation/analysis of models
3) Novel approaches and benchmarking efforts for parameter estimation
4) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, parallel computing, model pre-emption, model ensembles, etc.)
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) Methods for evaluating model consistency and reliability as well as detecting and characterizing model inadequacy
7) Analyses of over-parameterised models enabled by AI/ML techniques
8) Robust quantification of predictive uncertainty for model surrogates and machine learning (ML) models
9) Approaches to define meaningful priors for ML techniques in hydro(geo)logy

The invited speaker of this session is Francesca Pianosi (University of Bristol).

Forecasting the weather, in particular severe and extreme weather has always been the most important subject in meteorology. This session will focus on recent research and developments on forecasting techniques, in particular those designed for operations and impact oriented. Contributions related to nowcasting, meso-scale and convection permitting modelling, ensemble prediction techniques, and statistical post-processing are very welcome.
Topics may include:
 Nowcasting methods and systems, use of observations and weather analysis
 Mesoscale and convection permitting modelling
 Remote sensing and data assimilation
 Ensemble prediction techniques
 Ensemble-based products for severe/extreme weather forecasting
 Seamless deterministic and probabilistic forecast prediction
 Post-processing techniques, statistical methods in prediction
 Use of machine learning, data mining and other advanced analytical techniques
 Impact oriented weather forecasting
 Presentation of results from relevant international research projects of EU, WMO, and EUMETNET etc.

Key Words: Forecast technique, nowcasting, ensemble prediction, statistics, AI

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, as well as advances in improving the 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 as well as statistical predictions (including machine learning methods) and their combination. This includes predictions of climate phenomena, including extremes and natural hazards, from global to regional scales, and from seasonal to multi-decadal timescales ("seamless predictions"). The session also covers physical processes relevant to long-term predictability sources (e.g. ocean, cryosphere, or land) and predictions of large-scale atmospheric circulation anomalies associated to teleconnections as well as observational and emergent constraints on climate variability and predictability. Also relevant is the time-dependence of the predictive skill and windows of opportunity. Analysis of predictions in a multi-model framework and innovative ensemble-forecast initialization and generation strategies are another focus of the session. The session pays particular attention to innovative methods of quality assessment and verification of climate predictions, including extreme-weather frequencies, post-processing of climate hindcasts and forecasts, and quantification and interpretation of model uncertainty. We particularly invite contributions presenting the use of seasonal-to-decadal predictions for assessing risks from natural hazards, adaptation and further applications.

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 the developments in 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 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.

The nonlinear nature of fluid flow gives rise to a wealth of interesting and beautiful phenomena. Many of these are of fundamental importance in the understanding of lakes, oceans and the atmosphere because of their role in such things as transport, the energy cascade and, ultimately, in mixing.

This session is intended to bring together researchers interested in the fundamental nature of nonlinear processes. Particular attention will be paid to intrinsically nonlinear flows that are driven by a gravitational forces acting on density variations, e.g. those 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). 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 is difficult to predict.

We therefore welcome contributions including (but not limited to) diverse occurrences of geophysical gravity currents, nonlinear and solitary waves, wave-mean flow and wave-wave interactions, flow instabilities and their nonlinear evolution, frontogenesis, double diffusion and the nonlinear equation of state, convection, and river plumes.

This session then aims to present complementary physical-based approaches, by gathering researchers from different communities, all focusing on these flows by studying field data, using analogue laboratory experiments or numerical simulations or focusing on analytical modelling. We particularly encourage the participation of early-career researchers and students.

This session will focus on studies in geophysical fluids including atmospheres and oceans (on Earth and elsewhere) that are approached from a Lagrangian perspective, together with topics associated with turbulence.

Lagrangian tools allow to predict the dispersion of pollutants and track their sources, capture unresolved physics, and reveal transport pathways and barriers between flow regimes that have different dynamical fates. As such, Lagrangian methods are used in a vast array of applications from turbulent scales to planetary scales in the atmosphere, oceans, and cryosphere.

Furthermore, turbulence is a major driver of nonlinear behavior and variability in geophysical fluids, influencing both passive and active scalars via changes in the velocity field and fluxes (air-sea exchanges). As such, turbulence is a key forcing in marine ecology: it modulates the contact rate between organisms and nutrients, re-suspension processes, the formation of blooms and thin layers, and even the adaptation of organisms to their environment.

We invite presentations on topics including – but not limited to – the following:
- Large-scale circulation studies (jets, gyres, overturning circulations) using direct Lagrangian modeling and/or age and chemical tracers;
- Exchanges between reservoirs and mixing studies (e.g. transport barriers 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, cirrus seeding by aviation, etc);
- Inverse modeling techniques for the assessment and constraint of emission sources;
- Turbulent flows, physical oceanography, biogeochemistry, marine ecology, marine sciences;
- Lagrangian Coherent Structures;
- Model and tool development, numerical and computational advances.

Urban Boundary Layer (UBL) Dynamics is determined by city morphology, latent and sensible heat fluxes (including anthropogenic heat), and interactions with rural surroundings. The physical processes in such UBLs are characterized by great strong spatial and temporal heterogeneity, and have the potential to affect societally relevant issues like human thermal comfort, air quality, aviation operations and energy supply.
The goal of this session is to highlight research work and promote discussions on this often underrepresented aspect of urban meteorology and climatology. Hence, we invite and encourage contributions on the following topics:

- Numerical modeling of urban boundary layer dynamics at all scales (from regional to street level)
- Observational methods in the UBL: field campaigns and remote sensing (e.g., flux towers, LIDAR, drones)
- Wind tunnel experiments
- Interaction between local circulations (e.g., UHIC, thermal circulation in complex terrain, sea/lake breeze) and the built environment
- Role of turbulent fluxes and impact of turbulence on wind flow
- Intra-canopy and canyon ventilation
- Impact of urban vegetation (e.g., street trees) on wind flow
- Urban air quality (e.g., pollutant transport and dispersion)
- Urban wind energy potential

This session focuses on the non-linear processes in space, laboratory, and astrophysical plasma. In many cases, these processes are not separated but appear interlinked. For instance, magnetic reconnection is an established ingredient of the turbulence cascade, and it is also responsible for the production of turbulence in reconnection outflows; shocks can be accountable for turbulence formation, for example, in the turbulent magnetosheath, or can be efficient particle accelerators through their interaction with the ambient turbulence. All these and other non-linear processes shape plasma dynamics in the environments where they occur and govern the energy transfer between the electromagnetic field and the particles.

The study of these processes has seen significant progress in recent years thanks to a synergistic approach based on simulations and observations. On the one hand, simulations can deliver output in a temporal and spatial range of scales going from fluid to electron kinetic. That is also due to the advent of GPU facilities that contribute to increasing computational algorithms' power in plasma physics. On the observational side, high cadence measurements of particles and fields and high-resolution 3D measurements of particle distribution functions are currently provided by the missions MMS, Parker Solar Probe, and Solar Orbiter, opening new research scenarios in heliophysics and providing a consistent amount of new data to be analyzed. Furthermore, other present and future missions that will give unique plasma measurements around solar system compact objects such as Bepi Colombo, Juice, and Comet Interceptor, or multi-point measurements in the solar wind such as Helioswarm are demanding the development of new numerical tools for a successful interpretation of the observations.

This session welcomes simulation, observational, and theoretical works relevant to studying the abovementioned processes. Particularly welcome this year will be works focusing on how non-linear processes are responsible for the energy transfer between fields and species and energy partition among species. We also encourage papers proposing new methods in simulation techniques and data analysis, for example, those rooted in Artificial Intelligence and GPU algorithms or those based on multi-point satellite observations.

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 currently operating missions (e.g., Wind, Cluster, MMS, STEREO, THEMIS, Van Allen Probes, DSCOVR) and in particular for the Solar Orbiter and the Parker Solar Probe.