Cities are complex multi-scale systems, composed of multiple sub-components (e.g. for population, energy, transport, climate) that interact with each other on various time scales (e.g. hourly, seasonal, annual). Urban models and digital twins for urban planning applications and policies aimed at shaping healthier and more sustainable urban environments should account for such complex interactions as they regulate the growth and functioning of cities, often resulting in emergent large-scale phenomena. Yet our ability to quantitatively describe city behaviour is still limited due to the variety of processes, scales, and feedbacks involved.
In this session we welcome modelling and monitoring studies that focus on multi-sector dynamics and city-biosphere interactions. These include – but are not limited to – demography, urban transport networks, energy consumption, anthropogenic emissions, urban climate, pollution, epidemiology, urban hydrology and ecology.
The aim is to elucidate complex urban dynamics, identify strategies, methods, and protocols for the development of monitoring campaigns, models, and digital twins of cities, and understand how the form and function of urban environments can improve liveability and well-being of their citizens.

Far beyond the rocket science jargon, there has been a fast digitalisation of Urban Geosciences and Geo-Health. This is particularly illustrated by the almost immediate establishment of the Covid-19 database at the Johns Hopkins University Center for Systems Science and Engineering, which has enabled numerous studies of the environmental spread of the virus. Health threats are not limited to epidemics, as the recent spate of dramatic heatwaves, droughts, massive floods and resulting pollutions shows. It also includes ancient historical episodes like the demise of the ancient Maya culture or the abandoned settlements along the Silkroad. Geophysical databases, e.g. the EU Copernicus programme, are increasingly processing data relevant to Urban Geosciences and Geo-Health, especially at higher resolution.

However, there are scientific deadlocks: both Urban Geosciences and Geo-Health deal with complex systems that have strong interrelationships and common features. The Nobel Committee for Physics strongly emphasised, in awarding its 2021 prize, the fundamental roles of complexity and intermittency for geophysics and climate science, as well as the capacity of multiscale techniques to master them, notably multifractals.

In the line of the previous EGU sessions and great debates on Urban Geosciences and/or Geo-Health 2018, this ITS1 session welcomes data and/or theory driven studies dealing with Urban Geosciences and/or Geo-Health either at the methodological or original applications level.

Several subsystems of the Earth's climate and ecosystems have been suggested to react abruptly at critical levels of anthropogenic forcing. Well-known examples include the Atlantic Meridional Overturning Circulation, the polar ice sheets, tropical and boreal forests, but also drylands. Interactions between different Tipping Elements may either have stabilizing or destabilizing effects on the other subsystems, potentially leading to cascades of abrupt transitions.

It is paramount to determine the critical forcing levels (and the associated uncertainties) beyond which the systems in question could abruptly change their state, with potentially devastating climatic, ecological, and societal impacts. Similarly, it is crucial to understand how to help such systems to increase their resilience and evade tipping. 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, precursor signals have to be identified and monitored in both observations and models.

Given the often stochastic nature of the nonlinear and multiscale Earth system processes underlying abrupt behavior, it is important to avoid false sense of confidence that arises from perspectives that ignore the stochastic nature of such processes. This can also be the case when machine learning is used for modelling of such processes. As such this session also seeks to highlight the use of probabilistic data-driven and especially machine learning approaches.

This multidisciplinary session invites contributions 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,
- early-warning signals
- the implications of abrupt transitions for Climate sensitivity and response,
- ecological and societal impacts, as well as
- decision theory in the presence of uncertain Tipping Point estimates
- probabilistic modelling of Earth system processes
- climate change impacts on ecosystem resilience
-processes aiding ecosystem restoration and building climate resilient ecosystems

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, the mathematical, and the theoretical physical communities. Our goal is to stimulate the interaction among scientists of these and related disciplines interested in solving geophysical 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 of the climate system, and the search for new data analysis methods.

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, parametrizations, 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.

Solicited speakers: David Stainforth, Oana Lang

Abstracts are solicited related to the understanding and prediction 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 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, 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;
· Linking the dynamics of extreme events to their impacts.

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.

Analysis of energy transfers between and within climate components has been at the core of many step changes in the understanding of the climate system. Large-scale atmospheric circulation, hydrological cycle and heat/moisture transports are tightly intertwined. Dynamics and radiative exchanges are linked at the global scale, through the net impact of cloud feedbacks, sea-ice albedo changes, surface absorption by vegetation.

In the Tropics, the zonal mean Hadley circulation determines meridional energy transports, while Rossby and planetary-scale waves modulate the energy exchanges carried by extratropical eddies. In the ocean, the role of Atlantic Meridional Overturning Circulation is essential for the heat budget of continental regions in the Northern Hemisphere: long-term oceanic and sea-ice variability is crucial to understand and predict the dynamics in high latitudes. Observational and model studies have indeed shown that the Arctic is very susceptible to climate change, and climate perturbations in the Arctic likely have wide-spread influence. High-latitude atmosphere, biosphere, oceans and cryosphere have experienced significant changes over the observational era. Hence, advancing the understanding of variability and change, governing mechanisms and global implications, improving predictions and projections of high latitude climate in both hemispheres is highly important for global society.

We invite submissions on the interplay between Earth’s energy exchanges and the general circulation through modeling, theory, and observations, on the forced response and natural variability of the general circulation, understanding present-day climate, past and future changes, impacts of global features and change on regional climate. This session also aims to improve knowledge and representation of the multi-scale mechanisms that control high-latitude climate variability and predictability in both hemispheres from sub-seasonal to multi-decadal and longer time scales. We thus invite contributions on the causes, mechanisms and climate feedbacks associated with the Arctic and Antarctic climate, ocean and sea ice change, including the potential links of the pronounced Arctic amplification to weather and climate outside the Arctic, and teleconnections of high latitude climate with lower latitude climate. We also aim to link climate variability, predictions and projections to potential ecosystem and socio-economic impacts and encourage submissions on this topic.

This session focuses on advances in theoretical, methodological and applied studies in hydrologic and broader earth system dynamics, regimes, transitions and extremes, along with their physical understanding, predictability and uncertainty, across multiple spatiotemporal scales.

The session further encourages discussion on interdisciplinary physical and data-based approaches to system dynamics in 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.

Contributions are gathered from a diverse community in hydrology and the broader geosciences, working with diverse approaches ranging from dynamical modelling to data mining, machine learning and analysis with physical process understanding in mind.

The session further encompasses practical aspects of working with system analytics and information theoretic approaches for model evaluation and uncertainty analysis, causal inference and process networks, hydrological and geophysical automated learning and prediction.

The operational scope ranges from the discussion of mathematical foundations to development and deployment of practical applications to real-world spatially distributed problems.

The methodological scope encompasses both inverse (data-based) information-theoretic and machine learning discovery tools to first-principled (process-based) forward modelling perspectives and their interconnections across the interdisciplinary mathematics and physics of information in the geosciences.

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.

We welcome contributions that improve quantification, understanding, and prediction of climate variability in the Earth system across space and timescales through case studies, idealized or realistic modeling, synthesis, and model-data comparison studies that provide insights into past, present and future climate variability on local to global, and synoptic to orbital timescales. In particular, we welcome contributions making use of paleoclimate data and modelling to understand changes in the climate system dynamics and variability during the last glacial cycle, and the related implications for the future.

This session aims to provide a forum to present work on:
1. Characterization of climate dynamics using a variety of techniques (e.g. scaling and multifractal techniques and models, recurrence plots, variance analyses).

2. Proxy-system modelling to improve paleoclimate reconstructions and model-data comparisons

3. Relationship between mean state changes (e.g. glacial to interglacial or pre-industrial to present to future), and higher-order moments of relevant climate variables, including extreme-event occurrence and predictability.

4. Role of the ocean, atmosphere, cryosphere and land-surface processes in fostering long-term climate variability through linear – or nonlinear – feedbacks and mechanisms.

5. Attribution of climate variability to internal and/or forced dynamics, including natural (e.g. volcanic and solar) and anthropogenic forcing changes.

6. Synchronization and pacing of glacial cycles through dynamical interaction of external forcing (e.g. orbital forcing) and internal variability.

7. Characterization of the probabilities of extremes, including linkage between slow climate variability and extreme event recurrence.

Members of the PAGES working group on Climate Variability Across Scales (CVAS) and the German Climate Modeling Initiative PalMod are particularly welcome.

Environmental systems often span spatial and temporal scales covering different orders of magnitude. The session is oriented toward collecting studies relevant to understand multiscale aspects of these systems and in proposing adequate multi-platform and inter-disciplinary surveillance networks monitoring tools systems. It is especially aimed to emphasize the interaction between environmental processes occurring at different scales. In particular, special attention is devoted to the studies focused on the development of new techniques and integrated instrumentation for multiscale monitoring of high natural risk areas, such as volcanic, seismic, energy exploitation, slope instability, floods, coastal instability, climate changes, and another environmental context.
We expect contributions derived from several disciplines, such as applied geophysics, geology, seismology, geodesy, geochemistry, remote and proximal sensing, volcanology, geotechnical, soil science, marine geology, oceanography, climatology, and meteorology. In this context, the contributions in analytical and numerical modeling of geological and environmental processes are also expected.
Finally, we stress that the inter-disciplinary studies that highlight the multiscale properties of natural processes analyzed and monitored by using several methodologies are welcome.

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.

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.

The Earth's surface is shaped by many processes occurring over a wide range of time and length scales, all of which are interdependent with each other. Unraveling this complex system is challenging, especially because of the wide range of scales involved, which makes observation difficult. However, in recent years, major advances in understanding are being driven by new methods (e.g. by using fibre optic cables, or via environmental seismology) as well as the use of simplified and controlled experiments, which is widely used to monitor isolated processes or their interactions. Field observations may provide new opportunities to design and adapt the laboratory scale experiments, while laboratory experiments will help in better interpreting field observations. Together results from field and laboratory will provide insights to test and refine numerical models.

Thus, this session aims to bring together researchers from different communities that on one hand are working in the laboratory to reproduce and on the other hand are using novel field methods to monitor the natural processes in various systems and on various scales.

We welcome contributions but not limited to:
- fluvial and coastal systems
- aeolian processes and arid environments
- systems associated with melting, dissolution and precipitation
- gravity-driven flows
Finally, we particularly encourage participation from students and early career scientists.

Time series obtained within the different geoscientific disciplines commonly exhibit a large degree of irregularity, complexity and/or nonstationarity. In such cases, the use of classical (linear) concepts for the statistical analysis and modelling of time series (such as power spectra, autoregressive or other linear models) may be insufficient to obtain reliable and correct process-related information from the available data. Conversely, applying emergent concepts developed in fields like dynamical system theory, stochastic processes or computer science may provide useful tools to foster the knowledge discovery from complex geoscientific systems. Many of the corresponding methods from nonlinear time series analysis have meanwhile matured and reached a stage of broad applicability while still undergoing further methodological refinements, extensions and adaptations.

This session brings together researchers developing time series analysis approaches tailored to nonlinear deterministic and/or stochastic dynamical systems with such applying those concepts across the different geoscientific disciplines and beyond. We are confident that methodological knowledge transfer across the different topical fields present at EGU is of utmost relevance to improving our capability, as a community, to derive the most useful pieces of information from the growing amount of available data on various geoscientific phenomena. Therefore, we cordially invite contributions using different types of approaches, including (but not limited to) multi-scale methods for time series, information theoretic concepts, statistical complexity measures, causal inference, state space methods, stochastic process descriptions, etc., addressing recent methodological developments and/or successful applications to time series from any geoscience discipline and beyond.

Machine learning (ML) is currently transforming data analysis and modelling of the Earth system. While statistical and data-driven models have been used for a long time, recent advances in machine learning now allow for encoding non-linear, spatio-temporal relationships robustly without sacrificing interpretability. This has the potential to accelerate climate science, by providing new physics-based modelling approaches; improving our understanding of the underlying processes; reducing and better quantifying climate signals, variability, and uncertainty; and even making predictions directly from observations across different spatio-temporal scales. The limitations of machine learning methods need to also be considered, such as requiring, in general, rather large training datasets, data leakage, and/or poor generalisation abilities, so that methods are applied where they are fit for purpose and add value.

This session aims to provide a venue to present the latest progress in the use of ML applied to all aspects of climate science and we welcome abstracts focussed on, but not limited to:
- Causal discovery and inference: causal impact assessment, interventions, counterfactual analysis
- Learning (causal) process and feature representations in observations or across models and observations
- Hybrid models (physically informed ML, emulation, data-model integration)
- Novel detection and attribution approaches
- Probabilistic modelling and uncertainty quantification
- Explainable AI applications to climate data science and climate modelling
- Distributional robustness, transfer learning and/or out-of-distribution generalisation tasks in climate science

Please note that a companion session “ML for Earth System modelling” focuses specifically on ML for model improvement, particularly for near-term time-scales (including seasonal and decadal) forecasting, and related abstracts should be submitted there.

Unsupervised, supervised, semi-supervised as well as reinforcement learning are now increasingly used to address Earth system-related challenges for the atmosphere, the ocean, the land surface, or the sea ice.
Machine learning could help extract information from numerous Earth System data, such as in-situ and satellite observations, as well as improve model prediction through novel parameterizations or speed-ups. This session invites submissions spanning modeling and observational approaches towards providing an overview of state-of-the-art applications of these novel methods for predicting and monitoring the Earth System from short to decadal time scales. This includes (but is not restricted to):
- The use of machine learning to reduce or estimate model uncertainty
- Generate significant speedups
- Design new parameterization schemes
- Emulate numerical models
- Fundamental process understanding

Please consider submitting abstracts focused on ML applied to observations and modeling of the climate and its constituent processes to the companion "ML for Climate Science" session.

Modern challenges of climate change, disaster management, public health and safety, resources management, and logistics can only be addressed through big data analytics. A variety of modern technologies are generating massive volumes of conventional and non-conventional geospatial data at local and global scales. Most of this data includes geospatial data components and are analysed using spatial algorithms. Ignoring the geospatial component of big data can lead to an inappropriate interpretation of extracted information. This gap has been recognised and led to the development of new spatiotemporally aware strategies and methods.
This session discusses advances in spatiotemporal machine learning methods and the softwares and infrastructures to support them.

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. Abstracts are solicited 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) Integrating deep learning with process-based models and/or physical understanding.
(3) Improving understanding of the (internal) states/representations of deep learning models.
(4) Understanding the reliability of deep learning, e.g., under non-stationarity.
(5) Deriving scaling relationships or process-related insights with deep learning.
(6) Modeling human behavior and impacts on the hydrological cycle.
(7) Extreme event analysis, detection, and mitigation.
(8) Natural Language Processing in support of models and/or modeling workflows.

Artificial intelligence (in particular, machine learning) can be used to predict and respond to natural disasters. The ITU/WMO/UNEP Focus Group AI for Natural Disaster Management (FG-AI4NDM) is building a community of experts and stakeholders to identify best practices in the use of AI for data processing, improved modeling across spatiotemporal scales, and providing effective communication. This multidisciplinary FG-AI4NDM-session invites contributions addressing challenges and opportunities related to the use of AI for the detection, forecasting, and communication of natural hazards and disasters. In particular, it welcomes presentations highlighting innovative approaches to data collection (e.g., via sensor networks), data handling (e.g., via automating annotation), data storage and transmission (e.g., via edge- and cloud computing), novel modeling or explainability methods (e.g., integrating quantum computing methods), and outcomes of operational implementation.

Computational earth science often relies on modelling to understand complex physical systems which cannot be directly observed. Over the last years, numerical modeling of earthquakes provides new approaches to apprehend the physics of earthquake rupture and the seismic cycle, seismic wave propagation, fault zone evolution and seismic hazard assessment. Recent advances in numerical algorithms and increasing computational power enable unforeseen precision and multi-physics components in physics-based simulations of earthquake rupture and seismic wave propagation but also pose challenges in terms of fully exploiting modern supercomputing infrastructure, realistic parameterization of simulation ingredients and the analysis of large synthetic datasets while advances in laboratory experiments link earthquake source processes to rock mechanics. This session aims to bring together modelers and data analysts interested in the physics and computational aspects of earthquake phenomena and earthquake engineering. We welcome studies focusing on all aspects of seismic hazard assessment and the physics of earthquakes — from slow slip events, fault mechanics and rupture dynamics, to wave propagation and ground motion analysis, to the seismic cycle and inter seismic deformation — and studies which further the state-of-the art in the related computational and numerical aspects.

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 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.

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.

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 for 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), have proved to be very helpful.

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 approaches for parameter estimation, data inversion and data assimilation
5) Novel methods for spatial and temporal evaluation/analysis of models
6) 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.)
7) The role of SA in evaluating model consistency and reliability
8) Novel approaches and benchmarking efforts for parameter estimation
9) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, parallel computing, model pre-emption, model ensembles, etc.)

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. However, our ability to predict future climate conditions and potential pathways to them is dependent on our models' abilities to reproduce just such phenomena. Thus, our climatic and environmental history is ideally suited to thoroughly test and evaluate models against data, so they may be better able to simulate the present and make future climate projections.

We invite papers on palaeoclimate-specific model development, model 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 anything between time-slice equilibrium experiments to long transient climate simulations (e.g. transient simulations covering the entire glacial cycle as per the goal of the PalMod project) with timescales of processes ranging from synoptic scales to glacial cycles and beyond. Comparisons may include past, historical as well as future simulations and focus on comparisons of mean states, gradients, circulation or modes of variability using reconstructions of temperature, precipitation, vegetation or tracer species (e.g. δ18O, δD or Pa/Th).

Evaluations of results from the latest phase of PMIP4-CMIP6 are particularly encouraged. However, we also solicit comparisons of different models (comprehensive GCMs, isotope-enabled models, EMICs and/or conceptual models) between different periods, or between models and data, including an analysis of the underlying mechanisms as well as contributions introducing novel model or experimental setups.

A big challenge in Earth system science is providing reliable climate predictions on sub-seasonal, seasonal, decadal and longer timescales. Resulting data can potentially be translated into climate information for better assessment of global and regional climate-related risks. Latest developments and progress in climate forecasting on different timescales will be discussed and evaluated, including predictions for different time horizons from dynamical ensemble and statistical/empirical forecast systems, and the aspects required for their application: forecast quality assessment, multi-model combination, bias adjustment, downscaling, etc. Contributions on initialization methods that use observations from different Earth system components, on assessing and mitigating impacts of model errors on skill and on ensemble methods will be included, much as 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 development of early warning systems.
Another focus is on the use of operational climate predictions (C3S, NMME, S2S), results from CMIP5-CMIP6 decadal prediction experiments, and climate-prediction research and application projects. Since an important part of climate forecast is to apply appropriate downscaling methods -dynamic, statistical or a combination- to generate time series and fields with appropriate spatial or temporal resolution, this will be covered by the session, which aims to bring together scientists from all geoscientific disciplines working on the prediction and application problems. Following the new WCRP strategic plan for 2019-2029, prediction enhancements are also sought that embrace climate forecasting from an Earth system science perspective, including study of coupled processes between atmosphere, land, ocean and sea-ice components, and the impacts of coupling and feedbacks in physical, chemical, biological and human dimensions including migration. On migration, the focus is on migratory species or those that are forced to migrate due to a change in the frequency and severity of climatic disturbances or human intervention, i.e. land use land cover change. This part of the session is for researchers working on terrestrial, marine or freshwater species and studies covering all aspects of migration including trait and behavioral changes as a response to sudden or gradual environmental changes, at all temporal scales.

This session covers predictions of climate from seasonal to decadal timescales and their applications. With a time horizon from a few months up a few decades, such predictions are of major importance to society, and improving them presents an interesting scientific challenge. This session embraces advances in our understanding of the origins of seasonal to decadal predictability, as well as in improving the forecast skill 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, 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 ensemble forecast initialization and generation, including innovative ensemble approaches to minimize initialization shocks, 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 risk assessment, adaptation and further applications.

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 in rivers, lakes, oceans and the atmosphere. Examples include, but are not limited to, nonlinear and solitary waves, wave-current and wave-wave interactions, flow instabilities and their nonlinear evolution, turbulence, frontogenesis, double diffusion and the nonlinear equation of state, convection, and river plumes. Presentations on theoretical, modelling, experimental or observational work are welcome.

Space, laboratory, and astrophysical plasmas are seemingly different environments, which however host very similar processes: among them, turbulence, magnetic reconnection, kinetic instabilities and shocks, which all result in particle acceleration and plasma heating. These processes are highly non-linear, and closely interlinked. On one hand, the turbulence cascade favors the onset of magnetic reconnection between magnetic islands and, on the other hand, magnetic reconnection can trigger turbulence in the reconnection outflows and separatrices. Similarly, shocks may form in collisional and collisionless reconnection processes and can be responsible for turbulence generation, as for instance in the turbulent magnetosheath.
The investigation of these processes based on simulations and observations are converging. Simulations can deliver output that is approaching, in temporal and spatial scales and in the coexistence of several scales, the complexity of an increasing number of the processes of interest. On the observation side, high cadence measurements of particles and fields, high resolution 3D measurements of particle distribution functions and multipoint measurements make it easier to reconstruct the 3D space surrounding the spacecraft. The ever growing amount of data that both simulations and observations produce can be then combined through and exploited with Artificial Intelligence and Machine Learning methods.
This session welcomes numerical, observational, and theoretical works relevant for the study of the above mentioned plasma processes. Particularly welcome this year will be works focusing on the common aspects of turbulence, reconnection, and shocks in space, laboratory, and astrophysical plasmas.

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 of fluid parcels that have different dynamical fates. As such, Lagrangian tools are used in a vast array of applications in geophysical fluid dynamics, from turbulent scales to planetary scales.

This session brings together scientists with experimental, numerical, and theoretical backgrounds.

Latest advancements will be presented on the following topics:
• Mesoscale to planetary-scale studies of transport and mixing (e.g. the AMOC, mixing in the surface ocean, identification of eddies and transport barriers);
• Tracking anthropogenic and natural influence (e.g. spread of microplastics, oil spills, volcanic ashes, and diseases);
• Micro-scale studies of turbulent flows (e.g. bubbles in the ocean surface layer, turbulence in the ocean and atmosphere);
• Tool development and numerical advances (e.g. use of machine learning, dynamic mode decomposition, trajectory rotation average, effects of model resolution);

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, 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, as well as the role of specific phenomena such as the magnetic reconnections, shock waves, expansion, and plasma instabilities and their relationship with the turbulent cascade and dissipation.
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.

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. The presence of multiple fractures makes geomaterials non-linear. Therefore the analysis of wave propagation and interaction with pre-existing or emerging fractures is central to geophysics. Recently new observations and theoretical concepts were introduced that point out to the limitations of the traditional concept. 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)
• Non-linear mechanics of hydraulic fracturing
• Synchronization in fracture processes including earhtquakes 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 first part of the session is designed as a forum for discussing these and relevant topics.

The second part of the session aims to nurture the development of fractals, multifractals and related nonlinear methodologies applicable to a wide range of hydrological, meteorological systems and their multiscale interactions. Theories considering scalar and vector fields, applications in the area of hydrometeorology (e.g. rainfall extremes, urban flood control, water management etc.), analysis of in-situ, remotely sensed data and simulation techniques are of interest.

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 hydro-meteorological events drive many hydrologic and geomorphic hazards, such as floods, landslides and debris flows, which pose a significant threat to modern societies on a global scale. The continuous increase of population and urban settlements in hazard-prone areas in combination with evidence of changes in extreme weather events lead to a continuous increase in the risk associated with weather-induced hazards. To improve resilience and to design more effective mitigation strategies, we need to better understand the triggers of these hazards and the related aspects of vulnerability, risk, and mitigation.
This session aims at gathering contributions dealing with various hydro-meteorological hazards that address the aspects of vulnerability analysis, risk estimation, impact assessment, mitigation policies and communication strategies. Specifically, we aim to collect contributions from academia, the industry (e.g. insurance) and government agencies (e.g. civil protection) that will help identify the latest developments and ways forward for increasing the resilience of communities at local, regional and national scales, and proposals for improving the interaction between different entities and sciences.
Contributions focusing on, but not limited to, novel developments and findings on the following topics are particularly encouraged:
- Physical and social vulnerability analysis and impact assessment of hydro-meteorological hazards
- Advances in the estimation of socioeconomic risk from hydro-meteorological hazards
- Characteristics of weather and precipitation patterns leading to high-impact events
- Relationship between weather and precipitation patterns and socio-economic impacts
- Hazard mitigation procedures
- Strategies for increasing public awareness, preparedness, and self-protective response
- Impact-based forecast, warning systems, and rapid damage assessment.
- Insurance and reinsurance applications
This session is linked to an active special issue in Natural Hazards and Earth System Sciences (NHESS): https://nhess.copernicus.org/articles/special_issue1203.html

Hydroclimatic conditions and availability of water resources in space and time constitute important factors for maintaining adequate food supply, the quality of the environment, and the welfare of citizens and inhabitants, in the context of a post-pandemic sustainable growth and economic development. This session is designed to explore the impacts of hydroclimatic variability, climate change, and temporal and spatial availability of water resources on different factors, such as food production, population health, environment quality, and local ecosystem welfare.
We particularly welcome submissions on the following topics:
• Complex inter-linkages between hydroclimatic conditions, food production, and population health, including: extreme weather events, surface and subsurface water resources, surface temperatures, and their impacts on food security, livelihoods, and water- and food-borne illnesses in urban and rural environments.
• Quantitative assessment of surface-water and groundwater resources, and their contribution to agricultural system and ecosystem statuses.
• Spatiotemporal modeling of the availability of water resources, flooding, droughts, and climate change, in the context of water quality and usage for food production, agricultural irrigation, and health impacts over a wide range of spatiotemporal scales.
• Smart infrastructure for water usage, reduction of water losses, irrigation, environmental and ecological health monitoring, such as development of advanced sensors, remote sensing, data collection, and associated modeling approaches.
• Modelling tools for organizing integrated solutions for water supply, precision agriculture, ecosystem health monitoring, and characterization of environmental conditions.
• Water re-allocation and treatment for agricultural, environmental, and health related purposes.
• Impact assessment of water-related natural disasters, and anthropogenic forcing (e.g. inappropriate agricultural practices, and land usage) on the natural environment (e.g. health impacts from water and air, fragmentation of habitats, etc.)

In 2015, the UN Sustainable Development Goals and the Paris Agreement on climate recognized the deteriorating resilience of the Earth system, with planetary-scale human impacts constituting a new geological epoch: the Anthropocene. Earth system resilience critically depends on the nonlinear interplay of positive and negative feedbacks of biophysical and increasingly also socio-economic processes. These include dynamics and interactions between the carbon cycle, the atmosphere, oceans, large-scale ecosystems, and the cryosphere, as well as the dynamics and perturbations associated with human activities.

With rising anthropogenic pressures, there is an increasing risk we might be hitting the ceiling of some of the self-regulating feedbacks of the Earth System, and cross tipping points which could trigger large-scale and partly irreversible impacts on the environment, and impact the livelihood of millions of people. Potential domino effects or tipping cascades could arise due to the interactions between these tipping elements and lead to a further decline of Earth resilience. At the same time, there is growing evidence supporting the potential of positive (social) tipping points that could propel rapid decarbonization and transformative change towards global sustainability.

In this session we invite contributions on all topics relating to tipping points in the Earth system, positive (social) tipping, as well as their interaction and domino effects. We are particularly interested in various methodological approaches, from Earth system modelling to conceptual modelling and data analysis of nonlinearities, tipping points and abrupt shifts in the Earth system.

Glacial Isostatic Adjustment (GIA) describes the dynamic response of the solid Earth to the waxing and waning of ice sheets and corresponding spatial and temporal sea-level changes, which causes surface deformation and changes in the gravity field, rotation, and stress state of the Earth. The process of GIA is mainly influenced by the ice-sheet evolution and solid Earth structure, and in turn influences other components of the Earth system such as the cryosphere (e.g., ice sheets) and hydrosphere (e.g., ocean and sea level). A large set of observational data (e.g., relative sea level, GNSS measurements, tide gauges, terrestrial and satellite gravimetry, satellite altimetry, glacially induced faults) that can be used to constrain highly sophisticated GIA models is available nowadays in standardized form, which will further help in investigating the ice-sheet and sea-level evolution histories and rheological properties of the Earth, and understanding the interactions between ice sheets, the solid Earth and sea levels.

This session invites contributions discussing observations, analysis, and modelling of GIA and its effects on the Earth system across a range of spatial and timescales. Examples include, but not limited to, geodetic measurements of crustal motion and gravitational change, GIA modelling with complex Earth models (e.g., 3D lithosphere and/or viscosity, non-linear rheologies), GIA-induced global, regional and local sea-level changes, coupled GIA-ice sheet modelling for investigating past and future ice sheets/shelves changes and associated sea-level changes, glacially triggered faulting as well as the Earth’s (visco-)elastic response to present-day ice-mass changes. We also welcome abstracts that address GIA effects on nuclear waste repositories, groundwater distribution and migration of carbon resources. This session is co-sponsored by the SCAR sub-committee INSTANT-EIS, Earth - Ice - Sea level, in view of instabilities and thresholds in Antarctica https://www.scar.org/science/instant/home/ and PALMOD, the German Climate Modeling Initiative https://www.palmod.de.

Activities including the recent ESA Voyage 2050 exercise and the ongoing Heliophysics Decadal Survey in the US have triggered various new ideas on fundamental science themes in the area of solar, solar wind, magnetosphere, ionosphere-thermosphere physics at Earth and different planets, plasma physics around the moon and small body, as well as in the heliospheric boundary region. While the idea of implementation scheme (mission) may differ, there are commonality in the underlying physical processes of interest. Discussing the outstanding problems throughout the heliosphere by bringing the different discipline should bring us new perspective of the broad science area. In this session we invite papers which highlight the outstanding science questions in the different area of space plasma physics throughout the solar system and beyond. Ideas on new observations in space and from ground, new modeling, suggestions on new data analysis schemes are also welcome. We welcome active participation of Early Career Scientists and experts from the broad international heliophysics community

The Earth's inner magnetosphere contains different charged particle populations, such as the Van Allen radiation belts, ring current particles, and plasmaspheric particles. Their energy range varies from eV to several MeV, and the interplay among the charged particles provides feedback mechanisms that couple all those populations together. Ring current particles can generate various waves, for example, EMIC waves and chorus waves, which play important roles in the dynamic evolution of the radiation belts through wave-particle interactions. Ring current electrons can be accelerated to relativistic radiation belt electrons. The plasmaspheric medium can also affect these processes. In addition, precipitation of ring current and radiation belt particles will influence the ionosphere, while up-flows of ionospheric particles can affect dynamics in the inner magnetosphere. Understanding these coupling processes is crucial.

While the dynamics of outer planets’ magnetospheres are driven by a unique combination of internal coupling processes, these systems have several fascinating similarities which make comparative studies particularly interesting. We invite a broad range of theoretical, modeling, and observational studies focusing on the dynamics of the inner magnetosphere of the Earth and outer planets, including the coupling of the inner magnetosphere and ionosphere and coupling between the solar wind disturbances and various magnetospheric processes. Contributions from all relevant fields, including theoretical studies, numerical modeling, and observations from satellite and ground-based missions are welcome. In particular, we encourage presentations using data from MMS, THEMIS, Van Allen Probes, Arase (ERG), Cluster, cube-sat missions, Juno, SuperDARN, magnetometer, optical imagers, IS-radars, and ground-based VLF measurements.

First part - Mining the future

Research and innovation in exploration and mining of raw materials is increasingly focused on the prospect of developing new methods and technologies to reduce the environmental footprint of mineral extraction and exploration.

The robotization of exploration/production platforms, such as robotic autonomous explorers and miners, will allow to reconsider “non-economical” deposits (abandoned, small, ultra-depth), and to open as well towards the autonomous exploration and exploitation of other non-terrestrial bodies, including asteroids and moons.

Technological advances in the production process, included, but not limited to, X-ray sensors, hyper spectral techniques, LIBS, electromagnetic, combined with machine learning, AI models and efficient mechatronic solutions, will pave the way to a green mining industry.

We welcome contributions from researchers working on applied or interdisciplinary studies associated with mining exploration, geophysics, geochemistry, metallurgy, selective mining.

Second part - Novel developments in understanding the petrogenesis of REE resources

As a result of the critical need for rare earth elements (REE) in new technologies, in particular green energy production, the number of geological studies focusing on their ore formation have recently increased. REE deposits form in a variety of igneous and sedimentary environments. However, depending on factors such as relative and absolute REE content, mineralogy of the REE-bearing phases, host rock properties etc., their economic value can vary significantly. In addition to economic geology, REE deposits are ideal laboratories for understanding the elemental and isotopic behaviour of these elements in different geological environments, as well as the petrogenesis of their host rocks (e.g., carbonatites, alkaline igneous rocks, laterites, phosphorites etc.).
In this session, we will discuss new developments in understanding the formation of already known and recently discovered REE deposits. Studies based on different methodologies including new mathematical modelling techniques, field mapping, experimental petrology, mineralogical observations, in-situ and whole rock elemental and isotopic characterization will be discussed.

We welcome submission of studies conducted on different geological environments with different techniques discussing the conditions leading to concentration (and possibly differentiation) of the REE.

Since Claude Shannon coined the term 'Information Entropy' in 1948, Information Theory has become a central language and framework for the information age. Across disciplines, it can be used for i) characterizing systems, ii) quantifying the information content in data and theory, iii) evaluating how well models can learn from data, and iv) measuring how well models do in prediction. Due to their generality, concepts and measures from Information Theory can be applied to both knowledge- and data-based modelling approaches, and combinations thereof, which makes them very useful in the context of Machine Learning and hybrid modeling.
In this short course, we will introduce the key concepts and measures of Information Theory (Information, Entropy, Conditional Entropy, Mutual Information, Cross Entropy and Kullback-Leibler divergence), with practical examples of how they have been applied in Earth Science, and give a brief introduction to available open-source software.
This course assumes no previous knowledge or experience with Information Theory and welcomes all who are intrigued to learn more about this powerful theory.

The climate is highly variable over wide ranges of scale in both space and time so that the amplitude of changes systematically depends on the scale of observations. As a consequence, climate variations recorded in time series or spatial distributions, which are produced through modelling or empirical analyses are inextricably linked to their space-time scales and is a significant part of the uncertainties in the proxy approaches. Rather than treating the variability as a limitation to our knowledge, as a distraction from mechanistic explanations and theories, in this course the variability is treated as an important, fundamental aspect of the climate dynamics that must be understood and modelled in its own right. Long considered as no more than an uninteresting spectral “background”, modern data shows that in fact it contains most of the variance.

We review techniques that make it possible to systematically analyse and model the variability of instrumental and proxy data, the inferred climate variables and the outputs of GCM’s. These analyses enable us to cover wide ranges of scale in both space and in time - and jointly in space-time - without trivializing the links between the measurements, proxies and the state variables (temperature, precipitation etc.). They promise to systematically allow us to compare model outputs with data, to understand the climate processes from small to large and from fast to slow. Specific tools that will be covered include spectral analysis, scaling fluctuation analysis, wavelets, fractals, multifractals, and stochastic modeling; we discuss corresponding software. We also include new developments in the Fractional Energy Balance Equation approach that combines energy and scale symmetries.

Observations and measurements of geoscientific systems and their dynamical phenomena are genuinely obtained as time series or spatio-temporal data whose dynamics usually manifests a nonlinear multiscale (in terms of time and space) behavior. During the past decades, dynamical system, information theoretic, and stochastic approaches have rapidly developed and allow gaining novel insights on a great diversity of phenomena like weather and climate dynamics, turbulence in fluids and plasmas, or chaos in dynamical systems.

In this short course, we will provide an overview on a selection of contemporary topics related with complex systems based approaches and their utilization across the geosciences, exemplified by recent successful applications from various fields from paleoclimate over present-day atmospheric dynamics to Space Weather. The focus will be on tipping points and associated early warning indicators, the identification of causal relations among a multitude of observables, and how to combine both approaches in a multi-scale dynamical framework. The discussed data analysis tools are promising for investigating various aspects of both known and unknown physical processes.

Science impacts human society in many ways but of
particular importance is the application of scientific
results to the design of forecasting systems.
Forecasting systems are indispensable for making
informed decisions under risk. Informative and reliable
weather forecasts for instance help to better prepare
for or to reduce the exposure to adverse weather.
Therefore, there is a need for an objective and well
understood framework for ``forecast verification'',
i.e. qualitative and quantitative assessment of
forecast performance.

Statistical methods compare historical forecasts with
corresponding verifications, indicating whether the
forecasting system behaved significantly different (in
a statistical sense) from what was expected.

This short course will introduce the participants to
the fundamentals of statistical forecast verification.
Some necessary statistical theory will be discussed as well, and some hands-on numerical experiments will take place using freely available code. More specifically, the course will cover the following topics (more or less in that order)

* Forecast types and scoring rules
* Tests and p-values
* How to cope with dependent data
* How to cope with forecasts of spatial fields
* Code, literature, and further resources

Target audience are researchers (both from academic institutions and operational centres) who are either new to forecast verification or who have practical experience but want to know more about the theory. The course is NOT restricted to atmospheric forecasts, nor exclusively to the assessment of operational forecasting systems. The discussed methods are applicable in many other fields such as parameter estimation, data assimilation, model evaluation, and machine learning.

The proposed short course is one that we have taught twice in-person and once virtually at the EGU over the past 4 years, and that has always been attended to full capacity and with very positive feedback, so that we propose to teach it again this year.

The climate system as a whole can be viewed as a highly complex thermal/heat engine, in which numerous processes continuously interact to transform heat into work and vice-versa. As any physical system, the climate system obeys the basic laws of thermodynamics, and we may therefore expect the tools of non-equilibrium thermodynamics to be particularly useful in describing and synthesising its properties. The main aim of this short course will be twofold. Part 1 will provide an advanced introduction to the fundamentals of equilibrium and non-equilibrium thermodynamics, irreversible processes and energetics of multicomponent stratified fluids. Part 2 will illustrate the usefulness of this viewpoint to summarize the main features of the climate system in terms of thermodynamic cycles, as well as a diagnostic tool to constrain the behavior of climate models. Although the aim is for this to be a self-contained module, some basic knowledge of the subject would be beneficial to the participants.
- The first part, chaired by Remi Tailleux, will provide an advanced introduction on the fundamentals of equilibrium and non-equilibrium thermodynamics, irreversible processes and energetics.
- The second part, chaired by Valerio Lembo and Gabriele Messori, will illustrate some applications of thermodynamics to the study of the climate system and its general circulation.