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, equations, and feature representations in observations or across models and observations
- Hybrid models (physically informed ML, emulation, data-model integration)
- Novel detection and attribution approaches, including for extreme events
- Probabilistic modelling and uncertainty quantification
- Super-resolution for climate downscaling
- Explainable AI applications to climate data science and climate modelling
- Distributional robustness, transfer learning and/or out-of-distribution generalisation tasks in climate science

Machine learning (ML) methods have emerged as powerful tools to tackle various challenges in ocean science, encompassing physical oceanography, biogeochemistry, and sea ice research.
This session aims to explore the application of ML methods in ocean science, with a focus on advancing our understanding and addressing key challenges in the field. Our objective is to foster discussions, share recent advancements, and explore future directions in the field of ML methods for ocean science.
A wide range of machine learning techniques can be considered including supervised learning, unsupervised learning, interpretable techniques, and physics-informed and generative models. The applications to be addressed span both observational and modeling approaches.

Observational approaches include for example:
- Identifying patterns and features in oceanic fields
- Filling observational gaps of in-situ or satellite observations
- Inferring unobserved variables or unobserved scales
- Automating quality control of data

Modeling approaches can address (but are not restricted to):
- Designing new parameterization schemes in ocean models
- Emulating partially or completely ocean models
- Parameter tuning and model uncertainty

The session welcomes also submissions at the interface between modeling and observations, such as data assimilation, data-model fusion, or bias correction.

Researchers and practitioners working in the domain of ocean science, as well as those interested in the application of ML methods, are encouraged to attend and participate in this session.

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

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

Machine learning (ML) is being used throughout the geophysical sciences with a wide variety of applications. Advances in big data, deep learning, and other areas of artificial intelligence (AI) have opened up a number of new approaches to traditional problems.

Many fields (climate, ocean, NWP, space weather etc.) make use of large numerical models and are now seeking to enhance these by combining them with scientific ML/AI techniques. Examples include ML emulation of computationally intensive processes, data-driven parameterisations for sub-grid processes, ML assisted calibration and uncertainty quantification of parameters, amongst other applications.

Doing this brings a number of unique challenges, however, including but not limited to:
- enforcing physical compatibility and conservation laws, and incorporating physical intuition,
- ensuring numerical stability,
- coupling of numerical models to ML frameworks and language interoperation,
- handling computer architectures and data transfer,
- adaptation/generalisation to different models/resolutions/climatologies,
- explaining, understanding, and evaluating model performance and biases.
- quantifying uncertainties and their sources
- tuning of physical or ML parameters after coupling to numerical models (derivative-free optimisation, Bayesian optimisation, ensemble Kalman methods, etc.)

Addressing these requires knowledge of several areas and builds on advances already made in domain science, numerical simulation, machine learning, high performance computing, data assimilation etc.

We solicit talks that address any topics relating to the above. Anyone working to combine machine learning techniques with numerical modelling is encouraged to participate in this session.

Climate change results from atmosphere constituent modulation affecting the top-of-atmosphere energy balance, or land use changes at the Earth’s surface, altering surface albedo, amongst other “forced” changes. These natural or anthropogenic climate drivers are termed “climate forcing” agents. This session highlights research assessing and quantifying uncertainties in forcing agent evolution and their climate influence using Earth System Model simulations, or Earth observations. We invite contributions on all climate forcing research aspects, including the development of historical and future forcing time-series, analyses that use idealized, single- or multi-model approaches, or observational methods to evaluate the climate change impacts. We are especially interested in studies that examine the responses to forcing changes through time, using next-generation (CMIP7), current (CMIP6, CMIP6Plus), or previous CMIP phases. Research considering multiple components of the climate system (the ocean, atmosphere, cryosphere, land surface/subsurface, and biology) is highly encouraged.

The Coupled Model Intercomparison Project (CMIP) is instrumental in advancing our understanding of the Earth’s climate system and its future projections. However, Earth system models (ESM) exhibit disparities in critical aspects, particularly in their responses to anthropogenic forcings and the dynamical coupling of physical and biogeochemical systems. Given that the Earth system science community, and notably the IPCC, relies on CMIP outputs to inform policy and mitigation strategies, it becomes imperative to address these inherent uncertainties through a multidisciplinary approach that unites atmospheric, oceanic, and terrestrial modeling analyses. In this session, we invite studies that investigate uncertainties and model disagreements across all facets associated with the CMIP ensembles. These may include, but are not limited to, the following contributions:

1. Identification of processes and key entities with significant disparities across CMIP models: Quantifying sources of uncertainty across CMIP models, which may include i) internal variability, ii) process representations/model parameterization, iii) ESM architecture, and iv) external forcing.

2. Use of reduced complexity models and emulators: Exploring the uncertainty range with computationally fast model approaches, particularly the parts of the distribution not well represented by the CMIP ensembles.

3. Critical scientific priorities for future CMIP/Earth system model development: Recognizing and comprehending uncertainties and their underlying mechanisms are essential for guiding future model development and refining climate projections. We welcome contributions that focus on enhancing model performance and reducing uncertainties across disciplines for future CMIP iterations.

4. Opportunities, challenges, and constraints in using CMIP output for impact research: Uncertainties are amplified at regional scales; nevertheless, CMIP model projections are extensively utilized for impact studies by researchers unfamiliar with these sources of uncertainty and structural limitations of CMIP projections. We invite contributions that use innovative approaches to address these challenges in impact studies with CMIP output.

In summary, this session aims to foster collaboration and dialogue among climate scientists and modelers to increase the efficient use of CMIP output and meet the pressing challenges of climate change.

Join us for an interdisciplinary session, where we will explore how cutting-edge omics technologies are transforming our understanding of ecosystems and their resilience in response to climatic change across all scales. Over billions of years, spatial and temporal shifts in environmental conditions have driven the evolution of diverse microbial, fungal, plant and animal species, shaping the ecosystems, atmosphere, and climate of Earth. Gaining insights into how these organisms and biomes function, adapt, and interact requires a deep understanding of their components and the complex feedback systems they form.

Technological innovations in measuring and interpreting “meta-omics” datasets are now providing unprecedented mechanistic insights across diverse organisms, scales, and environmental spheres. These advances also drive the development of next-generation models to predict ecosystem function. In this session, we bring together ecologists, geochemists, and evolutionary biologists to examine the available omics toolkits for studying organisms and communities and to discuss ongoing efforts to integrate this knowledge across biological and temporal scales to address pressing Earth system science questions.

By combining eco-evolutionary insights with ecosystem-level concepts like community traits and resilience, we aim to foster future ITS sessions that apply integrated omics approaches alongside geoscience techniques for a deeper, mechanistic understanding of ecosystems.

We welcome contributions studying all Earth’s spheres (Biosphere, Atmosphere, Hydrosphere, Cryosphere, Geosphere), using a wide range of omics datasets (metagenomics, metatranscriptomics, metabolomics, proteomics, lipidomics, spectranomics, ionomics, elementomics, and isotopomics) as well as other large datasets such as trait, phenotype, inventory, pollen, and fossil records. We are particularly interested in studies involving control experiments, long-term ecological surveys, or flux networks, as well as research that provides mechanistic insights and employs big data in Earth system models or machine learning to scale patterns across space and time.

Advances in forest system modelling and monitoring techniques are crucial for deepening our understanding of forest ecosystems and their dynamic responses to environmental stresses and disturbances. These advancements are instrumental in addressing global environmental challenges by improving predictions and adapting management strategies accordingly. This session aims to bring together scientists and researchers focused on the latest advancements in forest systems modelling, observational techniques, and analytical methodologies to enhance our understanding of forest structural dynamics, soil carbon (C) dynamics, and the impacts of natural disturbances such as wildfires, insect’s outbreaks, pathogens/disease, droughts, and windstorms. Specifically, this session covers the following topics:

• Advancements in Forest System Modelling: Presentations on new models or significant improvements in existing models, that help predict and analyse forest growth, structural dynamics, C sequestration in biomass and soils, and ecosystem resilience. This includes models that integrate hydrological, meteorological, and biological processes.

• Innovative Monitoring Techniques: Studies showcasing novel observational technologies or methodologies, including remote sensing, isotopic tracing, or ground-based monitoring systems that provide new insights into forest mortality, growth patterns, and C cycling.

• Impact of Natural Disturbances: Research on how wildfires, insect’s outbreaks, pathogens/disease, droughts, and severe wind events alter forest structure, soil C stocks, and overall ecosystem functions. Contributions may include forward-looking information, post-disturbance recovery processes, disturbance modelling, and strategies for disturbance mitigation and adaptation.

• Cross-Scale Integration: Contributions that demonstrate the integration of innovative integrations of data and models across different spatial and temporal scales to understand forest biomass and soil dynamics comprehensively.

• Implications for future Management Strategies: Insights into how advanced modelling and monitoring approaches can shape policy development, offer a range of adaptation strategies, and inform management practices to enhance forest resilience and C retention.

It is undeniable reality the fact of increasing frequency and severity of natural hazards on a global scale. A trend that seems likely to continue in the future, as a consequence of increase in extreme weather events and climate change, constituting one of the most significant risks for the natural, technological and human environment. This session concerns the use of Geoinformatics technologies, specifically the use of Geographical Information Systems and Remote Sensing technologies as well as Artificial Intelligence methodologies, in order to understand the mechanisms of the manifestation and evolution of catastrophic phenomena, mostly related to floods, landslides, droughts and wildfires.
New data, remotely or in-situ acquired, advanced methodologies for their analysis and integration aimed at managing natural hazards are welcome in this session. Particular emphasis is placed on the application of explainable Artificial Intelligence methods, through techniques such as Shapley Additive explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), Permutation Importance, Partial Dependence Plot, Explainable Boosting Machine, etc., aimed at understanding the decision-making mechanism in problems related to the occurrence and evolution of natural hazards. Participants will be exposed to state-of-the-art technologies and practical applications to gain a full picture of the possibilities available for building applications of good disaster management practices. The intention of the session is to present successful cases that cover natural hazards in different environments and climate scenarios, leveraging cutting-edge technologies and contributing to the formation of a safer and more resilient society in the light of increased environmental challenges.

Data imperfection is a common feature in Geosciences. Scientists and managers alike are faced with uncertain, imprecise, heterogeneous, erroneous, missing or redundant multi-source data. Traditionally, statistical methods were used to address these shortcomings. With the advent of Big Data, Machine Learning methods, the development of new techniques in data mining, knowledge representation and extraction as well as artificial intelligence, new avenues are being offered to tackle the shortcomings of data imperfection.
This session aims to provide a venue to exchange on the latest progress in assessing, quantifying and representing data imperfection in all of its forms. We welcome abstracts focused on, but not limited to:
- Use cases and applications from all fields of Geosciences on missing value imputation, data fusion, imprecision management, model inversion. Examples may be built on any type of data: alpha-numerical time series, georeferenced field data, satellite, areal or ground imagery, geographical vector data, videos, etc...
- Theoretical developments for data fusion and completion; uncertainty assessment and quantification, knowledge extraction and representation from heterogeneous data, reasoning and decision making under uncertainty.
- Multi-disciplinary approaches including artificial intelligence and geosciences are encouraged. Contributions addressing data issues and solutions related to participatory sciences, crowd-sourced data and opportunistic measurements will be particularly appreciated.

Earth System Science is witnessing an ever-increasing availability of textual, digital trace, social sensing, mobile phone, opportunistic sensing, audiovisual, and crowdsourced data. These data open unprecedented new research avenues and opportunities but also pose important challenges, from technical hurdles to skewed coverage, difficulties in quality control, and reproducibility limits.

Textual data is a case in point. Digital newspaper repositories, social media platforms, and archives of peer-reviewed articles provide vast amounts of digitalized text data. At the same time, large language models, such as ChatGPT, have opened new scalable ways of extracting research-relevant and actionable information from texts. However, such models are far from unbiased and may not be transparent, interpretable, or open access, hindering reproducibility. The same holds true for other types of data and associated data mining methods, such as knowledge extraction from images, audio, and videos.

This session welcomes abstracts that explore using text and other emerging data sources in Earth System Sciences, especially in hydrology, natural hazards, and climate research. The session scope spans data analysis methodologies, scientific advances from the analysis of emerging data, and broader perspectives on the opportunities and challenges that these data sources present. Specific topics include but are not limited to, for example: assessment of natural hazard impacts (e.g. floods, droughts, landslides, temperature extremes, windstorms), real-time monitoring of disasters, evidence synthesis, public sentiment analysis, policy and awareness tracking, discourse and narrative analyses, natural language processing, large language models, social media analysis, historical data rescue, image mining, deep learning, and machine learning.

Earth System Reconstructions provide vital insights across all geological spatiotemporal scales, from the depths of deep time to future projections, crucial for understanding the complex interplay among the geosphere, atmosphere, and biosphere. These reconstructions are underpinned by paleogeographic research at regional to global scales. They leverage emerging modeling techniques and expanding databases to elucidate the interactions and feedback driving major past environmental crises and long-term evolutionary changes. In the current era of climate, biodiversity, and energy crises, such reconstructions are increasingly pivotal in shaping informed decision-making, with applications spanning environmental risk assessments, climate forecasting, and resource exploration.

The field is witnessing significant advancements through the application of machine learning, large language models, and other sophisticated statistical and nonlinear optimization techniques. These methods enhance our ability to interpret complex and often obscure geological, environmental, and geophysical data. By integrating approaches from various disciplines, we enhance the quantifiability of geological processes over a broad spectrum of spatial and temporal scales. This integration is critical for incorporating better quantifications of uncertainty in both parameter values and model choice, as well as the fusion between geophysical, geological and environmental sensing constraints with data analyses and numerical modelling of Earth Systems.

We invite contributions from all disciplines focused on modeling or constraining Earth Systems, from deep geological times to anticipated future scenarios, whether regional or global in scope. We welcome submissions that are analytical or lab-focused, field-based, or involve numerical modelling. This session also aims to explore cutting-edge methods, tools, and approaches that push the boundaries of inference and uncertainty analysis, and interdisciplinary model-data fusion. We ask the question `Where to next?’ in our collective quest to develop digital twins of our planet.

We also celebrate the contributions of early career researchers, open/community research philosophy, and innovations that have adopted interdisciplinary approaches.

NFDI4Earth recognises the crucial role of data centers in bridging disciplinary divides within Earth System Sciences (ESS) and beyond. This session, proposed by co-applicants of the NFDI4Earth (www.nfdi4earth.de), aims to leverage the ITS program group's focus on interdisciplinary and transdisciplinary approaches. Contributions from both data center providers and researchers to explore data center challenges in interoperability and opportunities within ESS are welcome.
The session will explore how data centers can facilitate collaboration and address complex challenges by:
Integrating Disciplines: Explore interdisciplinary data fusion and stakeholder engagement for data management.
Addressing Socially Relevant Problems: Address how data centers can aid transdisciplinary research on sustainability challenges and utilize data from public authorities for interdisciplinary ESS research and public engagement.
We encourage proposals from researchers and data center experts addressing:
I) Innovative Data Fusion: How can data centers seamlessly integrate diverse ESS data to tackle societal challenges?
II) Engaging Communities: How can data centers integrate stakeholder knowledge (academia, policymakers, public) for problem-solving?
III) Sustainability & Public Engagement: What opportunities exist for data centers to contribute to transdisciplinary research on sustainability challenges (e.g., climate change and impacts, biodiversity loss and safeguarding food security) and foster public engagement with ESS issues through citizen science data?
IV) Operation & Sustainability Models: How can we manage the diversity of ESS data centers and foster cooperation and interoperability?
Expected Outcomes:
Identify diverse interdisciplinary data management approaches in ESS.
Recommend enhanced collaboration between data centers, researchers, and stakeholders.
Develop strategies for leveraging data centers to support transdisciplinary research addressing societal challenges.
Create a roadmap for integrating stakeholder needs into NFD4Earth data center practices.

Downscaling aims to process and refine global climate model output to provide information at spatial and temporal scales suitable for impact studies. In response to the current challenges posed by climate change and variability, downscaling techniques continue to play an important role in the development of user-driven climate information and new climate services and products. In fact, the "user's dilemma" is no longer that there is a lack of downscaled data, but rather how to select amongst the available datasets and to assess their credibility. In this context, model evaluation and verification is growing in relevance and advances in the field will likely require close collaboration between various disciplines.

Furthermore, epistemologists have started to revisit current practices of climate model validation. This new thread of discussion encourages to clarify the issue of added value of downscaling, i.e. the value gained through adding another level of complexity to the uncertainty cascade. For example, the ‘adequacy-for-purpose view’ may offer a more holistic approach to the evaluation of downscaling models (and atmospheric models, in general) as it considers, for example, user perspectives next to a model’s representational accuracy.

In our session, we aim to bring together scientists from the various geoscientific disciplines interrelated through downscaling: atmospheric modeling, climate change impact modeling, machine learning and verification research. We also invite philosophers of climate science to enrich our discussion about novel challenges faced by the evaluation of increasingly complex simulation models.

Contributions to this session may address, but are not limited to:

- newly available downscaling products,
- applications relying on downscaled data,
- downscaling method development, including the potential for machine learning,
- bias correction and statistical postprocessing,
- challenges in the data management of kilometer-scale simulations,
- verification, uncertainty quantification and the added value of downscaling,
- downscaling approaches in light of computational epistemology.

The advancement of Open Science and the affordability of computing services allow for the discovery and processing of large amounts of information, boosting data integration from diverse scientific domains and blurring traditional discipline boundaries. However, data are often heterogeneous in format and provenance, and the capacity to combine them and extract new knowledge to address scientific and societal problems relies on standardisation, integration and interoperability.
Key enablers of the OS paradigm are ESFRI Research infrastructures, of which ECCSEL (www.eccsel.org), EMSO (https://emso.eu/) and EPOS (www.epos-eu.org), are examples currently enhancing FAIRness and integration within the Geo-INQUIRE project. Thanks to decades of work in data standardisation, integration and interoperability, they enable scientists to combine data from different disciplines and data sources into innovative research to solve scientific and societal questions.
But while data-driven science is ripe with opportunity to groundbreaking inter- and transdisciplinary results, many challenges and barriers remain.

This session aims to foster scientific cross-fertilization exploring real-life scientific studies and research experiences from scientists and ECS in Environmental Sciences. We also welcome contributions about challenges in connection to data availability, collection, processing, interpretation, and the application of interdisciplinary methods.
A non-exhaustive list of of topics includes:
- multidisciplinary studies involving data from different disciplines, e.g. combining seismology, geodesy, oceanography and petrology to understand subduction zone dynamics;
- interdisciplinary works, integrating two or more disciplines to create fresh approaches, e.g. merging solid earth and ocean sciences data to study coastal/oceanic areas and earth dynamics;
- showcase activities enabling interdisciplinarity and open science, e.g. enhancing FAIRness of data and services, enriching data provision, enabling cross-domain AI applications, software and workflows, transnational access and capacity building for ECS;
- transdisciplinary experiences that surpass disciplinary boundaries, integrate paradigms and engage stakeholders from diverse backgrounds, e.g. bringing together geologists, social scientists, civil engineers and urban planners to define risk maps and prevention measures in urban planning, or studies combining volcanology, atmospheric, health and climate sciences.

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

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

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

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

NASA successfully launched the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission on February 8, 2024. The spacecraft carries three groundbreaking instruments: the Ocean Color Imager (OCI), the Hyper-Angular Rainbow Polarimeter #2 (HARP 2), contributed by the University of Maryland Baltimore County, and the Spectro-polarimeter for Planetary Exploration (SPEXone), contributed by the Netherlands. This mission makes simultaneous measurements of the optical properties of water bodies, land, and the atmosphere that are first of their kind. This interdisciplinary session invites research on oceans, lakes, land, aerosols, and clouds, covering topics such as radiative transfer theory, algorithm development (including machine learning), validation, ocean and aquatic system biogeochemistry, terrestrial processes, and atmospheric process studies. Submissions demonstrating the connections between the atmosphere, ocean/aquatic systems, and land, as well as the synergistic use of PACE’s three sensors, are highly encouraged. We welcome the submissions that are using data collected during PACE validation campaigns (e.g., PACE-PAX). The session aims to strengthen collaboration across disciplines to fully utilize PACE’s unique dataset.

Natural hazards (e.g., earthquakes, volcanic eruptions, floods, landslides and ground subsidence), their cascading effects and societal risks, can strongly influence, and be influenced by human activities (e.g., migration, construction, architectural design, urban planning, forestation, deforestation, damming and drainage re-routing). The relationship between environmental risks and human behavior is dynamic in space and time. Understanding and using well this transdisciplinary interconnectedness is critical for improving disaster preparedness, urban planning, and environmental management. Investigating such complex relationships requires innovative joint analysis of the modern big earth observation data (e.g., optical, hyperspectral, GRACE, GNSS, RADAR, LiDAR), together with historical and paleo records of multi-hazards (e.g., literature, catalogue, geomorphology, and trenching), as well as anthropogenic (e.g., indigenous wisdom and tales), demographic (e.g., population, ethnicity, age), and developmental (e.g., economy, public policy) datasets. This session solicits contributions that employ earth observation (especially imaging geodesy) and interdisciplinary data sets for disaster risk reduction. We aim to encourage transdisciplinary discussions between data providers, researchers, and stakeholders, and thus welcome instrument designers, geodesists, natural scientists, social scientists, historians, anthropologists, engineers, architects, policy makers, and community workers to come together to celebrate success and highlight challenges in the integration of earth observation data in promoting resilience building and sustainable development.

High-impact climate and weather events typically result from the interaction of multiple climate and weather drivers, as well as vulnerability and exposure, across various spatial and temporal scales. Such compound events often cause more severe socio-economic impacts than single-hazard events, rendering traditional univariate extreme event analyses and risk assessment techniques insufficient. It is, therefore, crucial to develop new methodologies that account for the possible interaction of multiple physical and societal drivers when analysing high-impact events under present and future conditions. Despite the considerable attention from the scientific community and stakeholders in recent years, several challenges and topics must still be addressed comprehensively.

These include: (1) identifying the compounding drivers, including physical drivers (e.g., modes of variability) and/or drivers of vulnerability and exposure, of the most impactful events; (2) Developing methods for defining compound event boundaries, i.e. legitimate the ‘cut-offs’ in the considered number of hazard types to ultimately disentangle enough information for decision-making; (3) Understanding whether and how often novel compound events, including record-shattering events, will emerge in the future; (4) Explicitly addressing and communicating uncertainties in present-day and future assessments (e.g., via climate storylines/scenarios); (5) Disentangling the contribution of climate change in recently observed events and future projections; (6) Employing novel Single Model Initial-condition Large Ensemble simulations from climate models, which provide hundreds to thousands of years of weather, to better study compound events. (7) Developing novel statistical methods (e.g., machine learning, artificial intelligence, and climate model emulators) for compound events; (8) Assessing the weather forecast skill for compound events at different temporal scales; (9) Evaluating the performance of novel statistical methods, climate and impact models, in representing compound events and developing novel methods for reducing uncertainties (e.g., multivariate bias correction and emergent constraints); and (10) engaging with stakeholders to ensure the relevance of the aforementioned analyses.

We invite presentations on all aspects of compound events, including but not limited to the topics and research challenges described above.

Life on earth evolved through various geological ages in close interaction with the climate system. While the past climate changes have played a crucial role in shaping the terrestrial life distribution by modifying habitat and resource availability, modern humans have compounded these impacts by inducing a dramatic shift in the global biodiversity patterns. The evolutionary history of terrestrial life is characterized by migrations, adaptations, speciation and mass extinctions, with constant restructuring of the global ecosystem. Understanding the complex linkage between climate and terrestrial life forms is crucial in managing the present environmental challenges and developing effective conservation strategies for addressing potential biodiversity crisis in the future.

This session aims at bringing together multidisciplinary research on how climate has impacted and will impact terrestrial life forms and ecosystem structure in the past, present and future.

Topics of interest include,
- Mass extinctions in the past
- Climate and human influences on global biodiversity patterns
- Climate-driven species migrations
- Genetic diversification and speciation
- Vegetation dynamics and biome shifts
- Habitat degradation and effects on species distribution
- Species interactions and changes in ecosystem composition
- Climate-ecosystem modelling
- Conservation ecology

This multidisciplinary session at the nexus between climate change research and ecology will provide an opportunity for researchers to interact, forge new collaborations and exchange knowledge.

Over the past 50 years, climate extremes have caused more than 2 million deaths and an estimated $3.64 trillion in economic losses worldwide. Beyond these direct impacts, the effects on population health have become an urgent concern. Research has highlighted far-reaching consequences, particularly in terms of excess mortality and morbidity associated with cardiovascular and respiratory diseases, associated with climate extremes. The burden of these health impacts is not evenly distributed. Socioeconomic, demographic, and geographical factors heavily influence vulnerability, leading to significant disparities in health outcomes across different populations. For example, marginalized and disadvantaged groups, including the elderly, children, individuals with pre-existing health conditions, and residents of low-income or geographically vulnerable regions bear a disproportionate share of the health burden. Intersectionality plays a key role in this disparity; including overlapping social factors such as race, gender, age, and income interact to intensify existing vulnerabilities to climate extremes, climatic factors and health inequalities. This differential vulnerability underscores the critical link between climate justice and population health, emphasizing the need to address inequalities to strengthen resilience and mitigate population health impacts of climate extremes. This session welcomes all contributions that explore the complex impacts of climate extremes on population health, including studies on how intersecting socioeconomic, demographic, and geographical factors shape vulnerability.

The interconnection between climate, environment, and health is evident, with climate change posing significant threats to human welfare. As global temperature rise, extreme weather events such as heatwaves, floods, hurricanes, and droughts, directly and indirectly impact public health, alongside environmental exposures like air pollution. Climate and land use changes can influence the spread of vector-borne diseases such as malaria and increase the risk of waterborne illnesses. Additionally, climate change may result in severe wildfires and episodes of air pollution.
Addressing these complex challenges requires fostering interdisciplinary collaboration among climate researchers, epidemiologists, public health researchers, and social scientists, which is the primary focus of this session. The goal is to create a platform for presenting the latest innovations in using remote sensing and other large datasets to characterize exposures relevant to human health, especially in data-limited regions. The session encompasses various topics, including satellite data applications in human health, planetary epidemiology, risk mapping of infectious diseases, exposure mapping of heat and air pollution to quantify their impacts on human health, health co-benefits of mitigation actions, and the use of machine learning and AI for climate and health applications. The session emphasizes the examination of historical exposure-health outcome relationships, forecasts for the near future, and changes under progressive climate change.

As highlighted by the UN development goals, climate change is a reality to which we need to adapt. However, the many disciplines required to effectively plan and adapt to climate change often work in isolation. For example, physical climate modelling, hydrology, and hazard impact and risk assessment are largely separate disciplines with difficulties interacting due to different terminologies and backgrounds. Moreover, until recently, climate modellers did not have the capability to generate long-term projections at a spatial and temporal resolution useful for impact studies.

With the advent of kilometre-scale atmospheric models, called convection-permitting models CPMs, high resolution remote sensed data sets, and global sub-daily rainfall observations, we are now in a position to bridge the gap between disciplines, sharing knowledge and understanding. With all these tools at our disposal we have substantially improved the representation of sub-daily precipitation characteristics and have model output at a spatial resolution closer to what many impacts modellers, for example hydrologists, need. Now is the time to exploit these high-resolution, consistent datasets as input for impact studies and adaptation strategies; to foster interdisciplinary collaboration to build a common language and understand limitations and needs of the different fields; to learn together how to provide policymakers with information that can be used to design effective measures at to adapt to climate change as well as to inform mitigation decisions.

This interdisciplinary session invites contributions that address the linkages between high-resolution climate scientists, impact modellers, and end users with a special focus on:
- Recent advances in climate modelling for impact studies, particularly using high resolution convection- permitting models.
- Bias correction techniques to overcome bias in climate models affecting impact models.
- Analysis of the uncertainty propagation from climate into impact models.
- Improved understanding of processes that will alter hazards resulting from climate change.
- Novel use of new and existing observational data sets in characterising and quantifying climate change hazards.
- Examples of good practice, storylines and communication to both stakeholders and policymakers.

As climate change causes impacts from weather extremes to increase around the world, decision makers in government and industry are increasingly required to address changes to climate hazards when considering, disclosing, and acting to mitigate risks. Given that risk is the nexus of hazard, vulnerability, and exposure, a complete understanding of risk requires an interdisciplinary approach with input from experts in changes to all three of these pillars. In this session we address specifically those risks related to extreme weather events, including temperature, precipitation, and wind extremes, with a focus on interdisciplinary approaches that bridge the gap between the physical sciences and decision makers. We invite contributions from interdisciplinary teams working to address these challenges, as well as from those working in single disciplines but seeking to make interdisciplinary connections. Topics of interest include storyline approaches in which societal challenges are considered alongside physical climate risks; addressing knowledge gaps in physical hazard understanding when providing information to decision makers; issues related to the financial and insurance sectors’ responses to extreme weather events; impact-based forecasting as a tool for risk understanding; and studies of early-warning systems and associated decision making.

Disturbances, such as extreme weather events, play a key role in shaping ecosystems. Under climate change, extreme weather hazards undergo changes in frequency, intensity and seasonality. While ecosystem-based adaptation and nature-based solutions are gaining traction, it is crucial to elucidate the diverse interactions between extreme weather risk, ecosystems, and their services.

This session seeks to highlight research on the nexus of extreme weather events and ecosystems. This includes: 1) investigations into the key attributes and patterns of extreme weather events which affect ecosystem composition, structure and functioning. 2) studies on how ecosystems respond to and recover from extreme weather events across past, present, and future climates are of interest. 3) Implications of extreme weather impacts on ecosystems for biodiversity and ecosystem service provision. We welcome a diverse array of contributions, including theoretical analyses, modeling approaches, field studies, experimental designs, and remote sensing analysis.

Key topics include:
- Ecosystem (terrestrial, coastal or marine) responses to extreme weather
- Role of extreme weather in shaping ecosystem composition, biodiversity, structure and functioning
- Vulnerability assessments of ecosystems
- Natural hazard risk to ecosystems in past, present and future climates
- Changes in ecosystems service provisions due to extreme weather events
- Resilience and recovery dynamics
- Impact and efficacy of Nature-Based Solutions (NBS) under extreme conditions, risk of maladaptation or disservices
- Regime shift / tipping points in ecosystems due to extreme weather events
- Extreme weather disturbance regimes affecting ecosystems across time
- Identification of extreme weather risk hotspots
- Interactions of natural hazard and anthropogenic disturbances to ecosystems

Extreme weather events such as tropical cyclones, heatwaves and floods threaten populations around the world. Climate change is increasing the frequency and intensity of many kinds of extreme weather events, which can combine with community exposure, inequalities and vulnerabilities to cause substantial harm. There is growing literature at the intersection of the natural and social sciences studying the impacts of extreme weather events on populations as well as peoples’ behavioral, attitudinal, and emotional responses. For instance, studies have investigated how extreme weather and climatic changes influence food and water security, conflict and security risks, and health outcomes. Additionally, the field of environmental human mobility has witnessed remarkable progress in data collection, analytical methods, and modeling techniques. Further research has examined the responses of individuals and households to these threats, including climate-related emotions, environmental concerns, and climate policy support. These studies have been conducted in interdisciplinary settings, where social scientists closely collaborate with natural scientists to study populations that have been, or will be, impacted by extreme weather events.

Yet only few studies are currently harnessing the full potential of interdisciplinary collaborations in this space and several challenges pertaining to the choice of methods and the scale of analysis (e.g., regional, national) remain underexplored. This session aims to provide a platform for interdisciplinary work on extreme weather events and invites contributions from natural and social scientists interested in interdisciplinary studies on the societal impacts of and responses to extreme weather events. Furthermore, we highlight the topic of human (im)mobility with a perspective on addressing recent advancements, methodological innovations, novel use of data, challenges, or future prospects in modeling human mobility in the past, present, and future.

We invite contributions including but not limited to studies of:

- Environmental attitudes and behaviors influenced by extreme events
- Health and wellbeing effects of climate change and extreme events
- Migration and displacement due to extreme events
- Food production and security in relation to extreme weather
- The interplay between climate change, environment, and conflict
- Methodological challenges to interdisciplinary collaborations

Mountain environments are dynamic systems shaped by interconnected physical, biological, and chemical processes. Recent changes in climate, including elevation-dependent warming, shifting precipitation patterns, retreating glaciers, degrading permafrost and intensifying storms are reshaping these critical landscapes. These changes have a direct impact on ~35% of the global population, who live and work within or downstream of these regions. To address this complex, global challenge, this session aims to explore the diverse problems and approaches to monitoring, modelling, and predicting environmental change. It is essential to draw upon perspectives across the physical sciences to reduce uncertainties around future compounding hazard and risk. We welcome contributions focused on mountain system dynamics through, for example, remote sensing, numerical modelling, laboratory techniques, and field observations. This session is closely related to the objectives of the ‘Sediment Cascades and Climate Change (1)’ initiative and encourages an interdisciplinary dialogue between and beyond the fields of climatology, hydrology, sedimentology, and geomorphology.
(1) https://sedimentcascades.webspace.durham.ac.uk/

Atmosphere and Cryosphere are closely linked and need to be investigated as an interdisciplinary subject. Most of the cryospheric areas have undergone severe changes in last decades while such areas have been more fragile and less adaptable to global climate changes. This AS-CR session invites observational-, model- and remote sensing-based investigations on any aspects of linkages between atmospheric processes and snow and ice on local, regional and global scales. Emphasis is given on the Arctic, high latitudes and altitudes, mountains, sea ice, Antarctic-, and Alpine regions. In particular, we encourage studies that address aerosols (such as Black Carbon, Organic Carbon, dust, volcanic ash, diatoms, bioaerosols, bacteria, microplastics, etc.) and changes in the cryosphere, e.g., effects on snow/ice melt and albedo. The session also focus on dust transport, aeolian deposition, and volcanic dust, including health, environmental or climate impacts at high latitudes, high altitudes and cold Polar Regions. We include contributions on biological and ecological sciences including dust-organisms interactions, cryoconites, bio-albedo, eco-physiological, biogeochemical and genomic studies. Related topics are light absorbing impurities, cold deserts, dust storms, long-range transport, glaciers darkening, polar ecology, and more. The scientific understanding of the AS-CR interaction needs to be addressed better and linked to the global climate predictions scenarios.

Environmental issues are not only ecological but also societal and cultural. To address them effectively, we need to understand how human societies interact with the environment. This session highlights the importance of social science in environmental research and vice versa, and invites contributions that explore how interdisciplinary collaboration can lead to innovative and sustainable solutions. We welcome scientists from all disciplines of environmental and social sciences, data analysts, methodologists, and metadata experts to share their insights, case studies, and challenges. We aim to foster meaningful discussions and exchange of ideas across academic groups, research infrastructures, the private sector, and policy makers. By integrating the expertise of social scientists with environmental research, we can develop a more comprehensive and holistic understanding of environmental problems leading to pathways for viable climate action plans and supporting policies. Let's work together to contribute to a more sustainable relationship between people and the environment.
Topics may include, but are not limited to:
– Climate action plans and solutions for green and sustainable cities
– Cultural heritage and environmental sustainability
– Environmental policy and governance
– Air quality and climate indicators
– Sustainable agriculture and land use
– Biodiversity conservation and ecosystem services
– Climate adaptation and resilience
– Development of resilient communities through disaster risk reduction
– Citizen and participatory science and public engagement
– Best practice methodologies for specific use cases
– Metadata standards for integration of data from different research domains
– Project reports or infrastructure requirements related to multidisciplinary use cases

Our solicited speaker is Bonnie Wolff-Boenisch, CEO of CESSDA ERIC. Bonnie has 25 years of work experience in research and infrastructures, management and advocacy across different cultures, countries and disciplines. She is a member of Scientific Advisory Boards in Germany, Italy, France and the US, and has a PhD in Isotope Geochemistry from the Max-Planck Institute in Mainz, Germany.

Knowledge co-creation is key for participatory and transdisciplinary research and is often described as “science with society”, rather than science for society. Co-creation, co-production, and co-design refer to methods of participatory collaborative research, with adjacent terms including “public engagement”, or “community-led". All these methods are becoming increasingly recognised as necessary for solving complex societal and sustainability problems and challenges such as climate change, with joint efforts required from academia, enterprises, governments, and local/indigenous communities. Another advantage of co-creating with communities is that collaboratively designed solutions are more likely to be implemented and sustained long-term.

There are a wide variety of co-creation methodologies, including citizen science methods, which differ in levels of community collaboration depending on the question and goals of knowledge production. This session welcomes topics and case studies of co-creation from all disciplines and levels of participation of non-academic actors, from community consultation during the planning phases of the project goals to citizen scientists as data crowdsourcing. The idea is to not only highlight best practice, but also identify challenges associated with community co-creation. By sharing major learnings, best practices, and strategies, the session aims to promote increased participatory methods in mainstream science activities. Those participating in the session may also choose to submit a full paper in a special issue of Geoscience Communication (an EGU journal that covers outreach, public engagement, widening participation, and knowledge exchange in the geosciences), which will be based on the contributions of this session.

This session is a call for researchers to recognise that they are more than mere observers, and that non-academia actors are more than those observed. By enabling discussions and knowledge production on equal basis, transdisciplinary co-creation can empower communities, especially underrepresented communities who are often not heard.

Extreme climate and weather events, associated disasters and emergent risks are becoming increasingly critical in the context of global environmental change and interact with other stressors. They are a potential major threat to reaching the Sustainable Development Goals (SDGs) and are one of the most pressing challenges for future human well-being.
This session explores the linkages between extreme climate and weather events, associated disasters, societal dynamics and resilience. Emphasis is laid on 1) Which impacts on ecosystems and societies are caused by extreme events (including risks emerging from compound events)? 2) Which feedbacks and cascades exist across ecosystems, infrastructures and societies? 3) Where do these societal and environmental dynamics threaten to cross critical thresholds and tipping points? 4) Can we learn from past experiences? 5) What are key obstacles towards societal resilience and reaching the SDGs and Sendai Framework for Disaster Risk Reduction (SFDRR) targets, while facing climate extremes and compound events?

We welcome empirical, theoretical and modelling studies from local to global scale from the fields of natural sciences, social sciences, humanities and related disciplines.

Urban areas are major contributors to climate change and are especially vulnerable to its effects. Over the coming decades, millions of urban residents are expected to face rising sea levels, more intense storms, inland flooding, and extreme temperature variations. These challenges will strain urban infrastructure, reducing access to essential services and lowering the quality of life. Most critical economic and social infrastructure is located in cities, making them highly exposed to climate risks. However, many cities are not yet equipped to respond effectively due to outdated policies, limited resources, and low public awareness.
Citizen science offers a valuable way to address these challenges by enhancing our understanding of urban climate, health, and air quality. Through the active involvement of citizens and stakeholders, communities can collect critical data on air quality and other environmental factors. This participatory approach not only improves our knowledge of climate risks but also strengthens adaptation strategies for urban areas. Simple, low-cost tools can be used by citizens to gather atmospheric data, while stakeholders provide insights into local vulnerabilities. Additionally, unconventional data sources, such as crowdsourced observations and urban cellular networks, can offer important information on climate impacts and response strategies.
By engaging citizens in these efforts, we foster a sense of responsibility for the environment and build stronger support for adaptation initiatives. Citizen participation in data collection provides hands-on experience with the real effects of climate change, leading to greater awareness and climate-friendly behaviors. This is essential for meeting climate mitigation goals, along with technological and societal actions. Citizen science projects that monitor climate variables, health impacts, and air quality in urban settings, as well as those that develop digital tools to enhance public knowledge, play a critical role in combating misinformation and advancing climate adaptation.
This session encourages contributions that explore participatory science, crowdsourced data collection, and best practices for involving citizens in Europe’s climate adaptation strategies.

In the Anthropocene, water resources are simultaneously under unprecedented stress and the foundation for most ecosystem and societal processes. It is more important than ever to thoroughly understand the hydrological cycle and its interactions with other complex physical systems and social dimensions to address water-related challenges and develop actionable, sustainable solutions. To do this effectively, we need to move beyond a “science-as-usual” approach and leverage transdisciplinary knowledge involving multiple actors, including scientists, policymakers, local communities and indigenous peoples, NGOs and local associations, media, and businesses. Each of these actors brings a unique perspective and expertise, and we must empower and value their contributions with practices such as co-creation, to arrive at integrated solutions for complex water management issues. Co-creation can be defined as an iterative and collaborative process of mutual learning in which different knowledge interact and are integrated to address complex societal issues. Such approaches are common in policy creation and public services development but up until now have been under-described, -formalized, and -utilized in the context of water resources management and hydrological sciences.
Therefore, this session welcomes studies on co-creation approaches in hydrology and water resources management. More specifically, we welcome studies including, but not limited to: experiences and case studies of participatory and co-creation approaches applied to hydrology and water resources management; co-modelling approaches and socio-hydrological studies involving participation of stakeholders; meta-analyses, review of other experiences, and literature reviews; critical geography, political ecology and other critical approaches to co-creation and stakeholders involvement in water resources decision making.

Co-organized by the Working Group on Co-Creation of Water Knowledge of the International Association of Hydrological Sciences: https://iahs.info/uploads/HELPING/WG%20Proposal%20Co-Creating%20Water%20Knowledge%20v2.pdf

This session aims to (re)introduce biodiversity, an essential component of many aspects of life on Earth, as a notion that offers a wide array of multidisciplinary work from numerous fields of research, including but not limited to the geosciences and ecology. While biological diversity is vital for natural ecosystems such as forests and wetlands, and crucial for maintaining healthy freshwater ecosystems, soil systems, and oceans, it is also a factor that affects an ecosystem’s response to disturbances, in turn affecting notions such as (ecosystem) integrity, health and resilience. Biodiversity is also intrinsically linked with the Earth’s processes, geomorphology, formation, and development. The United Nation’s definition of biodiversity, or biological diversity, is: the variety of life on Earth and the natural patterns it forms. A wide range of studies on biological diversity also encompass ecological diversity and ecosystem diversity, since the diversity of ecosystems also affects the diversity of organisms that inhabit them. Earth Science recognizes the role of biotic factors in governing geophysical processes, and feedback mechanisms, across a wide range of spatial and temporal scales. Studies show that the control of biota might be part of a longer-term cycle, in which the dominance of biotic and abiotic processes not only switch, but depend on each other. Biota and abiotic processes may have co-evolved over both longer and shorter timescales. Scientific evidence from the geoscience community is therefore valuable in many political decisions for restoration, or rewilding, including the recent EU Nature Restoration Law. Also, research in these fields may contribute to policy on preparation for and/or prevention from geohazards, including those that may be triggered by anthropogenic interference and/or climate change, acting as stressors which affect diversity within a system.
Thus, in this session we aim to recognize the wide range of geoscience research projects that focus on or highlight aspects of biodiversity, while welcoming those that favor inter- and/or transdisciplinary approaches. Through these presentations, we hope to demonstrate the broad spectrum of biodiversity-related areas in which the geosciences contribute and where geoscience research gaps need to be addressed.

Mountains are complex social-ecological systems and natural laboratories in which to tangibly explore and understand how drivers and processes of global change manifest in specific places. In this session, we invite inter- and transdisciplinary contributions that examine past, present, and future environmental change, their