Machine learning (ML) is 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 ML and deep learning now allow for encoding non-linear, spatio-temporal relationships robustly without sacrificing interpretability. This has the potential to accelerate climate science through new approaches for modelling and understanding the climate system. For example, ML is now used in the detection and attribution of climate signals, to merge theory and Earth observations in innovative ways, and to directly learn predictive models from observations. The limitations of machine learning methods also need to 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:

More accurate, robust and accountable ML models:
- Hybrid models (physically informed ML, parameterizations, emulation, data-model integration)
- Novel detection and attribution approaches
- Probabilistic modelling and uncertainty quantification
- Uncertainty quantification and propagation
- Distributional robustness, transfer learning and/or out-of-distribution generalisation tasks in climate science
- Green AI

Improved understanding through data-driven approaches:
- Causal discovery and inference: causal impact assessment, interventions, counterfactual analysis
- Learning (causal) process and feature representations in observations or across models and observations
- Explainable AI applications
- Discover governing equations from climate data with symbolic regression approaches

Enhanced interaction:
- The human in the loop - active learning & reinforcement learning for improved emulation and simulations
- Large language models and AI agents - exploration and decision making, modeling regional decision-making
- Human interaction within digital twins

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.

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.

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.
Examples include ML emulation of computationally intensive processes, training on high resolution models or data-driven parameterisations for sub-grid processes, and Bayesian optimisation of model parameters and ensembles amongst several others.

Doing this brings a number of unique challenges, however, including but not limited to:
- enforcing physical compatibility and conservation laws, and incorporating physical intuition into ML models,
- 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.

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.

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, urban hydrology and ecology.
The aim is to elucidate complex urban dynamics, identify strategies and methods for the development of 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.
This session welcomes concepts, methodologies and disruptive models to overcome current scientific bottlenecks, to better deal with non-linearities, multi-component systems and extremes over a wide range of scales in geophysical and urban systems.

Climate change and widespread biodiversity loss are urgent challenges facing humanity, whose effects threaten human wellbeing, economies and planetary stability. There is increasing evidence that these two crises are strongly interconnected and might even be mutually reinforcing. However, climate- and biodiversity change are typically investigated through siloed approaches. This limits our ability to assess the feedbacks between these two major trends and to ultimately/eventually design policy solutions that fully take into account the trade-offs and synergies between climate change mitigation, adaptation, and biodiversity conservation.

In this session, we invite scientists from all disciplines working at the interface of these fields, and in particular on the linked relationships and processes between climate (change, variability, extremes) and biodiversity (taxonomic, functional, structural). We are especially interested in studies that investigate feedbacks mechanisms between biodiversity and the climate system at different spatial and temporal scales, from experimental, observational, data-science, and/or modelling perspectives, as well as on how human activities, such as land cover conversion or nature conservation, might influence these interactions.

Digital twins of our planet, at present-day and over geological timescales, are becoming central to decision-making and de-risking for a broad range of applications from natural hazard risk assessments, climate modelling, and to resource analysis. Emerging modelling techniques are promising to value-add to complex, and sometimes obscure, geological and geophysical data through machine learning, artificial intelligence, and other advanced statistical and nonlinear optimisation techniques. In addition, these new techniques provide an avenue to increase the quantifiability of geological processes at a wide range of spatial and temporal scales. This includes the key requirement to incorporate better quantifications of uncertainty in both parameter values and model choice, as well as the fusion between geophysical sensing and geological constraints with numerical modelling of Earth Systems.

We invite submissions from all disciplines that aim to model or constrain one or more Earth Systems over modern and geological timeframes. 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 geophysical inference and uncertainty analysis, and geological data fusion. We ask the question `Where to next?’ in our collective quest to develop digital twins of our planet.

The session will also celebrate the contributions of early career researchers, open/community philosophy of research, and innovations that have adopted inter-disciplinary approaches.

The Coupled Model Intercomparison Project (CMIP) advances climate system understanding, but Earth System Models (ESM) exhibit disparities, particularly in responses to forcings and system coupling. As the IPCC relies on CMIP to provide information for policy decisions, a multidisciplinary approach is crucial to address uncertainties across the full CMIP production line. This session invites studies on climate forcings, climate responses, uncertainties in forcing agents, and model disparities in CMIP projections.

We welcome diverse climate-forcing research, including historical and future, anthropogenic and natural forcing development, idealized Earth System Model studies, observational evaluations, and works spanning all climate system components. Topics may include identifying disparities in CMIP ESMs, quantifying uncertainties, and addressing key scientific priorities for future model development. Contributions on opportunities, challenges, and constraints in using CMIP output for impact research, especially at regional scales, are encouraged.
This session ultimately aims at fostering collaboration among climate scientists, observationalists and modelers to address climate change challenges. Convened by WCRP CMIP Forcing Task Team and Fresh Eyes on CMIP, it aims to enhance understanding of CMIP uncertainties and prepare for CMIP6Plus and CMIP7 climate-forcing datasets.

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

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

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.

Modelling and exploring forest ecosystems under future climate and management has never been more critical in the face of accelerated climate change and human-induced disturbances. Consequently, understanding the dynamics of forest ecosystems, which not only act as essential carbon sinks but also support biodiversity and a wide range of ecosystem services, and predicting their responses to changing environmental conditions and future management actions has become vital. To this end, this session aims to shed light on the innovations and advancements in forest modelling and assessment of ecosystem services, within the following focus areas:
1. Next-Generation Forest Models and Climate Dynamics: Overview of models designed to dynamically intertwine climate drivers with forest growth patterns, offering a thorough representation of carbon, nitrogen, and phosphorus cycles to predict forest responses to climate impact.
2. Model-Data Fusion in Forest Modelling: Discussion on how to integrate data from different sources (e.g., remote sensing, forest inventories, eddy-covariance) into forest modelling frameworks. Overview of computational techniques applied for model calibration, evaluation, and averaging and for data assimilation.
3. Large-Scale Forest Modelling for Feedback Mechanisms: Exploration of tools that assess the complex feedback loops among forest adaptive/mitigative strategies, localised climate changes, natural disturbances, and the permanence of forest carbon stocks.
4. Future Climate and Management Driven Forest Structural Modelling: In-depth look at how alternative management practices and climate drivers influence forest architecture, yielding structural indicators pivotal for the evaluation of biodiversity and ecosystem services.
5. Framework for Linking Forest Structural Indicators to Biodiversity and Ecosystem Services: Discussion on innovative methodologies establishing connections between forest structural variables from cutting-edge models and biodiversity and ecosystem service indicators, prioritizing the assessment and economic evaluation of potential synergies and trade-offs in forest management decisions.
This session invites contributions from researchers, practitioners, and policymakers. It seeks to become a vibrant forum for exchanging knowledge, insights, and best practices, furthering our collective goal of ensuring sustainable and resilient forest ecosystems in a rapidly changing world.

Space-based measurements of the Earth System, including its atmosphere, oceans, land surface, cryosphere, biosphere, and interior components, require extensive prelaunch and post-launch calibration and validation activities to evaluate scientific accuracy, characterise uncertainties and ensure the fitness for purpose of the geophysical information provided throughout lifetime of satellite missions. This stems from the need to demonstrate unambiguously that space-based measurements, which are typically based on engineering measurements by the detectors (e.g. photons), are sensitive to and can be used to reliably retrieve the geophysical and/or biogeochemical parameters of interest across the Earth.

Most geophysical parameters vary in time and space, and the retrieval algorithms used must be accurate and tested under the representative range of conditions encountered. Satellite missions also benefit from the availability of precursor data made available from other satellite missions, field campaigns, and/or surface-based measurement networks that are used in the definition of geophysical products and for the development and testing of the retrieval algorithms prior to launch during the satellite and ground segment development. Post-launch calibration and validation over the lifetime of missions assure that any long-term variation in observation can be unambiguously tied to the evolution of the Earth system. Such activities are also critical in ensuring that measurements from different satellites can be inter-compared and used seamlessly to create long-term multi-instrument/multi-platform data sets, which serve as the basis for large-scale international science investigations into topics with high societal or environmental importance.

This session seeks presentations on the use of surface-based, airborne, and/or space-based observations to develop precursor data sets and support both pre- and post- launch calibration/validation and retrieval algorithm development for space-based satellite missions measuring our Earth system. A particular but not exclusive focus will be on collaborative activities carried out jointly by NASA and ESA as part of their Joint Program Planning Group Subgroup on provision of precursor data sets for future ESA, NASA, and related partner missions, and the full range of pre- and post-launch calibration and validation and field activities for these satellite projects.

Modelling is fundamental for assessing various soil processes and interactions at different scales and resolution, while healthy soils are fundamentally important in sustaining a wide range of ecosystem services. Crossing interdisciplinary borders and integrating knowledge from various fields is essential in developing more accurate and comprehensive models to better capture the complexity of soil processes/mechanisms in natural and cultivated systems, address knowledge-gaps, and tackle the challenges related to data-integration, heterogeneity and uncertainty of modelling predictions across disciplines. An interdisciplinary approach is also needed in light of recent technological advances, such as computational approaches, model-coupling, geomatics, remote sensing/earth observation, machine learning, surveying and data collection sensors/sensor platforms, real-time data-streams, all of which provide opportunities for promoting new modelling generations integrating soil science across disciplines.

Integration of various disciplines and modelling is also essential for better understanding of the role of soil health, which includes concepts soil capacity and functionality towards a wide range of ecosystem services. Several measures to support soil health and tackle soil degradation have been proposed in the scientific literature, as well as several indicators to monitor expected benefits. The need for standardized data covering the broad concept of soil health and degradation is arising, along with the lack of information on relationships between soil quality and agriculture, forest and grassland resilience, and the socio-economic and environmental impacts of these measures. The scattered data availability and their complex integration for agronomic/environmental management and policy decisions may partly be covered by many European/international/national initiatives in the frameworks of the H2020, Horizon Europe, PRIMA, FAO programs, and other programs.

This session aims to promote and enhance communication and exchange of knowledge among scientists from modelling community, soil research and various related projects, linking different disciplines, and is open to contributions in a wide range of related topics, ranging from modelling soil systems to ecosystem and landscape modelling, soil health, degradation and living labs, while striving to contribute towards tackling current research challenges, addressing the knowledge-gaps, and informing policy.

The life evolution history on earth is closely intertwined with the multiple stressors within the ever-changing climate system. Ranging from large-timescale oscillations associated with orbital cycles and the glacial-interglacial transitions to regionalized extreme events, a wide range of climatic fluctuations in the past may have contributed to shaping the distribution and evolution of various life forms in the terrestrial environment. The recent developments in observational and coupled climate-ecological modelling approaches have provided a better understanding on the past climate impacts on the evolution of life. However, few occasions have allowed for a general bridge across these fields. Integrating multi-dimensional scientific approaches will provide us with a deeper understanding on the complex climate-ecological interactions and evolution in the past, throwing light into the potential ecological impacts of future climate change.

This session aims at bringing together multidisciplinary research addressing the climate-ecological interactions in the past, present and future, combining observational techniques/methods and ecosystem modelling. We welcome all kind of research contributions in this context and the topics of interests include,

- Past climate change and mass extinctions
- Global biodiversity patterns
- Chemical analyses on the geological materials (teeth, bone collagen, guano/feces, middens, sediment cores
- Geochemical mapping and dietary reconstructions across food webs
- DNA extraction, and taxonomic profiling of microorganisms
- Vegetation dynamics
- Climate and biome modelling
- Species adaptations and ecological strategies
- Genetic diversification and speciation
- Vulnerability and extinction risk, under anthropogenic warming and land use change.

We hope that through this session, individuals can discover new methodologies, applications and collaborations within their research areas that would help push science forward.

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 considering all aspects of compound events, including but not limited to the topics and research challenges described above.

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.

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 a 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

The polar climate system is strongly affected by interactions between the atmosphere and the cryosphere. Processes that exchange heat, moisture and momentum between land ice, sea ice and the atmosphere, such as katabatic winds, blowing snow, ice melt, polynya formation and sea ice transport, play an important role in local-to-global processes. Atmosphere-ice interactions are also triggered by synoptic weather phenomena such as cold air outbreaks, polar lows, atmospheric rivers, Foehn winds and heatwaves. However, our understanding of these processes is still incomplete. Despite being a crucial milestone for reaching accurate projections of future climate change in Polar Regions, deciphering the interplay between the atmosphere, land ice and sea ice on different spatial and temporal scales, remains a major challenge.
This session aims at showcasing recent research progress and augmenting existing knowledge in polar meteorology and climate and the atmosphere-land ice-sea ice coupling in both the Northern and Southern Hemispheres. It will provide a setting to foster discussion and help identify gaps, tools, and studies that can be designed to address these open questions. It is also the opportunity to convey newly acquired knowledge to the community.
We invite contributions on all observational and numerical modelling aspects of Arctic and Antarctic meteorology and climatology, that address atmospheric interactions with the cryosphere. This may include but is not limited to studies on past, present and future of:
- Atmospheric processes that influence sea-ice (snow on sea ice, sea ice melt, polynya formation and sea ice production and transport) and associated feedbacks,
- The variability of the polar large-scale atmospheric circulation (such as polar jets, the circumpolar trough and storm tracks) and impact on the cryosphere (sea ice and land ice),
- Atmosphere-ice interactions triggered by synoptic and mesoscale weather phenomena such as cold air outbreaks, katabatic winds, extratropical cyclones, polar cyclones, atmospheric rivers, Foehn winds and heatwaves,
- Role of clouds in polar climate and impact on the land ice and sea ice through interactions with radiation,
- Teleconnections and climate indices and their role in land ice/sea ice variability.

Climate change may regionally intensify the threat posed by future floods to societies. The space-time dynamics of floods are controlled by atmospheric, catchment, riverine and anthropogenic processes, and their interactions. From a global change perspective, Holocene and historical floods and their spatial and temporal patterns are of particular interest because they can be linked to former climate patterns, a proxy for future climate predictions. Millennial and centennial time series include the very rare extreme events, which are often considered by society as 'unprecedented'. By understanding their timing, magnitude and frequency in conjunction with prevailing climate regimes and human activities, we can overcome our lack of information and disentangle the so-called “unknown unknowns”. The reconstruction and modelling of space-time flood patterns, related atmospheric variability and flood propagation in river basins under different environmental settings are the foci of this session supported by the PAGES Floods Working Group. Flood-prone areas are, in many regions, hotspots of economic, social, and cultural development. Hence, the historical role of human action in altering flood frequencies, hydro-sedimentary, and environmental processes is a priority topic. The session will further stimulate scientific discussion on detection and attribution of flood risk change.
We welcome interdisciplinary contributions using natural and documentary archives, instrumental data, and model reconstructions, which:
i) provide knowledge from short-term to long-term development of cultural river-landscapes and human-environmental interaction,
ii) reconstruct and model temporal and spatial flood patterns related to climate variability and change, including long-term changes in rainfall patterns,
iii) analyse the role of catchment conditions in shaping flood patterns,
iv) develop (supra-) regional historical maps of extreme floods (MEF),
v) highlight historical risk mitigation strategies of local communities and assess the flood risk of cultural heritage sites,
vi) collect evidence of the Anthropocene in floodplains and wetlands,
vii) detect changes in flood exposure and vulnerability.
The interdisciplinary integration of information is critical for the provision of robust data sets and baseline information for future flood risk scenarios, impacts, adaptation and mitigation strategies, and integrated river management.

Coastal areas are of particular concern due to their high susceptibility to environmental shifts and to the expected human pressure over the next few decades. Sea level rise, storm-surges, flooding, extreme precipitation events, coastal erosion are only some of the challenges the coastal communities copy worldwide. Moreover, other climate-related hazards such as strong winds, droughts, heatwaves and cold spells can also be significant in coastal areas. Studies addressing coastal impacts in the future pose new questions on the spatiotemporal of such impacts and on the effectiveness of adaptation activities. To enhance the understanding of the complex dynamics of climate change impacts in coastal areas is of critical importance for hazard assessment and for the development of sustainable mitigation and adaptation solutions for vulnerable urban areas.
This interdisciplinary session focuses on climate-change-related hazards in coastal areas and on tools and approaches to address physical and socioeconomic consequences of extreme events in coastal areas. We especially encourage studies related, but not limited to: i) the historical characterization and future prediction and assessment of coastal hazards and risks, ii) climate and marine services, data and models to develop urban-scale hazard modeling and scenarios, iii) smart technologies for real-scenario interventions (e.g., digital twin, low-cost sensors, Nature-Based Solutions), iv) participative approaches (e.g., living labs, citizen science) and strategies (e.g., coastal zone management plans) to make informed decisions that foster climate change resilience and adaptation in coastal areas.

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.

This session aims to bring together traditional and non-traditional perspectives on environmental change. It features contributions, collaborations, perspectives and data from geosciences, historiology, humanities, social sciences, academics, science communicators, civil society, indigenous peoples and citizens. Such a broad and interdisciplinary approach is required to address current “anthroposcenic" challenges, in particular the effective communication of research.

One such challenge we focus on is armed conflict. War is resulting in widespread environmental change in different contexts across the world, often compounding climate and biodiversity challenges. Contributions use innovative techniques and perspectives to characterise the environmental and geophysical dimensions of war, based on research methods including: satellite remote sensing, in-situ and lab-based pollution measurements, and machine learning.

We hope for the session to illustrate the role non-traditional perspectives have in geosciences to broaden and deepen our understanding of compound challenges past and present. This will help open up space for discussion and collaboration between different disciplines and actors into the future.

The United Nations (UN) 2030 Agenda for Sustainable Development set a milestone in the evolution of society's efforts towards sustainable development which must combine social inclusion, economic growth, and environmental sustainability. The definition of the Sustainable Development Goals (SDGs) and the associated Global Indicator Framework represent a data-driven framework helping countries in evidence-based decision-making and development policies.

Earth observation (EO) data, including satellite and in-situ networks, and EO data analytics and machine learning plays a key role in assessing progress toward meeting the SDGs, since it can make the 2030 Agenda monitoring and reporting viable, technically and financially and be beneficial in making SDG indicators' monitoring and reporting comparable across countries.

This session invites contributions on how to make use of Earth Observations data to address SDG monitoring and reporting, in particular welcomes presentations about EO-driven scientific approaches, EO-based tools, and EO scientific initiative and projects to build, assess and monitor UN SDGs indicators.

Landscapes and land use change dynamics taking place over centuries have resulted in considerable environmental change conditions in many places worldwide, posing challenges for regional sustainability and resilience to climate and global change. However, these intricate social-ecological systems, such as mountains, watersheds and beyond, can also serve as natural laboratories through which an understanding of global change processes can be enhanced, as well as promote opportunities for learning and implementing solutions to address these challenges. In this inclusive EGU session, we delve into the complexity of diverse environments and their changes, emphasizing the heterogeneous landscapes shaped by traditional activities over centuries. A primary focus of the session is the imperative for effective land management strategies in response to these challenges. The discussion encompasses the diverse impacts of land use and other processes of change on water resources and the critical need for adaptive strategies to mitigate environmental risks. Ecosystem services, including soil fertility, biomass provision, and biodiversity, play a pivotal role in the assessment of land management strategies, aiming to enhance resilience and reduce climate change risks. The interdisciplinary nature of mountains and other systems is underscored, recognizing the difficulties in adequately parameterizing complex terrain in models and the scarcity of high-elevation monitoring infrastructure, to name a few such constraints. We seek contributions that bridge disciplinary boundaries, incorporating empirical studies of mountain climate, cryosphere, ecology, hazards, and hydrology. Understanding socio-economic dimensions and risks is prioritized, integrating demographic changes, land-use alterations, and projections to understand hazards, vulnerability, and exposure interactions. This collaborative session provides a pivotal platform to advance knowledge, encourage interdisciplinary research, and design comprehensive strategies for sustainable management in mountains, watersheds, and other regions. It stands as a testament to the collective commitment to address the intricate challenges faced by these unique environments, fostering a holistic understanding of their dynamics under global change.

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 ecosystems response to disturbances, affecting notions such as (ecosystem) integrity, health and resilience. Biodiversity is also intrinsically linked with the Earth’s processes, geomorphology, formation, and development. 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 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 natural hazards, including those that may be triggered by climate change. However, to be able to contribute to these processes, we need to be able to recognize the range of areas where our expertise is relevant and useful.

This session aims 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 more geoscience research is needed.