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

The UN development goals highlight we need to adapt to the reality of climate change. However, climate modelling, hydrology, hazard impact, risk and economic assessment – the disciplines needed for adaptation – all largely work in isolation with different terminologies and backgrounds. Moreover, until only recently, climate modellers were not able to generate long-term projections at the spatial and temporal resolution required for impact studies.

With the advent of kilometre-scale 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 gaps between disciplines. We have substantially improved the representation of sub-daily precipitation characteristics and have model output at a spatial resolution closer to what impacts modellers, for example hydrologists, need.

Unfortunately, impact studies at regional or sub-regional scale, which are crucial for effective adaptation strategies, are often limited to the direct economic impact of specific extreme events occurred in the past, like hurricane Katrina. As a result, impact studies rarely consider the indirect socio-economic effect or/and apply a probabilistic methodology to assess the potential direct and indirect impacts of extreme events in the future in specific regions.

This interdisciplinary session invites contributions that address the linkages between high-resolution climate scientists, impact as well as macro-economic and economic network models 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 data sets in characterising and quantifying climate change hazards and their socio-economic impacts.
- Examples of good practice, storylines and communication to both stakeholders and policymakers.
- Novel probabilistic approaches to assess not only the direct impacts generated by extremes, but also their indirect effects due to their propagation along supply chains and economic production and financial networks.

High-impact climate and weather events typically result from the interaction of multiple hazards 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 drivers when analysing high-impact events. Such an endeavour requires (i) a deeper understanding of the interplay of mechanisms causing compound events and (ii) an evaluation of the performance of climate/weather, statistical and impact models in representing compound events.

We invite papers studying all aspects of compound events, which might relate to (but are not limited to) the following topics:

Synthesis and Analysis: What are common features for different classes of compound events? Which variables need to be assessed jointly in order to address related impacts? How much is currently known about the dependence between these variables?
Stakeholders and science-user interface: Which events are most relevant for stakeholders? What are novel approaches to ensure continuous stakeholder engagement?
Impacts: What are the currently available sources of impact data? How can they be used to link observed impacts to climate and weather events?
Statistical approaches, model development and evaluation: What are possible novel statistical models that could be applied in the assessment of compound events?
Realistic model simulations of events: What are the physical mechanisms behind different types of compound events? What type of interactions result in the joint impact of the hazards that are involved in the event? How do these interactions influence risk assessment analyses?

Need for Smart Solutions in earth, environmental and planetary sciences: Tackling data challenges and incorporating applied earth and planetary sciences into artificial intelligence (AI) models opened a new avenue for creating comprehensive methodologies and strategies to answer a wide variety of theoretical and practical questions from detecting, modelling, interpreting and predicting changes in the earth and environment’s ecosystems in response to climate change to understanding interactions among the ocean, atmosphere, and land in the climate system. Therefore, AI and Data Science (DS) in earth, environmental and planetary sciences are one of the fastest growing areas. The performance of the AI/DS models improves as it gains experience over time. Various mathematical and statistical models need to be investigated to determine the performance of AI models. Once the learning process is completed, then the model can then be used to make an assumption, classify and test data. This is achieved after gaining experience in the training process. This session aims to make available to the world community of earth, environment and planetary sciences-related professionals a collection of scientific papers on the current state of the art and recent developments of AI and DS applications in the field. This session will shed light on many recent research activities on applying AI/DS techniques into a single comprehensive document to address engineering, social, political, economic, safety, health, and technological issues of earth, environment and planetary sciences challenges and opportunities. The purpose of this session is to improve and facilitate the application of intelligent systems for the earth, environmental and planetary sciences to highlight new insight for creating comprehensive methodologies for analyzing/processing/predicting/management strategies in the fields of fundamental and applied sciences problems through the decision-making abilities of artificial intelligence and machine learning techniques.

This session is a compilation of two independent sessions: ITS1.7 ‘Sandy solutions for coastal safety, measurements and modelling’ and GM7.3 ‘Arctic coastal processes’.

Future projections show that coastal regions are among the most vulnerable ecosystems on our planet. From nearshore to dunes, the coastal system provides ecosystem services such as water supply and storage, recreation, biodiversity and flood protection, all of which can be considered of critical importance for human well-being. Climate change, sea level rise and anthropogenic impacts can affect these services by altering topography and habitat development. Flexible nature-based solutions have been proposed to promote resilience against climate change and safeguard coastal services for current and future generations. For this session we aim to bring together experts from varying disciplines focused on measuring, modelling and designing nature-based solutions in a changing world. This includes but is not limited to topics related to coastal morphology, sediment and vegetation dynamics, hydrology, and anthropogenic impacts.

Decreasing extent and duration of sea ice cover, changes in storm patterns as well as rising sea surface and air temperatures impact coastal processes in the Arctic. Wave overtopping, flooding and coastal erosion pose risks to societies and infrastructure located at the coast. There is a pressing need to understand the rates and mechanisms of coastal change to better predict future trajectories under the changing climate. In this session, we invite contributions from a range of disciplines and across time scales on local to pan-Arctic studies related to coastal processes in the Arctic. Those can include observational (satellite and instrumental) data, historical data, geological records and proxy data, model simulations as well as forecasts, for the past, present and future rates and drivers of Arctic coastal change. The common denominator of these studies will be their focus on a better understanding of short- to long-term mechanisms and feedbacks that drive Arctic coastal changes, and their impact on coastal communities and infrastructure, at local to global scales.

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.

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

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

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

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

Recent advances in the field of Artificial Intelligence, Machine Learning and Data Assimilation have been massively applied to model, anticipate, and predict the natural catastrophes, such as earthquakes, floods, landslides, volcanic eruptions, tsunamis, wildfires, glacier instabilities, in addition to multi-hazard and cascading effects due to climate change. However, the adopted algorithms require a solid inductive bias, provided by the physics of the phenomenon at stake (or at least the understanding of it). Furthermore, due to simplified assumptions, analytical models might encounter limits while modeling these natural catastrophes. Therefore, several hybrid strategies, utilizing the growing computational resources, are currently being developed, to achieve more flexibility and full synergy between numerical physics-based simulations, machine learning and data-driven approaches.
The hybrid modeling of natural hazards benefits from the interpretability of numerical simulations and from the extrapolation and generalization capabilities of advanced Machine Learning methods. This synergy leads to multi-fidelity predictive tools that leverage all the available knowledge on the phenomenon at stake. Moreover, to tackle lack of data and representation, observational databases can be integrated with the synthetic results for re-analysis and for training machine learning algorithms on never-before-seen disaster scenarios. This multidisciplinary session invites contributions addressing hybrid solutions to predict and to mitigate natural catastrophes. It also welcomes presentations on hybrid tools for vulnerability assessment.

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

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

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

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

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

Humans have been successfully mapping the remotest and most inhospitable places on Earth, and the surfaces and interiors of other planets and their moons at highest resolution. The remaining blank spots are located in areas that are hardly accessible either through field surveys, geophysical methods or remote sensing due to technical and/or financial challenges. Some of these places are key areas that would help to reveal geologic history, or provide access to future exploration endeavors.

Such extreme and remote locations are commonly associated with the ocean floor, or planetary surfaces, but these extreme worlds might also be found in hot deserts, under the ice, in high-mountain ranges, in volcanic edifices, hidden underneath dense canopy cover, or located within the near-surface crust. All such locations are prime targets for remote sensing mapping in a wider sense. The methodological and technical repertoire to investigate extreme and remote locations is thus highly specialized and despite different contexts there are commonalities not only with respect to technical mapping approaches, but also in the way how knowledge is gathered and assessed, interpreted and vizualized regarding its scientific but also its economic value.

This session invites contributions to this field of geologic mapping and cartography of extreme (natural) environments with a focus on the scientific synthesis and extraction of information and knowledge.

A candidate contribution might cover, but is not limited to, topics such as:

- ocean mapping using manned and unmanned vehicles and devices,
- offshore exploration using remote sensing techniques,
- crustal investigation through drilling and sampling,
- subsurface investigation using radar techniques,
- planetary geologic and geophysical mapping,
- geologic investigation of desert environments,
- subglacial geologic mapping...

The aim of this session is to bring together researchers mapping geologically and geophysically inaccessible environments, thus relying on geophysical and remote sensing techniques as single source for collecting data and information. We would like to keep the focus on geologic and geophysical mapping of spots for which we have no or only very limited knowledge due to the harsh environmental conditions, and we would thus exclude areas that are inaccessible for political reasons.

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

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

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

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

- the mathematical theory of abrupt transitions in (random) dynamical systems,
- paleoclimatic studies of past abrupt transitions,
- data-driven and process-based modelling of past and future transitions,
- early-warning signals
- the implications of abrupt transitions for Climate sensitivity and response,
- ecological and societal impacts, as well as
- decision theory in the presence of uncertain Tipping Point estimates
- probabilistic modelling of Earth system processes

Scientific drilling through the International Ocean Discovery Program (IODP) and the International Continental Scientific Drilling Program (ICDP) continues to provide unique opportunities to investigate the workings of the interior of our planet, Earth’s cycles, natural hazards and the distribution of subsurface microbial life. The past and current scientific drilling programs have brought major advances in many interdisciplinary fields of socio-economic relevance, such as climate and ecosystem evolution, palaeoceanography, the deep biosphere, sustainable georesources, deep crustal and tectonic processes, geodynamics and geohazards. This session invites contributions that present and/or review recent scientific results from deep Earth sampling and monitoring through ocean and continental drilling projects. Furthermore, we encourage contributions that outline perspectives and visions for future drilling projects, in particular projects using a multi-platform approach.

The interactions between aerosols, climate, and weather are among the large uncertainties of current atmospheric research. Mineral dust is an important natural source of aerosol with significant implications on radiation, cloud microphysics, atmospheric chemistry and the carbon cycle via the fertilization of marine and terrestrial ecosystems. In addition, properties of dust deposited in sediments and ice cores are important (paleo-)climate indicators.

This interdivisional session --building bridges between the EGU divisions CL, AS, SSP, BG and GM-- had its first edition in 2004 and it is open to contributions dealing with:

(1) measurements of all aspects of the dust cycle (emission, transport, deposition, size distribution, particle characteristics) with in situ and remote sensing techniques,

(2) numerical simulations of dust on global, regional, and local scales,

(3) meteorological conditions for dust storms, dust transport and deposition,

(4) interactions of dust with clouds and radiation,

(5) influence of dust on atmospheric chemistry,

(6) fertilization of ecosystems through dust deposition,

(7) any study using dust as a (paleo-)climate indicator, including sediment archives in loess, ice cores, lake sediments, ocean sediments and dunes.

We especially encourage the submission of papers that integrate different disciplines and/or address the modelling of past, present and future climates.

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 model- and observational-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 and Antarctic regions, high latitudes and altitudes, mountains, sea ice and permafrost regions. In particular, we encourage studies that address aerosols (such as Black Carbon, Organic Carbon, dust, volcanic ash, microplastics, pollen, sea salt, diatoms, bioaerosols, bacteria, etc.) and changes in the cryosphere, e.g., effects on snow/ice melt and albedo. The session also focuses 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.

This session explores the driving mechanism for the timing of a monsoon season, which is key for a number of climate-sensitive sectors such as agriculture, hydropower that are highly dependent on the spatial and temporal distribution of rainfall throughout the season. In particular, we welcome submissions advancing the link between large-scale atmospheric and oceanic systems and the timing of the onset. This session will also discuss the different definition of onset currently used by NMHS and regional climate institutes for various applications (e.g., agriculture, climate model analysis)
The session aims to bring together, amongst others, climate service providers, numerical modelers, observation community and other disciplines for onset is relevant (e.g. Hydrology, ecology and agriculture), with the aim of advancing the understanding of onset definitions and their driving mechanisms. Of particular interest are new insights on the dynamical drivers that control the timing of monsoon onset. For example: physical mechanisms, interannual and decadal variability, differences in climate change signal on onset, interactions across scales and land-atmosphere interactions. Further, we welcome studies that explore rainfall onset in a variety of contexts whether they be past, present or future change. Studies that move towards improving the forecast skill of onset at seasonal and sub-seasonal timescales are especially encouraged.

Additional topics include, though are not limited to:
- Monsoon systems under climate change
- Event based case studies (cases of very early and late onset)
- Interannual and decadal variability of rainfall in tropical regions
- Inclusion of onset in climate services in Africa
- Model evaluation on timing of onset
- Understanding the variability of onset dates on agriculture
- Coproduction of onset forecast for specific application

In the face of ecological collapse and natural resources depletion, largely driven by our current linear economic system and exacerbated by continue economic growth, a new sustainability framework proposes to “bend” linear process and create a circular economy (CE). The aim of this economic circularity is to feedback energy and materials into the economic system as much as possible, resulting on a net reduction in the extraction and transformation of energy and materials from ecosystems and the generation of emissions and waste, with respect to economic activity (an eco-economic decoupling).
The extent to which this new economic paradigm becomes a reality depends on our ability to solve several economic transformation challenges and on being able to accurately assess the net savings of energy and materials and the reductions in emissions and waste that would constitute an absolute eco-economic decoupling.
In CE, waste is used as a resource by closing material loops through different types and levels of recovery. The objective of product recovery management is to recover as much of the economic (and ecological) value as reasonably possible, thereby reducing the ultimate quantities of waste (Thierry et al., 1995).
This session presents studies related to:
1. Resource extraction/recovery from wastes
2. Metals and REE extraction and recovery techniques
3. Reuse of waste materials in construction materials
4. Methods to quantify the CE
5. CE models and alternative value chains of secondary resources
This session also welcomes contributions that address the CE, including critical analyses, case studies, and monitoring frameworks that consider the energy and geo-resources (and/or their corresponding emissions and wastes) associated to economic activities/sectors. In particular, contributions that assess the decarbonization and/or dematerialization of energy systems, water systems, and/or food systems, from the perspective of (but not limited to) the net use of energy, water, and minerals, and the generation of GHG emissions, across value chains (hence, works that apply Input-Output Analysis and/or Life-Cycle Assessments are particularly welcomed). Lastly, studies that delve into the socio-political arrangements that allow (or obstruct) economic circularity transformations are also included.

Providing sufficient energy while minimizing climate impacts has become an essential challenge for our society, and the difficult geopolitical situation in Europe, combined with energy scarcity adds momentum to finding solutions quickly. At the Austrian Academy of Sciences, a working group has formed to explore the situation in Austria and Europe regarding pathways of a sustainable energy transition. Findings so far have demonstrated that the issue can’t be solved entirely on technological progress, but it needs a general re-thinking on how much energy is needed for decent living towards provision of services rather than energy. Increased efficiency, reduced energy consumption and smart grids as well as consuming devices are identified to be key pillars of a successful energy transition.
The context of the climate crisis as laid open in the recent IPCC reports (www.ipcc.ch) and the political response in form of the European Green Deal (https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en) demand treatment at highest scientific standards to provide well-founded responses to the society.
This session welcomes contributions that address energy transition from various perspectives, e.g. (but not limited to): energy production, transmission, storage, smart systems, efficiency, human behavior, energy markets and legislation, energy saving. In particular, we welcome contributions that address energy transition from a holistic point of view, that integrates the technological with societal aspects.

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 one of the most pressing challenges for future human well-being. Nature-based solutions (NBS), defined as “'inspired and supported by nature, which are cost-effective, simultaneously provide environmental, social and economic benefits and help build resilience'”, are a fundamental part of the efforts to “repair the way we interact with nature”, established as a goal within the European Green Deal. NBS can provide multiple benefits, such as mitigating climate hazard risks (e.g. floods, droughts) and enhancing climate resilience. Although NBS have received increasing interest over the last years, there are still doubts regarding their efficacy in comparison with more well tested civil engineering solutions, depending on the type and magnitude of hazards and the robustness of the NBS. This session aims to explore the linkages between extreme climate and weather events, associated disasters, societal dynamics and resilience, as well as the technical, financial and operational feasibility and performance of NBS solutions. Specific topics include, but are not limited to:
• Impacts of extreme climate events (including risks emerging from compound events) and cascades of impacts on various aspects of ecosystems and societies;
• Key obstacles towards societal resilience and achievement of SDGs while facing climate extremes;
• Evidence-base of NBS solutions to support disaster risk reduction and climate adaptation;
• New methods and tools to investigate the role of NBS to enhance resilience and adaptation to climate change;
• Case studies of inspirational practice for successful implementation and upscaling of NBS projects;
• Financial instruments and business opportunities to stimulate NBS implementation;
• Future NBS performance under various climate change scenarios;
• Co-governance of climate mitigation and adaptation with NBS.

Nature-Based Solutions (NbS) are actions to protect, sustainably manage, and restore natural or modified ecosystems, that address societal challenges, simultaneously providing human well-being and biodiversity benefits (IUCN, 2018). Within the framework of a global ecosystem approach, NbS must encompass ecological, societal, political, economic and cultural issues at all levels, from the individual to the collective, from local to national, from the public or private sphere.

As recently highlighted by IPCC and IPBES, climate change and biodiversity degradation cannot be separated, and must be considered together. For this reason, this session is especially focused on the way NbS can act as climate change adaptation solutions. Considering various ecosystems (marine and coastal, urban, cropland, mountainous, forest, rivers and lakes,.,), NbS as interventions for climate adaptation includes the adaptation to: sea level rise (flooding and erosion), changes of the water regime (floods, droughts, water quality and availability), rise in temperatures (heat waves, forest fires, drought, energy consumption), plant stress and increase of pests (variation of yields, forest dieback), to minimize their associated social and economic negative impacts.

Therefore, this session aims to promote interdisciplinary research related to ecosystem restoration, preservation and management, to put forward the complexity that is often hidden by simplifying hypotheses and approaches (sector-based silo approach, homogeneity of environments, ...).

Specific topics of interest are the followings:
- Complexity: nature of ecosystems and the risk of oversimplification, interconnection between NbS and complementary areas, consideration of uncertainties (future climate and associated impacts...)
- Scales: spatial scales with the integration of NbS in their environment, and temporal scales considering sustainability over time, variability of bio-physical processes and climate change effects
- Ecosystem services: understanding the bio-geophysical processes, spatial shift between the location of NbS and the location of beneficiaries, modification under climate change (threshold and inflection point), co-benefits or on the contrary degradation, negative effects ("misadaptation")
- Assessment and indicators: measurement and modelling protocols to evaluate NbS performances, capacity to measure the complexity, resilience and stability of the solutions.

Empowering the natural primary production capacity of the Earth System Carbon Cycle, without the risks of engineering the composition of the environment itself, to remove excess atmospheric CO2, is the subject of this Session. Activities and mechanisms that decrease CO2, without increasing acidification, and which, importantly, allow the economies of the world to continue to grow and prosper are encouraged; particularly global Nature Based Carbon Management Solutions (NBCMS) effecting an efficiency gain in the natural capture and storage of carbon, enabling the control and regulation of CO2 levels in the atmosphere via natural mechanisms. NBCMS should provide no mechanism for a preferential pressure on naturally determined biodiversity.

The Earth has a carbon cycle, where carbohydrate and hydrocarbon structures produce carbon dioxide (CO2), through respiration and combustion just below or at the Earth’s surface. The CO2 released into the atmosphere is then taken up by biological primary production, through photosynthesis, and converted back into carbohydrates and hydrocarbons. There is a growing consensus that this carbon cycle has a natural balance in carbon mass of around 210 billion tonnes annually; considerably less than 0.5% of the combined terrestrial and marine carbon reservoirs. We have unbalanced this cycle through our harvesting of locked up fossil carbon at a rate much greater than that at which it is being laid down. This was at the heart of the industrial revolution; which as a result of, and possibly partly a cause of, both a once exponentially growing global population and an unprecedented rate of innovation, the burning of fossil fuels has, until recently, been increasing at an exponentially growing rate.

We have become so accustomed to being instructed that there is no ‘silver bullet’ to the anthropogenic climate crisis that most of us have begun to accept it as an irrefutable fact. However, there are no published papers demonstrating this, if indeed it is something that could be demonstrated. In a more simple thought process having worked out how to supercharge the combustion side of the Earth’s carbon cycle it doesn’t seem too far fetched to imagine that there are NBCMSs for supercharging the photosynthetic side of this natural cycle and rebalancing the system.

Several cross-boundary kinds of science are emerging in the field of geosciences.
Citizen science is gaining momentum across multiple disciplines, increasing multi-scale data production that is extending the frontiers of knowledge. Successful participatory science enterprises and citizen observatories can potentially be scaled-up in order to contribute to larger policy strategies and actions (e.g. the European Earth Observation monitoring systems), for example to be integrated in GEOSS and Copernicus. Making credible contributions to science can empower citizens to actively participate as citizen stewards in decision making, helping to bridge scientific disciplines.
Critical zone science is an integrative, transdisciplinary approach where the spatio-temporal interactions between life, energy and matter cycles in natural and anthropogenic environments are jointly considered through the combined lens of climatology, hydrology, soil science, ecology, geomicrobiology, biogeochemistry, geology and/or other fields. The number and richness of critical zone observatories established around the world are increasing and gaining strength (e.g., TERENO, OZCAR, DOE watersheds, eLTER).
Both citizen science and critical zone science can be seen in the context of Open Science, which is a broad movement embracing Open Data, Open Technology, Open Access, Open Educational Resources, Open Source, Open Methodology, and Open Peer Review. Increasingly, access to research data has become a core issue in the advance of science.
Open science, citizen science and critical zone science pose great challenges for researchers to facilitate effective participatory and actionable science, yet they are of critical importance to modern research and decision-makers.

We want to ask and find answers to the following questions:
Which approaches and tools can be used in Earth and planetary observation?
What are the biggest challenges in bridging geoscientific disciplines and how to overcome them?
How can we make knowledge on critical zone functioning transferable from one observatory to another place?
What kind of participatory citizen scientist involvement and open science strategies exist?
What kind of community-level perspectives exist regarding the limitations, challenges, and ethical considerations for collaborative, transdisciplinary and/or open science?
How can citizen science and open science approaches and initiatives be supported on different levels?

The use of geological evidence may help the judicial system to solve cases of homicides, corpse concealments, hit-and-run accidents, kidnappings, sexual assaults, geohazard problematics, environmental damages, animal maltreatment, wildlife crimes, gemstone and fossil frauds. Forensic geologists may be supported by a team of experts during the scientific investigation.
Earth and Natural Sciences may be simultaneously involved in a holistic approach for analyzing inorganic, anthropogenic, and organic materials found on the outdoor crime scenes. These sciences may also be devoted to environmental issues due to the human-environmental interactions responsible for crucial human-driven changes in the Anthropocene and hazards in which biodiversity, climate, and public health and safety are at stake.
Different analytical methods aim to obtain information on the compatibility degree among unknown and known samples and the possible provenance.
Based on the above, different experts may collaborate with geologists and investigate geological evidence and environmental issues, together in research teams. Geologists approaching forensic geology need to master sedimentology, micropaleontology, physical geology, petrography, gemology, geochemistry, hydrogeology, soil sciences, geomorphology, stratigraphy, regional geology, remote sensing, and applied geology and geophysics. Botanists address their investigation in forensic botany by studying plant ecology, vegetal anatomy, systematics, palynology, algology, and plant DNA in soil/sediment. On the other hand, entomologists approach forensic entomology by studying chemistry, biology, human/animal health, molecular science, and animal DNA in soil/sediment.
We encourage submission of studies presenting new insights derived from different inter-disciplinary- and transdisciplinary perspectives, including earth, natural, and environmental sciences (geology, geophysics, geochemistry, ecology, geological medicine, botany, entomology, ecology, and climatology applied to the Anthropocene Epoch), legal medicine, geological medicine. Particular attention will be given to the following topics: comparative analyses; reconstruction of walking in crime scenes; search for clandestine graves; geographical profiling; gemstone frauds; pollutants in groundwaters and soil matrices and environmental forensics; ecological and human health risks.

Both society and industry are becoming increasingly aware of the physical risks and potential knock-on impacts posed by climate change. This awareness is leading to an increased demand for specific and actionable information about such risks across a wide range of sectors and domains. Awareness of and demand for data on climate-related impacts across multiple different hazards and across large geographical areas is also rising - in many nations this is driven by new and upcoming legislation requiring businesses or governments to understand and be prepared for any and all climate risk. Although researchers in climate and geosciences are best-informed to provide expertise, traditional research outputs such as publications and corresponding data are not necessarily useable by non-experts within these domains. In this session, we explore how focusing on the needs of non-expert users and decision makers changes the way in which research is carried out, disseminated, and scaled.

We welcome abstracts across all natural hazard types and climate impacts on a broad range of themes, including: how to work effectively with stakeholders and other users; how user-engagement and data-delivery requirements change the way
science goals are set and met; and how to scale climate risk research to provide information beyond localised case-studies.