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

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

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

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

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

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

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

This multidisciplinary session invites contributions from the different perspectives of all relevant disciplines, including

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

Taking inspiration from the Mathematics of Planet Earth 2013 initiative, this session aims at bringing together contributions from the growing interface between the Earth science, the mathematical, and the theoretical physical communities. Our goal is to stimulate the interaction among scientists of these and related disciplines interested in solving geophysical challenges. Considering the urgency of the ongoing climate crisis, such challenges refer, for example, to the theoretical understanding of the climate and its subsystems as a highly nonlinear, chaotic system, the improvement of the numerical modelling of the climate system, and the search for new data analysis methods.

Specific topics include: PDEs, numerical methods, extreme events, statistical mechanics, thermodynamics, dynamical systems theory, large deviation theory, response theory, tipping points, model reduction techniques, model uncertainty and ensemble design, stochastic processes, parametrizations, data assimilation and machine learning. We invite contributions both related to specific applications as well as more speculative and theoretical investigations. We particularly encourage early career researchers to present their interdisciplinary work in this session.

Solicited speakers: David Stainforth, Oana Lang

Abstracts are solicited related to the understanding and prediction of weather, climate and geophysical extremes, from both an applied and theoretical viewpoint.

In this session we propose to group together the traditional geophysical sciences and more mathematical/statistical and impacts-oriented approaches to the study of extremes. We aim to highlight the complementary nature of these viewpoints, with the aim of gaining a deeper understanding of extreme events. This session is a contribution to the EDIPI ITN, XAIDA and CLINT H2020 projects, and we welcome submissions from both project participants and the broader scientific community.

Potential topics of interest include but are not limited to the following:

· Dynamical systems theory and other theoretical perspectives on extreme events;
· Data-driven approaches to study extreme events, incl. machine learning;
· Representation of extreme events in climate models;
· Downscaling of weather and climate extremes;
· How extremes have varied or are likely to vary under climate change;
· Attribution of extreme events;
· Early warning systems and forecasts of extreme events;
· Linking the dynamics of extreme events to their impacts.

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

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

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

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

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

The session further encourages discussion on interdisciplinary physical and data-based approaches to system dynamics in hydrology and broader geosciences, ranging from novel advances in stochastic, computational, information-theoretic and dynamical system analysis, to cross-cutting emerging pathways in information physics.

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

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

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

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

Take part in a thrilling session exploring and discussing promising avenues in system dynamics and information discovery, quantification, modelling and interpretation, where methodological ingenuity and natural process understanding come together to shed light onto fundamental theoretical aspects to build innovative methodologies to tackle real-world challenges facing our planet.

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

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

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

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

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

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

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

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

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

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

Rainfall is a “collective” phenomenon emerging from numerous drops. Understanding the relation between the physics of individual drops and that of a population of drops remains an open challenge, both scientifically and at the level of practical implications. This remains true also for solid precipitation. Hence, it is much needed to better understand small scale spatio-temporal precipitation variability, which is a key driving force of the hydrological response, especially in highly heterogeneous areas (mountains, cities). This hydrological response at the catchment scale is the result of the interplay between the space-time variability of precipitation, the catchment geomorphological / pedological / ecological characteristics and antecedent hydrological conditions. Therefore, (1) accurate measurement and prediction of the spatial and temporal distribution of precipitation over a catchment and (2) the efficient and appropriate description of the catchment properties are important issues in hydrology.

This session will bring together scientists and practitioners who aim to measure and understand precipitation variability from drop scale to catchment scale as well as its hydrological consequences. Contributions addressing one or several of the following topics are especially targeted:
- Novel techniques for measuring liquid and solid precipitation variability at hydrologically relevant space and time scales (from drop to catchment scale), from in situ measurements to remote sensing techniques, and from ground-based devices to spaceborne platforms. Innovative comparison metrics are welcomed;
- Precipitation drop (or particle) size distribution and its small scale variability, including its consequences for precipitation rate retrieval algorithms for radars, commercial microwave links and other remote sensors;
- Novel modelling or characterization tools of precipitation variability from drop scale to catchment scale from various approaches (e.g. scaling, (multi-)fractal, statistic, deterministic, numerical modelling);
- Novel approaches to better identify, understand and simulate the dominant microphysical processes at work in liquid and solid precipitation.
- Applications of measured and/or modelled precipitation fields in catchment hydrological models for the purpose of process understanding or predicting hydrological response.

Ice sheets play an active role in the climate system by amplifying, pacing, and potentially driving global climate change over a wide range of time scales. The impact of interactions between ice sheets and climate include changes in atmospheric and ocean temperatures and circulation, global biogeochemical cycles, the global hydrological cycle, vegetation, sea level, and land-surface albedo, which in turn cause additional feedbacks in the climate system. This session will present data and modelling results that examine ice sheet interactions with other components of the climate system over several time scales. Among other topics, issues to be addressed in this session include ice sheet-climate interactions from glacial-interglacial to millennial and centennial time scales, the role of ice sheets in Cenozoic global cooling and the mid-Pleistocene transition, reconstructions of past ice sheets and sea level, the current and future evolution of the ice sheets, and the role of ice sheets in abrupt climate change.

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

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

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

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

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

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

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

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

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

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

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

Deep Learning has seen accelerated adoption across Hydrology and the broader Earth Sciences. This session highlights the continued integration of deep learning and its many variants into traditional and emerging hydrology-related workflows. Abstracts are solicited related to novel theory development, new methodologies, or practical applications of deep learning in hydrological modeling and process understanding. This might include, but is not limited to, the following:

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

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

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

Statistical post-processing techniques for weather, climate, and hydrological forecasts are powerful approaches to compensate for effects of errors in model structure or initial conditions, and to calibrate inaccurately dispersed ensembles. These techniques are now an integral part of many forecasting suites and are used in many end-user applications such as wind energy production or flood warning systems. Many of these techniques are flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias correction up to very sophisticated distribution-adjusting techniques that take into account correlations among the prognostic variables.

At the same time, a lot of efforts are put in combining multiple forecasting sources in order to get reliable and seamless forecasts on time ranges from minutes to weeks. Such blending techniques are currently developed in many meteorological centers. These forecasting systems are indispensable for societal decision making, for instance to help better prepare for adverse weather. Thus, there is a need for objective statistical framework for "forecast verification'', i.e. qualitative and quantitative assessment of forecast performance.

In this session, we invite presentations dealing with both theoretical developments in statistical post-processing and evaluation of their performances in different practical applications oriented toward environmental predictions, and new developments dealing with the problem of combining or blending different types of forecasts in order to improve reliability from very short to long time scales.

Inverse Problems are encountered in many fields of geosciences. One class of inverse problems, in the context of predictability, is assimilation of observations in dynamical models of the system under study. Furthermore, objective quantification of the uncertainty during data assimilation, prediction and validation is the object of growing concern and interest.
This session will be devoted to the presentation and discussion of methods for inverse problems, data assimilation and associated uncertainty quantification throughout the Earth System like in ocean and atmosphere dynamics, atmospheric chemistry, hydrology, climate science, solid earth geophysics and, more generally, in all fields of geosciences.
We encourage presentations on advanced methods, and related mathematical developments, suitable for situations in which local linear and Gaussian hypotheses are not valid and/or for situations in which significant model or observation errors are present. Specific problems arise in situations where coupling is present between different components of the Earth system, which gives rise to the so called coupled data assimilation.
Of interest are also contributions on weakly and strongly coupled data assimilation - methodology and applications, including Numerical Prediction, Environmental forecasts, Earth system monitoring, reanalysis, etc., as well as coupled covariances and the added value of observations at the interfaces of coupled models.
We also welcome contributions dealing with algorithmic aspects and numerical implementation of the solution of inverse problems and quantification of the associated uncertainty, as well as novel methodologies at the crossroad between data assimilation and purely data-driven, machine-learning-type algorithms.

Proper characterization of uncertainty remains a major research and operational challenge in Environmental Sciences, and is inherent to many aspects of modelling impacting model structure development; parameter estimation; an adequate representation of the data (inputs data and data used to evaluate the models); initial and boundary conditions; and hypothesis testing. To address this challenge, methods for a) uncertainty analysis (UA) that seek to identify, quantify and reduce the different sources of uncertainty, as well as propagating them through a system/model, and b) the closely-related methods for sensitivity analysis (SA) that evaluate the role and significance of uncertain factors (in the functioning of systems/models), have proved to be very helpful.

This session invites contributions that discuss advances, both in theory and/or application, in methods for SA/UA applicable to all Earth and Environmental Systems Models (EESMs), which embraces all areas of hydrology, such as classical hydrology, subsurface hydrology and soil science.

Topics of interest include (but are not limited to):
1) Novel methods for effective characterization of sensitivity and uncertainty
2) Analyses of over-parameterised models enabled by AI/ML techniques
3) Single- versus multi-criteria SA/UA
4) Novel approaches for parameter estimation, data inversion and data assimilation
5) Novel methods for spatial and temporal evaluation/analysis of models
6) The role of information and error on SA/UA (e.g., input/output data error, model structure error, parametric error, regionalization error in environments with no data etc.)
7) The role of SA in evaluating model consistency and reliability
8) Novel approaches and benchmarking efforts for parameter estimation
9) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, parallel computing, model pre-emption, model ensembles, etc.)

Modelling past climate states, and the transient evolution of Earth’s climate remains challenging. Time periods such as the Paleocene, Eocene, Pliocene, the Last Interglacial, the Last Glacial Maximum or the mid-Holocene span across a vast range of climate conditions. At times, these lie far outside the bounds of the historical period that most models are designed and tuned to reproduce. However, our ability to predict future climate conditions and potential pathways to them is dependent on our models' abilities to reproduce just such phenomena. Thus, our climatic and environmental history is ideally suited to thoroughly test and evaluate models against data, so they may be better able to simulate the present and make future climate projections.

We invite papers on palaeoclimate-specific model development, model simulations and model-data comparison studies. Simulations may be targeted to address specific questions or follow specified protocols (as in the Paleoclimate Modelling Intercomparison Project – PMIP or the Deep Time Model Intercomparison Project – DeepMIP). They may include anything between time-slice equilibrium experiments to long transient climate simulations (e.g. transient simulations covering the entire glacial cycle as per the goal of the PalMod project) with timescales of processes ranging from synoptic scales to glacial cycles and beyond. Comparisons may include past, historical as well as future simulations and focus on comparisons of mean states, gradients, circulation or modes of variability using reconstructions of temperature, precipitation, vegetation or tracer species (e.g. δ18O, δD or Pa/Th).

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

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

This session covers predictions of climate from seasonal to decadal timescales and their applications. With a time horizon from a few months up a few decades, such predictions are of major importance to society, and improving them presents an interesting scientific challenge. This session embraces advances in our understanding of the origins of seasonal to decadal predictability, as well as in improving the forecast skill and making the most of this information by developing and evaluating new applications and climate services.

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