During the last century, nonlinear sciences have greatly matured, moving from first ideas by Lewis Fry Richardson, passing through the evidence of the so-called deterministic chaos by Lorenz, up to the concept of fractals introduced by Mandelbrot. New paradigms have been introduced like scaling laws, scale invariance, self-organization, stochastic processes, predictability, tipping points, extreme events, and so on, involving both theoretical and methodological approaches, making nonlinear sciences a highly multidisciplinary field with an important role in fundamental geoscience.
During the last decades, methodologies, modelling and data analysis techniques have been significantly improved to provide a deeper understanding of nonlinear processes in geosciences, both testifying “classical” approaches and looking forward to future challenges and developments.
In this Townhall Meeting, some scientific officers of the Nonlinear Processes in Geosciences (NP) Division of EGU will provide their view on our understanding and challenges within our NP community. This is an excellent opportunity, especially for the young scientists, to meet with the experts and discuss the future of our community. We will be glad to follow a bottom-up approach in which the community, both established researchers and the Early Career Scientists, is invited to propose specific topics, themes, aspects, and questions for an open discussion with the NP officers about future opportunities, challenges, and directions in our field.

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.

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

One of the most challenging sustainable goals of the UN 2030 Agenda and other international agreements is that urban systems have to increase well-being and health. Indeed, these networked systems already host more than half of the world's population and are going to host most of its growth, while they have been mostly designed and managed with limited visions, in particular with respect to their geophysical environment.
This goal got an unforeseen acuity with the Covid-19 pandemic, starting with the confinement strategies that radically brought into question the functioning of these systems, e.g., drastically reducing mobility and breaking its ever increasing trend. Covid-19 was not without precursor (e.g., SARS, MERS) and will not be without successors.

Long term visions based on transdisciplinary scientific advances are therefore indispensable, particularly from the geoscience community. As a consequence, this session calls for contributions from data-driven and theory-driven approaches of urban health under global change. This includes:
- qualitative improvements of epidemic modelling, as trans-disciplinary and nonlinear as possible
- possible interplays between meteorological and/or climate drivers and epidemic/health issues
- novel monitoring capabilities (including contacts tracking), data access, assimilation and multidimensional analysis techniques
- managing field works, geophysical monitoring and planetary missions
- how to have the highest science output during corona pandemic
- a fundamental revision of our urban systems, their greening as well as their mobility offer
- a particular focus on urban biodiversity, in particular to better manage virus vectors
- urban resilience must include resilience to epidemics, and therefore requires revisions of urban governance.

The global-scale cycling of hydrogen, carbon, nitrogen, sulphur etc. controls the mass, composition and state of the outermost volatile layer of terrestrial planets over time, thereby controlling their habitability. These planetary volatile cycles involve the atmosphere, hydrosphere, crust, mantle and perhaps even core. On geological timescales, they are controlled by plate tectonics and mantle convection, but also by magmatism. Indeed, mantle melting is a key process that partitions (volatile) elements between the various planetary reservoirs. On Earth, for instance, ingassing and outgassing mainly occur at subduction zones, and major sites of volcanism (i.e., mid-ocean ridges and hotspots), respectively. Indeed, major volatile cycles are balanced to first order through ingassing and outgassing, particularly on plate-tectonic planets such as Earth. In planetary interiors, volatiles are partitioned into the existing minerals, or stabilize minor phases such as diamond or various hydrous phases in the mantle and crust, something that directly influences the spatial distribution of melt formation. Conversely, melt transport induces volatile exchanges between planetary reservoirs and favors outgassing. Understanding the complex dynamics (e.g., multi-phase flow) of melt/fluid segregation or accumulation is thus crucial for understanding global-scale volatile/material cycling. Further, melt retention as well as volatile content and speciation strongly and non-linearly affect rock properties such as viscosity, modal mineralogy, melting behavior, oxidation state, seismic velocity and attenuation, electrical conductivity and density.

In this session, we invite contributions from researchers in all disciplines of the Earth and Planetary Sciences that study volatile cycling and reservoir exchanges through fluid/melt percolation as well as magmatism from regional to global scales, and from short to long timescales. We also invite contributions such as, e.g., on the effects of volatiles on material properties, melt stabilization and planetary surface conditions, related observations or processes. Experimental, observational, modeling, and truly integrated multidisciplinary studies are highly welcome.

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.

Recent years have seen a substantial progress in the understanding of the nonlinear and stochastic processes responsible for important dynamical aspects of the complex Earth system. The Earth system is a complex system with a multitude of spatial and temporal scales which interact nonlinearly with each other. For understanding this complex system new methods from dynamical systems, complex systems theory, complex network theory, statistics, machine learning and climate and Earth sciences are needed.

In this context the session is open to contributions on all aspects of the nonlinear and stochastic dynamics of the Earth system, including the atmosphere, the ocean and the climate system. Communications based on theoretical and modeling studies, as well as on experimental investigations are welcome. Studies that span the range of model hierarchy from idealized models to complex Earth System Models (ESM), data driven models, use observational data and also theoretical studies are particularly encouraged.

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

In this session we propose to group together the traditional geophysical sciences and more mathematical/statistical approaches to the study of extremes. We aim to highlight the complementary nature of these two viewpoints, with the aim of gaining a deeper understanding of extreme events.

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

· How extremes have varied or are likely to vary under climate change;
· How well climate models capture extreme events;
· Attribution of extreme events;
· Emergent constraints on extremes;
· Linking dynamical systems extremes to geophysical extremes;
· Extremes in dynamical systems;
· Downscaling of weather and climate extremes.
· Linking the dynamics of climate extremes 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; low frequency, decadal and paleo variability; theoretical approaches; ENSO diversity; global teleconnections; impacts on climate, society and ecosystems; seasonal forecasting and climate change projections of ENSO and its tropical basins interactions. Studies aimed at understanding ENSO and its tropical basins interactions in models of a range of complexity are especially welcomed, including analysis of CMIP model intercomparisons.

Geoscience fields and time series show deterministic and stochastic fluctuations over a very large range of scales. Often due to the influence of turbulence or due to other forcing, such fluctuations often possess intermittent fluctuations over some given range of scales, with red noise spectra, presenting scale invariance in time or in space. This session focuses on methods, observations, and data analyses aiming to identify such scaling ranges and characterize them. We consider this scale and intermittency topic in the ocean, the atmosphere, the coupled atmosphere-ocean-climate system, in hydrology and earth sciences. The latter includes multifractals and singularity analysis in mineral exploration and environmental assessment.
The session welcomes also methodological presentations comparing different techniques or proposing new methods to extract relevant scaling information from field data or geophysical time series.

The Earth's climate is highly variable on all spatial and temporal scales, and this has direct consequences for society. For example, changes in variability (spatial or temporal) can impact the recurrence frequency of extreme events. Yet it is unclear if a warmer future is one with more or with less climate variability, and at which scales, as a multitude of feedbacks is involved and the instrumental record is short.
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.

The session is multidisciplinary and brings together people working in the geosciences, atmospheric science, oceanography, glaciology, paleoclimatology and environmental physics, to examine the complementarity of ideas and approaches. Members of the PAGES working group on Climate Variability Across Scales (CVAS) and others are welcome.

This session aims to provide a forum to present work on:

1- the characterization of climate dynamics using a variety of techniques (e.g. scaling and multifractal techniques and models, recurrence plots, or variance analyses) to study its variability including periodicities, noise levels, or intermittency)

2- the relationship between changes in the mean state (e.g. glacial to interglacial or preindustrial to present to future), and higher-order moments of relevant climate variables, to changes in extreme-event occurrence and the predictability of climate

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

4- the attribution of climate variability to internal dynamics, or the response to natural (volcanic or solar) and anthropogenic forcing

5- the interaction of external forcing (e.g. orbital forcing) and internal variability such as mechanisms for synchronization and pacing of glacial cycles

6- the characterization of probabilities of extremes, including linkage between slow climate variability and extreme event recurrence

This session aims to foster the development of multifractal methodologies and tools with applications to a wide variety of nonlinear, geophysical systems, including their interactions with urban systems. Theories range from scalar to vector fields, applications range from urban geosciences (e.g., land use patterns, water management and ecosystems) to atmospheric and oceanic turbulence (e.g., wind energy, meso-scale scaling anisotropy) and climate (e.g., across scale evolution of the extremes). Data include in-situ and remotely sensed data, as well as outputs from models.

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.

Artificial Intelligence (AI), especially Machine Learning (ML), has proven a powerful, effective tool for gaining insight from Earth data that otherwise would be hard to attain. However, for deeper insights AI/ML has to operate on unprecedented volumes of data - a fusion of AI/ML with Big Data technologies is a cornerstone for further progress in these domains. While Big Data volume and velocity are being addressed, variety and veracity remain challenges so far.

This session aims at bringing together researchers working with big data sets generated from monitoring networks, extensive observational campaigns and detailed modeling efforts across various fields of geosciences. Topics of this session will include the identification and handling of specific problems arising from the need to analyze such large-scale data sets, together with methodological approaches towards semi or fully automated inference of relevant patterns in time and space aided by computer science-inspired techniques. Among others, this session shall address approaches from the following fields:

* Big Data and AI in the Earth sciences
* Machine Learning, Deep Learning, and Data Mining applications in the Earth sciences
* Visualization and visual analytics of Big, multi-, and high-dimensional Data
* Dimensionality and complexity of Big Data sets
* Emerging Big Data paradigms, such as Datacubes
* Computer and Data Science aspects in the Earth sciences

This interdisciplinary session welcomes contributions on novel conceptual and/or methodological approaches and methods for the analysis and statistical-dynamical modeling of observational as well as model time series from all geoscientific disciplines.
Methods to be discussed include, but are not limited to linear and nonlinear methods of time series analysis. time-frequency methods, statistical inference for nonlinear time series, including empirical inference of causal linkages from multivariate data, nonlinear statistical decomposition and related techniques for multivariate and spatio-temporal data, nonlinear correlation analysis and synchronisation, surrogate data techniques, filtering approaches and nonlinear methods of noise reduction, artificial intelligence and machine learning based analysis and prediction for univariate and multivariate time series.
Contributions on methodological developments and applications to problems across all geoscientific disciplines are equally encouraged. We particularly aim at fostering a transfer of new methodological data analysis and modeling concepts among different fields of the geosciences.

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Sub-Session "Mathematical Climatology and Space-time Data Analysis" (Abdel Hannachi, Amro Elfeki, Christian Franzke, Muhammad Latif, Carlos Pires)

The recent progress in mathematical methods to solve various problems in weather & climate nonlinear dynamics and data analysis calls for the need to develop a new session that focus on those methods. Novel and powerful mathematical methods have been developed and used in different subjects of climate. Because those methods are used within specific contexts they go unnoticed most of the time by climate researchers. The proposed new session will provide the opportunity to climate scientists and researchers working on developing mathematical methods for climate to come together and present their findings in a transparent way. This will also be easily accessible to other climate scientists who look for, and are interested in specific methods to solve their problems.
Contributions are encouraged from researchers working on mathematical methods and their application to weather and climate. We particularly welcome contributions on optimization, dimension reduction and data mining, space-time patterns identification, machine learning, statistical prediction modelling, nonlinear methods , Bayesian statistics, and Monte-Carlo Markov Chain (MCMC) methods in stochastic modelling.

There are many ways in which machine learning promises to provide insight into the Earth System, and this area of research is developing at a breathtaking pace.
Unsupervised, supervised as well as reinforcement learning are now increasingly used to address Earth system related challenges.
Machine learning could help extract information from numerous Earth System data, such as satellite observations, as well as improve model fidelity through novel parameterisations or speed-ups. This session invites submissions spanning modelling and observational approaches towards providing an overview of the state-of-the-art of the application of these novel methods

Most of the processes studied by geoscientists are characterized by variations in both space and time. These spatio-temporal phenomena have been traditionally investigated using linear statistical approaches, as in the case of physically-based models and geostatistical models. Additionally, the rising attention toward machine learning, as well as the rapid growth of computational resources, opens new horizons in understanding, modeling, and forecasting complex spatio-temporal systems through the use of stochastics non-linear models.

These issues are particularly relevant in the field of performance evaluations of Earth Systems Science Prediction (ESSP) systems. A central issue in this domain deals with the representativeness of observational data used for evaluation, which are often not representative of the physical structures that are being predicted. While many large spatial and temporal observations datasets can help provide this information, adequate tools to integrate these large datasets to provide meaningful physical insights on the strengths and weaknesses of predicted fields are required. Other challenges deal with the large storage volumes to handle model simulations, large spatio-temporal datasets, and verification statistics which are difficult to maintain.

This session aims at exploring the new challenges and opportunities opened by the spread of data-driven statistical learning approaches in Earth and Soil Sciences. We invite cutting-edge contributions related to methods of spatio-temporal geostatistics or data mining on topics that include, but are not limited to:
- meaningful and informative model evaluation frameworks and platforms for ESSP;
- advances in spatio-temporal modeling using geostatistics and machine learning;
- uncertainty quantification and representation;
- innovative techniques of knowledge extraction based on clustering, pattern recognition and, more generally, data mining.

The main applications will be closely related to the research in Earth system science and quantitative geography. A non-complete list of possible applications includes:
- weather and climate (e.g. numerical weather prediction, hydrologic prediction, climate prediction and projection);
- natural and anthropogenic hazards (e.g. floods; landslides; earthquakes; wildfires; air pollution);
- interaction between geosphere and anthroposphere (e.g. land degradation; urban sprawl);
- socio-economic sciences (e.g. census data; transport; commuter traffic).

This is a merged session of “Spatio-temporal data science: theoretical advances and applications in computational geosciences” and “Innovative Evaluation Frameworks and Platforms for Weather and Climate Research”.

Clustering analysis is a well-known exploratory task for partitioning databases into smaller groups based on patterns or inherent similarity in data. Clustering methods have found many applications in many disciplines due to growing interest in unravelling the hidden and meaningful patterns that exist in large amounts of available data. Due to its unsupervised nature, clustering data is a complex task that requires attention to optimal choice alternatives regarding methods, model parameters and performance metrics. However, the suitability of clustering algorithms depends on their application. Different methods and approaches co-exist in a large pool. The challenge is to obtain application-specific insights while enabling a practical knowledge perspective for benchmarking. There are still research gaps in the wider clustering literature, and hydrology-specific knowledge is fragmented and difficult to find.

In hydrology, unsupervised classification of multivariate data is often used but typically in rather basic forms and as an intermediate step. Recently, the number of studies using clustering methods has rapidly increased. However, a clear and integrative vision on clustering algorithms is currently missing. Despite advances in other fields, the scope of hydrological studies is limited. Knowledge exchange on hydrology-specific ways of dealing with the issues related to clustering is needed.

The aim of this session is to explore theoretical and conceptual underpinnings of well-known clustering methods, offer fresh insights into applications of new clustering methods, gain thorough understanding of pearls and pitfalls in clustering algorithms, provide a critical overview of the main challenges associated with data clustering process, discuss major research trends and highlight open research issues. It is expected to improve scientific practice within the hydrology domain, and foster scientific debate on benchmarking in cluster analysis.

We invite contributions (case studies, comparative analyses, theoretical experiments) on a wide range of topics including (but not limited to): hard vs fuzzy clustering; comparison of clustering algorithms; benchmarking in cluster analysis; clustering as an exploratory tool vs clustering as a hypothesis testing tool; determination of number of clusters; selecting variables to cluster upon; evaluation of clustering performance; alternative clustering methods (sequential, evolutionary, deep, ensemble, etc.)

Machine learning (ML) is now widely used across Hydrology and the broader Earth Sciences and especially its subfield deep learning (DL) has recently enjoyed increased attention.. This session highlights the continued integration of ML, and its many variants, including deep learning (DL), into traditional and emerging Hydrology-related workflows. Abstracts are solicited related to novel theory development, novel methodology, or practical applications of ML and DL in hydrological modeling. This might include, but is not limited to, the following:

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

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, 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.
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.

This year, our solicited speaker is Ross Bannister from University of Reading / UK National Centre for Earth Observation.

Accurate predictions of geophysical systems’ evolution remain to have significant uncertainties at different time and spatial scales. Although some dynamical, statistical, and their combined (“scholastic”) approaches were often used to make predictions and showed their respective usefulness, there exist substantial limitations in improving the prediction level. This session will bring together experts to jointly address new approaches to predictions of geophysical systems behavior and to identify and quantify uncertainties associated with predictability and create an exchange of ideas likely to advance the state of predictions. Papers are invited on all aspects of dynamical and statistical approaches to predictions and predictability estimation and underlying that justification of the appropriateness of the use of any of them is particularly welcome. Papers on techniques that combine the dynamical and statistical approaches with newly emerging techniques of machine learning are highly welcome.

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.

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.

The assessment of precipitation variability and uncertainty is crucial in a variety of applications, such as flood risk forecasting, water resource assessments, evaluation of the hydrological impacts of climate change, determination of design floods, and hydrological modelling in general. Within this framework, this session aims to gather contributions on research, advanced applications, and future needs in the understanding and modelling of precipitation variability, and its sources of uncertainty.
Specifically, contributions focusing on one or more of the following issues are particularly welcome:
- Novel studies aimed at the assessment and representation of different sources of uncertainty versus natural variability of precipitation.
- Methods to account for different accuracy in precipitation time series, e.g. due to change and improvement of observation networks.
- Uncertainty and variability in spatially and temporally heterogeneous multi-source precipitation products.
- Estimation of precipitation variability and uncertainty at ungauged sites.
- Precipitation data assimilation.
- Process conceptualization and modelling approaches at different spatial and temporal scales, including model parameter identification and calibration, and sensitivity analyses to parameterization and scales of process representation.
- Modelling approaches based on ensemble simulations and methods for synthetic representation of precipitation variability and uncertainty.
- Scaling and scale invariance properties of precipitation fields in space and/or in time.
- Physically and statistically based approaches to downscale information from meteorological and climate models to spatial and temporal scales useful for hydrological modelling and applications.

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 are glad to welcome Mahesh Maskey (dynamical systems) and Uwe Ehret (information theory) as our invited authors for this eclectic session, where we promote a fruitful cross-fertilisation between complementary visions of the world.

Proper characterization of uncertainty remains a major challenge and is inherent to many aspects of modelling such as structural development, hypothesis testing and parameter estimation, and the adequate characterization of parameters, forcing data and initial and boundary conditions. To address this challenge, useful methods are uncertainty analysis, sensitivity analysis and inversion (calibration), either in Bayesian, geostatistical or conventional manners.

This session invites contributions that discuss advances, both in theory and/or application, in methods for SA/UA and inversion applicable to all Earth and Environmental Systems Models (EESMs). This includes 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) Novel approaches for parameter estimation, data inversion and data assimilation,
3) Novel methods for spatial and temporal evaluation/analysis of models,
4) Single- versus multi-criteria SA/UA/inversion,
5) The role of data information and error on SA/UA (e.g., input/output error, model structure error, worth of data etc.), and
6) Improving the computational efficiency of SA/UA/inversion (efficient sampling, surrogate modelling, parallel computing, model pre-emption, etc.).

Contributions addressing any or all aspects of sensitivity/uncertainty, including those related to structural development, hypothesis testing, parameter estimation, data assimilation, forcing data, and initial and boundary conditions are invited. We also invite instances of the above research questions applied to scientifically built machine-learning models.

Predictions of climate from seasonal to decadal time scales and their applications will be discussed in this session. With a time horizon from a few months up to thirty years, such predictions are of major importance to society, and improving them presents an interesting scientific challenge. This session aims to embrace advances in our understanding of the origins of seasonal to decadal predictability, as well as in improving the respective forecast skill and making the most of this information by building and testing new applications and climate services.

The session will cover dynamical as well as statistical predictions (including machine learning methods), and their combination. It will investigate predictions of various climate phenomena, including extremes, from global to regional scales, and from seasonal to multidecadal time scales ("seamless predictions"). Physical processes relevant to long-term predictability sources (e.g. ocean, cryosphere, or land) as well as predicting large-scale atmospheric circulation anomalies associated to teleconnections will be discussed, as will observational and emergent constraints on climate variability and predictability on the seasonal-to-(multi)decadal time scale. Also, the time-dependence of the predictive skill, or windows of opportunity (hindcast period), will be investigated. Analysis of predictions in a multi-model framework, and ensemble forecast initialization and generation, including innovative ensemble approaches to minimize initialization shocks, will be another focus of the session. The session will pay 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.

One of the big challenges in Earth system science consists in providing reliable climate predictions on sub-seasonal, seasonal, decadal and longer timescales. The resulting data have the potential to be translated into climate information leading to a better assessment of multi-scale global and regional climate-related risks.
The latest developments and progress in climate forecasting on subseasonal-to-decadal and longer timescales will be discussed and evaluated. This will include presentations and discussions of predictions for a time horizon of up to ten years from dynamical ensemble and statistical/empirical forecast systems, as well as the aspects required for their application: forecast quality assessment, multi-model combination, bias adjustment, downscaling, etc.
Following the new WCPR strategic plan for 2019-2029, prediction enhancements are solicited from contributions embracing climate forecasting from an Earth system science perspective. This includes the study of coupled processes, impacts of coupling and feedbacks, and analysis/verification of the coupled atmosphere-ocean, atmosphere-land, atmosphere-hydrology, atmosphere-chemistry & aerosols, atmosphere-ice, ocean-hydrology, ocean-ice, ocean-chemistry and climate-biosphere (including human component). Contributions are also sought on initialization methods that optimally use observations from different Earth system components, on assessing and mitigating the impacts of model errors on skill, and on ensemble methods.
We also encourage 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 the development of early warning systems.

A special focus will be put on the use of operational climate predictions (C3S, NMME, S2S), results from the CMIP5-CMIP6 decadal prediction experiments, and climate-prediction research and application projects (e.g. EUCP, APPLICATE, PREFACE, MIKLIP, MEDSCOPE, SECLI-FIRM, S2S4E, CONFESS).
An increasingly important aspect for climate forecast's applications is the use of most appropriate downscaling methods, based on dynamical or statistical approaches or their combination, that are needed to generate time series and fields with an appropriate spatial or temporal resolution. This is extensively considered in the session, which therefore brings together scientists from all geoscientific disciplines working on the prediction and application problems.

Space, laboratory, and astrophysical plasmas are seemingly different environments, which however host very similar processes: among them, turbulence, magnetic reconnection, and shocks, which all result in particle acceleration. These processes are highly non-linear, and closely interlinked. On the one hand, the turbulence cascade favors the onset of magnetic reconnection between magnetic islands and, on the other hand, magnetic reconnection can trigger turbulence in the reconnection outflows and separatrices. Similarly, shocks may form in collisional and collisionless reconnection processes and can be responsible for turbulence formation, as for instance in the turbulent magnetosheath.

We are now in a fortunate time when the investigation of these processes based on simulations and observations are converging: simulations can deliver output which is approaching, in temporal and spatial scales, and in the coexistence of several scales, the complexity of an increasing number of the processes of interest. On the observation side, high cadence measurements of particles and fields, high resolution 3D measurements of particle distribution functions and multipoint measurements make it easier to reconstruct the 3D space surrounding the spacecrafts. The ever growing amount of data that both simulations and observations produce can be then combed through and organized with Artificial Intelligence and Machine Learning methods.

This session welcomes simulations, observational, and theoretical works relevant for the study of the above mentioned plasma processes. Particularly welcome this year will be works focusing on the common aspects of turbulence, reconnection, and shocks in space, laboratory, and astrophysical plasmas. We also encourage papers proposing new methods, especially those rooted in Artificial Intelligence and Machine Learning, to extract new knowledge from these big observational and simulated data sets.

Lagrangian trajectories are currently used for a vast range of purposes in ocean and atmosphere sciences. Examples include studying the connectivity of ocean basins, forecasting the spreading of ash clouds, mapping global ocean diffusivities, observing the deep ocean, or tracing plastics and other forms of pollutants in the ocean, etc. There is thus a need for numerical models capable of simulating Lagrangian particles in the ocean and atmosphere as well as accurate methods for analysing the data from surface drifters, floats, and simulated particles.

This session aims at bringing together scientists working on all sorts of Lagrangian methods, e.g. observed or simulated particles in the atmosphere and ocean, and a variety of use cases e.g. studying oceanic mixing/diffusivity, tracing pollution in the atmosphere or ocean, iceberg tracking etc. We welcome presentations on e.g.:

* Connectivity and pathways of air- or water-masses in the atmosphere and the ocean
* Quantifying water mass transformations and fluxes between regions in the ocean
* Development of Lagrangian particle-tracking algorithms and approaches to model particles with active behaviours, e.g. icebergs, fish, ash clouds, plastics etc.
* New methods and tools to analyse observed or simulated Lagrangian particles, e.g. diffusivity, spreading rates, etc.
* New developments in in-situ observations such as balloons, surface drifters or floats.

The multitude of processes of various scales occurring simultaneously under strong winds in the air and sea boundary layers presents a true challenge for nonlinear science. We want to understand the physics of these processes, their specific role, their interactions and how they can be probed remotely, how these processes differ from their counterparts under moderate/weak winds. We welcome theoretical, experimental and numerical works on all aspects of processes in turbulent boundary layers above and below the ocean surface. Although we are particularly interested in the processes and phenomena occurring under strong wind conditions, the works concerned with similar processes under weaker winds which might provide an insight for rough seas are also welcomed. We are also very interested in works on remote sensing of these processes.
The areas of interest include the processes at and in the vicinity of the interface (nonlinear dynamics of surface water, wave-turbulence interactions, wave breaking, generation and dynamics of spray and air bubbles, thermodynamics of the processes in the boundary layers, heat and gas exchange), all the processes above and below the aIr/water interface, as long as they are relevant for strong wind conditions (such as, e.g. inertial waves generated by changing winds). Relevant nonlinear biological phenomena are also welcomed.
The main aims of the session is to initiate discussion of the multitude of processes active under strong winds across the narrow specializations as a step towards creating an integrated picture. Theoretical, numerical, experimental and observational works are welcomed.

Geophysical Fluid Dynamics (GFD) is a truly interdisciplinary field, including different topics dealing with rotating stratified fluids. It emerges in the late 50s, when scientists from meteorology, oceanography, astrophysics, geological fluid dynamics, and applied mathematics began to mathematically model complex flows and thereby unify these fields. Since then many new aspects were added and deeper insight into many problems has been achieved. New mathematical and statistical tools were developed, standard techniques were refined, classical problems were varied. In this session we primarily focus on contributions from dynamic meteorology and physical oceanography that model flows by mathematical analysis. However, it is also a forum for experimental GFD and for astrophysical and geological aspects of GFD as well.

Waves in the Earth’s crust are often generated by fractures in the process of their sliding or propagation. Conversely, the waves can trigger fracture sliding or even propagation. The presence of multiple fractures makes geomaterials non-linear. Therefore the analysis of wave propagation and interaction with pre-existing or emerging fractures is central to geophysics. Recently new observations and theoretical concepts were introduced that point out to the limitations of the traditional concept. These are:
• Multiscale nature of waves and fractures in geomaterials
• Rotational mechanisms of wave and fracture propagation
• Strong rock and rock mass non-linearity (such as bilinear stress-strain curve with high modulus in compression and low in tension) and its effect on wave propagation
• Apparent negative stiffness associated with either rotation of non-spherical constituents or fracture propagation and its effect on wave propagation
• Triggering effects and instability in geomaterials
• Active nature of geomaterials (e.g., seismic emission induced by stress and pressure wave propagation)
• Non-linear mechanics of hydraulic fracturing
• Synchronization in fracture processes including earhtquakes and volcanic activity

Complex waves are now a key problem of the physical oceanography and atmosphere physics. They are called rogue or freak waves. It may be expected that similar waves are also present in non-linear solids (e.g., granular materials), which suggests the existence of new types of seismic waves.

It is anticipated that studying these and related phenomena can lead to breakthroughs in understanding of the stress transfer and multiscale failure processes in the Earth's crust, ocean and atmosphere and facilitate developing better prediction and monitoring methods.

The session is designed as a forum for discussing these and relevant topics.

This session welcomes contributions presenting advances in, and approaches to, the modelling, monitoring, and forecasting of internal waves in stratified estuaries, lakes and the coastal oсean.
Internal solitary waves (ISWs) and large-amplitude internal soliton packets are a commonly observed event in oceans and lakes. In the oceans ISWs are mainly generated by the interaction of the barotropic tides with bottom topography. Large amplitude solitary waves are energetic events that generate strong currents. They can also trap fluid with larvae and sediments in the cores of waves and transport it a considerable distance. ISWs can cause hazards to marine engineering and submarine navigation, and significantly impact marine ecosystems and particle transport in the bottom layer of the ocean and stratified lakes. Contributions studying flows due to internal waves, their origin, propagation and influence on the surrounding environment are thus of broad scientific importance.
The scope of the session involves all aspects of ISWs generation, propagation, transformation and the interaction of internal waves with bottom topography and shelf zones, as well as an evaluation of the role of internal waves in sediment resuspension and transport. Breaking of internal-waves also drives turbulent mixing in the ocean interior that is important for climate ocean models. Discussion of parameterizations for internal-wave driven turbulent mixing in global ocean models is also invited.

We invite presentations on ocean surface waves, and wind-generated waves in particular, their dynamics, modelling and applications. This is a large topic of the physical oceanography in its own right, but it is also becoming clear that many large-scale geophysical processes are essentially coupled with the surface waves, and those include climate, weather, tropical cyclones, Marginal Ice Zone and other phenomena in the atmosphere and many issues of the upper-ocean mixing below the interface. This is a rapidly developing area of research and geophysical applications, and contributions on wave-coupled effects in the lower atmosphere and upper ocean are strongly encouraged.

Internal gravity waves (IGWs) still pose major questions both to the atmospheric and ocean sciences, and to stellar physics. Important issues are IGW radiation from their various relevant sources, IGW reflection at boundaries, their propagation through and interaction with a larger-scale flow, wave-induced mean flow, wave-wave interactions in general, wave breaking and its implications for mixing, and the parameterization of these processes in models not explicitly resolving IGWs. The observational record, both on a global scale and with respect to local small-scale processes, is not yet sufficiently able to yield appropriate constraints. The session is intended to bring together experts from all fields of geophysical and astrophysical fluid dynamics working on related problems. Presentations on theoretical, modelling, experimental, and observational work with regard to all aspects of IGWs are most welcome, including those on major collaborative projects, such as MS-GWaves.

Recent extreme weather and climate episodes, including the Siberian heatwave of early summer 2020 and the cold-air outbreak over the US in late summer 2020, highlight the need to further our understanding of linear and non-linear (quasi-stationary) planetary and synoptic-scale Rossby wave dynamics in the atmosphere, and their impacts on weather and climate events. Abstracts are solicited that are dedicated to:

i) the dynamics of linear wave propagation or quasi-stationarity, of wave breaking, atmospheric blocking, or jets as atmospheric Rossby waveguides. This includes the role of local and remote drivers (e.g., the tropics, Arctic, or stratosphere).

ii) exploring the links between extreme weather/climate events and linear and non-linear Rossby waves, including wave breaking and/or blocking.

iii) quantifying model representation of Rossby waves in climate and numerical weather prediction models, including wave propagation and breaking.

iv) exploring the role of Rossby wave trains on predictability at lead times from medium range (~2 weeks) to seasonal time-scales. This includes blocking and wave propagation.

v) analyzing projected future changes in planetary or synoptic-scale Rossby waves, or in their future impacts on weather and climate events.

Hydroclimatic conditions and the availability of water resources in space and time constitute important factors for maintaining an adequate food supply, the quality of the environment, and the welfare of inhabitants, in the context of sustainable growth and economic development. This session is designed to explore the impacts of hydroclimatic variability, climate change, and the temporal and spatial availability of water resources on: food production, population health, the quality of the environment, and the welfare of local ecosystems. We particularly welcome submissions on the following topics:

- Complex inter-linkages between hydroclimatic conditions, food production, and population health, including: extreme weather events, surface and subsurface water resources, surface temperatures, and their impacts on food security, livelihoods, and water- and food-borne illnesses in urban and rural environments.
- Quantitative assessment of surface-water and groundwater resources, and their contribution to agricultural system and ecosystem statuses.
- Spatiotemporal modeling of the availability of water resources, flooding, droughts, and climate change, in the context of water quality and usage for food production, agricultural irrigation, and health impacts over a wide range of spatiotemporal scales.
- Intelligent infrastructure for water usage, irrigation, environmental and ecological health monitoring, such as development of advanced sensors, remote sensing, data collection, and associated modeling approaches.
- Modelling tools for organizing integrated solutions for water, precision agriculture, ecosystem health monitoring, and characterization of environmental conditions.
- Water re-allocation and treatment for agricultural, environmental, and health related purposes.
- Impact assessment of water-related natural disasters, and anthropogenic forcings (e.g. inappropriate agricultural practices, and land usage) on the natural environment; e.g. health impacts from water and air, fragmentation of habitats, etc.

Worldwide over 500 million people live in low-lying coastal deltaic areas, existential to global food security, economic activities and biodiversity. Despite climate change severity at global scale, in many densely populated deltas its effect is currently evidently dwarfed by anthropogenic pressures in the river basin such as river flow modifications, damming and the overexploitation of the natural resources groundwater or sand, as well as profound land use changes and process such as urbanisation. As a result, many major deltas rapidly sink and shrink because of accelerated land subsidence and erosion rates. This increases relative sea-level rise and vulnerability to floods and storms, increases salinization of surface and groundwater and reduces freshwater availability, leading to significant losses in biodiversity, habitat degradation, reduced agricultural and economic productivity. A fundamental change in management approach is required to address these trends and challenges to sustain deltas environments, economies and populations through the 21st Century.

The processes resulting in sinking, shrinking and saltier deltas are interconnected and developing sustainable and inclusive management requires a multidisciplinary system approach. For this, we need to understand the full range of interrelated disciplines, including, amongst others, geology, river and estuarine dynamics, sediment dynamics, hydrology, hydrogeology, geomechanics, bio-morphodynamics as well as the human dimension of delta demography, economy and land use. This session aims to bring together contributions from the full range of scientific disciplines involved in understanding and managing the combined integrated environmental threats that our world’s deltas face. These includes recent advancements in measuring, modeling and projecting environmental dynamics, especially focused on distinguishing (quantifying) anthropogenic and climate change impacts on observed natural dynamics. In particular, inter- and multidisciplinary contributions on the interactions between different environmental processes and efforts towards developing integrated management and development strategies for our sinking, shrinking and saltier deltas are warmly welcomed.

In 2015, the UN Sustainable Development Goals and the Paris Agreement on climate recognised the deteriorating resilience of the Earth system, with planetary-scale human impacts constituting a new geological epoch: the Anthropocene. Earth system resilience critically depends on the nonlinear interplay of positive and negative feedbacks of biophysical and increasingly also socio-economic processes. These include dynamics in the carbon cycle, large-scale ecosystems, atmosphere, ocean, and cryosphere that can absorb geophysical shocks (e.g. volcanic eruptions), as well as the dynamics and perturbations associated with human activities.

Maintaining Earth in the Holocene-like interglacial state within which the world’s societies evolved over the past ~10,000 years will require industrialised societies to embark on rapid global-scale socio-economic transformations. In addition to incrementally increasing environmental hazards, there is a risk of crossing tipping points in the Earth system triggering partly irreversible and potentially cascading changes.

In this session we invite contributions on all topics relating to Earth resilience, such as assessing the biophysical and social determinants of the Earth’s long-term stability, negative feedback processes, modelling and data analysis and integration of nonlinearity, tipping points and abrupt shifts in the Earth system, and the potential for rapid social transformations to global sustainability.

Environmental systems often span spatial and temporal scales covering different orders of magnitude. The session is oriented in 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, a special attention is devoted to the studies focused on the development of new techniques and integrated instrumentation for multiscale monitoring high natural risk areas, such as: volcanic, seismic, energy exploitation, slope instability, floods, coastal instability, climate changes and other 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.

Numerical models used for weather and climate prediction have traditionally been formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from sub-grid scale motions and parametrised processes is estimated and used to predict the evolution of the large-scale flow. However, knowledge uncertainties, necessary simplifications in representing the physical processes in numerical models, and the lack of scale-separation in the Earth System mean that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to represent the effects of uncertain small-scale and parametrised processes. Instead of predicting the most likely forcing effect of these processes on the resolved scales, a Monte-Carlo approach is used. Integrations of the numerical model sample possible realisations of the forcing.

Stochastic parametrisations are now the norm in ensemble weather and seasonal forecasts worldwide. By accounting for uncertainty in the forecast due to the limitations of numerical models, stochastic parametrisations improve the reliability of ensemble forecasts. We are now seeing their adaptation for use in climate models, with stochastic parametrisations being developed to represent a wide range of processes in the Earth System, including processes in the atmosphere, oceans, and land surface.

This course will introduce the art and science of stochastic parametrisation, including

> Purpose: model uncertainty, ensemble forecasting, climate applications
> Foundations: stochastic processes
> Theory: how to design a stochastic scheme
> Realisation: the path from a well-designed scheme to an operational implementation in a numerical model

This course is aimed at PhD students, Early Career Scientists, and all those interested in an overview of key concepts in stochastic parametrisation. The course will be taught through a combination of presentations and interactive exercises using python notebooks. No prior knowledge of python is necessary.

In order to be able to have predictive power on extreme events we need to rely on mathematical approaches that provide us with some degree of universality, so that we have rigorous ways to extrapolate information beyond what has been already recorded. In this short course we will introduce frameworks based on dynamical systems theory and statistical mechanics that allow for a rigorous and effective treatment and analysis of extreme events. We will show how extreme value theory and large deviation theory allows for a better understanding of high-impact weather and climate extremes as well as of the basic dynamical properties of the atmosphere. We will introduce the basic theory and show applications on a range of datasets, including outputs of numerical models of various levels of complexity as well as observational data.

The climate system as a whole can be viewed as a highly complex thermal/heat engine, in which numerous processes continuously interact to transform heat into work and vice-versa. As any physical system, the climate system obeys the basic laws of thermodynamics, and we may therefore expect the tools of non-equilibrium thermodynamics to be particularly useful in describing and synthesising its properties. The main aim of this short course will be twofold. Part 1 will provide an advanced introduction to the fundamentals of equilibrium and non-equilibrium thermodynamics, irreversible processes and energetics of multicomponent stratified fluids. Part 2 will illustrate the usefulness of this viewpoint to summarize the main features of the climate system in terms of thermodynamic cycles, as well as a diagnostic tool to constrain the behavior of climate models. Although the aim is for this to be a self-contained module, some basic knowledge of the subject would be beneficial to the participants. Registration is not needed, but indication of interest would be helpful for planning purposes.

Publishing your research in a peer reviewed journal is essential for a career in research. The EGU Journals are fully open access which is great, but the open discussion can be daunting for first time submitters and early career scientists. This short course will cover all you need to know about the publication process from start to end for EGU journals, and give you a chance to ask the editors some questions. This includes: what the editor looks for in your submitted paper, how to deal with corrections or rejections, and how best to communicate with your reviewers and editors for a smooth transition from submission to publication. An open discussion will be served to give you time for questions to the editors,and for them to suggest some ‘top tips’ for a successful publication. This course is aimed at early-career researchers who are about to step into the publication process, and those who are yet to publish in EGU journals. Similarly, this course will be of interest to those looking to get involved in the peer-review process through reviewing and editing.

This short course introduces and gives practical examples on how to use the TRACMASS Lagrangian trajectory code version 7 to study the oceans and the atmosphere. Lagrangian trajectories are a powerful tool for studying connectivity, transport, dispersion etc. in geophysical flows. TRACMASS v7.0 includes a variety of new features and has also been extensively rewritten to make it more user-friendly and improve performance.

We will provide a brief introduction to the algorithm used to solve trajectory positions in TRACMASS as well as showcase examples of case studies on a variety of spatial and temporal scales in oceanography and meteorology. Case studies will include: tracing deep water from the Southern Ocean to the Atlantic, calculating trajectories using data from satellite altimetry, etc. We will also demonstrate a new tool to post process and analyse trajectory data from TRACMASS.

A large part of the short course will be devoted to a walkthrough on how to download, install and configure TRACMASS. This will be followed by running TRACMASS on some example data from the NEMO ocean model and atmospheric reanalysis (ERA-5) and then analyse the results. The analysis will cover e.g. calculating Lagrangian stream functions, connectivity and some Lagrangian statistics like dispersion etc.

Participants will be able to set up TRACMASS for their own application and understand the results which will be particularly useful for early-career scientists looking to add new tools to their scientific arsenal. Users should bring their own laptops.
The course is aimed for those with no previous experience with Lagrangian trajectories but more advanced participants will also have the opportunity to further develop their skills. Basic knowledge of Linux/UNIX commands is necessary. Knowledge in Fortran and Python is preferable, but not necessary.
For more information about TRACMASS, see: https://tracmass.readthedocs.io/en/latest/