AS – Atmospheric Sciences
Programme Group Chair: Philip Stier
- AS1 – Meteorology
- AS2 – Boundary Layer Processes
- AS3 – Atmospheric Composition, Chemistry and Aerosols
- AS4 – Interdisciplinary Processes
- AS5 – Methods and Techniques
- AS6 – Short Courses
Proposals are marked in red.
Using observational constraints for better prediction of future weather and climate extremes
CL3.1 | Future Climate – Climate Change: From Regional to Global
HS7 | Precipitation and climate
NH1 | Hydro-Meteorological Hazards
Uncrewed Aircraft Systems (UAS, also commonly referred to as drones, UAV or RPAS) are an emerging technology that is significantly expanding observational capabilities across the geosciences. The rapid development of these platforms (including multicopters, fixed-wing UAS, and tethered systems) combined with major advances in miniaturized payloads—spanning meteorological sensors, multispectral/hyperspectral cameras, and geophysical instruments—has led to a rapidly growing dataset that supports diverse scientific disciplines.
This session invites abstracts discussing scientific contributions using UAS across all fields of geosciences. Topics of interest include, but are not limited to:
• Atmospheric and climate sciences: boundary-layer research, weather prediction, urban environment, and climate monitoring networks.
• Agricultural and environmental sciences: precision agriculture, soil characterization, hydrology, and ecology.
• Geophysics and volcanology: high-resolution drone-borne geophysical surveying (e.g., magnetometry, GPR, EMI), site zonation, and hazard monitoring.
We welcome presentations on the development of novel platforms and instrumentation, recent measurement efforts and field campaigns, data analysis and synthesis, and other scientific interpretations of UAS-based datasets to improve process understanding, numerical model prediction, and data assimilation.
As data-driven models increasingly rival or complement physics-based systems, a central question remains open: can AI reliably forecast the events that matter the most, the extremes that drive real-world impacts, and not just the average state of the atmosphere? This session brings together the latest advances in machine learning (ML) and artificial intelligence (AI) for forecasting weather, projecting climate, and simulating extreme events.
We invite contributions spanning the full range of timescales and methods, including but not limited to:
*data-driven and foundation weather models for short- and medium-range forecasting;
*generative and probabilistic approaches (e.g. diffusion models) for forecasting, downscaling, and uncertainty quantification;
*ML for sub-seasonal to seasonal (S2S) prediction and longer-term climate projections;
*hybrid AI-physics approaches that embed physical constraints into data-driven models or improve the representation of climate variables in numerical models and datasets;
*detection, attribution, and anticipation of extreme events such as hurricanes, floods, heatwaves, droughts, and compound extremes.
We particularly encourage submissions that go beyond forecast skill to address impacts on infrastructure, ecosystems, health, or energy systems, and that engage with questions of trust, explainability, and generalization to unseen or out-of-distribution extremes.
By bringing together experts from AI, data science, meteorology, climate science, and impact modelling, this session aims to foster interdisciplinary collaboration and push the boundaries of AI-driven understanding and prediction of extreme weather and climate events. We warmly welcome submissions from early-career scientists, established researchers, and industry professionals alike.
Atmospheric reanalyses combine historical observations with a consistent numerical weather prediction model and data assimilation system to provide spatially and temporally complete reconstructions of the atmosphere. They are indispensable tools for studying climate variability, atmospheric dynamics and high-impact weather. However, their utility in diagnosing the mechanisms behind extreme events can be limited by inherent structural uncertainties and data artefacts. While modern reanalyses generally agree on large-scale climatological means, discrepancies can emerge when evaluating transient sub-synoptic gradients, localised diabatic feedbacks, and circulation features during extreme events. These differences can be particularly pronounced in regions and periods with sparse observational coverage, where reanalyses are more weakly constrained by observations and therefore more susceptible to biases in the underlying model and data-assimilation system. Furthermore, changes in the assimilated observations can introduce non-climatic jumps, representation errors, and artificial trends.
This session provides a forum for diagnosing uncertainties, limitations and artefacts in global and regional reanalyses when studying tropospheric circulation and weather extremes. We invite contributions addressing:
1. Structural uncertainties from large-scale teleconnections to atmospheric blocking, jet stream waviness, Rossby wave breaking, and storm tracks across reanalysis products (e.g., ERA5, MERRA-2, JRA-3Q).
2. Artefacts, discontinuities, and spurious trends introduced by changes in the observing system over time, alongside robust variability and trends shared across reanalysis.
3. Representation errors in land-atmosphere and air-sea coupling, including how biases in sensible/latent heat fluxes, diabatic heating and water budgets alter synoptic- and mesoscale extremes (e.g., convective environments, atmospheric rivers, explosive cyclogenesis).
4. Physical consistency, uncertainties, and artefacts in emerging machine-learning-based or AI-assisted reanalyses, particularly during extreme weather.
We welcome both studies evaluating the suitability and robustness of reanalyses for diagnosing tropospheric circulation and weather extremes, and science-driven studies using multiple reanalyses to assess the robustness of specific atmospheric or climate questions.
The Earth system is a complex, multiphysics system with nonlinear interactions on multiple spatial and temporal scales. Understanding constituent processes (linear, nonlinear, stochastic, etc.) on the one hand, and the complexity of individual subsystems or the full integrated system on the other, is key to being able to better model the Earth System in a predictive fashion. The renaissance of machine and deep-learning in the past decade has led to rapid progress in the development of advanced approaches in, e.g., nonlinear time series analysis, dynamical and stochastic systems theory, critical slowing down theory, complex systems theory, and these approaches, in turn show promise in facilitating further advances in modeling the Earth system.
In this context, this session seeks contributions on all aspects of complexity, nonlinearity, tipping points and stochastic dynamics of the Earth system, including the atmosphere, the hydrosphere, the cryosphere, the solid earth, etc. Communications on theoretical, experimental and modeling studies are all welcome, where the latter modeling studies can span the range of model hierarchy from idealized models to complex Earth System Models (ESM). Studies based on emerging approaches such as data driven models, Artificial Intelligence approaches, complex network methods, critical slowing down analysis, dynamical and stochastic systems theory, etc., are particularly encouraged.
The year 2026 marks the centenary of Richardson’s seminal paper on turbulent diffusion. In this pioneering work, several fundamental ideas were introduced. Richardson notably recognized the non-differentiable nature of turbulent velocity and suggested that a fractal-like process could be used to represent it, proposing a Weierstrass function as an example. Based on experimental evidence, he also proposed that turbulent diffusivity follows a scaling law with an exponent of 4/3. Fifteen years before Kolmogorov’s 1941 theory, this result is equivalent to a velocity scaling characterized by a Hurst exponent of 1/3.
To mark the centenary of this landmark paper, which led the basis of modern theory of turbulent diffusion, we propose a EGU session devoted to this topic. In particular, we aim to address ocean and atmospheric applications, from small-to-large scale processes, in light of more recent results about the presence of intermittent corrections, or recent approaches in terms of continuous-time random walk or ballistic cascade phenomenologies.
We welcome contributions addressing turbulent transport and dispersion from both Eulerian and Lagrangian perspectives, including the diffusion of chemical and biological tracers, pair dispersion, and turbulent mixing. Theoretical, experimental, numerical, and observational studies are all welcome, across a broad range of spatial and temporal scales.
We also particularly encourage contributions addressing the historical development of ideas on turbulent diffusion, from Richardson’s pioneering work to contemporary approaches in oceanic and atmospheric turbulence.
Geophysical and astrophysical flows in stratified media exhibit stratified turbulence that gives rise to a variety of flow phenomena spanning a range of spatial scales from the Kolmogorov to planetary scales. Stratified turbulence significantly influences the flow dynamics on various temporal scales via complex nonlinear interactions, which continue to be challenging to understand, diagnose, and quantify from both theory and numerics. This understanding is fundamental to advance our knowledge of turbulent flow dynamics, and a prerequisite for improved turbulent closures and parameterizations for robust predictions of weather and climate. This session aims at bringing together the recent advancements in the field of fluid dynamics, with a focus on geophysical and astrophysical flows, as well as magneto-hydro dynamics.
Our session invites fundamental and applied contributions on stratified turbulence in fluids from theoretical, numerical, and experimental observational perspectives. The topics include, but are not limited to: two dimensional, three dimensional, isotropic, and anisotropic turbulence; regime transitions and energy cascades in turbulent flows; turbulent fluxes and transports; turbulent decay, mixing, and dissipation; stable atmospheric boundary layer flows and intermittent turbulence; wave-vortex dynamics in various turbulent regimes; wave turbulence; clear air turbulence; turbulence in weakly and strongly stratified flows and stratified shear flows.
We particularly encourage participation from early career researchers.
The Navier-Stokes equations, initially formulated in the early 19th century, have since become the cornerstone of fluid mechanics, subsequently extending their relevance to fluid geophysics. The existence and regularity of their solutions pose a significant challenge within a substantial domain of geophysics.
Over the years, a series of partial results have been obtained, particularly in the pursuit of proving one of the four statements proposed by Charles L. Fefferman for the Millennium Clay Prize. A definitive proof of the third statement regarding the breakdown of the Navier-Stokes equations was unveiled by OpenAI on September 8th, utilising extensive IA resources. This revelation has sparked a substantial debate, encompassing various aspects such as the physical significance of the blowing-up singularity, the utilisation of intensive AI resources in disruptive research, and the connections with concepts like intermittency, cascades, multifractals and enstrophy catastrophe. It may also inspire new approaches to resolve fundamental questions of geosciences.
This PICO session seeks to provide the geophysical community with an opportunity to contribute to this ongoing discourse.
Past climate variability and extreme events cannot be understood from any single source of information. Climate-model simulations provide physically consistent representations of past climate states and the processes governing variability and extremes, but they cannot reproduce the unique sequence of events that actually occurred. Geological and biological proxies, historical documents, archaeological records and other event-based archives instead preserve evidence of the realised past, but with heterogeneous temporal resolution, spatial coverage, sensitivity, preservation and uncertainty. This session invites contributions that combine, compare or jointly interpret multiple sources of information to investigate past climate change, variability and extremes.
We particularly welcome studies integrating climate-model simulations with geological, palaeoenvironmental, documentary, historical or archaeological evidence; approaches addressing rare or high-impact events such as storms, floods, droughts and heat extremes; statistical or probabilistic methods designed to account explicitly for dating uncertainty, proxy sensitivity, observational thresholds, preservation biases and model uncertainty.
Of particular interest are approaches that move beyond simple model–data comparison towards a process-based understanding of what different sources can reveal about past climate states and events. Contributions exploring data assimilation, probabilistic inference, multi-proxy synthesis, event attribution, high-resolution palaeoclimate modelling, machine learning, or methods for identifying convergence and disagreement among heterogeneous evidence are encouraged.
The session aims to foster dialogue across traditionally separate communities and to explore how complementary information from models and archives can be combined to produce more physically grounded and uncertainty-aware reconstructions of past climate variability and extremes.
Weather and climate extremes, such as recent events unprecedented in the observational record, have extensive impact globally. Some of these events would have been nearly impossible without human-made climate change, exhibiting conditions well beyond previous records due to complex, and at times unprecedented configurations of their underlying physical drivers. Furthermore, compounding hazards and cascading risks resulting from these high-impact extremes are becoming evident. Continued warming does not only increase the frequency and intensity of such extremes, it also potentially increases the risk of unseen non-linear behaviours or unprecedented impacts. To increase preparedness for high-impact climate events, developing novel methods, models and process-understanding that capture these hazards and their associated impacts is paramount.
This session aims to bring together the latest research quantifying and understanding high-impact climate events in past, present and future climates. We welcome studies across all spatial and temporal scales, and covering compound, cascading, and connected extremes as well as worst-case scenarios, with the ultimate goal to provide actionable climate information to increase societal preparedness to such extreme high-impact events.
We invite work addressing high-impact extreme events via, but not limited to, model experiments and intercomparisons, diverse storyline approaches such as event-based or dynamical storylines, climate projections including large ensembles and unseen events, insights from paleo archives, and attribution studies. We also especially welcome contributions focusing on physical understanding of high-impact events, on their ecological and socioeconomic impacts, as well as on approaches to potentially limit societal impacts.
The session is closely linked to the World Climate Research Programme lighthouse activities on Understanding High-Risk Events and Explaining and Predicting Earth System Change.
The Quaternary Period (the last 2.6 million years) is characterized by frequent and abrupt climate swings and rapid environmental change. Studying these changes requires accurate, precise dating methods that can be applied effectively to environmental archives. Different methods or a combination of various dating techniques can be used depending on the archive, time range, and research question. Varve counting and dendrochronology allow for the construction of high-resolution chronologies. In contrast, radiometric methods (radiocarbon, cosmogenic in-situ, U-Th, and even Pb-210 for the Anthropocene), luminescence dating, and electron spin resonance dating provide independent anchors for chronologies that span longer timescales. We particularly welcome contributions that aim to (1) reduce, quantify, and express dating uncertainties in any dating method, including high-resolution radiocarbon approaches; (2) use established geochronological methods to answer new questions; (3) use new methods including recognizing and critically examine their limitations to address longstanding issues, or; (4) combine different chronometric techniques for improved results, including the analysis of chronological datasets with novel methods, e.g., Bayesian age-depth modeling; (5) we also welcome contributions integrating multiple chronological and provenance tools including U-Pb geochronology and apatite fission track thermochronology to constrain sediment provenance and source to sink dynamics. Applications may aim to understand long-term landscape evolution, quantify rates of geomorphological processes, or provide chronologies for records of climate change and anthropogenic effects on Earth's system.
Earth's climate is undergoing rapid change, with anthropogenically forced trends emerging across the atmosphere, ocean, and cryosphere. At the same time, internally generated multidecadal variability continues to modulate regional and global climate evolution, complicating attribution and prediction. Therefore, understanding the interplay between externally forced change and natural variability is essential for improving confidence in climate projections, decadal predictions, and climate risk assessments.
Recognizing the importance of addressing these issues, the WCRP CLIVAR has initiated a research focus on “Confronting Earth System Model Trends and Multidecadal Variability with Observations (CEMT-MV)”. This session contributes to CEMT-MV, offering a venue to summarize recent work and to stimulate new research. We invite contributions on the detection and attribution of observed climate trends, separation of externally forced signals and internal climate variability, and multidecadal variability in the atmosphere, ocean, cryosphere, and coupled climate system. We are particularly interested in the evaluation of existing and new ESM simulations (e.g., CMIP7 and PMIP7) and reanalysis datasets, on the mechanisms underlying simulated and observed multidecadal variability, and on implications for decadal climate prediction. We welcome studies utilizing paleo-climate reconstructions and data-model comparisons for the last millenium and ones that introduce new methods, including AI and machine learning, for identifying trends and multidecadal variability. Implications for future climate projections, regional climate change, and climate services.
The large-scale atmospheric circulation is an essential component of the climate system. Understanding the drivers, variability and the dynamical processes of this circulation is important for improving global and regional climate projections under anthropogenic climate change, and for predicting the associated impacts on extreme weather and climate events. This session encourages theoretical, modelling and observational research on the large-scale atmospheric circulation, including (but not limited to) the following topics:
-Response of the large-scale atmospheric circulation to climate change, including shifts and changes in intensity of the jet stream, Hadley and Walker cells, intertropical convergence zone, and monsoons;
-Changes in storm track intensity and structure in response to climate change and/or internal variability;
-Representation of the large-scale atmospheric circulation in climate models: inter-model variability, model biases, and methodologies for reducing uncertainty in model projections;
-Novel metrics and analysis methods for studying the large-scale atmospheric circulation;
-Interactions between the different components of the large-scale circulation, including tropical-extratropical interactions and teleconnection patterns;
-Role of moisture in the large-scale atmospheric circulation;
-Energy transport by the large-scale atmospheric circulation;
-Stratospheric-tropospheric interactions affecting the large-scale circulation.
Sitting under a tree, you feel the spark of an idea, and suddenly everything falls into place. The following days and tests confirm: you have made a magnificent discovery — so the classical story of scientific genius goes…
But science as a human activity is error-prone, and might be more adequately described as "trial and error". Handling mistakes and setbacks is therefore a key skill of scientists. Yet, we publish only those parts of our research that did work. That is also because a study may have better chances to be accepted for scientific publication if it confirms an accepted theory or reaches a positive result (publication bias). Conversely, the cases that fail in their test of a new method or idea often end up in a drawer (which is why publication bias is also sometimes called the "file drawer effect"). This is potentially a waste of time and resources within our community, as other scientists may set about testing the same idea or model setup without being aware of previous failed attempts.
Thus, we want to turn the story around, and ask you to share 1) those ideas that seemed magnificent but turned out not to be, and 2) the errors, bugs, and mistakes in your work that made the scientific road bumpy. In the spirit of open science and in an interdisciplinary setting, we want to bring the BUGS out of the drawers and into the spotlight. What ideas were torn down or did not work, and what concepts survived in the ashes or were robust despite errors?
We explicitly solicit Blunders, Unexpected Glitches, and Surprises (BUGS) from modeling and field or lab experiments and from all disciplines of the Geosciences.
In a friendly atmosphere, we will learn from each other’s mistakes, understand the impact of errors and abandoned paths on our work, give each other ideas for shared problems, and generate new insights for our science or scientific practice.
Here are some ideas for contributions that we would love to see:
- Ideas that sounded good at first, but turned out to not work.
- Results that presented themselves as great in the first place but turned out to be caused by a bug or measurement error.
- Errors and slip-ups that resulted in insights.
- Failed experiments and negative results.
- Obstacles and dead ends you found and would like to warn others about.
For inspiration, see the collection of BUGS - ranging from clay bricks to atmospheric temperature extremes - at https://meetingorganizer.copernicus.org/EGU25/session/52496
Nonlinear waves transfer energy, momentum, and information across scales in the atmosphere and ocean. Rossby waves, atmospheric gravity waves, and ocean surface and internal waves interact with mean flows, turbulence, and other waves, influencing circulation, atmospheric blocking, teleconnections, ocean mixing, predictability, and extreme events. Their multiscale behaviour, nonlinear interactions, and limited observability remain challenging for conventional analysis and modelling.
This session invites contributions exploring how artificial intelligence, machine learning, and data-driven methods can improve the understanding, representation, and prediction of atmospheric and oceanic waves.
We welcome studies on Rossby-wave propagation and breaking, wave packets, wave–mean-flow interactions, blocking, teleconnections, circulation regimes, extremes, and predictability. Contributions addressing atmospheric gravity waves, ocean surface and internal waves, planetary and topographic waves, and wave–wave interactions are also encouraged.
Relevant approaches may include deep learning, neural operators, physics-informed AI, computer vision, explainable AI, reduced-order modelling, causal discovery, hybrid modelling, and machine-learning parameterizations. Applications may address wave detection, reconstruction from sparse observations, simulation acceleration, unresolved processes, prediction of wave evolution, and forecasting of wave-related extremes.
We also welcome assessments of the physical consistency, interpretability, uncertainty, and generalizability of AI models under changing climatic conditions.
Potential topics include:
* AI-based detection and tracking of waves and wave packets
* Rossby-wave breaking, blocking, and circulation regimes
* Wave–mean-flow and wave–wave interactions
* Gravity-wave detection and parameterization
* Data-driven modelling of ocean waves
* Neural operators and reduced-order models
* Physics-informed and physics-constrained AI
* AI-based simulation and prediction of wave evolution
* Waves, teleconnections, and climate variability
* Wave-related extreme and compound events
* Explainability and uncertainty quantification
* Comparisons of AI, numerical, and theoretical models
The statistical characterization and modelling of precipitation are crucial in a variety of applications, such as flood forecasting, water resource assessments, evaluation of climate change impacts, infrastructure design, and hydrological modelling. This session aims to gather contributions on research, advanced applications, and future needs in the understanding and modelling of precipitation, including its variability at different scales and its sources of uncertainty.
Contributions focusing on one or more of the following issues are particularly welcome:
- Process conceptualization and approaches to modelling precipitation at different spatial and temporal scales, including model parameter identification, calibration and regionalisation, and sensitivity analyses to parameterization and scales of process representation.
- Novel studies aimed at the assessment and representation of different sources of uncertainty of precipitation, including natural climate variability and changes caused by global warming.
- Uncertainty and variability in spatially and temporally heterogeneous multi-source ground-based, remotely sensed, and model-derived precipitation products.
- Estimation of precipitation variability and uncertainty at ungauged sites.
- Modelling, forecasting and nowcasting approaches based on ensemble simulations for synthetic representation of precipitation variability and uncertainty.
- Machine-learning approaches for precipitation modelling, forecasting, and downscaling: Machine-learning and hybrid (physics-informed) methods for precipitation simulation, uncertainty quantification, bias correction, and spatio-temporal downscaling, including baseline comparisons, cross-climate transfer tests, and evaluations of explainability and robustness.
- Scaling and scale invariance properties of precipitation fields in space and/or in time.
- Dynamical and statistical downscaling approaches to generate precipitation at fine spatial and temporal scales from coarse-scale information from meteorological and climate models.
Understanding how urban environments interact with hydrometeorological extremes is becoming increasingly important as cities face growing risks from extreme precipitation, flooding, drought, and compound events. The characteristics of these extremes are shaped by interactions among local atmospheric and hydrological processes, land-surface properties, and urban form. Yet these interactions remain difficult to generalise across cities, climates, and spatial and temporal scales. This complexity reflects the high heterogeneity of urban environments and the multiple physical pathways through which urbanisation can influence hydrometeorological extremes. Addressing this challenge requires approaches that can disentangle nonlinear and scale-dependent relationships while retaining physical interpretability.
Machine learning provides opportunities to identify dominant drivers, characterise nonlinear relationships, and reveal spatially and climatically varying responses that are difficult to isolate based on conventional approaches alone. In particular, explainable machine learning (XAI), physics-informed machine learning, causal modelling and hybrid modelling can further support the transition from predictive performance to robust interpretation, hypothesis testing, and process understanding.
This session welcomes studies using machine learning and related data-driven approaches to advance understanding of hydrometeorological extremes in complex urban environments. We particularly welcome studies that use machine learning for physical interpretation, hypothesis testing, and process understanding. Key topics of discussion include:
• Identifying the drivers and nonlinear interactions shaping extreme precipitation, flooding, drought, and compound events.
• Investigating how urban form, land-surface properties, and infrastructure interact with atmospheric and hydrological processes across spatial and temporal scales.
• Applying explainable AI (XAI), physics-informed machine learning, causal modelling, and hybrid approaches to support physical interpretation and process understanding.
• Integrating remote sensing and other multi-source observations to characterise heterogeneous patterns and their scale dependence.
AS1 – Meteorology
Proposals are marked in red.
CL | Climate: Past, Present & Future
HS | Hydrological Sciences
Atmospheric Rossby waves and Jet Stream Dynamics, and their Impacts on Extreme Weather and Climate Events
CL2 | Present Climate – Historical and Direct Observations
NH1 | Hydro-Meteorological Hazards
NP7 | Nonlinear Waves
CL3.1 | Future Climate – Climate Change: From Regional to Global
CR7 | The Cryosphere in the Earth system: interdisciplinary topics
CL2 | Present Climate – Historical and Direct Observations
NH1 | Hydro-Meteorological Hazards
ERE2 | Renewable energy
NH1 | Hydro-Meteorological Hazards
NP5 | Predictability
NP7 | Nonlinear Waves
OS1 | Ocean Circulation and Climate
Mid-latitude Cyclones and Storms: Dynamics, Diagnostics of Observed and Future Trends, and Related Impacts
CL | Climate: Past, Present & Future
HS | Hydrological Sciences
NH | Natural Hazards
The atmospheric water cycle: processes, dynamics, isotopic tracers, and characteristics
CL4 | Climate Studies Across Timescales
Advancing understanding of the circulation-coupling and Lagrangian evolution of clouds
Mesoscale deep convection and scale-interactions: from cloud processes to extremes, upscale growth, and large-scale circulation feedbacks
CL3.1 | Future Climate – Climate Change: From Regional to Global
Atmospheric rivers: Understanding their processes and impacts across past, present, and future climates
CL | Climate: Past, Present & Future
NH | Natural Hazards
Insights into polar clouds from recent field campaigns: Advances in understanding and new challenges to inform future campaigns
CR | Cryospheric Sciences
Developments in Convective-Scale Data Assimilation, Machine Learning and Observations
Numerical Weather Prediction as Applications of Physics and Artificial Intelligence
Dynamic and diabatic processes linking across scales in the midlatitudes – from convection and fronts to cyclones and storm tracks
This session covers climate predictions from seasonal to multi-decadal timescales and their applications. Continuing to improve such predictions is of major importance to society. The session embraces advances in our understanding of the origins of seasonal to decadal predictability and of the limitations of such predictions. This includes advances in improving forecast skill and reliability and making the most of this information by developing and evaluating new applications and climate services, including windows of opportunity.
The session welcomes contributions from dynamical modeling, machine-learning or other statistical methods and hybrid approaches. It will investigate predictions of various climate phenomena, including extremes, from global to regional scales, and from seasonal to multi-decadal timescales (including seamless predictions). Physical processes and sources relevant to seasonal to (multi-)decadal predictability (e.g. ocean, cryosphere, or land) as well as predicting large-scale atmospheric circulation anomalies associated with teleconnections will be discussed. Analysis of predictions in a multi-model framework, and ensemble forecast initialization and generation will be another focus of the session. We are also interested in approaches addressing initialization shocks and drifts. The session welcomes work on innovative methods of quality assessment and verification of climate predictions. We also invite contributions on the use of seasonal-to-decadal predictions for risk assessment, adaptation and further applications.
A longstanding pursuit in climate science is to better understand Earth’s climate sensitivity, which quantifies how global mean surface temperature responds to changes in radiative forcing. Uncertainty in climate sensitivity arises due to forcing and radiative feedbacks, which are influenced by processes ranging from cloud microphysics and local meteorology to large-scale atmospheric circulation and the spatial pattern of surface temperature changes. This session solicits work across theory, observations, and modeling focusing on climate sensitivity, radiative feedbacks, and the pattern effect. It aims to serve as an exchange platform for atmospheric and oceanic science communities, showcasing the full spectrum of modeling approaches from conceptual frameworks and CMIP ensembles to km-scale simulations and novel machine learning methods.
We welcome contributions related to, but not limited to:
- Process studies of feedbacks from clouds, convection, and moist processes
- The modulation of radiative feedbacks by surface warming patterns (the "pattern effect'')
- Theoretical and conceptual models of climate sensitivity (ECS, TCR)
- Relationships between idealized climate sensitivity measures and climate change projections
- Insights into CMIP6 "hot models'' as well as novel CMIP7 simulations
- Ocean heat uptake, air-sea interactions, and ocean dynamics shaping surface temperature patterns, radiative feedbacks, and transient climate sensitivity
- Carbon-climate feedbacks, carbon-cycle interactions, and Earth system/emission response metrics (TCRE, ZEC, ESS)
The climate system is changing rapidly, with some regions experiencing increases in extreme events beyond what is expected from climate model simulations. To improve the accuracy of climate predictions and projections, it is necessary to (1) identify and explain what factors and processes drive observed and predicted climate changes, (2) critically assess how key processes are represented in climate models, (3) understand and explain the predicted signals, which often result from the interaction of multiple drivers, and (4) use this knowledge to calibrate and further develop predictions to provide more reliable and thus useful information to society. In combination, these research activities contribute to building the capability for an integrated attribution and prediction of climate change - a key goal of the WCRP Lighthouse Activity on Explaining and Predicting Earth System Change (EPESC) and the Horizon-Europe project EXPECT.
Progress in integrated attribution and prediction will benefit from combining diverse data sources, such as Earth Observations, and various climate model experiments, including those at very high resolutions. This session invites contributions on advancing integrated attribution and prediction, with a particular focus on annual to decadal timescales, which involves explaining, predicting and constraining climate changes from regional to global scales. Relevant topics include, for example, studies attributing the drivers of specific climate phenomena and extremes such as the atmospheric circulation during the boreal summer and related surface extremes, evaluating climate responses to different forcings and internal variability, correcting biased climate responses e.g. using process-based constraints, providing calibrated prediction and projections of future climate based on these constraints, and methods that exploit a variety of data in combination with novel analysis techniques including Artificial Intelligence.
Rainfall is a “collective” phenomenon emerging from numerous drops. It reaches the ground surface with varying intensity, drop size and velocity distribution. Understanding the relation between the physics of individual drops and that of a population of drops remains an open challenge, both scientifically and for practical implications. This remains true also for solid precipitation. Hence, it is much needed to better understand small scale space-time 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. Similarly to the small scales, accurate measurement and prediction of the spate-time distribution of precipitation at hydrologically relevant scales still remains an open challenge.
This session brings 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 encouraged:
- 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;
- Drop (or particle) size distributions, small scale variability of precipitation, and their 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.
- Rainfall simulators developed to investigate the accuracy of disdrometer measurements in assessing drop size and fall velocity.
Traditionally, hydrologists focus on the partitioning of precipitation water on the land surface into evaporation and runoff, while ignoring factors that influence precipitation. However, more than half of the evaporation globally returns as precipitation on land. Given this important feedback of the water cycle, changes in land-use and water-use, as well as climate variability and change, impact not only the partitioning of precipitation water but also the atmospheric input of water as precipitation, at both remote and local scales.
This session aims to:
i. investigate the remote and local atmospheric feedbacks from human interventions such as greenhouse gasses, irrigation, deforestation, and reservoirs on the water cycle, precipitation and climate, based on observations and coupled modelling approaches,
ii. investigate the use of hydroclimatic frameworks such as the Budyko framework to understand the human and climate effects on both atmospheric water input and partitioning,
iii. explore the implications of atmospheric feedbacks on the hydrological cycle for land and water management.
Applied studies in this session may adopt fundamental characteristics of the atmospheric branch of the hydrological cycle on different scales. These fundamentals include, but are not limited to, atmospheric circulation, humidity, hydroclimate frameworks, residence times, recycling ratios, sources and sinks of atmospheric moisture, energy balance and climatic extremes. Studies may also evaluate different data sources for atmospheric hydrology and implications for inter-comparison and meta-analysis. Examples of data sources and methodological approaches include observation networks, isotopic studies, conceptual models, Budyko-based hydroclimatological assessments, back-trajectories, reanalysis and fully coupled Earth system model simulations.
AS2 – Boundary Layer Processes
Proposals are marked in red.
BG | Biogeosciences
HS | Hydrological Sciences
Surface Exchange Processes in the Polar Boundary Layer: Physics, Chemistry, and Aerosols
CR7 | The Cryosphere in the Earth system: interdisciplinary topics
Air-Sea Biogeochemical Fluxes: Impacts on Atmospheric Composition, Biogeochemistry and Climate
OS3 | Ocean Biogeochemistry and Biology
Atmospheric Boundary Layer: From Basic Turbulence Studies to Integrated Applications
Land–atmosphere interactions often play a decisive role in shaping climate extremes. As climate change continues to exacerbate the occurrence of extreme events, a key challenge is to unravel how land states regulate the occurrence of droughts, heatwaves, intense precipitation and other extreme events. This session focuses on how natural and managed land surface conditions (e.g., soil moisture, soil temperature, vegetation state, surface albedo, snow or frozen soil) interact with other components of the climate system – via water, heat and carbon exchanges – and how these interactions affect the state and evolution of the atmospheric boundary layer. Moreover, emphasis is placed on the role of these interactions in alleviating or aggravating the occurrence and impacts of extreme events. We welcome studies using field measurements, remote sensing observations, theory and modelling to analyse this interplay under past, present and/or future climates and at scales ranging from local to global but with emphasis on larger scales.
AS3 – Atmospheric Composition, Chemistry and Aerosols
Proposals are marked in red.
Science-based, measurement-based greenhouse gas emission monitoring to inform climate change mitigation across decision-making scales and sectors
BG8 | Biogeosciences, Policy and Society
ESSI4 | Advanced Technologies and Informatics Enabling Transdisciplinary Science
Anthropogenic and natural aerosols in regional climate change: From physical hazards to climate risk and impacts on nature and society
CL3.1 | Future Climate – Climate Change: From Regional to Global
Suggestion by Chunsong Lu (17 September 2026)
Suggestion by Chunsong Lu (17 September 2026)
Quantification and attribution of anthropogenic methane sources through measurement: Where to focus for mitigation?
Towards sustainable road traffic, shipping and aviation: from emissions to climate and air quality effects
Suggestion by Erika von Schneidemesser (22 September 2026)
ESSI1 | Next-Generation Analytics for Scientific Discovery: Data Science, Machine Learning, AI
Atmospheric impacts of spacecraft launches and re-entries: knowns, unknowns, and research priorities
BG9 | Earth System Remote Sensing and Modelling
CL3.1 | Future Climate – Climate Change: From Regional to Global
PS4 | Space weather and space weathering
ST3 | Ionosphere and Thermosphere
From Emission to Environmental Fate: Ageing and Transformation of Non-Exhaust Traffic Particles
BG | Biogeosciences
CL | Climate: Past, Present & Future
NH7 | Wildfire Hazards
Light Absorbing Carbonaceous Aerosols: From Observations to Chemistry, Climate–Health and Policy Nexus
BG | Biogeosciences
CL | Climate: Past, Present & Future
CR | Cryospheric Sciences
GM | Geomorphology
SSP | Stratigraphy, Sedimentology & Palaeontology
Atmospheric Radicals: Measurement, Modelling and Their Role in Air Quality and Climate
Indoor and Outdoor Air Pollution Interface: Emissions, Transport, Transformation, and Exposure
Multiscale investigations into atmospheric processes: theory, simulation, and observation
Understanding greenhouse gas exchange and carbon–climate feedbacks using in situ observations, remote sensing and machine learning
ERE6 | Inter- and Transdisciplinary Sessions (ITS)
Greenhouse gas and tracer observations: calibration, harmonisation, and cross-network consistency
Satellite observations of tropospheric composition and pollution, analyses with models and applications
Aerosols, cloud condensation nuclei, and ice nucleating particles: the particular role of biological and biogenetic particles
Advances in Aerosol Observations Across Marine, High-Altitude, Remote Continental, and Extreme Environments: Sources, Transformations, and Climate Interactions
CL2 | Present Climate – Historical and Direct Observations
The radioactive materials are known as polluting materials that are hazardous for human society, but are also ideal markers in understanding dynamics and physical/chemical/biological reactions chains in the environment. Therefore, man-made radioactive contamination involves regional and global transport and local reactions of radioactive materials through atmosphere, soil and water system, ocean, and organic ecosystem, and its relations with human and non-human biota. The topic also involves hazard prediction, risk assessment, nowcast, and countermeasures, which is now urgent important for the nuclear power plants in Ukraine, the Middle East, etc.
By combining long monitoring data (> halftime of Cesium 137 after the Chornobyl Accident in 1986, 16 years after the Fukushima Accident in 2011, and other events), we can improve our knowledgebase on the environmental behavior of radioactive materials and its environmental/biological impact. This should lead to improved monitoring systems in the future including emergency response systems, acute sampling/measurement methodology, and remediation schemes for any future nuclear accidents. Furthermore, the discharge of ALPS-treated water into the ocean, carried out as part of the decommissioning of the Fukushima Daiichi Nuclear Power Station, has attracted international attention and demonstrated that decommissioning a nuclear power plant that has suffered an accident requires a fundamentally different approach from that of a conventional decommissioning. Studies on past nuclear contamination events and other environmental radioactivity datasets are also welcome.
The following specific topics have traditionally been discussed:
(a) Atmospheric Science (emissions, transport, deposition, pollution);
(b) Hydrology (transport in surface and ground water system, soil-water interactions);
(c) Oceanology (transport, bio-system interaction);
(d) Soil System (transport, chemical interaction, transfer to organic system);
(e) Forestry;
(f) Natural Hazards (warning systems, health risk assessments, geophysical variability);
(g) Measurement Techniques (instrumentation, multipoint data measurements);
(h) Ecosystems (migration/decay of radionuclides).
The radiation budget of the Earth is a key determinant for the genesis and evolution of climate on our planet and provides the primary energy source for life. Anthropogenic interference with climate occurs first of all through a perturbation of the Earth radiation balance. We invite observational, modelling and data-driven papers on all aspects of radiation and energy flows in the climate system. A specific aim of this session is to bring together newly available information on the spatial and temporal variation of radiative and energy fluxes at the surface, within the atmosphere and at the top of atmosphere (including EEI). This information may be obtained from direct measurements, satellite-derived products, climate modelling as well as AI-based and process studies. Scales considered may range from local radiation and energy balance studies to continental and global scales. In addition, related studies on the spatial and temporal variation of cloud properties, albedo, water vapour and aerosols, which are essential for our understanding of radiative forcings, feedbacks, and related climate change, are encouraged. Studies focusing on the impact of radiative forcings on the various components of the climate system, such as on the hydrological cycle, on the cryosphere or on the biosphere and related carbon cycle, are also much appreciated.
Throughout Earth’s history, large explosive volcanic eruptions and asteroid impacts have episodically perturbed the Earth system, driving major climate disruptions with profound consequences for the biosphere. These events can modify atmospheric composition, perturb Earth’s radiative balance, trigger abrupt surface cooling, weaken the hydrological cycle, alter ocean circulation and biogeochemistry, suppress terrestrial and marine productivity, and generate cascading effects across ecosystems and food webs. Such environmental changes may further shape the evolutionary trajectories of species, including humans.
One notable example is the Chicxulub impact about 66 million years ago, which generated a global impact winter and almost certainly triggered the Cretaceous–Paleogene mass extinction. Another example is the Toba supereruption about 74,000 years ago, which likely caused substantial climatic effects and may have affected human populations. However, the magnitudes, timescales, spatial patterns, and underlying mechanisms of climate and biosphere responses to major perturbations remain incompletely understood and actively debated.
This session invites contributions that investigate how major perturbations—including volcanic eruptions, asteroid impacts, and other abrupt climatic events of varying magnitudes—affect the climate system, terrestrial and marine ecosystems, and the evolution of mammals and humans across a wide range of timescales and regions. We welcome theoretical, observational, proxy-based, and modeling studies from multidisciplinary perspectives spanning volcanology, planetary science, climate science, paleoclimatology, ecology, archaeology, and paleoanthropology.
AS4 – Interdisciplinary Processes
Proposals are marked in red.
Submesoscale-to-Mesoscale Air-Sea Interactions and Their Role in Weather and Climate
CL0 | Inter- and Transdisciplinary Sessions
OS | Ocean Sciences
Artificial Intelligence for Atmospheric Science: From Physical Process Understanding to Weather and Climate Prediction
CL2 | Present Climate – Historical and Direct Observations
ST3 | Ionosphere and Thermosphere
Bridging Disciplines: Interactions Between Polar Aerosols, Clouds, Sea Ice, and Oceans in a Changing Climate
Atmospheric Science for Action: Linking Air Quality, Climate, Health, Equity, and Policy
Understanding atmospheric transport and deposition of cosmogenic nuclides for tracing Earth surface processes
GM | Geomorphology
ST | Solar-Terrestrial Sciences
BG10 | Interdisciplinary topics in Biogeosciences
Clouds, moisture, and precipitation in the Polar Regions: Sources, processes and impacts
CL | Climate: Past, Present & Future
CR | Cryospheric Sciences
The terrestrial water cycle is usually studied one compartment at a time: precipitation by meteorologists, runoff and recharge by hydrologists, aquifers by hydrogeologists, and evapotranspiration by land-surface scientists. Yet climate change, land-use change and human water use act mainly on the links between these compartments. They change how rainfall is partitioned at the surface, how much reaches aquifers and how long it is stored there, and how much returns to the atmosphere to fall again as rain.
This session invites studies that cross at least one of these boundaries. Relevant topics include precipitation extremes, monsoons and atmospheric rivers and their imprint on runoff and recharge; soil moisture, infiltration, and snow and glacier melt; groundwater recharge, surface water–groundwater exchange, and storage change from GRACE and in-situ networks; evapotranspiration, moisture recycling and irrigation feedbacks; and the carbon, solutes and pollutants that water carries along its flow paths, including greenhouse-gas emissions from reservoirs. We also welcome interventions that deliberately reconnect the loop, such as managed aquifer recharge and nature-based solutions. Methods that link compartments are equally welcome: isotopes and tracers, remote sensing, coupled and hybrid models, and explainable machine learning.
We particularly encourage work on water-balance closure across scales, studies from data-scarce and monsoon-dominated regions, and research that turns whole-cycle understanding into water-security decisions. Early-career scientists are strongly encouraged to submit.
This session invites contributions advancing the understanding, modelling, and prediction of extreme events in weather, climate, and other geophysical systems. It brings together researchers from the geophysical sciences and those applying mathematical, statistical, and dynamical-systems approaches.
Topics of interest include, but are not limited to:
* Variability and projected changes in extremes under climate change
* Representation of extreme events in weather and climate models
* Attribution of extreme events
* Emergent constraints on extreme-event behaviour
* Predictability of extremes across meteorological and climate timescales
* Connections between extremes in dynamical systems and observed geophysical extremes
* Theoretical and applied studies of extremes in nonlinear and chaotic systems
* Downscaling methods for extreme events
* Links between the physical dynamics of extremes and their impacts on society and ecosystems
We particularly welcome interdisciplinary contributions, novel methodologies, and studies connecting theory with observed geophysical extremes. Submissions from early-career researchers are especially encouraged.
Land surface processes play a crucial role in shaping Earth's climate system, mediating land-atmosphere interactions, and driving terrestrial water-carbon-energy feedbacks. Land Surface Models, as core components of Earth System Models (ESMs), influence climate projections in benchmarks such as the CMIP7. However, land hydrology and its interactions with other components of the Earth system (e.g. biosphere, biogeochemical cycles) remain poorly represented in most ESMs, potentially inducing erroneous responses to anthropogenic climate forcings at global to local scales and leading to misrepresentations of droughts and floods. For instance, ESMs do not represent the observed decline of groundwater levels in water-limited regions that threatens groundwater-dependent ecosystems and exacerbates drought persistence, thereby increasing the risk of ecosystem shifts and progressive desertification. This crosscutting session provides an open, interdisciplinary platform to bridge the gap between hydrologists, hydrogeologists, ecohydrologists, and climate modelers.
We invite observational, theoretical, and numerical modeling contributions that advance the integrated representation of hydrological, hydrogeological, biophysical, and ecosystem processes within land surface models across spatial and temporal scales. Key areas of focus include the representation of the soil-plant-atmosphere continuum, plant hydraulics, vegetation stress dynamics, and biosphere-mediated moisture recycling, alongside subsurface hydrogeology such as explicit groundwater-table dynamics, lateral flow, and deep aquifer linkages. Contributions addressing human-water-ecosystem interlinkages (e.g., groundwater abstraction, irrigation, land-use change), high-resolution ESM configurations, advanced observational networks, and emerging AI/machine learning techniques are also strongly encouraged.
The overarching aim of this session is to overcome historical disciplinary silos and establish a shared agenda across modeling communities. By aligning interdisciplinary priorities, addressing cross-scale parameterization challenges, and improving the evaluation of land-based mitigation and adaptation strategies, this session seeks to define future needs and collaborative opportunities for the next ESM generation.
Planetary magnetospheres across the Solar System offer an exceptional opportunity to study universal plasma processes operating under fundamentally different magnetic, atmospheric, rotational and solar-wind conditions. Comparative investigations allow us to distinguish physical mechanisms that are common across planetary environments from those that are unique to individual systems, thereby advancing both fundamental plasma physics and planetary space weather.
Recent and ongoing missions, including BepiColombo, JUICE, Juno, Cassini, Arase, Cluster, MMS, THEMIS, Van Allen Probes, SMILE and complementary ground-based observations, together with advances in first-principles modelling, global simulations, data assimilation, machine learning and artificial intelligence are enabling a new era of comparative magnetospheric science.
We welcome observational, theoretical and modelling studies of planetary magnetospheres, including but not limited to:
• Particle acceleration, transport and loss
• Wave–particle interactions and plasma waves
• Radiation belts and energetic particle populations
• Magnetic reconnection and global plasma circulation
• Magnetosphere–ionosphere–atmosphere coupling
• Auroral processes and energetic particle precipitation
• Solar-wind-driven and internally driven magnetospheric dynamics
• Comparative studies of Mercury, Earth, Jupiter, Saturn, Uranus, Neptune, Mars, Venus and exoplanetary environments
• Numerical modelling, data assimilation, digital twins and machine learning
• Multi-mission analyses and future planetary exploration missions
We especially encourage contributions that bridge planetary science and solar-terrestrial physics, compare multiple planetary environments, or combine observations, theory and advanced data-driven methods. The session aims to strengthen interactions between the PS and ST communities, identify universal plasma processes across planetary magnetospheres, and stimulate new international collaborations and future mission concepts.
AS5 – Methods and Techniques
Proposals are marked in red.
Artificial Intelligence/Machine Learning (AI/ML) in Atmospheric, Climate, and Environmental Sciences: Application and Development
Advanced Spectroscopic Measurement Techniques and Applications for Atmospheric Science
CL5 | Tools for Climate Studies
ESSI1 | Next-Generation Analytics for Scientific Discovery: Data Science, Machine Learning, AI
NP5 | Predictability
Constraining atmospheric model uncertainty: ensembles, emulation and observations across models
Low-cost air quality sensors: challenges, opportunities, and collaborative strategies across the world
In recent years, technologies based on Artificial Intelligence (AI), such as image processing, smart sensors, and intelligent inversion, have garnered significant attention from researchers in the geosciences community. These technologies offer the promise of transitioning geosciences from qualitative to quantitative analysis, unlocking new insights and capabilities previously thought unattainable.
One of the key reasons for the growing popularity of AI in geosciences is its unparalleled ability to efficiently analyze vast datasets within remarkably short timeframes. This capability empowers scientists and researchers to tackle some of the most intricate and challenging issues in fields like Geophysics, Seismology, Hydrology, Planetary Science, Remote Sensing, and Disaster Risk Reduction.
As we stand on the cusp of a new era in geosciences, the integration of artificial intelligence promises to deliver more accurate estimations, efficient predictions, and innovative solutions. By leveraging algorithms and machine learning, AI empowers geoscientists to uncover intricate patterns and relationships within complex data sources, ultimately advancing our understanding of the Earth's dynamic systems. In essence, artificial intelligence has become an indispensable tool in the pursuit of quantitative precision and deeper insights in the fascinating world of geosciences.
For this reason, aim of this session is to explore new advances and approaches of AI in Geosciences.
This session invites contributions on the latest developments and results in lidar remote sensing of the atmosphere, covering • new lidar techniques as well as applications of lidar data for model verification and assimilation, • ground-based, airborne, and space-borne lidar systems, • unique research systems as well as networks of instruments, • lidar observations of aerosols and clouds, thermodynamic parameters and wind, and trace-gases. Atmospheric lidar technologies have shown significant progress in recent years. While, some years ago, there were only a few research systems, mostly quite complex and difficult to operate on a longer-term basis because a team of experts was continuously required for their operation, advancements in laser transmitter and receiver technologies have resulted in much more rugged systems nowadays, many of which are already operated routinely in networks and several even being fully automated and commercially available. Consequently, also more and more data sets with very high resolution in range and time are becoming available for atmospheric science, which makes it attractive to consider lidar data not only for case studies but also for extended model comparison statistics and data assimilation. Here, ceilometers provide not only information on the cloud bottom height but also profiles of aerosol and cloud backscatter signals. Scanning Doppler lidars extend the data to horizontal and vertical wind profiles. Raman lidars and high-spectral resolution lidars provide more details than ceilometers and measure particle extinction and backscatter coefficients at multiple wavelengths. Other Raman lidars measure water vapor mixing ratio and temperature profiles. Differential absorption lidars give profiles of absolute humidity or other trace gases (like ozone, NOx, SO2, CO2, methane etc.). Depolarization lidars provide information on the shapes of aerosol and cloud particles. In addition to instruments on the ground, lidars are operated from airborne platforms in different altitudes. Even the first space-borne missions are now in orbit while more are currently in preparation. All these aspects of lidar remote sensing in the atmosphere will be part of this session.
The MacGyver session focuses on novel sensors made, or data sources unlocked, by scientists. All geoscientists are invited to present:
- new sensor systems, using technologies in novel or unintended ways,
- new data storage or transmission solutions sending data from the field with LoRa, WIFI, GSM, or any other nifty approach,
- started initiatives (e.g., Open-Sensing.org) that facilitate the creation and sharing of novel sensors, data acquisition and transmission systems.
Connected a sensor to an Arduino or Raspberri Pi? Used the new Lidar in the new iPhone to measure something relevant for hydrology? 3D printed an automated water quality sampler? Or build a Cloud Storage system from Open Source Components? Show it!
New methods in hydrology, plant physiology, seismology, remote sensing, ecology, etc. are all welcome. Bring prototypes and demonstrations to make this the most exciting Poster Only (!) session of the General Assembly.
This session is co-sponsered by MOXXI, the working group on novel observational methods of the IAHS.
Hydrological predictions - simulations or forecasts - are fundamentally uncertain. This has been recognized more than a century ago (see Krzysztofowicz, 2001, and references therein), and it is still true today. For a complete picture, hydrological predictions should therefore not only provide point estimates, but probabilistic statements. Such probabilistic predictions are not only an honest account of what we know (and what we do not know), they also provide practical advantages for end users and decisionmakers (Buizza, 2008). Nevertheless, and despite considerable progress, to date the majority of hydrological models still provide single-valued output. With this session, we want to establish a platform to promote the paradigm-shift towards making probabilistic predictions in hydrology the standard rather than the exception.
We welcome contributions from the following fields (but not limited to these):
- Theory and methodology for identifying and quantifying sources and pathways of uncertainty from data through models to predictions, including approaches based on probability theory and information theory
- Development of model architectures and efficient training procedures enabling fast, accurate and reliable probabilistic predictions, including physics-based, data-driven, machine-learning and hybrid approaches, stochastic parameterisations, ensemble prediction systems, and post-processing
- Probabilistic benchmarks and evaluation frameworks, including benchmark models and datasets, verification methods, scoring rules, calibration, and large-scale initiatives for assessing probabilistic hydrological predictions
- Operational implementations and real-world applications of probabilistic hydrological modelling, including flood forecasting, climate change impact assessment, and water resources management
- Development of strategies for effectively communicating probabilistic predictions to end users
References
Buizza, R. (2008), The value of probabilistic prediction. Atmosph. Sci. Lett., 9: 36-42. https://doi.org/10.1002/asl.170
Krzysztofowicz, R.: The case for probabilistic forecasting in hydrology, Journal of Hydrology, 249, 2-9, https://doi.org/10.1016/S0022-1694(01)00420-6, 2001.
Climate modeling is pushing the frontier towards increasingly complex, high-resolution earth system models (ESMs). At the same time, nonlinearities and emergent phenomena in the climate system are often studied by means of conceptual models, which offer qualitative understanding and permit theoretical approaches. Recent advancements in statistical and physical emulators – ranging from reduced-complexity climate models to machine learning-based techniques – are enabling rapid and computationally efficient assessments of climate trajectories, impacts, and risks.
Between these approaches, a persistent “gap between simulation and understanding” (Held 2005) challenges our ability to transfer insights from conceptual models to reality, and to distill the physical mechanisms underlying the behavior of complex (climate) models. This calls for a concerted effort to learn from the entire model hierarchy, understanding the differences and similarities across its various levels of complexity, to increase confidence in climate projections.
In this session, we invite contributions from all subfields of climate science that showcase how different modeling approaches advance our understanding of the Earth system and its components, and/or highlight inconsistencies in the model hierarchy. We also welcome studies exploring a single modeling approach, as we aim to encourage exchange between researchers working on different rungs of the model complexity ladder. Contributions may employ dynamical systems models, physics-based low-order models, explainable machine learning, Earth System Models of Intermediate Complexity (EMICs), simplified or idealized setups of ESMs (radiative-convective equilibrium, single-column models, aquaplanets, slab-ocean models, idealized geography, etc.), full ESMs or standalone models of components of the Earth system, and km-scale models.
Processes and phenomena of interest include, but are not limited to:
* Earth system response to climate forcing
* Tipping behavior and critical transitions
* (Coupled) modes of climate variability
* Extremes and predictability
The analysis of datasets that represent comprehensive Earth-system processes can be greatly facilitated with the aid of existing tools and tutorials that have been developed within the global climate community. This session aims to bring together the developers and users of these resources to exchange knowledge, share best practices, and address scientific and technical challenges related to climate and Coupled Model Intercomparison Project (CMIP) data analysis. We welcome contributions presenting tools, workflows, tutorials, and practical approaches that support access, processing, analysis, visualization, and interpretation of climate and CMIP data.
This session is prepared together with the members of the CMIP Rapid Evaluation Framework (REF) and members of the Fresh Eyes on CMIP project on compiling existing tools and tutorials. An overview of currently collected tutorials and tools is available through the WCRP CMIP website:
https://wcrp-cmip.org/tutorials/
https://wcrp-cmip.org/tools/.
Global coupled models that resolve ocean mesoscale eddies, and increasingly atmospheric storms, can now be run over multidecadal to centennial timescales. This allows us to ask whether small-scale processes change the large-scale climate: its mean state, its modes of variability and its response to forcing. This session focuses on the rectified effect of resolved small scales in the ocean, the atmosphere and at the air-sea interface on the climate system.
We welcome contributions from global km-scale models as well as from eddy-rich coupled configurations with coarser atmospheres, regional high-resolution setups and model hierarchies. Topics include the role of ocean mesoscale and submesoscale dynamics, boundary currents and fronts in climate; mesoscale air-sea coupling and its influence on storm tracks, jets, and precipitation; interannual to decadal to centennial variability including the AMOC, the Southern Ocean and tropical modes; whether resolution alters forced responses, climate sensitivity and SST trend patterns; sea ice, and ice-sheet-ocean interactions at high resolution; biogeochemistry, impacts on marine ecosystems, and km-scale mechanisms underlying the exchange of carbon at the air-sea interface and its subsequent transport in the ocean.
We also welcome studies exploring how resolved oceanic and atmospheric small-scale processes influence extremes, including heatwaves, marine heatwaves, ocean carbon uptake and acidification, heavy precipitation and compound events, particularly where these provide insight into climate variability and climate change.
We also invite studies using pacemaker or filtered-forcing experiments and resolved-versus-parameterised comparisons to isolate mechanisms or transfer insight to coarser models, as well as storyline approaches, pseudo-global-warming experiments, uniform warming experiments (e.g. +4 K frameworks), and related targeted methodologies used to understand the role of resolved small-scale processes in climate variability, extremes, and climate change, as well as work on challenges specific to long coupled simulations such as spin-up, drift, tuning, and initialisation.
Contributions from EERIE, DestinE, nextGEMS, WarmWorld, DYAMOND, DYAMOND3, MESACLIP, HighResMIP and related efforts are encouraged.
Connect with colleagues across disciplines at the 5th Lagrangian session!
This session provides an open venue for scientists to share the latest advances in Lagrangian techniques, explore diverse applications, and build new connections.
We invite presentations on topics including, but not limited to:
- Planetary circulations and variability (fundamental processes shaping jets, gyres, waveguides, overturning circulations, transport barriers across atmosphere and ocean)
- Mesoscale eddies and coherent structures (eddy transport, wave-mean flow interactions, blocking)
- Turbulence and mixing (turbulent and convective entrainment, breaking internal waves, boundary layers)
- Numerical and computational advances (incl. data-driven techniques, GPU acceleration, graph-theoretical formulations, adaptive methods, data assimilation)
- Inverse modeling techniques (long-range transport of volcanic plumes, wildfire smoke, hazardous material, aerosols, plastics, micro-organisms, and their impacts on global composition, health, and climate)
- Field campaigns (drifters, floats, superpressure balloons, etc)
Time series are a common type of data generated by observational and modelling efforts across Earth, environmental and space sciences. Long-term observations are particularly important for understanding gradual changes and assessing risks, yet are often difficult to sustain and fund. Their characteristics can vary substantially, from short to long records, linear to nonlinear dynamics, univariate to multivariate data, and single- to multi-scale variability. These differences call for both tailored methodologies and general approaches.
A key challenge is distinguishing random fluctuations from long-term changes in order to better understand processes within and across Earth system components. This requires knowledge of temporal variability and, often, sufficiently long observations. For example, reliable sea-level trends may require several decades of continuous measurements because of decadal variability. Likewise, the stochastic variability of geophysical time series can exhibit power-law scaling, requiring long records for robust statistical assessment.
Time series analysis encompasses a broad range of tasks, including:
- characterizing nonlinear variability in the time and/or frequency domain;
- quantifying complexity, predictability and scaling properties;
- identifying statistical interdependencies within and between time series;
- distinguishing co-variability from causal relationships;
- reducing dimensionality and identifying meaningful modes of variability; and
- developing stochastic and deterministic statistical or dynamical models.
This session invites contributions on the development and application of modern methods for analysing observational and model time series across the EGU community, including geophysical, geodynamic, oceanographic, geodetic and climate observations from terrestrial observatories and remote sensing. Contributions addressing advances in sensors, instrumentation, monitoring, analysis and interpretation, as well as comparisons of different approaches, are welcome. Studies using novel methods, including AI, for the analysis of long time series are particularly encouraged. We aim to foster interdisciplinary exchange and cross-fertilization between different EGU divisions.
AS6 – Short Courses
Forecasting systems are indispensable for making informed decisions under uncertainty. Therefore, there is a need for an objective and well-understood framework for ``forecast verification'', i.e., qualitative and quantitative assessment of forecast performance.
Statistical methods compare historical forecasts with corresponding verifications, indicating whether the forecasting system behaved significantly differently (in a statistical sense) from what was expected. This requires that the forecasts have a well--defined statistical interpretation; whether a forecast represents a mean or a quantile makes a difference with regards to how we evaluate that forecast.
This short course will introduce the participants to the fundamentals of statistical forecast verification. Some necessary statistical theory will be presented, along with the concept of risk measures, which allows to provide forecasts with a precise statistical meaning. We furthermore illustrate the relation to scoring and identification functions, and discuss practical challenges with evaluating forecasts as spatial fields (as opposed to point by point). Specifically, the course will cover the following topics (more or less in that order)
(1) Forecast types, risk measures, scoring functions, and identification functions (20min)
(2) Tests and p-values (10min)
(3) How to evaluate forecasts for specific risk measures
(with hands-on part, 30min)
(3) How to evaluate forecasts of spatial fields
(with hands-on part, 30min)
(4) Open challenges (15min)
The target audience is researchers (from both academic institutions and operational centers) who are either new to forecast verification or have practical experience but want to learn more about the theory. The discussed methods are applicable not only in atmospheric forecasts but in many other fields such as parameter estimation, data assimilation, model evaluation, and machine learning.