Internal gravity waves (IGWs) still pose major questions in atmospheric and ocean sciences and stellar physics. Fundamental issues include IGW radiation from various sources, reflection at boundaries, propagation through and interaction with larger-scale flows, wave-induced mean flow, wave-wave interactions, wave breaking and its implications for mixing, and parameterizing these processes in models that do not explicitly resolve IGWs. The observational record, both globally and for local small-scale processes, is not yet sufficient to yield appropriate constraints. The session aims to bring together experts from all fields of geophysical fluid dynamics working on related problems. Presentations on theoretical, modeling, experimental, and observational work on all aspects of IGWs are welcome, including those on major collaborative projects that seek to accurately resolve or parameterize IGWs in numerical models. Participation from early-career researchers is 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.

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)

Complementary middle-atmosphere sounding techniques—such as infrasound, lidar, radar, microwave spectrometry, and mesospheric airglow observations, further strengthened by satellite measurements—have become significantly more accessible over the past decade. We expect that developing multi-instrument platforms will expand the community involved in operational infrasound monitoring for near-real-time observation of high-impact natural hazards—including volcanic eruptions, earthquakes, meteoroid entries, and bright fireballs—while creating new opportunities for atmospheric remote sensing research, such as improving gravity wave parameterizations.

In particular, global and regional infrasound station networks have proven highly effective in detecting and locating a wide range of natural and anthropogenic phenomena— including space debris re-entries. Recent studies have shown that multi-technology analyses provide foundations for addressing geophysical inverse problems, enabling novel probing of both global-scale and small-scale structures in the middle atmosphere for enhanced weather prediction and climate modeling.

In addition to data-driven contributions, this session invites model-based papers dealing with the dynamics of the middle atmosphere across scales and altitudes, as well as its predictability. We welcome contributions on the characterization of atmospheric phenomena (gravity or planetary waves for example), using complementary observational methods across both local and global scales. Contributions addressing advances in wave propagation modeling, signal processing, and machine learning applications are also of great interest. Additional areas of focus include the development of derived data products and services for scientific and civilian use, as well as innovative instrumentation, including sensors deployed on mobile or elevated platforms such as balloons on Earth or other planets. Seismo-acoustic studies investigating the coupled Earth–ocean–atmosphere system, particularly focusing on ionospheric responses to processes originating from the ocean and solid Earth, are also encouraged.

Climate change is taking place at all levels of the atmosphere, from the surface to the edge of space. It is well-known that increasing greenhouse gas concentrations, in particular CO2, cause cooling and contraction throughout the middle and upper atmosphere. In the middle atmosphere, stratospheric ozone depletion and subsequent recovery play an important role as well, and interactions with circulation changes are still being disentangled. There are still considerable unknowns regarding long-term changes in atmospheric dynamics, including winds, tides, planetary waves, gravity waves, and large-scale residual circulations. These can also be related to considerable spatial variations in long-term trends in for example temperature or ionospheric quantities, as well as dependencies of trends on season and local time. In the ionosphere, additional spatial variations and seasonal/local time dependencies arise from long-term changes in the Earth’s magnetic field, which can also interact with variability and changes in atmospheric dynamics. Building a consistent pattern of long-term change across different atmospheric layers, regions, seasons and local times is therefore a fundamental challenge, which is important to address, given the connections with climate change near the surface and impacts of upper atmospheric changes on space operations and sustainability. This session welcomes studies that contribute to a more comprehensive understanding of all aspects of long-term changes in the stratosphere, mesosphere, thermosphere, and ionosphere, as well as their broader impacts, from surface weather to space operations.

The stratospheric circulation exerts an important influence on tropospheric weather and climate. Key phenomena—including the stratospheric polar vortex, the Brewer–Dobson circulation, and the Quasi-Biennial Oscillation—modulate atmospheric wave propagation, drive downward dynamical influences on the troposphere, and control the transport and mixing of climatically important chemical constituents. Understanding, observing, and accurately simulating stratospheric dynamics are therefore essential for improving predictions of weather and climate variability and change. This session will examine the causes and consequences of variations in stratospheric circulation, including natural and anthropogenic drivers, chemical transport and mixing processes, stratosphere–troposphere coupling, and implications for weather and climate prediction. We welcome abstracts that address these topics from observational, modeling, or theoretical perspectives and across a wide range of spatial and temporal scales.

Rossby wave dynamics stands at the intersection of several open research questions, ranging from our basic understanding of mid-latitude variability, to the short- and medium-range predictability of high-impact weather events, and to the circulation changes expected from anthropogenic global warming. Rossby waves exist and propagate along vorticity gradients such as the one related to the tropopause-level jet stream, whose complex meandering often "breaks" creating nonlinear circulation features, such as atmospheric blocking.

Recent extreme weather and climate episodes, like heavy rainfall events leading to flash floods, recurrent and concurrent summer heatwaves or unforeseen winter cold spells, highlight the need to improve our understanding of jet streams and of the associated linear and non-linear, planetary and synoptic-scale Rossby wave dynamics in the atmosphere to better constrain the impacts of Rossby waves and of atmospheric blocking on extreme weather and climate events.

Abstracts are invited on a wide range of topics, with a focus on, but not limited to, the following areas:

(1) Theoretical developments in the dry and moist dynamics of Rossby waves, wave breaking, atmospheric blocking, and of jet streams acting as atmospheric Rossby waveguides. This includes the role of local and remote drivers (e.g., the tropics, Arctic, or stratosphere) in affecting Rossby wave evolution.
(2) Linkages between extreme weather/climate events and the jet stream, as well as the associated linear and non-linear Rossby wave evolution during such events, including Rossby wave breaking, cut-off formation and re-absorption, and atmospheric blocking.
(3) Application of cutting-edge methods to study the multi-scale interaction of Rossby waves from the convective scale to the large-scale dynamics, and its representation in existing weather and climate models (e.g. hierarchical and/or high-resolution modelling, machine learning/AI-based approaches).
(4) Exploring the effect of Rossby wave packets on predictability at lead times from medium range (~2 weeks) to seasonal time-scales. This includes the potential role of blocking and of teleconnections involving Rossby wave propagation.
(5) Projected future changes in planetary or synoptic-scale Rossby waves, and in their connection to extreme weather and events in a changing climate.

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 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.

Midlatitude dynamics emerge from the interaction of different scales, ranging from planetary-scale atmospheric jets and synoptic storms to mesoscale circulations associated with fronts and convection. While we have well-established theories for the development of fronts, extratropical cyclones, climatological jet streams, and convection, our understanding on the feedback and dynamical linkages between these spatio-temporal scales is still incomplete. Another prominent challenge is related to diabatic processes that are regularly not well represented in weather and climate models and for which we often lack a theoretical foundation. These processes, including surface fluxes, latent heating, and radiative processes, provide key dynamical linkages across scales.
Recent efforts attributing climatological signals to synoptic-scale features have revealed nuanced dynamical insights in the functioning of the general circulation as well as the role of sub-synoptic scale structures, such as fronts, warm conveyor belts, and atmospheric rivers, on the evolution of synoptic storms and large-scale waves. Relatedly, there has been significant progress on linking limits in predictability to up- and down-scale error propagation, ranging from convective to planetary scales. However, our fundamental understanding of the dynamic and diabatic interactions between these scales remains rather limited.
We therefore invite contributions that aim to address our understanding of the role of dynamic and diabatic interactions across scales including, but not limited to, the following topics:
• Dynamic and diabatic processes connecting scales from convection, clouds, and fronts to cyclones, storm tracks, and large-scale waves
• Coupling between the ocean and land surface and the atmosphere across scales
• Predictability and forecasting across scales, including representation in AI-based weather and climate models
• Innovative methods (for example linking Eulerian, feature-based, and Lagrangian methods) to diagnose cross-scale interactions

Mid-latitude cyclones and storms are key drivers of weather variability, extremes, and associated socio-economic impacts across densely populated regions of the globe. Understanding their observed and projected trends is crucial for improving climate diagnostics, risk assessments, and adaptation strategies in a warming climate. This topic therefore addresses both fundamental scientific challenges and urgent societal needs by linking physical processes, climate change signals, and potential impacts.

This session encourages contributions covering mid-latitude storm systems, including but not limited to the following topics:

• Fundamental dynamics of cyclones - in all different stages of their life cycle - and their mesoscale features (e.g., fronts, jets, precipitation structures, dry intrusions, and diabatic processes)
• Representation of mid-latitude storms in AI-based weather and climate models
• Diagnostics of observed and projected trends in cyclone frequency, intensity, and storm tracks; including potential insights from contributions from measurement campaigns
• Predictability and forecasting on synoptic to sub-seasonal time scales
• Innovative methods, including AI/ML approaches, for cyclone detection, classification, or impact assessment
• Storm-related impacts, vulnerabilities, and risk-transfer mechanisms under a changing climate

By bringing together communities working on dynamics, diagnostics, impacts, field campaigns, and new methodologies, this session aims to provide a comprehensive platform for advancing our understanding of mid-latitude cyclones and their role in the past, present, and future climate system.

Mountains cover approximately one quarter of the earth’s land surface, and a significant fraction of the world’s population lives within them, in their vicinity, and downstream of them. Orography critically affects weather and climate processes across all scales and, in connection with factors such as land-cover heterogeneity, is responsible for high spatial variability in mountain weather and climate. This session showcases research that contributes to improving our understanding of weather and climate processes in mountain and high-elevation areas around the globe, as well as their modification induced by global environmental change, including studies connected to the TEAMx research programme.

We invite theoretical, modeling and observational contributions that address the influence of mountains on the atmosphere from meteorological to climate time scales, including terrain-induced flows, orographic gravity waves, orographic precipitation, land-atmosphere exchange over mountains, forecasting and predictability of mountain weather, and the interaction of mountain weather and climate with the terrestrial cryosphere.

Furthermore, we invite studies that investigate climate processes and climate change in mountain areas based on monitoring and modeling activities, including in particular contributions that address the interdisciplinary objectives of the Elevation-Dependent Climate Change (EDCC) working group of the Mountain Research Initiative, as well as the application and development of high-resolution (kilometer-scale) climate models over complex mountainous terrain, such as advances in model design, challenges, and observational gaps needed for robust model evaluation and improvement.

The subtropics are global hotspots of climate change, yet climate model simulations and projections for these regions remain highly uncertain. These regions are home to a large share of the world's population, including many communities with the least historical GHG emissions that are disproportionately exposed to extreme events and climate change. As a zone of interaction between tropical and extratropical circulation regimes, the subtropics are characterised by a unique hydroclimate, with strong spatial gradients, distinct seasonal cycles, and high sensitivity to climate variability and change. Despite their importance for both society and the climate system, subtropical weather and climate processes remain comparatively understudied. This session invites contributions that advance process understanding and prediction of weather and climate with a particular focus on the subtropics. We welcome studies based on observations, theory, numerical models, and machine learning. Topics of interest include, but are not limited to:
- Atmospheric processes, such as tropical–extratropical interactions, subtropical jet fluctuations, Rossby wave dynamics, transient eddies, monsoon circulations, and convergence zones, shaping surface weather, clouds, and transport of dust and air pollution.
- Weather, climate, and compound extremes – droughts, heatwaves, wildfires, heavy precipitation, floods, dust storms, and windstorms – spanning their drivers, future changes, and impacts on society and ecosystems.
- The water cycle – rainfall, evapotranspiration, and moisture transport – modulated by weather systems such as cyclones, cut-off lows, cold air outbreaks, mesoscale convective systems, easterly waves, atmospheric rivers, and cloud bands.
- Coupled interactions between Earth system components, including land-atmosphere and ocean-atmosphere feedbacks, and the role of sea surface temperature patterns in shaping subtropical climate.
- Climate variability and remote linkages with ENSO, the Madden-Julian Oscillation (MJO), and the Hadley circulation.
- Observations and climate model simulations addressing past and future changes in regional circulation patterns and surface weather, and novel approaches for identifying model biases and for reducing uncertainties in projections.

Regional monsoons have profound impacts on water, energy, and food security. Monsoons cause severe floods and droughts as well as undergoing variability on subseasonal, interannual and decadal-to-multi-decadal time scales. In addition to their profound local effects, monsoon variability also causes global-scale impacts via teleconnections, and the monsoons are linked together as part of the global monsoon via the divergent circulation, with aspects of coherent variability and interactions with planetary scale transports of heat and moisture.

Monsoons are complex phenomena involving coupled atmosphere-ocean-land interactions and remain notoriously difficult to forecast at NWP, subseasonal and seasonal scales, casting doubt also on our future climate projections. A better understanding of monsoon physics and dynamics, with more accurate simulation, prediction and projection of monsoon systems is therefore of great importance.

This session invites presentations on any aspects of monsoon research in present-day, future and palaeoclimate periods, involving observations, modelling, attribution, prediction and climate projection. Topics ranging from theoretical works based on idealized planets and ITCZ frameworks to the latest field campaign results are equally welcomed, as is work on impacts, extremes, NWP modelling, S2S and decadal forecasting, and the latest CMIP or PMIP findings to help inform the IPCC AR7.

The understanding of tropical phenomena and their representation in numerical models still raise important scientific and technical questions, particularly in the coupling between the dynamics and diabatic processes. Among these phenomena, tropical cyclones (TC) are of critical interest because of their societal impacts and because of uncertainties in how their characteristics (cyclogenesis processes, occurrence, intensity, latitudinal extension, translation speed) will change in the framework of global climate change. The monitoring of TCs, their forecasts at short to medium ranges, and the prediction of TC activity at extended range (15-30 days) and seasonal range are also of great societal interest.

The aim of the session is to promote discussions between scientists focusing on the physics and dynamics of tropical phenomena. This session is thus open to contributions on all aspects of tropical meteorology between the convective and planetary scale, such as:

- Tropical cyclones,
- Convective organisation,
- Diurnal variations,
- Local circulations (i.e. island, see-breeze, etc…),
- Monsoon depressions,
- Equatorial waves and other synoptic waves (African easterly waves, etc…),
- The Madden-Julian oscillation and intraseasonal variability,,
- Large-scale circulations (i.e. ITCZ, etc…)
- etc…

We especially encourage contributions of observational analyses and modelling studies of tropical cyclones and other synoptic-scale tropical disturbances including the physics and dynamics of their formation, structure, and intensity, and mechanisms of variability of these disturbances on intraseasonal to interannual and climate time scales.

Findings from recent field campaigns are also encouraged.

Atmospheric rivers (ARs) are narrow and transient channels of intense water vapor transport in the lower troposphere. They account for 90% of poleward moisture transport and drive high-impact weather and hydrological extremes all around the globe. Future projections suggest that landfalling ARs will become even more hazardous as they further intensify in a warmer climate. Given the fundamental role of ARs in the global water cycle, relevant research is rapidly expanding across different disciplines. With new data sources and novel methodological approaches, the multidisciplinary AR community has been breaking ground and posing fundamental questions for the understanding of AR processes and impacts.

By bringing together experts from diverse disciplines, this session aims to provide a comprehensive platform for discussing the latest advances in AR science. We invite all contributions that aim at a better understanding of AR uncertainties, processes, and impacts across past, present, and future climates at regional to global scales. Relevant topics of the session include, but are not limited to:

• Observation, identification, and monitoring of ARs
• Physical, dynamical, & microphysical aspects of ARs
• Aerosol & biochemical aspects of ARs
• ARs and the surface energy budget
• Environmental and socioeconomic impacts of AR-induced weather extremes
• ARs as a component of compound events
• AR dynamics and impacts in understudied regions
• Role of ARs in the changing Cryosphere
• Forecasting of ARs
• ARs in past, present, and future climates

The atmospheric water cycle is a key component of the climate system, and links across many scientific disciplines. Processes interact with dynamics at different scales throughout the atmospheric life cycle of water vapour from evaporation to precipitation. This session sets the focus on understanding the interaction between processes, their dynamics and characteristics of the water cycle, covering the entire atmospheric life cycle from evaporation, atmospheric moisture transport, to cloud microphysics and precipitation processes as observed from in-situ and remote sensing instrumentation, recorded by paleo-/climate archives, and as simulated by models for past, present and future climates.

We invite studies
* focusing on the understanding and impacts of features of the atmospheric water cycle related to weather systems, with a special focus on the role of Atmospheric Rivers, Cold-Air Outbreaks, Warm Conveyor Belts, Tropical Moisture Exports, and the global Monsoon systems;
* investigating the large-scale drivers behind the past, ongoing and future variability and trends within the atmospheric water cycle, from field campaigns (CAESAR, NAWDIC, (AC)3, ISLAS, AWACA etc.), long-term observations, reanalysis data, regional to global model simulations, or (isotopic) data assimilation;
* reconstructing past hydroclimates based on paleo-proxy records from archives such as ice cores, lake sediments, tree-rings or speleothems;
* applying methods such as tagged water tracers and Lagrangian moisture source diagnostics to identify source-sink relationships and to evaluate model simulations of the water cycle;
* using the isotopic fingerprint of atmospheric processes and weather systems to obtain new mechanistic insights into changes in the water cycle through isotope-enabled models and observations;
* describing the global and regional state of the atmospheric water cycle (e.g. monsoon systems) with characteristics such as the recycling ratio, life time of water vapour, and moisture transport properties.

We particularly encourage contributions linking across neighbouring disciplines, such as atmospheric science, climate, paleoclimate, glaciology, and hydrology.

This session welcomes contributions on atmospheric convection, including dry, shallow, or deep convection. A particular session focus is the organization of convection, such as mesoscale convective systems, convectively-coupled waves, idealized studies of self-aggregation, or research on the importance of organization for climate sensitivity. Additionally, submissions that address other aspects of convection like the convective lifecycle and structures including cold pools, interactions of convection with other physical processes or the representation of convection in numerical weather prediction and climate models are strongly encouraged. The research can use any tool, from idealized theoretical models, large-eddy simulations, convection-permitting simulations, to coarser-resolution simulations using parameterised convection, machine learning techniques, or observations and field campaigns.

Interactions between organised deep convection and large-scale scale circulations are increasingly recognised to play a crucial role in shaping atmospheric dynamics. Convective organisation itself depends on smaller-scale interactions involving individual convective plumes and cloud microphysical processes. A cascade of interactions across scales thereby underpins mesoscale convective systems and other meteorological phenomena that drive extreme hydroclimatic events. Understanding these interactions is important for assessing changes in hazardous convective weather and its impacts in a warming climate. Challenging the conventional assumption of strict scale separation, these interactions may also be key to understanding persistent discrepancies between observed and simulated regional climate change.

Global kilometre-scale atmospheric models and multidecadal observations over large domains are opening new opportunities to gain insights into the role of mesoscale convection in shaping circulation patterns for the first time. This session aims to brings together recent advances in understanding how atmospheric convective scale-interactions feedback on larger-scale circulations, and influence local and downstream climate extremes and their changes under warming. The session is intended to translate advances in modelling and observations into improved physical understanding of atmospheric dynamics.

We invite contributions focusing on mesoscale convective system dynamics and cloud microphysics, upscale effects of convection, and large-scale controls on convective organisation. Relevant contributions include advances in high-resolution modelling, forecasts and climate projections of these interactions, and insights from novel observations. We also invite contributions on the processes driving the formation and evolution of organised deep convection, and how these factors shape spatio-temporal patterns of deep convective intensity, precipitation, and on-the-ground hazards. This includes, but is not limited to, contributions on land-convection interactions in connection with mesoscale and severe storms. Examples include effects of complex topography, soil moisture feedbacks, and land use or land-use change, such as urbanisation, deforestation, or irrigation. We welcome diverse perspectives towards advancing physical understanding of convective processes, and quantifying their interactions across scales.

Severe convective storms (SCS) are among the most impactful weather phenomena, producing hazards such as hail, extreme precipitation, damaging winds, tornadoes, and lightning. Understanding them requires bridging scales that are usually studied separately: hydrometeor growth and updraft dynamics operate on scales of metres and minutes, storm organisation and longevity on the mesoscale, and the environments that permit severe convection are set by synoptic and large-scale circulation. The resulting hazard climatologies, and how they shift under a warming climate, emerge from the interaction of all of these.
Progress on the central questions — why some storms produce severe hazards and others do not, how far ahead this is predictable, and how hazard frequency and intensity will change — depends on work that connects these scales rather than treating them in isolation. This session provides a venue for that exchange across the SCS community, from process studies to global assessments and societal impacts.
This session welcomes contributions on:
- dynamical and microphysical processes governing storm severity and hazard production, including updraft dynamics, storm development, organisation, evolution and intensity
- pre-storm environments, convective parameters, and the large-scale drivers of severe convection
- storm-scale and environmental predictability, nowcasting and forecasting, and their verification
- hazard, storm, and environmental climatologies and trends, including regional climatologies and under-observed regions
- changes in storm dynamics, hazard evolution, and storm frequency and intensity under climate change
- societal impacts of severe convective storms, including compound and consecutive events
- aerosol–cloud interactions and their influence on storm microphysics and hazard severity
We welcome studies using high-resolution numerical modelling, machine learning, statistical analysis, in-situ observations and field campaigns, remote sensing, crowd-sourced data, reanalyses, and global climate models. Contributions from operational services and the private sector are explicitly welcome.

Clouds are ubiquitous and play a fundamental role in Earth’s climate by modulating incoming solar and outgoing terrestrial radiation. A challenge in understanding the impact of clouds arises from the multi-scale nature of cloud processes, which span from aerosol activation at the nanometer scale to the dynamics of cloud systems at the scale of hundreds of kilometres. Key microphysical processes, including droplet collision-coalescence, ice crystal formation, and their modulation by turbulence, occur at scales smaller than 100 m, which poses a challenge to observe or simulate them. The uncertainty is further exacerbated by turbulent interactions with the environment through entrainment, mixing of air, and radiative changes within the cloud. Hence, we need to improve our understanding on the small-scale to increase our confidence in climate projections.

The superposition of small-scale processes calls for an integrated approach that combines laboratory experiments, field observations, and numerical modelling. Field observations characterise cloud processes within their natural, dynamic environment using a combination of remote sensing and in-situ measurements. Recent advances in observational platforms (e.g., uncrewed aerial systems), measurement techniques (e.g., multi-frequency cloud radar), and experimental designs have enhanced these capabilities. Controlled laboratory experiments allow for the isolation and systematic study of specific cloud processes under defined and repeatable conditions. High-resolution, process-oriented numerical modelling enables the study of fundamental interactions, can test hypotheses, and synthesises datasets. These models need constraints and validation by data from both laboratory and field campaigns.

This session invites contributions that advance the understanding of small-scale cloud processes, with particular interest in the regime beyond the single particle, at scales of metres to hundreds of meters. A particular emphasis is placed on synergistic studies that combines laboratory experiments, field observations and/or numerical modelling.

Cold clouds (mixed-phase and ice) play an important role in the Earth’s radiation budget because of their high temporal and spatial coverage and their interaction with long wave and short wave radiation. Yet, the variability and complexity of their macro- and microphysical properties, the consequence of intricate ice particle nucleation and growth processes, make their study extremely challenging. As a result, large uncertainties still exist in our understanding of cold cloud processes, their radiative effects, and their interaction with their environment (in particular, aerosols).

This session aims to advance our comprehension of cold clouds by bringing observation- and modelling-based research together.

A diversity of research topics shall be covered, highlighting recent advances in cloud observation techniques, modelling, and subsequent process studies:

(1) Airborne, space borne, ground- or laboratory-based measurements and their derived products (retrievals), which are useful to constrain cloud properties like extent, emissivity, or crystal size distributions, to clarify formation mechanisms, and to provide climatology.

(2) Process-based, regional, and global model simulations that employ observations for better representation of cloud microphysical properties and radiative forcing under both current and future climate.

The synthesis of these approaches can uniquely answer questions regarding dynamical influence on cloud formation, life cycle, coverage, microphysical and radiative properties, crystal shapes, sizes, and variability of ice particles in mixed-phase as well as ice clouds. Joint observation-modelling contributions are therefore particularly encouraged.

Cold cloud thinning covers proposed climate interventions that looks to seed ice clouds with efficient ice nucleating particles, so they trap less outgoing longwave radiation. With cirrus cloud thinning, seeding aims to shift ice formation away from homogenous to heterogenous freezing nucleation, producing fewer but larger ice crystals that fall out faster, giving thinner, shorter-lived clouds. More recently, mixed-phase cloud thinning has sought to target supercooled clouds over the polar oceans in the winter, where cloud glaciation could reduce longwave radiation from reaching the surface and so help slow sea ice loss. Model estimates of the resulting cooling varies from net negative to net positive when clouds are overseeded, and much of this spread comes from processes that are difficult to constrain, such as the competition between nucleation modes, pre-existing ice, small-scale updrafts, and gravity waves.

This session focusses on work that helps improve our understanding of the ice cloud processes that decide whether cold cloud thinning could work, and what its side effects might be. We welcome laboratory studies of ice nucleation and possible seeding particles, field experiments including targeted glaciogenic seeding, in situ and satellite observations of cirrus and mixed-phase clouds (including early results from EarthCARE), and modelling from process- to global-scale climate models. Natural analogues are of interest, since mineral dust, volcanic eruptions and aviation all perturb ice clouds in ways that let us test seeding hypotheses in the atmosphere. Contributions on detectability, attribution, risks, and wider impacts are also welcome, and we encourage early career researchers to submit.

The uncertain response of clouds to global warming is a major contributor to uncertainty in climate sensitivity. Cloud feedback uncertainty is related to a limited understanding of the coupling between clouds, convection and the large-scale circulation across various spatial and temporal scales. Today's wealth of advanced remote-sensing observations and high-resolution modelling data provides comprehensive and complementary information that enables detailed process and lifecycle-based analyses. This session focuses on (1) efforts to advance our understanding of the cloud-circulation coupling and its role in climate change, and (2) Lagrangian studies related to clouds and water vapour.

We invite contributions from dedicated field campaigns, from ground-based and satellite remote sensing or in situ measurements, as well as modelling and theoretical studies. We particularly welcome the results from various ongoing model inter-comparison projects, like EUREC4A-MIP, CP-MIP and Lagrangian LES MIP. We also invite abstracts focusing on the role of mesoscale convective organisation, aerosol-cloud interactions, feature tracking, and Lagrangian cloud modelling.

Clouds remain a leading source of uncertainty in climate projections. This PICO session brings together research on how clouds are represented in global climate and Earth system models, and on how choices in model formulation, parameterisation, and process representation influence simulated clouds, climate feedbacks, and projection uncertainty.

We welcome contributions on new cloud parameterisations and model developments, evaluations of simulated clouds against observations, and experiments that perturb cloud processes to quantify their climatic impacts, including studies of cloud-based climate intervention. We also welcome studies using large-eddy simulations, kilometre-scale models, and Lagrangian approaches that provide process-level constraints for the development and evaluation of cloud representations in global models.

Precipitation, both liquid and solid, is central to the global water/energy cycle through its coupling of clouds, water vapor, atmospheric motion, ocean circulation, and land surface processes. While precipitation is the primary source of freshwater, it also has tremendous socio-economic impacts associated with extreme weather events such as hurricanes, floods, droughts, and landslides. Knowledge of precipitation characteristics from local to global scales is essential for understanding how the Earth system operates under changing climatic conditions and for improved societal applications that range from numerical weather prediction to freshwater resource management.

This session will host papers on all aspects of precipitation, especially contributions in the following four research areas:

1. Precipitation measurements (amount, duration, intensity etc) by ground-based in-situ sensors (e.g., rain gauges, disdrometers); estimation of accuracy of measurements, comparison of instrumentation.
2. Precipitation climatologies at regional to global scales; areal distribution of measured precipitation; classification of precipitation patterns; spatial and temporal characteristics of precipitation; methodologies adopted and their uncertainties; comparative studies.
3. Remote sensing of precipitation (spaceborne, airborne, ground-based, underwater, or shipborne sensors) and retrieval techniques; methodologies to estimate areal precipitation (interpolation, downscaling, combination of measurements and/or estimates of precipitation); methodologies used for the estimation (e.g., QPE), validation, and assessment of error and uncertainty of precipitation as estimated by remote sensors.
4. Contributions to current and future missions, such as the international Global Precipitation Measurement (GPM) mission, Atmospheric Observing System (AOS), EUMETSAT Polar System-Second Generation (EPS-SG), Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS), Arctic Weather Satellite (AWS), Earth Clouds, Aerosol and Radiation Explorer (EarthCARE), tomorrow.io constellation and the Advanced Microwave Scanning Radiometer-3 (AMSR-3).

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.

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.

Snow cover characteristics (e.g., spatial distribution, surface and internal physical properties) are continuously evolving over a wide range of scales due to meteorological conditions, such as precipitation, wind, and radiation.
Most processes occurring in the snow cover depend on the vertical and horizontal distribution of its physical properties, which are primarily controlled by the microstructure of snow (e.g., density and specific surface area). In turn, snow metamorphism changes the microstructure, leading to feedback loops that affect the snow cover on coarser scales. This can have far-reaching implications for a wide range of applications, including snow hydrology, weather forecasting, climate modelling, avalanche hazard forecasting, and the remote sensing of snow. The characterization of snow thus demands synergistic investigations of the hierarchy of processes across the scales, ranging from explicit microstructure-based studies to sub-grid parameterizations for unresolved processes in large-scale phenomena (e.g., albedo and drifting snow).
This session is therefore devoted to modelling and measuring snow processes across scales. The aim is to gather researchers from various disciplines to share their expertise on snow processes in seasonal and perennial snowpacks. We invite contributions ranging from “small” scales, as encountered in microstructure studies, over “intermediate” scales typically relevant for 1D snowpack models, up to “coarse” scales, that typically emerge for spatially distributed modelling over mountainous or polar snow- and ice-covered regions. Specifically, we welcome contributions reporting results from field, laboratory, and numerical studies of the physical and chemical evolution of snowpacks. We also welcome contributions reporting statistical or dynamic downscaling methods of atmospheric driving data, assimilation of in-situ and remotely sensed observations, representation of sub-grid processes in coarse-scale models, and evaluation of model performance and associated uncertainties.

Weather prediction and climate modelling extensively use numerical models of the Earth system. Both the atmosphere and ocean components of such models consist of a fluid dynamics solver (dynamical core) that solves a system of partial differential equations numerically. The dynamical core is coupled to physical parameterizations that represent processes that occur below the grid scale (physics). Enabled also by substantial improvements of the underlying numerical algorithms, these models can deliver accurate and efficient simulations.

Researchers are constantly working to further improve the accuracy, efficiency, and scalability of the dynamical core, the physics, and their coupling. The rapid development of computing systems towards massive use of graphics processing units and extreme parallelism requires adaptation of algorithms to further increase model efficiency via strong or weak parallel scaling. Recent years have also seen a rapid increase in hybrid approaches that combine physics-based modelling with data driven techniques, adopting techniques from scientific machine learning for Earth system modelling.

This session invites presentations on the development, testing, and application of novel numerical techniques for Earth system models in a broad sense. The scope includes modifications to the governing equations, horizontal and vertical discretizations, structure preserving methods, time stepping schemes (including parallel in time schemes), advection schemes, adaptive multi-scale models, physics-dynamics coupling, regional and global models, classical and stochastic physical parameterizations, as well as hybrid schemes combining numerical methods and machine learning.

This session welcomes papers on applying physical principles and artificial intelligence, or their fusion, to develop, evaluate, and improve numerical weather prediction models. Papers on using physics, machine learning techniques and observations to address research and applications questions in the following topics are highly encouraged:

1) Forecasting and simulating high-impact weather events - research on using advanced artificial intelligence and machine learning techniques to improve numerical weather model prediction of severe weather events (such as winter storms, tropical storms, and severe mesoscale convective storms);

2) Development and improvement of model numerics - basic research on advanced numerical techniques for weather and climate models (such as cloud-resolving global models and high-resolution regional models specialized for extreme weather events on sub-synoptic scales);

3) Development and improvement of model physics - progress in research on advanced model physics parameterization schemes (such as stochastic physics, air-wave-ocean coupling physics, turbulent diffusion and interaction with the surface, sub-grid condensation and convection, grid-resolved cloud and precipitation, land-surface parameterization, and radiation);

4) Verification of model physics and forecast products against theories and observations;

5) Data assimilation systems - progress in the development of data assimilation systems for operational applications (such as reanalysis and climate services), research on advanced methods for data assimilation on various scales (such as treatment of model and observation errors in data assimilation, and observational network design and experiments);

6) Ensemble forecasts and predictability - strategies in ensemble construction, model resolution and forecast range-related issues, and applications to data assimilation;

7) Advances and challenges in applying data from various conventional and avant-garde observation platforms to evaluate and improve high-resolution simulations and forecasting.

Weather forecasting and its application is one of the most important subject in meteorology. This session will focus on R&D on understanding the weather, forecasting techniques and its applications, both with AI and physics based. Contributions related to nowcasting, numerical weather prediction, ensemble techniques, NWP post-processing and forecast application are very welcome.

Topics may include:
- Nowcasting and warning methods and systems, use of observations and weather analysis
- Studies on high impact weather
- Data driven and physics based numerical modelling; explainable Artificial Intelligence (xAI) in weather models
- Development of AI for ensemble prediction techniques and products
- Weather forecasting application
- Seamless prediction and application
- Statistical and AI NWP Post-processing
- Use of machine learning, data mining and other advanced analytical techniques
- ML for weather warning

Storm and convective-scale weather data analysis and prediction still present significant challenges for the atmospheric sciences. Making progress requires synergy of advances in high spatial and temporal resolution observations, storm and convective-scale data assimilation methodology, machine learning and ensemble modeling. This session intent is to provide a platform for exchange on the state-of-the-art developments, ongoing progress and ideas for future directions in all aspects of this multifaceted challenge

This session invites contributions from developments in

• Storm and convective-scale data assimilation techniques
• Multi-scale data assimilation techniques
• Use of machine learning in convective-scale data assimilation
• Applications of machine learning to forecasting on convective scales
• Convective-scale model and observation uncertainty representation
• Ensemble design and uncertainty quantification at storm and convective scales using machine learning
• Convective-scale modeling and parameter estimation
• Assimilation of ground and space-based radar data
• Assimilation of geostationary satellite observations
• All-sky satellite data assimilation at storm and convective scales
• Observation operators for remote sensing
• Assessment of the impact of convective-scale data assimilation on weather prediction
• Observations at convective scales: new observing technologies and strategies

Downscaling aims to process and refine global climate model output to provide information at spatial and temporal scales suitable for impact studies. In response to the current challenges posed by climate change and variability, downscaling techniques continue to play an important role in the development of new services and products. While the refinement of downscaling techniques proceeds at an unprecedented pace, users of climate information are facing the novel challenge of how to select amongst the choice of available datasets or how to assess their credibility with respect to a particular application. In this context, model evaluation and verification is growing in relevance and advances in the field will likely require close collaboration between various disciplines.

Recent developments, including the integration of AI and machine learning applications, the emergence of kilometre-scale simulations, and the widespread availability of open-source downscaling products, add new dimensions to this challenge. These advances raise important questions about the ‘added value’ of downscaling, especially in light of the cascade of uncertainty and the need for robust evaluation frameworks.

In our session, we aim to bring together scientists from the various geoscientific disciplines interrelated through downscaling: atmospheric modeling, climate change impact modeling, machine learning and verification research. We also invite philosophers of climate science to stimulate our discussion about the novel challenges that arise from evaluating complex models and modelling chains in the face of the increasingly heterogeneous needs of the growing user communities.

Contributions to this session may address, but are not limited to:
- newly available downscaling products,
- applications relying on downscaled data and impact assessments,
- downscaling method development and machine learning,
- bias correction and statistical postprocessing,
- challenges in the data management of kilometer-scale simulations,
- verification, uncertainty quantification and the added value of downscaling,
- downscaling approaches in light of computational epistemology.

This session invites contributions spanning all aspects of prediction and predictability on the subseasonal (2 weeks to 2 months) forecasting timescale, also known as subseasonal-to-seasonal (S2S) prediction. We welcome research that covers predictions, processes, warning capabilities and which supports applications and decision-making across sectors (including, but not limited to, the examples listed below). In light of recent advances in artificial intelligence (AI) and machine learning (ML) techniques for subseasonal prediction, contributions on AI/ML model developments, benchmarking frameworks and applications are very welcome. Of special interest are contributions related to the AI Weather Quest, an open international competition benchmarking AI-based subseasonal forecasts in real-time.
Physical drivers and processes:
- Role of the atmosphere, ocean, land, and ice processes in extended-range/S2S predictability
- Modes of variability (e.g. Madden Julian Oscillation (MJO), quasi-biennial oscillation (QBO), polar vortex strength, and others) impacting the extended-range/S2S predictability
- Impact of global warming on early warning systems, changes in risks

Prediction systems:
- Evaluation and improvement of S2S prediction systems, including advancements in model physics and comparison between dynamical and data-driven prediction models, data assimilation, ensemble forecasting, and initialization techniques
- Use of AI/ML methods for S2S prediction, data-driven models, post-processing, and attribution, including innovative techniques for improving forecast accuracy

Extreme events and early warnings:
- Early warnings for single- and multi-hazard events
- Sources of predictability for extreme events, including multi-hazards events, on the S2S timescale (including driver identification and teleconnections)
- Case studies of extreme or high-impact event prediction and impacts on early warnings
- Predictability and predictive skill of atmospheric or surface variables, and other variables relevant for socio-economic sectors, such as sea ice, snow cover, soil moisture, and land surface

Applications and societal relevance:
- Sector-specific applications, impact studies on the S2S/extended range timescale
- New approaches for incorporating S2S forecast in decision-making
- Examples of integration of S2S predictions into decision support systems at local, regional, or global levels and co-production of knowledge with stake-holders and decision-makers

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 frequencies and intensities of extreme events such as floods, tropical cyclones, heat waves, droughts etc. are increased in many regions across the globe and now of serious concern due to their socio-economic Impact. Hence understanding of the mechanism, pattern and characteristics of such events have been the focus of many recent studies. This session invites abstracts on observational and numerical modeling studies aimed to enhance the understanding of the spatial and temporal characteristics and predictability of the extreme events. This session also welcomes the submissions on model simulations and evaluations aimed to advance the understanding of the physics and dynamics associated with the extreme events. In particular, abstracts are encouraged on regional-scale analysis of the historical extreme events and their projections which would assist the policy makers to build more resilient societies to face the extreme event related disasters.

The impacts of weather and climate extremes depend on interactions between physical hazards, exposure, and vulnerability. Beyond intensity, the duration, timing, spatial extent, and co-occurrence of extremes can strongly influence their consequences. Understanding these relationships is essential for assessing how climate change alters risks to societies and ecosystems.
This interdisciplinary session connects research on the physical drivers of weather and climate extremes with assessments of their societal and environmental impacts. We particularly welcome studies that investigate how the mechanisms and characteristics of extremes shape their consequences, and how these relationships change under different climatic and socioeconomic conditions.
Contributions are invited on:
- Physical drivers and impact pathways: Atmospheric dynamics, ocean–atmosphere interactions, land–atmosphere coupling, and thermodynamic processes shaping extremes and their consequences. Topics include the roles of persistence, spatial extent, antecedent conditions, and compound or sequential hazards in determining impacts.
- Attribution of extremes and their consequences: Assessments of how anthropogenic climate change and natural variability influence extremes and associated impacts, including approaches that distinguish climatic influences from changes in exposure and vulnerability.
- Prediction and assessment of climate risks: Integration of observations, climate simulations, statistical approaches, and impact models to understand and anticipate risks under present and future conditions. Contributions may address cross-sector and cascading impacts, impact-based forecasting, and the implications of improved physical understanding for preparedness and adaptation.
We welcome observational, modelling, theoretical, and empirical studies addressing heatwaves, droughts, heavy precipitation, floods, fire weather, and compound extremes. Contributions may examine individual links between physical drivers, hazards, and impacts; interdisciplinary studies connecting these elements are particularly encouraged. The session aims to strengthen dialogue between extreme-event science and impact research, advancing understanding of how physical climate processes translate into societal and environmental risks.

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.

Weather and climate extremes, such as intense precipitation, tropical and extratropical storms, drought, heatwaves, and compound events, pose substantial risks to society, infrastructure, and ecosystems across the globe. Yet directly and explicitly modelling such extremes and their future behaviour remains challenging, owing to the high computational cost of resolving relevant processes, incomplete understanding of many driving mechanisms, and the chaotic nature of atmospheric dynamics, which further limits predictability at longer lead times. Consequently, model-based projections often carry large uncertainties, especially in how complex and non-linear interactions among atmospheric variables shape these extremes in a future warmer world.

Observational constraints and statistical approaches offer a complementary pathway. Observations from ground-based networks, radars, and satellites, together with reanalysis simulations, can provide information on the characteristics and physical drivers that can be used to evaluate and constrain models. Statistical and data-driven models, when constrained by associated temperature, moisture, storm characteristics, and regional and global circulation teleconnections, can provide viable alternatives for problems that remain too computationally expensive, while incorporation of constraints in explicit physical simulations can help narrow uncertainties and reduce model biases.

This session aims to bring together research on the use of observational constraints, physical theory, and statistical approaches for improved understanding, prediction and projection of past and future extremes. We welcome contributions including, but not limited to, assessing the drivers of extremes, scaling relationships based on physical theory, storyline approaches, and statistical and hybrid approaches, as well as studies critically evaluating the physical basis and robustness of the proposed constraints. Contributions spanning various types of extremes, spatial and temporal scales, and observational and modelling frameworks are encouraged.

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.

As a result of a warming Arctic, there is an increased frequency and intensity of extreme weather events such as marine and terrestrial heatwaves, rain-on-snow events, and intense windstorms. Such extreme weather events can trigger cascading, multidisciplinary impacts across the Arctic system. This session bridges the gap between physical mechanisms and on-the-ground realities by investigating the physical drivers of extreme events within oceanic, atmospheric, and cryospheric systems, alongside their wide spectrum of impacts. These impacts range from rapid physical shifts, such as enhanced meltwater runoff, sudden sea-ice loss, and snowpack depletion, to societal and ecological disruptions, including infrastructure damage, traffic disruptions, and threats to reindeer herding and Indigenous livelihoods.
We welcome submissions based on modeling, observations, or interdisciplinary frameworks, such as those incorporating local and Indigenous knowledge systems, that investigate extreme weather phenomena, their physical drivers, and their direct and cascading impacts on the Arctic environment, ecosystems, and local communities.

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)

Polar clouds play a fundamental role in high latitude climate but are challenging to model due to uncertainties in representing fundamental cloud processes, such as microphysical processes that determine phase partitioning between ice and radiatively important cloud liquid water, which drives the turbulence that maintains the system. New observations of polar clouds can address questions about the role of surface fluxes, coupling with atmospheric radiation, potential secondary ice production, sources of aerosols, impacts on sea ice and the surface energy budget, and triggers for transitions, among other processes. Recent field campaigns provide new insights, new hypotheses, but also new questions, about the mechanisms and feedback that cause the formation and persistence of polar clouds.

This session welcomes papers on scientific results from recent campaigns such as ARCSIX, CAESAR, (AC)³, MOSAiC, among other Arctic expeditions and campaigns. Clouds of interest include mixed-phase clouds, ice clouds, cold-air outbreaks, and clouds over different surface conditions. In addition, we welcome papers on new technologies and strategies for observing polar clouds.

Clouds play an important role in the Polar climate due to their interaction with radiation and their role in the hydrological cycle linking poleward water vapour transport with precipitation. Cloud and precipitation properties depend on the atmospheric dynamics and moisture sources and transport, as well as on aerosol particles, which can act as cloud condensation and ice nuclei. These processes are complex and are not well represented in the models. While measurements of cloud and precipitation microphysical properties in the Arctic and Southern Ocean/Antarctic regions are challenging, they are highly needed to evaluate and improve cloud processes representation in the models used for polar and global climate and cryosphere projections.

This session aims at bringing together researchers using observational and/or modeling approaches (at various scales) to improve our understanding of polar tropospheric clouds, precipitation, and related mechanisms and impacts. Contributions are invited on various relevant processes including (but not limited to):
- Drivers of cloud/precipitation microphysics at high latitudes,
- Sources of cloud nuclei both at local and long range,
- Linkages of polar clouds/precipitation to the moisture sources and transport, including extreme transport events (e.g., atmospheric rivers, moisture intrusions),
- Relationship of moisture/cloud/precipitation processes to the atmospheric dynamics, ranging from synoptic and meso-scale processes to teleconnections and climate indices,
- Interactions between clouds and radiation, including impacts on the surface energy balance,
- Impacts that the clouds/precipitation in the Polar Regions have on the polar and global climate system, surface mass and energy balance, sea ice and ecosystems.

Papers including new methodologies specific to polar regions are encouraged, such as (i) improving polar cloud/precipitation parameterizations in atmospheric models, moisture transport events detection and attribution methods specifically in the high latitudes, and (ii) advancing observations of polar clouds and precipitation.

This year, we would particularly like to emphasize new observational activities and opportunities linked to Antarctica InSync, including in-situ, remote-sensing and autonomous observations.

Driven by atmospheric turbulence, and integrating surface processes to free atmospheric conditions, the Atmospheric Boundary Layer (ABL) plays a key role not only in weather and climate, but also in air quality and wind/solar energy. It is in this context that this session invites theoretical, numerical and observational studies ranging from fundamental aspects of atmospheric turbulence, to parameterizations of the boundary layer, and to renewable energy or air pollution applications. Below we propose a list of the topics included:

- Observational methods in the Atmospheric Boundary Layer
- Simulation and modelling of ABL: from turbulence to boundary layer schemes
- Stable Boundary Layers, gravity waves and intermittency
- Evening and morning transitions of the ABL
- Convective processes in the ABL
- Boundary Layer Clouds and turbulence-fog interactions
- Micro-Mesoscale interactions
- Micrometeorology in complex terrain
- Agricultural and Forest processes in the ABL
- Diffusion and transport of constituents in the ABL
- Turbulence and Air Quality applications
- Turbulence and Wind Energy applications
- Urban boundary layers

The session is addressed to experimentalists and modellers working on air-land interactions from local to regional scales. The programme is open to a wide range of new studies in micrometeorology and related atmospheric and remote sensing disciplines. It is also open to studies investigating how turbulent transport and surface-layer processes upscale from the atmospheric boundary layer to mesoscale dynamics and ultimately to large-scale circulation in weather and climate systems. The topics include the development of new devices, measurement techniques, experimental design, data analysis methods, as well as novel findings on surface layer theory and parametrization, including local and non-local processes. The theoretical parts encompass soil-vegetation-atmosphere transport, internal boundary-layer theories and flux footprint analyses. Of special interest are synergistic studies employing experimental data, parametrisations and models. This includes energy and trace gas fluxes (inert and reactive) as well as water, carbon dioxide and other GHG fluxes. Specific focus is given to outstanding problems in land surface boundary layer descriptions such as complex terrain, effects of horizontal heterogeneity on sub-meso-scale transport processes, energy balance closure, stable stratification and night time fluxes, dynamic interactions with atmosphere, plants (in canopy and above canopy) and soils.

This session is intended to provide an interdisciplinary forum to bring together researchers working in the areas of atmospheric and snow/ice physics, atmospheric chemistry, air quality, biogeochemistry, oceanography, meteorology and climate in the polar regions.

The emphasis is on the role of polar boundary layer processes that drive exchange fluxes of heat, momentum and mass between the Earth's surface (snowpack, sea-ice, surface ocean and land) and the atmosphere as well as the local to large-scale influences on these exchanges. An adequate understanding and quantification of these processes is necessary to improve modelling and prediction of future changes in the polar regions and their teleconnections with mid-latitude weather and climate. These changing processes include meridional transport of heat, moisture, chemical trace species, and aerosols; regional emission and vertical mixing of climate active trace gases and aerosol, such as cloud condensation nuclei (CCN) and ice nucleating particles (INP), their precursors, and their radiative effects; and air-sea(ice) fluxes of CO2 and CH4.
It is expected that observations from recent field campaigns, data from existing networks, and modelling efforts, will help diagnose long-range and local moisture, trace gas, aerosol and greenhouse gas sources as well as the coupling between local and large-scale dynamics and their impacts on climate, health and ecosystems. Reporting on progress as well as critical knowledge gaps will contribute to upcoming research programmes Antarctica InSync and the 5th International Polar Year.

We encourage submissions such as (but not limited to):
(1) External controls on the boundary layer such as clouds, radiation and long-range transport processes
(2) Results from field programs and routine observatories, insights from laboratory studies, and advances in modeling and reanalysis,
(3) Use of data from pan-Arctic and Antarctic observing networks,
(4) Surface processes involving snow, sea-ice, surface ocean, land/atmosphere chemical exchanges, natural aerosol sources and gas air-sea flux
(5) Studies on atmospheric chemistry and air pollution during polar winter
(6) The role of boundary layers in polar climate change and implications of climate change for physical and chemical surface exchange processes, especially in the context of changes in sea ice coverage and age, wetter snow packs, and increased glacial discharge.

Surface exchange fluxes of mass over the global oceans have significant impacts on atmospheric composition, biogeochemistry and climate at regional to global scales. Atmospheric boundary layer processes mediate these fluxes. This session is intended to provide an interdisciplinary forum to bring together researchers working in the areas of meteorology, atmospheric chemistry, air quality, biogeochemistry, stable isotope research, oceanography, and climate over the global oceans, focusing on new research in a range of relevant topics. These include: air-sea fluxes of radiatively active trace gases (CO2, CH4, N2O, CO), regional emissions and transport of climate-relevant aerosols and their precursors (e.g., dimethyl sulfide [DMS], marine organic compounds, and halogenated species), atmospheric deposition of nutrients (e.g., nitrogen, phosphorus, and iron) and associated impacts on ocean biological systems, and biogeochemistry-climate feedback loops in the ocean-atmosphere system. Studies on characterization of these fluxes, their underlying control processes and how they may change in response to climate change in different scales are welcome.
The session has long-standing links to the Surface Ocean ̶ Lower Atmosphere Study (SOLAS) and GESAMP Working Group 38 on atmospheric input of chemicals to the ocean. We welcome submissions from all remit areas of these programs, and from a range of analysis approaches: field measurements, remote sensing, laboratory studies, and atmospheric and oceanic numerical models.
This year we particularly welcome contributions on the following specialist themes: The impacts of extreme events on air-sea gas exchange of climate-relevant compounds in marine systems. Studies addressing physical drivers such as marine heatwaves, storms and tropical cyclones, flooding events circulation anomalies, or changes in sea ice; biogeochemical drivers, like hypoxic or anoxic conditions and acidification pulses; biological drivers, like harmful algal blooms; or compound events. The role of anthropogenic activities in the production and air-sea exchange of gases, aerosols, pollutants in coastal regions (e.g. direct ship traffic emissions, indirect emissions caused by ship wakes, effects of wind parks, effluents from waste water treatment plants, and mariculture activities). Studies on these special topics may address impacts in all oceanic domains; e.g., open ocean, shelf waters, and shallow coastal ecosystems.

The session focuses on the variability of the tropospheric and stratospheric chemical composition on the timescales from diurnal to decadal. It discusses the processes driving this variability and attribution of changes to specific processes. Special emphasis is put on the value of high-quality long-term measurement data sets, including in the context of science-policy applications, and observations sustainability. Supporting model simulations on different scales that utilize observational data will also be discussed. Contributions related to emerging constituents, new data sources and approached to atmospheric composition measurements (e.g. low cost sensor, emerging measurement techniques), measurement campaign that addresses specific processes and long-term projections of the atmospheric chemical composition are also welcome in the session.
Researchers are invited to present novel scientific results from mid- and long-term observational time series from various programmes and networks such as the Global Atmosphere Watch (GAW) Programme, European Monitoring, and Evaluation Programme (EMEP), Network for the Detection of Atmospheric Composition Change (NDACC), Southern Hemisphere Additional Ozonesondes (SHADOZ), Advanced Global Atmospheric Gases Experiment (AGAGE), National Oceanic and Atmospheric Administration (NOAA), regular airborne (e.g. CARIBIC, IAGOS, CONTRAIL) and other campaigns as well as satellite data and model simulations. Data relevant to tropospheric and stratospheric composition, in particular, related to climate change, ozone depletion, ecosystems and health impacts, and air quality as well as firn data on past atmospheric composition are welcome. We welcome contributions from multi-year modeling studies and inter-comparison exercises that address past and future tropospheric or stratospheric composition changes, carried out in the framework of international projects and initiatives.

Air pollution remains a pressing global issue, prompting nations worldwide to implement various mitigation strategies, often targeting specific pollutants such as particulate matter. While these strategies are crafted with the best intentions, they sometimes yield unintended effects, including changes in atmospheric chemical composition that lead to phenomena such as increases in ozone or unforeseen climate impacts.

This session aims to shed light on the unintended consequences of air pollution mitigation strategies, focusing on the changes in chemical composition induced by these strategies. We invite contributions that delve into the observational data, modeling approaches, and projections of future changes to better understand the full spectrum of effects of pollution control measures. By fostering a discussion grounded in rigorous scientific analysis, we hope to pave the way for more holistic and effective strategies in the future, balancing the urgent need to reduce pollution with a deep understanding of potential unintended effects. We welcome submissions that employ a range of research methods, including - but not limited to - observational studies and modeling to forecast future changes, encouraging a multidisciplinary approach to a complex issue.

Molecular hydrogen (H₂) is gaining global attention as a key component of the transition to sustainable energy systems, with the potential to significantly reduce greenhouse gas emissions and air pollutants. However, the biogeochemical cycle of H₂ and its impacts on atmospheric chemistry and climate present critical knowledge gaps. While hydrogen itself is not a greenhouse gas, its chemical oxidation can influence methane, ozone, and stratospheric water vapor, with potential effects on the Earth’s radiative balance.

This session aims at advancing our understanding of the hydrogen budget and the implications of increased hydrogen use. Topics of interest include:

- Quantifying hydrogen emissions from direct sources, including leakages, venting, and incomplete combustion, as well as oxidation by volatile organic compounds.
- Investigating the removal of hydrogen by soil bacteria and by reaction with OH in the atmosphere.
- Assessing the indirect climate effects of hydrogen emissions on methane, ozone, stratospheric water vapor and aerosols/clouds.
- Utilizing observations and modeling to refine estimates of hydrogen sources and sinks across various spatial and temporal scales.
- Exploring scenarios of future hydrogen economies, including their potential to reduce fossil fuel emissions and the associated environmental and climatic co-benefits.

We welcome studies that employ experimental, observational, and theoretical approaches to hydrogen biogeochemistry and atmospheric processes, contributing to a more comprehensive understanding of H₂ in the context of the global energy transition.

Atmospheric radicals (OH, HO₂, RO₂, NO₃ and halogen oxides) control the oxidation of trace gases, drive the formation of secondary pollutants such as ozone, and influence climate. Their sources, such as HCHO, HONO and ClNO₂, and their chemical fate determine how primary pollutants are transformed, so understanding them is central to addressing regional air pollution and climate change. Yet radicals remain among the hardest species to study. Their concentrations are extremely low, they react very quickly, and the reaction networks they initiate are highly complex.
This session brings together experimentalists and modellers working on radical chemistry across laboratory, chamber and field settings. We invite contributions on the development of techniques for detecting radicals, their precursors and intermediate species, and on the adaptation of instruments to ground-based, mobile, shipborne, airborne and other platforms. We also welcome work on quality assurance, including calibration procedures and inter-comparison of different techniques, as well as on model development, from new reactions and mechanisms to revised model configurations and uncertainty analysis. Finally, we encourage studies that apply radical measurements and modelling in field and chamber experiments.

Short-lived greenhouse gases (GHG), including methane (CH4) and ozone (O3), are important contributors to global warming. Unlike long-lived GHGs, their tropospheric abundance is strongly influenced by reactive processes and depends on complex photochemical formation and removal pathways and the availability of precursors and reactants. Additionally, CH4 and O3 are photochemically coupled in the troposphere: the hydroxyl (OH) radical, whose production is largely driven by sunlight, is the dominant atmospheric sink of CH4, while CH4 oxidation contributes to O3 formation and impacts atmospheric oxidation capacity.

This session focuses on the photochemical sources, sinks and mechanisms governing short-lived GHGs in the troposphere, with a particular emphasis on CH4 and O3, their precursors and key reactants. Relevant topics include, but are not limited to: the chemistry of CH4 and O3 and their interactions with nitrogen oxides (NOx), volatile organic compounds (VOC), hydroxyl (OH) and peroxy (HO2, RO2) radicals, carbon monoxide (CO), and halogen species (Cl, Br, I).

The goal of this session is to discuss how photochemical pathways affect the abundance and spatio-temporal distribution of short-lived GHGs, associated uncertainties and challenges, and their future climate implications. We welcome contributions from observational, laboratory, and modeling studies across the entire troposphere, including innovative measurement technologies, advances in global modeling and other novel methodological approaches. Submissions may address photochemical mechanisms and reaction pathways, tropospheric oxidation capacity, ozone formation sensitivity, methane lifetime, regional and global long-term trends, precursor emission changes, and the broader impacts of short-lived GHGs on climate and human health, both now and in the future.

Reactive halogen species can have an important influence on the chemistry of the troposphere. For instance, chlorine atoms react faster with most hydrocarbons than OH does and inorganic bromine and iodine can catalytically destroy tropospheric ozone and oxidise mercury. These reactions have been shown to be important in in environments as different as the polar troposphere during the springtime ozone depletion events, the boundary layer over salt lakes, and volcanic plumes. There is strong evidence that halogens play a spatially even wider role in the marine boundary layer and free troposphere for ozone destruction, changes in the ratios of OH/HO2 and NO/NO2, destruction of methane, in the oxidation of mercury and in the formation of secondary aerosol. There are indications that both, oceanic sources as well as the chemistry of halogens and volatile organic compounds (VOCs) and oxygenated VOCs (OVOCs) in the tropics are linked with potential implications not only for the photochemistry but also the formation of secondary organic aerosol (SOA). More recently, marine emissions of active halogens have been linked to potential impacts on oxidants loading in coastal cities. Finally, bromine and iodine are also being proposed as proxies of past sea ice variability.

We invite contributions in the following areas dealing with tropospheric halogens on local, regional, and global scales:

- Model studies: Investigations of the chemical mechanisms leading to release, transformation and removal of reactive halogen species in the troposphere. Studies of consequences of the presence of reactive halogen species in the troposphere.

- Laboratory studies: Determination of gas- and aqueous-phase rate constants, study of complex reaction systems involving halogens, Henry's law and uptake coefficients, UV/VIS spectra, and other properties of reactive halogen species.

- Field experiments and satellite studies: Measurements of inorganic (X, XO, HOX, XONO2, ..., X = Cl, Br, I) and organic (CH3Br, CHBr3, CH3I, RX, ...) reactive halogen species and their fluxes in the troposphere with in situ and remote sensing techniques.

- Measurements and model studies of the abundance of (reactive) halogen species in volcanic plumes and transformation processes and mechanisms.

- All aspects of tropical tropospheric halogens and links to (O)VOCs: their chemistry, sources and sinks, and their impact on local, regional, and global scales.

The composition of the upper troposphere and lower stratosphere (UTLS) plays a key role in the climate system, influencing radiation, atmospheric chemistry, and dynamics. Recent advances in observations and modeling are providing new insights into the processes governing the variability and long-term evolution of this region. The session focuses on topics related to UTLS dynamics, transport, and chemical processes that determine atmospheric composition and its implications for radiation and climate across a range of spatial and temporal scales.
We encourage contributions based on in situ and remote-sensing observations from recent and ongoing field campaigns as well as on models of varying complexity, ranging from comprehensive chemistry–climate models to idealized and conceptual approaches. Especially, we welcome studies connecting observational datasets with process-level understanding or model evaluation.

Understanding the complex physical and chemical transformations of biogenic and anthropogenic emissions is essential for predicting their effects on air quality and climate. This session focuses on investigations ranging from fundamental molecular-level mechanisms to complete chemical mechanisms that drive and explain these large-scale atmospheric processes.

We welcome contributions that bridge the gap between microscopic processes and macroscopic atmospheric phenomena, spanning gas-phase kinetics, multiphase chemistry, and the nanophysics that drives aerosol forcing. Topics of interest include: 1) computational and theoretical advances, including quantum chemistry, molecular dynamics simulations, and machine learning; 2) experimental insights from laboratory, atmospheric simulation chamber, and field studies; and 3) the application of novel experimental techniques and the use of research infrastructures (such as ACTRIS, ICOS, and IAGOS) for detailed process studies.

Aerosol particles are key components of the Earth system, with important roles in atmospheric chemistry, climate and radiative balance, air quality, human health, and other areas of societal concern. Understanding their formation, evolution, properties, and impacts requires contributions from multiple disciplines, including laboratory experiments, field observations, remote sensing, theory, and numerical modelling. This session welcomes contributions across the full breadth of aerosol chemistry and physics, and submissions do not need to address the focus topic described below.

Alongside this broad scope, this year we particularly welcome studies highlighting emerging technologies for aerosol research, spanning both physical and digital approaches. These may include advances in aerosol sensing and measurement, instrumentation and ionisation techniques, new experimental platforms, high-resolution and computationally efficient modelling, cloud computing, digital twins, and AI. Such technologies are creating new opportunities across scales, from individual particles and molecular processes to regional and global aerosol systems, and from fundamental process understanding to prediction and impact assessment.

We welcome submissions across a broad range of aerosol topics, including but not limited to:
- Fundamental aerosol chemistry and physics, including particle formation, composition, mixing state, phase state, morphology, and physicochemical properties
- Primary and secondary aerosol formation and evolution, including new particle formation, gas-to-particle conversion, photochemistry, aqueous and heterogeneous chemistry, and aerosol ageing
- Aerosol observations from laboratory experiments, field campaigns, long-term monitoring, and remote sensing
- Aerosol interactions with radiation, clouds, climate, air quality, ecosystems, and human health
- Development and application of aerosol measurement, sensing, instrumentation, ionisation, and experimental technologies
- Development of process-based, data-driven, and hybrid process–machine-learning aerosol models
- Advances in numerical methods, model resolution, computational efficiency, and multiscale aerosol modelling
- Applications of AI and emerging digital approaches, including machine learning, generative AI, foundation models, cloud computing, and digital twins
- Open science approaches, including benchmark datasets, reproducible workflows, model and data sharing, and evaluation standards

Organic compounds play a key role in biosphere-atmosphere exchange, anthropogenic emissions, and the reactive chemistry responsible for ozone and particulate matter production. Coming from diverse sources and constituting thousands of individual compounds, with varying oxidation mechanisms, the organic composition of the troposphere is complex. With their wide range of lifetimes and volatilities, these species partition between gas and particle phases and make up a substantial fraction of fine particulate matter. Organics are also a major source of atmospheric reactivity, with implications for the oxidative capacity of the atmosphere. Some individual organic compounds are of interest due to their toxicity or use as specific source tracers. Because of organics’ role in secondary pollutant formation and reactivity, this chemistry is highly relevant to air quality from urban to remote regions. Finally, while global budgets of organic species are central to understanding tropospheric oxidative chemistry and aerosol budgets, they remain poorly constrained.

This session invites contributions about tropospheric organics on local, regional and global scales, from theoretical studies, laboratory experiments, field measurements, modeling studies, satellite studies, and including measurement technique development. The emphasis of this session is on gas-phase organics, including aerosol precursors and semi-volatile species.

Organic aerosols (OA) are a significant fraction of atmospheric particulate matter (PM) in different environments from urban landscapes to pristine regions, and from the boundary layer to the upper troposphere. Due to their complex chemical composition, OA remains one of the least understood parts of PM, with effects on Earth's climate and human health that are still inadequately characterized. Ongoing efforts enhance our understanding of the origin and (trans)formation processes of OA. This encompasses studying natural sources and assessing how anthropogenic emissions change the chemical composition and physical properties of organic aerosols.
This session welcomes submissions on ambient observations, chamber and modelling studies of OA, which contribute to a deeper understanding of their origins (such as secondary OA formation or biomass burning), analysis of the molecular composition (e.g. targeted analysis of organic pollutants), investigation of physico-chemical properties, exploration of atmospheric transformation reactions (for example aging or brown carbon formation), and examination of gas-to-particle partitioning of organic molecules.

This session focuses on volatile organic compounds (VOCs) at the biosphere-atmosphere interface, encompassing innovative analytical methods, laboratory and field studies, and emission modeling approaches. We invite contributions on plant and other biogenic VOC emission sources (e.g., from soil, litter, and freshwater) under environmental changes, methodological advances in sampling and analysis techniques, and modeling frameworks that bridge experimental observations with atmospheric processes.

Bioaerosols influence public health, climate dynamics and economy. Up to 30% of the global population now suffers from pollen allergies and asthma, a burden that has steadily intensified over recent decades and costs an estimated €50–150 billion annually to the public health system. Beyond human health, pollen and fungal spores also impair agricultural and forestry productivity, reducing crop yields and forest vitality. These challenges are further compounded by climate change, as rising temperatures and elevated CO₂ levels alter the life cycles of fungi and plants, leading to intensified pollen and spore emissions.

Accurate quantification of bioaerosols in different regions around the globe with markedly different emission patterns is therefore critical across a broad spectrum of research domains, including cloud-aerosol interactions and climate feedback, or public health responses to allergenic species. In response to these demands, bioaerosol monitoring is undergoing a paradigm shift that builds upon recent advances in novel automatic bioaerosol measurement techniques, including in situ monitoring, DNA-based and omics techniques and remote sensing, gradually replacing traditional manual methods. Nevertheless, significant technical challenges remain. Real-time methods with high temporal resolution often suffer from limited discriminatory power, whereas offline techniques provide rich taxonomic and physiological details but lack temporal resolution and struggle to yield absolute quantitative atmospheric concentrations. Furthermore, the emergence of this high throughput and taxonomically resolved techniques paves the way to advancing current modelling and forecasting capabilities for aeroallergens and raises further challenges in producing climatological time series that allow for assessing long term trends in bioaerosol concentrations and quantifying their response to climate change.

This session seeks to bridge these gaps by uniting interdisciplinary expertise in bioaerosol research. We invite contributions on novel detection methods and laboratory and field validation, indoor and outdoor ambient observations,  chemical composition detection, model development and forecasting, as well as exposure assessment and associated health impacts, including impacts of bioaerosol-air pollution interactions on all the above. Our goal is to foster cross-cutting dialogue that advances both fundamental understanding and practical monitoring capabilities.

Atmospheric aerosols influence cloud formation and Earth's radiative balance by acting as cloud condensation nuclei (CCN) and ice nucleating particles (INPs). Among the diverse aerosol population, biological and biogenetic particles, including bacteria, fungal spores, pollen, plant debris, marine organic matter, and their molecular fragments, have emerged as disproportionately efficient nuclei, capable of initiating ice formation at temperatures far warmer than mineral dust or other inorganic aerosols. Despite growing recognition of their importance, the sources, atmospheric transport, transformation, and ice- or cloud-nucleating mechanisms of these particles remain incompletely understood, limiting their representation in cloud microphysics and climate models.

This session invites contributions that advance understanding of the origin, composition, and atmospheric processing of biological and biogenetic aerosols and their role as CCN and INPs. We welcome studies spanning laboratory investigations of nucleation mechanisms, field observations and sampling campaigns, novel detection and characterization techniques, and modeling efforts that quantify the climatic relevance of bio-aerosols across scales. Contributions addressing the coupling between terrestrial and marine ecosystems and atmospheric ice/cloud nucleation, as well as the impact of a changing climate and land use on bio-aerosol emissions, are particularly encouraged. By bringing together atmospheric scientists, microbiologists, and modelers, this session aims to foster interdisciplinary dialogue.

In recent years, microplastics and nanoplastics have become recognised as ubiquitous atmospheric pollutants. However, many open questions remain regarding emissions, transport and deposition of microplastics and nanoplastics, along with atmospheric processes that determined their fate. In this session we welcome contributions from observational, laboratory and modelling studies that advance the field of airborne microplastics and nanoplastics research, including:

- Sampling and analysis of airborne micro- and nanoplastics
- Atmospheric microplastics and nanoplastics and their interactions with different environmental compartments (oceans, land and the cryosphere)
- Contributions of soils, roads and other terrestrial sources to the atmospheric micro- and nanoplastic burden
- Ocean-atmosphere exchange of microplastics and nanoplastics
- Microplastics and nanoplastics in the cryosphere
- Interactions between micro- and nanoplastics and other sources of aerosol
- Interactions between microplastics, nanoplastics, radiation and clouds
- Airborne microplastics as vectors for chemical and pathogen transport
- Indoor, outdoor, urban, rural and remote microplastics and nanoplastics (measurements, observations, modelling)
- Toxicological and exposure studies related to airborne micro- and nanoplastics
- Degradation of macro-, micro- and nanoplastics in real and simulated atmospheric conditions
- Airborne sources and sinks of micro- and nanoplastics (measurements and modelling)

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

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

(1) measurements and theoretical concepts of all aspects of the dust cycle (emission, transport, deposition, size distribution, particle characteristics),
(2) numerical simulations (physical or machine learning) of dust on global, regional, and local scales
(3) meteorological conditions for dust storms,
(4) interactions of dust with clouds and radiation,
(5) influence of dust on atmospheric chemistry,
(6) fertilization of ecosystems through dust deposition,
(7) interactions with the biosphere, cryosphere, and hydrosphere,
(8) any study using dust as a (paleo-)climate indicator, including sediment archives in loess, ice cores, lake sediments, ocean sediments and dunes,
(9) impacts of dust on climate and climate change, and associated feedbacks and uncertainties,
(10) implications of dust for health, transport, energy systems, agriculture, infrastructure, etc.

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

Globally, wildfires are intensifying and becoming more frequent, significantly altering the atmospheric composition due to the emitted smoke aerosol and fire-related gaseous emissions. Smoke particles interact with radiation and cloud microphysics, as well as affecting on the biosphere and human health. Extreme wildfires that trigger the formation of pyrocumulonimbus clouds can inject smoke aerosols into the stratosphere, where they can have long-lasting climate effects similar to those of volcanic eruptions. Increasingly frequent climate extremes and wildfires, such as those experienced in the summer of 2026, and shifting patterns of wildfires change atmospheric smoke distributions. Thus, it is crucial to understand the interconnected processes from smoke formation to its impacts.
The aim of this interdivisional session is therefore to bring together scientists from different disciplines to explore the impact of wildfire smoke and co-emitted aerosol particles, such as bioaerosols and dust, on atmospheric processes, air quality, and biosphere dynamics. Possible contributions include, but are not limited to:
• Characterization of the emission, transport, and deposition processes of smoke and its interaction with other aerosol particles
• Interactions of smoke and related aerosol particles with atmosphere, radiation, clouds, and climate including large-scale effects
• Relation between vegetation, burning conditions, and smoke properties
• Chemical composition and atmospheric ageing of smoke particles
• Modelling, remote sensing, and field and laboratory studies of all processes from smoke formation to its atmospheric impacts
• Impacts of wildfire emissions on terrestrial and aquatic ecosystems
• Health effects of smoke
By integrating research from atmospheric science, chemical analysis, combustion physics, vegetation ecology, and other disciplines, we aim to clarify the complex relationships between wildfires, smoke formation, transport, and ageing, and their effects on the environment.

Aerosols significantly influence Earth’s climate, atmospheric chemistry, and air quality through their direct interactions with radiation and their effects on cloud formation, properties, lifetime, and precipitation. Aerosol-cloud interactions are particularly important in marine, high-altitude, remote, and extreme environments, including the Himalayas, and the Polar regions, where unique atmospheric conditions provide valuable insights into global climate and atmospheric processes. Understanding how aerosols form, grow, and transform; act as cloud condensation nuclei (CCN) and ice-nucleating particles (INPs); interact with clouds and radiation; and are ultimately removed from the atmosphere is essential for improving climate models and reducing uncertainties in climate predictions.
Despite their importance, aerosol properties, sources, and transformation processes in these environments remain poorly constrained. Observational data are limited because of logistical challenges, harsh conditions, and restricted accessibility. This session aims to address these knowledge gaps by bringing together observational and modelling studies from marine environments, high-altitude regions, remote continental environments including Amazon Tall Tower Observatory, and extreme environments: including the Himalayas, the Arctic, and Antarctica.
We invite contributions addressing:
• Light-absorbing aerosols, including black carbon (BC), brown carbon (BrC), and mineral dust;
• Aerosol-cloud interactions and the properties of CCN and INPs;
• Microphysical properties and mixing state;
• New particle formation, growth processes, and formation pathways;
• Source apportionment of aerosols;
• Results from recent field campaigns involving ground-based, shipborne, aircraft, and measurements from autonomous platforms;
• Emerging observational techniques and research infrastructure, including uncrewed aerial vehicles (UAVs), balloon-borne sensors, and automated ground-based systems that enable high-resolution measurements under extreme conditions;
• The physical and chemical properties of aerosols and their precursors, including the roles of different chemical species in regional and global atmospheric processes; and
• Interactions at air-sea and air-snow interfaces and their relationships with meteorological and chemical processes.

Marine aerosols are a major source of uncertainty in climate forcing estimates because marine clouds are highly sensitive to the cloud condensation nuclei (CCN) and ice nucleating particles (INP) provided by these aerosols. Over the remote oceans, CCN and INP populations are shaped by primary sea spray and secondary formation, and are increasingly influenced by transported dust, smoke, and anthropogenic pollution. Marine aerosols are also vertically coupled, but this coupling remains poorly quantified: the free troposphere delivers aerosols down into the marine boundary layer, while the marine boundary layer feeds particles and precursors back aloft. Constraining the resulting climate impacts requires ambient observations, laboratory experiments and modelling across scales to be brought together.

This session brings together work on the sources, formation processes and impacts of marine aerosols across the full depth of the marine atmosphere - from the boundary layer to the free troposphere - and across primary, secondary and transported origins. We welcome observations from diverse platforms, alongside laboratory experiments and modelling from regional to global scales. Studies that connect emission and formation processes to cloud and climate response are particularly encouraged.

Topics of interest include, but are not limited to:
• Primary marine aerosol: production mechanisms, fluxes, size distribution, composition, CCN/INP activity
• Secondary marine aerosol: iodine, sulfur, ammonia, amine and organic emissions and mechanisms, their chemical transformation and new particle formation
• Transported aerosols: dust, smoke, anthropogenic and volcanic plumes over the ocean, their fates and impacts
• Vertical coupling: entrainment, convective transport and detrainment, aerosol and precursor exchange between the marine boundary layer and free troposphere
• Marine aerosol-cloud interactions: CCN/INP control of clouds; cloud processing and wet removal of aerosols; radiative and precipitation responses
• Perturbations of the marine background: shipping emissions and fuel-sulfur regulation, and proposed interventions such as marine cloud brightening
• Climate response and model evaluation: marine aerosol radiative effects and feedbacks, the pre-industrial baseline and future projections, and representation of marine aerosol in Earth system models

Clouds and aerosols play a key role in climate and weather-related processes over a wide range of spatial and temporal scales. An initial forcing due to changes in the aerosol concentration and composition may also be enhanced or dampened by feedback processes such as modified cloud dynamics, surface exchange or atmospheric circulation patterns. This session aims to link research activities in observations and modelling of radiative, dynamical and microphysical processes of clouds, aerosols, and their interactions. Studies addressing several aspects of the aerosol-cloud-radiation-precipitation system are encouraged. Contributions related to the EU projects CERTAINTY (Cloud-aERosol inTeractions & their impActs IN The earth sYstem) and CleanCloud (Clouds and climate transitioning to post-fossil aerosol regime) are also invited.

Topics covered in this session include, but are not limited to:
- Cloud and aerosol macro- and microphysical properties, precipitation formation mechanisms and their role in the
energy budget
- New constraints on aerosol, clouds or precipitation from EarthCARE and PACE
- Impacts of changes in aerosol composition and sources on clouds, radiation and precipitation
- Observational constraints on aerosol-cloud interactions
- Use of observational simulators to constrain aerosols, clouds and their radiative effects in models
- Explorations of cloud seeding or artificial cloud brightening techniques
- Experimental cloud and aerosol studies
- High-resolution modelling, including large-eddy simulation and cloud-resolving models
- Parameterization of cloud and aerosol microphysics/dynamics/radiation processes

Black carbon, brown carbon, and mineral dust—collectively known as light-absorbing aerosols (LAA)—are key drivers of climate. Their influence spans the atmosphere, land (e.g., snow and ice albedo), and water (e.g., oceans and lakes) systems. Major uncertainties remain around their emissions, physical-chemical properties, and mixing states. The estimates of LAA effective radiative forcing in earth system models differ by over two-fold globally. To address this, we need enhanced observations and improved model representations of LAA spatial distribution, microphysical features, and lifecycle evolution. Such progress would reconcile conflicting data and ensure accurate aerosol treatment in climate models.
We welcome contributions covering: (a) LAA characterization, transformations, and removal processes; (b)multi-platform observational analysis, field campaign experiments on LAA; (c) relationships among LAA chemical, microphysical, and optical attributes; (d) numerical modelling of LAA across scales; (e) evaluation of LAA radiative forcing and climatic effects; and (f) AI and data-driven techniques for studying LAA-relevant processes.

Light Absorbing Carbonaceous aerosols (LACs), including black carbon and brown carbon, are major components of atmospheric particulate matter and play a critical role in Earth’s radiative balance, air quality, climate and health burden. Their complex sources, chemical transformation, and transport create substantial challenges for accurately quantifying their environmental impacts and developing effective mitigation strategies.
This session aims to bring together observational, modeling and interdisciplinary research on carbonaceous aerosols from local to global scales. We welcome studies investigating emission sources and drivers, including anthropogenic (fossil fuel combustion, biomass burning, industrial emissions fossil-fuel combustion, residential combustion, transportation and agricultural burning) and natural processes (wildfires, biogenic volatile organic compound oxidation). Contributions addressing source apportionment, emission inventories, chemical and physical characterization, optical properties, atmospheric lifetimes, ageing and transformation are particularly encouraged.
The session will also highlight advances in ground-based, airborne and satellite remote sensing, as well as innovative measurement techniques for characterizing LAC abundance, composition and optical properties. We also invite studies that integrate observations with atmospheric models, data assimilation, machine learning and multi-platform datasets to improve understanding of these carbonaceous aerosol variability and trends.
The central focus is on the carbonaceous aerosols, climate and health nexus. Contributions addressing radiative effects, aerosol–cloud interactions, health risks (exposure assessment, epidemiological linkages, and attributable disease burden), environmental inequalities and climate-air quality interactions are encouraged. We also welcome research examining emission-control strategies and the translation of atmospheric science into evidence based air quality and climate policy, while integrating Clean-air regulatory frameworks, targeted reduction strategies (e.g., short-lived climate pollutants), and climate-air quality co-benefit evaluations.
We aim to connect aerosol sources and processes with observations, impacts and policy applications, the session seeks to foster collaboration among researchers of all career stages across atmospheric chemistry, aerosol science, remote sensing, climate science, health research and policy communities.

Anthropogenic and natural aerosols play key roles in driving climate change over a range of spatial and temporal scales, both close to emission sources and also remotely through teleconnections. Aerosols can directly interact with radiation by scattering and absorption and indirectly through modulating cloud properties, and thereby modify the surface and atmospheric energy balance, cloud dynamics and precipitation patterns, and the atmospheric and oceanic circulation. Changes in regional aerosol emissions accelerate greenhouse gas-driven climate changes in some regions, counteract them in others, and may interact with natural variability to further stress human and ecological systems. However, our understanding of these impacts still lags those due to greenhouse gases. The poor aerosol integration in many climate risk and impact studies currently leads to potentially dangerous omissions in projections of near-term climate change impacts.

This session addresses: the strong and spatially complex trends in temperature, hydroclimate, air quality, and extreme events driven by aerosol changes over the historical era, and those expected in the near future; the interplay between aerosol-driven changes and those induced by other forcing factors; and their extensions to climate risk and impact studies. We encourage contributions based on model and observation-based approaches to investigate the effects of aerosols on regional decadal climate variability and extremes, tropical-extratropical interactions and teleconnections, and the interactions with modes of variability such as the NAO, ENSO, AMV, and PDO. This year we especially welcome studies focusing on the climate effects of African air pollution, notably how absorbing aerosols influence Sub-Saharan precipitation, and any analyses using the RAMIP dataset. We also welcome focused studies on aerosol influences on monsoon systems, midlatitude and Arctic responses, extreme temperature and precipitation, atmospheric and oceanic circulation changes, tropical cyclones, and daily variability, using for example CMIP6 and CMIP7 projections, large ensemble simulations, or specifically designed experiments. We also encourage studies focusing on climate risk and concrete regional impacts on nature and society resulting from changes in anthropogenic and natural aerosol emissions.

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.

Volcanic aerosol clouds from major tropical eruptions cause periods of strong surface cooling in the historical climate record and are dominant influences within decadal surface temperature trends. Advancing our understanding of the influence of volcanoes on climate relies upon better knowledge of:

(i) the radiative forcings of past eruptions and the microphysical, chemical and dynamical processes which affect the evolution of stratospheric aerosol properties and

(ii) the response mechanisms governing post-eruption climate variability and their dependency on the climate state at the time of the eruption.

This can only be achieved by combining information from satellite and in-situ observations of recent eruptions, stratospheric aerosol and climate modelling activities, and reconstructions of past volcanic histories and post-eruption climate state from proxies.
In recent years the smoke from intense wildfires in North America and Australia has also been an important component of the stratospheric aerosol layer, the presence of organic aerosol and meteoric particles in background conditions now also firmly established.

This session seeks presentations from research aimed at better understanding the stratospheric aerosol layer, its volcanic perturbations and the associated impacts on climate through the post-industrial period (1750-present) and also those further back in the historical record.

Observational and model studies on the stratosphere and climate impacts from the 2022 eruption of Hunga Tonga are also especially welcomed.

We also welcome contributions to understand the societal impacts of volcanic eruptions and the human responses to them. Contributions addressing volcanic influences on atmospheric composition, such as changes in stratospheric water vapour, ozone and other trace gases are also encouraged.

The session aims to bring together research contributing to several current international co-ordinated activities: SPARC-SSiRC, CMIP7-VolMIP, CMIP7-PMIP, and PAGES-VICS.

The atmosphere integrates surface–atmosphere exchange across spatial and temporal scales. Monitoring and interpreting atmospheric greenhouse gases (GHGs), such as carbon dioxide (CO2) and methane (CH4), therefore provide fundamental information on carbon fluxes from natural and anthropogenic sources and their links to energy and water cycling. Combining observations with process-based and data-driven modelling can constrain poorly understood processes, particularly in the terrestrial biosphere, and reveal the mechanisms and climatic drivers governing GHG exchange and carbon–climate feedbacks. Such process-level understanding is essential for predicting how these feedbacks will evolve in a changing climate. Machine-learning (ML) approaches provide additional opportunities to extract information from increasingly diverse and extensive datasets.

This session focuses on top-down, bottom-up, and hybrid modelling frameworks that investigate GHG exchange using observational platforms such as surface and ecosystem networks (e.g. ICOS Atmosphere and Ecosystem, FLUXNET, NOAA), aircraft campaigns (e.g. MAGIC and COMET), and active and passive remote-sensing missions (e.g. ECOSTRESS, OCO-2/3, TROPOMI, and GOSAT). We particularly welcome studies that combine data sources not traditionally used together to constrain GHG fluxes and the underlying processes. Contributions may explore how the integration of diverse observations with process-based models, machine learning, or novel data-assimilation approaches can identify climatic controls, improve process representation, and advance understanding of carbon–climate feedbacks.

Monitoring carbon and greenhouse gas exchange between atmosphere, land, ocean and human systems underpins the Global Stocktake, national inventories, carbon markets and carbon dioxide removal (CDR). Yet bottom-up (inventories, process-based and data-driven models), top-down (atmospheric inversions) and bookkeeping (land-use change) estimates remain uncertain and often disagree, while the credibility of measurement, reporting and verification (MRV) rests on independent methods agreeing. Machine learning (ML) offers new ways to enhance, complement, emulate, integrate or even replace established observational, modelling and analytical workflows.

This session invites contributions using ML to advance carbon cycle monitoring and understanding across all compartments (atmosphere, vegetation, soils, permafrost, wetlands, inland waters, coastal and open ocean, anthropogenic sources) and all relevant trace gases, including CO2, CH4, N2O, CO, COS and isotopes. We welcome the full breadth of ML: foundation models, transformers, GNNs, CNNs, generative models (diffusion, flow matching), hybrid and differentiable modelling, Bayesian ML, symbolic regression, interpretable, robust and trustworthy ML, and tabular methods such as gradient-boosted trees. We particularly encourage work bridging science, policy and industry.
Topics include, but are not limited to:

- ML-enhanced bottom-up estimates: upscaling, mapping stocks and fluxes, inventories
- ML for top-down inversions: transport emulators, data assimilation, retrievals, point-source detection
- ML for bookkeeping and land-use change emissions
- Emulators and foundation models for process-based and Earth system models
- Hybrid modelling, and generative models for gap-filling, downscaling and ensemble generation
- Fusion of remote sensing with in-situ data (eddy covariance, flask networks, ocean pCO2)
- ML for carbon cycle process understanding and discovery: drivers, feedbacks and emergent constraints, with explainability, causality and uncertainty quantification
- Reconciling lines of evidence across methods, compartments and gases
- Monitoring for policy and industry: MRV, national inventories, carbon markets, CDR verification
- ML-based decision support and policy pathways (reinforcement learning, integrated assessment models)
- Benchmarks, datasets & open tools

Reliable greenhouse gas (GHG) observations (e.g. CO2, CH4, N2O) and complementary chemical and isotopic tracer measurements (e.g. δ13C(CO2), δ18O(CO2), δ13C(CH4), δ2H(CH4), as well as radioactive tracers e.g. radon, 14CH4, 14CO2) are critical for tracking emissions, detecting changes in atmospheric composition, understanding sources and sinks, and informing effective climate policy. As GHG mitigation strategies and reduction targets increasingly rely on precise, long-term data, it is essential to ensure that measurements are accurate and comparable across time and space. Metrology, the science of measurement, plays a vital role in this process by providing standardised calibration methods, uncertainty assessment, and traceability to international reference standards and scales. While metrology has traditionally been applied in controlled laboratory settings, its principles are increasingly critical to real world, in situ atmospheric observations, where long-term consistency, cross-network comparability, and data integrity are key. Additionally, emerging measurement platforms including UAVs, mobile laboratories, and low-cost sensor networks present new calibration challenges that require innovative metrological approaches. Strengthening the connection between metrology and in situ atmospheric observations is key to building globally consistent datasets that support climate research, emissions verification, and international reporting.
This session aims to foster cross-disciplinary dialogue and highlight advances that improve the comparability of GHG and tracer measurements across spatial and temporal scales. We invite contributions that explore novel or improved calibration strategies, measurement and analytical strategies, uncertainty quantification, interlaboratory comparisons, and traceability chains for GHGs, chemical, stable isotope, and radioactive tracers. Of particular interest are studies that address harmonisation between measurement networks, improvements in temporal and spatial resolution, and integration with satellite data or modelling frameworks.

The urgency, complexity, and economic implications of greenhouse gas (GHG) emission reductions demand strategic investments in science-based information for planning, implementing, and tracking the impact of emission reduction policies and actions. An increasing number of applications succeed by combining activity-based emissions data with atmospheric GHG measurements and analyses – this hybrid approach can yield additional insights and practical information on GHG emissions and removals at different scales. Inspired by this potential, the Integrated Global Greenhouse Gas Information System (IG3IS) of the World Meteorological Organization works to identify and document good practice guidelines for creation and utilisation of decision-relevant information, while promoting scientific advances in methodologies and facilitating two-way linkages between practitioners and stakeholders.
This session continues to showcase how scientific data and analyses can be created, characterized and transformed into actionable information services for a wide range of user-communities.
This session seeks contributions from researchers, inventory compilers, government decision and policy makers, non-government and private sector service providers that produce or utilize science-based methods for detecting, quantifying, tracking GHG emissions using a combination of observations, activity-based emissions data and modelling and analysis tools. We especially welcome presentations of work guided by IG3IS good practice research guidelines at urban and national scale and for specific economic sectors. The scope of the session spans measurements of all GHGs and from all tiers of observation.

Methane is an important greenhouse gas that has contributed to ∼25% of the increase in radiative forcing experienced to date. Despite methane’s short atmospheric lifetime (~10 years), the global average methane mole fraction has increased three times faster than carbon dioxide since 1750. Rapid and severe reductions in methane emissions are required to lower peak warming, reduce the likelihood of overshooting warming limits and reduce reliance on net negative carbon dioxide emissions. In order to track mitigation efforts and ensure emission quantification required in legislation can be met, we must be able to accurately attribute and quantify emissions and are actively doing so through activities such as the UNEP International Methane Emissions Observatory (IMEO).

This session will highlight measurement studies at all scales and from ground-based to satellites, that focus on quantification and source attribution of methane emissions from human activities. We especially encourage submissions from both IMEO and non-IMEO funded work that focus on the following topics: (1) new technologies / methods to provide accurate and repeatable emissions measurements, (2) demonstration of affordable and reliable quantification methods for mitigation tracking, (3) attribution of emissions to specific sources and, (4) methods for upscaling measurements into inventories and creating policy relevant datasets.

Methane is a potent short-lived climate forcer, and rapid mitigation measures are crucial for meeting climate targets and limiting currently expected temperature overshoots. This session will explore these methane mitigation measures, spanning emissions reduction approaches, novel point-source oxidation technologies, and potential atmospheric methane removal approaches.
We invite contributions covering:
- Assessment of methane emission reduction approaches
- Novel and emerging methane mitigation approaches, including emissions destruction and atmospheric removal
- Measurement and verification of mitigation effectiveness
- The role of methane mitigation in future climate scenarios

Non-exhaust emissions (tyre-wear particles, TWP; brake-wear particles, BWP; road dust) are a major urban pollution source as exhaust declines. These heterogeneous particles contain metals, minerals, carbonaceous material, polymers, organics and micro-/nanoplastics; their behavior cannot be understood from emission composition alone.
During transport, deposition and resuspension, they undergo multi-stressor ageing (precipitation, drying, UV, temperature, freeze–thaw, oxidation, interactions with salts, organic matter and particles). Ageing alters morphology, size, surface chemistry, mineralogy, magnetic properties, aggregation, mobility and reactivity, modifying signatures and environmental fate. TWP and BWP are chemically and mineralogically complex. Constituents transform, dissolve, oxidise, fragment, aggregate and interact with natural minerals, affecting transport, resuspension, deposition and transfer between compartments. The session focuses on post-emission evolution of non-exhaust particles, coupling composition, ageing, mineral transformations and mobility.
We welcome studies addressing:
• quantification of non-exhaust particles in the atmosphere;
• source apportionment via multivariate statistics and modelling, including signature changes;
• shifting traffic source signatures;
• health impacts and risk assessment of fresh vs aged particles, including bioavailability;
• generation and composition of BWP, TWP and road dust;
• inorganic constituents of BWP/TWP;
• environmental ageing under UV, oxidation, humidity, temperature, wetting–drying and freeze–thaw;
• changes in size, morphology, surface chemistry, aggregation and stability;
• mineral transformations, dissolution, oxidation and secondary mineral formation;
• mineral, elemental and magnetic signatures as tracers and their relevance through time;
• environmental magnetism identifying Fe-bearing components;
• interactions and heteroaggregation of TWP/BWP with minerals, metals, organics and micro-/nanoplastics;
• resuspension, transport and deposition of fresh/aged particles;
• transfer to soils, runoff, surface waters and sediments;
• changes in particle reactivity, mobility, persistence and bioavailability;
• lab experiments, field observations and modelling linking transformation with fate;
• advanced analytical approaches combining mineralogical, magnetic, spectroscopic, microscopic, thermal and geochemical methods.

The transport sector, comprising road traffic, shipping and aviation, is a significant contributor to global warming and has detrimental effects on air quality. The combustion of fossil fuels emits gases and particles that alter the chemical composition of the atmosphere. In addition to CO₂, non-CO₂ effects (NOx, contrails, aerosols, and aerosol-cloud interactions) are also important, some of which have warming and others cooling effects on the climate.
Driven by growing demand, the contribution of aviation to climate change is expected to increase, and emissions from road traffic and shipping may also rise depending on evolving mobility patterns and technological development. It is therefore crucial to develop and implement measures that reduce the climate footprint of transport. Mitigation options include alternative fuels such as electricity and hydrogen, technological improvements such as exhaust after-treatment systems, and operational measures such as optimized routing. A change in the share of demand between transport modes (modal shift) can complement these technical and operational measures.
Assessing the effects of such measures with numerical atmospheric models relies heavily on state-of-the-art emission inventories. Providing information on the uncertainties in emission data is essential for a reliable assessment of air quality and climate effects; these uncertainties further interact with those associated with the representation of physical, chemical, and dynamical processes in atmospheric models.
The objective of this session is to bring together the community that develops transport emission inventories with the community that uses them. The session has two aims: first, to establish a shared understanding of the requirements for, and uncertainties in, transport emission inventories; second, to review the latest research on the non-CO₂ and air-quality effects of transport emissions. The overarching goal is to reduce the uncertainties in the assessment of the climate and air-quality impacts of the transport sector. Contributions are welcome on topics ranging from measurement campaigns and modelling studies to strategies for reducing the environmental impact of transport.

Aircraft emit a mix of gases and particles in the Earth’s atmosphere, near the surface and at altitude, up to 12 km. Aviation emissions degrade air quality in the vicinity of airports and globally, and contribute to climate change through pathways that are specific to emissions released at altitude, including but not limited to NOx-induced ozone, methane and stratospheric water vapour changes, and aerosol–radiation and aerosol–cloud interactions. These impacts remain uncertain, while the sector continues to grow at ~5% every year. At the same time, the atmospheric implications of mitigation options, including Sustainable Aviation Fuel (SAF) and hydrogen, are also not well understood, despite their high policy relevance.

This specialised session synthesises measurement and modelling contributions towards better understanding the present and future impacts of aviation emissions on atmospheric composition, air quality and climate, alongside the substantial recent progress on contrail research. We invite contributions on:
- the characterisation and quantification of gaseous and particulate emissions from current and future fuels, engines and combustor technologies, including ultrafine particles (volatile and non-volatile), lubrication oil and emerging or unregulated pollutants, based on laboratory, engine test-cell, airport and in-flight measurements as well as emission models and inventories;
- the atmospheric fate of these emissions: plume processing, transport pathways, chemistry, aerosol microphysics and removal processes;
- their air quality effects near airports and at regional and global scales, and the associated exposure and health implications;
- their non-CO2 climate effects through ozone, methane, water vapour and aerosol–radiation and aerosol–cloud interactions, including feedbacks, metrics and uncertainty assessment;
- the atmospheric implications of mitigation options, including fuel composition, sustainable aviation fuels, hydrogen and electrified propulsion, engine technology and operational measures.

We hope that these contributions will bring the community together and spark fruitful discussions and collaborations towards an improved understanding of aviation’s growing impacts on the atmosphere.

Aircraft condensation trails (contrails) are currently estimated to contribute a similar effective radiative forcing as all CO2 emitted from the aviation sector thus far. They also constitute the ultimate short-lived forcer, with lifetimes estimated in hours and local radiative forcings on the order of several watts per meter squared. This has made them the subject of an ongoing explosion in interest from the scientific, aviation, and regulatory communities, including fierce debate regarding potential measurement and regulation of contrail formation alongside aircraft CO2 emissions.

Although there have been several recent in-situ measurement campaigns and significant recent advances in the detection of contrails using remote sensing data, understanding their overall climate effects - as well as the development of effective strategies to mitigate said impacts - requires accurate models of contrail formation and evolution. This is a substantial challenge, given that contrails form at the scale of an aircraft engine exhaust but can spread to be tens or hundreds of kilometers wide. Furthermore there remains relatively little observational data to directly constrain contrails due to the difficulty of directly sampling contrail ice and the high degree of temporal and spatial variability in cruise altitude conditions.

With these concerns in mind, this session complements ongoing efforts in contrail observation by aiming to bring together the growing community of contrail modellers who are seeking to advance our ability to represent and understand the full lifetime of a contrail and its long term impacts. This includes those who are working on: high-fidelity modelling of contrail formation in the exhaust plume; LES or RANS models of the exhaust plume and contrail; 0-, 1-, 2-, or 3-D models of long-term contrail behaviour; models of regional- and global-scale contrail evolution and climate responses, including potential cloud feedbacks; and studies using new datasets to evaluate the performance of existing contrail models. Particular attention will be given to research using open-source models and data.

Contrail cirrus are among the largest non-CO₂ climate impacts of aviation and have become a major focus of research and mitigation efforts. Recent advances in laboratory experiments, flight campaigns, remote sensing techniques, and operational demonstrations have substantially improved our understanding of contrail formation and evolution. At the same time, increasing interest in alternative aviation fuels, new engine technologies, and operational contrail avoidance has highlighted the need for observational evidence to quantify the effectiveness of mitigation strategies, support future implementation and monitoring, and assess their economic costs.
With these developments in mind, this session aims to bring together the growing community working on observations and investigation and evaluation of contrail mitigation strategies. The session complements ongoing efforts in contrail modelling by focusing on experimental and observational studies across a range of spatial scales.
Contributions may include laboratory, ground-based, and in-flight measurements of aerosols, contrails, and atmospheric background conditions. Ground-based cameras, active and passive remote sensing, including ground and airborne lidar, and satellite observations of contrails and cirrus are also of interest. Experimental studies of contrail mitigation using alternative aviation fuels, including SAF, and new engine technologies such as hydrogen combustion are particularly encouraged. Air traffic management, weather and climate model evaluations, and observational validation of operational contrail management and avoidance strategies are also relevant topics. The session also covers research assessing the environmental, economic, and life-cycle implications of contrail mitigation. Contributions combining observations with model evaluation or validation are also welcome.

Rocket launches and re-entries of reusable and discarded objects inject familiar and exotic anthropogenic gases and aerosols to all layers of the atmosphere. The space sector is the only anthropogenic source that releases these gases and aerosols directly to the middle and upper layers of the atmosphere. Once emitted to these layers, pollutants may persist for years or decades, leaving a long legacy of atmospheric pollution. These pollutants are increasingly ubiquitous due to recent exponential space sector growth, yet there are no regulatory controls to mitigate harm. Quantification of the complex risks to the atmosphere is hindered by many uncertainties and data gaps, such as the chemical composition of exhaust from novel propellants, the locations and trajectories of ablative re-entry, the radiative properties of pollutants, the material composition of objects ablating on re-entry, and the physical and chemical evolution of chemicals released during re-entry. Lack of openly-available modelling tools is compounded by a scarcity of real-world experiments and ambient observations, and expected massive growth of space launches and, consequently, re-entries. This session invites submissions across all geophysical and related disciplines in and beyond academia to share planned, ongoing, or recently completed research that provides new knowledge in this area, explores new open-source modelling techniques, or exposes methodological gaps that need to be resolved to inform sustainability initiatives and global regulation. We are also interested in innovative and interdisciplinary approaches adopted by researchers in other domains that could be applied to advance understanding of environmental harm from the space sector. These include related topics such as geoengineering, space weather, space engineering, upper atmosphere circulation and chemistry, meteoritics, stratospheric aerosols and space debris management.

Over the last years, more and more satellite data on tropospheric composition have become available and are now being used in numerous applications. In this session, we aim to bring together reports on new or improved data products and their validation, as well as studies using satellite data for applications in tropospheric chemistry, emission inversions and air quality. This includes both studies on trace gases and aerosols.

We welcome presentations based on studies analysing data from current and future satellite missions, including the geostationary GEMS, TEMPO and Sentinel-4 platforms and the recent S5, IRS, IASI-NG and 3MI instruments.
Topics also include the inter-comparisons of different remote sensing measurements dedicated to tropospheric chemistry sounding and/or analyses with ground-based measurements and chemical transport models.

Remote sensing of clouds and aerosols is of central importance when studying processes and changes in the climate system. The new space-borne generation of active sensors (e.g. EarthCare), passive multi-angular polarimeters (e.g. PACE/SPEX, PACE/HARP-2, 3MI, CO2M, MAP) together with single-viewing instruments (e.g. hyperspectral Sentinel 5P/5/4, OLCI and SLSTR on Sentinel 3) will take the characterisation of aerosols and clouds to a new level. This will significantly improve our understanding of physical and chemical processes in the atmosphere, particularly aerosol-cloud interactions, and climate and radiation studies. Nevertheless, there are still many challenges and unsolved problems in remote sensing algorithms and their applications.

The aim of this session is to discuss current developments, challenges and opportunities in the characterisation of aerosols and clouds, in the study of aerosol-cloud interactions and their long-term effects, using ground-based, airborne and spaceborne active and passive remote sensing systems. We invite submissions of theoretical, methodological and empirical studies to advance the field of aerosol/cloud remote sensing, and to improve our understanding of aerosol-cloud interactions and their effect on the climate.

Significant uncertainties remain in our understanding of Carbon Dioxide (CO2) and Methane (CH4) fluxes across land, ocean, and atmosphere at both regional and global scales. Remote sensing of CO2 and CH4 offers great potential to enhance our understanding of the natural carbon cycle and to monitor anthropogenic emissions. Recent advances in CO2 and CH4 remote sensing technologies, spanning spaceborne, airborne, and ground-based platforms, have delivered unprecedented accuracy and coverage. Furthermore, next-generation platforms such as CO2M, Merlin, TANGO, and CO2Image promise to further enhance observational capabilities. When integrated with ground-based observation networks and modeling tools, these space-based observations can significantly improve our understanding of the carbon cycle from local to global scales.
This session invites contributions on all aspects of CO2 and CH4 remote sensing, including current missions (e.g., GOSAT/2/GW, OCO-2/3, S5P/S5, IASI-NG, Carbon Mapper, MicroCarb, GHGSat), upcoming and planned missions (e.g., CO2M, Merlin, TANGO), as well as ground-based networks (e.g., TCCON and COCCON), airborne platforms, and other remote sensing instruments. We welcome contributions on advances in retrieval techniques, instrument concepts, and validation activities, with an emphasis on interpreting observations related to natural fluxes and anthropogenic emissions.

This session focuses on the application of spectroscopic remote sensing techniques for measuring atmospheric trace gases and related properties. We welcome studies on any method that exploits absorption along open atmospheric light paths using natural or artificial light sources. This encompasses the full range of DOAS applications (e.g. MAX-DOAS, zenith-sky, direct-sun, long-path DOAS), hyperspectral imaging techniques, direct-sun and open-path Fourier-transform spectroscopy (FTS), and related or emerging techniques.

Collectively, contributions to this session help to bridge the spatial and temporal scales between in-situ point measurements and satellite remote sensing, including:
– Greenhouse gas and air quality monitoring,
– Gas transport and quantification of emission fluxes,
– Retrieval of spatial distribution of trace gases and aerosols.
Moreover, the aim of this session is to gain insights into algorithm developments, instrument improvements, and practical field application, including instrument synergies, satellite validation, and cross-platform comparison.

This session is organized for the third time at EGU General Assemblies and reflects the recent emerging trends of atmospheric monitoring: (i) realizing multi-instrument synergy retrieval and (ii) bridging three branches of atmospheric research (modelling, in situ measurements, and remote sensing).

This session encourages discussions that explore the synergies of complimentary observations, such as synergies of passive imagery with active vertical profiling of the atmosphere, synergies of observations in different spectral ranges and at different time and/or spatial scales, and synergies of satellite observations with sub-orbital observations and chemical transport model simulations. Synergy is important because the quality of measurements cannot be radically improved once the instruments have been deployed, but algorithms can continuously evolve and notably improve results with data fusion and optimization of the joint sensitivity of multi-instrument datasets. The the session especially welcomes the ides and demonstrations of synergy methods and Interdisciplinary applications using novel observations from the , EPS-SG, MTG, PACE, Copernicus Sentinels, EarthCARE and other recent and forthcoming advanced satellite missions (e.g., CO2M) as well as field campaigns.

The session also invites presentations that demonstrate the benefits of collaboration amongst the three core fields of atmospheric aerosol studies outlined in the Models, In situ, and Remote sensing of Aerosols (MIRA) international working group, which was formed to facilitate collaborations and improve discussions amongst these fields of study and across regional boundaries. For this year, presentations relevant to coordinated campaigns, including the forthcoming NASA/ESA experiment CoSENSE, which bring together ground-based and airborne remote sensing and in situ measurements, satellite observations and atmospheric modelling for EarthCARE Cal/Val and scientific exploitation, are particularly welcome. More information can be found at https://science.larc.nasa.gov/mira-wg/.

This session brings together advances in artificial intelligence, Earth observation, and atmospheric modelling to improve air quality monitoring and emission inversion. We welcome studies on the synergistic uses of satellite-based monitoring and deep learning—ranging from data-driven surrogates and emulators to hybrid physical-AI integration within chemistry transport models, alongside synthetic data approaches. While advances for well-monitored regions are of interest, we particularly encourage applications in resource-constrained, data-scarce, and under-monitored environments where traditional infrastructure is lacking

The aim of this general session is to bring together the scientific community within air pollution modelling. The focus is ongoing research, new results and current problems related to the field of modelling the atmospheric transport and transformation of air pollutants and precursors on global, regional and local scales.

All presentations covering the research area of air pollution modelling are welcome, including recent model developments, applications and evaluations, physical and chemical parameterisations, process understanding, model testing, evaluation and uncertainty estimates, emissions, numerical methods, model systems and integration, forecasting, event-studies, scenarios, ensembles, assessment, machine learning, etc.

Indoor air quality is vital to human health, as people spend most of their time indoors, including at home and in the workplace. While indoor activities such as cooking or cleaning elevate indoor pollution levels, outdoor pollutants simultaneously enter buildings through windows, doors, ventilation, or structural leaks. Conversely, pollutants generated indoors, such as volatile organic compounds or particulate matter, exit through these same pathways, contributing to urban air pollution.


Moreover, the indoor processing of primary pollutants differs significantly from outdoor dynamics (e.g., oxidants and surface interactions). This divergence adds complexity to how pollutants transform during transport and produce secondary pollutants, such as secondary organic aerosols and ozone. Understanding this dynamic is essential for developing effective mitigation strategies and informing policy. It requires integrated insights into emission sources, chemical transformation, indoor-outdoor exchange mechanisms, and the influence of occupant behaviour.


This session invites contributions from laboratory/chamber experiments, indoor and outdoor field observations (including both lived-in and model-house environments), and modelling studies that advance our knowledge of the indoor-outdoor air pollution interface. We particularly encourage submissions addressing indoor emission sources, pollutant transport, transformation, and exposure assessments.

Satellite, ground-based, and other observational platforms, together with chemical transport models, have greatly advanced our ability to characterize atmospheric composition and air quality from local to global scales. Recent advances in polar-orbiting and geostationary satellite sensors, ground-based networks, and field campaigns are providing unprecedented observations of aerosols, reactive trace gases, and greenhouse gases with improved spatial, temporal, and spectral resolution. These complementary observing systems offer new opportunities to characterize diurnal variability, extreme pollution events, emission sources, and transboundary transport, while improving our understanding of human exposure to air pollution and its associated health impacts. At the same time, advances in artificial intelligence (AI), data fusion, and atmospheric modeling are creating new opportunities to integrate satellite observations, ground-based measurements, meteorological data, and chemical transport models. These approaches enable high-resolution and gap-free characterization of atmospheric composition, support near-real-time monitoring and forecasting, and improve assessments of air pollution exposure, radiative effects, emissions, and health impacts. This session invites contributions that advance our understanding of atmospheric composition, air quality, and health through observations, modeling, data integration, and emerging computational approaches. We particularly welcome studies using satellite and ground-based observations, field measurements, chemical transport models, AI and machine learning, multi-source data fusion, and novel approaches for air pollution monitoring, exposure assessment, and environmental health applications.

One of the most intriguing and challenging frontiers in climate science is small-scale air-sea interactions and their potential impact on large-scale circulations. Recent advances in numerical modelling and observational capacity, particularly increased horizontal resolution, have revealed rich small-scale features spanning the ocean submesoscale through the mesoscale (O[1-100 km]) in both the ocean surface and marine atmospheric boundary layer. Submesoscale (permitting) ocean-atmosphere coupled simulations have shown that these small-scale features interact across the fluid interface, thereby modifying exchanges between the ocean and the atmosphere. These exchanges – air-sea fluxes of heat, moisture and momentum – are essential for large-scale circulations, modes of variability in the coupled climate, extreme events, cloudiness, rainfall, and winds, as well as ocean waves, upper-ocean mixing and stratification, and sea surface temperature.

This session welcomes submissions that examine air-sea interactions at ocean submesoscale-to-mesoscales, including high-resolution air-sea flux variability, influences of surface heterogeneity and coupled boundary layer dynamics, as well as their impacts on large-scale weather statistics, overturning circulations, and climate. All observational, modelling, and theoretical approaches are welcome. We particularly encourage submissions that use km-scale coupled modelling outputs (including those generated at regional-to-global scale hackathon events) and new observational data from space or field campaigns; present novel theoretical understanding; and those that apply integrated observation-modelling-theory or multi-scale modelling approaches.

The polar regions are experiencing rapid and profound environmental changes, with the Arctic warming at nearly four times the global average. Aerosol-cloud interactions remain one of the largest sources of uncertainty in climate models, particularly in these fragile and rapidly evolving environments. Mixed-phase clouds, biogeochemical processes in the ocean and sea ice, and atmospheric dynamics all contribute to complex feedbacks that modulate Earth's radiative balance, influence weather systems, and drive climate change.

This session, inspired by the goals of initiatives such as QuiESCENT (Quantifying the Indirect Effect: from Sources to Climate Effects of Natural and Transported aerosol in the Arctic), CATCH (Cryosphere and ATmospheric CHemistry), CIce2Clouds (Coupling of ocean-ice-atmosphere processes: from sea-Ice biogeochemistry to aerosols and Clouds), BEPSII (Biogeochemical exchanges at Sea Ice Interfaces), and PICAASO (Partnerships for Investigations of Clouds and the biogeochemistry of the Atmosphere in Antarctica end the Southern Ocean) aims to foster interdisciplinary collaboration by bringing together researchers studying aerosols, clouds, ocean–ice biogeochemistry, and their coupled interactions in both the Arctic and Antarctic. We seek to bridge disciplinary gaps to advance our understanding of polar climate systems.

We invite contributions that address the following key topics:
- Aerosol-cloud interactions: Their influence on cloud microphysics, radiative properties, and precipitation patterns in polar environments, including the contrasting effects of anthropogenic pollution and natural aerosols.
- Biogeochemical cycling: Marine and terrestrial sources of aerosols, including sea salt, mineral dust, biological particles, black carbon, and organic aerosols, and their feedbacks with atmospheric components.
- Boundary layer dynamics: The role of atmospheric and oceanic boundary layer processes in shaping aerosol and cloud properties, as well as their impact on boundary layer mixing and local pollution processing.
- Observational and modeling advances: Innovations in field campaigns, remote sensing, laboratory experiments, and modeling frameworks to improve the representation of polar aerosol–cloud–ocean–ice systems in climate models.
- Climate feedbacks and projections: Implications of polar aerosol–cloud interactions for climate model performance and global climate predictions, with a focus on reducing uncertainties in projections.

Recent changes across the Antarctic atmosphere–ocean–cryosphere system have been profound, and projections suggest that many of these changes will continue or even accelerate. These changes arise from interactions spanning scales from turbulent boundary-layer exchange and individual weather systems to basin-scale ocean circulation and large-scale atmospheric variability. These components are often studied separately, despite forming connected physical pathways. Large-scale circulation and storm tracks modulate cyclones, katabatic winds and air-mass intrusions; atmospheric heat, moisture and momentum fluxes affect sea-ice formation, drift, export and polynya activity; sea-ice changes modify air–sea exchange, ocean stratification, mixing and continental-shelf circulation; and ocean heat and freshwater exchanges influence ice-shelf basal melting, cavity circulation and downstream climate feedbacks. Resolving these multiscale pathways is essential for understanding recent Antarctic change and improving future projections.
We invite contributions based on in situ observations, field campaigns, satellite remote sensing, reanalyses, theory, process studies, regional coupled models and Earth system models. Topics include multiscale atmospheric forcing of sea ice, polynyas and ice shelves; interactions among katabatic winds, cyclones, large-scale circulation and surface fluxes; feedbacks from leads and open water to the atmospheric boundary layer; sea-ice–wave–ice-shelf interactions; upper-ocean buoyancy forcing, stratification and convection; continental-shelf circulation, dense-water formation and export; ice-shelf meltwater and coupled feedbacks; and emergent behaviour under future climate change. We particularly encourage studies that connect two or more components through testable physical mechanisms, quantify interactions across weather-to-climate scales, assess observational or modelling uncertainties, or identify priorities for future observing systems and coupled-model development.

This session aims to showcase recent research progress and augment existing knowledge in polar meteorology and climate, and the atmosphere-land ice-sea ice coupling in both Hemispheres. It will provide a setting to foster discussion and help identify gaps, tools, and studies needed to address these open questions. It is also an opportunity to convey newly acquired knowledge to the community.
We invite contributions on all observational and numerical modelling aspects of Arctic and Antarctic meteorology and climatology that address atmospheric interactions with the cryosphere. This may include but is not limited to studies on past, present and future of:
- Atmospheric processes that influence sea ice (snow on sea ice, sea ice melt, polynya formation and sea ice production and transport) and associated feedbacks,
- Surface energy balance above the snow/ice surface.
- The variability of the polar large-scale atmospheric circulation (such as polar jets, the circumpolar trough and storm tracks) and impact on the cryosphere (sea ice and land ice),
- Atmosphere-cryosphere interactions triggered by synoptic and meso-scale weather phenomena such as cold air outbreaks, katabatic winds, extratropical cyclones, polar cyclones, atmospheric rivers, Foehn winds, and heatwaves,
- Role of clouds in polar climate and impact on the land ice and sea ice through interactions with radiation,
- High-latitude dust and pollution, deposition on snow and ice, and related impacts.
- Teleconnections and climate indices and their role in land ice/sea ice variability.
Presentations including new observational (ground- and satellite-based) and modelling methodologies specific to polar regions are encouraged. Contributions related to results from recent field campaigns in Greenland, the Arctic, and in the Southern Ocean/Antarctica are most welcome.

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.

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.

Land-atmosphere coupling modulates the exchange of water, heat and carbon between the land surface and the atmosphere. Therefore, it can dampen or exacerbate climate extremes (e.g., heat waves and droughts) and affect ecosystem processes (e.g., the land carbon sink). However, there are several key aspects related to uncertainties that hinder the accurate quantification of land-atmosphere coupling. Firstly, different metrics/indices can be used to describe (aspects of) land-atmosphere coupling, introducing uncertainties between the respective estimates. Secondly, an increasing amount of data describing land-atmosphere coupling is available, together with increasingly powerful methods for analysing them. In response to this, a more thorough consideration of uncertainty stemming from data and methods is critical in interpreting the robustness of emerging patterns. Lastly, physical processes underlying land-atmosphere coupling occur on different spatiotemporal scales ranging from in-situ to the regional scale and from seconds to months, which may or may not be reflected in the data we use to study them, in turn further inducing uncertainties.
Only by considering these aspects it is possible to determine accurately where available information is (not) sufficient to draw robust conclusions. Addressing the uncertainty challenge aims at a more precise identification of e.g. accurate observational constraints for process-based modelling or a more accurate determination of the role of land-atmosphere interactions for changes in (surface) weather and climate.
We invite abstracts that quantify uncertainties of various origins relevant for studying land-atmosphere interactions, including but not limited to inter- and intracomparisons of observations and model output, evaluations across spatiotemporal scales, and assessments of variables capturing surface carbon or water fluxes and indices/metrics.
We are especially interested in abstracts that move beyond solely quantifying the uncertainties, but motivate us to address and reconcile them and abstracts focusing on the implications of uncertainties related to land-atmosphere interactions on the evolution and location of extremes, carbon processes or the location of land-atmosphere coupling hot spots, among others.

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.

Atmospheric science research provokes and reacts to policy, and the two fields remain intertwined to the present. Air quality and climate hazards endanger exposed communities, and compounding exposures often harm public health, deepen inequality, and erode resilience, prompting policymakers to act. Evidence-informed action designed to improve air quality can create co-benefits for climate mitigation and simultaneously address health and environmental injustice challenges. This session calls for research seeking to understand how mitigation and adaptation actions to air pollution and climate change could influence atmospheric chemistry and dynamics in the present and future and how these actions could affect health and equality. We welcome abstracts that leverage remote sensing, statistical and earth system modeling, ground-based observations, machine-learning approaches, and policy analysis to test how well policies improve air quality and reduce climate change, health harms, and environmental injustices. We also seek novel atmospheric science research identifying where policy is needed.

Urban areas are increasingly vulnerable to severe weather events, including localized intense precipitation, extreme heat, and urban windstorms. Developing effective Early Warning Systems (EWS) that are fit for purpose requires bridging high-resolution physical science, cutting-edge AI methodologies, and actionable co-produced risk communications and policies. This requires understanding both physical forecast performance and how warnings are operationalized to drive effective anticipatory action, particularly in vulnerable urban environments.

This session, organized as part of the WMO Urban-PREDICT initiative, explores recent advances in high-resolution urban weather forecasting, multi-hazard prediction, and the interdisciplinary co-design of early warnings. We welcome contributions covering:
• High-Resolution Physical Modelling: Sub-kilometer and hectometric-scale Numerical Weather Prediction for urban environments and their application to early warning.
• AI & Machine Learning Applications: Standalone AI or physics-informed AI prediction systems, fast emulators, data-driven downscaling, and novel observational datasets (e.g., radar, crowdsourced sensors, urban IoT), including pathways for operational deployment and data accessibility.
• Impact & Hazard Modelling: Translating meteorological variables into actionable hazard metrics for urban infrastructure, energy systems, and public health, considering the consequences for community response.
• Interdisciplinary Co-Production: Frameworks and case studies linking physical hazard forecasts to societal response, policy, and community-level EWS resilience.

This session will feature contributions from ongoing research under the Urban-PREDICT framework, including international intercomparison initiatives (e.g., RUMI high-resolution modelling over Hong Kong, HEAT-X AI heat downscaling) and local case study approaches to early warning and data availability (e.g., Hong Kong, Lusaka, and Montevideo). We encourage submissions across physical meteorology, machine learning, and social sciences.

Microorganisms (i.e. bacteria, archaea, fungi, and protists) as well as viruses and macromolecules are ubiquitous in the atmosphere. They originate from surfaces including terrestrial, aquatic, marine, cryospheric, and built environments, and redistribute across large spatial and temporal scales. The continuous exchange of biological material by atmospheric transport provides an important but still poorly understood link between distant ecosystems and habitats. Once airborne, microbial cells are exposed to temperature fluctuations, radiation, oxidants, and limited availability of water and nutrients. These conditions can affect their survival, biological functioning, potential infectivity and virulence, as well as their physical characteristics, with important implications for microbial and viral dispersal, gene flow, and for the ability of microorganisms to establish and spread in new environments.

Microorganisms, viruses, and microbially-produced macromolecules can also influence atmospheric and Earth-system processes. Biological particles and macromolecules can act as cloud condensation nuclei (CCN) and ice-nucleating particles (INP), affecting cloud formation, precipitation, and potentially the climate. Microbial activity may also affect atmospheric chemical budgets and biogeochemical cycles. Finally, dry and wet deposition returns airborne microorganisms, viruses, and macromolecules to soils, vegetation, water, and ice impacting microbial distribution across these environments. Thus, the atmosphere is as an important interface connecting microbial communities and biogeochemical cycles across ecosystems.

This session aims to bring together researchers from microbiology, atmospheric science, biogeochemistry, climate science, One Health, and related disciplines to advance our understanding of atmospheric microorganisms, their interaction with the atmosphere, and their various effects. We welcome contributions addressing (I) microbial survival, activity, and interactions; (II) atmospheric stress responses and microbial evolution; (III) airborne pathogens, infectivity, virulence, and antimicrobial resistance; (IV) emission, transport, transformation, and deposition; (V) biological ice-nucleating particles and cloud–microbe interactions; (VI) microbial contributions to atmospheric chemistry and biogeochemical cycling; and (VII) the consequences of atmospheric microorganisms and viruses for ecosystems, agriculture, climate, and human and animal health.

Atmospherically produced cosmogenic nuclides provide unique tracers connecting processes in the atmosphere with those operating at Earth’s surface. Their application requires understanding not only cosmogenic production but also how these nuclides are transported through the atmosphere and delivered to terrestrial and marine environments. Atmospheric circulation, aerosol transport, precipitation, and scavenging can modify depositional fluxes and thereby influence signals recorded in soils, sediments, ice, and other environmental archives. Recent advances in atmospheric modelling increasingly allow this chain from production to deposition to be quantified for cosmogenic nuclides such as ¹⁰Be and ³⁶Cl. Physics-based production functions can be coupled with atmospheric transport and deposition models to predict spatially and temporally resolved fluxes, which can be tested against observational records. Multi-nuclide approaches may further help distinguish variations in production from those arising during atmospheric transport and deposition.
Improved constraints on cosmogenic-nuclide deposition are particularly important for quantitative applications at Earth’s surface. Meteoric ¹⁰Be and ¹⁰Be/⁹Be, for example, are used to quantify soil residence times, sediment transport, weathering, erosion, and catchment-wide denudation, while other cosmogenic nuclides provide complementary constraints on surface processes. Linking atmospheric models to such applications can provide spatially resolved depositional flux estimates and improve the interpretation of cosmogenic-nuclide inventories and ratios in landscapes.
This session aims to bring together communities working on cosmogenic-nuclide production, atmospheric transport and deposition, environmental archives, and Earth-surface applications. We welcome contributions on production and transport models, aerosol interactions and scavenging, deposition, model downscaling and validation, as well as studies linking atmospheric modelling with soil and sediment dynamics, weathering and denudation, landscape evolution, and landform dating. Contributions from ice-core, lacustrine, and marine archives and multi-nuclide studies are equally welcome. By connecting these perspectives, the session seeks to advance our understanding of how cosmogenic signals are transferred from the atmosphere to Earth’s surface and used quantitatively to investigate Earth-surface processes.

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).

We welcome contributions that address a range of environmental and societal effects of nuclear war. Areas of interest include, but are not limited to:
- Radioactive contamination
-Radioactive fallout and long-range transport
-Effects on supply chains and infrastructure
-Nuclear detonations in space and EMP attacks
-Nuclear winter and climatic disruption
-Fires and blast effects
-Smoke transport and aerosol plume dynamics
-Impacts on the hydrological cycle and oceans
-Impacts on ecological systems and ecosystem services
-The use of science to contribute to reduction of nuclear threats.

Artificial intelligence and machine learning are opening new opportunities across atmospheric science, enabling information to be extracted from increasingly complex observations, improving the representation and understanding of atmospheric processes, and providing new approaches to weather and climate modelling and prediction. At the same time, the growing use of AI raises fundamental scientific questions about how data-driven approaches can be integrated with physical knowledge, numerical models, and observations while maintaining physical consistency, interpretability, robustness, and generalizability.
This session aims to bring together researchers developing and applying AI and machine-learning approaches across the atmospheric sciences. We welcome contributions spanning scales from clouds, aerosols, precipitation, radiation, and boundary-layer processes to regional and global weather and climate. Topics may include, but are not limited to, AI-assisted analysis and retrievals from atmospheric observations; process discovery and understanding; data-driven and hybrid parameterizations; emulation of computationally expensive model components; differentiable and hybrid physics–AI modelling; foundation models and other data-driven approaches for weather and climate prediction; AI-enhanced data assimilation; and methods for improving physical consistency, interpretability, uncertainty quantification, and robustness.
By connecting methodological developments with atmospheric applications, the session seeks to foster exchange between the AI, observational, process-modelling, weather, and climate communities. We particularly encourage studies that go beyond predictive skill alone to investigate how AI can advance physical understanding, improve atmospheric models, and contribute to more reliable prediction across spatial and temporal scales.

The wave of the Information Technology revolution is propelling us into a new era of research in atmospheric and environmental sciences. New techniques, including Artificial Intelligence/Machine Learning (AI/ML) are enabling a deeper understanding of complex atmospheric and environmental systems, as well as the interactions among weather/climate, air quality, public health, and socioeconomic factors. Meanwhile, the unprecedented growth in both the volume and dimensionality of diverse and heterogeneous data is creating new opportunities while challenging conventional research paradigms for data integration, analysis, and interpretation. These emerging techniques are therefore becoming increasingly central to addressing the scientific challenges of this new era and supporting science-based policymaking in response to evolving environmental and societal challenges.

In this session, we welcome submissions addressing the latest advances in emerging techniques applied to all aspects of atmospheric, climate, and environmental sciences, including but not limited to:
- Applications of AI/ML and other emerging techniques for:
• Advancing our understanding of the complex Earth system, especially the underlying mechanisms of weather/climate systems, atmospheric environmental systems, and their interactions
• Facilitating faster and more accurate weather/climate/air quality modeling and forecasting, especially for extreme weather events, climate change, and air pollution episodes
• Providing new insights into the mechanisms of atmospheric chemistry and physics
• Improving air pollution tracing and source attribution
• Supporting policymakers in making decisions that promote environmental sustainability (e.g., considering interactions among extreme weather, climate change, air quality, socioeconomic factors, and public health
- The adaptation and development of AI/ML and other emerging techniques through:
• Explainable AI (XAI)
• Hybrid methods (e.g., hybrid ML, physics-integrated ML)
• Multi-source heterogeneous data fusion and mining
• Transfer learning
• Advanced modeling frameworks

We believe that exchanges across research fields could help break down the limitations of disciplinary thinking and enabling technological innovations. Therefore, contributions from fields other than atmospheric, climate, and environmental sciences are also encouraged.

The recent revolution of data-driven forecasting systems based on artificial intelligence (AI) has opened new research possibilities in weather forecasting, climate science, and various other areas. At the same time, many open questions remain–such as how to properly evaluate the model outputs in terms of generalizability under climate change, whether models extrapolate to unseen extremes, and to what extent they are consistent with physical principles. This session focuses on new scientific approaches emerging from this AI revolution, limitations of current models, and strategies to overcome them. We encourage submissions that explore a wide range of topics, including evaluations of outputs and comparisons to numerical models, technical advancements in initial condition optimization or model fine-tuning, novel techniques from explainable AI, and other relevant studies. Bringing together experts from AI, climate sciences, statistics, and applied math will foster interdisciplinary collaborations and guide scientific progress in this quickly evolving field of research.

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.

Deep learning is causing a major paradigm shift in weather forecasting from physical modelling to data-driven or hybrid approaches. While the first ML-systems are now used in operational medium-range weather forecasting, the adaptation of deep learning techniques to the subseasonal-to-seasonal (S2S), decadal and multidecadal range is still lagging behind. New approaches are needed for S2S since atmospheric initial conditions are no longer the main driver of predictability. Similarly, deep-learning approaches are emerging for the decadal and multidecadal timescales, when climate forcing becomes a dominant influence on the atmosphere. The scarcity of simulation data and the lack of ground truth pose serious challenges at these time ranges. For this session, we solicit abstracts that present current ideas and results at the intersection of deep learning, S2S-forecasting and climate emulation and simulation.

In particular, we welcome contributions from the following areas.
- Generative modelling for time-scales beyond medium-range
- Calibration and uncertainty estimation for data-driven long-term forecasting
- Coupled generative models of earth system components
- Predictability limits and failure modes in data-driven long-term forecasting
- Out-of distribution emulation and forecasting
- Benchmarking and validation of long-term forecasts with data-driven models
- Identification of drivers of increased predictability and forecast skill for data-driven models (XAI)

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.

Air pollution remains a critical global challenge, disproportionately impacting vulnerable communities in low- and middle-income countries. Weak policies, fragmented institutions, limited financial and computational resources, and lack of comprehensive monitoring infrastructure hinder effective air quality (AQ) management. Accessible and affordable AQ sensors (low-cost sensor systems, LCS) offer a promising solution by enabling dense spatial networks, rapid deployment, citizen engagement, and integration with other data sources. Yet, persistent challenges remain around data quality, calibration, standardisation, integration with regulatory frameworks, long-term sustainability, and equitable access. Addressing these issues requires strong local capacity-building and international collaboration.
This session will showcase best practices in using LCS for AQ monitoring. We will explore case studies where LCS enhance air quality monitoring products (integrating satellite with air sensor information), verify air quality forecasting systems, and support public health and community monitoring initiatives, particularly, but not limited, to resource-limited settings. This session promotes cutting-edge research, case studies, performance evaluations, standardisation efforts, mobile measurements and best practices for using air sensors in air quality monitoring, incl. indoor air quality. These aspects will help to enhance air quality monitoring capabilities and foster strong local and international collaboration. It will also explore strategies for sustainable practices that empower communities and ensure equitable partnerships.

The "Advanced Spectroscopic Measurement Techniques and Applications for Atmospheric Science" session focuses on the development of novel spectroscopic methods and instrumentation and their use in atmospheric applications. These applications target measurements of concentrations and optical properties of atmospheric constituents, physical properties of the atmosphere, as well as the study of atmospheric processes in laboratories, atmospheric simulation chambers, and field deployments.
The session aims to be a forum for sharing information on the state-of-the-art and emerging developments in atmospheric sensing using spectroscopic methods.
Topics include developments, demonstrations and early applications of novel spectroscopic methods and instruments from the UV to THz spectral regions for measuring atmospheric aerosols, isotopologues, air pollutants, greenhouse gases and trace gases, as well as spectroscopic measurements of meteorological variables.
Spectroscopic methods include high sensitivity and selectivity spectroscopy (such as frequency comb spectroscopy, cavity-enhanced absorption/Raman spectroscopies, photoacoustic & photothermal spectroscopies), low-cost optical sensors, heterodyne radiometry, imaging spectroscopy, and wildfire detection and characterization.
Applications include laboratory and simulation chamber demonstrations, vertical concentration profiles and flux measurements. Submissions are also encouraged on novel adaptations of instruments to mobile platforms (e.g., vehicles, in backpacks, UAVs, ships and balloons) and on new spectral data analysis tools (including deep learning spectroscopy). Contributions from scientists and engineers in academic and scientific institutes, regulatory agencies, and commercial companies are welcomed.

This session presents an integrated approach based on infrasound records from the I17CI station to reconstruct the atmospheric dynamics and thermal structure of the atmospheric column over Côte d’Ivoire and the wider West African region. The session will cover the full methodological chain, including spectro-temporal signal processing, inversion of propagation parameters, and joint assimilation with MERRA-2 reanalysis data. It will also highlight operational applications such as seismic and explosion detection, early monitoring of convective storms, detection of volcanic eruptions, and identification of industrial incidents. Finally, the session will discuss the technical implementation using MATLAB tools, validation through case studies, and the benefits for forecasting, civil security, and national capacity building.

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.

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.

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

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.

Understanding and reducing uncertainty in atmospheric and Earth system models requires effective ways to explore model behaviour, compare different models and connect simulations with observations. Key challenges include identifying the processes that cause model uncertainty, distinguishing uncertain parameters from differences in model structure, and establishing whether observational constraints remain robust across models.

This session brings together modelling, observational and statistical communities developing methods and applications that address these challenges. We welcome both single-model and multi-model studies, including perturbed parameter ensembles, coordinated model experiments and model intercomparison projects.

Topics include:

Ensemble design, sensitivity analysis and attribution of uncertainty within and across models.

Statistical and machine-learning emulation of individual models and multi-model ensembles, including approaches that capture shared behaviour and model-specific differences.

Bayesian calibration, history matching, model weighting, and emergent or process-based observational constraints.

Joint use of multiple observations, accounting for observational uncertainty, model dependence and structural differences.

Evaluation of how uncertainties propagate across coupled model components and how coupling alters relationships between model variables and observations.

Diagnosing structural model deficiencies using model–observation comparisons to guide model development.

Designing observations and model experiments to improve constraints on climate uncertainty.

Applications across atmospheric composition, clouds, physical atmospheric processes and their coupling with other Earth system components are encouraged, from regional to global scales. We particularly welcome studies investigating whether constraints derived from one model remain informative across other models, and whether improved agreement with observations leads to more reliable climate projections.

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.

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.

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

The landscape of earth science education is shifting quickly due to emerging technologies and shifting priorities, which present both challenges and opportunities in pedagogy. Satellites, genetic sequencing, long-term in situ sensors, model results and reanalyses, digitized collections and citizen science are producing massive datasets, requiring students in earth and environmental sciences to learn scientific computing skills to utilise them. However, many students in earth and environmental science degree programs report lacking the computational skills needed to work with earth observation data.
At the same time, ubiquitous access to computing can help universities meet goals around educating more scientists by accelerating learning and lowering the barrier to entry with technologies such as virtual labs and cloud-based learning environments. Emerging technologies, including artificial intelligence, further expand these possibilities but also raise questions about ethical use and how best to support meaningful student engagement and scientific understanding.
This session invites researchers, educators, industry practitioners, and representatives from other organisations working in Earth and environmental science education to share innovative and effective approaches to developing geospatial and computational skills in Earth and environmental science education. We welcome contributions describing successful courses, programmes, learning environments, and other educational tools, as well as experiences evaluating their impact and accessibility. We are particularly interested in highlighting initiatives with a proven track record of targeting and including intersectional identities traditionally under-represented among earth, environmental and/or computer scientists.
This session is being organized jointly with a short course with the same name, where there will be a hands-on opportunity to share educational tools. If you are looking to demonstrate your educational tool or module in an interactive setting, we encourage you to apply to this session, and we will contact you about the short course.