Regional climate modeling has tremendously grown in the last decades, encompassing a large and diverse scientific community. Regional climate models (RCMs) are run on a wide range of resolutions, from a few to a few tens of km, and applications, from process studies to past and future climate simulations. In addition, the Coordinated Regional climate Downscaling EXperiment (CORDEX) has been recently implemented to increase international coordination among different research efforts and produce large ensembles of high resolution climate projections over regiona worldwide. Specifically, the Phase II CORDEX framework has been launched, including a CORDEX-CORE initiative aimed at generating a coherent set of projections over all CORDEX domains at increased resolution (12-25 km) and a number of Flagship Pilot Studies (FPSs) addressing specific research challenges, such as convection permitting modeling. These two initiatives are also intended to provide a strong input to the upcoming sixth assessment report of the Intergovernmental panel on Climate Change (IPCC). The session seeks for contributions on:
1) New developments in RCM research
2) Key methodological issues, such as Added Value and metrics for model assessment
3) Application of RCMs for different scientific challenges, e.g. extreme events, the hydrologic cycle, effect of land-use change and aerosols
4) Contributions to the CORDEX CORE and FPS initiatives
5) Use of RCMs to provide multi-model ensemble-based climate information for Vulnerability, Impacts and Adaptation (VIA) studies)

Accurate and homogeneous long-term data records (i.e., data that are forced to look like a common reference) are essential for researching, monitoring, or attenuating changes in climate, for example to describe the state of climate or to detect climate extremes. Likewise, reanalysis needs accurate and homogenized/harmonized data records (i.e., data records in which the unique nature of each sensor is maintained). Temporal changes, such as degradation of instruments, changes of instruments, changes of observation practices, or changes of station location and exposure, cause artificial non-climatic sudden or gradual changes in data records. The magnitude and uncertainty of these changes impact the results of climate trend analyses. Therefore, data intended for applications, such as making a realistic and reliable assessment of historical climate trends and variability, require to be homogenized or harmonized consistently so as to obtain well calibrated data records including measurement uncertainties.

The above described factors influence the quality of different essential climate variables, including atmospheric (e.g., air temperature, precipitation, wind speed), oceanic (e.g., sea surface temperature), and terrestrial (e.g., albedo, snow cover) variables from in-situ observing networks, satellite observing systems, and climate/earth-system model simulations. Our session calls for contributions related to the:

• Calibration, quality control, homogenization/harmonisation and validation of either fundamental or essential climate data records.

• Development of new data records and their analysis (spatial and temporal characteristics, particularly of extremes).

• Examination of observed trends and variability, as well as studies that explore the applicability of techniques/algorithms to data of different temporal resolutions (annual, seasonal, monthly, daily, and sub-daily).

• Rescue and analysis of centennial meteorological observations, with focus on data prior to the 1960s, as a unique source to fill in the gap of knowledge of climate variability over century time-scales. In particular, we encourage wind studies dealing with the observed slowdown (termed “stilling”) of near-surface winds in the last 30-50 years.

This session explores advances and challenges in convection-permitting atmospheric modelling: using the newest generation of atmospheric models that allow for the explicit treatment of convective processes (grid spacing ≤ 4 km).

Convection-permitting models (CPMs) are a rapidly growing area of research and have been shown to improve both the diurnal convective cycle and the representation of convective precipitation, particularly extremes. Furthermore, CPMs often exhibit important differences in feedback mechanisms and climate change signals compared to models with parametrized deep convection. CPMs offer a promising tool to better understand fine-scale processes and provide critical information to stakeholders, especially in areas affected by convective extremes and mountainous regions, and have thus sparked wider interest in their applications and development. For example, the CORDEX Flagship Pilot Study (FPS) on convective phenomena over Europe and the Mediterranean.

The session brings together numerical modellers, the observational community, cloud physicists, forecasters and CORDEX-FPS participants, with the aim of advancing understanding of convection and high-resolution modelling in general (including convective storm life cycle and convective organization) with new modelling and statistical observation approaches. Contributions on new high-resolution/sub-daily observational datasets, and their application to CPM evaluation, are particularly welcome. This session calls for papers on state-of-the-art development and application of CPM activities, including examination of interactions between convection and other atmospheric phenomena (e.g. boundary layers, cloud physics, radiation), as well as CPM investigations of local- to regional-scale phenomena (e.g. land-use change, land-ocean contrasts, flow-orography interactions, urban-rural transitions, aerosol effects, etc.). We welcome studies of past, present or future climates, and CPM modelling across time scales. Particular attention is given to extremes.

Other topics include, but are not limited to:
-- Model setup and parametrization, including sensitivity to resolution and dynamics
-- Model evaluation and new evaluation metrics/methods
-- Ensemble-based approaches to quantify predictability/uncertainty at convective scale
-- Physical understanding of the added value compared to coarser models
-- Land-atmosphere coupling at convection-permitting scale
-- Application to climate studies
-- Tropical phenomena
-- Convection, energy balance and hydrological cycle
-- Lightning in CPMs
-- Teleconnection across scales
-- Novel high-resolution experiments

The session will include a solicited talk from Christoph Schär (ETH Zürich) on prospects and challenges in convection-resolving modelling. The session will be chaired by M Tölle and H Truhetz.

The quality of predictions of weather and climate depends on both resolution and complexity of the models that are used. However, resolution and complexity are limited by the computational performance that is available on today's supercomputers. While weather and climate models run on some of the fastest supercomputers of the world, models typically fail to run close to peak performance such that there is still room for a significant speed-up if efficiency is improved. The increase in parallelisation in high performance computing and the availability of various computing platforms is imposing significant challenges for the community to find the optimal hardware/model configuration and to achieve the best performance. On the other hand, the evaluation of high resolution simulations is often tedious due to large data volumes, limited statistic that is affordable and changed model behaviour that needs to be studied (e.g. if convection or eddies are resolved explicitly or if non-hydrostatic equations need to be used).
These challenges can only be addressed appropriately in a close collaboration between Computing and Earth System Scientists. This session aims to bring together scientists who run and evaluate atmosphere and ocean models with high resolution and complexity as well as scientists who enable these models to run as efficiently as possible on existing and future high performance computing architectures (regarding both model development and model optimisation). The session will also be an opportunity for scientists from the EU projects PRIMAVERA, ESCAPE and ESiWACE as well as HighResMIP from CMIP6 to meet and interact.

V. Balaji from Princeton University will be our keynote speaker invited by the ESiWACE EU Horizon2020 COE (grant number 675191).

Weather and climate models used for weather forecasts, seasonal predictions and climate projections, are essential for decision making on timescales from hours to decades. However, information about future weather and climate relies on complex, though imperfect, numerical models of the Earth-system. Systematic biases and random errors have detrimental effects on predictive skill for dynamically driven fields on weather and seasonal time scales. Biases in climate models also contribute to the high levels of uncertainty in many aspects of climate change as the biases project strongly on future changes. A large source of uncertainty and error in model simulations is unresolved processes, represented through parameterization schemes. However, these errors typically materialize at large spatial scales. Our physical understanding of the mechanical and dynamical drivers of these large-scale biases is incomplete. Incomplete mechanistic understanding hinders marked improvements in models, including identification of the parameterizations most in need of improvement.

Understanding and reducing the errors in weather and climate models due to parameterizations and poorly represented mesoscale to regional scales processes is a necessary step towards improved weather and climate prediction. This session aims to bring together these two perspectives, and unite the weather and climate communities to address this common problem and accelerate progress in this area.

This session seeks submissions that aim to quantify, understand, and reduce sources of error and bias in weather and climate models. Themes covered in this session include:

- Theory and development of parameterization. Impact on errors in mean state, model variability and physical process representation;

- Improved physical understanding of the drivers of large-scale biases including the use of process studies, idealized modeling studies and studies with strong observational components;

- Growth and propagation of error and bias in models; model errors across temporal and spatial scales; dependency of errors on model resolution and the development of scale-aware parameterization schemes;

- Use of “emergent constraints” to relate present day model biases with the climate change signal;

- Understanding and representing random model error.

Invited presentations: Felix Pithan (AWI) and Bob Plant (University of Reading)

Lead Convenors: Hannah Christensen and Stefan Sobolowski
Co-convenors: Craig Bishop, Ariane Frassoni, Daniel Klocke, Erica Madonna, Isla Simpson, Keith Williams, Giuseppe Zappa

From the perspective of Earth System predictions, the use of machine learning, and in particular deep learning, is still in its infancy. There are many possible ways how machine learning could improve model quality, generate significant speed-ups for simulations or help to extract information from numerous Earth System data, in particular satellite observations. However, it has yet to be shown that machine learning can hold what it is promising for the specific needs of the application of Earth System predictions. This session aims to provide an overview how machine learning can/will be used in the future and tries to summarise the state-of-the-art in an area of research that is developing at a breathtaking pace.

In both climate modelling and numerical weather prediction, numerical models of the Earth System are used extensively. For the both the atmosphere and ocean such models consist of a fluid dynamics solver (dynamical core) coupled to physics parameterizations to represent processes that occur below the grid scale (physics). Over time these models have become capable of sophisticated simulations, incorporating such features as multi-scale prediction, structure-preserving discretization and a detailed treatment of physics. New work is constantly being undertaken to improve the accuracy and efficiency of these models, both the dynamical core and the physics.

This session encompasses the development, testing and application of novel numerical techniques for Earth system models, including new discretizations, test cases, advection schemes, vertical discretizations, adaptive multi-scale models, physics-dynamics coupling, global and regional climate and NWP models, structure-preserving discretizations and parameterizations (that are not covered in other sessions).

Models of the class used in the CMIP6 experiment to make global
climate projections are imperfect representations of reality that
differ widely in regard to the overall magnitude of warming, in their
regional projections, and in their short-range predictions. While
better models of the underlying physical processes are ultimately
needed, immediate improvement may come simply from better methods to
combine existing models. Contributions are solicited on new methods to
fuse models of climate and weather ranging from output averaging techniques to methods that
dynamically combine model components in a synchronizing, interactive
ensemble. The importance (or lack thereof) of nonlinearities in
determining the sufficiency of output averaging is a topic of special
interest.

Climate services challenge the traditional interface between users and providers of climate information as it requires the establishment of a dialogue between subjects, who often have limited knowledge of each-other’s activities and practices. Increasing the understanding and usability of climate information for societal use has become a major challenge where economic growth, and social development crucially depends on adaptation to climate variability and change.

To this regard, climate services do not only create user-relevant climate information, but also stimulate the need to quantify vulnerabilities and come up with appropriate adaptation solutions that can be applied in practice.

The operational generation, management and delivery of climate services poses a number of new challenges to the traditional way of accessing and distributing climate data. With a growing private sector playing the role of service provider is important to understand what are the roles and the responsibilities of the publicly funded provision of climate data and information and services.

This session aims to gather best practices and lessons learnt, for how climate services can successfully facilitate adaptation to climate variability and change by providing climate information that is tailored to the real user need.
Contributions are strongly encouraged from international efforts (GFCS, CSP, ClimatEurope…); European Initiatives (H2020, ERA4CS, C3S, JPI-Climate…) as well as national, regional and local experiences.

Geochronological frameworks are essential for the study of landscape evolution. Over the last decades, geochronological techniques such as cosmogenic nuclides, thermochronology, radiocarbon and luminescence dating have improved in accuracy, precision, and temporal range. Recently, the development of new approaches, new isotopic/mineral systems, and the increasing combination of these techniques are expanding their range of applications. This session explores these advances and novel applications, which include the study of erosional rates and processes, sediment provenance, burial and transport times, bedrock exposure or cooling histories, landscape dynamics, and the examination of potential biases and discordances in geochronological data. We welcome contributions that use dating tools which are established or in development, particularly those that quantify geomorphological processes with novel approaches and/or generic implications. We encourage studies that combine different techniques (e.g. CRN, luminescence, thermochronology, etc.) or data sets (e.g. field, remote sensing, numerical modelling), and/or highlight the latest developments and open questions in the application of geochronometers to landscape evolution questions.

Invited speakers: Prof. Kristina Hippe and Prof. Todd Ehlers.

The aim of this session is to present the latest research and case studies related to various data analysis and improvement methods and modeling techniques, and demonstrate their applications from the various fields of earth sciences like: hydrology, geology and paleogeomorphology, to geophysics, seismology, environmental and climate change.

Instrumentation and measurement technologies are currently playing a key role in the monitoring, assessment and protection of environmental resources. Climate study related experiments and observational stations are getting bigger and the number of sensors and instruments involved is growing very fast. This session deals with measurement techniques and sensing methods for the observation of environmental systems, focusing on climate and water. We welcome contributions about advancements on field measurement approaches, development of new sensing techniques, low cost sensor systems and whole environmental sensor networks, including remote observation techniques.
Studies about signal and data processing techniques targeted to event detection and the integration between sensor networks and large data systems are also very encouraged. This session is open for all works about an existing system, planning a completely new network, upgrading an existing system, improving streaming data management, and archiving data.

The new scenario related to the global urbanization process and its impact on environmental sustainability and resilience to natural disasters, especially the ones related to the Climate Change, strongly call holistic multidisciplinary and multi-sectorial approaches for the management of urban areas and Cultural heritages.
These approach aim at providing solutions based on the integration of technologies, methodologies and best practices (remote and local monitoring, simulating and forecasting, characterizing, maintaining, restoring, etc.), with the purpose to increase the resilience of the assets, also thanks to the exploitation of dedicated ICT architectures and innovative eco-solutions and also by accounting the social and economic value of the investigated areas, especially in CH frame.
In this context, attention is also focused on the high-resolution geophysical imaging is assuming a great relevance to manage the underground and to adopt new strategies for the mitigation of geological risks.
This session represents a good forum to present, technologies best practices and share different experiences in the field of the urban areas and CH management and protection, against the multi-risk scenarios and for the different situations at European and worldwide level. Finally, great attention will be devoted to the success cases, with a specific focus on recent international projects on smart cities and Cultural heritage in Europe and other countries.

A wide range of processes in the earth system directly affect geodetic observations. This session invites a wide array of contributions which showcase the use of geodesy for Earth science and climate applications, providing crucial insights into the state and change of the earth system and/or understanding its processes.

Data driven quantification of water mass fluxes through boundaries of Earth’s different regions and spheres provides important insights to other geoscience communities and informs model validation and improvement. Changes in regional sea level and ocean circulation are observed by altimetry and gravimetry. Natural and anthropogenic alterations of the terrestrial water cycle lead to changes in river runoff, precipitation, evapotranspiration, and water storage which may cause surface deformation sensed by GNSS stations and InSAR measurements as well as mass/gravity changes observed by satellite/ground gravimetry. Mass changes in the ice sheets and glaciers are detectable by both geometrical and gravimetric techniques. And other novel applications of geodetic techniques are emerging in many fields.

In addition, individual sensor recordings are often affected by high-frequency variability caused by, e.g., tides in the solid Earth, oceans, and atmosphere and their corresponding crustal deformations affecting station positions; non-tidal temperature and moisture variability in the troposphere modifying microwave signal dispersion; rapid changes in the terrestrially stored water caused by hydrometeorologic extreme events; as well as swift variations in relative sea-level that are driven by mass and energy exchange of the global oceans with other components of the Earth system, which all might lead to temporal aliasing in observational records. 

This session invites a wide array of contributions which showcase the use of geodesy for Earth science and climate applications. This session aims to cover innovative ways to use GRACE, GRACE-FO and other low Earth orbiters, GNSS techniques, InSAR, radar altimetry, and their combination with in-situ observations. We welcome approaches which tackle the problem of separating signals of different geophysical origin, by taking advantage of model output and/or advanced processing and estimation techniques. Since the use of geodetic techniques is not always straightforward, we encourage authors to think of creative ways to make their findings, data and software more readily accessible to other communities in hydrology, ocean, cryospheric, atmospheric and climate sciences. With author consent, highlights from the oral and poster session will be tweeted with a dedicated hashtag during the conference in order to increase the impact of the session.

This session aims at bringing together multidisciplinary studies that address the current state of Arctic observing systems, including strategies to improve them in the future. We invite contributions covering atmosphere, ocean, cryosphere and terrestrial spheres, or combinations thereof, by use of remote sensing, in situ observation technologies, and modeling. Particular foci are placed on (i) the analysis of strengths, weaknesses, gaps in spatial/temporal coverage, and missing monitoring parameters in existing observation networks and databases, and (ii) studies describing the development and/or deployment of new sensors or observation platforms that extend the existing observing infrastructure with multidisciplinary measurements. This session will be supported by the EU-H2020 project INTAROS, and welcomes contributions from other pan-Arctic networks (e.g. INTERACT, GTN-P, NEON, ICOS, SIOS, IASOA, AOOS), multi-disciplinary campaigns (e.g. ABoVE, NGEE Arctic, Arctic Ocean 2018, RV Polarstern cruises) or databases.

Modeling soil and vadose zone processes is vital for estimating physical states, parameters and fluxes from the bedrock to the atmosphere. While the media soil, air and water physically affect biogeochemical processes, transport of nutrients and pollutants, their implications on ecosystem functions and services, and terrestrial storage capacities are vital to the understanding of global, land use and climate change. This session aims to bring together scientists advancing the current status in modelling soil processes from the pore to the catchment and continental scale. We welcome contributions with a specific focus on soil hydrological processes but also those that address the role of soil structure on land surface processes, soil biogeochemical processes and their interactions with hydrology, transport of pollutants, soil vegetation atmosphere modelling and root-soil processes.

Statistical post-processing techniques for weather, climate, and hydrological forecasts are powerful approaches to compensate for effects of errors in model structure or initial conditions, and to calibrate inaccurately dispersed ensembles. These techniques are now an integral part of many forecasting suites and are used in many end-user applications such as wind energy production or flood warning systems.

Many of these techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias correction up to very sophisticated distribution-adjusting techniques that take into account correlations among the prognostic variables.

In this session, we invite papers dealing with both theoretical developments in statistical post-processing and evaluation of their performances in different practical applications oriented toward environmental predictions.

Taking inspiration from the Mathematics of Planet Earth 2013 initiative, this session aims at bringing together contributions from the growing interface between the geophysical, the mathematical, and the theoretical physical communities. Specific topics include: PDEs, numerical methods, extreme events, statistical mechanics, large deviation theory, response theory, model reduction techniques, coarse graining, stochastic processes, parametrizations, data assimilation, and thermodynamics. We invite talks and poster both related to specific applications as well as more speculative and theoretical investigations. We particularly encourage early career researchers to present their interdisciplinary work in this session.

This interdisciplinary session welcomes contributions on novel conceptual approaches and methods for the analysis of observational as well as model time series and associated uncertainties from all geoscientific disciplines.

Methods to be discussed include, but are not limited to:
- linear and nonlinear methods of time series analysis
- time-frequency methods
- predictive approaches
- statistical inference for nonlinear time series
- nonlinear statistical decomposition and related techniques for multivariate and spatio-temporal data
- nonlinear correlation analysis and synchronisation
- surrogate data techniques
- filtering approaches and nonlinear methods of noise reduction

We particularly encourage submissions addressing the problem of uncertainty of geoscientific time series and its treatment in the context of statistical and dynamical analysis, including:
- representation of time series with uncertain dating (in particular paleoclimatic records from ice cores, sediments, speleothems etc.)
- uncertainties in change point / transition detection
- uncertainty propagation in time series methods like correlation, synchronization, spectral analysis, PCA, networks, and similar techniques
- uncertainty propagation in empirical (i.e., data-derived) inverse models

This session aims to bring together researchers working with big data sets generated from monitoring networks, extensive observational campaigns and detailed modeling efforts across various fields of geosciences. Topics of this session will include the identification and handling of specific problems arising from the need to analyze such large-scale data sets, together with methodological approaches towards semi or fully automated inference of relevant patterns in time and space aided by computer science-inspired techniques. Among others, this session shall address approaches from the following fields:
• Dimensionality and complexity of big data sets
• Data mining in Earth sciences
• Machine learning, including deep learning and other advanced approaches
• Visualization and visual analytics of big data
• Informatics and data science
• Emerging big data paradigms, such as datacubes