Robust and reliable climatic studies, particularly those assessments dealing with climate variability and change, greatly depend on availability and accessibility to high-quality/high-resolution and long-term instrumental climate data. At present, a restricted availability and accessibility to long-term and high-quality climate records and datasets is still limiting our ability to better understand, detect, predict and respond to climate variability and change at lower spatial scales than global. In addition, the need for providing reliable, opportune and timely climate services deeply relies on the availability and accessibility to high-quality and high-resolution climate data, which also requires further research and innovative applications in the areas of data rescue techniques and procedures, data management systems, climate monitoring, climate time-series quality control and homogenisation.
In this session, we welcome contributions (oral and poster) in the following major topics:
• Climate monitoring , including early warning systems and improvements in the quality of the observational meteorological networks
• More efficient transfer of the data rescued into the digital format by means of improving the current state-of-the-art on image enhancement, image segmentation and post-correction techniques, innovating on adaptive Optical Character Recognition and Speech Recognition technologies and their application to transfer data, defining best practices about the operational context for digitisation, improving techniques for inventorying, organising, identifying and validating the data rescued, exploring crowd-sourcing approaches or engaging citizen scientist volunteers, conserving, imaging, inventorying and archiving historical documents containing weather records
• Climate data and metadata processing, including climate data flow management systems, from improved database models to better data extraction, development of relational metadata databases and data exchange platforms and networks interoperability
• Innovative, improved and extended climate data quality controls (QC), including both near real-time and time-series QCs: from gross-errors and tolerance checks to temporal and spatial coherence tests, statistical derivation and machine learning of QC rules, and extending tailored QC application to monthly, daily and sub-daily data and to all essential climate variables
• Improvements to the current state-of-the-art of climate data homogeneity and homogenisation methods, including methods intercomparison and evaluation, along with other topics such as climate time-series inhomogeneities detection and correction techniques/algorithms, using parallel measurements to study inhomogeneities and extending approaches to detect/adjust monthly and, especially, daily and sub-daily time-series and to homogenise all essential climate variables
• Fostering evaluation of the uncertainty budget in reconstructed time-series, including the influence of the various data processes steps, and analytical work and numerical estimates using realistic benchmarking datasets

Spatially comprehensive representations of past weather and climate, for example in the form of gridded datasets, are an important basis for analyzing climate variations and for modelling weather-related impacts on the environment and natural resources. They are also indispensable for validation and downscaling of climate models. Increasing demands for, and widespread application of grid data, call for efficient methods of spatial analysis from observations, and profound knowledge of the potential and limitations of these datasets in applications. At the same time, the growing pool of observational data (radar data, satellite based data…) offers the opportunity to improve the accuracy and reduce uncertainty of gridded climate data. Modern spatial climatology therefore deals with a wide range of space and time scales. As a result, actual developments in the field are concerned with a range of challenging issues. These include for example the spatial characteristics and representation of extremes, the representation of small-scale processes (auxiliary variables), the integration of several observational data sources (e.g. station, radar, satellite, re-analysis data), the quantification of uncertainties, the analysis at sub-daily time scales, and the long-term consistency as well as cross-variable consistency in grid datasets.

This session addresses topics related to the development, production, quality assessment and application of gridded climate data with an emphasis on statistical methods for spatial analysis and interpolation applied on observational data. Contributions dealing with modern methodological challenges and applications giving pertinent insights are particularly encouraged. Spatial analysis by applying e.g. GIS is a very strong tool for visualizing and disseminating climate information. Examples showing developments, application and dissemination of products from such analyses for climate services are also very welcome.

The session intends to bring together experts, scientists and other interested people analyzing spatio-temporal characteristics of climatological elements, including spatial interpolation and GIS modeling within meteorology, climatology and other related environmental sciences.

The Objective of the session is to provide overview of the current applications of Satellite Climate datasets with potential downstream applications.

Satellites make routine observations from which key climate parameters can be derived. Observations from space have the advantage of broad spatial and temporal coverage that complement in situ measurements. Satellite based Climate data Records have reached a high level of maturity in terms of time coverage, accuracy and accessibility. Such products come from long-term international programs that allow continuity and constant quality and are freely and openly available to government, academic, commercial and general public users.

The session will present existing applications and products based on Satellite Climate Data Records and “near real time” data with relevance to Climate to motivate and support the use of satellite data. Thus, a broad ensemble of thematic variables is encouraged, from Climate Monitoring to Atmospheric Composition. Contribution may address novel science-driven (operational) applications, methodological approach, data fusion, innovative products and services.

The Copernicus Climate Change service will combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. The service will benefit from a network of observations, both from in situ and satellite sensors, and modelling capabilities. Moreover, it will provide key indicators on climate change drivers (such as carbon dioxide) and impacts (such as reducing glaciers). This information will serve a number of sectors sensitive to climate change, including energy, water management, agriculture & forestry, tourism, insurance, transport, health, disaster risk reduction, coastal areas and infrastructure.

This session will provide the opportunity to discuss how the service can deliver substantial economic value to Europe by:
• informing policy development to protect citizens from climate-related hazards such as high-impact weather events;
• improving planning of mitigation and adaptation practices for key human and societal activities;
• promoting the development of new services for the benefit of society.

Contributions from climate service providers and sectoral users are solicited, as well as from relevant FP7 and H2020 precursor projects.

The prediction of changes in the climate mean state, variability and extremes remains a key challenge on decadal to centennial timescales. Recent advances in climate modelling and post-processing techniques (statistical downscaling, bias correction and ensemble techniques) provide the basis for developing future climate information on local to regional and global scales. To make such information actionable for users, relevant information needs to be derived and provided in a way that can support decision-making processes. This requires a close dialogue between the producers and users of future climate information.

Recent projects such as CMIP5, CMIP6, CORDEX, SPECS, EUPORIAS, MiKlip, COST-VALUE have tackled some of these aspects and the learning has fed into the development of national climate change scenarios which are now operational climate services in many European countries (e.g. KNMI14 and KNMI21 in the Netherlands, UKCP18 in the UK, CH2018 in Switzerland, ÖKS15 in Austria). The session invites papers related to these projects and covers the topics:

•Practical challenges and best practices in developing national, regional and global climate scenarios and predictions to support adaptation action.

•Developments in dynamical and statistical downscaling techniques, process-based model evaluations and model weighting, methods to quantify uncertainties from climate model ensembles, combination of decadal predictions and projections to provide seamless user information.

•Challenges in the co-production of future climate information for different target audiences. Examples of tailored information for impact assessments and decision-making. Methods for eliciting user requirements.

•Achievements to increase the uptake of future climate information in decision-making (e.g. case studies, targeted communications campaigns)

Skilful climate predictions on seasonal-to-decadal timescales can generate large socio-economic benefits, but current forecast quality is relatively low over Europe and the Mediterranean Sea. In general, our limited understanding of the mechanisms and processes responsible for predictability and model systematic errors hamper our capability to simulate and forecast seasonal-to-decadal climate variability, especially over the Euro–Mediterranean region. Improved global climate model calibration and regionalization techniques, as well as better forecast verification methods need to be developed specifically for this region to both extract as much climate information as possible from operational forecast systems, and tailor this information to produce top–quality climate services for sectors with high societal impacts in the Mediterranean.

This Session is devoted to research and development aimed at improving climate prediction capabilities and related services on seasonal-to-decadal timescales in the Euro–Mediterranean region, and it invites contributions on (but not only restricted to):

- understanding of sources and mechanisms for predictability in the target region,
- novel, process-based methods for bias correction, downscaling, optimal combination of different sources of information,
- innovative empirical forecasting models,
- climate services based on end-user tailored climate forecasts, in relevant socio–economic sectors for the Mediterranean (e.g., water management, renewable energy, agriculture, tourism, forestry and fire risk)

The Session will act as a workshop organized by the JPI–ERA4CS EU–project MEDSCOPE (www. medscope–project.eu)