Challenges in retrieving and using climate services' data for local-scale impact studies: insights from the SCORE project

Coastal climate services play a crucial role in developing customised climate information for diverse end-users and stakeholders. To build climate-resilient societies, decision-makers should be empowered through easy access to powerful tools that enable timely adaptation to future and ongoing hazards. For this reason, fit-for-purpose climate services are needed to conduct accurate historical characterisation and projections for interpretative studies on climate- and water-related risks at the local coastal scale. The EU-funded SCORE project (Smart Control of Climate Resilience in European Coastal Cities) utilises climate and marine services for the development of smart technologies that support nature-based solutions to address specific concerns, including rising sea levels, coastal erosion, and coastal flooding due to extreme weather events. As part of the SCORE project, decision-makers will be able to address climate change-related coastal effects in their own cities through novel participatory approaches (Coastal City Living Labs—CCLLs). As part of this framework, this work (i) discusses the main requirements for the identification of fit-for-purpose coastal climate services for local-scale impact studies in European coastal cities based on CCLL requests and prior knowledge and (ii) provides relevant parameters and features that fulfil the users’ needs.

Data from the marine environment are a valuable asset for marine industries, decision-making bodies and scientific research, but in Europe marine data are stored in a wide range of national, regional and international databases and repositories using different formats and standards which makes it difficult to find, assemble and use them efficiently. The European Marine Observation and Data Network (EMODnet) is a network of organisations set up in 2007 by the European Commission in the framework of EU’s Integrated Maritime policy to address the fragmented marine data collection, storage and access in Europe. This paper introduces EMODnet in the context of EU’s Marine Knowledge 2020 Strategy and highlights some of the main features of the EMODnet Data Portals, as well as those being developed by the EMODnet central portal. Finally, the paper zooms in on the features and endeavours of one specific EMODnet activity: the EMODnet portal with access to near-real time and historical data of physical parameters.

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 47:83-94 (2011) - DOI: https://doi.org/10.3354/cr00950 Contribution to the CR Special: 'Climate services for sustainable development' Provision of climate information for adaptation to climate change Xianfu Lu* United Nations Framework Convention on Climate Change Secretariat, Martin-Luther-King Strasse 8, 53175 Bonn, Germany *Email: xlu@unfccc.int ABSTRACT: Climate data and information is central to understanding the risks of climate change and to planning for adaptive actions to reduce such risks. The multi-scale, multi-disciplinary and multi-stakeholder nature of climate change impacts, vulnerability and adaptation require effective and sustained collaboration between the providers and users in order to ensure the relevance, practicality and applicability of climate data and information. The Nairobi Work Programme (NWP) on impacts, vulnerability and adaptation to climate change was launched as a mechanism under the United Nations Framework Convention on Climate Change (UNFCCC) to engage stakeholders, facilitate knowledge sharing and collaboration, and catalyze targeted actions on adaptation. The NWP has engaged a wide array of stakeholders, providers, users and knowledge intermediaries for climate data and information. Through discussions under the NWP, Parties to the UNFCCC recognized that there is an urgent need to improve the provision and delivery of climate information, particularly in developing countries. Improvement in the availability of and access to high quality observed climate data and climate scenarios, practical guidance for the use of climate information, and the systematic documentation and wide dissemination of good practices in applying climate information to support adaptation are all identified as priority areas of work. These clearly defined priorities, together with synergies among relevant initiatives, represent considerable opportunities to enhance the provision and delivery of climate information and services, particularly within the context of the development and implementation of the Global Framework for Climate Services (GFCS). KEY WORDS:Climate information · Adaptation · Nairobi Work Programme Full text in pdf format PreviousNextCite this article as: Lu X (2011) Provision of climate information for adaptation to climate change. Clim Res 47:83-94. https://doi.org/10.3354/cr00950 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 47, No. 1-2. Online publication date: March 31, 2011 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2011 Inter-Research.

Climate data for the European forestry sector: From end-user needs to opportunities for climate resilience

There is a growing need within the paleoclimate community for robust soil paleoproxies capable of reconstructing past terrestrial environments with high precision. Existing proxies for past mean annual air temperature (MAT), such as branched GDGTs (1) and chironomids (2), suffer from large uncertainties (i.e., ≥ 4°C error on these land temperature reconstructions), which limit their applicability.Bacteriohopanepolyols (BHPs) are pentacyclic triterpenoid membrane lipids produced by bacteria that are ubiquitous in terrestrial and aquatic environments (3). Functionalized BHPs have a large structural diversity both in the rings and head groups. They have been detected in sedimentary archives extending back 1.2 Myr (4), underscoring their considerable potential as tools for reconstructing past climatic conditions.BHPs with nucleoside (adenosyl and inosyl) head groups (Nu-BHPs) have been widely used as indicators of terrestrial organic matter input into marine systems (Rsoil) (5). Recently, a large range of previously unknown Nu-BHPs were identified thanks to a newly developed method using Ultra High Performance Liquid Chromatography – high resolution Orbitrap Mass Spectrometry (6). The relative abundances of several Nu-BHPs found in Alaskan soils were shown to correlate with pH and temperature and thus are potential paleotemperature proxies (7). To validate these correlations on a global scale, we present Nu-BHP abundances analyzed across 89 globally distributed surface soil samples. These include soils previously used to calibrate branched GDGTs (1), as well as soils from Northern Norway and Finland and Brazil, to complete coverage from the Arctic to the tropics. Complementary analyses included six soil environmental variables (pH, latitude, total organic carbon (TOC), C/N, δ¹³C, δ¹⁵N) and four climate parameters (mean annual and warmest quarter air temperature, obtained from CHELSA climatological data (8), annual and wettest quarter precipitation, retrieved from the Copernicus Climate Change Service (9)).Forty-eight Nu-BHPs were identified in soils with a pH range of 3.3-8.1 and total organic carbon (TOC) range of 0.2 and 48.4%. The most dominant compound in the dataset is adenosylhopane with 0 methylations. Of the forty-eight Nu-BHPs, thirty compounds were present in trace amounts (less than 1% of total relative abundances). The remaining eighteen Nu-BHPs were further used to investigate climatic controls on Nu-BHP abundances.This showed that only a few Nu-BHPs showed a good correlation with pH (R2 ~0.65), while temperature did not appear to influence Nu-BHP distributions. Non-metric multidimensional scaling analysis was conducted on relative abundance of these eighteen Nu-BHPs, along with the soil environmental variables and climate parameters (Fig. 1). This revealed that none of the measured parameters measured fully explains the variability in Nu-BHP distributions. We hypothesize that the main control factors instead are related to nutrient availability and/or bacterial community diversity. Future work includes investigating these variables using samples with strong nutrient and pH gradients; and known bacterial community abundances. ReferencesWeijers et al., 2007.Brooks et al., 2001. Cooke et al., 2009. Zhu et al., 2011.Talbot et al., 2014. Hopmans et al., 2021. O’Connor, 2025. Krager et al., 2017. Dorigo et al., Copernicus Climate Change Service (C3S) Climate Data Store (CDS).

The increased focus on climate change impacts over recent years and the recognised need for adaptation across society has led to a rise in the use of climate information such as the Climate Projections (UKCP18) in the UK. However, while public sector engagement with the Projections has been explored, there is very limited knowledge on how the private sector engages with and uses climate information to provide services to their end-users. The REVEAL project aimed to understand how these climate service purveyors use UKCP18 and other climate data, their relationships with both producers and users, and emerging climate information needs. For the purposes of this study, purveyors were defined as “Organisations beyond government bodies that use, enhance or add value to UKCP18 climate information to address specific users' needs.” Building upon initial Met Office research conducted in 2023 the project initially carried out a landscape assessment of purveyors in the UK who would likely use climate data and information. A total of 31 semi-structured interviews were then conducted with purveyor organisations to explore in more in-depth themes around: organisational and client profile; current and future use of UKCP18; barriers to using UKCP18; suggested developments for the next UK Projections; and wider recommendations for climate services in the UK. This study highlighted key findings around the expanding and heterogeneous landscape of purveyors in the UK, their perceptions and current use of UKCP18, the type of activities pursued and their clients, requirements for improving the next phase of projections as well as recommendations for linking up with other derived products such as the recently launched Local Authority Climate Service and Climate Risk Indicators. The study also offers insights into the expanding role of these intermediaries in the climate services value chain over the last few years as well as wider considerations on the governance of climate information and services in the context of climate adaptation efforts in the UK and Europe.

SeaDataNet is a major pan-European infrastructure for managing and providing access to marine data sets, acquired by European organisations from research cruises and other observational activities in European coastal marine waters, regional seas and the global ocean. Founding partners are National Oceanographic Data Centres (NODCs), and major marine research institutes. The SeaDataNet network gradually expanded its network of data centres and infrastructure, during a series of dedicated EU RTD projects, and by engaging as core data management infrastructure and network in leading European Commission initiatives such as the European Marine Observation and Data network (EMODnet), Copernicus Marine Service (CMS), and the European Open Science Cloud (EOSC).SeaDataNet develops, governs and promotes common standards, vocabularies, software tools, and services for marine data management, which are widely adopted. A core service is the CDI data discovery and access service which provides online unified discovery and access to vast resources of data sets, managed by 115+ connected SeaDataNet data centres from 34 countries around European seas, both from research and monitoring organisations. Currently, it gives access to more than 3 Million data sets, originating from 1000+ organisations in Europe, covering physical, geological, chemical, biological and geophysical data, acquired in European waters and global oceans. Standard metadata and data formats are used, supported by an ever-increasing set of controlled vocabularies, resulting in rich and highly FAIR metadata and data sets. SeaDataNet provides core services in EMODnet Chemistry, Bathymetry, and Physics for bringing together and harmonizing large amounts of marine data sets, which are used by EMODnet groups for generating thematic data products.EMODnet Bathymetry is active since 2008 and maintains a Digital Terrain Model (DTM) for the European seas. This is published every 2 years, each time extending coverage, and improving quality and precision. The DTMs are produced from surveys and aggregated data sets that are referenced with metadata via the SeaDataNet Catalogue services. Bathymetric survey data sets are gathered and populated by national hydrographic services, marine research institutes, and companies in the SeaDataNet CDI Data Discovery & Access service. Currently, this amounts to more than 45.000 datasets from 78 data providers. A major selection of these datasets has been used for preparing the 2024 release of the EMODnet DTM for all European waters and Caribbean, which has been published on the EMODnet portal. Currently, work is ongoing for a new 2026 version. The EMODnet DTM has a grid resolution of 1/16 * 1/16 arc minutes (circa 115 * 115 m), covering all European seas. It is based upon circa 22.000+ in situ datasets. It can be downloaded in tiles and viewed as map layers in the EMODnet portal. The maps are derived from EMODnet Bathymetry OGC WMS, WMTS, and WFS services. The EMODnet Bathymetry products are very popular and in 2024 – 2025 more than 100.000 EMODnet DTM files were downloaded, and more than 60 million OGC service requests were registered over the 2 years. EMODnet Bathymetry is also managing the European contribution to the international Seabed 2030 project.

<p>Producing and providing useful information for climate services requires vast volumes of data to come together which requires technical standards. Especially in the case of extreme climate events, where scientific methods for appropriate assessments, detection or even attribution are facing high complexity for the data processing workflows, therefore the production of climate information services requires optimal technical systems to underpinn climate services with science. These climate resilience information systems like the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S) can be enhanced when scientific workflows for extreme event detection are optimized as information production service, accordingly deployed to be usable by extreme event experts to facilitate their work through a frontend. Deployment into federated data processing systems like CDS requires that scientific methods and their algorithms be wrapped up as technical services following standards of application programming interfaces (API) and, as good practice, even FAIR principles. FAIR principles means to be <strong>Findable</strong> within federated data distribution architectures, including public catalogues of well documented scientific analytical processes. Remote storage and computation resources should be operationally <strong>Accessible</strong> to all, including low bandwidth regions and closing digital gaps to ‘Leave No One Behind’. including Data inputs, outputs, and processing API standards are the necessary conditions to ensure the system is <strong>Interoperable</strong>. And they should be built from <strong>Reusable</strong> building blocks that can be realized by modular architectures with swappable components, data provenance systems, and rich metadata.<br>Here we present challenges and preliminary prototypes for service which are based on OGC API standards for processing (https://ogcapi.ogc.org/processes/) open geospatial consortium (OGC). We are presenting blueprints on how AI-based scientific workflows can be ingested into climate resilience information systems to enhance climate services related to extreme weather and impact events. The importance of API standards will be pointed out to ensure reliable data processing in federated spatial data infrastructures. Examples will be taken from the EU H2020 Climate Intelligence (CLINT; https://climateintelligence.eu/) project, where extreme events components will be developed for C3S. Within this project, appropriate technical services will be developed as building blocks ready to deploy into digital data infrastructures like C3S but also European Science Cloud, or the DIAS. This deployment flexibility results out of the standard compliance and FAIR principles. In particular, a service employing state-of-the-art deep learning based inpainting technology to reconstruct missing climate information of global temperature patterns will be developed. This OGC-standard based web processing service (WPS) will be used as a prototype and extended in the future to other climate variables. Developments focus on heatwaves and warm nights, extreme droughts, tropical cyclones and compound and concurrent events, including their impacts, whilst the concepts are targeting generalised opportunities to transfer any kind of scientific workflow to a technical service underpinning scientific climate service. The blueprints are taking into account how to chain the data processing from data search and fetch, event index definition and detection as well as identifying the drivers responsible for the intensity of the extreme event to construct storylines guiding to the event.</p>

Climate warming in mountain regions is resulting in glacier shrinking, seasonal snow cover reduction, changes in the amount and seasonality of meltwater runoff, with consequences on water availability. Droughts are expected to become more severe in the future with economical and environmental losses both locally and downstream. Effective adaptation strategies involve multiple time scales, and seasonal forecasts can help in the optimization of the available snow/water resources with lead times of several months. In the framework of the ERA4CS MEDSCOPE project we developed a prototype to generate seasonal forecasts of snow depth and snow water equivalent with starting date November 1st and lead times of 7 months, so up to May 31st of the following year. The prototype has been co-designed with end users in the field of water management, hydropower production and of mountain ski tourism, meeting their needs in terms of indicators, time resolution of the forecasts, visualization of the forecast outputs. In this paper we present the modeling chain, based on the seasonal forecasts of ECMWF and Météo-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S) Climate Data Store. Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and relative humidity are bias-corrected and downscaled to three sites in the Western Italian Alps, and finally used as input for the physically-based multi-layer snow model SNOWPACK. The RainFARM stochastic downscaling procedure is applied to precipitation data in order to allow an estimate of uncertainties due to the downscaling method. The skill of the prototype in predicting the monthly snow depth evolution from November to May in each season of the hindcast period 1995–2015 are demonstrated using both deterministic and probabilistic metrics. Forecast skills are determined with respect to a simple forecasting method based on the climatology, and station measurements are used as reference data. The prototype shows very good skills at predicting the tercile category, i.e. snow depth below- and above-normal, in the winter (lead time 2-3-4 months) and spring (lead times 5-6-7 months) ahead: snow depth is predicted with higher accuracy (Brier Skill Score) and higher discrimination (Area Under the ROC Curve Skill Score) with respect to a simple forecasting method based on the climatology. Ensemble mean monthly snow depth forecasts are significantly correlated with observations not only at short lead time 1 and 2 months (November and December) but also at lead time 5 and 6 months (March and April) when employing the ECMWFS5 forcing. Moreover the prototype shows skill at predicting extremely dry seasons, i.e. seasons with snow depth below the 10th percentile, while skills at predicting snow depth above the 90th percentile are model-, station- and score-dependent. No remarkable differences are found among the skill scores when the precipitation input is bias-corrected, downscaled or bias-corrected and downscaled compared to the case in which raw data are employed, suggesting that skill scores are weakly sensitive to the treatment of the precipitation input.

In recent years, an increase in the frequency and intensity of cyclones with tropical characteristics has been observed in areas of the Mediterranean Sea (De la Vara et al., 2021). The rise in this type of cyclones has triggered extreme weather conditions in many Mediterranean coastal regions (Romero & Emanuel, 2013). With the increasing focus on the study and characterisation of these types of cyclones (Gutiérrez-Fernández et al., 2024), numerous research efforts have employed different methodologies to investigate the favourable conditions for the development of cyclones with tropical characteristics in the Mediterranean (Romero & Emanuel, 2017). Among the methods used to study the favourable conditions for the development of tropical cyclones, one of the most widely applied is the Potential Intensity (PI; Holland, 1997). This index measures the theoretical maximum intensity a cyclone with tropical characteristics can achieve under specific atmospheric conditions. This method has been investigated in numerous studies on tropical cyclones and demonstrates that high values of this index are associated with a higher likelihood of intense tropical cyclone development (Emanuel, 2005; Wing & Camargo, 2007).Therefore, the main motivation for this study is to analyse the evolution of the Potential Intensity (PI) over the past 70 years in the Mediterranean region. For this purpose, ERA5 reanalysis data (Hersbach et al., 2023) covering the period from 1950 to 2023 over the Mediterranean basin were used. The results indicate an increase in the values of this index, particularly during the autumn months, when most cyclones with stronger tropical characteristics occur. Finally, an individual analysis of this index was conducted under pre-development conditions for recent cyclones with tropical characteristics in the Mediterranean.de la Vara, A., Gutiérrez‐Fernández, J., González‐Alemán, J. J., & Gaertner, M. A. (2021). Characterization of medicanes with a minimal number of geopotential levels. International Journal of Climatology, 41(5), 3300-3316.Emanuel, K. (2005). Genesis and maintenance of" Mediterranean hurricanes". Advances in Geosciences, 2, 217-220.Gutiérrez‐Fernández, J., Miglietta, M. M., González‐Alemán, J. J., & Gaertner, M. A. (2024). A new refinement of Mediterranean tropical‐like cyclones characteristics. Geophysical Research Letters, 51(8), e2023GL106429.Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.bd0915c6 (Accessed on 29/10/2024)Holland, G. J. (1997). The maximum potential intensity of tropical cyclones. Journal of the atmospheric sciences, 54(21), 2519-2541.Romero, R., & Emanuel, K. (2013). Medicane risk in a changing climate. Journal of Geophysical Research: Atmospheres, 118(12), 5992-6001.Romero, R., & Emanuel, K. (2017). Climate change and hurricane-like extratropical cyclones: Projections for North Atlantic polar lows and medicanes based on CMIP5 models. Journal of Climate, 30(1), 279-299Wing, A. A., Sobel, A. H., & Camargo, S. J. (2007). Relationship between the potential and actual intensities of tropical cyclones on interannual time scales. Geophysical research letters, 34(8).

Carbon dioxide (CO2) and methane (CH4) are important atmospheric greenhouse gases (GHG) and, therefore, classified as essential climate variables (ECVs). Previously, satellite-derived atmospheric CO2 and methane CH4 ECV data sets have been generated and made available via the GHG-CCI project of the European Space Agency’s (ESA) Climate Change Initiative (CCI, http://www.esa-ghg-cci.org/). The latest GHG-CCI data set, Climate Research Data Package No. 4 (CRDP 4), covers the time period 2003–2015 and is available since February 2017. Currently, the production and provision of these data sets is being continued (pre-)operationally via the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/), which is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. The C3S satellite GHG sub-project (C3S_312a_Lot6) is led by University of Bremen supported by University of Leicester (UK), SRON (The Netherlands) and CNRS-LMD (France). The first Climate Data Record (CDR) data set produced and delivered within the C3S framework covers the time period 2003–2016 and consists of column-average dry-air mole fraction CO2 and CH4 products, i.e., XCO2 and XCH4, from SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT. Furthermore, mid-tropospheric CO2 and CH4 mixing ratios from IASI Metop-A and Metop-B are part of this data set. It is planned to extend this data set each year by one additional year. The data products are available via the Climate Data Store (CDS) of C3S. Here a short overview about this new Earth Observation data set is presented.

This volume covers multidisciplinary Research and Development contributions on geology, geophysics, bathymetric and biologic aspects, towards data sampling and acquisition, data analysis and its results, and innovative ways of data access. It also presents the development of processes to map, harmonize and integrate marine data across EEZ boundaries, an impressive example of which is the European EMODnet (European Marine Observation and Data network) initiative. EMODnet assembles scattered and partially hidden marine data into continentally harmonized geospatial data products for public benefit and increasingly within overseas collaboration. The volume also aims to shed light on an evaluation of biological and mineral resources and environmental assessments at continental shelf to slope depths. Western Pacific examples provide excellent case studies for this topic.

<p>Modern numerical ocean models have matured over the last decades and are able to provide accurate fore- and hind-cast of the ocean state. The most accurate data could be obtained from the reanalysis where the model run in a hindcast mode with assimilation of available observational data. An obvious benefit of model simulation is that it provides the spatial density and temporal resolution which cannot be achieved by in-situ observations or satellite derived measurements. It is not unusual that even a relatively small area of the ocean model can have in access of 100,000 nodes in the horizontal, each containing vertical profiles of temperature, salinity, velocity and other ocean parameters with a temporal resolution theoretically as high as a few minutes. Remotely sensed (satellite) observations of sea surface temperature can compete with the models in terms of spatial resolution, however they only produce data at the sea surface not the vertical profiles. On the other hand, in-situ observations have a benefit of being much more precise than model simulations. For instance a widely used CTD profiler SBE 911plus has accuracy of about 0.001 °C, which is not achievable by models.</p><p>In the creation of a climatic atlas the higher accuracy of individual profiles provided by in-situ measurements may become less beneficial. Assuming the normal distribution of data at each location, the standard error of the mean (SEM) is calculated as SE=S/SQRT(N), where S is the standard deviation of individual data points around the mean, and N is the number of data points. The climatic data are obtained by averaging a large number of individual data points, and here the benefit of having more data points may become a greater advantage than the accuracy of a single observation.  </p><p>In this study we have created an ocean climate atlas for the northern part of the Indian Ocean including the Red Sea and the Arabian Gulf using model generated data. The data were taken from Copernicus Marine Environment Monitoring Service (CMEMS) reanalysis product GLOBAL_REANALYSIS_PHY_001_030 with 1/12° horizontal resolution and 50 vertical levels for the period 1998 to 2017. The model component is the NEMO platform driven at the surface by ECMWF ERA-Interim reanalysis. The model assimilates along track altimeter data, satellite Sea Surface Temperature, as well as in-situ temperature and salinity vertical profiles where available. The monthly data from CMEMS were then averaged over 20 years to produce an atlas at the surface, 10, 20, 30, 75, 100, 125, 150, 200, 250, 300, 400, and 500 m depths.  The standard error of the mean has been calculated for each point and each depth level on the native grid (1/12 degree).</p><p>The atlas based on model simulations was compared with the latest version of the World Ocean Atlas (WOA)  2018 published by the NCEI.  WOA has objectively analysed climatological mean fields on a ¼  degree grid. The differences between the mean values and SEMs from observational and simulated atlases are analysed, and the potential causes of mismatch are discussed.</p>

<p>The provision of climate services to users is a fast developing field. In support of this development, the IS-ENES3 project, funded within the EC Horizon2020 program, organized three schools on “Climate data for impact assessments” in 2020 and 2021. In an Autumn school, a Spring school and a Summer school, climate scientists and impact scientists were brought together. An important aim of the schools was to enhance interaction between Vulnerability-Impact-Adaptation (VIA) researchers, climate services providers and climate researchers. Another aim was to provide an overview of information on climate modeling, climate data, impact modelling and climate services based on the work of the IS-ENE3 project.</p><p>In the first three weeks a series of lectures was given, covering topics such as climate data and modelling, impact models, portals for accessing and processing climate data, setting-up impact assessments, and communication of results to stakeholders. In the last three weeks the participants worked in small groups of one climate scientist with one impact scientist on a case study under the guidance of the course lecturers. Impact and climate researchers were combined on purpose to let them experience how they could help each other.</p><p>Originally the schools were planned to take place on-site (e.g. in Prague) during one week; however, due to COVID-19 the schools had to be transformed to virtual schools with two weekly sessions during six weeks. Although the virtual set-up had some disadvantages (e.g. less possibilities for networking), there were also some advantages (e.g. the possibility to record the lectures and make them available to a broader audience; more time to explore and work with climate data in between the sessions, no CO<sub>2</sub> emissions for travelling). During this presentation we will present the set-up of the schools and the conversion to a virtual school. We will focus on the lessons learnt and the evaluation of the virtual schools by the participants and give some recommendations for similar schools and how to link the climate and VIA research communities .</p>

Spatially enabling the Global Framework for Climate Services: Reviewing geospatial solutions to efficiently share and integrate climate data & information