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The FAIR data principles form the core OGC mission that renders in the open geospatial standards and the open-data initiatives that use them. Although OGC is best known for the technical interoperability, the domain modelling and semantic level play an inevitable role in the standards definition and the exploitation. On the one hand, we have a growing number of specialised profiles and implementations that selectively use the OGC modular specification model components. On the other hand, various domain ontologies exist already, enabling a better understanding of the data. As there could be multiple semantic representations, common data models support cross ontology traverses. Defining the service in the technical-semantic space requires fixing some flexibility points, including optional and mandatory elements, additional constraints and rules, and content including normalised vocabularies to be used.The proposed solution of the OGC Definition Server is a multi-purpose application built around the triple store database engine integrated with the ingestion, validation, and entailment tools and exposing customized end-points. The models are available in the human-readable format and machine-2-machine aimed encodings. For manual processes, it enables understanding the technical and semantic definitions/relationships between entities. Programmatic solutions benefit from a precise referential system, validations, and entailment.Currently, OGC Definition Server is hosting several types of definitions covering:Register of OGC bodies, assets, and its modules Ontological common semantic models (e.g., for Agriculture) Dictionaries of subject domains (e.g., PipelineML Codelists) In practice, that is a step forward in defining the bridge between conceptual and logical models. The concepts can be expressed as instances of various ontological classes and interpreted within multiple contexts, with the definition translated into entities, relationships, and properties. In the future, it is linking the data to the reference model and external ontologies that may be even more significant. Doing so can greatly improve the quality of the knowledge produced based on the collected data. Ability to verify the research outcomes and explainable AI are just two examples where a precise log of inferences and unambiguous semantic compatibility of the data will play a key role.

The recent OGC Cloud Concept Development Study [1] has shown that the major (big) Geospatial Data providers are going towards Cloud solutions not only to make more data more accessible, but also to locate data processing next to the data. Meanwhile, recent experiences from the H2020 e-shape project show that the EO developers community still needs support to fully adopt the Cloud all the more that based on the feedback received during e- shape’s first sprint, the Earth Observation Cloud platforms still need to mature to be more attractive. In order to support the good connection between Data providers, Technology providers and EO developers, it is critical that sponsors keep on supporting the efforts from the Earth Observation community at a number of levels: Enhancing Copernicus and other open data accessibility, developing Clouds and platforms interoperability and operational maturity, increasing cloud skills among developers and scientists, sustaining funding mechanisms long enough to allow the rendez-vous in the Cloud of all the critical stakeholders with good timing to reach the critical point of self-sustainability.During this process it is important to not only develop the technical skills and new platforms capacities, but also to develop a good understanding of the pricing mechanisms and how to optimize the costs. This is very needed to develop the trust that outsourcing infrastructures will lead to the expected budget savings and to trigger the budgets organization evolutions that moving to Cloud technologies requires. [1] Echterhoff, J., Wagermann, J., Lieberman, J.: OGC 21-023, OGC Earth Observation Cloud Platform COncept Development Study Report. Open Geospatial Consortium (2021). https://docs.ogc.org/per/21-023.html

Cloud-based big earth data workflow architectures for operational decision making across communities need to follow FAIR (Findable, Accessible, Interoperable, Reusable) principles in order to be effective. This presentation highlights mature implementations of OGC standards-based building blocks for climate data processing and service provision that are deployed in leading climate services information server systems such as the COPERNICUS Climate Change Service C3S. OGC Web Processing Services (WPS) form the bases of component operations in these implementations, from simple polygon subsetting to climate indices calculation and complex hydrological modelling. Interoperable building blocks also handle security functions such as user registration, client-site utilities, and data quality compliance. A particular focus will be the ROOCS (Remote Operations on Climate Simulations) project, a set of tools and services to provide "data-aware" processing of ESGF (Earth System Grid Federation) and other standards-compliant climate datasets from modelling initiatives such as CMIP6 and CORDEX. One example is the WPS service ‘Rook’, that enables remote operations, such as spatio-temporal subsetting, on climate model data. It exposes all the operations available in the ‘daops’ library based on Xarray. Finch is a WPS-based service for remote climate index calculations, also used for the analytics of ClimateData.ca, that dynamically wraps Xclim, a Python-based high-performance distributed climate index library. Finch automatically builds catalogues of available climate indicators, fetches data using “lazy”-loading, and manages asynchronous requests with Gunicorn and Dask. Raven-WPS provides parallel web access to a dynamically-configurable ‘RAVEN’ hydrological modelling framework with numerous pre-configured hydrological models (GR4J-CN, HBV-EC, HMETS, MOHYSE) and terrain-based analyses. Coupling GeoServer-housed terrain datasets with climate datasets, RAVEN can perform analyses such as hydrological forecasting without requirements of local access to data, installation of binaries, or local computation.The EO Exploitation Platform Common Architecture (EOEPCA) describes an app-to-the-data paradigm where users select, deploy and run application workflows on remote platforms where the data resides. Following OGC Best Practices for EO Application Packages, Weaver executes workflows that chain together various applications and WPS inputs/outputs. It can also deploy near-to-data applications using Common Workflow Language (CWL) application definitions. Weaver was developed especially with climate services use cases in mind.The architectural patterns illustrated by these examples will be exercised and tested in the upcoming OGC Climate Services Pilot initiative, whose outputs will be also incorporated into disaster risk indicators developed in the upcoming OGC Disaster Pilot 2022.Further reading:https://docs.google.com/document/d/1IrwlEiR-yRLcoI9fGh2B1leH4KU0v0SUMWQqiaxc1BM/edit

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 Findable within federated data distribution architectures, including public catalogues of well documented scientific analytical processes. Remote storage and computation resources should be operationally Accessible 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 Interoperable. And they should be built from Reusable building blocks that can be realized by modular architectures with swappable components, data provenance systems, and rich metadata.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.

Ongoing climate change is increasingly impacting ecosystems and living conditions. To understand climate change effects on all scales ranging from regional to global and to develop appropriate response strategies, reliable, easily accessible climate location information is crucial. The United Nations framework of climate change policy emphasizes the role of open data as an essential component to enable efficient implementation of appropriate climate change strategies. Data offered at the various portals and climate services needs to be Findable, Accessible, Interoperable, and Reusable (FAIR). This is particularly important when several communities need to work together in order to develop the most effective response strategies. These communities not only involve climate scientists and meteorologists, but also climate impact analysts, hydrologists, agronomists, urban planners, ecologists, and many more. Screening the web for available data, it becomes apparent that there is no shortage of portal solutions built upon climate data archives. Portal solutions have turned out in the past of often being targeted towards a specific, and sometimes rather small, number of users from within a single community. Cross-community integration and thus enhanced reusability and interoperability was not in focus. Due to recent ongoing international domain crossing efforts, FAIR principles are increasingly respected also for the portal architectures of the information systems itself. For example, the Open Geospatial Consortium (OGC) develops standards and best practices that enable FAIR principles across communities.FAIR principles across communities require a set of essential ingredients to work effectively. These ingredients include metadata models that allow discovery (Findable), interfaces to access the data (Accessible), data models that are well documented (Interoperable) and can be efficiently consumed by others (Reusable). Because data volumes are continuously growing and therefore require new approaches for efficient data processing, OGC has extended the ‘Reusable’ component in FAIR. ‘Reusable’ now includes mechanisms for executing applications close to the physical location of the data. What was previously a data provisioning system now needs to be extended to support processing capacities up to the level where user-defined applications can be deployed and executed. In a sense, for data to be FAIR, it needs to be accompanied by equally FAIR services. This presentation is showing current realisations of leading climate services information systems that implement the extended FAIR principle. The presentation will sort out roles and capabilities of standardized web APIs that can be assembled in line with data and processing environments for interoperable climate data across communities in the most efficient way. Once paired with OGC’s new “Applications-to-the-Data” architecture and strong metadata models, the web APIs enable effective integration of climate data with data from other disciplines within state-of-the art cloud environments that feature not only reusability of data, but also of applications, data processes, and scientific workflows.

A detailed analysis is carried out to assess the HadGEM3-A global atmospheric model skill in simulating extreme temperatures, precipitation and storm surges in Europe in the view of their attribution to human influence. The analysis is performed based on an ensemble of 15 atmospheric simulations forced with observed sea surface temperature of the 54 year period 1960–2013. These simulations, together with dual simulations without human influence in the forcing, are intended to be used in weather and climate event attribution. The analysis investigates the main processes leading to extreme events, including atmospheric circulation patterns, their links with temperature extremes, land–atmosphere and troposphere-stratosphere interactions. It also compares observed and simulated variability, trends and generalized extreme value theory parameters for temperature and precipitation. One of the most striking findings is the ability of the model to capture North-Atlantic atmospheric weather regimes as obtained from a cluster analysis of sea level pressure fields. The model also reproduces the main observed weather patterns responsible for temperature and precipitation extreme events. However, biases are found in many physical processes. Slightly excessive drying may be the cause of an overestimated summer interannual variability and too intense heat waves, especially in central/northern Europe. However, this does not seem to hinder proper simulation of summer temperature trends. Cold extremes appear well simulated, as well as the underlying blocking frequency and stratosphere-troposphere interactions. Extreme precipitation amounts are overestimated and too variable. The atmospheric conditions leading to storm surges were also examined in the Baltics region. There, simulated weather conditions appear not to be leading to strong enough storm surges, but winds were found in very good agreement with reanalyses. The performance in reproducing atmospheric weather patterns indicates that biases mainly originate from local and regional physical processes. This makes local bias adjustment meaningful for climate change attribution.

International audience; Analyses of extreme weather events and their impacts often requires big data processing of ensembles of climate model simulations. Researchers generally proceed by downloading the data from the providers and processing the data files " at home " with their own analysis processes. However, the growing amount of available climate model and observation data makes this procedure quite awkward. In addition, data processing knowledge is kept local, instead of being consolidated into a common resource of reusable code. These drawbacks can be mitigated by using a web processing service (WPS). A WPS hosts services such as data analysis processes that are accessible over the web, and can be installed close to the data archives. We developed a WPS named 'flyingpigeon' that communicates over an HTTP network protocol based on standards defined by the Open Geospatial Consortium (OGC) [23], to be used by climatologists and impact modelers as a tool for analyzing large datasets remotely. Here, we present the current processes we developed in flyingpigeon relating to commonly-used processes (preprocessing steps, spatial subsets at continent, country or region level, and climate indices) as well as methods for specific climate data analysis (weather regimes, analogues of circulation, segetal flora distribution, and species distribution models). We also developed a novel, browser-based interactive data visualization for circulation analogues , illustrating the flexibility of WPS in designing custom outputs. Bringing the software to the data instead of transferring the data to the code is becoming increasingly necessary, especially with the upcoming massive climate datasets.

International audience; An attribution study has been performed to investigate the degree to which the unusually cold European winter of 2009/10 was modified by anthropogenic climate change. Two different methods have been included for the attribution: one based on large HadGEM3-A ensembles and one based on a statistical surrogate method. Both methods are evaluated by comparing simulated winter temperature means, trends, standard deviations, skewness, return periods, and 5% quantiles with observations. While the surrogate method performs well, HadGEM3-A in general underestimates the trend in winter by a factor of ⅔. It has a mean cold bias dominated by the mountainous regions and also underestimates the cold 5% quantile in many regions of Europe. Both methods show that the probability of experiencing a winter as cold as 2009/10 has been reduced by approximately a factor of 2 because of anthropogenic changes. The method based on HadGEM3-A ensembles gives somewhat larger changes than the surrogate method because of differences in the definition of the unperturbed climate. The results are based on two diagnostics: the coldest day in winter and the largest continuous area with temperatures colder than twice the local standard deviation. The results are not sensitive to the choice of bias correction except in the mountainous regions. Previous results regarding the behavior of the measures of the changed probability have been extended. The counterintuitive behavior for heavy-tailed distributions is found to hold for a range of measures and for events that become more rare in a changed climate

BACKGROUND Central European outbreak populations of the bank vole (Myodes glareolus Schreber) are known to cause damage in forestry and to transmit the most common type of Hantavirus (Puumala virus, PUUV) to humans. A sound estimation of potential effects of future climate scenarios on population dynamics is a prerequisite for long-term management strategies. Historic abundance time series were used to identify the key weather conditions associated with bank vole abundance, and were extrapolated to future climate scenarios to derive potential long-term changes in bank vole abundance dynamics. RESULTS Classification and regression tree analysis revealed the most relevant weather parameters associated with high and low bank vole abundances. Summer temperatures 2 years prior to trapping had the highest impact on abundance fluctuation. Extrapolation of the identified parameters to future climate conditions revealed an increase in years with high vole abundance. CONCLUSION Key weather patterns associated with vole abundance reflect the importance of superabundant food supply through masting to the occurrence of bank vole outbreaks. Owing to changing climate, these outbreaks are predicted potentially to increase in frequency 3–4-fold by the end of this century. This may negatively affect damage patterns in forestry and the risk of human PUUV infection in the long term. © 2014 Society of Chemical Industry

The low energy polarimeter (LEP [1]) is a polarimeter in the injection beam line of the Cooler Synchrotron (COSY [2]). COSY is a facility for cooled polarized beams at the Forschungszentrum in Julich. The beam polarization is measured using scattering off carbon and polyethylene (CH2) targets. Up to now only elastic scattering off the carbon atoms is used for polarimetry. The outgoing particles are detected using twelve plastic scintillators installed in groups of three to the left, to the right, above, and below the beam. LEP is the routine tool for beam set-up, but limited in performance. The current read-out electronics consists of analog NIM modules. It can either be used to measure pulse height spectra at a data rate limited to about 50 kHz or to merely count the pulses within an adjustable range in amplitude, which allows for a faster measurement. A further motivation for the upgrade is the missing ability to determine the tensor polarization Pzz. A new system using analog pulse sampling and an FPGA chip for signal processing was installed and tested. The ejectile particles were identified by relative time of flight measurement using a signal from the RF amplifier of the cyclotron used for acceleration as a reference. The new system is able to measure the time at which a particle arrives to an accuracy in the order of 50 ps. The presentation includes a review of available systems and the report about first measurements in May 2015.

A new high-resolution regional climate change ensemble has been established for Europe within the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) initiative. The first set of simulations with a horizontal resolution of 12.5 km was completed for the new emission scenarios RCP4.5 and RCP8.5 with more simulations expected to follow. The aim of this paper is to present this data set to the different communities active in regional climate modelling, impact assessment and adaptation. The EURO-CORDEX ensemble results have been compared to the SRES A1B simulation results achieved within the ENSEMBLES project. The large-scale patterns of changes in mean temperature and precipitation are similar in all three scenarios, but they differ in regional details, which can partly be related to the higher resolution in EURO-CORDEX. The results strengthen those obtained in ENSEMBLES, but need further investigations. The analysis of impact indices shows that for RCP8.5, there is a substantially larger change projected for temperature-based indices than for RCP4.5. The difference is less pronounced for precipitation-based indices. Two effects of the increased resolution can be regarded as an added value of regional climate simulations. Regional climate model simulations provide higher daily precipitation intensities, which are completely missing in the global climate model simulations, and they provide a significantly different climate change of daily precipitation intensities resulting in a smoother shift from weak to moderate and high intensities., Regional Environmental Change, 14 (2), ISSN:1436-3798, ISSN:1436-378X

Daniela Jacob • Juliane Petersen • Bastian Eggert • Antoinette Alias • Ole Bossing Christensen • Laurens M. Bouwer • Alain Braun • Augustin Colette • Michel Deque • Goran Georgievski • Elena Georgopoulou • Andreas Gobiet • Laurent Menut • Grigory Nikulin • Andreas Haensler • Nils Hempelmann • Colin Jones • Klaus Keuler • Sari Kovats • Nico Kroner • Sven Kotlarski • Arne Kriegsmann • Eric Martin • Erik van Meijgaard • Christopher Moseley • Susanne Pfeifer • Swantje Preuschmann • Christine Radermacher • Kai Radtke • Diana Rechid • Mark Rounsevell • Patrick Samuelsson • Samuel Somot • Jean-Francois Soussana • Claas Teichmann • Riccardo Valentini • Robert Vautard • Bjorn Weber • Pascal Yiou

Central European outbreak populations of the bank vole (Myodes glareolus Schreber) are known to cause damage in forestry and to transmit the most common type of Hantavirus (Puumala virus, PUUV) to humans. A sound estimation of potential effects of future climate scenarios on population dynamics is a prerequisite for long-term management strategies. Historic abundance time series were used to identify the key weather conditions associated with bank vole abundance, and were extrapolated to future climate scenarios to derive potential long-term changes in bank vole abundance dynamics.Classification and regression tree analysis revealed the most relevant weather parameters associated with high and low bank vole abundances. Summer temperatures 2 years prior to trapping had the highest impact on abundance fluctuation. Extrapolation of the identified parameters to future climate conditions revealed an increase in years with high vole abundance.Key weather patterns associated with vole abundance reflect the importance of superabundant food supply through masting to the occurrence of bank vole outbreaks. Owing to changing climate, these outbreaks are predicted potentially to increase in frequency 3-4-fold by the end of this century. This may negatively affect damage patterns in forestry and the risk of human PUUV infection in the long term.

Climate is the main environmental driver determining the spatial distribution of most tree species at the continental scale. We investigated the distribution change of European beech and Norway spruce due to climate change. We applied a species distribution model (SDM), driven by an ensemble of 21 regional climate models in order to study the shift of the favourability distribution of these species. SDMs were parameterized for 1971–2000, as well as 2021–2050 and 2071–2100 using the SRES scenario A1B and three physiological meaningful climate variables. Growing degree sum and precipitation sum were calculated for the growing season on a basis of daily data. Results show a general north-eastern and altitudinal shift in climatological favourability for both species, although the shift is more marked for spruce. The gain of new favourable sites in the north or in the Alps is stronger for beech compared to spruce. Uncertainty is expressed as the variance of the averaged maps and with a density function. Uncertainty in species distribution increases over time. This study demonstrates the importance of data ensembles and shows how to deal with different outcomes in order to improve impact studies by showing uncertainty of the resulting maps.