2024-03-28T14:22:29Z
https://zenodo.org/oai2d
oai:zenodo.org:3634508
2020-02-03T19:20:50Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-02-03
<p><strong>Climate Index: </strong>RX5day</p>
<p><strong>Definition:</strong> Greatest five-day precipitation amount.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3634508
oai:zenodo.org:3634508
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3634507
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
RX5day
Precipitation
EURO-CORDEX
open-data
output-data
H2020
Future climate
Ensemble calculations of "RX5day" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3635289
2020-02-05T07:20:52Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-02-04
<p><strong>Climate Index: </strong>Consecutive Dry Days</p>
<p><strong>Definition:</strong> Maximum annual number of consecutive dry days (daily rainfall minimum < 1mm).</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3635289
oai:zenodo.org:3635289
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3635288
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Consecutive dry days
Drought
EURO-CORDEX
open-data
output-data
H2020
Future climate
Ensemble calculations of "Consecutive Dry Days" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3529265
2020-01-20T17:19:11Z
user-clarity
user-eu
Loibl, Wolfgang
Etminan, Ghazal
Österreicher, Doris
Ratheiser, Matthias
Stollnberger, Romana
Tschannett, Simon
Tötzer, Tanja
Vuckovic, Milena
Walal, Karoline
2019-04-02
<p>The paper discusses interrelations of urban densification and urban climate under global warming conditions by means of microclimate simulations as well as urban densification scenarios, referring to two research projects exploring test areas in Vienna and Linz, Austria. The impact of the extension of building heights on microclimate in densely urbanized areas is tested applying 3D city models describing building height distribution, surface properties and open space characteristics. In Vienna the densification impact on the local climate is explored for a larger study area by extruding the buildings’ footprints towards the maximum height as allowed by the current zoning regulations. In Linz the urban densification impact on the local climate is tested by adding high-rise buildings which are planned to be developed in a selected neighbourhood. Building height extension scenarios allow on the one side examining the densification potential to create new residential floorspace without requiring additional building land and on the other side to investigate the impact of densification on climate conditions by modelling the effects on heat storage during sunlight hours, nocturnal heat radiation from buildings and air flow. Microclimate simulations show significant differences in the diurnal variation pattern of the mean radiant temperature depending on increase or decrease of shading and heat storage effects due to densification.</p>
ISBN 978-3-9504173-6-4
REALCORP conference proceedings:
https://archive.corp.at/cdrom2019/papers2019/CORP2019_28.pdf
https://doi.org/10.5281/zenodo.3529265
oai:zenodo.org:3529265
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3529264
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
climate simulation, climate-urban fabric interaction, climate change, urban densification, climate adaptation
Urban densification and urban climate change – assessing interaction through densification scenarios and climate simulations
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:3832152
2020-08-10T09:55:17Z
user-clarity
software
user-eu
Pascal Dihé
therter
Itsman-AT
DanielRodera
2020-05-18
The Map Component is a reusable, flexible and highly configurable Building Block meant to be used throughout CSIS.
https://doi.org/10.5281/zenodo.3832152
oai:zenodo.org:3832152
Zenodo
https://github.com/clarity-h2020/map-component/tree/2.6.0
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3642873
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/map-component: Map Component v2.6.0
info:eu-repo/semantics/other
oai:zenodo.org:3978050
2020-08-11T00:59:23Z
user-clarity
software
user-eu
Pascal Dihé
therter
Itsman-AT
DanielRodera
2020-08-10
The Map Component is a reusable, flexible and highly configurable Building Block meant to be used throughout CSIS.
https://doi.org/10.5281/zenodo.3978050
oai:zenodo.org:3978050
Zenodo
https://github.com/clarity-h2020/map-component/tree/2.7.2
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3642873
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/map-component: Map Component v2.7.2
info:eu-repo/semantics/other
oai:zenodo.org:2565211
2020-01-20T15:31:02Z
user-clarity
openaire
user-eu
Kainz, Astrid
2019-02-14
<p>The CLARITY methodology, as well as the application of the urban climate model MUKLIMO_3 within the framework of the Austrian Demonstration Case, is presented. The current and future climatic situation in terms of urban heat stress and possible climate adaptation measures on urban scale, such as roof greening, increased albedo, decreased soil sealing, are shown for the city of Linz.</p>
https://doi.org/10.5281/zenodo.2565211
oai:zenodo.org:2565211
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2565210
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Side Event at the European Week of Regions & Cities, Brussels, 9-10 October 2018
CLARITY Demonstration Case Austria - Climate Change Adaptation for the city of Linz
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:4050326
2020-09-28T12:26:54Z
user-clarity
openaire
user-eu
José Cubillo
Denis Havlik
Luis Torres
Laura Parra
María Postigo
Jorge Paz
Iñaki Beltrán
2020-07-15
<p><strong>En este webinar presentamos un análisis del Cambio Climático: Cómo se mejora la resiliencia de la Infraestructura de Transportes y Urbana?</strong></p>
<p>El presente webinar tiene como objetivo presentar dicho servicio climático y los resultados obtenidos. Así mismo, se van a presentar otros dos proyectos europeos, INFRA SIS y FORESEE también dedicados al estudio de los posibles cambios vinculados al clima que hay que tener en cuenta en el diseño y explotación de infraestructuras. Para cerrar el webinar se ha organizado una mesa redonda en la que participarán todos los ponentes y se debatirán las aplicaciones y ventajas de incluir la información ambiental en la toma de decisiones.</p>
<p>En España, la inminente ley de cambio climático es el marco perfecto para conseguir que todos los actores involucrados en estas actividades incorporen en sus actividades información sobre las proyecciones climáticas.</p>
<p>En este sentido, el proyecto CLARITY, financiado en el marco del programa H2020 de la UE, ha desarrollado un servicio climático (CSIS) que alberga una herramienta online para la evaluación del riesgo en infraestructuras urbanas y de transportes, proporcionando las proyecciones de las variables climáticas que se han considerado más representativas de las potenciales amenazas a las que se pueden enfrentar, tanto las ciudades como las carreteras.</p>
This webinar is part of th eCLARITY4ClimateResilience webinar series. Other webinars from this series can be accessed at https://www.gotostage.com/channel/climate-adaptation
https://doi.org/10.5281/zenodo.4050326
oai:zenodo.org:4050326
spa
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4050325
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate adaptation
Climate change adaptation
Adaptación al cambio climático
infraestructura de transporte,
infraestructura urbana
Climate resilience
Transport infrastructure
Urban Infrastructure
Análisis de vulnerabilidad y riesgo frente a cambio climático en infraestructuras de transporte. Proyecto CLARITY
info:eu-repo/semantics/lecture
oai:zenodo.org:3336025
2020-10-19T09:33:59Z
user-clarity
openaire
user-eu
Hahn, Claudia
Goler, Robert
Zuvela-Aloise, Maja
Kainz, Astrid
de Wit, Rosmarie
Zuccaro, Giulio
Leone, Mattia
Capolupo, Alessandra
Havlik, Denis
Skarbal, Bernhard
2019-07-15
<p>Climate research and modelling efforts provide a large amount of data and knowledge on how the climate will change in different regions of the world. Translating available climate data such that decision-makers can incorporate the information into their decisions is crucial to increase resilience at local level.</p>
<p>Within the EU-Horizon-2020 funded project CLARITY (http://www.clarity-h2020.eu) an integrated Climate Services Information System (CSIS) is being developed to transfer knowledge about climate change and its implications for urban areas and traffic infrastructure to decision-makers and thus to support urban infrastructure planning. CSIS implements the standardized CLARITY methodological framework that comprises the hazard characterisation, exposure analysis, vulnerability analysis, risk and impact assessment and the identification and appraisal of adaptation options.</p>
<p>Hazard analysis is the first step needed to provide an assessment on the risk and impact of such hazards. The following hazards are being evaluated for inclusion in CSIS: heat, cold, floods, storms, droughts, forest fires and landslides. For each included hazard (e.g. heat), several climate indices (e.g. consecutive summer days, tropical nights, etc.) will be provided to fully capture its effects. Climate indices will be available for the whole of Europe - for the historical period 1971-2000 as well as for three future time periods (2011-2040, 2041-2070, 2071-2100). The indices are being calculated for 16 Global Climate Model – Regional Climate Model combinations from the EURO-CORDEX simulations at 0.11_ resolution (EUR-11) to account for inter-model variability. An ensemble mean will be available for three representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5). Combining climate data with additional data sources (e.g. EUROSTAT) will then enable the evaluation of the exposure of certain elements at risk, which, together with an assessment of their vulnerability, is necessary for the risk and impact analysis.</p>
<p>While the CSIS methodology will be tested and demonstrated in detail in four different study areas within Europe, the CLARITY CSIS platform will also provide an easy to use screening tool for risk assessment. It will demonstrate: (1) how a core set of hazards and exposure data, based on available Open Data, can be easily provided for any major urban region within Europe; and (2) the added value of the screening based on high resolution regional data sets.</p>
<p>The CLARITY screening tool together with the more detailed studies in four demonstration cases will display the potential of climate services and will help to develop business models for further exploitation of climate services among decision-makers interested in expert studies for climate resilient planning.</p>
https://doi.org/10.5281/zenodo.3336025
oai:zenodo.org:3336025
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3336024
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
European Geosciences Union General Assembly 2019 (EGU 2019), Vienna, 7-12 April 2019
CLARITY, climate services, hazard characterisation
The CLARITY Climate Services Information System – providing hazard characterisation on European scale
info:eu-repo/semantics/lecture
oai:zenodo.org:2557587
2020-01-20T15:31:51Z
user-clarity
openaire
user-eu
Denis Havlik
2018-10-09
<p>"CLARITY climate service concept" was presented on the joint CLARITY/Climate Fit workshop that was organised as a side-event of the "European Week of Regions and Cities", on October 9-th and 10-th 2018, in Brussels. </p>
<p>Interactive CSIS mockups illustrates the "screening" part of the CSIS service, where users can discover the main hazards at the project location, baseline exposure for the key elements at risk, vulnerabilities and understand the risk profile of their projects.</p>
Interactive mockup does not work in the preview mode. Download the file, open it in acrobat reader and press Ctrl-L to switch to full screen mode.
https://doi.org/10.5281/zenodo.2557587
oai:zenodo.org:2557587
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2557586
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
European Week of Regions and Cities, Brussels, 7-10 October 2019
CLARITY, climate services, climate change adaptation, concept
CLARITY climate service concept and mockups
info:eu-repo/semantics/lecture
oai:zenodo.org:3529276
2020-01-20T16:44:21Z
user-clarity
user-eu
Vuckovic, Milena
Loibl, Wolfgang
Tötzer, Tanja
Stollnberger, Romana
2019-07-10
<p>Global increase of urban population has brought about a growing demand for more dwelling space, resulting in various negative impacts, such as accelerated urbanization, urban sprawl and higher carbon footprints. To cope with these growth dynamics, city authorities are urged to consider alternative planning strategies aiming at mitigating the negative implications of urbanization.</p>
<p>In this context, the present contribution investigates the potential of urban densification to mitigate the heat island effects and to improve outdoor thermal conditions. Focusing on a quite densely urbanized district in Vienna, Austria, we carried out a set of simulations of urban microclimate for pre- and post-densification scenarios using the parametric modelling environment Rhinoceros 3D and a set of built-in algorithms in the Rhino’s plug-in Grasshopper. The study was conducted for a hot summer period. The results revealed a notable solar shielding effect of newly introduced vertical extensions of existing buildings, promoting temperature decrease and improved thermal conditions within more shaded urban canyons and courtyards. However, a slight warming effect was noted during the night-time due to the higher thermal storage and lower sky view factor.</p>
Urban microclimate modelling and results described in the paper was further funded through Austrian Climate Research Program 2018 (ACRP)
https://doi.org/10.3390/environments6070082
oai:zenodo.org:3529276
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
urban heat island; urban densification; solar radiation shielding; climate adaptation measures., urban climate modelling
Potential of Urban Densification to Mitigate the Effects of Heat Island in Vienna, Austria
info:eu-repo/semantics/article
oai:zenodo.org:3532656
2020-01-20T17:03:52Z
user-clarity
openaire
user-eu
Kainz, Astrid
Zuvela-Aloise, Maja
Goler, Robert
de Wit, Rosmarie
Hahn, Claudia
2019-11-08
<p>Urban areas and traffic infrastructure are particularly affected by climate change, thus raising the need for well-founded climate adaptation strategies. The project CLARITY, funded by EU Horizon 2020 Programme, aims at implementing a climate services information system (CSIS) specifically designed to address climate related hazards and to provide climate change adaptation strategies for supporting urban infrastructure development. The CSIS is tested and demonstrated on four study areas in different regional and climatological contexts. In this study, we focus on the Austrian demonstration case that addresses the compound effects of heat waves and urban heat islands in the city of Linz, which under climate change are expected to worsen.</p>
<p>The dynamical urban climate model MUKLIMO_3, developed by the Deutscher Wetterdienst (DWD), is used to investigate urban heat island effects and to carry out sensitivity simulations of climate adaptation measures for the city of Linz and its surrounding area. The model simulations, performed at a horizontal resolution of 100m, are based on Copernicus Urban Atlas land cover data combined with local data provided by the city administration of Linz to consider city-specific structures. A dynamical-statistical downscaling method is applied to derive climate indices for long-term historical and future climate periods by combining high-resolution model output with observational data and regional climate projections.</p>
<p>Model results are used to analyze the current and future climatic situation in the city of Linz in terms of urban heat load. Furthermore, several climate adaptation scenarios are tested with respect to their efficiency in reducing urban heat stress. These include, amongst others, roof greening, increased albedo of roofs and walls, unsealing of surfaces and increased vegetation cover. Depending on the scenario, moderate to strong cooling effects are found as indicated by a reduction in the mean annual number of summer days, hot days and tropical nights.</p>
<p>The main findings obtained in this study are used to demonstrate how urban climate models promote the efficiency assessment of different climate adaptation strategies and how they contribute to climate resilient urban planning.</p>
https://doi.org/10.5281/zenodo.3532656
oai:zenodo.org:3532656
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3532655
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY, climate services, ECCA2019
Demonstrating the effects of climate adaptation measures for the Austrian city of Linz as part of CLARITY's climate services
info:eu-repo/semantics/lecture
oai:zenodo.org:2557364
2020-01-20T16:29:18Z
user-clarity
user-eu
Dihé, Pascal
2018-03-20
<p>This report is the first deliverable of WP4 “Technology Support” of the CLARITY project, funded by the EU’s Horizon 2020 Programme under Grant Agreement number 730355. WP4 intends to provide the technological backbone of the CLARITY Climate Service Information System (CSIS) by tailoring the technological background foreseen in the CLARITY work package descriptions to project needs. For this, WP4 will integrate and adapt all required and existing (background) tools and services that are necessary for realisation of the CLARITY reference scenarios (Demonstration Cases) and implementation of the EU-GL into the CLARITY Climate Services.</p>
<p>The aim of this deliverable is to provide an initial plan for the WP4 work per task, taking into account the input from D1.1 “Initial Workshops and the CLARITY Development Environment”:</p>
<ul>
<li>T4.2 "Catalogue of elements at risk and adaptation options" provides a catalogue that will serve as repository of element at risk types and adaptation options and a dedicated Catalogue of Data Sources and Simulation Models that makes climate-related information accessible and discoverable.</li>
<li>T4.3 "Scenario Management" will offer the Scenario Management tool which supports and enforces first and foremost the standardised workflows of the EU-GL for each of the distinct planning steps, a Data Dashboard that provides an overview of all the different datasets that are used, produced, ordered, collected, requested, exchanged etc. by an end user and a Data Package Export and Import Tool for exporting any data that is directly available in the CSIS.</li>
<li>T4.4 "Scenario Transferability" provides a Map Component for clear and easy visualization of different maps and layers as well as the Scenario Transferability Component that offers general matchmaking functionality by means of graphical user interfaces (e.g. map visualisations) for side-by-side comparison of alternate (adaptation) scenarios.</li>
<li>T4.5 Scenario Analysis, Decision Support and Report Generation provides software that will support the analysis and comparison of scenario candidates (options) regarding performance indicators that can be defined by the end user.</li>
</ul>
<p>Detailed information for each of the WP4 tasks is provided as a separate section in this document. Annex 1 also provides an overview of the tools that are inherited from previous projects and explains the initial plan for using and/or extending these inputs in CLARITY.</p>
https://doi.org/10.5281/zenodo.2557364
oai:zenodo.org:2557364
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2557363
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
H2020
Technology Support
Building Blocks
Climate Change
Software Components
CLARITY D4.1 Technology Support Plan
info:eu-repo/semantics/report
oai:zenodo.org:2563051
2020-01-21T07:22:35Z
user-clarity
openaire_data
user-eu
Kainz, Astrid
2019-02-12
<p>Climate indices (e.g. mean annual number of summer days, hot days, tropical nights) for 30-year historical/future climate periods. The calculation method is based on the cuboid method, a statistical-dynamical downscaling procedure that combines high-resolution (100m) urban climate simulations with long-term climate information from monitoring data/regional climate projections.</p>
<p><strong>Climate indices for historical/current periods:</strong> - Background climate information: monitoring data from the airport station Linz Hoersching (1961-2010) - Background climate information: historical (bias-corrected) EURO-CORDEX simulations (1971-2000)</p>
<p><strong>Climate indices for future periods:</strong> - Background climate information: bias-corrected EURO-CORDEX model simulations for different representative concentration pathways (2021-2100)</p>
Provenance: EURO-CORDEX + MUKLIMO
Note: These datasets are preliminary results and will eventually be updated.
Conditions: MUKLIMO_3 results are intended for non-commercial public use, complying to the conditions of the MUKLIMO_3 modelling license for non-commercial tasks in research and teaching.
https://doi.org/10.5281/zenodo.2563051
oai:zenodo.org:2563051
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/heat-load-maps
https://doi.org/10.5281/zenodo.2565211
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2563050
info:eu-repo/semantics/openAccess
Other (Non-Commercial)
EURO-CORDEX
MUKLIMO_3
open-data
output-data
H2020
CLARITY
Climate Indices
Heat
GeoTIFF
NetCDF
for non commercial use only
hot days
tropical nights
future climate
historical climate
Heat load maps at 100m resolution (Linz)
info:eu-repo/semantics/other
oai:zenodo.org:2560307
2020-01-24T19:23:49Z
user-clarity
openaire_data
user-eu
Nuñez, Mario
2019-02-08
<p>Urban Atlas based data subset, where every element with CODE 11220 was extracted as medium urban fabric elements with the next information:</p>
<p>gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real run_off_coefficient real context real fua_tunnel real</p>
<p>This data is an input for local effects calculation.</p>
Provenance: Urban Atlas
https://doi.org/10.5281/zenodo.2560307
oai:zenodo.org:2560307
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/medium-urban-fabric
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2560306
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
H2020
CLARITY
DC1
Land Use
Local Effects
Medium urban fabric
Urban Atlas
open-data
Naples
output-data
GeoTIFF
ESRI Shapefile
Medium Urban Fabric (Comune di Napoli)
info:eu-repo/semantics/other
oai:zenodo.org:2562214
2020-05-04T14:13:23Z
user-clarity
openaire_data
user-eu
Nuñez, Mario
2019-02-11
<p>Urban Atlas based data subset, where every element with CODE 50000 was extracted as a water element with the next information:</p>
<p>gid integer area numeric perimeter numeric geom geometry(polygon,3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint</p>
<p>This data is an input for local effects calculation.</p>
Provenance: Urban Atlas
https://doi.org/10.5281/zenodo.2562214
oai:zenodo.org:2562214
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/naples-water
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2562213
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
H2020
ESRI Shapefile
GeoTIFF
DC1
Land Use
Local Effects
Naples Urban
Atlas Water
open-data
output-data
Water
info:eu-repo/semantics/other
oai:zenodo.org:4046113
2020-09-24T00:26:50Z
user-clarity
openaire_data
user-eu
Hundecha, Yeshewatesfa
2020-09-23
<p>Hourly river flow and total runoff were computed for the southern part of Sweden using the hourly version of a high resolution hydrological model S-HYPE, which is operationally used by SMHI. The model was calibrated and validated using radar based hourly precipitation and an operationally used hourly reanalysis temperature data. Projection of the impact of climate change was performed by running the model with hourly forcing data from an ensemble of EURO-COREX climate model simulations over 1971 - 2100. Four GCM-RCM combinations were used under two emission scenarios, RCP4.5 and RCP8.5. The results can be used to assess the risk of riverine flooding in areas located along a small to meso-scale river basin. The results can, in particular, be used to assess the risk of flash flooding that can result from heavy precipitation of short duration.</p>
https://doi.org/10.5281/zenodo.4046113
oai:zenodo.org:4046113
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4046112
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Sweden
extreme river flow
hourly flow extremes
climate impact projection
SMHI
DC2 High resolution future hydrological data for Sweden
info:eu-repo/semantics/other
oai:zenodo.org:3822051
2020-05-13T20:20:39Z
user-clarity
user-eu
Amorim, Jorge H.
Segersson, David
Körnich, Heiner
Asker, Christian
Olsson, Esbjörn
Gidhagen, Lars
2020-05-07
<p>Stockholm is expanding fast in response to increasing housing needs. This paper evaluates the consequences of land-use changes on summer temperatures. Urban development scenarios for 2030 and 2050 were developed together with the municipality. The spatial and temporal variations of the changes in the urban air temperature are simulated at 1 km grid resolution applying a dynamical downscaling technique. The comparison against observations obtained during 5 years at 11 weather stations shows that the high resolution model captures the dynamics of the intra-urban air temperature gradients with good performance skills. Scenario results indicate that the temperature of summer 2014 would increase over the new built-up areas by, on average, 0.29 °C in 2030 and 0.46 °C in 2050, up to a local maximum of 1.35 °C in the latter, as a consequence of urbanization. The number of days with temperature above the 75th percentile for the summer months increases by up to 10, with locations closer to the sea being less prone to temperature maxima. The spatial coverage of this warming effect is predominantly local, occurring mostly over the transformed/densified area. Better knowledge on how urban temperatures are affected by on-going urbanization is needed also in high latitude cities.</p>
https://doi.org/10.1016/j.uclim.2020.100632
oai:zenodo.org:3822051
Zenodo
https://doi.org/10.5281/zenodo.3796277
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Urban Climate, 32, 100632, (2020-05-07)
Urban development
Urban heat island
Northern city
Dynamical downscaling
NWP model
Intra-city temperature
High resolution simulation of Stockholm's air temperature and its interactions with urban development
info:eu-repo/semantics/article
oai:zenodo.org:2562203
2020-01-24T19:23:02Z
user-clarity
openaire_data
user-eu
Nuñez, Mario
2019-02-11
<p>Urban Atlas based data subset, where every element with CODES 12210 and 12220 were extracted as roads elements with the next information:</p>
<p>gid integer area numeric, perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint hillshade_building real</p>
<p>This data is an input for local effects calculation.</p>
Provenance: Urban Atlas
https://doi.org/10.5281/zenodo.2562203
oai:zenodo.org:2562203
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/roads
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2562202
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Urban Atlas
CLARITY
H2020
DC1
Land Use
Local Effects
Naples Roads
Urban Atlas
open-data
output-data
Roads (Comune di Napoli)
info:eu-repo/semantics/other
oai:zenodo.org:3532753
2020-01-20T15:01:47Z
user-clarity
openaire
user-eu
Kainz, Astrid
Goler, Robert
Zuvela-Aloise, Maja
Hahn, Claudia
de Wit, Rosmarie
Zuccaro, Giulio
Leone, Mattia
Capolupo, Alessandra
Nardone, Stefano
Havlik, Denis
Loibl, Wolfgang
Köstl, Mario
Hager, Wilfried
2019-11-08
<p>The CLARITY project, funded by Horizon 2020 (<a href="http://www.clarity-h2020.eu">http://www.clarity-h2020.eu</a>), aims to derive actionable information about climate change and climate change impacts and to make this information usable for local decision-makers through an integrated Climate Services Information System (CSIS) that is specifically designed for transferring knowledge on climate-related risks to urban and transport infrastructure sectors.</p>
<p> </p>
<p>The CSIS development follows a standardized methodology that incorporates the characterisation of extreme events and related hazards, evaluation of exposure, analysis of vulnerability, risk and impact assessment as well as the identification and appraisal of adaptation options. This requires a profound analysis and harmonisation of available climate data.</p>
<p> </p>
<p>An ensemble of 16 different global climate model (GCM) and regional climate model (RCM) combinations from the EURO-CORDEX initiative is used to extract information on climate-related extreme events (e.g. severe heat, cold, heavy precipitation, storms, droughts) at European level. To account for potential systematic errors in the model simulations, the data are bias-corrected against the gridded observational dataset E-OBS using a quantile mapping technique. Following this procedure, the current climate conditions (baseline) as well as future changes, considering three different representative greenhouse gas concentration pathways (RCP2.6, RCP4.5, RCP8.5), are provided at a spatial resolution of 0.11°.</p>
<p> </p>
<p>Based on this, detailed climate information can be obtained at finer scales that are suitable for urban planning and climate adaptation applications within the framework of high-resolution, ‘add-on’ expert studies. The dynamical urban climate model MUKLIMO_3, developed by the Deutscher Wetterdienst (DWD), that takes into account urban land use and elevation data is used to investigate urban heat load at a spatial resolution of 20 – 250 m. A dynamical-statistical downscaling approach that combines urban climate simulations with long-term monitoring data and regional climate projections from EURO-CORDEX, is used to derive high-resolution climate indices for past and future climate periods on urban scale.</p>
<p> </p>
<p>Results will be shown for entire Europe, as well as for two urban study areas (Linz and Naples) that are part of demonstration cases used for showcasing and testing the CSIS methodology. Especially focussing on heat-related hazards, different climate indices (e.g. mean annual number of summer days, tropical nights etc.) will be analysed and their representation on European and urban scale will be assessed. Additionally, urban climate simulations will be used to evaluate the efficiency of several climate adaptation options (e.g. rooftop greening, unsealing of surfaces, increasing the albedo) on local scale.</p>
https://doi.org/10.5281/zenodo.3532753
oai:zenodo.org:3532753
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3532752
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
EMS Annual Meeting 2019, Copenhagen, Denmark, 9-13 September 2019
CLARITY, climate services, EMS2019
CLARITY's climate services: Using EURO-CORDEX simulations and including dynamical-statistical downscaling to allocate current and future climate-related hazard patterns at different spatial scales
info:eu-repo/semantics/lecture
oai:zenodo.org:2560309
2020-01-24T19:22:58Z
user-clarity
openaire_data
user-eu
Nuñez, Mario
2019-02-08
<p>Urban Atlas based data subset, where every element with CODES 11100 and 11210 were extracted as dense urban fabric elements with the next information:</p>
<p>gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035), albedo real emissivity real transmissivity real run_off_coefficient real context real fua_tunnel real</p>
<p>This data is an input for local effects calculation.</p>
Provenance: Urban Atlas
https://doi.org/10.5281/zenodo.2560309
oai:zenodo.org:2560309
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/dense-urban-fabric
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2560308
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ESRI Shapefile
GeoTIFF
CLARITY
DC1
Dense urban fabric
Land Use
Local Effects
Naples Urban Atlas
Zenodo
open-data
output-data
Dense Urban Fabric (Comune di Napoli)
info:eu-repo/semantics/other
oai:zenodo.org:2562127
2020-01-24T19:23:41Z
user-clarity
openaire_data
user-eu
Nuñez, Mario
2019-02-11
<p>STL and Urban Atlas based data subset, where every Urban Atlas element with CODE 31000 as well as all STL elements were extracted as tree elements with the next combined information:</p>
<p>gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint hillshade_green_fraction real</p>
<p>This data is an input for local effects calculation.</p>
Provenance: Urban Atlas + the European Settlement Map 2017 Release
https://doi.org/10.5281/zenodo.2562127
oai:zenodo.org:2562127
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/trees
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2562126
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
European Settlement Map
Urban Atlas
CLARTIY
H2020
GeoTIFF
ESRI Shapefile
DC1
Land Use
Local Effects
Naples
Trees
Urban Atlas
open-data
output-data
Trees (Comune di Napoli)
info:eu-repo/semantics/other
oai:zenodo.org:2560314
2020-05-04T13:33:14Z
user-clarity
openaire_data
user-eu
Nuñez, Mario
2019-02-08
<p>ESM data subset, generated by extracting band 30 as buildings with the next information:</p>
<p>gid integer geom geometry(Polygon,EPSG:3035) albedo real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint hillshade_building real</p>
<p>This data is an input for local effects calculation.</p>
Provenance: The European Settlement Map 2017 Release
https://doi.org/10.5281/zenodo.2560314
oai:zenodo.org:2560314
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/built-open-spaces
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2560313
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ESRI Shapefile
GeoTIFF
Built open spaces
CLARITY
DC1
European Settlement Map
Land Use
Local Effects
Naples
open-data
output-data
H2020
Built Open spaces
info:eu-repo/semantics/other
oai:zenodo.org:3632408
2020-02-25T13:14:12Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-01-31
<p><strong>Climate Index: </strong>Tx90p</p>
<p><strong>Definition:</strong> Average number of days that the daily maximum temperature is above the 90th percentile of daily maximum temperatures of a five day window.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3632408
oai:zenodo.org:3632408
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3632407
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Tx90p
EURO-CORDEX
open-data
output-data
H2020
Heat
Future climate
Ensemble calculations of "Tx90p" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3631582
2020-01-30T19:20:47Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-01-30
<p><strong>Climate Index: </strong>Summer days</p>
<p><strong>Definition:</strong> Number of days with daily maximum temperature above 25°C.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3631582
oai:zenodo.org:3631582
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3631581
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Summer days
EURO-CORDEX
open-data
output-data
H2020
Heat
Future climate
Ensemble calculations of "Summer Days" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:2562232
2020-01-24T19:24:55Z
user-clarity
openaire_data
user-eu
Nuñez, Mario
2019-02-11
<p>Urban Atlas based data subset, where every element with CODE 21000,22000,23000,24000 and 25000 was extracted as an agricultural area with the next information:</p>
<p>gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint</p>
<p>This data is an input for local effects calculation.</p>
Provenance: Urban Atlas
https://doi.org/10.5281/zenodo.2562232
oai:zenodo.org:2562232
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/agricultural-areas
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2562231
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
H2020
ESRI Shapefile
GeoTIFF
DC1
Land Use
Local Effects
Naples
Urban Atlas
open-data
output-data
Agricultural areas (Comune di Napoli)
info:eu-repo/semantics/other
oai:zenodo.org:3828572
2020-08-03T15:37:24Z
user-clarity
software
user-eu
Pascal Dihé
Eugene Maximov
2020-05-15
<p>Download the <a href="https://myclimateservices.eu/">CLARTIY H2020</a> Data Management Plan generated by p-a-s-c-a-l from <a href="https://ckan.myclimateservice.eu/">CLARITY CKAN</a> meta-data catalogue <a href="https://github.com/clarity-h2020/data-management-plan/releases/">here</a>.</p>
<p><em>Build #21 triggered by e12170e058bb69e19c39143a630de619be00c246.</em></p>
https://doi.org/10.5281/zenodo.3828572
oai:zenodo.org:3828572
Zenodo
https://github.com/clarity-h2020/data-management-plan/tree/v0.2.3
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3776132
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/data-management-plan: Data Management Plan v0.2.3
info:eu-repo/semantics/other
oai:zenodo.org:3678203
2020-02-25T09:16:39Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-02-21
<p><strong>Climate Index: </strong>Snow Days</p>
<p><strong>Definition:</strong> Annual average number of days that the daily precipitation is equal to or greater than 1mm and the maximum temperature is below 4°C.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3678203
oai:zenodo.org:3678203
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3678202
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Snow days
Precipitation
EURO-CORDEX
open-data
output-data
H2020
Future climate
Ensemble calculations of "Snow Days" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3957603
2020-07-29T11:22:21Z
user-clarity
software
user-eu
Pascal Dihé
DanielRodera
2020-07-23
JavaScript Helper Library for communicating with Drupal 8 JSON:API
https://doi.org/10.5281/zenodo.3957603
oai:zenodo.org:3957603
Zenodo
https://github.com/clarity-h2020/csis-helpers-js/tree/0.6.4
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3957298
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/csis-helpers-js: v0.6.4
info:eu-repo/semantics/other
oai:zenodo.org:3957299
2020-07-29T11:22:19Z
user-clarity
software
user-eu
Pascal Dihé
2020-07-23
<p>JavaScript Helper Library for communicating with Drupal 8 JSON:API</p>
https://doi.org/10.5281/zenodo.3957299
oai:zenodo.org:3957299
Zenodo
https://github.com/clarity-h2020/csis-helpers-js/tree/0.6.1
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3957298
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/csis-helpers-js: v0.6.1
info:eu-repo/semantics/other
oai:zenodo.org:4018509
2020-09-09T00:59:26Z
user-clarity
openaire
Lena Strömbäck
2020-09-08
<p>Presentation of Clarity resulults from DC2 for the climate council in Jönköping County.</p>
https://doi.org/10.5281/zenodo.4018509
oai:zenodo.org:4018509
Zenodo
https://zenodo.org/communities/clarity
https://doi.org/10.5281/zenodo.4018508
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Presentation of results from Clarity DC 2.
info:eu-repo/semantics/lecture
oai:zenodo.org:4018514
2020-09-09T00:59:26Z
user-clarity
openaire
Lena Strömbäck
2020-09-08
<p>Introduction to results from Clarity DC2 with a focus on hydrology held as part of a webinar in June.</p>
https://doi.org/10.5281/zenodo.4018514
oai:zenodo.org:4018514
Zenodo
https://zenodo.org/communities/clarity
https://doi.org/10.5281/zenodo.4018513
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Introduction to results from Clarity DC2
info:eu-repo/semantics/lecture
oai:zenodo.org:4071686
2020-10-08T00:26:58Z
user-clarity
openaire
user-eu
Åkesson, Anna
Kaiser, Gunilla
Thurin, Sofia
Lindell, Måns
Sannebro, Magnus
Johansson, Christer
Strömbäck, Lena
Hundecha, Yeshewatesfa
Almer, Anne-Catrin
Moberg, Frida
2020-09-14
<p>Short overview of the demonstration cases which WSP has been an active participant. Collaborations with SMHI, CABJON and StockCity, Contact WSP for further details regarding these case studies.</p>
https://doi.org/10.5281/zenodo.4071686
oai:zenodo.org:4071686
swe
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4028487
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
flooding
climate change
DC2 demonstration cases related to flooding
info:eu-repo/semantics/lecture
oai:zenodo.org:3336068
2020-01-20T15:50:59Z
user-clarity
user-eu
Skarbal, Bernhard
Dihé, Pascal
2019-07-15
<p>This report is the third deliverable of WP4 “Technology Support” of the CLARITY project, funded by the EU’s<br>
Horizon 2020 Programme under Grant Agreement number 730355. WP4 provides the technological<br>
backbone of the CLARITY climate service Information System (CSIS) by tailoring the technological background<br>
to project needs. For this, WP4 will integrate and adapt existing (background) tools and services that are<br>
necessary for realisation of the CLARITY reference scenarios (demonstration cases) and the implementation<br>
of the EU-GL [1] into the CLARITY climate services.<br>
It is an accompanying report to the technologies and software components that are adapted, extended,<br>
customised and deployed by WP4 and configured, assembled and integrated by WP1 to implement the<br>
CLARITY CSIS. It reports on the work performed in WP4 since the project start, provides links to access the<br>
software, tools and documentation resulting from this work and summaries the continuously updated work<br>
plan that takes emerging requirements from the agile co-creation process and feedback from the end users<br>
of the CSIS and the demonstration cases into account</p>
https://doi.org/10.5281/zenodo.3336068
oai:zenodo.org:3336068
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3336067
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
H2020
climate-services
architecture
software
D4.3 Technology Support Report v1
info:eu-repo/semantics/report
oai:zenodo.org:3523954
2020-01-20T16:46:14Z
user-clarity
openaire
user-eu
de Wit, Rosmarie
2019-05-30
<p>By combining different layers of data, climate services aim at translating state-of-the art climate science to information that can efficiently be incorporated in (urban) planning processes. Within the EU-Horizon-2020 funded project CLARITY (<a href="http://www.clarity-h2020.eu/">www.clarity-h2020.eu</a>), a standardized methodological framework as well as expert knowledge are united in a new generation climate service, specifically designed to assess adaptation measures at the city level under the effects of weather extremes in the context of climate change. To assess these effects, climate indices derived from observations as well as from Intergovernmental Panel on Climate Change climate projections are used to determine changes in climate extremes. To address the fine spatial scales (100 m) relevant for urban planning, regional climate model results are downscaled using a dynamical-statistical method. As a result, this modelling chain provides urban microclimate projections and enables climate sensitivity simulations of adaptation measures (e.g. the effect of green roofs, blue infrastructure changes) on the urban scale. Here, the CLARITY-developed modelling chain will be discussed in detail, and results will be shown for the project’s test sites. In addition, the usage of these methods within the CLARITY climate service as well as the connection to urban climate change resilience will be highlighted.</p>
https://doi.org/10.5281/zenodo.3523954
oai:zenodo.org:3523954
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3523953
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ECCA 2019, 4th European Climate Change Adaptation conference
The CLARITY Climate Services Information System - Modelling chain supporting urban climate change resilience
info:eu-repo/semantics/lecture
oai:zenodo.org:3515361
2020-01-20T17:07:35Z
user-clarity
openaire
user-eu
de Wit, Rosmarie
Havlik, Denis
Geyer-Scholz, Andrea
2019-10-08
<p><em><strong>How are climate change impacts gaining importance to urban infrastructure planning?</strong></em></p>
<p>Regulations and investors increasingly require the consideration of climate risks and opportunities both over the lifetime or in the planning phase of urban infrastructure. This form of climate resilient planning can increase, and <strong>improve urban quality of living</strong>.</p>
<p>This interactive session showcases <strong>how multi-level collaboration can unlock the potential for local climate action</strong> to shape a greener Europe. In the context of EU regional policy, two roundtables will discuss lessons learnt from two approaches to local projects: 'municipal climate partnerships' and 'smart cities'. Participants will share experiences and challenges of supporting communities through these approaches.</p>
<p>Based on a <strong>hands-on lab </strong>conducted prior to the session, participants identify climate risks and impacts to their infrastructure projects based on data of their specific location through the CLARITY Climate Services Information System (CSIS) screening tool. H<strong>azard-specific resilient solutions </strong>will be presented, guided by<strong> pilot initiatives</strong> with climate-resilience experience from Austria, Sweden and Italy. Results of the lab will be discussed at a panel discussion by experts and innovators from the investment, climate risk, and adaptation sectors.</p>
<p>This session is aimed at <strong>urban and infrastructure planners and developers, municipal decision-makers, energy and climate policy experts</strong> with interest in bringing together adaptation and mitigation measures at the local level.</p>
<p>Inputs to the session are based on the Horizon-2020-co-funded project <a href="https://myclimateservices.eu/">CLARITY</a> providing the CSIS tool and a climate services marketplace and the project Bridging European and Local Climate Action (<a href="https://www.euki.de/en/euki-projects/bridging-european-and-local-climate-action-beacon/">BEACON</a>) financed by the European Climate Initiative of the German Environmental Ministry (BMU). BEACON brings together 34 municipalities and 57 schools from seven EU Member States in central-eastern and south-eastern Europe.</p>
<p>These slides were used in the input presentation for the CLARITY project: Pilot city with recent insights on climate proofing project. </p>
https://europa.eu/regions-and-cities/programme/sessions/512_en
https://doi.org/10.5281/zenodo.3515361
oai:zenodo.org:3515361
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3515360
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
European Week of Regions and Cities, Brussels, Belgium
Steering Local Adaptation: Hands-On Lab on Measuring Climate Risks in Muncipalities - The CLARITY Project
info:eu-repo/semantics/lecture
oai:zenodo.org:2557390
2020-01-20T15:30:20Z
user-clarity
user-eu
Dihé, Pascal
2017-12-20
<p>This report is the first deliverable of Task 5.1 “Exploitation Requirements” of the CLARITY project, funded by the EU’s Horizon 2020 Programme under Grant Agreement number 730355. Task 5.1 intends to make sure that the project partners can recognize realistic exploitation and innovation aspects during the co-design and implementation of CLARITY Climate Service right from the start of the project, when no detailed and focused market study and business model are available yet.</p>
<p>The work foreseen in this task is performed in two stages. The first stage concentrates on the technical perspective and the impact of potential Exploitation Requirements identified on basis of a general and broad assessment of Climate Service market conditions, needs and gaps. Thereby, especially the early results of the EU-MACS (Project ID: 730500. Funded under: H2020-EU.3.5.1.), reported in EU-MACS deliverables D1.1 “Review and Analysis of CS Market Condition”, D1.2 “Existing Resourcing and Quality Assurance of Current Climate Services” and D1.3 “Analysis of existing Data Infrastructures for Climate Services” are taken into account. In a second stage, the results of CLARITY’s market analysis and business model (D5.3 “Exploitation and business plan v1”) are used to re-evaluate and/or validate the findings of the initial Exploitation Requirements assessment and to concretize innovative aspects of CLARITY products and service.</p>
<p>This document defines CLARITY’s general approach towards Exploitation Requirements and Innovation Design and presents the results of the first stage of Task 5.1, that is, Exploitation Requirements elicitation and assessment. It describes the consolidated Exploitation Requirements, discusses the potential impact and implications of Exploitation Requirements on the Climate Services co-design process, product and service implementation and the CSIS architecture and formulates concrete technical recommendations for WP1 “Co-Creation” and WP4 “Technology Support”.</p>
https://doi.org/10.5281/zenodo.2557390
oai:zenodo.org:2557390
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2557389
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
H2020
Exploitation
Climate Services
European Market for Climate Services
Innovation Design
Requirements
CLARITY D5.1 Exploitation Requirements and Innovation Design
info:eu-repo/semantics/report
oai:zenodo.org:4050358
2020-09-28T12:26:54Z
user-clarity
openaire
user-eu
Denis Havlik
Laura Parra
Luis Torres
José Cubillo
Ernesto Rodriguez
2020-09-18
<p>Presentación de herramienta online para análisis de vulnerabilidad y riesgo en carreteras frente a cambio climático. En este webinar presentamos un análisis de la afección del Cambio Climático a infraestructuras de transporte: ¿Cómo se mejora la resiliencia de la Infraestructura de Carreteras?</p>
<p>En este sentido, el proyecto CLARITY, financiado en el marco del programa H2020 de la UE, ha desarrollado un servicio climático CSIS (Climate Service Information System) que alberga una herramienta online para la evaluación del riesgo en infraestructuras urbanas y de transportes, proporcionando las proyecciones de las variables climáticas que se han considerado más representativas para las potenciales amenazas a las que se pueden enfrentar, tanto las ciudades como las carreteras, siendo estas últimas en las que se centra este webinar.</p>
<p>En España, la inminente ley de cambio climático es el marco perfecto para conseguir que todos los actores involucrados en estas actividades incorporen en sus actividades información sobre las proyecciones climáticas, basadas el conocimiento existente. Herramientas como CLARITY ayudarán a acometer este apasionante reto, con el objetivo de que nuestras infraestructuras sean más sostenibles y resilientes ante los posibles efectos del cambio climático.</p>
This webinar is part of the CLARITY4ClimateChange webinars series. Other webinars from this series can be found at https://www.gotostage.com/channel/climate-adaptation
https://doi.org/10.5281/zenodo.4050358
oai:zenodo.org:4050358
spa
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4050357
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate change
Climate adaptation
Climate resilience
Traffic infrastructure
Planning
Herramientas para análisis de vulnerabilidad y riesgo en carreteras frente a cambio climático
info:eu-repo/semantics/lecture
oai:zenodo.org:3784863
2020-05-04T20:20:24Z
user-clarity
openaire_data
user-eu
Nuñez, Mario
2020-05-04
<p>Urban Atlas based data subset, where every element with CODE 50000 was extracted as a water element with the next information:</p>
<p>gid integer area numeric perimeter numeric geom geometry(polygon,3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint</p>
<p>This data is an input for local effects calculation.</p>
Provenance: Urban Atlas
https://doi.org/10.5281/zenodo.3784863
oai:zenodo.org:3784863
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/naples-water
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2562213
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
H2020
ESRI Shapefile
GeoTIFF
DC1
Land Use
Local Effects
Naples Urban
Atlas Water
open-data
output-data
Water
info:eu-repo/semantics/other
oai:zenodo.org:3642874
2020-08-10T09:55:16Z
user-clarity
software
user-eu
Herter, Thorsten
Dihé, Pascal
Itsman-AT
DanielRodera
2020-02-05
<p>Map Component</p>
<p>The Map Component is understood as a reusable, flexible and highly configurable Building Block meant to be used throughout CSIS. It is envisioned as an embeddable component that can be easily adapted to different parts of the common CSIS UI. The core functionalities of this component must be a clear and easy visualization of different maps and layers. It is also a key feature of the map component to allow for a degree of interactivity meant to enable users to better define locations, elements at risk, hazards, scenario results, etc.</p>
<p>Requested functionality</p>
<p>Baseline requirements elicitation and the assessment of presently available Test Cases have yielded the following functional requirements for this Building Block:</p>
<p><strong>Baseline functionality</strong></p>
<ul>
<li>
<p>Basic map functionality such as zoon in, zoom out, pan, click on a point (and get info related to it if available), draw polygon/bbox, etc.</p>
</li>
<li>
<p>Visualize different types of hazard maps in relation to climate change projections for an area of interest</p>
</li>
<li>
<p>Advanced layer management: the user must be able to add individual (hazards) maps as layers (e.g. from existing (local) WMS), and to provide a set predefined climate change projection layers (e.g. from C3S.)</p>
</li>
<li>
<p>Support for Map Layer Timeline, e.g. visualize temperature change between 2020-2050 in an area of interest. This can be achieved using the TIME attribute in WMS GetMap requests.)</p>
</li>
<li>
<p>Generate geo-referenced information to exchange with planning services (data might be obtained as of SHP, NetCDF, geoTIF, etc. export) with help of external services (GeoServer).</p>
</li>
<li>
<p>Show map layers from both internal and external WMS Services (CLARITY cloud file storage / GeoServer / or public Open Data inventories.)</p>
</li>
<li>
<p>Spatial data import: the user must be able to upload (hazards) maps in a standardised format, add them to a private data repository and the workspace, and show them as a layer.</p>
</li>
<li>
<p>Predefined layers: provide a set predefined climate change projection layers (e.g. from C3S.)</p>
</li>
<li>
<p>Tabular visualisation of GML Feature\'s attributes obtained from an OGC WFS.</p>
</li>
<li>
<p>Editing of GML Feature\'s attributes via OGC WFS-T.</p>
</li>
</ul>
<p><strong>Functionality requested by CSIS Test Cases</strong></p>
<ul>
<li>
<p>from US-CSIS-100: For the pre-feasibility study, where the user selects some random location, the system should be able to extract the information from the map at the selected location. So, the map component must be able to account for these interactions too. This information must also be available within the system (not offline, by some expert), so it can be used for the automatic evaluation: pre feasibility risk analysis and reporting. For the expert study, the expert must be able to upload/download the maps to analyse them offline, this will depend on the US/TC.</p>
</li>
<li>
<p>from TC-CSIS-0053: The map component is used to specify, view and change the geospatial project location. It will also display some standard background layers (topographic, aerial, etc.)</p>
</li>
</ul>
<p><strong>Functionality requested by DC Test Cases</strong></p>
<ul>
<li>
<p>from US-DC1-150: The results of CLARITY simulations and climate services could be visualized as Georeferenced maps.</p>
</li>
<li>
<p>from US-DC1-110: Visualize heat wave, landslide and pluvial flood hazard maps in relation to climate change projections for the area of the Metropolitan City of Naples.</p>
</li>
<li>
<p>from TC DC1: Display results of impact scenario (no adaptation) on a map (note that map visualization must always include a legend based on the layers included).</p>
</li>
<li>
<p>from TC DC1: Map widget should allow the comparison among \"non adaptation\" and \"adaptation\" scenarios (e.g. two maps juxtaposed on the same screen), see also Scenario Transferability Component.</p>
</li>
<li>
<p>from TC DC1: The Map View must provide an user interface that will allow the user to visualize the location of the current project under assessment (e.g. city) and to specific the spatial extent (area under assessment) that should be considered by a local model (e.g. urban climate model) when producing a specific hazard map (e.g. heat waves) for that particular area.</p>
</li>
<li>
<p>from TC DC1: Displays hazards maps resulting from local models (e.g. urban climate models) run \"offline\" by experts.</p>
</li>
<li>
<p>from TC DC4: Displays the hazards using a map. It must allow the user to configure how to represent them.</p>
</li>
<li>
<p>from TC DC4: The user selects a specific geographical area. The user needs to modify the geographical location of a selected element at risk.</p>
</li>
<li>
<p>from TC DC4: Needed to upload / store / compute / maps at a regional or local scale to allow to evaluate the climate risks.</p>
</li>
<li>
<p>from TC DC4: The user defines the geographical area covered by the study and loads the elements of the area</p>
</li>
<li>
<p>from TC RA: Position the elements at risk on a map, to show the hazard map layers and to show a colour-coded map with the results of the HxExV calculation (alternative to showing it in a table.).</p>
</li>
<li>
<p>from TC RA: Select and show an entire inventory of elements at risk (e.g. buildings layer) on the map.</p>
</li>
</ul>
<p>Technology support</p>
<p>The high interactivity and flexibility expected from this component requires an approach based on responsive and highly adaptable technologies. This approach can be achieved using client-side rendering along with libraries and tools that have already proved their usability and popularity, meaning that a big and active community is supporting their development and use. To ensure this high interactivity approach of this web application a good approach would be to use <strong>React</strong>, React allows rich site interactions, fast website rendering after the initial load, and a good selection of JavaScript libraries. It is also designed to build encapsulated components that can be composed to make complex UIs. In consequence, the Map Component is developed as independent <strong>HTML5/AJAX RIA</strong> that is loosely embedded as <strong>HTML5 iframe</strong> in the UI Integration Platform (<strong>Drupal 8</strong>, see 7.5) and relies as backend on Data Repository (7.4) and various OGC Services, respectively.</p>
<p>In terms of an open-source solution for the map itself, a proposed solution will be the use of <strong>Mapbox GL</strong> and <strong>Leaflet</strong> depending on the necessities of each map or layer. Mapbox provides a number of tools to build maps into a website or application. It is an open source JavaScript library that can be used to display maps, add interactivity, and customize the map experience. There are also a number of plugins for extending the map's functionality with drawing tools and interfaces to Mapbox web services APIs like the Mapbox Geocoding API or Mapbox Directions API.</p>
<p><a href="https://www.mapbox.com/help/define-mapbox-gl/">https://www.mapbox.com/help/define-mapbox-gl/</a></p>
<p>While Leaflet is meant to be as lightweight as possible, and focuses on a core set of features, an easy way to extend its functionality is to use third-party plugins developed by an active community.</p>
<p><a href="http://leafletjs.com/">http://leafletjs.com/</a></p>
https://doi.org/10.5281/zenodo.3642874
oai:zenodo.org:3642874
eng
Zenodo
https://github.com/clarity-h2020/map-component/tree/2.5.5
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3642873
info:eu-repo/semantics/openAccess
Other (Open)
reactjs
javascript
webapp
ogc client
clarity-h2020/map-component: Map Component v2.5.5
info:eu-repo/semantics/other
oai:zenodo.org:3686570
2020-02-26T09:12:50Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-02-25
<p><strong>Climate Index: </strong>Tx75p-max-consecutive</p>
<p><strong>Definition:</strong> Maximum number of consecutive days that the daily maximum temperature is above the 75th percentile of daily maximum temperatures during the warm season of April-September of the period 1971-2000.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3686570
oai:zenodo.org:3686570
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3686569
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Tx75p-max-consecutive
EURO-CORDEX
open-data
output-data
H2020
Heat
Heat wave
Future climate
Ensemble calculations of "Tx75p-max-consecutive" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3711675
2020-08-10T09:55:17Z
user-clarity
software
user-eu
therter
Pascal Dihé
Itsman-AT
DanielRodera
2020-03-16
The Map Component is a reusable, flexible and highly configurable Building Block meant to be used throughout CSIS.
https://doi.org/10.5281/zenodo.3711675
oai:zenodo.org:3711675
Zenodo
https://github.com/clarity-h2020/map-component/tree/2.5.0
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3642873
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/map-component: v2.5.0
info:eu-repo/semantics/other
oai:zenodo.org:3796277
2020-05-13T20:20:41Z
user-clarity
openaire_data
user-eu
Amorim, Jorge
2020-05-06
<p>A number of heat scenarios studying how green infrastructure would affect the future climate of Stockholm. The dataset consist of a number of scenarios which simulates how building plans could affect heat exposure in Stockholm. The scenarios are:</p>
<ul>
<li>Stockholm 2014: Baseline scenario simulating the heatwave during the summer of 2014 in Stockholm</li>
<li>Stockholm 2030: Simulating the effects of the heatwave 2014 with new building according to plans for city expansion 2030.</li>
<li>Stockholm 2050: Simulating the effects of the heatwave 2014 with new buildings according to available plans for city expansion 2050.</li>
<li>Grey Scenario: Simulating the effects of the heatwave 2014 in a city where green infrastructure has been minimized.</li>
</ul>
https://doi.org/10.5281/zenodo.3796277
oai:zenodo.org:3796277
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/heat-scenarios-over-stockholm
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3796276
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Meteorological data
Stockholm
climate change
heatwave
NetCDF
exposure
adaptation
H2020
climate simulation
SMHI
Heat scenarios over Stockholm
info:eu-repo/semantics/other
oai:zenodo.org:4046204
2020-09-24T00:26:50Z
user-clarity
openaire_data
user-eu
Hundecha, Yeshewatesfa
2020-09-23
<p>Hourly river flow and total runoff were computed for the southern part of Sweden using the hourly version of a high resolution hydrological model S-HYPE, which is operationally used by SMHI. The model was calibrated and validated using radar based hourly precipitation and an operationally used hourly reanalysis temperature data. Projection of the impact of climate change was performed by running the model with hourly forcing data from an ensemble of EURO-COREX climate model simulations over 1971 - 2100. Four GCM-RCM combinations were used under two emission scenarios, RCP4.5 and RCP8.5. The results can be used to assess the risk of riverine flooding in areas located along a small to meso-scale river basin. The results can, in particular, be used to assess the risk of flash flooding that can result from heavy precipitation of short duration.</p>
https://doi.org/10.5281/zenodo.4046204
oai:zenodo.org:4046204
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4046203
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Sweden
extreme river flow
hourly flow extremes
climate impact projection
SMHI
DC2 High resolution future hydrological data for Sweden
info:eu-repo/semantics/other
oai:zenodo.org:2560292
2020-01-24T19:25:38Z
user-clarity
openaire_data
user-eu
Mario Nuñez
2019-02-08
<p>Urban Atlas based data subset, where every element with CODES 11230, 11240, 11300 were extracted as low urban fabric elements with the next information:</p>
<p>gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real run_off_coefficient real context real fua_tunnel real</p>
<p>This data is an input for local effects calculation.</p>
Provenance: Urban Atlas
https://doi.org/10.5281/zenodo.2560292
oai:zenodo.org:2560292
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/low-urban-fabric
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2560291
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Urban Atlas
CLARITY
H2020
open-data
ESRI Shapefile
GeoTIFF
DC1
Land Use
Local Effects
Low urban fabric
Naples
Low Urban Fabric (Comune di Napoli)
info:eu-repo/semantics/other
oai:zenodo.org:2560328
2020-01-24T19:23:06Z
user-clarity
openaire_data
user-eu
Nuñez, Mario
2019-02-08
<p>ESM and Urban Atlas based data subset, where every Urban atlas element with CODE 14100,14200,32000,33000 was extracted togheter with band40 ESM elements to gather all as vegetation elements with the next combined information:</p>
<p>gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035), albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint</p>
<p>This data is an input for local effects calculation.</p>
Provenance: Urban Atlas + the European Settlement Map 2017 Release
https://doi.org/10.5281/zenodo.2560328
oai:zenodo.org:2560328
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/vegetation
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2560327
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
European Settlement Map
H2020
GeoTiff
ESRI Shapefile
CLARITY
DC1
Land Use
Local Effects
Naples
Urban Atlas
Vegetation
open-data
output-data
Vegetation (Comune di Napoli)
info:eu-repo/semantics/other
oai:zenodo.org:3707892
2020-03-12T20:20:16Z
user-clarity
openaire_data
user-eu
Hahn, Claudia
2020-03-12
<p><strong>Climate Index: </strong>torro17</p>
<p><strong>Definition:</strong> Number of days per year with daily maximum wind speed equal or greater than 17 m/s, average over a 30-year time-period.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily maximum near-surface wind speed (sfcWindmax).</p>
<p>Results (ensemble mean and ensemble standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) time periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>SMHI-RCA4/ ICHEC-EC-EARTH, SMHI-RCA4/ MOHC-HadGEM2-ES</li>
<li>CLMcom-CCLM4-8-17/ ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/ MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ ICHEC-EC-EARTH, KNMI-RACMO22E/ MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3707892
oai:zenodo.org:3707892
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3707891
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate Index
Wind Index
EURO-CORDEX
Ensemble calculations of "torro17" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:2557611
2020-01-20T15:32:26Z
user-clarity
openaire
user-eu
de Wit, Rosmarie
Kainz, Astrid
Zuvela-Aloise, Maja
Goler, Robert
2019-02-05
<p>Worldwide, climate services are emerging as an essential tool to connect the advances in climate science with the domains of climate change adaptation. The modelling methodology developed within the CLARITY project (EU-Horizon 2020, <a href="https://webmail.zamg.ac.at/owa/UrlBlockedError.aspx">www.clarity-h2020.eu)</a> is aimed at implementing a new generation of climate services specifically designed to assess adaptation measures at the city level under the effects of extreme weather events in the context of climate change. These effects are assessed based on observations combined with Intergovernmental Panel on Climate Change climate projections, and the subsequent derivation of climate indices to address changes in climate extremes. The dynamical-statistical downscaling of regional climate model results is used to obtain this information on fine spatial scales (100 m), hence providing urban microclimate projections and enabling climate sensitivity simulations of adaptation measures on the urban scale. The climate adaptation strategies encompass, among others, green roofs, increasing roof albedo, as well as blue (water) and green (parks, trees) infrastructure changes. Here, the climate assessment methodology developed within CLARITY will be discussed in detail, and results will be shown for the city of Linz (Austria). In addition, the usage of these methods and results within the CLARITY climate service as well as the connection to urban climate change resilience will be highlighted.</p>
https://doi.org/10.5281/zenodo.2557611
oai:zenodo.org:2557611
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2557610
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ICUC, 10th International Conference on Urban Climate/14th Symposium on the Urban Environment, New York City (NY), USA, 6-10 August 2018
CLARITY Climate Service Promoting Urban Climate Change Resilience through the Modelling of Climate Adaptation Strategies
info:eu-repo/semantics/lecture
oai:zenodo.org:3631622
2020-01-30T19:20:47Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-01-30
<p><strong>Climate Index: </strong>Tropical Nights</p>
<p><strong>Definition:</strong> Number of days with daily minimum temperature above 20°C.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3631622
oai:zenodo.org:3631622
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3631621
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Tropical nights
EURO-CORDEX
open-data
output-data
H2020
Heat
Future climate
Ensemble calculations of "Tropical Nights" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:2562827
2020-01-20T15:41:11Z
user-clarity
user-eu
Zuccaro, Giulio
Leone, Mattia Federico
Zuvela-Aloise, Maja
Capolupo, Alessandra
2019-01-28
<p>Climate Services are emerging worldwide as an essential tool to bridge the advancement in climate science and meteo/earth observations with a variety of operational fields in the domains of Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA). The modelling methodology developed within H2020-CLARITY project (<a href="http://www.clarity-h2020.eu)/">www.clarity-h2020.eu)</a> is aimed at implementing a new generation of climate services specifically designed to address adaptation measures at city level, thus dealing with planning and urban design issues in the context of new construction and retrofitting of buildings, open spaces and transport networks.</p>
https://doi.org/10.5281/zenodo.2562827
oai:zenodo.org:2562827
ita
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2562826
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Ecoscienza, ISSN:2039-0424(6/2018), 30-32, (2019-01-28)
CLARITY
climate services
climate change adaptation
Una nuova generazione di servizi climatici a supporto della progettazione e pianificazione resiliente delle città: metodologia e applicazione del modello Clarity
info:eu-repo/semantics/article
oai:zenodo.org:1491532
2020-01-20T17:46:04Z
user-clarity
user-eu
Schimak, Gerald
Zuvela-Aloise, Maja
Leone, Mattia
Dihé, Pascal
2017-11-30
<p>This report is the first deliverable of Task 7.3 “Data Management” and describes the initial Data Management Plan (DMP) for the CLARITY project, funded by the EU’s Horizon 2020 Programme under Grant Agreement number 730355. The purpose of the DMP is to provide an overview of all datasets collected and generated by the project and to define the CLARITY consortium’s data management policy that is used with regard to these datasets.</p>
<p>The CLARITY DMP follows the structure of the Horizon 2020 DMP template. It reflects the status of the data that is collected, processed or generated and following what methodology and standards, whether and how this data will be shared and/or made open, and how it will be curated and preserved. This initial version of the DMP defines the general policy and approach to data management in CLARITY that handles data management related issues on the administrative and technical level. This includes for example topics like data and meta-data collection, publication and deposition of open data, the data repository infrastructure and compliance to the Open Access Infrastructure for Research in Europe (OpenAIRE).</p>
<p>It furthermore summarises the intermediate results of the data collection activities in Task 2.2 “Demonstrator-specific data collection” that are being carried out according to the data collection concept introduced in Task 2.2 “Data requirements definition, data collection concept, demonstration and result validation concept” and the guidelines on FAIR (Findable, Accessible, Interoperable and Reusable) data management. The DMP will evolve during the lifespan of the project. Next versions will refine and enhance policy aspects and will go into more detail regarding the datasets collected and produced by the CLARITY project.</p>
https://doi.org/10.5281/zenodo.1491532
oai:zenodo.org:1491532
eng
Zenodo
https://csis.myclimateservice.eu/
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.1491531
info:eu-repo/semantics/openAccess
Creative Commons Attribution Share Alike 4.0 International
https://creativecommons.org/licenses/by-sa/4.0/legalcode
Data Management Plan
Open Research Data Pilot
OpenAIRE
H2020
Data Management Policy
climate change adaptation
climate data
CLARITY D7.8 Data Management Plan
info:eu-repo/semantics/report
oai:zenodo.org:3245237
2020-01-20T12:54:48Z
user-clarity
user-eu
Emilio Blas
María Postigo
Miguel Ángel Esbrí
Mario Núñez
Laura Parra
Laura Asensio
2019-06-13
<p><em>There is no doubt that transport infrastructures management is facing new challenges today; <em>among them are those linked to climate change: more extreme temperatures, larger termal <em>oscillations between day and night, increased risks of flooding due to intense rainfall and others. <em>Given this situation, it becomes necessary to evaluate how new climatic variables can affect the <em>different elements of the road.</em></em></em></em></em></p>
<p><em>To this end, the CLARITY project, funded by the European Union within the framework of the <em>Horizon 2020 research and development program, aims to offer decision support services to <em>investigate the effects of adaptation measures, as well as risk reduction options in the specific <em>context of the project (through comparison of alternative strategies).</em></em></em></em></p>
<p><em>The purpose of this article is to present the methodology of the study and its application to the <em>Spanish pilot case (A2 motorway, between Guadalajara and Alcolea del Pinar). On the one hand, <em>the needs of the main stakeholders will be determined (administrations, construction and <em>consultancy companies) and, on the other hand, workflows will be established: conceptualization of <em>the study (identification of road elements that may be affected by climatic risks and proposal of <em>indicators), identification of relevant databases, implementation of the models and analysis of <em>alternatives. As a result, the technology developed (climate services) will allow estimating the <em>effects of climate change in the context of a specific transport infrastructure, giving information on <em>the most appropriate adaptation and mitigation measures.</em></em></em></em></em></em></em></em></em></p>
https://doi.org/10.5281/zenodo.3245237
oai:zenodo.org:3245237
ssp
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3245236
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
climate change
climate services information systems for roads
adaptation
Gestión de infraestructuras y cambio climático en el marco del proyecto CLARITY
info:eu-repo/semantics/article
oai:zenodo.org:2557378
2020-02-13T16:28:08Z
user-clarity
user-eu
Dihé, Pascal
2019-02-05
<p>This report is the second deliverable of WP4 “Technology Support” of the CLARITY project, funded by the EU’s Horizon 2020 Programme under Grant Agreement number 730355. WP4 intends to provide the technological backbone of the CLARITY Climate Service Information System (CSIS) by tailoring the technological background foreseen in the CLARITY work package descriptions to project needs. For this, WP4 will integrate and adapt all required and existing (background) tools and services that are necessary for realisation of the CLARITY reference scenarios (Demonstration Cases) and implementation of the EU-GL into the CLARITY Climate Services.</p>
<p>The main aim of this deliverable is to describe the CSIS Architecture in such a concise and simple manner so that its goals and major concepts can be understood by all stakeholders (including the end users) involved in the co-creation process. It does this by communicating the most significant design decisions that shape CSIS and equips the agile development teams with "just enough" conceptual and technical knowledge to successfully implement the presented Conceptual Innovation Design.</p>
<p>Unlike as initially foreseen in the DoA, the CSIS Architecture follows an agile and emergent approach that aims to quickly respond to unavoidable changes imposed by the agile co-creation approach of WP1 "Co-Creation". Moreover, technology moves fast and many of the software components and technologies mentioned in the DoA are outdated or do not suit the emergent use cases and requirements introduced during the first year of the project. The impact to project plans with respect to tasks, deliverables, resources requested etc., however, are minimal and do not collide with general project objectives.</p>
https://doi.org/10.5281/zenodo.2557378
oai:zenodo.org:2557378
eng
Zenodo
https://doi.org/10.5281/zenodo.3336068
https://doi.org/10.5281/zenodo.2557390
https://doi.org/10.5281/zenodo.2557364
https://doi.org/10.5281/zenodo.1494828
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2557377
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
agile software development
H2020
software architecture
agile architecture
emergent architecture
agile
scrum
kanban
co-creation
TOGAF
CLARITY D4.2 CLARITY CSIS Architecture
info:eu-repo/semantics/report
oai:zenodo.org:2560133
2020-05-04T13:33:46Z
user-clarity
openaire_data
user-eu
Nuñez, Mario
2019-02-08
<p>Urban Atlas based data subset for Comune di Napoli, where every element with CODE 12100 was extracted as Public, military and industrial units elements with the next information:</p>
<p>gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real run_off_coefficient real context real fua_tunnel real</p>
<p>This data is an input for local effects calculation.</p>
Provenance: Urban Atlas
https://doi.org/10.5281/zenodo.2560133
oai:zenodo.org:2560133
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/public-military-and-industrial-units
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2560132
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
open-data
CLARITY
H2020
Local Effects
Naples
Public military and industrial units
Urban Atlas
DC1
ESRI Shapefile
Public, military and industrial units
info:eu-repo/semantics/other
oai:zenodo.org:2562210
2020-01-24T19:23:04Z
user-clarity
openaire_data
user-eu
Nuñez, Mario
2019-02-11
<p>Urban Atlas based data subset, where every element with CODE 12230 was extracted as railways elements with the next information:</p>
<p>gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint</p>
<p>This data is an input for local effects calculation.</p>
Provenance: Urban Atlas
https://doi.org/10.5281/zenodo.2562210
oai:zenodo.org:2562210
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/railways
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2562209
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARTIY
H2020
ESRI Shapefile
GeoTIFF
DC1
Land Use
Local Effects
Naples Railways
Urban Atlas
open-data
output-data
Railways (Comune di Napoli)
info:eu-repo/semantics/other
oai:zenodo.org:3632322
2020-01-31T19:20:51Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-01-31
<p><strong>Climate Index: </strong>Frost days</p>
<p><strong>Definition:</strong> Number of days with daily minimum temperature below 0°C.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3632322
oai:zenodo.org:3632322
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3632321
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Frost days
EURO-CORDEX
open-data
output-data
H2020
Cold
Future climate
Ensemble calculations of "Frost Days" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:2560323
2020-01-24T19:23:06Z
user-clarity
openaire_data
user-eu
Nuñez, Mario
2019-02-08
<p>ESM data subset, generated by extracting band 50 as buildings with the next information:</p>
<p>gid integer geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint height real</p>
<p>This data is an input for local effects calculation.</p>
Provenance: The European Settlement Map 2017 Release
https://doi.org/10.5281/zenodo.2560323
oai:zenodo.org:2560323
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/buildings
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2560322
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
GeoTiff
H2020
ESRI Shapefile
Buildings
CLARITY
DC1
European Settlement Map
Land Use
Local Effects
Naples
open-data
output-data
Buildings (Comune di Napoli)
info:eu-repo/semantics/other
oai:zenodo.org:3970982
2020-08-04T00:59:22Z
user-clarity
software
user-eu
Pascal Dihé
Eugene Maximov
2020-08-03
CLARITY Data Management Plan
https://doi.org/10.5281/zenodo.3970982
oai:zenodo.org:3970982
Zenodo
https://github.com/clarity-h2020/data-management-plan/tree/0.5
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3776132
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/data-management-plan: Data Management Plan v0.5
info:eu-repo/semantics/other
oai:zenodo.org:3965710
2020-07-30T00:59:26Z
user-clarity
software
user-eu
Pascal Dihé
DanielRodera
2020-07-29
JavaScript Helper Library for communicating with Drupal 8 JSON:API
https://doi.org/10.5281/zenodo.3965710
oai:zenodo.org:3965710
Zenodo
https://github.com/clarity-h2020/csis-helpers-js/tree/0.6.5
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3957298
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/csis-helpers-js: v0.6.5
info:eu-repo/semantics/other
oai:zenodo.org:3957598
2020-07-29T11:22:20Z
user-clarity
software
user-eu
Pascal Dihé
DanielRodera
2020-07-23
JavaScript Helper Library for communicating with Drupal 8 JSON:API
https://doi.org/10.5281/zenodo.3957598
oai:zenodo.org:3957598
Zenodo
https://github.com/clarity-h2020/csis-helpers-js/tree/0.6.3
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3957298
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/csis-helpers-js: v0.6.3
info:eu-repo/semantics/other
oai:zenodo.org:3635451
2020-02-05T07:20:52Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-02-04
<p><strong>Climate Index: </strong>RR90p</p>
<p><strong>Definition:</strong> Average number of days that the daily precipitation is above the 90th percentile of daily precipitation of a five day window.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3635451
oai:zenodo.org:3635451
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3635450
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
RR90p
Precipitation
EURO-CORDEX
open-data
output-data
H2020
Future climate
Ensemble calculations of "RR90p" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3759363
2020-05-13T05:34:02Z
user-clarity
openaire
AIT
2020-04-21
<p>Presentation of used methodology, input data, results and adaptation measures for various test sites/areas in the City of Linz </p>
https://doi.org/10.5281/zenodo.3759363
oai:zenodo.org:3759363
Zenodo
https://zenodo.org/communities/clarity
https://doi.org/10.5281/zenodo.3759362
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
DC3
Microclimate simulations Linz
info:eu-repo/semantics/lecture
oai:zenodo.org:4050396
2020-09-28T16:00:17Z
user-clarity
openaire
user-eu
Denis Havlik
Andrea Geyer-Scholz
Adriaan Perrels
Alessia Pietrosanti
Stefania Manca
2020-07-01
<p>This is the second webinar in the CLARITY for Climate Resilience (Clarity4CR) webinar series. It is dedicated to "Climate Marketplace".</p>
<p>The market for climate services is dominated by public sector users and providers so far. Numerous opportunities arise within private sectors that provide considerable growth prospects but also multiple barriers need to be tackled.</p>
This webinar is part of the CLARITY4ClimateResilience series. Other webinars from this series can be found at https://www.gotostage.com/channel/climate-adaptation
https://doi.org/10.5281/zenodo.4050396
oai:zenodo.org:4050396
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4050395
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate Services
Climate Change
Climate Adaptation
Marketplace
Climate Resilience
Climate Services as emerging market - latest trends
info:eu-repo/semantics/lecture
oai:zenodo.org:3631508
2020-01-30T19:20:47Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-01-30
<p><strong>Climate Index: </strong>Hot days</p>
<p><strong>Definition:</strong> Number of days with daily maximum temperature above 30°C.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3631508
oai:zenodo.org:3631508
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3631507
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
EURO-CORDEX
Hot days
open-data
output-data
H2020
Heat
Future climate
Ensemble calculations of "Hot Days" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:2557667
2019-02-12T10:26:28Z
user-clarity
user-eu
Denis Havlik
2018-10-18
<p>This draft CLARITY busines canvas is a result of the interactive session with potential users from the western Balkans region at the CLIMATEUROPE FESTIVAL 2018. It was used as input for designing the CLARITY busines canvas / busines models. </p>
https://doi.org/10.5281/zenodo.2557667
oai:zenodo.org:2557667
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2557666
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLIMATEUROPE FESTIVAL 2018 - Climate information at your service, Belgrade, Serbia, 17-19 October 2018
CLARITY, climate services, busines canvas
CLARITY busines canvas (draft)
info:eu-repo/semantics/other
oai:zenodo.org:3687620
2020-02-26T19:21:01Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-02-26
<p><strong>Climate Index: </strong>Tx75p</p>
<p><strong>Definition:</strong> Number of days that the daily maximum temperature is above the 75th percentile of daily maximum temperatures during the warm season of April-September of the period 1971-2000.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3687620
oai:zenodo.org:3687620
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3687619
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Tx75p
EURO-CORDEX
open-data
output-data
H2020
Heat
Future climate
Ensemble calculations of "Tx75p" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:2557605
2020-01-20T15:30:18Z
user-clarity
openaire
user-eu
de Wit, Rosmarie
Zuvela-Aloise, Maja
Kainz, Astrid
Goler, Robert
2019-02-05
<p>With more than 50% of the global population living in cities, urban climate change resilience is key to protect humans from the adverse effects of a changing climate. In order to translate climate change knowledge to information that can be used by policy makers or in areas such as urban planning, climate services are essential tools, bridging the gap between climate science and adaptation practitioners. The modelling methodology developed within the CLARITY project (funded as part of Horizon 2020, www.clarity-h2020.eu) is aimed at implementing a new generation of climate services specifically designed to assess adaptation measures at the city level under the effects of extreme weather events influenced by climate change. Climate indices derived from observations combined with IPCC climate projections are used to assess these effects. Using the urban climate model MUKLIMO_3 developed by the Deutscher Wetterdienst (DWD), regional climate model results are used to obtain this information on fine spatial scales (100 m), hence providing urban microclimate projections and enabling climate sensitivity simulations of adaptation measures on the urban scale. The climate adaptation strategies addressed in CLARITY are, among others, the effect of green roofs, increasing roof albedo, as well as blue (water) and green (parks, trees) infrastructure changes. Here, the climate assessment methodology developed within CLARITY will be presented in detail, and results will be shown for the Austrian city of Linz. In addition, the usage of these methods and results within the CLARITY climate service as well as the derivation of actionable information for e.g. urban planners and policy makers will be highlighted.</p>
https://doi.org/10.5281/zenodo.2557605
oai:zenodo.org:2557605
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2557604
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
EMS, European Meteorological Society – Annual Meeting 2018, Budapest, Hungary, 3-7 September 2018
Exploring urban climate change adaptation measures with CLARITY's climate service
info:eu-repo/semantics/lecture
oai:zenodo.org:2557633
2020-09-28T09:16:17Z
user-clarity
openaire
user-eu
Denis Havlik
2018-12-12
<p>"My Climate Services Marketplace" explains the CLARITY offer for climate resilience "experts". Once the end-user has finished the screening, and based on the screening results, CLARITY CSIS will indicate which "expert services" the user should consult, if any. </p>
<p>"Expert services" offer could e.g. cover additional hazards or element at risk classes that aren't available in the screening data package, micro climate simulations, dependable cost/benefit calculations, what/if scenario simulations or even the realisation of concrete adaptation measures. </p>
<p>By registering as a service provider, experts can become a part of the CLARITY marketplace and improve the visibility of their expert service offers, while at the same time minimizing the effort spent at customer acquisition and understanding what the potential customers really need from them.</p>
<p> </p>
https://doi.org/10.5281/zenodo.2557633
oai:zenodo.org:2557633
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2557632
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
COP24, COP24 United Nations Climate Change Conference, Katowice, Poland, 02 Dec 2018 - 14 Dec 2018
CLARITY
Climate adaptation
Climate Resilience
marketplace
climate change adaptation services
My Climate Services Marketplace
info:eu-repo/semantics/lecture
oai:zenodo.org:4049987
2020-09-28T12:26:54Z
user-clarity
openaire
user-eu
Denis Havlik
Wolfgang Loibl
Wilfried Hager
Claudia Hahn
Tanja Tötzer
Robert Goler
2020-07-08
<p>Dies ist das dritte Webinar in der „CLARITY für die Klimaresilienz“ (Clarity4CR) Webinarreihe und das erste in deutscher Sprache. Es präsentiert die CLARITY-Methodik zur Klimawandel-Risikobewertung, Impakt-Analyse, und Anpassungsplanung, stellt den "Advanced Urban Screening" Service vor und erläutert die Ergebnisse der CLARITY-Expertenstudie in Linz. </p>
This webinar is part of the CLARITY4ClimateResilience series. Other webinars from this series are available at https://www.gotostage.com/channel/climate-adaptation
https://doi.org/10.5281/zenodo.4049987
oai:zenodo.org:4049987
deu
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4049986
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Climate Change
Climate Adaptation
Climate Resilience
Urban Planning
Linz
Austria
CLARITY für Klimaresilienz - "In meiner Region: Linz, Österreich"
info:eu-repo/semantics/lecture
oai:zenodo.org:4106407
2020-10-19T15:18:39Z
user-clarity
user-eu
de Wit, Rosmarie
Kainz, Astrid
Goler, Robert
Žuvela-Aloise, Maja
Hahn, Claudia
Zuccaro, Giulio
Leone, Mattia
Loibl, Wolfgang
Tötzer, Tanja
Hager, Wilfried
Geyer-Scholz, Andrea
Havlik, Denis
2020-08-07
<p>In recent years, the representation of climate information in a way to support decision making<br>
has been gaining momentum. Worldwide, these so-called climate services are emerging as an<br>
essential tool to connect the advances in climate science with the domains of climate change<br>
adaptation. The methodology developed within the CLARITY project (funded through European<br>
Union funding program Horizon 2020) is aimed at implementing a new generation of climate<br>
services specifically designed to assess adaptation measures at the city level under the effects of<br>
extreme weather events in the context of climate change. These effects are assessed based on<br>
observations as well as climate projections, and the subsequent derivation of climate indices to<br>
address changes in climate extremes. The dynamical-statistical downscaling of regional climate<br>
model results is used to obtain this information on fine spatial scales (100 m), hence providing<br>
urban scale projections and enabling climate sensitivity simulations of adaptation measures on<br>
the urban scale. The climate adaptation strategies encompass, among others, green roofs, increasing<br>
roof albedo, as well as changes in soil sealing. Here, the climate assessment methodology<br>
developed within CLARITY will be discussed in detail, and results for the city of Linz (Austria)<br>
presented. In addition, the usage of these methods and results within the CLARITY climate service<br>
as well as the connection to urban climate change resilience will be highlighted.</p>
https://doi.org/10.1016/j.uclim.2020.100675
oai:zenodo.org:4106407
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution Non Commercial No Derivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
Urban Climate, 34, (2020-08-07)
climate services, CLARITY, climate adaptation strategies, urban climate modelling
Supporting climate proof planning with CLARITY's climate service and modelling of climate adaptation strategies – the Linz use-case
info:eu-repo/semantics/article
oai:zenodo.org:2563046
2020-05-04T13:28:48Z
user-clarity
openaire_data
user-eu
Torres, Luis
2019-02-12
<p>Hazard level of heat waves depending on different base temperatures for the city of Naples.</p>
https://doi.org/10.5281/zenodo.2563046
oai:zenodo.org:2563046
eng
Zenodo
https://ckan.myclimateservice.eu/dataset/historical-heat-wave-temperature-local-effects
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2563045
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
H2020
GeoTIFF
Heat
Naples
open-data
output-data
ArcGrid
Historical heat wave temperature (Comune di Napoli)
info:eu-repo/semantics/other
oai:zenodo.org:3632592
2020-01-31T19:20:51Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-01-31
<p><strong>Climate Index: </strong>Extreme temperature range (ETR)</p>
<p><strong>Definition:</strong> Greatest daily extreme temperature range (Tmax-Tmin).</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3632592
oai:zenodo.org:3632592
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3632591
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Extreme temperature range
ETR
EURO-CORDEX
open-data
output-data
H2020
Future climate
Ensemble calculations of "ETR" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3632445
2020-01-31T19:20:51Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-01-31
<p><strong>Climate Index: </strong>Tn10p</p>
<p><strong>Definition:</strong> Average number of days that the daily minimum temperature is below the 10th percentile of daily minimum temperatures of a five day window.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3632445
oai:zenodo.org:3632445
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3632444
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Tn10p
EURO-CORDEX
open-data
output-data
H2020
Cold
Future climate
Ensemble calculations of "Tn10p" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:2559262
2020-01-20T15:49:16Z
user-clarity
openaire
user-eu
Kainz, Astrid
Zuvela-Aloise, Maja
de Wit, Rosmarie
Goler, Robert
2019-02-07
<p>Urban areas and traffic infrastructure are especially vulnerable to climate change impacts. Heat waves, together with urban heat island effects represent a climate related concern that particularly affects cities, resulting from an interplay of climate warming, densification of urban areas, vegetation loss and increased sealed surfaces.</p>
<p>The CLARITY project (EU-Horizon 2020, <a href="http://www.clarity-h2020.eu">www.clarity-h2020.eu</a>) aims at implementing a climate service tool designed for assessing climate change induced hazards and for considering possible adaptation strategies in order to improve resilience measures at the urban level and to support urban infrastructure development. Using a dynamical-statistical downscaling approach, information on climate change and related climate extremes can be assessed on the urban scale by combining future climate projections provided by the EURO-CORDEX project with urban climate simulations.</p>
<p>Here, we use the dynamical urban climate model MUKLIMO_3, developed by DWD, to provide high-resolution (100 m) urban climate projections and climate sensitivity simulations for the city of Linz in Austria, based on high-resolution Urban Atlas land-use data and Copernicus topography data. The modelling methodology, as developed within CLARITY, are outlined and furthermore, the current and future climatic situation in terms of urban heat stress are analysed and possible climate adaptation measures, such as roof greening, increased albedo, decreased soil sealing, amongst others, are tested with respect to their efficiency towards resilient urban planning.</p>
https://doi.org/10.5281/zenodo.2559262
oai:zenodo.org:2559262
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2559261
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
EGU 2018, European Geosciences Union General Assembly 2018, Vienna, Austria, 8–13 April 2018
CLARITY Climate Services - Supporting Urban Climate Change Resilience and Adaptive Planning through Modelling of Possible Climate Adaptation Strategies
info:eu-repo/semantics/lecture
oai:zenodo.org:2557603
2020-09-28T09:13:01Z
user-clarity
openaire
user-eu
Denis Havlik
2018-12-11
<p>"Digitized general workflow for climate impact assessment" explains the CLARITY offer for potential institutional users of the CSIS service. "Tailored" data package can e.g. include hazards and elements at risk classes that are relevant for the specific sector or region, localised vulnerability functions and/or realistic exposure and impact scenarios.</p>
<p>By requesting that all project proposals perform the screening against such data package, the institutional users can enforce a low-cost, standardized and highly transparent procedure for initial climate change resilience screening that will clearly indicate the need (if any) for implementation of the adaptation options and possible further steps for climate proofing the project.</p>
https://doi.org/10.5281/zenodo.2557603
oai:zenodo.org:2557603
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2557602
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
COP24, COP24 United Nations Climate Change Conference, Katowice, Poland, 02 Dec 2018 - 14 Dec 2018
CLARITY
climate change adaptation services
business offer
Climate adaptation
Climate resilience
Digitized general workflow for climate impact assessment
info:eu-repo/semantics/lecture
oai:zenodo.org:2557801
2020-01-20T15:38:37Z
user-clarity
openaire
user-eu
Zuccaro, Giulio
Zuvela-Aloise, Maja
Leone, Mattia Federico
2019-02-05
<p>Climate Services are emerging worldwide as an essential tool to bridge the advancement in climate science and meteo/earth observations with a variety of operational fields in the domains of Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA).</p>
<p>The modelling methodology developed within CLARITY project (EU-Horizon 2020, <a href="http://www.clarity-h2020.eu)">www.clarity-h2020.eu)</a> is aimed at implementing a new generation of climate services specifically designed to address adaptation measures at city level, thus dealing with planning and urban design issues in the context of new construction and retrofitting of buildings, open spaces and green and blue infrastructure.</p>
<p>The general logic follows the six-steps approach outlined in the EU document “Non-paper Guidelines for Project Managers: Making vulnerable investments climate resilient”, modified and integrated according to the updated risk assessment methodology and terminology as proposed within IPCC-AR5 framework, which reconnects the climate change domain with the conventional DRR modelling approach (R=HxExV).</p>
<p>The approach intends to support the assessment and appraisal of adaptation measures within foreseen planning and urban design activity, by providing a detailed impact quantification on selected elements at risk (e.g. population, buildings, transport infrastructure, local economy, etc.) under the effect of extreme weather events in context of climate change based on high resolution climate projections. In this sense, a major effort is devoted to embedding urban microclimate projections as additional refining step in the conventional GCM-RCM downscaling approach and performing climate sensitivity simulations of adaptation measures on the urban scale. </p>
<p>Vulnerability and impact assessment represent an essential component of the proposed simulation-based scenario assessment methodology, aimed at increasing the potential for use of scientific results by decision-makers to streamline national to local DRR and CCA policies.</p>
<p>The envisaged climate services tool combines multi-hazard and dynamic impact scenarios with cost-benefit and multi-criteria analyses, tailored according the specific applications to assess the effectiveness of alternative “Adaptive Mitigation” and “Build Back Better” options, as well as to identify trade-offs, co-benefits, common resilience pathways and management approaches, highlighting synergies of integrated actions in a “all-hazards” perspective.</p>
https://doi.org/10.5281/zenodo.2557801
oai:zenodo.org:2557801
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2557800
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
climate services
climate change
IPCC Cities
Next Generation of Climate Services to Support Adaptive Planning and Design in Cities: CLARITY modelling methodology and applications
info:eu-repo/semantics/lecture
oai:zenodo.org:6606784
2023-07-12T17:56:50Z
user-clarity
openaire_data
Gaitan, E
Paradinas, C
Redolat, D
Prado, C
de Diego, E
Asensio, L
Torres, I
Pacheco, D
Juncosa, L
Monjo, R
Torres, L
2022-06-02
<p>CRISI-ADAPT II project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision making of the related stakeholders. These projections were obtained with also the purpose to be freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas.</p>
<p>For further details, find here a brief of the methodology followed:</p>
<p> </p>
<p><strong> Methodology</strong></p>
<p>Information provided by 10 models belonging to CMIP6 have been included. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table: </p>
<p><em>Table. Information about the ten climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the sixth report of the IPCC. Models were supplied by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) archives. </em></p>
<table>
<tbody>
<tr>
<td>
<p><strong>CMPI6 MODELS</strong> </p>
</td>
<td>
<p><strong>Resolution</strong> </p>
</td>
<td>
<p><strong>Responsible Centre</strong> </p>
</td>
<td>
<p><strong>References</strong> </p>
</td>
</tr>
<tr>
<td>
<p><strong>BCC-CSM2-MR</strong> </p>
</td>
<td>
<p>1,125º x 1,121º </p>
</td>
<td>
<p>Beijing Climate Center (BCC), China Meteorological Administration, China. </p>
</td>
<td>
<p>Wu, T. et al. (2019) </p>
</td>
</tr>
<tr>
<td>
<p><strong>CanESM5</strong> </p>
</td>
<td>
<p>2,812º x 2,790º </p>
</td>
<td>
<p>Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. </p>
</td>
<td>
<p>Swart, N.C. et al. (2019) </p>
</td>
</tr>
<tr>
<td>
<p><strong>CNRM-ESM2-1</strong> </p>
</td>
<td>
<p>1,406º x 1,401º </p>
</td>
<td>
<p>CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. </p>
</td>
<td>
<p>Seferian, R. (2019) </p>
</td>
</tr>
<tr>
<td>
<p><strong>EC-EARTH3</strong> </p>
</td>
<td>
<p>0,703º x 0,702º </p>
</td>
<td>
<p>EC-EARTH Consortium </p>
</td>
<td>
<p>EC-Earth Consortium. (2019) </p>
</td>
</tr>
<tr>
<td>
<p><strong>GFDL-ESM4</strong> </p>
</td>
<td>
<p>1,250º x 1,000º </p>
</td>
<td>
<p>National Oceanic and Atmospheric Administration (NOAA), E.E.U.U. </p>
</td>
<td>
<p>Krasting, J.P. et al. (2018) </p>
</td>
</tr>
<tr>
<td>
<p><strong>MPI-ESM1-2-HR</strong> </p>
</td>
<td>
<p>0,938º x 0,935º </p>
</td>
<td>
<p>Max-Planck Institute for Meteorology (MPI-M), Germany. </p>
</td>
<td>
<p>Von Storch, J. et al. (2017) </p>
</td>
</tr>
<tr>
<td>
<p><strong>MRI-ESM2-0</strong> </p>
</td>
<td>
<p>1,125º x 1,121º </p>
</td>
<td>
<p>Meteorological Research Institute (MRI), Japan. </p>
</td>
<td>
<p>Yukimoto, S. et al. (2019) </p>
</td>
</tr>
<tr>
<td>
<p><strong>UKESM1-0-LL</strong> </p>
</td>
<td>
<p>1,875º x 1,250º </p>
</td>
<td>
<p>Uk Met Office, Hadley Centre, United Kingdom </p>
</td>
<td>
<p>Good, P. et al. (2019) </p>
</td>
</tr>
<tr>
<td>
<p><strong>NorESM2-MM</strong> </p>
</td>
<td>
<p>1,250º x 0,942º </p>
</td>
<td>
<p>Norwegian Climate Centre (NCC), Norway. </p>
</td>
<td>
<p>Bentsen, M. et al. (2019) </p>
</td>
</tr>
<tr>
<td>
<p><strong>ACCESS-ESM1-5</strong> </p>
</td>
<td>
<p>1,875º x 1,250º </p>
</td>
<td>
<p>Australian Community Climate and Earth System Simulator (ACCESS), Australia </p>
</td>
<td>
<p>Ziehn, T. et al. (2019)</p>
</td>
</tr>
</tbody>
</table>
<p>Since the case studies are distributed among Portugal, Spain, Italy, Malta and Cyprus, a grid covering the whole Mediterranean area, between latitudes 30°N and 50°N and longitudes between 15°W and 40°E, has been chosen for the study. The atmospheric variables available from CMIP6 are wind, temperature, humidity and rainfall at a daily timescale and sea level rise at a monthly timescale. However, it is possible simulate sub-daily rainfall (e.g. for the sector of Flooding and Emergency Response) thanks to the index-n method (Monjo <em>et al.</em> 2016). Other variables such as fog and wave height requires to be obtained from model post-processing. </p>
<p>In addition to these models, information has also been combined to the ERA5-LAND, which has a resolution of 0.07°×0.07°. For each climate variable simulated by the CMIP6 models, a statistical downscaling was applied according to seven steps: </p>
<ol>
<li>
<p>Firstly, as a reference field, a purely geo-statistical downscaling of the original Era5-Land grid (0.07°×0.07°) was performed for each variable to a 1km×1km grid, using linear stepwise regression with topological and geographical parameters (altitude, latitude, longitude and distance to the Atlantic Ocean and Mediterranean Sea), and bilinear model for the residual errors. </p>
</li>
<li>For all models and their corresponding scenarios, the average values for the study area have been calculated for the periods 1981-2010, 2021-2050 and 2071-2100 and their rate of variation between the periods 2071-2100 and 2021-2050. </li>
<li>
<p>The model scenario with the highest rate of variation and the model scenario with the lowest rate of variation have been chosen to range future variations of the variables. Quantiles 90th, 50th and 10th scenarios have been called Upper, Medium and Lower, respectively. </p>
</li>
<li>For these scenarios, Upper, Medium and Lower, the empirical values corresponding to the return periods of 5, 10, 20 and 30 years for the periods 1981-2010, 2021-2050, 2046-2075 and 2071-2100 have been calculated for each grid point in the model. </li>
<li>
<p>Once the above results were obtained, an interpolation to a grid of 1km×1km was performed using the bilinear method. </p>
</li>
<li>Then, the increment or difference with respect to the same return periods of the period 1981-2010 has been calculated for each period of 30 years (2021-2050, 2046-2075 and 2071-2100) and for each return period. Relative increment (instead of absolute increment) was considered for some variable such as precipitation and wind. </li>
<li>
<p>Finally, the absolute o relative increment of each scenario and return period (step 6) was added to the reference values of each variable (step 1), obtaining climate scenarios in a 1km×1km grid (see for instance Figure 8). This entire process, applied to return-period values, is an empirical quantile mapping by increment from reanalysis (Monjo et al. 2013). </p>
</li>
</ol>
https://doi.org/10.5281/zenodo.6606784
oai:zenodo.org:6606784
Zenodo
https://zenodo.org/communities/clarity
https://doi.org/10.5281/zenodo.6606783
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Climate
Climate change
Climate projections
CMIP6
Statistical downscaling
CRISI-ADAPT II: free downscaled climate projection layers
info:eu-repo/semantics/other
oai:zenodo.org:3631667
2020-01-30T19:20:47Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-01-30
<p><strong>Climate Index: </strong>Consecutive Frost days</p>
<p><strong>Definition:</strong> Maximum number of days with daily minimum temperature below 0°C.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3631667
oai:zenodo.org:3631667
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3631666
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Consecutive frost days
EURO-CORDEX
open-data
output-data
H2020
Cold
Future climate
Ensemble calculations of "Consecutive Frost Days" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:4050264
2020-09-28T09:22:30Z
user-clarity
openaire
user-eu
Andrea Geyer-Scholz
Denis Havlik
Robert Goler
Claudia Hahn
Mattia Leone
Jorge Armorim
Wolfgang Loibl
2020-07-09
<p>This is a fourth Climate Thursdays and also a third CLARITY for Climate Resilience (Clarity4CR) webinar. It is <strong>dedicated to future (urban) heat waves and possible adaptation options that can mitigate the risks and generally improve the quality of life in European urban areas</strong>.</p>
This webinar is part of the CLARITY4ClimateResilience webinar series. Other webinars from this series are available on https://www.gotostage.com/channel/climate-adaptation
https://doi.org/10.5281/zenodo.4050264
oai:zenodo.org:4050264
eng
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4050263
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate change
Climate adaptation
Climate resilience
Urban planning
Urban Heat Islands
In my region: Urban heat adaptation in Southern, Central and Northern Europe
info:eu-repo/semantics/lecture
oai:zenodo.org:3958827
2020-08-10T09:55:18Z
user-clarity
software
user-eu
Pascal Dihé
therter
Itsman-AT
DanielRodera
2020-07-24
The Map Component is a reusable, flexible and highly configurable Building Block meant to be used throughout CSIS.
https://doi.org/10.5281/zenodo.3958827
oai:zenodo.org:3958827
Zenodo
https://github.com/clarity-h2020/map-component/tree/2.7.1
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3642873
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/map-component: Map Component v2.7.1
info:eu-repo/semantics/other
oai:zenodo.org:3925259
2020-08-10T09:55:17Z
user-clarity
software
user-eu
Pascal Dihé
therter
Itsman-AT
DanielRodera
2020-07-01
<p>The Map Component is a reusable, flexible and highly configurable Building Block meant to be used throughout CSIS.</p>
https://doi.org/10.5281/zenodo.3925259
oai:zenodo.org:3925259
Zenodo
https://github.com/clarity-h2020/map-component/tree/2.7.0
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3642873
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/map-component: Map Component v2.7.0
info:eu-repo/semantics/other
oai:zenodo.org:4028488
2020-10-07T12:29:11Z
user-clarity
openaire
Åkesson, Anna
Kaiser, Gunilla
Thurin, Sofia
Lindell, Måns
Sannebro, Magnus
Johansson, Christer
Strömbäck, Lena
Hundecha, Yeshewatesfa
Almer, Anne-Catrin
Moberg, Frida
2020-09-14
<p>Short overview of the demonstration cases which WSP has been an active participant. Collaborations with SMHI, CABJON and StockCity, Contact WSP for further details regarding these case studies.</p>
https://doi.org/10.5281/zenodo.4028488
oai:zenodo.org:4028488
swe
Zenodo
https://zenodo.org/communities/clarity
https://doi.org/10.5281/zenodo.4028487
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
flooding
climate change
DC2 demonstration cases related to flooding
info:eu-repo/semantics/lecture
oai:zenodo.org:6607603
2023-07-12T17:56:51Z
user-clarity
Monjo, R
Paradinas, C
Prado, C
Redolat, D
Gaitan, E
Rivera, A
de Diego, E
Torres, I
Russo, B
Paindelli, A
Bojic, O
Skouroupathi, M
2022-06-02
<p>The main CRISI-ADAPT II product is a holistic-based decision-support tool that is offered as a specialised climate service to validate adaptation planning and operations. This tool consists of two modules, the Climate Risk Information Tool (CRIT) and the Monitoring Extreme Events Tool (MEET). These modules enable end-users to improve their decisions to face climate-related impacts with adaptation planning or to identify new opportunities for improving efficiency in their operations.</p>
<p>This tool offers several services that are included in what was budgeted to be developed during CRISI-ADAPT II project, but has the potential to include some others that could be developed specifically under request. CRISI-ADAPT II consortium has also the potential to offer different more advanced climate services if needed. All of this can be consulted in the attached PDF file at this post.</p>
https://doi.org/10.5281/zenodo.6607603
oai:zenodo.org:6607603
eng
Zenodo
https://zenodo.org/communities/clarity
https://doi.org/10.5281/zenodo.6607602
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Climate
Climate change
Climate services
Climate adaptation
CBA
Statistical downscaling
Urban flooding
Hydrological drought
CRISI-ADAPT II: Climate services description
info:eu-repo/semantics/other
oai:zenodo.org:3631650
2020-01-30T19:20:47Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-01-30
<p><strong>Climate Index: </strong>Consecutive Summer days</p>
<p><strong>Definition:</strong> Maximum number of days with daily maximum temperature above 25°C.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3631650
oai:zenodo.org:3631650
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3631649
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Consecutive summer days
EURO-CORDEX
open-data
output-data
H2020
Heat
Future climate
Ensemble calculations of "Consecutive Summer Days" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3964441
2020-08-12T11:53:01Z
user-clarity
software
user-eu
Pascal Dihé
2020-07-28
<p>Customised versions of the CRISMA Scenario Comparison and Analysis and the Multi-Criteria-Analysis and Decision Support Functional Building Blocks for integration within the CLARITY CSIS.</p>
https://doi.org/10.5281/zenodo.3964441
oai:zenodo.org:3964441
Zenodo
https://github.com/clarity-h2020/scenario-analysis/tree/2.3.0
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3862011
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/scenario-analysis: Scenario Analysis v2.3
info:eu-repo/semantics/other
oai:zenodo.org:3862012
2020-08-12T11:53:01Z
user-clarity
software
user-eu
Pascal Dihé
2020-05-28
<p>AngularJS implementation of the Scenario Comparison and Analysis and the Multi-Criteria-Analysis and Decision Support Functional Building Block.</p>
https://doi.org/10.5281/zenodo.3862012
oai:zenodo.org:3862012
Zenodo
https://github.com/clarity-h2020/scenario-analysis/tree/2.2
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3862011
info:eu-repo/semantics/openAccess
Other (Open)
AngularJS
clarity-h2020/scenario-analysis: Scenario Analysis v2.2
info:eu-repo/semantics/other
oai:zenodo.org:3707751
2020-03-12T20:20:16Z
user-clarity
openaire_data
user-eu
Hahn, Claudia
2020-03-12
<p><strong>Climate Index: </strong>Fmax</p>
<p><strong>Definition:</strong> Average of the annual maximum of the daily maximum wind speed over a 30-year time-period.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily maximum near-surface wind speed (sfcWindmax).</p>
<p>Results (ensemble mean and ensemble standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) time periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>SMHI-RCA4/ ICHEC-EC-EARTH, SMHI-RCA4/ MOHC-HadGEM2-ES</li>
<li>CLMcom-CCLM4-8-17/ ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/ MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ ICHEC-EC-EARTH, KNMI-RACMO22E/ MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3707751
oai:zenodo.org:3707751
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3707750
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate Index
Wind index
EURO-CORDEX
Ensemble calculations of "Fmax" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3980900
2020-08-12T12:59:24Z
user-clarity
software
user-eu
Pascal Dihé
2020-08-12
Customised versions of the CRISMA Scenario Comparison and Analysis and the Multi-Criteria-Analysis and Decision Support Functional Building Blocks for integration within the CLARITY CSIS.
https://doi.org/10.5281/zenodo.3980900
oai:zenodo.org:3980900
Zenodo
https://github.com/clarity-h2020/scenario-analysis/tree/2.3.1
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3862011
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/scenario-analysis: Scenario Analysis v2.3.1
info:eu-repo/semantics/other
oai:zenodo.org:3634456
2020-02-03T19:20:50Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-02-03
<p><strong>Climate Index: </strong>RX1day</p>
<p><strong>Definition:</strong> Greatest one-day precipitation amount.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3634456
oai:zenodo.org:3634456
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3634455
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
RX1day
EURO-CORDEX
open-data
output-data
H2020
Future climate
Precipitation
Ensemble calculations of "RX1day" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3776133
2020-08-03T15:37:23Z
user-clarity
software
user-eu
Pascal Dihé
2020-04-29
<p><a href="https://myclimateservices.eu/">CLARTIY H2020</a> Data Management Plan refs/tags/v0.2.1 generated by p-a-s-c-a-l from <a href="https://ckan.myclimateservice.eu/">CLARITY CKAN</a> meta-data catalogue. Build #19 triggered by 87898724045628d25936de409b85c8d3e913efbc.</p>
https://doi.org/10.5281/zenodo.3776133
oai:zenodo.org:3776133
Zenodo
https://github.com/clarity-h2020/data-management-plan/tree/v0.2.1
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3776132
info:eu-repo/semantics/openAccess
Other (Open)
Data Management Plan v0.2.1
info:eu-repo/semantics/other
oai:zenodo.org:3776164
2020-08-03T15:37:23Z
user-clarity
software
user-eu
Pascal Dihé
Eugene Maximov
2020-04-29
<p>Download the <a href="https://myclimateservices.eu/">CLARTIY H2020</a> Data Management Plan generated by p-a-s-c-a-l from <a href="https://ckan.myclimateservice.eu/">CLARITY CKAN</a> meta-data catalogue <a href="https://github.com/clarity-h2020/data-management-plan/releases/">here</a>.</p>
<p><em>Build #20 triggered by dfa02282ed49063470bf4337e2cad3ccb81bb34d.</em></p>
https://doi.org/10.5281/zenodo.3776164
oai:zenodo.org:3776164
Zenodo
https://github.com/clarity-h2020/data-management-plan/tree/v0.2.2
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3776132
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/data-management-plan: Data Management Plan v0.2.2
info:eu-repo/semantics/other
oai:zenodo.org:3843055
2020-08-03T15:37:24Z
user-clarity
software
user-eu
Pascal Dihé
Eugene Maximov
2020-05-25
<p>Download the <a href="https://myclimateservices.eu/">CLARTIY H2020</a> Data Management Plan generated by p-a-s-c-a-l from <a href="https://ckan.myclimateservice.eu/">CLARITY CKAN</a> meta-data catalogue <a href="https://github.com/clarity-h2020/data-management-plan/releases/">here</a>.</p>
<p><em>Build #22 triggered by c906e3703acc4e78f3247649085f4a0c6757445b.</em></p>
https://doi.org/10.5281/zenodo.3843055
oai:zenodo.org:3843055
Zenodo
https://github.com/clarity-h2020/data-management-plan/tree/v0.3
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3776132
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/data-management-plan: Data Management Plan v0.3
info:eu-repo/semantics/other
oai:zenodo.org:3854964
2020-08-03T15:37:24Z
user-clarity
software
user-eu
Pascal Dihé
Eugene Maximov
2020-05-26
<p>Download the <a href="https://myclimateservices.eu/">CLARTIY H2020</a> Data Management Plan generated by p-a-s-c-a-l from <a href="https://ckan.myclimateservice.eu/">CLARITY CKAN</a> meta-data catalogue <a href="https://github.com/clarity-h2020/data-management-plan/releases/">here</a>.</p>
<p><em>Build #23 triggered by 169882642ef0109e191059f5e6126d0b860692b4.</em></p>
https://doi.org/10.5281/zenodo.3854964
oai:zenodo.org:3854964
Zenodo
https://github.com/clarity-h2020/data-management-plan/tree/v0.4
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3776132
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/data-management-plan: Data Management Plan v0.4
info:eu-repo/semantics/other
oai:zenodo.org:3634551
2020-02-03T19:20:50Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-02-03
<p><strong>Climate Index: </strong>Consecutive Wet Days</p>
<p><strong>Definition:</strong> Maximum annual number of consecutive wet days (daily rainfall minimum ≥ 1mm).</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3634551
oai:zenodo.org:3634551
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3634550
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Consecutive wet days
Precipitation
EURO-CORDEX
open-data
output-data
H2020
Future climate
Ensemble calculations of "Consecutive Wet Days" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3632361
2020-01-31T19:20:51Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-01-31
<p><strong>Climate Index: </strong>Ice days</p>
<p><strong>Definition:</strong> Number of days with daily maximum temperature below 0°C.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3632361
oai:zenodo.org:3632361
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3632360
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Ice days
EURO-CORDEX
open-data
output-data
H2020
Cold
Future climate
Ensemble calculations of "Ice Days" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3957397
2020-07-29T11:22:20Z
user-clarity
software
user-eu
Pascal Dihé
DanielRodera
2020-07-23
JavaScript Helper Library for communicating with Drupal 8 JSON:API
https://doi.org/10.5281/zenodo.3957397
oai:zenodo.org:3957397
Zenodo
https://github.com/clarity-h2020/csis-helpers-js/tree/0.6.2
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3957298
info:eu-repo/semantics/openAccess
Other (Open)
clarity-h2020/csis-helpers-js: v0.6.2
info:eu-repo/semantics/other
oai:zenodo.org:3634638
2020-02-03T19:20:50Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-02-03
<p><strong>Climate Index: </strong>Wet days</p>
<p><strong>Definition:</strong> Average number of wet days (daily precipitation ≥ 1mm).</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3634638
oai:zenodo.org:3634638
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3634637
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Wet days
Precipitation
EURO-CORDEX
open-data
output-data
H2020
Future climate
Ensemble calculations of "Wet Days" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:6606644
2023-07-12T17:56:53Z
user-clarity
Rivera, A
de Diego, E
Monjo, R
Rubio, S
Paradinas, C
2022-06-02
<p>The challenge of the adaptation to climate change and extreme events demands decision-support tools to manage risks. The main CRISI-ADAPT-II product is a holistic-based decision-support tool that is offered as a specialised climate service to validate adaptation planning and operations. This tool consists of two modules, the Climate Risk Information Tool (CRIT) and the Monitoring Extreme Events Tool (MEET). These modules enable end-users to improve their decisions to face climate-related impacts with adaptation planning or to identify new opportunities for improving efficiency in their operations.</p>
<p>Both tools, interconnected, were co-designed and co-developed by the CRISI-ADAPT II consortium and stakeholders to be adapted to each of the 4 sectors studied in the project, providing a specific set of data to each of the case studies. This platform offers different modules, being: weather forecast, seasonal forecast, downscaled climate projections and adaptation and mitigation measures for identified vulnerable elements.</p>
<p>The platform here offered is a free-to-access general version for the area surrounding the Case Studies and it can be found at the link <a href="https://tool.crisi-adapt2.eu/">https://tool.crisi-adapt2.eu/</a>. Only information regarding seasonal and climate projections is offered due to the rest of info being under payment or restricted due to its confidential nature. Seasonal forecast is displayed from the ECMWF-SEAS5 model, collected from the Copernicus C3S platform, and combined with Model Output Statistics (MOS) on monthly basis. Obtained variables are similar, involving maximum and minimum temperature, total precipitation, wind gusts and relative humidity. Regarding climate projections, Shared Socioeconomic Pathways (SSP2-4.5, SSP3-7.0, and SSP5-8.5) scenarios of 10 CMIP6 models were considered and sorted regarding their changes; offered for mean and extreme climate variables, with ERA5-Land reanalysis selected as a reference baseline for all the cases. Variables are provided by 30-years span period, from historical, up to the end of the Century. Climate data are available for all the case studies, while tailored menu and options are displayed in function of the case study characteristics.</p>
URL: https://tool.crisi-adapt2.eu/
https://doi.org/10.5281/zenodo.6606644
oai:zenodo.org:6606644
eng
Zenodo
https://zenodo.org/communities/clarity
https://doi.org/10.5281/zenodo.6606643
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Climate
Decision Support System
Climate adaptation
CRISI-ADAPT II
Climate change
CRISI-ADAPT II: CRIT-MEET platform for supporting the decision making and adaptation planning
info:eu-repo/semantics/other
oai:zenodo.org:3707882
2020-03-12T20:20:16Z
user-clarity
openaire_data
user-eu
Hahn, Claudia
2020-03-12
<p><strong>Climate Index: </strong>98perc_sfcWindmax</p>
<p><strong>Definition:</strong> Average of the annual 98<sup>th</sup> percentile of the daily maximum wind speed over a 30-year time-period.</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily maximum near-surface wind speed (sfcWindmax).</p>
<p>Results (ensemble mean and ensemble standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) time periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>SMHI-RCA4/ ICHEC-EC-EARTH, SMHI-RCA4/ MOHC-HadGEM2-ES</li>
<li>CLMcom-CCLM4-8-17/ ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/ MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ ICHEC-EC-EARTH, KNMI-RACMO22E/ MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3707882
oai:zenodo.org:3707882
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3707881
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate Index
Wind Index
EURO-CORDEX
Ensemble calculations of "98perc_sfcWindmax" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other
oai:zenodo.org:3634646
2020-02-03T19:20:50Z
user-clarity
openaire_data
user-eu
Robert Goler
2020-02-03
<p><strong>Climate Index: </strong>Very heavy precipitation days</p>
<p><strong>Definition:</strong> Average number of very heavy precipitation days (daily precipitation ≥ 20mm).</p>
<p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.</p>
<p>Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p>
<p>The bias-corrected EURO-CORDEX climate model simulations used are:</p>
<ul>
<li>CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES</li>
<li>DMI-HIRHAM5/ICHEC-EC-EARTH</li>
<li>KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES</li>
<li>SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES</li>
</ul>
https://doi.org/10.5281/zenodo.3634646
oai:zenodo.org:3634646
Zenodo
https://zenodo.org/communities/clarity
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3634645
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CLARITY
Climate index
Very heavy precipitation days
Precipitation
EURO-CORDEX
open-data
output-data
H2020
Future climate
Ensemble calculations of "Very Heavy Precipitation Days" from EURO-CORDEX data for Europe
info:eu-repo/semantics/other