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These are the raw data generated in the 2do Joint Experiments sponsored by IRP Wind EU project

This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)-1 yr-1] or [g N (kg product)-1 yr-1] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector

This project aims to validate experimentally the High definition Modular Multilevel Converter (HD-MMC) which was developed by ORE Catapult. The concept can generate a lower THD than Conventional MMC (C-MMC) helping to increase power density and efficiency. You will find data set and final report here

This dataset contains spatial predictions of the potential environmental spaces for pingos, ice-wedge polygons and rock glaciers across the Northern Hemisphere permafrost areas. The potential environmental spaces, i.e. conditions where climate, topography and soil properties are suitable for landform presence, were predicted with statistical ensemble modelling employing geospatial data on environmental conditions at 30 arc-second resolution (~1 km). In addition to the baseline period (1950-2000), the predictions are provided for 2041-2060 and 2061-2080 using climate-forcing scenarios (Representative Concentration Pathways 4.5 and 8.5). The resulting dataset consists of five spatial predictions for each landform in GeoTIFF format.The data provide new information on 1) the fine-scale spatial distribution of permafrost landforms in the Northern Hemisphere, 2) the potential future alterations in the environmental suitability for permafrost landforms due to climate change, and 3) the circumpolar distribution of various ground ice types, and can 4) facilitate efforts to inventory permafrost landforms in incompletely mapped areas.

Herewith we present the extended 1Hz dataset of wind measurements from a Skipheia meteorological station on the island of Frøya on the western coast of Norway, Trondelag. The data binned in 10 min averages can be find at: https://doi.org/10.5281/zenodo.2557500 The site represents an exposed coastal wind climate with open sea, land and mixed fetch from various directions. UTM-coordinates of the Met-mast: 8.34251 E and 63.66638 N. See the map for details (NorwegianMapping Authority): https://www.norgeskart.no/#!?project=norgeskart&layers=1003&zoom=3&lat=7035885.49&lon=539601.41&markerLat=7077031.483032227&markerLon=170902.83203125&panel=searchOptionsPanel&sok=Titranveien Presented data were gathered between years 2009-2016. Data&hardware summary: Years 2009-2016: Mast2 equipped with 6 pairs of 2D sonic anemometers at 10, 16, 25, 40, 70, 100 m above the ground, independent temperature measurements at the same heights and near the ground; pressure and relative humidity from local meteostation (Sula, 20 km away). Years 2014-2016: Mast4 equipped with 2 pairs of 2D sonic anemometers at 40 and 100 m above the ground. The distance between the masts is 79 m. Data is binned in years and months and stored in a ‘*.txt’ tab-separated values file. Data column order is described in SkipheiaMast2_header.txt and SkipheiaMast4_header.txt, where WSx is the wind speed (m/s), WDx is the wind direction (360 deg), ATx is the air temperature (deg C) and x designates the instrument number. The instruments are numbered starting from the ground. Example: For Mast2 (6 pairs of anemometers, ground temperature + 6 temperature sensors on the mast) that means that AT0 is the ground temperature. WS1 and WS2 are wind speed records at 10 m level. WS3 and WS4 are wind speed records at 16 m. For Mast4 (2 pairs of anemometers) that means that WS1 and WS2 are wind speed records at 40 m level. WS3 and WS4 are wind speed records at 100 m. Detailed site description with wind climate description can be found in attached analysis: Site analysys.pdf. Additional information and analysis can be found in listed below works, using data from Frøya site: Bardal, L. M., & Sætran, L. R. (2016, September). Spatial correlation of atmospheric wind at scales relevant for large scale wind turbines. In Journal of Physics: Conference Series (Vol. 753, No. 3, p. 032033). IOP Publishing, doi:10.1088/1742-6596/753/3/032033, https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf Bardal, L. M., & Sætran, L. R. (2016). Wind gust factors in a coastal wind climate. Energy Procedia, 94, 417-424, https://doi.org/10.1016/j.egypro.2016.09.207 IEA Wind TCP Task 27 Compendium of IEA Wind TCP Task 27 Case Studies, Technical Report, Prepared by Ignacio Cruz Cruz, CIEMAT, Spain Trudy Forsyth, WAT, United States, October 2018; Chapter 1.8. https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0 Domagalski, P., Bardal, L. M., & Sætran, L. Vertical Wind Profiles in Non-neutral Conditions-Comparison of Models and Measurements from Froya. Journal of Offshore Mechanics and Arctic Engineering, doi: 10.1115/1.4041816, http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3 Møller, M., Domagalski, P., & Sætran, L. R. (2019, October). Characteristics of abnormal vertical wind profiles at a coastal site. In Journal of Physics: Conference Series (Vol. 1356, No. 1, p. 012030). IOP Publishing. https://iopscience.iop.org/article/10.1088/1742-6596/1356/1/012030 Møller, M., Domagalski, P., and Sætran, L. R.: Comparing Abnormalities in Onshore and Offshore Vertical Wind Profiles, Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-40 , in review, 2019.

Upwelling processes bring nutrient-rich waters from the deep ocean to the surface. Areas of upwelling are often associated with high productivity, offering great economic value in terms of fisheries. The sensitivity of spring/summer-time coastal upwelling systems to climate change has recently received a lot of attention. Several studies have suggested that their intensity may increase in the future while other authors have shown decreasing intensity in their equatorward portions. Yet, recent observations do not show robust evidence of this intensification. The Senegalo-Mauritanian upwelling system (SMUS) located at the southern edge of the north Atlantic system (12°N–20°N) and most active in winter/spring has been largely excluded from these studies. Here, the seasonal cycle of the SMUS and its response to climate change is investigated in the database of the Coupled Models Inter comparison Project Phase 5 (CMIP5). Upwelling magnitude and surface signature are characterized by several sea surface temperature and wind stress indices. We highlight the ability of the climate models to reproduce the system, as well as their biases. The simulations suggest that the intensity of the SMUS winter/spring upwelling will moderately decrease in the future, primarily because of a reduction of the wind forcing linked to a northward shift of Azores anticyclone and a more regional modulation of the low pressures found over Northwest Africa. The implications of such an upwelling reduction on the ecosystems and local communities exploiting them remains very uncertain.