- home
- Advanced Search
- Energy Research
- Open Access
- Restricted
- Open Source
- Embargo
- CA
- AT
- IN
- English
- Energy Research
- Open Access
- Restricted
- Open Source
- Embargo
- CA
- AT
- IN
- English
Research data keyboard_double_arrow_right Dataset 2020Publisher:Zenodo Funded by:EC | EdgeStressEC| EdgeStressAuthors:Thyrring, Jakob;
Wegeberg, Susse; Blicher, Martin E.;Thyrring, Jakob
Thyrring, Jakob in OpenAIREKrause-Jensen, Dorte;
+6 AuthorsKrause-Jensen, Dorte
Krause-Jensen, Dorte in OpenAIREThyrring, Jakob;
Wegeberg, Susse; Blicher, Martin E.;Thyrring, Jakob
Thyrring, Jakob in OpenAIREKrause-Jensen, Dorte;
Krause-Jensen, Dorte
Krause-Jensen, Dorte in OpenAIREHøgslund, Signe;
Høgslund, Signe
Høgslund, Signe in OpenAIREOlesen, Birgit;
Wiktor Jr, Jozef;Olesen, Birgit
Olesen, Birgit in OpenAIREMouritsen, Kim N.;
Mouritsen, Kim N.
Mouritsen, Kim N. in OpenAIREPeck, Lloyd S.;
Peck, Lloyd S.
Peck, Lloyd S. in OpenAIRESejr, Mikael K.;
Sejr, Mikael K.
Sejr, Mikael K. in OpenAIREThe data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.3920534&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.3920534&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2018Publisher:Zenodo Velocity-based macrorefugia for boreal passerine birds Citation for dataset -------------------- Stralberg, D. Velocity-based macrorefugia for boreal passerine birds. Boreal Avian Modelling Project. Edmonton, Alberta, Canada. DOI: 10.5281/zenodo.1299880 https://doi.org/10.5281/zenodo.1299880 Data layers ----------------- Refugia layers represent mid-century (2041-2070) and end-of-century (2071-2100) conditions for the SRES A2 emissions scenario at 4-km resolution ----------------- Combined index for 53 species (clipped to Brandt's boreal region): _refbrandt53_YYYYZZZZ Species-specific indices: XXXX_refYYYY where: YYYY = Time period (2050s or 2080s) ZZZZ = weighted or unweighted XXXX = Songbird Species Code (see Birdlookup.csv) Percentile values of refugia indices for mapping purposes 0.01 0.1 0.25 0.5 0.75 0.9 0.99 "2050s, weighted " 0.032 0.243 0.317 0.399 0.484 0.589 0.779 "2080s, weighted" 0.002 0.09 0.137 0.2 0.281 0.386 0.675 "2050s, unweighted" 0.006 0.108 0.159 0.218 0.292 0.358 0.421 "2080s, unweighted" 0.001 0.055 0.083 0.123 0.185 0.241 0.297 Projection information ------------------- """+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs""" ------------------- Projection LAMBERT Spheroid GRS80 Units METERS Zunits NO Xshift 0.0 Yshift 0.0 Parameters 49 0 0.0 /* 1st standard parallel 77 0 0.0 /* 2nd standard parallel -95 0 0.0 /* central meridian 0 0 0.0 /* latitude of projection's origin 0.0 /* false easting (meters) 0.0 /* false northing (meters)
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.1299879&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 485visibility views 485 download downloads 76 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.1299879&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | HELIXEC| HELIXAuthors:Thiery, Wim;
Lange, Stefan; Rogelj, Joeri; Schleussner, Carl-Friedrich; +33 AuthorsThiery, Wim
Thiery, Wim in OpenAIREThiery, Wim;
Lange, Stefan; Rogelj, Joeri; Schleussner, Carl-Friedrich; Gudmundsson, Lukas; Seneviratne, Sonia I.; Andrijevic, Marina; Frieler, Katja; Emanuel, Kerry; Geiger, Tobias; Bresch, David N.; Zhao, Fang; Willner, Sven N.; Büchner, Matthias; Volkholz, Jan; Bauer, Nico; Chang, Jinfeng; Ciais, Philippe; Dury, Marie; François, Louis; Grillakis, Manolis; Gosling, Simon N.; Hanasaki, Naota; Hickler, Thomas; Huber, Veronika; Ito, Akihiko; Jägermeyr, Jonas; Khabarov, Nikolay; Koutroulis, Aristeidis; Liu, Wenfeng; Lutz, Wolfgang; Mengel, Matthias; Müller, Christoph; Ostberg, Sebastian; Reyer, Christopher P. O.; Stacke, Tobias; Wada, Yoshihide;Thiery, Wim
Thiery, Wim in OpenAIREThis 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.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5497632&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 317visibility views 317 download downloads 197 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5497632&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: Narayanasetti, Sandeep;Panickal, Swapna;
Gopinathan, Prajeesh A.; Choudhury, Ayantika Dey; +2 AuthorsPanickal, Swapna
Panickal, Swapna in OpenAIRENarayanasetti, Sandeep;Panickal, Swapna;
Gopinathan, Prajeesh A.; Choudhury, Ayantika Dey; Singh, Manmeet; Raghavan, Krishnan;Panickal, Swapna
Panickal, Swapna in OpenAIREProject: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26050/wdcc/ar6.c6spciiits126&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.26050/wdcc/ar6.c6spciiits126&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Funded by:EC | Open ENTRANCEEC| Open ENTRANCEAuthors:O'Reilly, Ryan;
O'Reilly, Ryan
O'Reilly, Ryan in OpenAIRECohen, Jed;
Cohen, Jed
Cohen, Jed in OpenAIREReichl, Johannes;
Reichl, Johannes
Reichl, Johannes in OpenAIREThree data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.7182594&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 26visibility views 26 download downloads 33 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.7182594&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Publisher:PANGAEA Authors:Anhaus, Philipp;
Anhaus, Philipp
Anhaus, Philipp in OpenAIRESchiller, Martin;
Planat, Noémie;Schiller, Martin
Schiller, Martin in OpenAIREKatlein, Christian;
+1 AuthorsKatlein, Christian
Katlein, Christian in OpenAIREAnhaus, Philipp;
Anhaus, Philipp
Anhaus, Philipp in OpenAIRESchiller, Martin;
Planat, Noémie;Schiller, Martin
Schiller, Martin in OpenAIREKatlein, Christian;
Katlein, Christian
Katlein, Christian in OpenAIRENicolaus, Marcel;
Nicolaus, Marcel
Nicolaus, Marcel in OpenAIRETransmitted solar irradiance was measured using an ACC (Advanced-Cosine-Collector) RAMSES hyper-spectral radiometer (TriOS) mounted on the ROV during the ARTofMELT2023 expedition in May and June 2023 and normalized by the incident solar irradiance as measured using an ACC RAMSES hyper-spectral radiometer (TriOS) installed on-board the ship. All times are given in Universal Coordinated Time (UTC).
PANGAEA - Data Publi... arrow_drop_down PANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2024License: CC BYData sources: DatacitePANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2024License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1594/pangaea.964482&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert PANGAEA - Data Publi... arrow_drop_down PANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2024License: CC BYData sources: DatacitePANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2024License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1594/pangaea.964482&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 27 Mar 2023Publisher:Dryad Authors:Bouderbala, Ilhem;
Labadie, Guillemette; Béland, Jean-Michel; Boulanger, Yan; +4 AuthorsBouderbala, Ilhem
Bouderbala, Ilhem in OpenAIREBouderbala, Ilhem;
Labadie, Guillemette; Béland, Jean-Michel; Boulanger, Yan; Hébert, Christian; Desrosiers, Patrick; Allard, Antoine; Fortin, Daniel;Bouderbala, Ilhem
Bouderbala, Ilhem in OpenAIREAim Despite an increasing number of studies highlighting the impacts of climate change on boreal species, the main factors that will drive changes in species assemblages remain ambiguous. We study how species community composition would change following anthropogenic and natural disturbances. We determine the main drivers of assemblage dissimilarity for bird and beetle communities. Location Côte-Nord, Québec, Canada. Methods We quantify two climate-induced pathways based on direct and indirect effects on species occurrence under different harvest management scenarios. The direct climate effects illustrate the impact of climate variables while the indirect effects are reflected through habitat-based climate change. We develop empirical models to predict the distribution of more than 100 species over the next century. We analyze the regional and the latitudinal species assemblage dissimilarity by decomposing it into 'balanced variation in species occupancy and occurrence' and 'occupancy and occurrence gradient'. Results Both pathways increased dissimilarity in species assemblage. At the regional scale, both effects have an impact on decreasing the number of winning species. Yet, responses are much larger in magnitude under mixed climate effects (a mixture of direct and indirect effects). Regional assemblage dissimilarity reached 0.77 and 0.69 under mixed effects versus 0.09 and 0.10 under indirect effects for beetles and birds, respectively, between RCP8.5 and baseline climate scenarios when considering harvesting. Latitudinally, assemblage dissimilarity increased following the climate conditions pattern. Main conclusions The two pathways are complementary and alter biodiversity, mainly caused by species turnover. Yet, responses are much larger in magnitude under mixed climate effects. Therefore, the inclusion of climatic variables considers aspects other than just those related to forest landscapes, such as life cycles of animal species. Moreover, we expect differences in occupancy between the two studied taxa. This could indicate the potential range of change in boreal species concerning novel environmental conditions.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.ghx3ffbsb&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 14visibility views 14 download downloads 2 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.ghx3ffbsb&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Embargo end date: 22 Mar 2024Publisher:Dryad Authors:Pelle, Tyler;
Greenbaum, Jamin; Ehrenfeucht, Shivani; Dow, Christine; +1 AuthorsPelle, Tyler
Pelle, Tyler in OpenAIREPelle, Tyler;
Greenbaum, Jamin; Ehrenfeucht, Shivani; Dow, Christine; McCormack, Felicity;Pelle, Tyler
Pelle, Tyler in OpenAIRE# Dataset: Subglacial freshwater driven speedup of East Antarctic outlet glacier retreat [https://doi.org/10.5061/dryad.1vhhmgr0b](https://doi.org/10.5061/dryad.1vhhmgr0b) Journal: Journal of Geophysical Research: Earth Surface Principle Investigator: * Tyler Pelle, Scripps Institution of Oceanography, University of California San Diego, [tpelle@ucsd.edu](mailto:tpelle@ucsd.edu) Co-Authors: * Dr. Jamin Greenbaum, Scripps Institution of Oceanography, University of California San Diego * Dr. Shivani Ehrenfeucht, Department of Geography and Environmental Management, University of Waterloo * Prof. Christine Dow, Department of Geography and Environmental Management, University of Waterloo * Dr. Felicity S. McCormack, Securing Antarctica's Environmental Future, School of Earth, Atmosphere, & Environment, Monash University Created on October 4, 2023 ## Description of the data and file structure ### File description: 1. runme.m - MATLAB script used to run coupled ISSM-GlaDS SSP5-8.5_{F,M} simulation - includes melt rate parameterization. 2. ssp585.mat – Yearly ice sheet model output from 2017-2100 for SSP5-8.5 simulation. 3. ssp585_F.mat – Yearly ice sheet model output from 2017-2100 for SSP5-8.5_{F} simulation. 4. ssp585_M.mat – Yearly ice sheet model output from 2017-2100 for SSP5-8.5_{M} simulation. 5. ssp585_FM.mat – Yearly ice sheet model output from 2017-2100 for SSP5-8.5_{F,M} simulation. 6. ssp126.mat – Yearly ice sheet model output from 2017-2100 for SSP1-2.6 simulation. 7. ssp126_F.mat – Yearly ice sheet model output from 2017-2100 for SSP1-2.6_{F} simulation. 8. ssp126_M.mat – Yearly ice sheet model output from 2017-2100 for SSP1-2.6_{M} simulation. 9. ssp126_FM.mat – Yearly ice sheet model output from 2017-2100 for SSP1-2.6_{F,M} simulation. 10. ssp585_Totten_T.mat - Bi-weekly ocean temperature (Ta) for Totten Glacier from January 1, 2017 to December 31, 2099 (high emission). 11. ssp585_Moscow_T.mat - Bi-weekly ocean temperature (Ta) for Moscow University Glacier from January 1, 2017 to December 31, 2099 (high emission). 12. ssp585_Vander_T.mat - Bi-weekly ocean temperature (Ta) for Vander Glacier from January 1, 2017 to December 31, 2099 (high emission). 13. ssp585_Totten_S.mat - Bi-weekly ocean salinity (Sa) for Totten Glacier from January 1, 2017 to December 31, 2099 (high emission). 14. ssp585_Moscow_S.mat - Bi-weekly ocean salinity (Sa) for Moscow University Glacier from January 1, 2017 to December 31, 2099 (high emission). 15. ssp585_Vander_S.mat - Bi-weekly ocean salinity (Sa) for Vander Glacier from January 1, 2017 to December 31, 2099 (high emission). 16. ssp126_Totten_T.mat - Bi-weekly ocean temperature (Ta) for Totten Glacier from January 1, 2017 to December 31, 2099 (low emission). 17. ssp126_Moscow_T.mat - Bi-weekly ocean temperature (Ta) for Moscow University Glacier from January 1, 2017 to December 31, 2099 (low emission). 18. ssp126_Vander_T.mat - Bi-weekly ocean temperature (Ta) for Vander Glacier from January 1, 2017 to December 31, 2099 (low emission). 19. ssp126_Totten_S.mat - Bi-weekly ocean salinity (Sa) for Totten Glacier from January 1, 2017 to December 31, 2099 (low emission). 20. ssp126_Moscow_S.mat - Bi-weekly ocean salinity (Sa) for Moscow University Glacier from January 1, 2017 to December 31, 2099 (low emission). 21. ssp126_Vander_S.mat - Bi-weekly ocean salinity (Sa) for Vander Glacier from January 1, 2017 to December 31, 2099 (low emission). 22. TotBasin.exp - Polygon that contains Totten Glacier over which Totten's ocean temperature is applied. 23. MuisBasin.exp - Polygon that contains Moscow University Glacier over which Totten's ocean temperature is applied. 24. VandBasin.exp - Polygon that contains Vanderford Glacier over which Totten's ocean temperature is applied. ### File specific information: **ASB_IceHydroModel.mat**: All data associated with the ice sheet and subglacial hydrology model initial state is held in ASB_IceHydroModel.mat, which contains a MATLAB ‘model’ object (for more information, see [https://issm.jpl.nasa.gov/documentation/modelclass/](https://issm.jpl.nasa.gov/documentation/modelclass/). In MATLAB, the model can be loaded and displayed by running load(‘ASB_IceHydroModel.mat’), which will load in the model variable ‘md’. Of particular interest will be the following data contained in md: md.mesh (mesh information), md.geometry (initial ice sheet geometry, ice shelf geometry, and bed topography), md.hydrology (initial hydrology model fields), md.initialization (model initialization fields) and md.mask (ice mask and grounded ice mask). Note that all fields are defined on the mesh nodes, and one can plot a given field in MATLAB using the ISSM tool ‘plotmodel’ (e.g., plotmodel(md,'data',md.geometry.bed) will plot the model bed topography). For more information on plotting, please see [https://issm.jpl.nasa.gov/documentation/plotmatlab/](https://issm.jpl.nasa.gov/documentation/plotmatlab/). **Model output files (e.g. ssp585_FM.mat)**: Yearly ice sheet model results between 2017-2100 for all model simulations described in the paper. Fields appended with '*' are included in results with changing subglacial hydrology (ssp126_F, ssp126_M, ssp126_FM, ssp585_F, ssp585_M, ssp585_FM). Fields appended with '**' are included in results where ice shelf melt is enhanced by subglacial discharge (ssp126_M, ssp126_FM, ssp585_M, ssp585_FM). These files contain a MATLAB variable that is the same as the file name, which is a model object of size 1x83 that contains the following yearly variables: * \* Vel (velocity norm, m/yr) * \* Thickness (ice sheet thickness, m) * \* Surface (ice sheet surface elevation, m) * \* Base (ice sheet base elevation, m) * \* BasalforcingsFloatingiceMeltingRate (ice shelf basal melting rate field, m/yr) * \* MaskOceanLevelset (ground ice mask, grounded ice if > 0, grounding line position if = 0, floating ice if < 0) * \* IceVolume (total ice volume in the model domain, t) * \* IceVolumeAboveFloatation (total ice volume in the model domain that is above hydrostatic equilibrium, t) * \* TotalFloatingBmb (Total floating basal mass balance, Gt) * \* \\*ChannelDischarge\\_Node (GlaDS-computed channel discharge interpolated onto model node, m3/s) * \* \\*ChannelDiameter\\_Node (GlaDS-computed channel diameter interpolated onto model node, m) * \* \\*ChannelArea (GlaDS-computed channel area defined on model edges, m2) * \* \\*ChannelDischarge (GlaDS\\_computed channel discharge defined on model edges, m3/s) * \* \\*EffectivePressure (GlaDS-computed ice sheet effective pressure, Pa) * \* \\*HydraulicPotential (GlaDS computed hydraulic potential, - * \* \\*HydrologySheetThickness (GlaDS-computed after sheet thickness, m) * \* \\*GroundedIceMeltingRate (Grounded ice melting rate defined on all grounded nodes, m/yr) * \* \\*\\*melt\\_nodis (ice shelf basal melting rate computed when discharge is set to zero, m/yr) * \* \\*\\*zgl (grounding line height field, m) * \* \\*\\*glfw (grounding line fresh water flux field, m2/s) * \* \\*\\*chan\\_wid (Domain average subglacial discharge channel width, m) * \* \\*\\*maxdist (5L' length scale used in melt computation, m) * \* \\*\\*maxis (maximum discharge at each subglacial outflow location, m2/s) * \**\\*\\_T.mat**: Bi-weekly ocean temperature extracted from an East Antarctic configuration of the MITgcm (Pelle et al., 2021), where '\\*' ssp126 (low emission) or ssp585 (high emission). Ocean temperature was averaged adjacent to each target ice front in both depth and in the contours shown in figure 1b. * \**\\*\\_S.mat**: Same as above, but for salinity in units on the Practical Salinity Scale (PSU). * \***.exp**: Exp files that contain coordinates that outline a polygon for the drainage basins of each major glacier in this study (Vanderford Glacier contains the drainage basins for Adams, Bond, and Underwood Glaciers as well). Recent studies have revealed the presence of a complex freshwater system underlying the Aurora Subglacial Basin (ASB), a region of East Antarctica that contains ~7 m of global sea level potential in ice mainly grounded below sea level. Yet, the impact that subglacial freshwater has on driving the evolution of the dynamic outlet glaciers that drain this basin has yet to be tested in a coupled ice sheet-subglacial hydrology numerical modeling framework. Here, we project the evolution of the primary outlet glaciers draining the ASB (Moscow University Ice Shelf, Totten, Vanderford, and Adams Glaciers) in response to an evolving subglacial hydrology system and to ocean forcing through 2100, following low and high CMIP6 emission scenarios. By 2100, ice-hydrology feedbacks enhance the ASB’s 2100 sea level contribution by ~30% (7.50 mm to 9.80 mm) in high emission scenarios and accelerate retreat of Totten Glacier’s main ice stream by 25 years. Ice-hydrology feedbacks are particularly influential in the retreat of the Vanderford and Adams Glaciers, driving an additional 10 km of retreat in fully-coupled simulations relative to uncoupled simulations. Hydrology-driven ice shelf melt enhancements are the primary cause of domain-wide mass loss in low emission scenarios, but are secondary to ice sheet frictional feedbacks under high emission scenarios. The results presented here demonstrate that ice-subglacial hydrology interactions can significantly accelerate retreat of dynamic Antarctic glaciers and that future Antarctic sea level assessments that do not take these interactions into account might be severely underestimating Antarctic Ice Sheet mass loss. In this data publication, we present the model output and results associated with the following manuscript recently submitted to the Journal of Geophysical Research: Earth Surface: “Subglacial discharge accelerates ocean driven retreat of Aurora Subglacial Basin outlet glaciers over the 21st century”. We include yearly ice sheet model output between 2017-2100 for eight numerical ice-subglacial hydrology model runs. We also include the ice sheet and subglacial hydrology model initial states. In addition, we include all ocean forcing time-series (temperature and salinity for the low emission and high emission climate forcing scenarios for three glacial regions), which are used as input into the melt parameterization. Lastly, we include a MATLAB script that contains the code used to couple the ice-subglacial hydrology models as well as a "readme" file with further information on all data in this publication. Ice sheet model results: Direct results taken from the Ice-sheet and Sea-level System Model (ISSM, Larour et al. 2012) with no processing applied, provided yearly as *.mat files. Ice sheet and subglacial hydrology model initial states: Initial state of the ice sheet model (ice geometry, mesh information, inversion results, etc.) and subglacial hydrology model (steady-state water column thickness, effective pressure, channelized discharge, etc.) containing Aurora Subglacial Basin outlet glaciers with no processing applied, provided as a *.mat file. The contents of the *.mat file is a MATLAB variable of class "model", which is compatible with ISSM. Model coupling script: Documented MATLAB script ready to run with the provided data sets. Ocean temperature and salinity timeseries: Bottom ocean temperature (°C) and salinity (PSU) timeseries (January 1st, 2017 through December 31, 2099) extracted from an East Antarctic configuration of the ocean component of the MITgcm (Pelle et al., 2021). Temperature and salinity are provided bi-weekly and averged both in depth and along the ice fronts of Moscow University, Totten, and Vanderford Glaciers (see white dashed contour in figure 1b of the main manuscript text). Data are provided as *.mat files. Polygons that provide locaion to apply ocean temperature and salinity: Polygons provided as a list of x/y coordinates (meters) are provided in three *.exp files that cover the drainage basins of Moscow University, Totten, and Vanderford Glaciers (the polygon for Vanderford also includes the drainage basins of Adams, Bond, and Underwood Glaciers).
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.1vhhmgr0b&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.1vhhmgr0b&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Embargo end date: 10 Jul 2024Publisher:Dryad The response of the single-celled ciliates to increased temperature during global warming is critical for the structure and functioning of freshwater food webs. I conducted a meta-analysis of the literature from field studies and experimental evidence to assess the parameters characterising the thermal response of freshwater ciliates. The shape of the thermal performance curve predicts the ciliates’ survival at supraoptimal temperatures (i.e., the width of the thermal safety margin, TSM). The ciliates’ typical TSM is ~5°C. One-third of the freshwater ciliates dwelling permanently or occasionally in the pelagial cannot survive at temperatures exceeding 30°C. Likewise, cold-stenothermic species, which represent a significant fraction of euplanktonic ciliates, cannot survive by evolutionary adaptation to rapidly warming environments. The statistical analysis revealed that the ciliates’ thermal performance is affected by their planktonic lifestyle (euplanktonic versus tychoplanktonic), ability to form cysts, and nutritional ecology. Bactivorous ciliates have the widest temperature niche, and algivorous ciliates have the narrowest temperature niche. Phenotypic plasticity and genetic variation, favouring the selection of pre-adapted species in a new environment, are widespread among freshwater ciliates. However, the lack of evidence for the temperature optima and imprecisely defined tolerance limits of most species hamper the present analysis. The extent of acclimation and adaptation requires further research with more ciliate species than the few chosen thus far. Recent eco-evolutionary experimental work and modelling approaches demonstrated that the ciliates’ thermal responses follow general trends predicted by the metabolic theory of ecology and mechanistic functions inherent in enzyme kinetics. The present analysis identified current knowledge gaps and avenues for future research that may serve as a model study for other biota. Thermal adaptation may conflict with adaptation to other stressors (predators, food availability, pH), making general predictions on the future role of freshwater ciliates in a warmer environment difficult, if not impossible, at the moment. # Data from: Thermal response of freshwater ciliates: can they survive at elevated lake temperatures? [https://doi.org/10.5061/dryad.jdfn2z3jr](https://doi.org/10.5061/dryad.jdfn2z3jr) The dataset results from a meta-analysis to assess the parameters characterising the thermal response of freshwater ciliates (i.e., minimum and maximum temperature tolerated, temperature niche breadth). Cyst formation, the nutritional type, and the planktonic lifestyle were considered as factors affecting the ciliates’ thermal performance. ## Description of the data and file structure The main dataset reporting ciliate species and synonyms, taxonomic affiliation, minimum and maximum temperature and the temperature range tolerated, cysts formation, mixotrophic nutrition, food type, and planktonic lifestyle are reported in the 'Dataset_v4.xlsx' file. This is the main document. Taxonomic affiliation (i.e., order) following Adl et al. (2019, reference [65]J, the GBIF Backbone Taxonomy, and Lynn (2008; reference [66]). Details on the references - i.e., authors, publication year, title, journal/book, volume, and page/article numbers used to compile this dataset and some comments can be found in 'References.xlsx'. Empty cells mean that information is unavailable. References A-E are the main sources of the dataset, i.e., comprehensive review articles published by W. Foissner and colleagues in the 1990s. References 1-64 are case studies, published mainly after 1999. References 65 and 66 refer to the taxonomic affiliation of the ciliate species. More details about each column of the main document can be found in the 'Units_table.xlsx' file. ## Sharing/Access information Data was derived from the following sources: * ISI Web of Science (All Data Bases) * Google Scholar ## Code/Software R statistical software (v 4.0.5, R Core Team 2021) with the packages lme4, lmtest, multcomp, AICcmodavg. WebPlotDigitizer (Version 4.6) for data extraction from figures ## Version changes **06-aug-2024**: Taxonomic affiliation (order) corrected according to GBIF. Genus *Tintinnidium* is now in the order Oligotrichida. I scrutinised the detailed literature compilations by Foissner and colleagues published in the 1990s; these references are listed as primary sources A-E in the Dataset, see References.xlsx and README.txt) to obtain an overview of the thermal performance, resting cyst formation, and nutritional ecology of planktonic freshwater ciliates. I then searched the ISI Web of Science (All Data Bases) for updates and cross-references of Foissner’s works and further temperature records from (mainly) field studies. Search terms (in all fields) for the latter were ciliate* AND temperature NOT marine NOT ocean NOT soil NOT parasit* (1,339 hits). I followed the PRISMA guidelines in combination with EndNote 20 to filter out the records eligible for screening and analysis. Temperature data for assessing the minimum (Tmin) and maximum temperature (Tmax) of occurrence were eventually extracted from 68 publications. However, because Foissner’s works present extensive reviews, the actual number of publications used for the analysis is much higher. The final dataset obtained from field studies comprised 206 ciliate species. Next, I searched the ISI Web of Science for experimental results, using ciliate* AND temperature AND growth rate* NOT marine as search terms (218 records). Removing results from unsuitable research areas (mainly from medical research) reduced the records to 71 publications, which were screened. The combination of ciliate* AND numerical response NOT marine yielded 40 studies, ciliate* AND thermal performance 21 hits. I checked the selected articles for citations and cross-references using Google Scholar to identify any publications that might have slipped my attention. Eventually, I picked experimental results from 18 studies. If the literature data were only shown in figures, I extracted the data from the plots with WebPlotDigitizer (Version 4.6). I analysed the dataset with the R Statistical Software using the packages lme4, lmerTest, stats, multcomp, AICcmodavg and car.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.jdfn2z3jr&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.jdfn2z3jr&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Authors:Kalt, Gerald;
Kalt, Gerald
Kalt, Gerald in OpenAIREMayer, Andreas;
Mayer, Andreas
Mayer, Andreas in OpenAIREHaberl, Helmut;
Haberl, Helmut
Haberl, Helmut in OpenAIREKaufmann, Lisa;
+4 AuthorsKaufmann, Lisa
Kaufmann, Lisa in OpenAIREKalt, Gerald;
Kalt, Gerald
Kalt, Gerald in OpenAIREMayer, Andreas;
Mayer, Andreas
Mayer, Andreas in OpenAIREHaberl, Helmut;
Haberl, Helmut
Haberl, Helmut in OpenAIREKaufmann, Lisa;
Kaufmann, Lisa
Kaufmann, Lisa in OpenAIRELauk, Christian;
Lauk, Christian
Lauk, Christian in OpenAIREMatej, Sarah;
Matej, Sarah
Matej, Sarah in OpenAIRETheurl, Michaela C.;
Theurl, Michaela C.
Theurl, Michaela C. in OpenAIREErb, Karl-Heinz;
Erb, Karl-Heinz
Erb, Karl-Heinz in OpenAIREThe dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use. The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions. The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)". Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information. This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.4965052&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 133visibility views 133 download downloads 25 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.4965052&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu