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QTDIAN - Quantification of Technological DIffusion and sociAl constraiNts - is a toolbox of qualitative and quantitative descriptions of socio-technical and political aspects of the energy transition that influence the overall potential, the rate of energy-related technology and service diffusion and the design of the future energy system. The output of QTIDIAN is empirically founded datasets of social and political drivers and barriers of the transition, both in the form of raw data describing past and current developments and manipulated to constitute consistent quantifications of the storylines. Here you can download the data for six QTDIAN themes: Socially feasible scaling of energy technologies Policy preferences & dynamics Barriers to infrastructural development (wind energy, grid development) Citizen energy Private energy demand Further information on the QTDIAN modelling toolbox and the data can be found in the SENTINEL Deliverable 2.3 and Deliverable 2.4: S��sser, D., al Rakouki, H., & Lilliestam, J.(2021). The QTDIAN modelling toolbox���Quantification of social drivers and constraints of the diffusion of energy technologies. Deliverable 2.3. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS). S��sser, D., Pickering, B., Chatterjee, S., Oreggioni, G., Stavrakas, V., & Lilliestam, J.(2021). Integration of socio-technological transition constraints into energy demand and systems models. Deliverable 2.5. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS).

Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WGI 9.6.3.2. P-boxes derived from these distributions are available in the sister entry 'IPCC-DDC_AR6_Sup_PBox'. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.

Aim 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.

Project: 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.MOHC.UKESM1-0-LL.ssp245' 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 UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Les paysages arctiques se caractérisent par la présence de nombreux lacs et étangs qui possèdent des propriétés physico-chimiques et biologiques distinctes. Depuis 2018, nous étudions les communautés zooplanctoniques de plus de 22 lacs et 13 étangs d'eau douce situés au sud de l'Île Victoria à Cambridge Bay, Nunavut (69 ° N, 105 ° O). Pour chacun des lacs et étangs échantillonnés les communautés de zooplancton ont été récoltées et les spécimens ont été identifiés jusqu'à l'espèce. Au total, plus de 77 espèces différentes ont été identifiées incluant 56 rotifères, 6 copépodes, 11 cladocères, 2 crevettes arctiques, une espèce appartenant à la famille des Mysidacea et une crevette têtard. Arctic landscapes are characterized by the presence of many lakes and ponds that exhibit distinct physico-chemical and biological properties. Since 2018, we have been studying the zooplankton communities of more than 22 lakes and 13 freshwater ponds located on southern Victoria Island, Cambridge Bay, Nunavut (69°N, 105°W). For each of the lakes and ponds sampled, zooplankton communities were collected and specimens were identified to species. In total, more than 77 different species were found, including 56 rotifers, 6 copepods, 11 cladocerans, 2 fairy shrimps, a mysid and a tadpole shrimp.

This dataset includes data from two experiments.

Project: 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.DAMIP.MPI-M.MPI-ESM1-2-LR' 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 MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.