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Suitability maps (raster format: geotiff) of Betula pubescens, computed using the ForestFocus European dataset of species presence/absence. The adopted suitability model estimates the optimal environmental conditions for European tree species under present and future climates. Available years: 2000, 2020, 2050, 2080. For year 2000 the observed (WorldClim) climate conditions have been used. For years 2020, 2050, 2080 the climate conditions simulated for the climate change scenarios A2 and B2 have been used (by means of the climate models CCCMA, CSIRO, HANDCM3 and of an ensemble model of them).

Global change has dramatic impacts on grassland diversity. However, little is known about how fast species can adapt to diversity loss and how this affects their responses to global change. Here, we performed a common garden experiment testing whether plant responses to global change are influenced by their selection history and the conditioning history of soil at different plant diversity levels. Using seeds of four grass species and soil samples from a 14-year-old biodiversity experiment, we grew the offspring of the plants either in their own soil or in soil of a different community, and exposed them either to drought, increased nitrogen input, or a combination of both. Under nitrogen addition, offspring of plants selected at high diversity produced more biomass than those selected at low diversity, while drought neutralized differences in biomass production. Moreover, under the influence of global change drivers, soil history, and to a lesser extent plant history, had species-specific effects on trait expression. Our results show that plant diversity modulates plant-soil interactions and growth strategies of plants, which in turn affects plant eco-evolutionary pathways. How this change affects species' response to global change and whether this can cause a feedback loop should be investigated in more detail in future studies.

Suitability maps (raster format: geotiff) of Acer pseudoplatnus, computed using the ForestFocus European dataset of species presence/absence. The adopted suitability model estimates the optimal environmental conditions for European tree species under present and future climates. Available years: 2000, 2020, 2050, 2080. For year 2000 the observed (WorldClim) climate conditions have been used. For years 2020, 2050, 2080 the climate conditions simulated for the climate change scenarios A2 and B2 have been used (by means of the climate models CCCMA, CSIRO, HANDCM3 and of an ensemble model of them).

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.CMCC.CMCC-ESM2.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 CMCC-ESM2 climate model, released in 2017, includes the following components: aerosol: MAM3, atmos: CAM5.3 (1deg; 288 x 192 longitude/latitude; 30 levels; top at ~2 hPa), land: CLM4.5 (BGC mode), ocean: NEMO3.6 (ORCA1 tripolar primarly 1 deg lat/lon with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 50 vertical levels; top grid cell 0-1 m), ocnBgchem: BFM5.2, seaIce: CICE4.0. The model was run by the Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce 73100, Italy (CMCC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Impact assessments for agriculture are partly based on projections delivered by models. Sectoral policies are becoming more and more interrelated. Hence, there is a need to improve the capacity of current models, connect them or redesign them to deliver on an increasing variety of policy objectives, and to explore future directions for agricultural modelling in Europe. SUPREMA (SUpport for Policy RElevant Modelling of Agriculture) is a project that has received funding from the European Union’s Horizon 2020 research and innovation programme (under grant agreement No 773499 SUPREMA) and that came to address this challenge by proposing a meta-platform that supports modelling groups linked already through various other platforms and networks. SUPREMA should help close the gaps between expectations of policy makers and the actual capacity of models to deliver relevant policy analysis. The SUPREMA model family includes a set of ‘core models’ that are already used in support of key European impact assessments in agriculture, trade, climate and bioenergy policies. One of the work-packages of the project ("Testing the SUPREMA model family") had the objective of testing the SUPREMA model family comparing model outcomes of three applications, including: (i) harmonize baseline assumptions and to the extent possible align baseline projections across models in the platform, and (ii) showcase the potential of the models in the meta-platform to respond to the upcoming and existing policy needs by means of two exploratory policy scenarios. This open dataset includes 3 components: 1 - (Baseline scenario) - the harmonized baselines (for 2030 and 2050). Please note that the baseline projections do not take into account the 2020 and possible future effects of the SARS-CoV-2 pandemic 2 - (Agricultural policy scenario) - medium-term horizon scenarios aiming comparing different models and/or model combinations, that have a large degree of ‘similarity’ such as joined indicator variables, i.e.: AGMEMOD-MITERRA (combined) modelling tool and the CAPRI model. The main focus was comparing model results in both agronomic and biophysical domains. Two variants of the agricultural policy scenario have been simulated and compared: (i) a CAP greening scenario; and (ii) a sustainable diet scenario. Both scenarios are hypothetical but have been chosen in such a way that the can provide insights in future policy issues as: (i) a further greening of the CAP fits in the policy implementation space as it is included in the ongoing policy reform of the CAP after 2020; and (ii) as increasing consumer awareness about healthy diets and their relation to meat consumption, as well as the footprint/climate consequences are highly relevant with respect to the Green Deal roadmap (December 2019) and the Farm to Fork Strategy (May 2020) documents that have been recently published. 3 - (Climate change mitigation scenario) - scenarios that quantifies the GHG mitigation potential of the EU’s agricultural sector and domestic and global impacts of the EU policy, conditional on different levels of GHG mitigation efforts in the rest of the world. These are obtained through the SUPREMA models CAPRI, GLOBIOM and MAGNET and include scenarios where the EU only takes ambitious unilateral climate action up to scenario where the 1.5 C target is pursued globally SUPREMA has been coordinated by Wageningen Research with the participation of EuroCARE, Thünen Institute, Swedish University of Agricultural Sciences (SLU), European Commission Joint Research Centre (JRC) and Research Executive Agency (REA), International Institute for Applied Systems Analysis (IIASA) and Universidad Politécnica de Madrid (UPM). Impact assessments for agriculture are partly based on projections delivered by models. Sectoral policies are becoming more and more interrelated. Hence, there is a need to improve the capacity of current models, connect them or redesign them to deliver on an increasing variety of policy objectives, and to explore future directions for agricultural modelling in Europe. SUPREMA (SUpport for Policy RElevant Modelling of Agriculture) is a project that has received funding from the European Union’s

The survey is based on a questionnaire containing 56 closed questions that covers 111 home gardeners in Pune, India. Questions cover growing decisions and cultivation practice, including fertilization, pesticide use, irrigation and more, as well as the cultural and recreational use of the garden. Additionally socio-economic characteristics and motivations of gardeners are covered. The data was gathered by direct on-site interviews between January and May 2014. Respondents were recruited via snowball sampling starting with members of the local gardening club INORA (www.inora.in).

This data provides per building points data (each point is a buildings centroid) for all of Austria: building footprint PV yearly potential for this specific building per m2 and for the whole roof This data also provides open space data for Austria on a vector raster (point) dataset with a resolution of 250mx250m: each point provides data on kWh per m2 and year and numbers per kWp at the specific area data exists for: all open space areas agricultural areas vegetable areas crereal areas pasture areas

Although many studies have tested the direct effects of drought on alien plant invasion, less is known about whether drought affects alien plant invasion indirectly via interactions of plants with other groups of organisms such as soil mesofauna. To test for such indirect effects, we grew single plants of nine naturalized alien target species in pot-mesocosms with a community of five native grassland species under four combinations of two drought (well-watered vs drought) and two soil-mesofauna-inoculation (with vs without) treatments. We found that drought decreased the absolute and the relative biomass production of the alien plants, and thus reduced their competitive strength in the native community. Drought also decreased the abundance of soil mesofauna, particularly soil mites, but did not affect the abundance and richness of soil herbivores. Soil-fauna inoculation did not affect biomass of the alien plants but increased biomass of the native plant community, and thereby decreased the relative biomass production of the alien plants. This increased invasion resistance due to soil fauna, however, tended (p = 0.09) to be stronger for plants growing under well-watered conditions than under drought. Synthesis. Our multispecies experiment thus shows that soil fauna might help native communities to resist alien plant invasions, but that this effect might be weakened under drought. In other words, soil mesofauna may buffer the negative effects of drought on alien plant invasions. The file archives 'SoilFauna_Drought_PlantInvasion_Date_YJL.tar' include three dataset, one named 'SoilFauna_Drought_PlantInvasion.csv' (Biomass data), one named 'SoilFaunaData.csv' (Soil Fauna data), and one named 'SoilNitrogenData.csv' (soil nitrogen data). The file 'SoilFauna_Drought_PlantInvasion.Rmd' is the R script, and its output is 'SoilFauna_Drought_PlantInvasion.html'. All data were collected from a greenhouse expeirment at the Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences.

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.CMIP.MPI-M.ICON-ESM-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 ICON-ESM-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ICON-A (icosahedral/triangles; 160 km; 47 levels; top level 80 km), land: JSBACH4.20, landIce: none/prescribed, ocean: ICON-O (icosahedral/triangles; 40 km; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC, 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: 50 km, ocnBgchem: 50 km, seaIce: 50 km.