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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.NUIST.NESM3.ssp585' 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 NUIST ESM v3 climate model, released in 2016, includes the following components: atmos: ECHAM v6.3 (T63; 192 x 96 longitude/latitude; 47 levels; top level 1 Pa), land: JSBACH v3.1, ocean: NEMO v3.4 (NEMO v3.4, tripolar primarily 1deg; 384 x 362 longitude/latitude; 46 levels; top grid cell 0-6 m), seaIce: CICE4.1. The model was run by the Nanjing University of Information Science and Technology, Nanjing, 210044, China (NUIST) in native nominal resolutions: atmos: 250 km, land: 2.5 km, ocean: 100 km, seaIce: 100 km.

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.BCC.BCC-CSM2-MR.ssp370' 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 BCC-CSM 2 MR climate model, released in 2017, includes the following components: atmos: BCC_AGCM3_MR (T106; 320 x 160 longitude/latitude; 46 levels; top level 1.46 hPa), land: BCC_AVIM2, ocean: MOM4 (1/3 deg 10S-10N, 1/3-1 deg 10-30 N/S, and 1 deg in high latitudes; 360 x 232 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: SIS2. The model was run by the Beijing Climate Center, Beijing 100081, China (BCC) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 50 km, seaIce: 50 km.

This dataset provides grided species phenology (SP) maps of 24 woody plants and ground phenology (GP) maps of forests over China (18°N-54°N，72′°E-136°E) from 1951 to 2020, with a spatial resolution of 0.1° and a temporal resolution of 1 day. Three phenophases, namely the first leaf date (FLD), first flower date (FFD), and 100% leaf coloring date (LCD), were included for each species. Data Quality: The SP maps of 24 species are largely consistent with the in-situ observations in China, with an average error of 6.4, 7.5 and 10.8 days for FLD, FFD and LCD, respectively. The GP maps of forests have good consistency with the existing LSP products in China, particularly in DF areas, where the correlation coefficients between GP and LSP in FLD and LCD were 0.91 and 0.84, respectively, and the differences were 8.8 days and 15.1 days, respectively. Method: Based on the in-situ phenology observations from the Chinese Phenology Observation Network (CPON) in the past 70 years, this dataset employed three spring phenology models (Unichill, Unified and Temporal-Spatial Coupling) and two autumn phenology models (Multiple Regression, Temperature-Photoperiod) to simulate and upscale the phenology data on the national scale, and generate the SP maps of woody plants in China. Four aggregation methods (weighted average (mean), weighted percentile (pct50, pct20\80, pct10\90)) were used to generate the GP maps of forests in China based on the SP maps. The weight of each species was determined by the species distribution probability. Dataset composition: The dataset contains the yearly SP maps of 24 woody plants (China_SP.zip) and GP maps of forests(China_GP.zip) over China from 1951 to 2020, including spring FLD, FFD and autumn LCD. Each map is stored in a GeoTIFF formatted 16-bit signed integer file containing a raster with two dimensions (641 row × 361 column). Data files are named according to "China + phenophase (XXD) + species/method + year (YYYY)". For example, "China_FLD_Acer_pictum_2020.tif" is the SP map of Acer pictum’s FLD in 2020, and “China_FLD_mean_2020.tif” is the GP map of weighted averaged FLD in 2020. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is -1. The spatial reference system of the data is EPSG:4326 (WGS84). This dataset provides grided species phenology (SP) maps of 24 woody plants and ground phenology (GP) maps of forests over China (18°N-54°N，72′°E-136°E) from 1951 to 2020, with a spatial resolution of 0.1° and a temporal resolution of 1 day. Three phenophases, namely the first leaf date (FLD), first flower date (FFD), and 100% leaf coloring date (LCD), were included for each species. Data Quality: The SP maps of 24 species are largely consistent with the in-situ observations in China, with an average error of 6.4, 7.5 and 10.8 days for FLD, FFD and LCD, respectively. The GP maps of forests have good consistency with the existing LSP products in China, particularly in DF areas, where the correlation coefficients between GP and LSP in FLD and LCD were 0.91 and 0.84, respectively, and the differences were 8.8 days and 15.1 days, respectively. Method: Based on the in-situ phenology observations from the Chinese Phenology Observation Network (CPON) in the past 70 years, this dataset employed three spring phenology models (Unichill, Unified and Temporal-Spatial Coupling) and two autumn phenology models (Multiple Regression, Temperature-Photoperiod) to simulate and upscale the phenology data on the national scale, and generate the SP maps of woody plants in China. Four aggregation methods (weighted average (mean), weighted percentile (pct50, pct20\80, pct10\90)) were used to generate the GP maps of forests in China based on the SP maps. The weight of each species was determined by the species distribution probability. Dataset composition: The dataset contains the yearly SP maps of 24 woody plants (China_SP.zip) and GP maps of forests(China_GP.zip) over China from 1951 to 2020, including spring FLD, FFD and autumn LCD. Each map is stored in a GeoTIFF formatted 16-bit signed integer file containing a raster with two dimensions (641 row × 361 column). Data files are named according to "China + phenophase (XXD) + species/method + year (YYYY)". For example, "China_FLD_Acer_pictum_2020.tif" is the SP map of Acer pictum’s FLD in 2020, and “China_FLD_mean_2020.tif” is the GP map of weighted averaged FLD in 2020. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is -1. The spatial reference system of the data is EPSG:4326 (WGS84).

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.CSIRO.ACCESS-ESM1-5.hist-nat' 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 Australian Community Climate and Earth System Simulator Earth System Model Version 1.5 climate model, released in 2019, includes the following components: aerosol: CLASSIC (v1.0), atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m), land: CABLE2.4, ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: WOMBAT (same grid as ocean), seaIce: CICE4.1 (same grid as ocean). The model was run by the Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia (CSIRO) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

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.CAS.FGOALS-g3.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 FGOALS-g3 climate model, released in 2017, includes the following components: atmos: GAMIL3 (180 x 80 longitude/latitude; 26 levels; top level 2.19hPa), land: CAS-LSM, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

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.CAS.FGOALS-f3-L.ssp370' 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 FGOALS-f3-L climate model, released in 2017, includes the following components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

Livestock grazing is among the most intensive land-use activities in grasslands and can affect plant communities directly or indirectly via grazing-induced soil legacies. Under climate change, grasslands are threatened globally by recurrent drought. However, the extent to which drought influences grazing-induced soil legacy effects on plant biomass production and community composition remains largely unexplored. We grew five naturally co-occurring plant species (three dominants and two subordinates) in mixed communities in a glasshouse experiment in live and sterilized soil that had or had not been subjected to 19 years of grazing; these plant communities were then exposed to a subsequent drought. We tested the treatment effects on plant community biomass, proportional aboveground biomass of individual species, arbuscular mycorrhizal (AM) fungal root colonization, and soil nutrient availability. Under drought-free conditions, soils from grazed plots produced significantly higher plant aboveground and total community biomass compared to soils from ungrazed plots. In contrast, plant aboveground and total community biomass were similar between grazed and ungrazed soils under drought conditions. Similarly, soils from grazed plots increased the proportional biomass of dominant species but decreased the proportion of subordinate species; however, the proportional biomass of dominant and subordinate species was similar between grazed and ungrazed soils under drought conditions. Soil NO3--N in grazed soil was significantly higher compared to ungrazed soil. Drought dramatically increased soil NO3--N in sterilized soil and had a more pronounced increase in grazed soil than in ungrazed soil. Arbuscular mycorrhizal fungal root colonization from grazed soil was lower compared to ungrazed soil. Drought significantly increased the soil available phosphorus concentration, as well as plant community AM fungal root colonization. Synthesis. Our study suggests that drought can neutralize positive grazing effects on plant community biomass production via altered plant-soil interactions. Also, we found that drought can alleviate the negative effects of grazing legacies on subordinate species by reducing the competitiveness of dominant species. Our study provides new insights for understanding the underlying mechanisms of grazing effects on grassland productivity under climate change. Please see the README document and the accompanying published article: Duan, DD., Tian, Z., Wu, NN., Feng, XX., Hou, FJ., Nan, ZB., Kardol, P., and Chen, T. 2023. Drought neutralizes positive effects of long-term grazing on grassland productivity through altering plant-soil interactions. Functional Ecology. 

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.BCC.BCC-ESM1.historical' 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 BCC-ESM 1 climate model, released in 2017, includes the following components: atmos: BCC_AGCM3_LR (T42; 128 x 64 longitude/latitude; 26 levels; top level 2.19 hPa), atmosChem: BCC-AGCM3-Chem, land: BCC_AVIM2, ocean: MOM4 (1/3 deg 10S-10N, 1/3-1 deg 10-30 N/S, and 1 deg in high latitudes; 360 x 232 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: SIS2. The model was run by the Beijing Climate Center, Beijing 100081, China (BCC) in native nominal resolutions: atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 50 km, seaIce: 50 km.

This dataset reports results on seedling growth and survival for two hyphal exclusion experiments in a subtropical forest. The data include survival status, height, total biomass and the biomass of component plant parts, percentage root colonisation by mycorrhizas, for tree seedlings of ten common species including five ectomycorrhizal (ECM) and five arbuscular mycorrhizal (AM) species, which were transplanted in the in-growth cores with windows covering different sizes of nylon meshes (35 vs. 0.5 µm). The dataset provides raw data on growth and survival metrics for each seedling, plus identifying codes for the dominant sites where the experiments were conducted, as well as experimental block, mesh treatment, botanical names for the tree species, and mycorrhizal type. The data were entered into Excel spreadsheets and exported as comma separated value files (csv). Study area - the Heishiding Nature Reserve (111°53’E, 23°27’N, 150-927 m a.s.l.) in Guangdong Province of south China. Mesh-walled cores were assembled from 16 cm diameter × 30 cm deep PVC piping, perforated with six 8-cm-diameter windows which were regularly distributed along the side with three of them at the depth of 4-12 cm and the other three at 16-24 cm. The cores were lined with 35 µm or 0.5 µm nylon mesh (Plastok Associates Ltd, Birkenhead, UK) to cover the bottom and the windows, which was attached using transparent superglue (Pattex(R), Henkel Adhesives Ltd., Shantou, China). Nylon mesh with a pore size of 35 µm excludes roots of neighboring plants, but allows mycorrhizal hyphae access to the transplanted seedlings; while cores with a 0.5 µm mesh exclude both fine roots and hyphae, with only free-living soil microorganisms passing through. We then covered the sides and the bottoms of all cores with 2 mm nylon mesh, to prevent soil fauna damaging the smaller size meshes.

This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1° and a temporal resolution of 1 day. The quality evaluation of the grid phenology data shows that the average error of FLD and FFD is 7.9 and 7.6 days respectively, which has high simulation accuracy.Method: Based on the in-situ phenology observations from the Pan European Phenology Project (PEP725) in the past 70 years, this dataset employed three phenology models (Unichill, Unified and Temporal-Spatial Coupling) to predict and upscale the phenology data on the continental scale, and developed a grid phenology dataset of woody plants in Europe.Dataset composition: The dataset contains the gridded phenology data of six woody plants in Europe from 1951 to 2021, including the spring FLD (BBCH11.zip) and the spring FFD (BBCH60.zip). The annual data of each species is stored as a Geotiff file with 651 row × 371 column. The data is named according to "year (YYYY) + species genus (Genus) + phenophase (_xx)". For example, "2021Aesculus_11. tif" is the grid data file of the FLD of European Aesculus in 2021. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is 999. The spatial reference system of the data is EPSG:4326 (WGS84). This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1° and a temporal resolution of 1 day. The quality evaluation of the grid phenology data shows that the average error of FLD and FFD is 7.9 and 7.6 days respectively, which has high simulation accuracy.Method: Based on the in-situ phenology observations from the Pan European Phenology Project (PEP725) in the past 70 years, this dataset employed three phenology models (Unichill, Unified and Temporal-Spatial Coupling) to predict and upscale the phenology data on the continental scale, and developed a grid phenology dataset of woody plants in Europe.Dataset composition: The dataset contains the gridded phenology data of six woody plants in Europe from 1951 to 2021, including the spring FLD (BBCH11.zip) and the spring FFD (BBCH60.zip). The annual data of each species is stored as a Geotiff file with 651 row × 371 column. The data is named according to "year (YYYY) + species genus (Genus) + phenophase (_xx)". For example, "2021Aesculus_11. tif" is the grid data file of the FLD of European Aesculus in 2021. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is 999. The spatial reference system of the data is EPSG:4326 (WGS84).