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    Input files for the ForClim model (version 4.0.1) used in the associated paper. They can be used to to reproduce results of the simulation study. The ForClim model, including the source code, executable and documentation, is freely available under an Open Access license from the website of the original developers at https://ites-fe.ethz.ch/openaccess/. The original climatic dataset used to generate the ForClim input climate files at each site in South Tyrol is freely available at https://doi.pangaea.de/10.1594/PANGAEA.924502 while the CHELSA climate data for future scenarios are available at https://www.chelsa-climate.org. If interested in using this dataset for a research study or a project, please contact Marco Mina ----------------------------------------------------------------------- Hillebrand L, Marzini S, Crespi A, Hiltner U & Mina M (2023) Contrasting impacts of climate change on protection forests of the Italian Alps. Frontiers in Forests and Global Change, 6, 2023 https://doi.org/10.3389/ffgc.2023.1240235 ABSTRACT. Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • Authors: O’Gorman, E.J.; Warner, E.; Marteinsdóttir, B.; Helmutsdóttir, V.F.; +2 Authors

    Herbivory assessments were made at the plant community and species levels. We focused on three plant species with a widespread occurrence across the temperature gradient: cuckooflower (Cardamine pratensis, Linnaeus), common mouse-ear (Cerastium fontanum, Baumgerten), and marsh violet (Viola palustris, Linnaeus). For assessments of invertebrate herbivory at the species level, thirty individuals per species of C. pratensis, C. fontanum, and V. palustris were marked in each of ten plots, using a stratified random sampling method where individuals were randomly selected, but the full range of within-plot soil temperatures was represented. For assessments of invertebrate herbivory at the community level, five 50 × 50 cm quadrats were marked at random points in eight of the plots that best captured the full temperature gradient. The community-level herbivory assessment was conducted on 19th June. The number of damaged plants was recorded out of 100 random individuals, selected using a 10 × 10 grid within each 50 × 50 cm quadrat. For the species-level herbivory assessment, individual marked plants were surveyed for signs of invertebrate herbivory every two weeks from 30th May to 2nd July, generating three time-points per species. At each survey, all marked individuals for each species were assessed within a 48-hour period. Plants were recorded as damaged or not damaged by invertebrate herbivores at each time-point. Further details of how phenological stage of development, vegetation community composition, soil temperature, moisture, pH, nitrate, ammonium, and phosphate were recorded are provided in the supporting documentation. This is a dataset of environmental data, vegetation cover, and community- and species-level invertebrate herbivory, sampled at 14 experimental soil plots in the Hengill geothermal valley, Iceland, from May to July 2017. The plots span a temperature gradient of 5-35 °C on average over the sampling period, yet they occur within 1 km of each other and have similar soil moisture, pH, nitrate, ammonium, and phosphate.

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    Authors: Neubauer, David; Ferrachat, Sylvaine; Siegenthaler-Le Drian, Colombe; Stoll, Jens; +18 Authors

    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.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' 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-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, 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 ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Suitability maps (raster format: geotiff) of Quercus frainetto, 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).

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    European Union Open Data Portal
    Dataset . 2009
    License: CC_BY_4_0
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      European Union Open Data Portal
      Dataset . 2009
      License: CC_BY_4_0
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    Authors: Karjalainen, Olli; Luoto, Miska; Aalto, Juha; Etzelmüller, Bernd; +4 Authors

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

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    B2FIND
    Dataset . 2020
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2020
    License: CC BY
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2020
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2020
      License: CC BY
      Data sources: PANGAEA
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    Authors: Holmgren, M.; Lin, C.Y.; Murillo, J.E.; Nieuwenhuis, A.; +7 Authors

    Figure 1data_Exp 2Figure 1 data: Condition of experimental seedlings in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS) during the warmest growing season (2011) and at the end of the experiment (2013). Seedling condition was defined as: healthy (< 50% of the needles turned yellow or brown) or unhealthy (> 50% of the needles turned yellow or brown). Seedlings were 1 month old at plantation time in the July 2010.Table 1_environmental conditions_Exp 1Table 1 data: Environmental conditions and vegetation characteristics in hummocks (circular and bands) and lawns for Experiment 1. Water table depth below surface is an average for the four growing seasons (2010-2013)Table 2_ photosynthesis data_Exp 1Table 2 photosynthesis data: Photosynthesis rates for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1.Table 2_seedling responses_Exp 1Table 2 data: Responses of experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1 after 4 growing seasons. ST: Seeds inserted on top of moss; SB: Seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Emergence = % of planted seeds emerged after 1 year. Condition = % healthy seedlings. Stem growth corresponds to vertical stem growth for germinating (ST and SB) seedlings and new stem growth for older (small and large) seedlings.Table 3_regression seedling-environment_Exp 1Table 3 data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 during the whole experimental period (2010-2013). ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Condition = % healthy seedlings. Growth = stem growth.Table 4_Environmental data_Exp 2Table 4: Environmental conditions in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS).Table 4 and Table S5a_seedling performance_Exp 2Table 4: Seedling performance in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS). Seedling emergence, condition and survival from seeds inserted below the moss (SB), and from small planted seedlings.Table S3_cox regression (survival analysis)_Exp 1Table S3: Data for Cox survival analysis for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns during 2010-2013. ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time).Table S4_ regression seedling-environment 2011_Exp 1Table S4: Data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 in 2011. Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time). Condition = % healthy seedlings. Growth = stem growth. Boreal ecosystems are warming roughly twice as fast as the global average, resulting in woody expansion that could further speed up the climate warming. Boreal peatbogs are waterlogged systems that store more than 30% of the global soil carbon. Facilitative effects of shrubs and trees on the establishment of new individuals could increase tree cover with profound consequences for the structure and functioning of boreal peatbogs, carbon sequestration and climate. We conducted two field experiments in boreal peatbogs to assess the mechanisms that explain tree seedling recruitment and to estimate the strength of positive feedbacks between shrubs and trees. We planted seeds and seedlings of Pinus sylvestris in microsites with contrasting water-tables and woody cover and manipulated both shrub canopy and root competition. We monitored seedling emergence, growth and survival for up to four growing seasons and assessed how seedling responses related to abiotic and biotic conditions. We found that tree recruitment is more successful in drier topographical microsites with deeper water-tables. On these hummocks, shrubs have both positive and negative effects on tree seedling establishment. Shrub cover improved tree seedling condition, growth and survival during the warmest growing season. In turn, higher tree basal area correlates positively with soil nutrient availability, shrub biomass and abundance of tree juveniles. Synthesis. Our results suggest that shrubs facilitate tree colonization of peatbogs which further increases shrub growth. These facilitative effects seem to be stronger under warmer conditions suggesting that a higher frequency of warmer and dry summers may lead to stronger positive interactions between shrubs and trees that could eventually facilitate a shift from moss to tree-dominated systems.

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    ZENODO
    Dataset . 2015
    License: CC 0
    Data sources: ZENODO
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    Research@WUR
    Dataset . 2015
    Data sources: Research@WUR
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    B2FIND
    Dataset . 2015
    Data sources: B2FIND
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    EASY
    Dataset . 2015
    Data sources: EASY
    DRYAD
    Dataset . 2015
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2015
      License: CC 0
      Data sources: ZENODO
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      Research@WUR
      Dataset . 2015
      Data sources: Research@WUR
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      B2FIND
      Dataset . 2015
      Data sources: B2FIND
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      EASY
      Dataset . 2015
      Data sources: EASY
      DRYAD
      Dataset . 2015
      License: CC 0
      Data sources: Datacite
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    Suitability maps (raster format: geotiff) of Fraxinus excelsior, 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).

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    European Union Open Data Portal
    Dataset . 2009
    License: CC_BY_4_0
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      European Union Open Data Portal
      Dataset . 2009
      License: CC_BY_4_0
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    Authors: Ramirez F; Rodriguez C; Seoane J; Figuerola J; +1 Authors

    Global warming and direct anthropogenic impacts, such as water extraction, are largely affecting water budgets in Mediterranean wetlands, thereby increasing wetland salinities and isolation, and decreasing water depths and hydroperiods (duration of the inundation period). These wetland features are key elements structuring waterbird communities. However, the ultimate and net consequences of these dynamic conditions on waterbird assemblages are largely unknown. We combined a regular sampling on waterbird presence through the 2008 annual cycle with in-situ data on these relevant environmental predictors of waterbird distribution to model habitat selection for 69 individual species in a typical Mediterranean wetland network in south-western Spain. Species association with environmental features were subsequently used to predict changes in habitat suitability for each species under three climate change scenarios (encompassing changes in environment that ranged from 10% to 50% change as predicted by climatic models). Waterbirds distributed themselves unevenly throughout environmental gradients and water salinity was the most important gradient structuring the distribution of the community. Environmental suitability for the guilds of diving birds and vegetation gleaners will be reduced according to future climate scenarios, while most small wading birds will benefit from changing conditions. Resident species and those that breed in this wetland network will be also more impacted than those using this area for wintering or stopover. We provide here a tool that can be used in a horizon-scanning framework to identify emerging issues on waterbird conservation and to anticipate suitable management actions : Datasets as supporting information to article “How will climate change affect endangered Mediterranean waterbirds?” to be published in PLOS ONE. Address questions to Francisco Ramírez: ramirez@ub.edu

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    Digital.CSIC
    Dataset . 2017
    License: CC BY
    Data sources: Datacite
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    BioStudies
    Dataset . 2018
    Data sources: BioStudies
    Digital.CSIC
    Dataset . 2017 . Peer-reviewed
    Data sources: Digital.CSIC
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      Digital.CSIC
      Dataset . 2017
      License: CC BY
      Data sources: Datacite
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      BioStudies
      Dataset . 2018
      Data sources: BioStudies
      Digital.CSIC
      Dataset . 2017 . Peer-reviewed
      Data sources: Digital.CSIC
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    Authors: Neubauer, David; Ferrachat, Sylvaine; Siegenthaler-Le Drian, Colombe; Stoll, Jens; +18 Authors

    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.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.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 MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, 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 ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Beguería, Santiago; Vicente Serrano, Sergio M.;

    Format: raw binary. The raw binary archive is composed of 576 zipped files, corresponding to the SPEI index at time scales between 1 and 48 months for the whole World and divided by decades (except the last file, containing only data for the period 2001-2006). Each zipped file contains three files, one with the data itselt (.img), and two headers (.doc and .hdr). The information contained in the header files is equivalent, and allows direct access to the data using some widely used commercial programs. Naming convention: spei[tempscale]_[decade].zip, where [tempscale] is a number between 1 and 48 indicating the temporal scale of the index (months), and [decade] indicates the years of data contained in the file. Example: spei12_1910-1919.zip. All currently available gridded drought datasets at continental and global scales are based on either the PDSI or the sc-PDSI. A new global drought dataset based on the Standardised Precipitation-Evapotranspiration Index (SPEI) has been developed, which covers time scales from 1-48 months at a spatial resolution of 0.5°, and provides temporal coverage for the period 1901-2006. This dataset represents an improvement in spatial resolution and operative capability of previous gridded drought datasets based on the PDSI, and enables identification of various drought types. A monthly global dataset of a multiscalar drought index is presented and compared in terms of spatial and temporal variability with the existing continental and global drought datasets based on the Palmer drought severity index (PDSI, scPDSI). The new dataset is based on the standardized precipitation evapotranspiration index (SPEI). The index was obtained from the CRU TS3.0 data, covering time scales from 1 to 48 months for the period 1901-2006, and has a spatial resolution of 0.5°. The advantages of the new dataset are that: i) it improves the spatial resolution of the unique global drought dataset at a global scale; ii) it is spatially and temporally comparable to other datasets, given the probabilistic nature of the SPEI, and, in particular; iii) it enables identification of various drought types, given the multiscalar character of the SPEI. More details at: http://www.eead.csic.es/spei/spei.html A monthly global dataset of a multiscalar drought index is presented and compared in terms of spatial and temporal variability with the existing continental and global drought datasets based on the Palmer drought severity index (PDSI, scPDSI). The new dataset is based on the standardized precipitation evapotranspiration index (SPEI). The index was obtained from the CRU TS3.0 data, covering time scales from 1 to 48 months for the period 1901-2006, and has a spatial resolution of 0.5°. The advantages of the new dataset are that: i) it improves the spatial resolution of the unique global drought dataset at a global scale; ii) it is spatially and temporally comparable to other datasets, given the probabilistic nature of the SPEI, and, in particular; iii) it enables identification of various drought types, given the multiscalar character of the SPEI. More details at: http://www.eead.csic.es/spei/spei.html All currently available gridded drought datasets at continental and global scales are based on either the PDSI or the sc-PDSI. A new global drought dataset based on the Standardised Precipitation-Evapotranspiration Index (SPEI) has been developed, which covers time scales from 1-48 months at a spatial resolution of 0.5°, and provides temporal coverage for the period 1901-2006. This dataset represents an improvement in spatial resolution and operative capability of previous gridded drought datasets based on the PDSI, and enables identification of various drought types. The Global 0.5° gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. Use of the newest version is recommended. Older versions are still available to allow replicability. The dataset is freely available on the web repository of the Spanish National Research Council (CSIC) in three different formats (NetCDF, binary raster, and plain text).

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    Digital.CSIC
    Dataset . 2010
    Data sources: Datacite
    Digital.CSIC
    Dataset . 2010
    Data sources: Digital.CSIC
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      Digital.CSIC
      Dataset . 2010
      Data sources: Datacite
      Digital.CSIC
      Dataset . 2010
      Data sources: Digital.CSIC
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    Input files for the ForClim model (version 4.0.1) used in the associated paper. They can be used to to reproduce results of the simulation study. The ForClim model, including the source code, executable and documentation, is freely available under an Open Access license from the website of the original developers at https://ites-fe.ethz.ch/openaccess/. The original climatic dataset used to generate the ForClim input climate files at each site in South Tyrol is freely available at https://doi.pangaea.de/10.1594/PANGAEA.924502 while the CHELSA climate data for future scenarios are available at https://www.chelsa-climate.org. If interested in using this dataset for a research study or a project, please contact Marco Mina ----------------------------------------------------------------------- Hillebrand L, Marzini S, Crespi A, Hiltner U & Mina M (2023) Contrasting impacts of climate change on protection forests of the Italian Alps. Frontiers in Forests and Global Change, 6, 2023 https://doi.org/10.3389/ffgc.2023.1240235 ABSTRACT. Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • Authors: O’Gorman, E.J.; Warner, E.; Marteinsdóttir, B.; Helmutsdóttir, V.F.; +2 Authors

    Herbivory assessments were made at the plant community and species levels. We focused on three plant species with a widespread occurrence across the temperature gradient: cuckooflower (Cardamine pratensis, Linnaeus), common mouse-ear (Cerastium fontanum, Baumgerten), and marsh violet (Viola palustris, Linnaeus). For assessments of invertebrate herbivory at the species level, thirty individuals per species of C. pratensis, C. fontanum, and V. palustris were marked in each of ten plots, using a stratified random sampling method where individuals were randomly selected, but the full range of within-plot soil temperatures was represented. For assessments of invertebrate herbivory at the community level, five 50 × 50 cm quadrats were marked at random points in eight of the plots that best captured the full temperature gradient. The community-level herbivory assessment was conducted on 19th June. The number of damaged plants was recorded out of 100 random individuals, selected using a 10 × 10 grid within each 50 × 50 cm quadrat. For the species-level herbivory assessment, individual marked plants were surveyed for signs of invertebrate herbivory every two weeks from 30th May to 2nd July, generating three time-points per species. At each survey, all marked individuals for each species were assessed within a 48-hour period. Plants were recorded as damaged or not damaged by invertebrate herbivores at each time-point. Further details of how phenological stage of development, vegetation community composition, soil temperature, moisture, pH, nitrate, ammonium, and phosphate were recorded are provided in the supporting documentation. This is a dataset of environmental data, vegetation cover, and community- and species-level invertebrate herbivory, sampled at 14 experimental soil plots in the Hengill geothermal valley, Iceland, from May to July 2017. The plots span a temperature gradient of 5-35 °C on average over the sampling period, yet they occur within 1 km of each other and have similar soil moisture, pH, nitrate, ammonium, and phosphate.

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    Authors: Neubauer, David; Ferrachat, Sylvaine; Siegenthaler-Le Drian, Colombe; Stoll, Jens; +18 Authors

    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.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' 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-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, 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 ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Suitability maps (raster format: geotiff) of Quercus frainetto, 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).

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    European Union Open Data Portal
    Dataset . 2009
    License: CC_BY_4_0
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      European Union Open Data Portal
      Dataset . 2009
      License: CC_BY_4_0
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    Authors: Karjalainen, Olli; Luoto, Miska; Aalto, Juha; Etzelmüller, Bernd; +4 Authors

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

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    B2FIND
    Dataset . 2020
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2020
    License: CC BY
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2020
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2020
      License: CC BY
      Data sources: PANGAEA
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    Authors: Holmgren, M.; Lin, C.Y.; Murillo, J.E.; Nieuwenhuis, A.; +7 Authors

    Figure 1data_Exp 2Figure 1 data: Condition of experimental seedlings in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS) during the warmest growing season (2011) and at the end of the experiment (2013). Seedling condition was defined as: healthy (< 50% of the needles turned yellow or brown) or unhealthy (> 50% of the needles turned yellow or brown). Seedlings were 1 month old at plantation time in the July 2010.Table 1_environmental conditions_Exp 1Table 1 data: Environmental conditions and vegetation characteristics in hummocks (circular and bands) and lawns for Experiment 1. Water table depth below surface is an average for the four growing seasons (2010-2013)Table 2_ photosynthesis data_Exp 1Table 2 photosynthesis data: Photosynthesis rates for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1.Table 2_seedling responses_Exp 1Table 2 data: Responses of experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1 after 4 growing seasons. ST: Seeds inserted on top of moss; SB: Seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Emergence = % of planted seeds emerged after 1 year. Condition = % healthy seedlings. Stem growth corresponds to vertical stem growth for germinating (ST and SB) seedlings and new stem growth for older (small and large) seedlings.Table 3_regression seedling-environment_Exp 1Table 3 data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 during the whole experimental period (2010-2013). ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Condition = % healthy seedlings. Growth = stem growth.Table 4_Environmental data_Exp 2Table 4: Environmental conditions in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS).Table 4 and Table S5a_seedling performance_Exp 2Table 4: Seedling performance in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS). Seedling emergence, condition and survival from seeds inserted below the moss (SB), and from small planted seedlings.Table S3_cox regression (survival analysis)_Exp 1Table S3: Data for Cox survival analysis for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns during 2010-2013. ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time).Table S4_ regression seedling-environment 2011_Exp 1Table S4: Data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 in 2011. Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time). Condition = % healthy seedlings. Growth = stem growth. Boreal ecosystems are warming roughly twice as fast as the global average, resulting in woody expansion that could further speed up the climate warming. Boreal peatbogs are waterlogged systems that store more than 30% of the global soil carbon. Facilitative effects of shrubs and trees on the establishment of new individuals could increase tree cover with profound consequences for the structure and functioning of boreal peatbogs, carbon sequestration and climate. We conducted two field experiments in boreal peatbogs to assess the mechanisms that explain tree seedling recruitment and to estimate the strength of positive feedbacks between shrubs and trees. We planted seeds and seedlings of Pinus sylvestris in microsites with contrasting water-tables and woody cover and manipulated both shrub canopy and root competition. We monitored seedling emergence, growth and survival for up to four growing seasons and assessed how seedling responses related to abiotic and biotic conditions. We found that tree recruitment is more successful in drier topographical microsites with deeper water-tables. On these hummocks, shrubs have both positive and negative effects on tree seedling establishment. Shrub cover improved tree seedling condition, growth and survival during the warmest growing season. In turn, higher tree basal area correlates positively with soil nutrient availability, shrub biomass and abundance of tree juveniles. Synthesis. Our results suggest that shrubs facilitate tree colonization of peatbogs which further increases shrub growth. These facilitative effects seem to be stronger under warmer conditions suggesting that a higher frequency of warmer and dry summers may lead to stronger positive interactions between shrubs and trees that could eventually facilitate a shift from moss to tree-dominated systems.

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    ZENODO
    Dataset . 2015
    License: CC 0
    Data sources: ZENODO
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    Research@WUR
    Dataset . 2015
    Data sources: Research@WUR
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    B2FIND
    Dataset . 2015
    Data sources: B2FIND
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    EASY
    Dataset . 2015
    Data sources: EASY
    DRYAD
    Dataset . 2015
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2015
      License: CC 0
      Data sources: ZENODO
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      Research@WUR
      Dataset . 2015
      Data sources: Research@WUR
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      B2FIND
      Dataset . 2015
      Data sources: B2FIND
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      EASY
      Dataset . 2015
      Data sources: EASY
      DRYAD
      Dataset . 2015
      License: CC 0
      Data sources: Datacite
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    Suitability maps (raster format: geotiff) of Fraxinus excelsior, 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).

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    European Union Open Data Portal
    Dataset . 2009
    License: CC_BY_4_0
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      European Union Open Data Portal
      Dataset . 2009
      License: CC_BY_4_0
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    Authors: Ramirez F; Rodriguez C; Seoane J; Figuerola J; +1 Authors

    Global warming and direct anthropogenic impacts, such as water extraction, are largely affecting water budgets in Mediterranean wetlands, thereby increasing wetland salinities and isolation, and decreasing water depths and hydroperiods (duration of the inundation period). These wetland features are key elements structuring waterbird communities. However, the ultimate and net consequences of these dynamic conditions on waterbird assemblages are largely unknown. We combined a regular sampling on waterbird presence through the 2008 annual cycle with in-situ data on these relevant environmental predictors of waterbird distribution to model habitat selection for 69 individual species in a typical Mediterranean wetland network in south-western Spain. Species association with environmental features were subsequently used to predict changes in habitat suitability for each species under three climate change scenarios (encompassing changes in environment that ranged from 10% to 50% change as predicted by climatic models). Waterbirds distributed themselves unevenly throughout environmental gradients and water salinity was the most important gradient structuring the distribution of the community. Environmental suitability for the guilds of diving birds and vegetation gleaners will be reduced according to future climate scenarios, while most small wading birds will benefit from changing conditions. Resident species and those that breed in this wetland network will be also more impacted than those using this area for wintering or stopover. We provide here a tool that can be used in a horizon-scanning framework to identify emerging issues on waterbird conservation and to anticipate suitable management actions : Datasets as supporting information to article “How will climate change affect endangered Mediterranean waterbirds?” to be published in PLOS ONE. Address questions to Francisco Ramírez: ramirez@ub.edu

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    Digital.CSIC
    Dataset . 2017
    License: CC BY
    Data sources: Datacite
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    BioStudies
    Dataset . 2018
    Data sources: BioStudies
    Digital.CSIC
    Dataset . 2017 . Peer-reviewed
    Data sources: Digital.CSIC
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      Digital.CSIC
      Dataset . 2017
      License: CC BY
      Data sources: Datacite
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      BioStudies
      Dataset . 2018
      Data sources: BioStudies
      Digital.CSIC
      Dataset . 2017 . Peer-reviewed
      Data sources: Digital.CSIC
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    Authors: Neubauer, David; Ferrachat, Sylvaine; Siegenthaler-Le Drian, Colombe; Stoll, Jens; +18 Authors

    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.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.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 MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, 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 ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Beguería, Santiago; Vicente Serrano, Sergio M.;

    Format: raw binary. The raw binary archive is composed of 576 zipped files, corresponding to the SPEI index at time scales between 1 and 48 months for the whole World and divided by decades (except the last file, containing only data for the period 2001-2006). Each zipped file contains three files, one with the data itselt (.img), and two headers (.doc and .hdr). The information contained in the header files is equivalent, and allows direct access to the data using some widely used commercial programs. Naming convention: spei[tempscale]_[decade].zip, where [tempscale] is a number between 1 and 48 indicating the temporal scale of the index (months), and [decade] indicates the years of data contained in the file. Example: spei12_1910-1919.zip. All currently available gridded drought datasets at continental and global scales are based on either the PDSI or the sc-PDSI. A new global drought dataset based on the Standardised Precipitation-Evapotranspiration Index (SPEI) has been developed, which covers time scales from 1-48 months at a spatial resolution of 0.5°, and provides temporal coverage for the period 1901-2006. This dataset represents an improvement in spatial resolution and operative capability of previous gridded drought datasets based on the PDSI, and enables identification of various drought types. A monthly global dataset of a multiscalar drought index is presented and compared in terms of spatial and temporal variability with the existing continental and global drought datasets based on the Palmer drought severity index (PDSI, scPDSI). The new dataset is based on the standardized precipitation evapotranspiration index (SPEI). The index was obtained from the CRU TS3.0 data, covering time scales from 1 to 48 months for the period 1901-2006, and has a spatial resolution of 0.5°. The advantages of the new dataset are that: i) it improves the spatial resolution of the unique global drought dataset at a global scale; ii) it is spatially and temporally comparable to other datasets, given the probabilistic nature of the SPEI, and, in particular; iii) it enables identification of various drought types, given the multiscalar character of the SPEI. More details at: http://www.eead.csic.es/spei/spei.html A monthly global dataset of a multiscalar drought index is presented and compared in terms of spatial and temporal variability with the existing continental and global drought datasets based on the Palmer drought severity index (PDSI, scPDSI). The new dataset is based on the standardized precipitation evapotranspiration index (SPEI). The index was obtained from the CRU TS3.0 data, covering time scales from 1 to 48 months for the period 1901-2006, and has a spatial resolution of 0.5°. The advantages of the new dataset are that: i) it improves the spatial resolution of the unique global drought dataset at a global scale; ii) it is spatially and temporally comparable to other datasets, given the probabilistic nature of the SPEI, and, in particular; iii) it enables identification of various drought types, given the multiscalar character of the SPEI. More details at: http://www.eead.csic.es/spei/spei.html All currently available gridded drought datasets at continental and global scales are based on either the PDSI or the sc-PDSI. A new global drought dataset based on the Standardised Precipitation-Evapotranspiration Index (SPEI) has been developed, which covers time scales from 1-48 months at a spatial resolution of 0.5°, and provides temporal coverage for the period 1901-2006. This dataset represents an improvement in spatial resolution and operative capability of previous gridded drought datasets based on the PDSI, and enables identification of various drought types. The Global 0.5° gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. Use of the newest version is recommended. Older versions are still available to allow replicability. The dataset is freely available on the web repository of the Spanish National Research Council (CSIC) in three different formats (NetCDF, binary raster, and plain text).

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    Digital.CSIC
    Dataset . 2010
    Data sources: Datacite
    Digital.CSIC
    Dataset . 2010
    Data sources: Digital.CSIC
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      Digital.CSIC
      Dataset . 2010
      Data sources: Datacite
      Digital.CSIC
      Dataset . 2010
      Data sources: Digital.CSIC
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