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This dataset contains the underlying data for the following publication: Doukas, H., Spiliotis, E., Jafari, M. A., Giarola, S. & Nikas, A. (2021). Low-cost emissions cuts in container shipping: Thinking inside the box. Transportation Research Part D: Transport and Environment, 94, 102815, https://doi.org/10.1016/j.trd.2021.102815.

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.

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.MOHC.HadGEM3-GC31-MM.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 HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.

Tree cores were sampled using increment borers. At each site three trees were chosen for coring, with two or three cores taken per tree. Cores were sanded and ring widths measured based on high-resolution images of the sanded cores. Cores were cross-dated and summary statistics used to compare cross-dating accuracy. The dataset contains the resulting dated ring width series. This dataset includes tree ring width data, derived from tree cores, that were sampled from sites across the Rhön Biosphere Reserve (Germany). At each chosen site three trees were cored, with two or three cores taken per cored tree. Data was collected in August 2021.

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.

Variable list for files: SW wind - Section table on Skomer (Standardised).csv / NW wind - Section table on Skomer (Standardised).csv / SE wind - Section table on Skomer (Standardised).csv /NE wind - Section table on Skomer (Standardised).csv and SW wind - Sections on Skokholm (Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanUMedian; MeanUIQR, MeanUSkewness, MeanUCV: Median, interquartile range,skewness and coefficient of variation of mean wind speed per section HorizontalMedian;HorizontalIQR,HorizontalSkewness,HorizontalCV: Median, interquartile range,skewness and coefficient of variation of horizontal wind speed per section PMedian;PIQR,PSkewness,PCV: Median, interquartile range,skewness and coefficient of variation of preessure per section TKEMedian;TKEIQR,TKESkewness,TKECV: Median, interquartile range,skewness and coefficient of variation of turbulent kinetic energy per section TIMedian;TIIQR,TISkewness,TICV: Median, interquartile range,skewness and coefficient of variation of turbulence intensity per section U_2Median;lU_2IQR;U_2Skewness;U_2CV: Median, interquartile range,skewness and coefficient of variation of vertical wind speed per section EpsilonMedian;EpsilonIQR,EpsilonSkewness,EpsilonCV: Median, interquartile range,skewness and coefficient of variation of turbulent dissipation rate per section NutMedian;NutIQR,NutSkewness,NutCV: Median, interquartile range,skewness and coefficient of variation of kinematic viscosity per section GustsMedian;GustsIQR,GustsSkewness,GustsCV: Median, interquartile range,skewness and coefficient of variation of instataneous gusts per section MeanSectorSlope: Mean slope per section ColPresence: Binomial variable, indicating whether a section has birds or not. This variable varies with classification, based on either the count of birds or the density per section Variable list for file: Section table on Skomer - with Mean cliff orientation and Slope (NOT-Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanSectorSlope: Mean slope per section MeanSectorAspectCircular: Mean cliff orientation per section ApsectClass: Factor indicating whether the mean cliff orientation is lee- or windward to the SW wind ColPresence: Binomial variable, indicating whether a section has birds or not. This variable varies with classification, based on either the count of birds or the density per section Variable list for file: SW wind - Sections on Skokholm to predict colonies using cliff orientation and slope model from Skomer (NON - Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) Sector: Section ID MeanSectorSlope: Mean slope per section MeanSectorAspectCircular: Mean cliff orientation per section Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well-studied seabirds. Here we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots (Uria aalge) breeding on Skomer island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/ wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions, and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions. Methods detailed in manuscript: https://doi.org/10.1111/ecog.05733.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.MOHC.HadGEM3-GC31-MM.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 HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.

Near continuous methane and CO2 fluxes measured along a transect on an ombrotrophic fen in Southern Sweden from August 2017-September 2019 using an automated greenhouse gas flux platform SkyLine2D. The impacts of drought (in 2018 the mire experienced drought conditions) and different vegetation types (sedge, heather, sphagnum or open water; 6 replicated for each) on the fluxes were determined. Fluxes were measured within collars of 20-cm diameter, 4-min at each collar. CH4 and CO2 fluxes were detected using a Licor infrared gas analyser (IRGA, LI-8100, Licor, NE, USA) to measure CO2 and a cavity ringdown laser (CRD, LGR U-GGA-91, Los Gatos Research, CA USA) to measure both CO2 and CH4. Fluxes of CO2 and CH4 were calculated using linear regression; a deadband of at least 20 seconds was allowed for the chamber headspace to mix and a window of 90 seconds was used for CO2 and 240 seconds used for CH4. Fluxes were adjusted for area, air temperature and gas volume. Further adjustment was made to the CO2 fluxes during daylight hours based upon the light response curve to account for attenuation of light by the chamber material, after. All data manipulation and analyses were carried out using SAS 9.4 (SAS Institute, CA 161 USA). GHG flux data (for both CO2 and CH4) were quality controlled in the first instance using the R2 statistic of the CO2 flux measurement, with values < 0.9 discarded. Measurements passing this threshold were then assessed using the output statistics from the regression calculation of CH4 fluxes, where regressions with a P value < 0.05 were accepted, while those that did not were treated as zero flux. Data outliers were defined as those ± 1.96 standard errors of the mean flux value for each collar and were excluded from the analyses. Data were further filtered to account for overestimation of fluxes during still atmospheric night-time conditions. Using the procedure fluxes where the mean CO2 concentration for the 20 second period before and after chamber closure dropped by more than 25 ppm where discounted. Net ecosystem exchange and methane fluxes were measured from a hemi-boreal ombrotrophic fen in Southern Sweden. An automated chamber system, SkyLine2D, was used to measure the fluxes near-continuously from August 2017 to September 2019. Four ecotypes were identified: sphagnum (Sphagnum spp), eriophorum, heather and water, to assess how these different ecotypes would respond to drought. The 2018 drought allowed comparison of fluxes between drought and non-drought years (May to September), and their recovery the following year.

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.