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This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper: Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. https://doi.org/10.5194/esd-10-741-2019 Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de). Climate change impact data File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries. File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action). File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action). In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, https://tntcat.iiasa.ac.at/SspDb/, last accessed 15Nov 2019). Climate change mitigation cost data The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2]. File 4: REMIND_scenario_results_economic_data.csv File 5: REMIND_scenarios_climate_data.csv Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature. In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios. The first dimension specifies the climate policy regime (delayed action, baseline scenarios): 1xx: climate action from 2010 5xx: climate action from 2015 2xx climate action from 2020 (used in this study) 3xx climate action from 2030 4x1 weak policy baseline (before Paris agreement) The second dimension specifies the technology portfolio and assumptions: x1x Full technology portfolio (used in this study) x2x noCCS: unavailability of CCS x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed x4x NucPO: phase out of investments into nuclear energy x5x Limited SW: penetration of solar and wind power limited x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases) x6x noBECCS: unavailability of CCS in combination with bioenergy The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.). xx1 0$/tCO2 (baseline) xx2 10$/tCO2 xx3 30$/tCO2 xx4 50$/tCO2 xx5 100$/tCO2 xx6 200$/tCO2 xx7 500$/tCO2 xx8 40$/tCO2 xx9 20$/tCO2 xx0 5$/tCO2 For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price). [1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a. [2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.

Wheat Biomass for Kherson and Poltava regions in Ukraine The dataset contains Dry Above Ground Biomass (DAM) estimates over the Kherson and Poltava regions in Ukraine for years 2020,2021 and 2022. - Processing:The processing is done using the AgriCarbon-EOv1.5 processing chain, using the TREX processing centre at CNES France.The input remote sensing data are L2A Sentinel-2 surface reflectances provided by the MAJA processing chain based on the Copernicus Sentinel-2 L1C data.The Landcover maps are provided using ML Deep learning based on the Copernicus L2A data.The daily weather data is extracted from ERA5Land products (C3S). -Geophysical variable:Dry Above ground biomass of winter wheat in g/m2. - Extents: * DAM estimates over the Copernicus Sentinel-2 tile 36TWT cover the Kherson region.* DAM estimates over the Copernicus Sentinel-2 tile 36UVA cover the Poltava region. - Spatial resolution:10m resolution estimlates over wheat plots identified in the landcover map. - Temporal coverage:Estimates are provided at the end of the wheat cycle for cycles:* The year 2020 correspond to cycle: 2019-2020* The year 2021 corresponds to cycle : 2020-2021* The year 2022 corresponds to cycle : 2021-2022 - Projection: EPSG:32636 - File content: Each Raster file has 2 bands containing respectively: * band1: mean value of DAM in g/m2. * band2: standard deviation of DAM in g/m2. - List of maps:* Dry_aboveground_biomass_2020_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2020_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2021_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2021_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2022_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2022_T36UVA_Poltava_Ukraine.tif

This database includes more than 100 decarbonisation innovations in Paper, Plastic, Steel and Meat & Dairy sectors, across their value chains, as well as in Finance. For each innovation there is a description, information about its contribution to decarbonisation, actors and collaborators involved, sources of funding, drivers, (co)benefits and disadvantages. More information on the method for selecting innovations for the database is available here. The database was created as part of REINVENT – a Horizon 2020 research project funded by the European Commission (grant agreement 730053). REINVENT involves five research institutions from four countries: Lund University (Sweden), Durham University (United Kingdom), Wuppertal Institute (Germany), PBL Netherlands Environmental Assessment Agency (the Netherlands) and Utrecht University (the Netherlands). More information can be found on our website: www.reinvent-project.eu.

This repository contains a series of .csv files developed for the study titled "Plant canopies promote climatic disequilibrium in Mediterranean recruit communities", authored by: Perez-Navarro MA, Lloret F, Molina-Venegas R, Alcántara JM and Verdú M. The author of these files is Perez-Navarro MA. These files are used to characterize species niches, estimate climatic disequilibrium for recruit communities growing under plant canopies and open spaces, and conduct statistical analyses. Variables description of each table is compiled in the METADATA.txt file. Please visit Github readme () to correctly place these files in the folder tree and check for the corresponding scripts where they are required. Please notice that although alternative approaches were calibrated to estimate species niche (accordingly producing multiple niche, distances and disequilibrium dataframes), only niche centroid calibrated discarding 95 percentile of lowest niche density was used for paper results and figures. Also, in case of univariate analyses only bio01, bio06 and bio12 were used in analyses, though species niche and further niche and community estimations were obtained for all 19 variables. This is version 2 (v2) and include extra intermediate .csv required to run all the R scripts included in the abovementioned Github repository. NAs or empty cells present in the .csv files of this repository means no data and do not contribute to the analyses. Visit METADATA.txt file for variables description. These data are under CC0 license. It is possible to share, copy and redistribute the material in any medium or format, and adapt, remix, transform, and build upon the material for any purpose. Studies using R scripts or any data files from these study should cite the abovementioned paper (Perez-Navarro MA, Lloret F, Molina-Venegas R, Alcantara JM, Verdu M. (2024). Plant canopies promote climatic disequilibrium in Mediterranean recruit communities). Please contact m.angeles582@gmail.com in case of having doubts or problems with the existing files and scripts. Current rates of climate change are exceeding the capacity of many plant species to track climate, thus leading communities to be in disequilibrium with climatic conditions. Plant canopies can contribute to this disequilibrium by buffering macro-climatic conditions and sheltering poorly adapted species to the oncoming climate, particularly in their recruitment stages. Here we analyze differences in climatic disequilibrium between understory and open ground woody plant recruits in 28 localities, covering more than 100,000 m2, across an elevation range embedding temperature and aridity gradients in the southern Iberian Peninsula. This study demonstrates higher climatic disequilibrium under canopies compared with open ground, supporting that plant canopies would affect future community climatic lags by allowing the recruitment of less arid-adapted species in warm and dry conditions, but also it endorse that canopies could favor warm-adapted species in extremely cold environments as mountain tops, thus pre-adapting communities living in these habitats to climate change.

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.

The Aneto Glacier, is the largest glacier in the Pyrenees. Its shrinkage and wastage have been continuous in recent decades, and there are signs of accelerated melting in recent years. In this study, changes in the surface and ice thickness of the Aneto Glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne LiDAR point clouds, and UAV imagery. A GPR survey conducted in 2020, combined with data from photogrammetric analyses, allowed us to reconstruct the current ice thickness and also the existing ice distribution in 1981 and 2011. Over the last 41 years, the total glaciated area has shrunk by 64.7% and the ice thickness has decreased, on average, by 30.5 m. The mean remaining ice thickness in autumn 2022 was 11.9 m, as against the mean thicknesses of 32.9 m, 19.2 m reconstructed for 1981 and 2011 and 15.0 m observed in 2020 respectively. The results demonstrate the critical situation of the glacier, with an imminent segmentation into two smaller ice bodies and no evidence of an accumulation zone. We also found that the occurrence of an extremely hot and dry year, as observed in the 2021–2022 season, leads to a drastic degradation of the glacier, posing a high risk to the persistence of the Aneto Glacier, a situation that could extend to the rest of the Pyrenean glaciers in a relatively short time.

Data type: Experimental measurements, correlations and Van't Hoff plot. Date format: .opj. Origin of the data: Experimental pressure composition isotherm measurements. Data generated by a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin.

As part of global efforts to reduce dependence on carbon-based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at-sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts. Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south-east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another. Within an operational wind farm, there was a close-to-significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause. There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p-p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non-piling scenario. Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement. Wash_diagWash_diag.xlsx is the historic location data (pre windfarm construction) for the 19 individuals used in the analysis described in Russell et al.