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The database presents the scenario results of an exploratory research, carried out at the International Institute for Applied Systems Analysis (IIASA): the Low Energy Demand (LED) study (Grubler et al. 2018). The LED scenario explored how far transformative changes that combine technological changes, end-use efficiency, and new business models for energy service provision can lead for lowering energy demand, and how these changes could drive deep decarbonisation in the long-term. The scenario development methodology included a bottom-up analysis of how currently existing, though often embryonic, social, institutional, and technological trends could become mainstream with resulting step-changes in efficiency and resulting lowered energy demand. The bottom-up demand estimations were then further explored for their supply side and emissions and climate implications with a top-down modeling framework drawing on the Shared Socioeconomic Pathways (SSP) framework (Riahi et al. 2017). The results show that global final energy demands can be drastically reduced in 2050, to around 245 EJ/yr, or 40% lower than today, whilst significantly expanding human welfare and reducing global development inequalities. According to the knowledge of the authors, LED is the lowest long-term global energy demand scenario ever published. The LED scenario meets the 1.5°C climate target in 2100 without overshoot and keeps the global mean temperature increase below 1.5°C with a probability of more than 60%, without requiring controversial negative emission technologies, such as bioenergy with carbon capture and storage (BECCS), that figure prominently in the emission scenario literature (Rogelj et al. 2015, Anderson and Peters 2016, Creutzig et al. 2016, Smith et al. 2016). Furthermore, the beneficial impacts of the LED scenario on a range of other sustainable development goals are also shown, demonstrating that efficiency of energy services provision plays a critical role in reaching low-energy futures without compromising increased living standards in the Global South, while at the same time reducing adverse social and environmental impacts of climate mitigation strategies that focus predominantly on large-scale supply-side transformations. The research is published in a peer-reviewed article in Nature Energy (Grubler et al. 2018) with ample supplementary information. Water consumption and withdrawal data are published in Parkinson et al. (2018). The data is available for download from the LED Database. The content of the LED database and any derived analysis may only be used for non-commercial scientific publications, articles, educational purposes, figures and data tables provided that the source reference pursuant to section 'Required citation' is included and all relevant publications are correctly cited. Partial reproductions of the database content may be stored in online repositories, if this is necessary to comply with a journal's data archiving and access requirements. Such reproductions must be limited to the scope of the manuscript in question, and must include a hyperlink to the source database hosted at https://db1.ene.iiasa.ac.at/LEDDB and the download date from the source database. However, any wholesale duplication, translation, reworking, processing, arrangement, transformation, or reproduction through the internet or any other channels, of the https://db1.ene.iiasa.ac.at/LEDDEB for commercial or non-commercial purposes is not permitted without the explicit written approval of IIASA.

# Heatwave grazing kelp microbes sequences [https://doi.org/10.5061/dryad.vhhmgqns7](https://doi.org/10.5061/dryad.vhhmgqns7) We experimentally simulated ocean warming and marine heatwaves (MHWs) to quantify effects on two dominant temperate seaweed species and their microbiota, as well as grazing by a tropical herbivore. The kelp *Ecklonia radiata*’s microbiota in sustained warming and MHW treatments were enriched with microorganisms associated with seaweed disease and tissue degradation. In contrast, the fucoid *Sargassum linearifolium*’s microbiota was unaffected by temperature\*.\* Consumption by the tropical sea-urchin *Tripneustes gratilla* was greater on *Ecklonia* where the microbiota had been altered by higher temperatures, while *Sargassum*’s consumption was unaffected. Elemental traits (carbon, nitrogen), chemical defences (phenolics) and tissue bleaching of both seaweeds were generally unaffected by temperature. ## Description of the data and file structure Juvenile *Ecklonia radiata* (length \~15cm; N=140) and *Sargassum linearifolium* (length \~10cm; N=140) were collected haphazardly (>2m apart) at Cronulla rocky reef, Sydney, Australia. We exposed seaweeds to one of four temperature profiles over seven weeks: Ambient, Warming, marine heatwave MHW, MHW variable. After seven weeks of exposure to temperature treatments, a subset of individuals from each species/temperature treatment (*Ecklonia*: n=4-6; *Sargassum*: n=3) were randomly selected. Sterile cotton swabs were used to sample microbiota on algal surfaces, with the same area (20cm2) and swabbing time (30s) sampled for all individuals. Swabs were immediately stored in liquid nitrogen and transported to the University of New South Wales (UNSW, Sydney) and kept at -80°C until DNA extraction. DNA was extracted from swabs using the DNeasy PowerSoil Kit (Qiagen) and amplified using Polymerase Chain Reaction (PCR) primers 341F (5’-CCTACGGGNGGCWGCAG-3’) and 785R (5’-GACTACHVGGGTATCTAATCC-3’), targeting the 16S rRNA gene V3-V4 regions (bacteria and archaea), and were sequenced with a 2x250bp MiSeq reagent kit v2 on the Illumina MiSeq2000 Platform. The range-expansion of tropical herbivores due to ocean warming can profoundly alter temperate reef communities by overgrazing the seaweed forests that underpin them. Such ecological interactions may be mediated by changes to seaweed-associated microbiota in response to warming, but empirical evidence demonstrating this is rare. We experimentally simulated ocean warming and marine heatwaves (MHWs) to quantify effects on two dominant temperate seaweed species and their microbiota, as well as grazing by a tropical herbivore. The kelp Ecklonia radiata’s microbiotain sustained warming and MHW treatments were enriched with microorganisms associated with seaweed disease and tissue degradation. In contrast, the fucoid Sargassum linearifolium’s microbiota was unaffected by temperature. Consumption by the tropical sea-urchin Tripneustes gratilla was greater on Ecklonia where the microbiota had been altered by higher temperatures, while Sargassum’s consumption was unaffected. Elemental traits (carbon, nitrogen), chemical defences (phenolics) and tissue bleaching of both seaweeds were generally unaffected by temperature. Effects of warming and MHWs on seaweed holobionts (host plus its microbiota) are likely species-specific. The effect of increased temperature on Ecklonia’s microbiota and subsequent increased consumption suggest that changes to kelp microbiota may underpin kelp-herbivore interactions, providing novel insights into potential mechanisms driving change in species’ interactions in warming oceans.

Workers employed for a hydropower project in Fujian province had their dormitories close to the river. On 7th May 2016, a landslide was triggered through heavy rainfall. More than 40 construction workers died in this event. The pre-event acquisition is from 7th February 2016 (Sentinel-2) and the post-event acquisition is from 26th July 2016 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image. Contains modified Copernicus Sentinel data (2016) {"references": ["http://blogs.agu.org/landslideblog/2016/05/10/chitan-hydropower-landslide/"]}

Description: The eddy covariance technique was used to record continuous, non-invasive measurements of CO2, H2O and energy exchange between the ecosystem and the atmosphere. The measuring system consists of a semi-open path infrared gas analyser LI-7200 (LI-COR, USA), and a CSAT3 Sonic Anemometer (Campbell Scientific, USA) at a measuring height of 52 m over a canopy height of ~25 m. Data were recorded at a frequency of 20 Hz that was treated using the post-processing software EddyPro® (v.7.0.6; www.licor.com/eddypro) to compute fluxes for each 30-minute averaging period. To treat the raw fluxes, primary data processing steps were applied, including spike removal (Vickers, 1997 J Atmos Ocean Technol), coordinate rotation, block averaging detrending of CO2, H2O and sonic temperature, time lag compensation using covariance maximisation detection method, random uncertainty estimation (Finkelstein et al. 2001 Journal of Geophysical Research Atmospheres), computation of turbulent fluxes and mean fluxes, spectral corrections (Moncrieff et al. 1997 J Hydrol Amst) using correction of low-pass filtering effects, planar fit rotation (Wilczak et al. 2001 Boundary Layer Meteorol) and quality flagging policy (Göckede et al. 2006 Boundary Layer Meteorol). Eddy covariance meteorological data from above and below canopy is available at DOI 10.5281/zenodo.3888374. Cells with -9999 represent not enough data collected, which can be regarded as NA. This data has been collected over a heavily logged landscape between 2012 - 2018, please note 2016 was removed from this dataset. Before 2015, the landscape was ~10 years recovering from it's previous round of logging (four times logged). During 2015 the landscape was salvaged logged, removing 75% of tree stand basal area. The first data sheet, named "Raw_data" contains all raw fluxes that have been treated by EddyPro, which have not been filtered or quality controlled. The second sheet, named "Daily_fluxes" contains daily mean fluxes of net ecosystem CO2 exchange (NEE), ecosystem respirationn (Reco) and gross primary productivity and their associated standard errors. Net ecosystem CO2 exchange (NEE) was calculated by adding the estimated CO2 storage flux to the observed CO2 flux. Data was subjecto quality control including the removal of quality flags 4 and 5 (Göckede et al. 2006 Boundary Layer Meteorol) and the application of a mean u* threshold of >0.29 m s-1 to the dataset, as established using the package "REddyProc" (v.1.2; (Wultzer et al. 2019 Biogeosciences)) in based on the Moving Point Method (Reichstein et al. 2005, GCB). Data was subsequently gap filled and partitioned, as descripted within the variable methods of this sheet. This data was part of an analysis of carbon fluxes within three periods of data collection: in 2012 – 2013, which captured the four-times logged ecosystem ~10 years after its previous round of logging, in 2015 during a new round of active salvage logging, and in 2017 – 2018 when the ecosystem was recovery 2-3 years after the salvage logging. Days with large standard errors for Reco (> ± 5 µmol m−2 s−1) were deemed as bad quality and removed from the dataset and we used only days that had four or more observed half-hourly values of NEE. Of the final dataset , 29.5% of the half-hourly values are original observed fluxes, and 70.5% gap-filled. Of the 455 days remaining after all filtering processes were applied, 65 days were during the 10-years recovery phase (2012-2013), 100 during the active salvage logging (2015) and 290 during the 2-3 years recovery from active salvage logging phase (2017-2018). Project: This dataset was collected as part of the following SAFE research project: Changing carbon dioxide and water budgets from deforestation and habitat modification XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: SAFE_EC_byYear.xlsx SAFE_EC_byYear.xlsx This file contains dataset metadata and 6 data tables: Raw_data_2012_2013 (described in worksheet Raw_data_2012_2013) Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower. Number of fields: 105 Number of data rows: 24213 Fields: Location: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location) date: Date of the end of the averaging period (Field type: date) time: Time of the end of the averaging period (Field type: time) DOY: decimal day of year (Field type: numeric) daytime: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric) file_records: Number of valid records found in the raw file (or set of raw files) (Field type: numeric) used_records: Number of valid records used for current the averaging period (Field type: numeric) Tau: Corrected momentum flux (Field type: numeric) qc_Tau: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_Tau: Random error for momentum flux, if selected (Field type: numeric) H: Corrected sensible heat flux (Field type: numeric) qc_H: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_H: Random error for momentum flux, if selected (Field type: numeric) LE: Corrected latent heat flux (Field type: numeric) qc_LE: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_LE: Random error for latent heat flux, if selected (Field type: numeric) co2_flux: CO2 flux (Field type: numeric) qc_co2_flux: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_co2_flux: Random error of CO2 flux (Field type: numeric) h2o_flux: H2O flux (Field type: numeric) qc_h2o_flux: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_h2o_flux: Random error of CO2 flux (Field type: numeric) H_strg: Estimate of storage sensible heat flux (Field type: numeric) LE_strg: Estimate of storage latent heat flux (Field type: numeric) co2_strg: Estimate of storage CO2 flux (Field type: numeric) h2o_strg: Estimate of storage H20 flux (Field type: numeric) co2_v.adv: Estimate of vertical advection flux of CO2 (Field type: numeric) h2o_v.adv: Estimate of vertical advection flux of H20 (Field type: numeric) co2_molar_density: Measured or estimated molar density of gas (Field type: numeric) co2_mole_fraction: Measured or estimated mole fraction of gas (Field type: numeric) co2_mixing_ratio: Measured or estimated mixing ratio of gas (Field type: numeric) co2_time_lag: Time lag used to synchronize gas time series (Field type: numeric) co2_def_timelag: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric) h2o_molar_density: Measured or estimated molar density of gas (Field type: numeric) h2o_mole_fraction: Measured or estimated mole fraction of gas (Field type: numeric) h2o_mixing_ratio: Measured or estimated mixing ratio of gas (Field type: numeric) h2o_time_lag: Time lag used to synchronize gas time series (Field type: numeric) h2o_def_timelag: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric) sonic_temperature: Mean temperature of ambient air as measured by the anemometer (Field type: numeric) air_temperature: Mean temperature of ambient air, either calculated from high frequency air temperature readings, or estimated from sonic temperature (Field type: numeric) air_pressure: Mean pressure of ambient air, either calculated from high frequency air pressure readings, or estimated based on site altitude (barometric pressure) (Field type: numeric) air_density: Density of ambient air (Field type: numeric) air_heat_capacity: Specific heat at constant pressure of ambient air (Field type: numeric) air_molar_volume: Molar volume of ambient air (Field type: numeric) ET: Evapotranspiration flux (Field type: numeric) water_vapor_density: Ambient mass density of water vapor (Field type: numeric) e: Ambient water vapor partial pressure (Field type: numeric) es: Ambient water vapor partial pressure at saturation (Field type: numeric) specific_humidity: Ambient specific humidity on a mass basis (Field type: numeric) RH: Ambient relative humidity (Field type: numeric) VPD: Ambient water vapor pressure deficit (Field type: numeric) Tdew: Ambient dew point temperature (Field type: numeric) u_unrot: Wind component along the u anemometer axis (Field type: numeric) v_unrot: Wind component along the v anemometer axis (Field type: numeric) w_unrot: Wind component along the w anemometer axis (Field type: numeric) u_rot: Rotated u wind component (mean wind speed) (Field type: numeric) v_rot: Rotated v wind component (should be zero) (Field type: numeric) w_rot: Rotated w wind component (should be zero) (Field type: numeric) wind_speed: Mean wind speed (Field type: numeric) max_wind_speed: Maximum instantaneous wind speed (Field type: numeric) wind_dir: Direction from which the wind blows, with respect to Geographic or Magnetic north (Field type: numeric) yaw: First rotation angle (Field type: numeric) pitch: Second rotation angle (Field type: numeric) u.: Friction velocity (Field type: numeric) TKE: Turbulent kinetic energy (Field type: numeric) L: Monin-Obukhov length (Field type: numeric) X.z.d..L: Monin-Obukhov stability parameter - (z-d)/L (Field type: numeric) bowen_ratio: Sensible heat flux to latent heat flux ratio (Field type: numeric) T.: Scaling temperature (Field type: numeric) model: Model for footprint estimation, 1- Kljun et al. (2004): A crosswind integrated parameterization of footprint estimations obtained with a 3D Lagrangian model by means of a scaling procedure.2 - Kormann and Meixner (2001): A crosswind integrated model based on the solution of the two dimensional advection-diffusion equation given by van Ulden (1978) and others for power-law profiles in wind velocity and eddy diffusivity, 3 - Hsieh et al. (2000): A crosswind integrated model based on the former model of Gash (1986) and on simulations with a Lagrangian stochastic model. (Field type: numeric) x_peak: Along-wind distance providing <1% contribution to turbulent fluxes (Field type: numeric) x_offset: Along-wind distance providing the highest (peak) contribution to turbulent fluxes (Field type: numeric) x_10.: Along-wind distance providing 10% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_30.: Along-wind distance providing 30% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_50.: Along-wind distance providing 50% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_70.: Along-wind distance providing 70% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_90.: Along-wind distance providing 90% (cumulative) contribution to turbulent fluxes (Field type: numeric) un_Tau: Uncorrected momentum flux (Field type: numeric) Tau_scf: Spectral correction factor for momentum flux (Field type: numeric) un_H: Uncorrected sensible heat flux (Field type: numeric) H_scf: Spectral correction factor for sensible heat flux (Field type: numeric) un_LE: Uncorrected latent heat flux (Field type: numeric) LE_scf: Spectral correction factor for latent heat flux (Field type: numeric) un_co2_flux: Uncorrected gas flux (Field type: numeric) co2_scf: Spectral correction factor for gas flux (Field type: numeric) un_h2o_flux: Uncorrected gas flux (Field type: numeric) h2o_scf: Spectral correction factor for gas flux (Field type: numeric) spikes_hf: Hard flags for individual variables for spike test (Field type: numeric) amplitude_resolution_hf: Hard flags for individual variables for amplitude resolution (Field type: numeric) drop_out_hf: Hard flags for individual variables for drop-out test (Field type: numeric) absolute_limits_hf: Hard flags for individual variables for absolute limits (Field type: numeric) skewness_kurtosis_hf: Hard flags for individual variables for skewness and kurtosis (Field type: numeric) skewness_kurtosis_sf: Soft flags for individual variables for skewness and kurtosis test (Field type: numeric) discontinuities_hf: Hard flags for individual variables for discontinuities test (Field type: numeric) discontinuities_sf: Soft flags for individual variables for discontinuities test (Field type: numeric) timelag_hf: Hard flags for gas concentration for time lag test (Field type: numeric) timelag_sf: Soft flags for gas concentration for time lag test (Field type: numeric) attack_angle_hf: Hard flags for gas concentration for time lag test (Field type: numeric) non_steady_wind_hf: Soft flags for gas concentration for time lag test (Field type: numeric) u_spikes: Number of spikes detected and eliminated for rotated u wind component (Field type: numeric) v_spikes: Number of spikes detected and eliminated forrotated v wind component (Field type: numeric) w_spikes: Number of spikes detected and eliminated for rotated w wind component (Field type: numeric) ts_spikes: Number of spikes detected and eliminated for ts variable (Field type: numeric) co2_spikes: Number of spikes detected and eliminated for co2 variable (Field type: numeric) h2o_spikes: Number of spikes detected and eliminated for h2o variable (Field type: numeric) Raw_data_2014 (described in worksheet Raw_data_2014) Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower. There is a significant data gap, with some intermittent records available during the daytime, between 17/2/2014-17/06/2014 due to the problems in the power supply. Number of fields: 105 Number of data rows: 17520 Fields: Location: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location) date: Date of the end of the averaging period (Field type: date) time: Time of the end of the averaging period (Field type: time) DOY: decimal day of year (Field type: numeric) daytime: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric) file_records: Number of valid records found in the raw file (or set of raw files) (Field type: numeric) used_records: Number of valid records used for current the averaging period (Field type: numeric) Tau: Corrected momentum flux (Field type: numeric) qc_Tau: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_Tau: Random error for momentum flux, if selected (Field type: numeric) H: Corrected sensible heat flux (Field type: numeric) qc_H: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_H: Random error for momentum flux, if selected (Field type: numeric) LE: Corrected latent heat flux (Field type: numeric) qc_LE: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_LE: Random error for latent heat flux, if selected (Field type: numeric) co2_flux: CO2 flux (Field type: numeric) qc_co2_flux: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_co2_flux: Random error of CO2 flux (Field type: numeric) h2o_flux: H2O flux (Field type: numeric) qc_h2o_flux: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best,

Studies have shown that food chain length is governed by interactions between species richness, ecosystem size, and resource availability. While redundant trophic links may buffer impacts of species loss on food chain length, higher extinction risks associated with predators may result in bottom-heavy food webs with shorter food chains. The lack of consensus in earlier empirical studies relating species richness and food chain length reflects the need to account robustly for the factors described above. In response to this, we conducted an empirical study to elucidate impacts of land-use change on food chain length in tropical forest streams of Southeast Asia. Despite species losses associated with forest loss at our study areas, results from amino acid isotope analyses showed that food chain length was not linked to land use, ecosystem size or resource availability. Correspondingly, species losses did not have a significant effect on occurrence likelihoods of all trophic guilds except herbivores. Impacts of species losses were likely buffered by high levels of initial trophic redundancy, which declined with canopy cover. Declines in trophic redundancy were most drastic amongst invertivorous fishes. Declines in redundancy across trophic guilds were also more pronounced in wider and more resource-rich streams. While our study found limited evidence for immediate land-use impacts on stream food chains, the potential loss of trophic redundancy in the longer term implies increasing vulnerability of streams to future perturbations, as long as land conversion continues unabated.

Climate change allows species to expand polewards, but non-changing environmental features may limit expansions. Daylength is unaffected by climate and drives life cycle timing in many animals and plants. Because daylength varies over latitudes, poleward-expanding populations must adapt to new daylength conditions. We studied local adaptation to daylength in the butterfly Lasiommata megera, which is expanding northwards along several routes in Europe. Using common garden laboratory experiments with controlled daylengths, we compared diapause induction between populations from the southern-Swedish core range and recently established marginal populations from two independent expansion fronts in Sweden. Caterpillars from the northern populations entered diapause in clearly longer daylengths than those from southern populations, with the exception of caterpillars from one geographically isolated population. The northern populations have repeatedly and rapidly adapted to their local daylengths, indicating that the common use of daylength as seasonal cue need not strongly limit climate-induced insect range expansions.

California policy is incentivizing rapid adoption of zero emission electric vehicles for light duty and freight applications. In this project, we explored how locating charging facilities at California’s highway rest stops, might impact electricity demand, grid operation, and integration of renewables like solar and wind into California’s energy mix. Assuming a growing population of electric vehicles to meet state goals, we estimated state-wide growth of electricity demand, and identified the most attractive rest stop locations for siting chargers. Using a California-specific electricity dispatch model developed at ITS, we estimated how charging vehicles at these stations would impact renewable energy curtailment in California. We estimated the impacts of charging infrastructures on California’s electricity system and how they can be utilized to decrease the duck curve effect resulting from a large amount of solar energy penetration by 2050.

Description This dataset contains a set of 13 climatological variables (Variable, VariableName) at a spatial resolution of 1x1km for Europe (nx = 13147, ny = 6071) for historical (ClimatePeriod) and future climate conditions. These variables are a subset of the so-called bioclimatic variables that are often part of global gridded datasets (e.g. WorldClim, CHELSA) that have been specifically developed for species distribution modelling and ecological applications. The climatological data correspond to 35-year (Startyear_Endyear = 1971_2005) and 30-year (Startyear_Endyear = 2041_2070) mean values representing respectively historical and future climate conditions. To account for the future climate conditions, three possible emission scenarios of greenhouse gases as defined by the Intergovernmental Panel on Climate Change (IPCC) are used (ClimatePeriod = rcp26, rcp45, rcp85). The complete set of variables (var[1-13]) for which historical and future climate data layers are produced are given below. The source data for the climate layers were assembled from the EURO-CORDEX archive (Kotlarski et al., 2014). More specifically, we have used the regional climate model simulations for Europe at a spatial resolution of 12.5x12.5km on which a three-step statistical downscaling approach has been applied: Processing (averaging, totals, …) of all available time series of the EURO-CORDEX model experiments (ClimatePeriod = evaluation, historical, rcp) for the climatological variables. Interpolation of the data layers from the 12.5x12.5km EURO-CORDEX grid to a 1x1km spatial CHELSA (Karger et al., 2017) reference grid (see files lat_1km.csv and lon_1km.csv). Calculate differences between the 1x1km-interpolated variables (Variable = only for var[1-9]) from the evaluation model experiments (or ClimatePeriod) and the corresponding reference bioclimatic CHELSA variables. In order to account for possible biases present in the EURO-CORDEX climate models, these differences (or biases) are then subtracted from the respective 1x1-km-interpolated variables for the historical and rcp model experiments (ClimatePeriod). The dimensions of the 1x1km grid (excl. the first row and column): y-dimension = number of columns = 6071 x-dimension = number of rows = 13147 The longitudes and latitudes of respectively the southwest and northeast corner of the grid are: longitude -44.592; latitude 21.991 (southwest corner) longitude 64.967; latitude 72.583 (northeast corner) The climatological variables are used as input data for the species distribution modelling of Invasive Alien Species for the Tracking Invasive Alien Species (TrIAS) project. Variables Variable (VariableName): Unit var1 (AnnualMeanTemperature): °C var2 (AnnualAmountPrecipitation): mm year-1 var3 (AnnualVariationPrecipitation): coefficient of variation var4 (AnnualVariationTemperature): stdev var5 (MaximumTemperatureWarmestMonth): °C var6 (MinimumTemperatureColdestMonth): °C var7 (TemperatureAnnualRange): °C var8 (PrecipitationWettestMonth): mm var9 (PrecipitationDriestMonth): mm var10 (30yrMeanAnnualCumulatedGDDAbove5degreesC): °C days var11 (AnnualMeanPotentialEvapotranspiration): mm day-1 var12 (AnnualMeanSolarRadiation): W m-2 var13 (AnnualVariationSolarRadiation): stdev Files varX_VariableName_ClimatePeriod_Startyear_Endyear.csv: climatological data layers for the 13 variables listed above lon_1km.csv: longitudes for the 1x1km grid lat_1km.csv: latitudes for the 1x1km grid {"references": ["Kotlarski et al. (2014). Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. https://doi.org/10.5194/gmd-7-1297-2014", "Karger et al. (2017). Climatologies at high resolution for the earth's land surface areas. https://doi.org/10.1038/sdata.2017.122"]} This work has been funded under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS). We also acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5.