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2,837 results for “climate data”
Data repository for "Spatio-temporal trends of Holocene peat carbon accumulation in China: climatic and human drivers"
<p>Dating results collected from peatlands in China are used to calculate the spatiotemporal trends of the Holocene peat accumulation rate (PAR) and net carbon balance (NCB), including all original dating, calculated intermediate results, and final composite results. This file includes a total of 14 tables (Supplementary Tables S1-S14).</p>
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
Supplementary data: Rain-on-snow events in mountainous catchments under climate change
<p>The file in this record represents supplementary data for the journal paper Hotovy, O., Nedelcev, O., Seibert, J., Jenicek, M. (2024): Rain-on-snow events in mountainous catchments under climate change submitted to Hydrology and Earth System Sciences.<br>The presented files contain daily simulations of the HBV rainfall-runoff model for 93 mountain catchments in Czechia, Germany and Switzerland. The model simulated different water balance components, such as runoff, base flow, snow water equivalent, evapotranspiration, and soil and groundwater storages for the study period 1980-2010 as well as hydrological projections assuming different increases in air temperature and precipitation.</p>
Soil, climatic, physiographic and stand data in Pinus sylvestris and Pinus halepensis plantations in Spain
<p>This dataset contains information about soil physical, chemical and biochemical, climatic, physiographic and stand parameters of 32 plots belonging to the Spanish National Forest Inventory (SNFI) located in <em>Pinus halepensis</em> Mill. plantations and 35 plots belonging to the Sustainable Forest Management Research Institute (iuFOR; University of Valladolid and INIA) located in <em>Pinus sylvestris </em>L. plantations in Spain.</p> <p>Parameters included in the dataset: <br> Plot: plot identification in the SNFI and iuFOR networks.<br> Species: species present in each plot (1: Pinus sylvestris; 2: Pinus halepensis)<br> Slope: gradient in the plot in percentage.<br> Altitude: elevation of the plot in meters above the sea level<br> Latitude and Longitude: geographical coordinates of the plots in degrees<br> Density: number of trees per hectare in the plot<br> Dg: quadratic mean diameter in centimetersç<br> Hm: mean height in meters of the trees in the plot<br> H0; dominant height in meters of the trees in the plot<br> BA: basal area of the plot in square meters per hectare<br> SI: site index; dominant height of the trees in the plot at the reference age (80 years for Pinus halepensis and 50 years for Pinus sylvestris stands) <br> SQ: the site quality class<br> Age: average age in years of the trees in the plot<br> AW: soil available water in percentage<br> CO: soil coarse particles in percentage<br> Porosity: soil porosity in percentage<br> CLAY: clay content in soil in percentage<br> SILTUS: silt content in soil following the USDA criteria in percentage<br> SILTIS: silt content in soil following the International criteria, in percentage<br> SANDUS: sand content in soil following the USDA criteria, in percentage<br> SANDIS: sand content in soil following the International criteria, in percentage<br> OHT: organic horizon thickness in the plot in centimeters<br> ([C/N]L): the total carbon to total nitrogen ratio in the litter fraction of the organic horizon<br> ([C/N]FH): the total carbon to total nitrogen ratio in the fragmented plus humified fractions of the organic horizon <br> L: amount of litter fraction in the organic horizon in tons per hectare<br> FH: amount of fragmented plus humified fraction in the organic horizon in tons per hectare. <br> pH: soil pH value <br> CEC: cation exchange capacity in soil in centimoles of charge per kilogram of soil (Bascomb, 1964)<br> EOC: amount of easily oxidizable C in soil in percentage (Walkley and Black, 1934)<br> AP: amount of available phosphorus in soil in miligrams per kilogram of soil extracted with anion exchange membranes and determined with colorimetry (Murphy and Riley, 1962)<br> TN: total N in soil in percentage<br> TOC/TN: total organic C to total N ratio in soil<br> Ca, Mg, Na, K: exchangeable calcium, magnesium, sodium and potassium in soil in centimoles of charge per kilogram of soil (Schollenberger and Simon, 1945)<br> WSP: water soluble phenols in soil in micrograms of TAE per gram of soil (Box, 1983)<br> Carbonates: amount of carbonates in soil in percentage (Bundy and Bremner, 1972)<br> React_carb: amount of reactive carbonates in soil in percentage (Bashour and Sayegh, 2007)<br> Gypsum: amount of gypsum in soil in centimoles of charge per kilogram of soil (Richards, 1954)<br> Cu, Fe, Mn, Zn: amount of copper, iron, manganese and zinc in miligrams per kilogram of soil (Lindsay and Norvell, 1978)<br> EA: soil exchangeable acidity in centimoles of charge per kilogram of soil (Bascomb, 1964)<br> Sat: base saturation of soil in percentage <br> AlA, FeA, MnA: amorphous aluminum, iron and manganese (AlA, FeA, MnA) in soil in centimoles of charge per kilogram of soil (Bascomb, 1968)<br> AlM, FeM, MnM: organically bound aluminum, iron and manganese in soil in centimoles of charge per kilogram of soil (Blakemore et al. 1987) <br> AlE: exchangeable aluminum in soil in centimoles of charge per kilogram of soil (Bertsch & Bloom, 1996)<br> AlI: inorganic aluminum in soil in centimoles of charge per kilogram of soil (Mc-Keague et al., 1971)<br> Cmic, Nmic, Pmic: amount of microbial biomass carbon, nitrogen and phosphorus in soil in milligrams per kilogram of soil (Vance et al. 1987)<br> Cmin: amount of mineralizable carbon in soil in milligrams per kilogram of soil (Isermeyer, 1952)<br> Cmin/TOC: mineralizable carbon to total organic carbon ratio <br> Cmic/TOC: microbial biomass carbon to total organic carbon ratio<br> qCO2: microbial metabolic quotient (Cmin/Cmic) in soil in grams per week and gram of soil<br> FDA: fluorescein diacetate hydrolysis reaction (Alef and Nannipieri, 1995) in milliunits per gram of dry soil (nanomoles of fluorescein diacetate produced per gram of soil and minute)<br> DHA: dehydrogenase activity (Casida et al., 1964) in milliunits per gram of dry soil (nanomoles of triphenyl formazan produced per gram of soil and minute)<br> AcPhos, AlkPhos: acid and alkaline phosphatase activity (Tabatabai and Bremner, 1969) in milliunits per gram of dry soil (nanomoles of p-nitrophenol produced per gram of soil and minute)<br> Urease: urease activity in soil (Hofmann, 1963) in milliunits per gram of dry soil (nanomoles of N per gram of soil and minute)<br> Catalase: catalase activity (Tabatabai and Beck, 1971) in milliunits per gram of dry soil (nanomoles of O<sub>2</sub> produced per gram of soil and minute)<br> MAT: mean annual temperature in degrees centigrade (Ninyerola et al., 2005)<br> MMWM: mean maximum temperature of the warmest month in degrees centigrade (Ninyerola et al., 2005)<br> MMCM: mean maximum temperature of the coldest month in degrees centigrade (Ninyerola et al., 2005)<br> MTWM: mean temperature of the warmest month in degrees centigrade (Ninyerola et al., 2005)<br> MTCM: mean temperature of the coldest month in degrees centigrade (Ninyerola et al., 2005)<br> TP: total precipitation in millimeters (Ninyerola et al., 2005)<br> PW, PSP, PSU, PA: winter, spring, summer and autumn precipitation in millimeters (Ninyerola et al., 2005)<br> PET, RET: potential and real evapotranspiration in millimetres (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> Deficit: mean annual hydric deficit in millimeters (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> Surplus: mean annual hydric surplus in millimetres (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> AHI: Annual Hydric Index (Thornthwaite, 1949)<br> Martonne: Martonne index (De-Martonne, 1926)<br> Lang: Lang index (Lang, 1919)</p> <p>Code -999.99 indicates missing values.</p>
Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)
<p><strong>Gridded historical climate </strong><strong>data over China, spanning 1851 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by 20th century reanalysis (20CRv2c, NOAA/ESRL PSD 20th Century Reanalysis version 2c, ensemble member 37).</strong></p> <p>This data has been un-rotated to true latitude longitude coordinates from its original rotate pole frame of reference. For more information on the PRECIS regional climate model, visit <a href="http://www.metoffice.gov.uk/precis">www.metoffice.gov.uk/precis</a>. Data near the boundaries should be used with caution due to model configuration aspects of regional climate modelling, and the interpolation method applied.</p> <p><strong>Domain</strong>: 17N to 58.84N, 73E to 135.7E</p> <p><strong>Countries covered</strong>: China, Nepal, Bhutan, Bangladesh, Taiwan, Mongolia, North Korea, South Korea, Kyrgzstan, and northern parts of India, Myanmar, Lao PDR & Vietnam.</p> <p><strong>Variables</strong>: pr (mean precipitation flux), tm (mean surface temperature), tn (minimum surface temperature) & tx (maximum surface temperature)</p> <p><strong>Time averaging</strong>: monthly</p> <p> </p> <p><em>This data set supplements the equivalent downscaled ERA-Interim data set: <a href="https://zenodo.org/record/2600192#.XJj3uKD7RWE">Downscaled ERA-Interim gridded historical climate data over China (1980-2010)</a> doi: 10.5281/zenodo.2600192</em></p>
Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"
<p>This repository contains the data for the paper:</p> <p>"Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019."</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)</p> <p> </p>
Raw data for: "Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness"
<p><strong>Raw data for the article:</strong> Bestion, E, Soriano-Redondo, A, Cucherousset, J, Jacob, S, White, J, Zinger, L, Fourtune, L, Di Gesu, L, Teyssier, A, Cote, J. Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness. Proceedings of the Royal Society: B. 2019. 286:20192227. https://doi.org/10.1098/rspb.2019.2227</p> <p><strong>This data should be cited as</strong>: Bestion, E, Soriano-Redondo, A, Cucherousset, J, Jacob, S, White, J, Zinger, L, Fourtune, L, Di Gesu, L, Teyssier, A, Cote, J (2019). Raw data for: "Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness", Bestion et al 2019 Proceedings B. (Version 1). Zenodo. https://doi.org/10.5281/zenodo.3475402</p> <p><strong>This data is composed of</strong> one dataset with 21 columns and a README file</p> <p>Composition of the Bestion_2019_isotopy_dataset_for_zenodo.csv dataset</p> <p>- Individual: numerical index corresponding to each of the 327 individuals in the dataset<br> - Age: age class, J = juvenile (<1 year old), A = adult (1 and 2+ year old)<br> - Sex: F (female) or M (male)<br> - Climate: Present-day climate or Warm climate<br> - Enclosure: enclosure number (10 enclosures, 5 per climatic treatment)<br> - delta13C_september: stable isotope values for delta13C in september<br> - delta15N_september: stable isotope values for delta15N in september<br> - delta13C_september_corrected: stable isotope values for delta13C in september corrected for the stable isotope value of the three invertebrate prey categories<br> - delta15N_september_corrected: stable isotope values for delta15N in september corrected for the stable isotope value of the three invertebrate prey categories<br> - Prop_predator_eaten: proportion of predatory invertebrates eaten by each individual derived from the corrected stable isotope values<br> - Prop_phytophagous_eaten: proportion of phytophagous invertebrates eaten by each individual derived from the corrected stable isotope values<br> - Prop_detritivorous_eaten: proportion of detritivorous invertebrates eaten by each individual derived from the corrected stable isotope values<br> - Levins_diet_index: levins' dietary index corresponding to lizard diet specialization (with 3 = completely generalist and 1 = completely specialist lizard)<br> - Body_Size_september: lizard body size (snout-vent length in mm)<br> - Body_Mass_september: lizard body mass (in g)<br> - Body_Condition_september: lizard body condition (residuals of body mass by body size)<br> - Microbiota_shannon_index: shannon index representing gut microbial bacteria community diversity<br> - Survival_winter: survival during the winter (1 = survived, 0 = died)<br> - Abundance_predator_enclosure: abundance of predatory invertebrates within the enclosure<br> - Abundance_phytophagous_enclosure: abundance of phytophagous invertebrates within the enclosure<br> - Abundance_detitivorous_enclosure: abundance of detritivorous invertebrates within the enclosure</p> <p> </p> <p> </p>
Climate change impact and mitigation cost data - The economically optimal warming limit of the planet
<p>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:</p> <p>Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. <a href="https://doi.org/10.5194/esd-10-741-2019">https://doi.org/10.5194/esd-10-741-2019</a></p> <p>Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).</p> <p> </p> <p><strong>Climate change impact data</strong></p> <p>File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv</p> <p>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.</p> <p> </p> <p>File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv</p> <p>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).</p> <p> </p> <p>File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv</p> <p>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).</p> <p><br> 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, <a href="https://tntcat.iiasa.ac.at/SspDb/">https://tntcat.iiasa.ac.at/SspDb/</a>, last accessed 15Nov 2019).</p> <p> </p> <p><strong>Climate change mitigation cost data</strong></p> <p>The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2].</p> <p>File 4: REMIND_scenario_results_economic_data.csv</p> <p>File 5: REMIND_scenarios_climate_data.csv</p> <p>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.</p> <p>In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios.</p> <p>The first dimension specifies the climate policy regime (delayed action, baseline scenarios):</p> <p>1xx: climate action from 2010<br> 5xx: climate action from 2015<br> 2xx climate action from 2020 (used in this study)<br> 3xx climate action from 2030<br> 4x1 weak policy baseline (before Paris agreement)</p> <p>The second dimension specifies the technology portfolio and assumptions:</p> <p>x1x Full technology portfolio (used in this study)<br> x2x noCCS: unavailability of CCS<br> x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed<br> x4x NucPO: phase out of investments into nuclear energy<br> x5x Limited SW: penetration of solar and wind power limited<br> x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases)<br> x6x noBECCS: unavailability of CCS in combination with bioenergy</p> <p>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.).</p> <p>xx1 0$/tCO2 (baseline)<br> xx2 10$/tCO2<br> xx3 30$/tCO2<br> xx4 50$/tCO2 <br> xx5 100$/tCO2<br> xx6 200$/tCO2<br> xx7 500$/tCO2<br> xx8 40$/tCO2<br> xx9 20$/tCO2<br> xx0 5$/tCO2</p> <p>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).</p> <p> </p> <p>[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.</p> <p>[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.</p>
Coral growth data for the research article "Reconstruction of long-term sub-lethal effects of warming on a temperate coral in a climate change hotspot" in Journal of Animal Ecology
<p>This repository contains the coral growth data files used to generate the results for the following article:</p> <p> </p> <p>MJ. Vergotti, JP. D’Olivo, T. Brachert, P. Capdevila, J. Garrabou, C. Linares, P. Spreter, DK. Kersting (2024) Reconstruction of long-term sub-lethal effects of warming on a temperate coral in a climate change hot-spot. <em>Journal of Animal Ecology</em>. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.14225</p> <p> </p> <p><strong>Abstract: </strong>The impact of warming on zooxanthellate corals is widespread, from tropical to temperate seas, with their associated mortalities causing global concern. The temperate coral <em>Cladocora caespitosa</em> is the only zooxanthellate coral with reef-building capacity in the Mediterranean Sea, a climate change hotspot with warming rates triple the global average. Over the past two decades, <em>C. caespitosa</em> populations have suffered severe mortality events associated with marine heatwaves (MHWs). However, with monitoring efforts beginning, at best, in the 2000s, the occurrence of MHWs before to that period, as well as the sub-lethal effects of these events remain poorly understood. Here we use sclerochronology to reconstruct the histories of past stress events and long-term sub-lethal effects on <em>C. caespitosa</em> in three locations within the NW Mediterranean Sea, each with different environmental conditions. Skeletal extension, density and calcification rates were compared to the <em>in situ</em> seawater temperature of each site to assess their relationship. Additionally, we assessed the occurrence of skeletal growth anomalies to reconstruct stress events between 1991 and 2021, a period that encompasses the onset and evolution of warming-related mass mortality events in the NW Mediterranean Sea. Our results reveal a positive association between calcification and temperature, following a latitudinal temperature gradient. However, the evolution of the likelihood distribution of growth rates in the warmest site (Columbretes Islands) since the 1990s indicates a decrease in linear extension and calcification rates during the most recent years. With the increase in the frequency of MHWs and growth anomalies during the last decade, this decline suggests a recurrence in physiological stress events. These results unravel information on the long-term impacts of warming on coral growth and highlight the potential of applying sclerochronology to reconstruct sub-lethal effects of warming using <em>C. caespitosa</em>. </p> <p> </p> <p> </p> <p><strong>Funding</strong>: This research is supported by the Horizon 2020 program of research and innovation of the European Union under the MaCoBioS grant agreement, by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project no. 401447620) and by the Spanish Ministry of Science, Innovation and Universities under the project UndResCoral (project no. PID2022-137539OA-C22). D.K.K. was supported by a Ramon y Cajal postdoctoral grant funded by the Ministry of Science and Innovation (PEICTI 2021–2023; grant no. RYC2021-033576-I). C.L. acknowledges the support by ICREA Academia. J.G. acknowledges the grant “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S) funded by AEI 10.13039/501100011033.</p>
Data from: Global climate disruption and regional climate shelters after the Toba supereruption
<p>CESM1.3 simulations for Toba eruption scenarios. Run numbers correspond to scenarios listed in Appendix 1, Table S1 of:</p> <p><strong>Black, Lamarque, Marsh, Schmidt, and Bardeen. Global climate disruption and regional climate shelters after the Toba supereruption. PNAS. DOI: 10.1073/pnas.2013046118</strong></p> <p> </p>
Supplementary Data for "Climate change may induce connectivity loss and mountaintop extinction in Central American forests"
<p>Supplementary data underlying the main figures presented in the publication "<strong>Climate change may induce connectivity loss and mountaintop extinction in Central American forests</strong>"</p>
Wildfire and climate teleconnection data in the Western Mediterranean Basin
<p>This dataset contains the data used to conduct all analyses of the manuscript entitled "<strong>Spatio-temporal domains of wildfire-prone teleconnection patterns in the Western Mediterranean Basin</strong>". Submitted to Geophysical Research Letters.</p> <p>File <em>fire_data.csv</em> contains monthly gridded data of total burned area (BA), number of fire ignitions (N) and the 95th percentile of fire size (S) at 0.5 degree spatial resolution. The spatial extent covers Portugal, Spain, Southern France, Corsica and Sardina. Original data sources have been acknowledged in the manuscript file.</p> <p>File <em>teleconnections.csv</em> contains monthly data of the the North Atlantic Oscillation (NAO), the East Atlantic (EA), the Atlantic Multidecadal Oscillation (AMO), the El Niño Southern Oscillation (ENSO), the Mediterranean Oscillation (MOI), the Pacific Decadal Oscillation (PDO), the Scandinavian pattern (SCAND) and the Western Mediterranean oscillation (WeMOi). Original data sources have been acknowledged in the manuscript file.</p>
Data and code for "Carbon neutrality should not be the end goal: Lessons for institutional climate action from U.S. higher education"
<p>Code and data for the paper "Carbon neutrality should not be the end goal: Lessons for institutional climate action from U.S. higher education"</p> <p>File descriptions:</p> <p>'HEI_analysis_OneEarth.Rmd' is the code with improved annotation and colorblind-friendly figures.</p> <p>All other data files are provided as excel and csv for convenience.</p> <p>'working_master_data' contains data from the Second Nature reporting platform on emissions by category for each institution analyzed in the paper (measured in metric tons). All adjustments necessary to fill in the data gaps in this file are documented at the beginning of 'HEI_analysis'.</p> <p>'offsets' contains data on the type(s) of offsets purchased by each school in their carbon neutral year (measured in metric tons). This data was assembled from a variety of sources which are documented at the beginning of 'HEI_analysis'.</p> <p>'carbon_neutral_years' contains yearly counts of higher education neutrality goals that were reported to Second Nature as of November 2020.</p>
Supporting data: "RECEIPT D7.3: Future climate scenarios: sea level rise and sea ice extent"
<p>This is the supporting data for deliverable D7.3 from work package 7 ( Sea level rise, infrastructure and coastal flooding ) of the RECEIPT H2020 project (No 820712).</p> <p>Deliverable 7.3 describes the development of future SLR (sea level rise) and sea ice extent scenarios. Each SLR contributor (e.g. thermal expansion, instability of Antarctic and Greenland ice sheets, melting glaciers, ocean circulation and land water storage) are included in the assessment (KNMI, Task 7.4). Sea ice extent is derived from CMIP5/6.</p> <p>This dataset is composed of three compressed files:</p> <p>cmip5_zos_zostoga_v2.zip and cmip6_zos_zostoga_v2.zip: Netcdf files of ocean thermal expansion and ocean dynamics computed from zos and zostoga data from the ESGF nodes.</p> <p>data_RECEIPT_D73.zip: Netcdf files of three sea level scenarios. Data is provided globally but scenarios are designed for the European coast.</p>
Data for the publication "Addressing complexity in global aerosol climate model cloud microphysics"
<p>This repository contains the data for the paper:</p> <p>Authors: Ulrike Proske, Sylvaine Ferrachat, and Ulrike Lohmann<br> Titel: Addressing complexity in global climate model cloud microphysics<br> Date: 2022</p> <p>Note that the scripts can be found in the accompanying package (https://doi.org/10.5281/zenodo.7375978).</p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Data for: Mercury contamination challenges the behavioral response of a keystone species to Arctic climate change
<p>Combined effects of multiple, climate change-associated stressors are of mounting concern, especially in Artic ecosystems. Elevated mercury (Hg) exposure in Arctic animals could affect behavioural responses to changes in foraging landscapes linked to climate change, generating interactive effects on behaviour and population resilience. We investigated this hypothesis in the little auk (<em>Alle alle</em>), a keystone Artic seabird. We compiled behavioural data using accelerometers, and quantified blood mercury and environmental conditions (sea surface temperature (SST), sea ice coverage (SIC)) across multiple years. These datasets contain the behavioral, blood Hg and environmental data (SST, SIC) used in our analyses. Details about the datasets are found in the accompanying word document.</p>
Italy, climate data analyst based on era5 land data
<p>These plots illustrate the results of a climatic analysis conducted in Italy using the ERA5 Land (Copernicus Climate Service), since 1950.</p>
Data from: Complex climate-mediated effects of urbanization on plant reproductive phenology and frost risk
<p>This dataset comprises crowdsourced data using digitized herbarium specimen images from two comprehensively digitized regional floras; the Consortium of Northeastern Herbaria (CNH; <a href="http://portal.neherbaria.org/portal/">http://portal.neherbaria.org/portal/</a>) and Southeast Regional Network of Expertise and Collections (SERNEC; <a href="http://sernecportal.org/portal/index.php">http://sernecportal.org/portal/index.php</a>) for 200 plant species in the eastern United States, and four reproductive phenophases (i.e., flowering, peak flowering, fruiting, and peak fruiting) extracted from the herbarium specimens with associated climate data from PRISM and human population density from US Census Bureau.</p>
Data: Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models
<p>These data accompany the publication "Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models". The data are organized as follows:</p> <p>- two files with time series (csv) of hourly near-surface climate and surface energy balance values for Neumayer station (ice shelf, East Antarctic ice sheet) and automatic weather station S5 (southwest Greenland ice sheet)</p> <p>- two files (nc) with monthly melt fields from the regional climate model RACMO2.3p2 forced by ERA5 over Greenland (0.05-degree resolution) and Antarctica (0.25-degree resolution)</p> <p>- two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) or the historical period (1950-2014)</p> <p>- two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) for the future emission scenario SSP5-8.5 (2015-2099)</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.