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2,837 results for “climate data”
Climate data from the SINERR/GCE/UGAMI weather station at Marsh Landing on Sapelo Island, Georgia, from 01-Jan-2014 to 31-Dec-2014
Air temperature, relative humidity, barometric pressure, precipitation, photosynthetically-available and total solar radiation, and wind speed and direction were measured using an automated Campbell Scientific Instruments climate station installed at Marsh Landing on Sapelo Island, Georgia. Observations were logged at 15 minute intervals throughout the study period. The sensors were mounted on a 10m aluminum tower, with wind sensors mounted at the top, light sensors at approximately 5m, and other sensors at 2-3m to minimize interference from the surrounding landscape. This climate station was jointly operated by the Sapelo Island National Estuarine Research Reserve, the Georgia Coastal Ecosystems LTER Project, and University of Georgia Marine Institute.
Jornada Basin LTER Weather Station Daily summary climate data
Daily summary values of averages of readings of the following parameters are made which are based on data recorded on a Campbell CR10, CR10X, then CR1000 data logger: maximum, minimum, and average air temperature; maximum and minimum relative humidity; total precipitation; average wind speed; maximum wind speed; average wind direction; total incoming solar radiation; average soil temperature at 5cm and 20cm; mean dew temperature. From 1983 - 5 June 1991, readings on which daily averages are based were made at 12 second intervals. From 6 June 1991 to present, readings on which daily averages are based are made at 10 second intervals.
NOAA's National Climatic Data Center including daily precipitation and USFS RDA datasets
This dataset was originally established as a subset of relevant NOAA daily precipitation data. This has been replaced with links to NOAA station websites which contain this data, please visit these links in the dataset file here. Previously, daily precipitation for 5 stations in or near the LEF were compiled from the NOAA National Climate Data Center and posted here. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
NOAA's National Climatic Data Center including maximum temperature and USFS RDA datasets
This dataset was originally established as a subset of relevant NOAA monthly average maximum air temperature data. This has been replaced with links to NOAA station websites which contain this data, please visit these links in the dataset file here. Previously, maximum air temperature at two stations in or near the LEF were compiled from the NOAA National Climate Data Center and posted here. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
NOAA's National Climatic Data Center including minimum temperature and USFS RDA datasets
This dataset was originally established as a subset of relevant NOAA monthly average minimum air temperature data. This has been replaced with links to NOAA station websites which contain this data, please visit these links in the dataset file here. Previously, minimum air temperature at two stations in or near the LEF were compiled from the NOAA National Climate Data Center and posted here. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Climate reconstructions for the SMPDSv1 modern pollen data set
<p>The dataset contains estimates of three bioclimatic variables at modern pollen sites from the SMPDSv1 modern pollen data set (Harrison, 2019). The bioclimatic variables are mean temperature of the coldest month (MTCO), growing degree days above 0°C (GDD0), and an annual Moisture Index, defined as the ratio of annual precipitation to annual potential evapotranspiration (MI). Estimates of these bioclimatic variables were derived using geographically-weighted regression of gridded climate data in order to correct for elevation differences between each pollen site and the corresponding grid cell. The climatological data (mean monthly temperature, precipitation, and fractional sunshine hours) were derived from the CRU CL v2.0 gridded dataset of modern (1961-1990) surface climate at 10 arc minute resolution (~18 km) (New et al., 2002).Geographically- weighted regression (GWR) was carried out in ArcGIS (v10.3, ESRI, 2014). A fixed bandwidth kernel of 1.06 ° (~140km) was used in the GWR because this optimized model diagnostics and reduced spatial clustering of residuals relative to other bandwidths. The climate of each pollen site was then estimated based on its longitude, latitude, and elevation. MTCO was taken directly from the GWR regression. GDD0 were estimated from daily data using a mean-conserving interpolation of the monthly mean temperatures. MI was calculated for each pollen site using code modified from SPLASH v1.0 (Davis et al., 2017) based on daily values of precipitation, temperature and sunshine hours again obtained using a mean-conserving interpolation of the monthly values of each.</p>
CanESM5 data for CCCma COVID-19 climate scenarios
<p>This data is associated with the publication:<br> <br> <strong> Quantifying the Influence of COVID-19 Emission Reductions on Climate</strong></p> <p><br> John C. Fyfe, Viatcheslav V. Kharin, Neil Swart, Gregory M. Flato, Michael<br> Sigmond and Nathan Gillett<br> <br> Canadian Centre for Climate Modelling and Analysis, Environment and Climate<br> Change Canada, Victoria, British Columbia, V8W 2Y2, Canada.<br> <br> The data includes the monthly CO2 emissions used to drive CanESM5, and also the<br> monthly CO2 concentrations, and Global Mean Screen Temperatures resulting from<br> the model simulations. Using this data, Figure 1 of the paper can be completely<br> reproduced.</p> <p> </p> <p>The organisation of the data is described in the readme.txt file. All contents are</p> <p>collected into a tar archive.<br> <br> </p>
JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.
<p>The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that <strong>(1)</strong> gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute <strong>(2)</strong> theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes</p> <p>We deliver (1) data collection and (2) numerical codes in the following files attached:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-GRL.pdf</strong> that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li><strong>JUMP-JetsInTheLab.zip</strong></li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter<strong> </strong> <ul> <li><strong>uvData-JupObs-istep-0-nstep-4-niz-1.nc</strong></li> <li><strong>StatisticalData-JupObs.nc</strong></li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li><strong>IPV-QGPV-Jupiter-Saturn.zip</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul> <p> </p> <p>The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver <strong>(1)</strong> velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and <strong>(2)</strong> guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.</p>
Data for: "Climatic drivers of (changes in) bat migration phenology at Bracken Cave (USA)"
<p>This dataset contains the spring and autumn migration phenology dataset used in Haest <em>et al.</em> (2020) to determine the drivers of migration phenology of Brazilian free-tailed bats at Bracken Cave (USA) over the period 1995-2017. The phenology dataset was derived from nightly colony population sizes estimated using weather radar data (Stepanian <em>et al.</em>, 2018). See the Materials and Methods section in Haest <em>et al.</em> (2020) for more details on the dataset. </p> <p>References:</p> <p>Haest, B., Stepanian, P. M., Wainwright, C. E., Liechti, F., & Bauer, S. (2021). Climatic drivers of (changes in) bat migration phenology at Bracken Cave (USA). <em>Global Change Biology</em>, 27(4), 768-780. <a href="https://doi.org/10.1111/gcb.15433">https://doi.org/10.1111/gcb.15433</a></p> <p>Stepanian, P. M., & Wainwright, C. E. (2018). Ongoing changes in migration phenology and winter residency at Bracken Bat Cave. <em>Global Change Biology</em>, <em>24</em>(7), 3266–3275. <a href="https://doi.org/10.1111/gcb.14051">https://doi.org/10.1111/gcb.14051</a></p> <p> </p>
Questionnaire data to research small-scale farmers' information sharing for adapting to climate change in Mozambique (2019-2020)
<p>Data collected from individual questionnaires with local communities of 4 districts of Mozambique in November 2019 and July 2020. It contains as well data from nine individual questionnaires to institutions (government and NGOs) working with local communities for their development.</p> <p>Data are replies from interviews containing open and closed questions about a) climate change adaptation options necessary for Mozambican small scale farmers, about b) the most used and preferred information sources of farmers, about c) the main barriers for a better exchange of information, and about d) proposals for improving it. The questionnaire can be consulted in Appendix A (in English and Portuguese). The open questions had the purpose to understand the causes and explanations about the themes presented. The closed questions followed a 0-5 likert scale approach, where 5 meant a very important factor and 0 non important one. This format was pursued for developing statistical analysis and comparison between the different types of participants. We used the same questions and format for interviewing farmers and stakeholders, although the questionnaire for farmers included also personal aspects like gender, age, and education.</p>
Data Visualization - Final Project - Global Climate Change
<p>This Project is part of the course work for Data visualization DATS 6401. In this project, I have created webpage to show data analysis on Global Climate Change. D3 & Google Visualization API is used for all visualization graphs in the webpage.</p>
Soil profile, climatic, physiographic, overstory and understory data in mixed and monospecific plots of Pinus sylvestris and Pinus pinaster in Spain
<p>This dataset provides valuable environmental information about a triplets’ essay of Scots pine and Maritime pine in Spain. The data characterizes the soil profile (physicochemical parameters of organic and mineral horizons), climate, physiography, understory and overstory.</p> <p>The essay, located in North-Central Spain, consists of eighteen forest plots divided in six triplets. Each triplet includes three circular plots of 15 m-radius located less than 1 km from each other: two monospecific plots dominated by <em>P. sylvestris</em> or <em>P. pinaster</em>, and one mixed plot of both species. In each plot, one pit up to 50 cm depth, one 15 m-radius overstory features inventory and ten understory 1x1 m inventories were carried out. Additionally, physiographic and climatic variables were collected per plot.</p> <p>The file contains information about the 218 environmental variables studied in the eighteen forest plots.</p> <p>Triplet: Triplet to which the plot belongs(1: Triplet 1; 2: Triplet 2; 3: Triplet 3; 4: Triplet 4; 5: Triplet 5; 6: Triplet 6).</p> <p>Stand_type: Type of stand (PS: monospecific stand of <em>Pinus sylvestris</em> L.; PP: monospecific stand of <em>Pinus pinaster</em> Ait.; MM: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait.).</p> <p>Plot: Plot identification (PS01: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 1; PS02: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 2; PS03: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 3; PS04: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 4; PS05: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 5; PS06: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 6; MM01: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait. of triplet 1; MM02: mixed stand of <em>Pinus sylvestris </em>L.and <em>Pinus pinaster</em> Ait. of triplet 2; MM03: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait. of triplet 3; MM04: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait. of triplet 4; MM05: mixed stand of <em>Pinus sylvestris </em>L.and <em>Pinus pinaster</em> Ait. of triplet 5; MM06: mixed stand of <em>Pinus sylvestris </em>L.and <em>Pinus pinaster Ait</em>. of triplet 6; PP01: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 1; PP02: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 2; PP03: monospecific stand of <em>Pinus pinaster </em>Ait. of triplet 3; PP04: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 4; PP05: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 5; PP06: monospecific stand of<em> Pinus pinaster </em>Ait. of triplet 6).</p> <p>Lat: Plot latitude in degrees.</p> <p>Long: Plot longitude in degrees.</p> <p>Province: Province to which the plot belongs (B: Province of Burgos; Sp: Province of Soria).</p> <p>Municipality: Municipality to which the plot belongs (M: Town of Mamolar; HP: Town of Hontoria del Pinar; N: Town of Navaleno; St: Town of Soria; CP: Town of Cabrejas del Pinar).</p> <p>Forest: Name of the forest where is located the plot (MB: Mata Blanca; MR: Mata Robledo; FP: Fuente del Pardo; PM: Pajar de la molinera; MP: Mojon Pardo; CM: Cueva de Matarubias).</p> <p>Alti: Plot elevation above sea level in m a.s.l.</p> <p>Slope: Slope (gradient) of the plot in percentage.</p> <p>Ori: Plot orientation in degrees.</p> <p>Clim: Climate classification according to Köppen classification (1936) (Cfb: Temperate without a dry season and temperate summer climate; Csb: Temperate with dry summer climate).</p> <p>XR: Accumulated rainfall in one year according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>JR: January rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ mm</p> <p>FR: February rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>MR: March rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>AR: April rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>MyR: May rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>JnR: June rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>JlR: July rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>AgR: August rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>SR: September rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>OR: October rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>NR: November rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>DR: December rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>XT: Anual mean temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>JT: January temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>FT: February temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>MT: March temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>AT: April temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>MyT: May temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>JnT: June temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>JlT: July temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>AgT: August temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>ST: September temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>OT: October temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>NT: November temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>DT: December temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>Par_mat: Soil parental material according to Spanish Geological Map on a 1M scale. (IGME , 2015) (SM: Sandstones and Marls).</p> <p>Geo_age: Geological age of plot according to Spanish Geological Map on a 1M scale. (IGME, 2015) (Mz: Mesozoic age).</p> <p>Soil: Soil type according to Soil-Survey-Staff (2014) (TpDx: Typic Dystroxerept; TpHx:: Typic Humixerept; AqHx:: Aquic humixerept)</p> <p>Litter_B: Total Leaf Litter Biomass in Mg/ha.</p> <p>FF_Th: Forest floor Thickness in cm.</p> <p>Fs: Percentage of Fresh to Total Leaf Litter in %.</p> <p>Fr: Percentage of Fragmented to Total Leaf Litter in %.</p> <p>Hm: Percentage of Humified to Total Leaf Litter in %.</p> <p>GH1: Fist genetic soil horizon according to Soil Survey-Staff (2014) (Ah: Mineral horizon with accumulation of organic matter. This horizon is formed at the soil surface or below an O horizon).</p> <p>GH2: Second genetic soil horizon according to Soil Survey-Staff (2014) (AB: Transition horizon between A and B. A is a mineral horizon formed at the surface or below an O horizon, B is a subsurface horizon in which the structure of the rock is obliterated; AC: Transition horizon between A and C. A is a mineral horizon formed at the surface or below an O horizon; C is a mineral horizon, excluding hard bedrock, that is little affected by pedogenetic processes; Bw: Mineral B horizon where the development of color or structure are its more important diagnostic characteristics).</p> <p>GH3: Third genetic soil horizon according to Soil Survey-Staff (2014) (Bw: Mineral B horizon where the development of color or structure are its more important diagnostic characteristics; C: Mineral horizon, excluding hard bedrock, that is little affected by pedogenetic processes; Cg: Mineral horizon in which a distinct pattern of mottling occurs that reflects alternating conditions of oxidation and reduction of sesquioxides, caused by seasonal surface waterlogging).</p> <p>Th_H1: Thickness of the first soil horizon in cm.</p> <p>Th_H2: Thickness of the second soil horizon in cm.</p> <p>Th_H3: Thickness of the third soil horizon in cm.</p> <p>moistCol_H1: Wet matrix color (Hue Value/Chroma) of the first soil horizon according to Munsell soil color chards (10YR2/1: black; 10YR2/2: very dark brown; 10YR3/1: very dark grey; 10YR3/2: very dark greyish brown; 10YR4/1: dark grey; 10YR6/3: pale brown).</p> <p>moistCol_H2: Wet matrix colour (Hue Value/Chroma) of the second soil horizon according to Munsell soil color chards (5YR5/8: yellowish red; 7.5YR4/6: strong brown; 10YR3/2: very dark greyish brown; 10YR4/1: dark grey; 10YR4/2: dark greyish brown; 10YR4/4: dark yellowish brown with chroma 4; 10YR4/6: dark yellowish brown with chroma 6; 10YR5/3: brown; 10YR5/4: yellowish brown with chroma 4; 10YR5/6: yellowish brown with chroma 6; 10YR5/8: yellowish brown with chroma 8; 10YR6/4: light yellowish brown; 10YR6/6: brownish yellow).</p> <p>moistCol_H3: Wet matrix colour (Hue Value/Chroma) of the third soil horizon according to Munsell soil color chards (5YR4/6: yellowish red; 10YR4/4: dark yellowish brown with chroma 4; 10YR4/6: dark yellowish brown with chroma 6; 10YR5/8: yellowish brown; 10YR6/1: grey).</p> <p>dryCol_H1:Dry matrix color (Hue Value/Chroma) of the first soil horizon according to Munsell soil color chards (10YR4/1: dark grey; 10YR4/2: dark greyish brown; 10YR5/1: grey with value 5; 10YR5/2: greyish brown; 10YR5/3: brown; 10YR6/1: grey with value 6; 10YR6/2: light yellowish brown; 10YR7/2: light grey).</p> <p>dryCol_H2: Dry matrix color (Hue Value/Chroma) of the second soil horizon according to Munsell soil color chards (7.5YR6/6: redish brown; 10YR4/1: dark grey; 10YR6/1: grey with value 6; 10YR6/2: light yellowish brown with chroma 2; 10YR6/3: pale brown; 10YR6/4: light yellowish brown with chroma 4; 10YR6/6: brownish yellow; 10YR7/3: very pale brown with value 7 and choma 3; 10YR7/4: very pale brown withvalue 7 and choma 4; 10YR8/4: very pale brown with value 8 and choma 4).</p> <p>dryCol_H3: Dry matrix color (Hue Value/Chroma) of the third soil horizon according to Munsell soil color chards (5YR5/6: yellowish red; 7.5YR5/6: strong brown; 10YR6/4: light yellowish brown; 10YR6/6: brownish yellow; 10YR7/4: very pale brown; 10YR8/1: white).</p> <p>Sand_H1: Percentage of sand of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Sand_H2: Percentage of sand of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Sand_H3: Percentage of sand of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Silt_H1: Percentage of silt of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Silt_H2: Percentage of silt of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Silt_H3: Percentage of silt of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Clay_H1: Percentage of clay of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Clay_H2: Percentage of clay of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Clay_H3: Percentage of clay of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Tex_H1: Textural class of the first soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam).</p> <p>Tex_H2: Textural class of the second soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam).</p> <p>Tex_H3: Textural class of the third soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam; CL: Clay loam; C: Clay).</p> <p>Stones_H1: Coarse soil material (> 2 mm) of the first soil horizon in % weight/weight.</p> <p>Stones_H2: Coarse soil material (> 2 mm) of the second soil horizon in % weight/weight.</p> <p>Stones_H3: Coarse soil material (> 2 mm) of the third soil horizon in % weight/weight.</p> <p>bD_H1: Bulk density of the first soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>bD_H2: Bulk density of the second soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>bD_H3: Bulk density of the third soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>pD_H1: Particle density of the first soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>pD_H2: Particle density of the second soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>pD_H3: Particle density of the third soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>Poro_H1: Porosity of the first soil horizon according to (Van-Reeuwijk 2002) in % vol/vol.</p> <p>Poro_H2: Porosity of the second soil horizon according to (Van-Reeuwijk 2002) in % vol/vol.</p> <p>Poro_H3: Porosity of the third soil horizon according to (Van-Reeuwijk 2002) in % vol/vol.</p> <p>pH_H1: pH (1:2.5 H2O) of the first soil horizon according to (Van-Reeuwijk 2002)</p> <p>pH_H2: pH (1:2.5 H2O) of the second soil horizon according to (Van-Reeuwijk 2002)</p> <p>pH_H3: pH (1:2.5 H2O) of the third soil horizon according to (Van-Reeuwijk 2002)</p> <p>EC_H1: Electrical conductivity of the first soil horizon according to (Van-Reeuwijk 2002) in dS/m.</p> <p>EC_H2: Electrical conductivity of the second soil horizon according to (Van-Reeuwijk 2002) in dS/m.</p> <p>EC_H3: Electrical conductivity of the third soil horizon according to (Van-Reeuwijk 2002) in dS/m.</p> <p>avP_H1: Available phosphorus of the first soil horizon according to Olsen and Sommers (1982) in mg/kg.</p> <p>avP_H2: Available phosphorus of the second soil horizon according to Olsen and Sommers (1982) in mg/kg.</p> <p>avP_H3: Available phosphorus of the third soil horizon according to Olsen and Sommers (1982) in mg/kg.</p> <p>avPstock_H1: Available phosphorus stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>avPstock_H2: Available phosphorus stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>avPstock_H3: Available phosphorus stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>avPstock_50: Available phosphorus stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TN_Fs: Total nitrogen of the fresh forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TN_Fg: Total nitrogen of the fragmented forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TN_Hm: Total nitrogen of the humified forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TN_H1: Total nitrogen of the first soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TN_H2: Total nitrogen of the second soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TN_H3: Total nitrogen of the third soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TNstock_H1: Total nitrogen stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TNstock_H2: Total nitrogen stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TNstock_H3: Total nitrogen stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TNstock_50: Total nitrogen stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOC_Fs: Total organic carbon of the fresh forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TOC_Fg: Total organic carbon of the fragmented forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TOC_Hm: Total organic carbon of the humified forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TOC_H1: Total organic carbon of the first soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TOC_H2: Total organic carbon of the second soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TOC_H3: Total organic carbon of the third soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TOCstock_H1:Total organic carbon stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOCstock_H2: Total organic carbon stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOCstock_H3: Total organic carbon stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOCstock_50: Total organic carbon stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>C/N_Fs: Ratio of total organic carbon to total nitrogen of the fresh forest floor </p> <p>C/N_Fg: Ratio of total organic carbon to total nitrogen of the fragmented forest floor </p> <p>C/N_Hm: Ratio of total organic carbon to total nitrogen of the humified forest floor </p> <p>C/N_H1: Ratio of total organic carbon to total nitrogen of the first soil horizon</p> <p>C/N_H2: Ratio of total organic carbon to total nitrogen of the second soil horizon</p> <p>C/N_H3: Ratio of total organic carbon to total nitrogen of the third soil horizon</p> <p>OxC_H1: Easily oxidizable carbon of the first soil horizon according to Walkley (1947) in mg/kg.</p> <p>OxC_H2: Easily oxidizable carbon of the second soil horizon according to Walkley (1947) in mg/kg.</p> <p>OxC_H3: Easily oxidizable carbon of the third soil horizon according to Walkley (1947) in mg/kg.</p> <p>OxCstock_H1: Easily oxidizable carbon stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>OxCstock_H2: Easily oxidizable carbon stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>OxCstock_H3: Easily oxidizable carbon stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>OxCstock_50: Easily oxidizable carbon stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>CEC_H1: Cation exchange capacity of the first soil horizon according to Mehlich (1953) in cmol<sub>+</sub>/kg.</p> <p>CEC_H2: Cation exchange capacity of the second soil horizon according to Mehlich (1953) in cmol<sub>+</sub>/kg.</p> <p>CEC_H3: Cation exchange capacity of the third soil horizon according to Mehlich (1953) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>_H1: Exchangeable sodium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>_H2: Exchangeable sodium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>_H3: Exchangeable sodium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>stock_H1: Exchangeable sodium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Na<sup>+</sup>stock_H2: Exchangeable sodium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Na<sup>+</sup>stock_H3: Exchangeable sodium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Na<sup>+</sup>stock_50: Exchangeable sodium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>_H1: Exchangeable potassium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>K<sup>+</sup>_H2: Exchangeable potassium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>K<sup>+</sup>_H3: Exchangeable potassium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>K<sup>+</sup>stock_H1: Exchangeable potassium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>stock_H2: Exchangeable potassium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>stock_H3: Exchangeable potassium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>stock_50: Exchangeable potassium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>_H1: Exchangeable calcium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Ca<sup>+2</sup>_H2: Exchangeable calcium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Ca<sup>+2</sup>_H3: Exchangeable calcium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Ca<sup>+2</sup>stock_H1: Exchangeable calcium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>stock_H2: Exchangeable calcium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>stock_H3: Exchangeable calcium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>stock_50: Exchangeable calcium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>_H1: Exchangeable magnesium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Mg<sup>+2</sup>_H2: Exchangeable magnesium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Mg<sup>+2</sup>_H3: Exchangeable magnesium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Mg<sup>+2</sup>stock_H1: Exchangeable magnesium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>stock_H2: Exchangeable magnesium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>stock_H3: Exchangeable magnesium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>stock_50: Exchangeable magnesium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SB_H1: Sum of bases of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>SB_H2: Sum of bases of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>SB_H3: Sum of bases of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>SBstock_H1: Sum of bases stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SBstock_H2: Sum of bases stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SBstock_H3: Sum of bases stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SBstock_50: Sum of bases stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>FC_H1: Field capacity of the first soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>FC_H2: Field capacity of the second soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>FC_H3: Field capacity of the third soil horizon according to Van-Reeuwijk (2002)) in %.</p> <p>PWP_H1: Permanent wilting point of the first soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>PWP_H2: Permanent wilting point of the second soil horizon according to Van-Reeuwijk (2002) in %. </p> <p>PWP_H3: Permanent wilting point of the third soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>AW_H1: Available water of the first soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>AW_H2: Available water of the second soil horizon according to MAPA (1994) in %.</p> <p>AW_H3: Available water of the third soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>WHC_H1: Water holding capacity of the first soil horizon according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>WHC_H2: Water holding capacity of the second soil horizon according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>WHC_H3: Water holding capacity of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>WHC_50: Water holding capacity of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>Pot_veg: Potential vegetation according to Rivas-Martínez (1987) (LfQp: <em>Luzulo forsteri-Querceto pyrenaicae </em>S.; FhQp: <em>Festuco heterophyllae-Querceto pyrenaicae</em> S.; Jht: <em>Junipereto hemisphaerico-thuriferae</em> S.).</p> <p>Cur_veg: Current vegetation according to WMS service of MAPAMA(<a href="http://wms.mapama.es/sig/Biodiversidad">http://wms.mapama.es/sig/Biodiversidad</a>) (ps: <em>Pinus sylvestris</em> L.; pp: <em>Pinus pinaster</em> Ait.; pi: <em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.).</p> <p>NT: Stems per hectare of both <em>Pinus </em>species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in trees/ha.</p> <p>NPs: Stems per hectare of <em>Pinus sylvestris</em> L. in trees/ha.</p> <p>NPp: Stems per hectare of <em>Pinus pinaster</em> Ait. in trees/ha.</p> <p>GT: Basal area per hectare of both <em>Pinus</em> species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in m<sup>2</sup>/ha.</p> <p>GPs: Basal area per hectare of <em>Pinus sylvestris</em> L. in m<sup>2</sup>/ha.</p> <p>GPp: Basal area per hectare of <em>Pinus pinaster</em> Ait. in m<sup>2</sup>/ha.</p> <p>%PS: Percentage of basal area of <em>Pinus sylvestris</em> L. from total basal area</p> <p>%PP: Percentage of basal area of <em>Pinus pinaster</em> Ait. from total basal area</p> <p>dgT: Quadratic mean diameter of both <em>Pinus </em>species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in cm.</p> <p>dgPs: Quadratic mean diameter of <em>Pinus sylvestris</em> L. in cm.</p> <p>dgPp: Quadratic mean diameter of <em>Pinus pinaster</em> Ait. in cm.</p> <p>HoT: Dominant height of both <em>Pinus</em> species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in cm.</p> <p>HoPs: Dominant height of <em>Pinus sylvestris</em> L. in m.</p> <p>HoPp: Dominant height of <em>Pinus pinaster</em> Ait. in m.</p> <p>AgePs: Normal age of <em>Pinus sylvestris</em> L. in years.</p> <p>AgePp: Normal age of <em>Pinus pinaster</em> Ait. In years.</p> <p>SIPs: Site index of <em>Pinus sylvestris</em> L. related at age 100 for total plot according to Rojo and Montero (1999)</p> <p>SIPp: Site index of <em>Pinus pinaster</em> Ait. related at age 100 for total plot according to Bravo-Oviedo et al. (2007)</p> <p>Litter_cov: Cover of leaf litter in %.</p> <p>Vasc: Cover of understory vascular plants in %.</p> <p>Bryo: Cover of understory bryophytes in %.</p> <p>Under_sp: More abundant specie of understory vegetation (Aica: <em>Aira caryophyllea</em> L.; Aruv: <em>Arctostaphylos uva-ursi</em> (L.) Spreng.; Cavu: <em>Calluna vulgaris</em> (L.) Hull; Erar: <em>Erica arborea </em>L.; Erau: <em>Erica australis</em> L.; Pipi: <em>Pinus pinaster</em> Aiton. (seedlings/saplings); Pisy: <em>Pinus sylvestris</em> L. (seedlings/saplings; Ptaq: <em>Pteridium aquilinum</em> (L.) Kuhn)</p> <p>Aqu: Understory cover of family Aquifoliaceae in %.</p> <p>Aste: Understory cover of family Asteraceae in %.</p> <p>Cari: Understory cover of family Cariophyllaceae in %.</p> <p>Cist: Understory cover of family Cistaceae in %.</p> <p>Cupr: Understory cover of family Cupresaceae in %.</p> <p>Eric: Understory cover of family Ericaceae in %.</p> <p>Faba: Understory cover of family Fabaceae in %.</p> <p>Faga: Understory cover of family Fagaceae in %.</p> <p>Junc: Understory cover of family Juncaceae in %.</p> <p>Lili: Understory cover of family Liliaceae in %.</p> <p>Pina: Understory cover of family Pinaceae in %.</p> <p>Poac: Understory cover of family Poaceae in %.</p> <p>Poli: Understory cover of family Poligalaceae in %.</p> <p>Rosa: Understory cover of family Rosaceae in %.</p> <p>Rubi: Understory cover of family Rubiaceae in %.</p> <p>Scro: Understory cover of family Scrofulariaceae in %.</p> <p>Viol: Understory cover of family Violaceae in %.</p> <p>Xant: Understory cover of family Xanthorrhoeaceae in %.</p>
C3-EURO4M-MEDARE Mediterranean historical climate data
<p>Historical surface climate data files and meta-data for stations in Mediterranean North Africa and Middle East areas (1852-2008)</p>
Extracted raw data from: Global dominance of lianas over trees is driven by forest disturbance, climate, and topography
<p>In a meta-analysis, we use an unprecedented dataset, representing 556 unique locations worldwide, distributed across 44 countries and six continents to show for the first time that lianas (woody vines) thrive relatively better than trees when forests are disturbed, temperature increase, precipitation decrease, and particularly in tropical lowlands. We demonstrate that liana dominance can persist for decades post-disturbance and hinder the recovery of disturbed forests, especially when climate favours lianas. With implications for the global carbon sink, our findings suggest that degraded tropical forests with environmental conditions favouring lianas should be the highest priority to consider for restoration management.</p>
Code and data to "Climate change contribution to the 2023 autumn temperature records in Vienna"
<p>The dataset consists of the code and data used for the preprint "Climate change contribution to the 2023 autumn temperature records in Vienna". </p> <p>It contains two objects:</p> <ul> <li>The station data of mean monthly temperature for Vienna Hohe-Warte from 1750 to 2023 (vienna_hohe-warte.csv), which also can be downloaded here: http://www.zamg.ac.at/histalp/dataset/station/csv.php. </li> <li>The code for modeling and producing the figures of the preprint (autumn_temperature.R).</li> </ul>
Supplementary data for "Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity"
<h3>This dataset is supplementary to the article "Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity".</h3> <h3>spectral_olr.nc</h3> <p>This file contains the spectral outgoing longwave radiation (OLR) calculated using the line-by-line radiative transfer model ARTS and the radiative-convective equilibrium model konrad. It contains spectral OLR for surface temperatures from 270K to 330K for different strengths of the water vapor continuum absorption.</p> <h3>opacity_emission_level.py</h3> <p>This file also contains the spectrally resolved optical depth and the emission level of outgoing longwave radiation for the considered absorption species (H2O lines, H2O continuum, H2O self continuum, H2O foreign continuum, CO2, N2, and O2).</p> <h3>continuum_reference_conditions.nc</h3> <p>This file contains the reference continuum absorption coefficients that were used to calculate the adjustment to the foreign continuum for the single-constraint experiment.</p> <h3>continuum_all_profiles.nc</h3> <p>This file contains the reference continuum absorption coefficients that were used to calculate the adjustment to the foreign continuum for the general-constraint experiment.</p> <h3>modified_continuum_input_files_single_constraint.zip and modified_continuum_input_files_general_constraint.zip</h3> <p>These files contain the modified continuum data files used for the implementation of the MT_CKD continuum model in the line-by-line model ARTS for the single-constraint and general-constraint experiments, respectively.</p> <h3>tau_column.nc and tau_profile.nc</h3> <p>These files contain separately for each absorption species the vertically integrated opacity spectra, and the opacity profiles at two selected wavenumbers.</p> <p> </p>
Supporting Data for "Impacts of Antarctic ice mass loss on New Zealand climate"
<p>Contains the model output necessary to reproduce the results of "Impacts of Antarctic ice mass loss on New Zealand climate" by Andrew G. Pauling, Inga J. Smith, Jeff K. Ridley, T. Martin, M. Thomas and D. P. Stevens. Submitted for publication to Geophysical Research Letters.</p> <p>Please use the "getdata.sh" script in the Github repository here: LINK to download and extract the data into the correct location for the notebooks to reproduce the results of the paper.</p>
Flood Hazard Maps and Associated Data for Case Study: Funding rules that promote equity in climate adaptation outcomes
<p>Inundation grids for multiple return periods and multiple scenarios. Please see the underlying study for more details about the methods. The data here can be reproduced following the code and instructions at this repository: https://github.com/CoRE-Lab-UCF/Pollack_et_al_2024/tree/main. Also available here: https://doi.org/10.5281/zenodo.14515896. </p>
Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"
<p>This dataset results from an experiment on young oil palm plants (<em>Elaeis guineensis</em>) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), <em>i.e.</em>, a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%).</p> <p>Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.</p>
Data set for the integrated Climate, Land, Energy and Water systems modelling exercise RCLEWs in OSeMOSYS
<p>This dataset refers to the modelling exercise (version01_210616RCLEWs). The dataset contains the OSeMOSYS code used to run the modelling exercise, the model input data, the scenarios model data files, and the results. The code for the results visualization is available at https://github.com/KTH-dESA/teaching-CLEWs_visualization.</p> <p>This is an update of version 01_210827 available at: https://doi.org/10.5281/zenodo.5293834</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.