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5,097 results for “Mediterranean”
Mediterranean Cyclone tracks between 1979-2018 (40 years) from a high-resolution perspective using ECMWF ERA5 dataset
<p>The present dataset presents the trajectories of the 13,157 cyclones identified within the Mediterranean Region (MR) between 1979 and 2018 (40 years). These cyclone tracks were obtained using the new Cyclone Detection and Tracking Method (CDTM) described in Aragão e Porcù (2021) to take advantage of the recent availability of a high-resolution reanalysis dataset of ECMWF ERA5. The CDTM uses hourly data of Geopotential Height at 1000 hPa with a spatial resolution of 0.25°x0.25°, and the analysis' domain covers the area within 15°W to 48° E and 21° N to 54°N. Additionally, trying to eliminate artificial low-pressure cores, short-living thermal-lows or too weak cyclones as much as possible, the present study only considered cyclones lasting more than 24h.<br> The dataset presents hourly information for all cyclones from the cyclogenesis time to the cyclolysis time. Each record presents: [1] Cyclone ID (integer, 8 digits), [2] Cyclone centre longitude position (°E, real, 8 digits, 3 decimal digits), [3] Cyclone centre latitude position (°N, real, 8 digits, 3 decimal digits), [4] Year (integer, 4 digits), [5] Month (integer, 2 digits), [6] Day (integer, 2 digits), [7] Hour (integer, 2 digits), [9] Cyclone centre Geopotential Height at 1000 hPa (m, real, 9 digits, 3 decimal digits).<br> The analyses presented in Aragão e Porcù (2021) revealed that the proposed CDTM is capable to capture almost the totality of the observed cyclones, as well as describing its respective area of cyclogenesis, trajectories, and durations. More than an adaptation to a high-resolution dataset, the method brings as its primary contribution a suitable set of parameters to systematically identify and track the cyclonic activities in the Mediterranean, where cyclones do not have sizeable horizontal pressure gradients and present a shorter lifetime compared to open-ocean cyclones.</p> <p>Cite this article</p> <p>Aragão, L., Porcù, F. Cyclonic activity in the Mediterranean region from a high-resolution perspective using ECMWF ERA5 dataset. <em>Clim Dyn</em> (2021). https://doi.org/10.1007/s00382-021-05963-x</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) October 2023 - May 2024
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from October 2023 up to May 2024 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: TIME in UTC [yyyy-MM-ddThh:mm:ssZ]; Latitude [deg]; Longitude [deg]; nominal depth [m]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature [°C]; Conductivity [mmS/cm]. Missing data are defined as NaN.</p>
The extrAIM dataset: A merged satellite-based daily precipitation dataset for the Mediterranean region (including an ensemble of 20 synthetic realisations)
<p><strong>extrAIM </strong>dataset is a <strong>new merged daily precipitation product</strong> (extraim_merged_data.nc) for the Mediterranean region with the following characteristics:</p> <ul> <li><strong>Dataset format:</strong> NetCDF</li> <li><strong>Spatial resolution:</strong> 25 x 25 km</li> <li><strong>Temporal resolution:</strong> 1 day</li> <li><strong>Spatial coverage:</strong> Longitude: from -6.25 to 38.25, Latitude: 27.75 to 49</li> <li><strong>Temporal coverage: </strong>01-01-2007 to 30-09-2021</li> <li><strong>Merging approach: </strong>Two-step merging (classification and regression) <ul> <li><strong>Algorithm: </strong>Random Forest for both classification and regression</li> <li><strong>Training strategy:</strong> Full training strategy</li> </ul> </li> <li><strong>Merged precipitation products: </strong>SM2Rain-ASCAT and GPM Late Run</li> <li><strong>Reference precipitation product:</strong> EMO5</li> <li><strong>Static covariates: </strong>Longitude, Latitude and Elevation, in both classification and regression step <ul> <li><strong>Classification step:</strong> probability dry and probability dry of the 5 neighboring points around the target locations</li> <li><strong>Regression step:</strong> mean, standard deviation and skewness of daily precipitation, of the entire series and non-zero amounts, as well as mean precipitation of the 5 neighboring points around the target locations</li> </ul> </li> </ul> <p>In addition, an <strong>ensemble of 20 synthetic realizations</strong> (equiprobable and bias-adjusted) of the merged dataset is provided (files named: “extraim_realisation_XX.nc”). The synthetic realisations were produced using the extrAIM’s uncertainty-quantification approach and the associated conditional sampling method.</p>
Majadas de Tietar: Ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean tree-grass ecosystem
<p>This dataset contains a subset of measurements collected at the experimental site Majadas de Tietar. We collected ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean Savanna using the eddy covariance technique and a series of meteorological sensors for the time period December 2015 - February 2018. The dataset is used for the development of a series of R packages including 'bigleaf' (Knauer et al., 2018).</p> <p>The experimental site is collected in Majadas de Tietar (Casals et al., 2009) located in western Spain (39°56′25″N 5°46′29″W). The ecosystem is a typical “Iberic Dehesa”, which is characterized by an herbaceous stratum of native pasture and sparse trees, for the majority (~98%) Quercus ilex. The tree density is about 20–25 trees/ha, the fractional cover of trees is about 20%, mean DBH of 46 cm, and a canopy height of about 8 m. (El-Madany et al., 2018). The herbaceous layer is composed of native annual species of the three main functional plant forms (grasses, forbs and legumes), whose fractional cover varies seasonally and is characterized by important inter-annual variations in the seasonal dynamics related to the onset of the dry period.</p> <p>Fluxes were measured with the eddy covariance technique with two different systems, one at ecosystem scale to characterize the fluxes of the whole ecosystem (15.5 m above ground), and one at 1.65 m above ground in an open space to measure the fluxes of the well-established understory grass layer.</p> <p>The description of the set-up, equipment and processing used to calculate ecosystem scale fluxes are described in El-Madany et al., (2018), while for the understory tower can be found in Perez-Priego et al., (2017).</p> <p>The dataset is composed of two files: 'ESLMa_MainTower', which is the ecosystem eddy covariance system, and 'ESLMa_SubCanopy', which is the understory eddy covariance system. The dataset contains half-hourly, processed eddy covariance of the ecosystem and understory tower, as well as the main biometeorological data used in the big-leaf package (net radiation, soil heat fluxes, horizontal wind velocity, atmospheric pressure, precipitation, air temperature). All the processing was conducted with EddyPro software (version 5.2.0, LI-COR Biosciences Inc., Lincoln, NE, USA) and the ustar filtering, gap-filling and partitioning with the R package REddyProc (Wutzler et al., 2018). The variables and the units are described in the Readme.txt file released with the dataset.</p> <p><strong>References</strong></p> <p>Casals, P. et al., 2009. Soil CO2 efflux and extractable organic carbon fractions under simulated precipitation events in a Mediterranean Dehesa. Soil Biol. Biochem. 41, 1915–1922. <a href="https://doi.org/10.1016/j.soilbio.2009.06.015">https://doi.org/10.1016/j.soilbio.2009.06.015</a>.</p> <p>El-Madany, T.S.,et al., 2018. Drivers of spatio-temporal variability of carbon dioxide and energy fluxes in a Mediterranean savanna ecosystem 21. <a href="https://doi.org/10.1016/j.agrformet.2018.07.010">https://doi.org/10.1016/j.agrformet.2018.07.010</a></p> <p>Knauer, J., et al., 2018. bigleaf - An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. PLOS ONE, doi:10.1371/journal.pone.0201114</p> <p>Perez-Priego O, et al., 2017. Evaluation of eddy covariance latent heat fluxes with independent lysimeter and sapflow estimates in a Mediterranean savannah ecosystem. Agricultural and Forest Meteorology. 236: 87-99. doi: 10.1016/j.agrformet.2017.01.009.</p> <p>Wutzler, T., et al., 2018. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences Discuss., p. 1-39.</p> <p> </p>
2005-2099 High resolution bioclimatic variables for the surface and bottom of the Mediterranean Sea.
<p><em><span>This dataset provides annual statistical descriptors (mean, minimum, maximum, range and standard deviation) of key biogeochemical and physical variables for the Mediterranean Sea. It covers the period 2005-2099 under the RCP8.5 scenario, with a spatial resolution of 1/24 degree (~4km²). Variables include temperature, salinity, pH, water velocity, nutrients (NO3, PO4, NH4), dissolved inorganic carbon, oxygen, and net primary production. Data are available for both surface and at bathymetry level. The original projections were generated using OGSTM-BFM and MFS16 models at daily time and 1/16 degree grid resolution. We downscaled these to 1/24 degree and applied Quantile Delta Mapping bias correction using CMEMS reanalysis products for 2005-2020. The dataset is provided in a user-friendly format, making it accessible for various ecological and environmental modelling applications.</span></em></p>
Last interglacial sea-level index points in the Western Mediterranean
<p>Sea-level index points, dated samples and correlated metadata for the Western Mediterranean. This dataset was assembled in the framework of the World Atlas of Last Interglacial Shorelines. Field descriptors are available at: https://walis-help.readthedocs.io/en/latest/</p> <p>See readme files for updates with respect to version 2.0</p>
Surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios.
<p>Surface maps and basin mean/total of annual mean surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios and for underlying the baseline projection (RCP4.5).</p> <p>Details on simulations and alkalinisation strategies are given in the reference article below.</p> <p> </p> <p>Reference:</p> <p>Butenschön, M., Lovato, T., Masina, S., Caserini, S., Grosso, M., 2021. Alkalinization Scenarios in the Mediterranean Sea for Efficient Removal of Atmospheric CO2 and the Mitigation of Ocean Acidification. Front. Clim. 3. <a href="https://doi.org/10.3389/fclim.2021.614537">https://doi.org/10.3389/fclim.2021.614537</a></p>
C3-EURO4M-MEDARE Mediterranean historical climate data - v.2
<p>Historical surface climate data files and meta-data for stations in Mediterranean North Africa and Middle East areas (1852-2008).</p>
Effects of intercropping on the herbage production of a binary grass-legume mixture (Hedisarum coronarium L. and Lolium multiflorum Lam.) under artificial shade in Mediterranean rainfed conditions
<p>This dataset refers to the experimental raw data (csv version) collected within the trial reported in the concerned article on the following parameters:</p> <p>1. crop aboveground biomass, splitted per field, mowing, crop, treatment and replicate (crop aboveground biomass.csv)</p> <p>2. cumulated crop aboveground biomass, splitted per field, year, crop, treatment and replicate (cumulated crop aboveground biomass_year.csv)</p> <p>3. cumulated crop aboveground biomass for the two years of the growing cycle, splitted per field, crop, treatment and replicate (cumulated crop aboveground biomass_2years.csv)</p> <p>4. partial and total RYT splitted per year, field and treatment (RYT_year)</p> <p>5. partial and total RYT for the two years of the growing cycle, splitted per field and treatment (RYT_2years)</p> <p> </p> <p><br> </p>
Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"
<p>This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea".</p> <p>To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory:</p> <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> <p>Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.</p>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data
<p>Live fuel moisture content (LFMC), defined as the mass of water in the foliage and small twigs relative to its total dry mass, is a key factor affecting fire potential and determining wildfire danger and activity. Fuel moisture is directly related to the amount of energy needed to evaporate water before ignition. Consequently, high moisture values reduce, or even inhibit, ignitability and subsequent fire spread.</p> <p>To cover the absence of a specific model to estimate LFMC for the Mediterranean Basin at the sub-continental scale, we built an empirical model based on Random Forests (LFMC<sub>RF</sub>) and combining MODIS spectral bands, vegetation indices, land surface temperature, and the day of year as predictors. The details on the modeling and validation methods, and the accuracy of the estimates are in the related publication <strong><a href="https://doi.org/10.3390/rs14133162">Cunill Camprubí et al., 2022</a></strong>.</p> <p>This dataset contains a collection of weekly LFMC maps from February 2000 to December 2021. The maps cover the Mediterranean and part of the Temperate biomes of the Mediterranean Basin. File <em>mapping_area_LFMC-RF_W-1.0.png</em> shows the target mapping areas.</p> <p>Metadata:</p> <ul> <li>Spectral Information: MODIS MCD43A4 C.6</li> <li>Land Surface Temperature: MODIS MOD11A2 C.6</li> <li>Land Cover Mask: MODIS MCD12Q1 C.6</li> <li>Coordinate Reference System: Native MODIS Sinusoidal</li> <li>Temporal Resolution: Weekly (W)</li> <li>Spatial Resolution: ~500 m</li> <li>File Format: NetCDF v.4</li> <li>Scale Factor: 0.01</li> </ul> <p>Fundings:</p> <p>The study was funded by the MICINN (RTI2018-094691-B-C31), European Union’s Horizon 2020-Research and Innovation Framework Programme under grant agreement no. 101003890 project FirEUrisk, the National Natural Science Foundation of China (U20A20179, 31850410483), and the talent proposals in Sichuan Province (2020JDRC0065) from Southwest University of Science and Technology (18ZX7131).</p>
Reconstruction of Mediterranean sea-level changes and contributions for 1960-2018
<p><strong>Data supporting the paper:</strong></p> <p><strong>Calafat, F. M., Frederikse, T., and Horsburgh, K. (2022). The Sources of Sea-Level Changes in the Mediterranean Sea since 1960, Journal of Geophysical Research Oceans, under review.</strong></p> <p>Please cite the paper above when using this data set.</p> <p>This new version of the data set has been published to support the paper above and includes more data than the previous version as well as several improvements and refinements. Version 2.3 has been created to include regional estimates of rates due to the inverse barometer effect.</p> <p><em>Data description:</em></p> <ul> <li><strong>Bayesian_estimates_Mediterranean_sea_level.nc:</strong> this file contains gridded estimates of relative sea-level changes and their instantaneous rates for 1960-2018 in the Mediterranean Sea, separated into the individual contributions of: <ol> <li>Sterodynamic changes (i.e., ocean dynamics and thermal expansion).</li> <li>Contemporary GRD (i.e., changes in Earth gravity, Earth rotation, and solid-earth deformation due to land-mass changes).</li> <li>GIA (i.e., glacial isostatic adjustment).</li> <li>Short-term variability (interannual to decadal). </li> <li>Inverse barometer effect.</li> </ol> </li> <li><strong>data_input.mat:</strong> this file contains all of the data needed to run the Bayesian hierarchical model. This includes the observational data from tide gauges and satellite altimetry as well as the ensemble-mean and ensemble covariance matrices for the sea-level fingerprints associated with contemporary GRD effects and GIA.</li> </ul> <p>The Bayesian estimates have been obtained using a spatiotemporal Bayesian hierarchical model (see paper). This work has been carried out within the framework of the EuroSea project funded by European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626.</p>
Data from: Carbon and Water Balances in a Watermelon Crop Mulched with Biodegradable Films in Mediterranean Conditions at Extended Growth Season Scale
<p><span>Abstract</span></p> <p><span>The uploaded data are relative to the investigation around (i) the carbon source/sink nature and, further, (ii) the water and carbon balances, of a drip-irrigated and mulched watermelon. The crop was cultivated under the semi-arid climate of the Apulia region, in south Italy.</span></p> <p><span>The used mulching films were biodegradable as indicate by the producer; plants and some non-standard fruits were left on the soil as green manure after harvesting, thus, the experiment spanned from planting to the subsequent crop (6 months of continuous measurement from June to November 2023). </span></p> <p><span>The results detailed in the original publication indicate that mulching films contribute to carbon sequestration in the soil (+19.3 gC m<sup>−2</sup>). However, this mulched watermelon represents a net carbon source, with a net biome exchange, as loss from ecosystems, equal to +230 gC m<sup>−2</sup>. This is primarily due to the substantial amount of carbon exported through marketable fruits. Fixed water scheduling led to water waste through deep percolation (approximately 1/6 of the water supplied), which also contributed to the loss of organic carbon via leaching (−4.3 gC m<sup>−2</sup>). </span></p> <p><span> </span></p> <p><span>Methods</span></p> <p><span>Site and crop</span></p> <p><span>The field site was at the CREA-AA Research Unit experimental farm located in southern Italy (Rutigliano–Bari, 41 01’ N, 17°01’ E, altitude 147 m a.s.l.)., characterized by a Mediterranean semi-arid climate (average annual rainfall of 535 mm). The soil is classified as Lithic Rhodoxeralf, with a clay texture, stable structure, shallow profile (0.6–1.1 m) and rapid drainage due to an underlying cracked limestone subsoil. The SOC content averages around 12.0 g kg<sup>−1</sup>. The field capacity and the permanent wilting point volumetric water contents are 0.36 and 0.21 m<sup>3</sup> m<sup>−3</sup>, respectively; with a bulk density of 1.15 Mg m<sup>−3</sup>, the available soil water ranges from 80 to 140 mm.</span></p> <p><span>The studied watermelon crop (seedless var. Lion king), followed a broccoli cabbage crop harvested in April and partially incorporated (0.81 kg m<sup>−2</sup> of fresh biomass in a soil layer depth of 0.30 m, corresponding to 0.69 kgH2O m<sup>−2</sup>) as green manure on 25 May 2023. Main tillage at medium depth ploughing (0.30 m) and seedbed preparation were performed between 25 and 30 May 2023; the biodegradable film mulch (model PC 100 d8, BASF, Italy, 1 m width) was applied on 1 June 2023. On the same day, driplines (2.1 Lh<sup>−1</sup> emitters, 0.60 m apart) and the main organic fertilization (Orga-Kem 6.11.8 + 11CaO, 300 kg ha<sup>−1</sup>) were also applied. The watermelon plants were transplanted on 9 June at a spacing of 2.70 m between rows and 1 m between plants, covering an area of about 4.0 ha, with a density of approximately 3200 plants ha<sup>−1</sup>. Every 6 rows, the inter-row distance was 5 m to facilitate machinery passage. The first irrigation was performed the day before planting. Crop management adhered to the usual treatments in the area including mechanical weed removal every 4 weeks, irrigation around three times per week to maintain optimal soil water conditions and monthly fertigation (ammonium sulphate 50 kg ha<sup>−1</sup>, magnesium nitrate 30 kg ha<sup>−1</sup>, calcium nitrate 60 kg ha<sup>−1</sup>, mycorrhizae 20 kg ha<sup>−1</sup>). The scalar harvest of marketable fruits occurred between 28 and 31 August 2023. After harvesting, on 25 September 2023, the fresh plant residues (0.6 kg m<sup>−2</sup> of fresh biomass, corresponding to 0.49 kgH2O m<sup>−2</sup>), unharvested fruits (4.0 kg m<sup>−2</sup> of fresh material, corresponding to 3.7 kgH2O m<sup>−2</sup>) and the mulching film were chopped by a tractor shredder and ploughed in two steps, on 2 and 13 October 2023, to a soil depth of 0.30 m. Measurements concluded at the end of November 2023, when tillage for the new winter crop commenced.</span></p> <p><span> </span></p> <p><span>Measurements of H<sub>2</sub>O and CO<sub>2</sub> fluxes; partitioning in evaporation, transpiration, photosynthesis and respiration</span></p> <p><span>The eddy covariance technique was employed to monitor water vapor (H<sub>2</sub>O) and carbon dioxide (CO<sub>2</sub>) fluxes. The equipment comprised a three-dimensional sonic anemometer (uSonic 3 Scientific, Metek GmbH, 25337 Elmshorn, Germany) and a fast response open-path infrared gas analyzer (LI-7500, Li-COR Inc., Lincoln, NE, USA). The three wind components, sonic temperature and atmospheric concentrations of CO<sub>2</sub> and H<sub>2</sub>O were continuously measured at 1.5 m above the crop canopy, with the sensor height adjusted to follow crop growth, reaching a maximum of 1.75 m. </span></p> <p><span>Data were recorded at a frequency of 10 Hz on a dedicated computer using the MeteoFlux software (Servizi Territorio, S.n.c., Cinisello Balsamo, Italy) and were stored on an hourly scale. Post-processing and computation of hourly fluxes of H<sub>2</sub>O (mmol m<sup>−2</sup> s<sup>−1</sup>) and CO<sub>2</sub> (</span>μ<span>mol m<sup>−2</sup> s<sup>−1</sup>) were conducted using EddyPro software, v7.0.9 (</span><a href="http://www.licor.com/eddypro"><span>http://www.licor.com/eddypro</span></a><span>), applying 60 min block averaging, double coordinate rotation, the statistical test, the maximum cross-covariance method, and the WPL density correction.</span></p> <p><span>H<sub>2</sub>O and CO<sub>2</sub> fluxes were partitioned into transpiration, evaporation, photosynthesis and respiration, respectively, using the flux variance similarity method. This method utilizes the Monin–Obukhov similarity theory to separate stomatal (photosynthesis, Fp, and transpiration, Ft) from non-stomatal (respiration, Fr, and evaporation, Fe) processes (Palatella et al., 2014). the H<sub>2</sub>O and CO<sub>2</sub> EC fluxes were partitioned using an adaptation of the code in Phyton provided by (Skaggs et al., 2018) and downloaded from <span> </span></span><a href="https://github.com/usda-arsussl/fluxpart"><span>https://github.com/usda-arsussl/fluxpart</span></a><span> (V0.2.10).</span></p>
Impact Areas and Dynamical Features associated with Mediterranean Cyclones (1980-2019)
<p>The dataset includes NetCDF files of Impact Areas and Dynamical Features associated with Mediterranean Cyclones (henceforth MedCyclones).</p> <p>MedCyclone tracks correspond to confidence-level 5 tracks from Flaounas et al. (2023), <a href="https://doi.org/10.5194/wcd-4-639-2023">https://doi.org/10.5194/wcd-4-639-2023</a>.</p> <p>Temporal frequency: 6h (00, 06, 12, 18 UTC)<br>Years: 1980 – 2019<br>Spatial resolution: 0.5 deg<br>Grid extension: 0-70N, 40W-65E</p> <p> </p> <h2>Dynamical Features</h2> <p>Files "dynfeats_bool_rmax2000_YYYY.nc" include the following list of variables, describing connected boolean objects:</p> <ul> <li><strong>r_500</strong>, <strong>r_1000</strong>: central areas of fixed 500 or 1000 km radius around MedCyclone centres;</li> <li><strong>WCB</strong>: warm conveyor belts related to MedCyclones (i.e., overlapping with r_500 in at least one grid point). Each <strong>WCB</strong> is eventually separated into inflow (<strong>WCBin</strong>, up to 800 hPa) and ascent (<strong>WCBout</strong>, between 800 and 400 hPa) regions. Ref. at <a href="https://doi.org/10.1175/JCLI-D-12-00720.1">https://doi.org/10.1175/JCLI-D-12-00720.1</a>, <a href="https://doi.org/10.5194/wcd-5-537-2024">https://doi.org/10.5194/wcd-5-537-2024</a>;</li> <li><strong>fronts</strong>: cold fronts related to MedCyclones (i.e., overlapping with r_500 in at least one grid point). Ref. at <a href="https://doi.org/10.5194/gmd-17-6137-2024">https://doi.org/10.5194/gmd-17-6137-2024</a>;</li> <li><strong>DI</strong>: dry instrusions related to MedCyclones (i.e., overlapping with r_1000 in at least one grid point). Ref. at <a href="https://doi.org/10.1175/JCLI-D-16-0782.1">https://doi.org/10.1175/JCLI-D-16-0782.1</a>;</li> <li><strong>r_1000_Nodynfeat</strong>: the central 1000 km area excluding regions of MedCyclone WCB, fronts and DI objects.</li> </ul> <p>The criteria for the identification of WCB, fronts and DI objects are described in Section 2.3 of Portal et al. (2024), <a href="https://doi.org/10.5194/wcd-5-1043-2024">https://doi.org/10.5194/wcd-5-1043-2024</a>.</p> <p>Additionally, we note that :<br>i. a weaker overlap constraint was used to associate DI objects to MedCyclones (r_1000 compared to r_500 for WCB and fronts objects) because of the relatively large distance of the DI airstream from the cyclone centre;<br>ii. in this dataset, all connected objects related to MedCyclones are cropped within a 2000 km area circle from the cyclone centre for two reasons. Firstly, the dynamical-feature related surface impacts usually weaken with the distance from the cyclone centre. Secondly, to cut connected objects composed by multiple overlapping features of the same kind - this often happens for fronts in summer because of their high detection density. Far from the cyclone centre, these objects are usually unrelated with the MedCyclone circulation.</p> <p> </p> <h2>Impact Areas</h2> <p>Files "IAs_bool_rmax2000_YYYY.nc" include boolean impact areas, combining a central area (r_1000 or r_500) and cyclone-related WCB, CF and DI objects. The three types of impact area are described in the following :</p> <ol> <li><strong>IA01</strong> is composed by a 1000 km radius circle around the cyclone centre (r_1000) extended by cyclone-related WCB, fronts and DI;</li> <li><strong>IA02</strong> is composed by a 500 km radius circle around the cyclone centre (r_500) extended by cyclone-related WCB, fronts and DI</li> <li><strong>IA03</strong> is composed by a 500 km radius circle around the cyclone centre (r_500) extended by cyclone-related WCB and fronts (DI is neglected).</li> </ol> <p>As discussed in Section 3.1 and Appendix A of Portal et al. (2024) (<a href="https://doi.org/10.5194/wcd-5-1043-2024">https://doi.org/10.5194/wcd-5-1043-2024</a>), IA01, composed by a central area of 1000 km, is adequate for intercepting long-range wind impacts associated with MedCyclones. IA02 and IA03, on the contrary, are better devised for detecting impacts expected at shorter distances from the cyclone centre, such as rainfall, thunderstorm and storm surges. In particular, IA03 neglects the DI region, which is normally of little interest for cyclone-related moist processes, involved in producing precipitation. Noetheless, DI remains relevant for the identification of strong cyclone-related winds.</p> <p> </p> <h3>Case Studies</h3> <p>A pdf file providing the visualisation and description of impact areas and dynamical features of all MedCyclones occurring in 1980 is available at the link <a href="https://boris.unibe.ch/192315/">https://boris.unibe.ch/192315/</a>. Note that in the examples the dynamical features are not cropped at 2000 km from the cyclone centres, as for the present dataset. </p> <p>Note that many of the "Annotations and Limitations" listed below derive from the attentive analysis of these study cases.</p> <h3>Annotations and limitations</h3> <ul> <li>In the case of more than one MedCyclone centre per timestep, the dasaset does not distinguish the impact areas / dynamical features associated with each centre.</li> <li>Because of the automated criteria for associating WCB, fronts and DI objects to MedCyclones, at times objects close to the centre but unrelated to the MedCyclone's circulation, are considered to be cyclone-related and included in the impact area.</li> <li>Elaborating on the point above, at times fronts responsible for Mediterranean cyclogenesis (and not produced by the cyclonic circulation itself) are included in the MedCyclone impact area.</li> <li>When computing statistics over a long time interval (e.g., climatology), the effects of erroneous associations of dynamical features to MedCyclone impact areas are attenuated by the aggregation of large quantity of data. </li> <li>Over a long time interval (e.g., climatology) the choice of a 1000 km fixed-radius impact area provides similar statistics to IA01, although in the first case it is not possible to isolate the role played by the different features composing the MedCyclones.</li> </ul>
Spectral library of vegetation from Mediterranean woodlands
<p>Site description:</p> <p>All reflectance measurements have been collected in Mediterranean oak woodland at <em>Herdade </em>da <em>Machoqueira do Grou</em>, located<em> </em>in Central Portugal (39° 08′ 18.9″ N, 9° 19′ 56.22″ W, 165-m height). The site is characterized by a Mediterranean climate with mild winters and hot dry summers. The average annual precipitation recorded at the climate station of Santarém (39° 12′ N, 8° 44′ W) for the period 1981–2010 was 652 mm, and mean daily temperature was 17°C (<a href="http://www.ipma.pt/pt/oclima/normais.clima/">www.ipma.pt/pt/oclima/normais.clima/</a>). Detailed meteorological measurements of radiation, temperature, and air humidity are also publicly available (Cerasoli et al., 2020). The soil is a cambisol (FAO) with 81% sand, 5% clay, and 14% silt. The tree layer is represented exclusively by cork oak trees (<em>Quercus suber</em> L.) with a tree density of 177 tree ha<sup>-1</sup> and leaf area index (LAI) of 1.5. The mean total tree height and height below the canopy are 7.9 and 3.1m respectively (Cerasoli et al., 2015). Tree canopy represents 36% of the soil cover fraction. The understorey is composed of a mixture of shrubs and herbaceous species. The site was plowed in 2013 (Correia et al., 2016), hence the cover fraction of shrubs changed across years. A field survey in 2017 estimated an 18% coverage of shrubs and 41% of herbaceous species, while the remaining 41% was represented by litter and bare soil (Heuschmidt et al., 2020). The most represented shrub species are <em>Cistus salvifolius</em> (cistus) and the <em>Ulex airensis</em> (ulex). In spite of occupying the same habitat, the two species have different growth habits and stress strategies. While the cistus is a semi-deciduous species with shallow roots, decreasing its canopy area during the summer period, the ulex has a deep root system and spine shaped leaves and shoots conferring high drought resistance (Correia et al., 2014). The herbaceous layer is composed of C3 species mainly grasses (44.5%) and legumes (28.7%) (Cerasoli et al., 2015).</p> <p> </p> <p>Reflectance measurements:</p> <p>All spectral observations were acquired with an ASD FieldSpec3 spectroradiometer (Malvern Panalytical, Boulder, USA) in the range of 350-2300nm. The visible and near-infrared region (350-1000nm) has a spectral resolution (full-width half maximum) of 3nm and a sampling interval of 1.4nm, while the mid infrared region (1000-2500nm) has a spectral resolution of 10nm and a sampling interval of 2.0nm. Canopy spectral data were collected by a fiber optic cable inserted into a pistol grip. A white reference of known reflectance (Spectralon panel, Labsphere, Inc., North Sutton, USA) was used to normalize for variation in atmospheric conditions and to convert the measurements into absolute reflectance. All targets were fully exposed to solar radiation at the time of the measurements. Measurements were performed on cork oak, cistus, and ulex canopies. Herbaceous plots were delimited by a 50X50 cm quadrat. Oak trees canopy measurements were done using a scaffold on the south side of the canopy. All canopy measurements were performed with a nadir view, a field of view angle of 25º, and a distance of about 90cm from the target, which resulted in a field of view of about 1256 cm<sup>2</sup>. All spectra were collected for 2 hours around solar noon, to minimize the effects of shadowing and solar zenith changes, with five replicates for each target, representing each the average of 25 spectra. All reflectance values in the range 1350-1400nm and 1800-1950nm were excluded, corresponding to the atmospheric water vapor absorption regions. A leaf clip including a white and a black standard was used for the measurement of the reflectance of cork oak leaf blades avoiding main veins.</p> <p> </p> <p>File description: </p> <p>The file "specveg_data_spectra" concerns all spectral data, the "specveg_metadata" covers the additional data of every single measured vegetation including photos (URL), and the "specveg_meta" describes all the existing variables.</p>
Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean
<p>Koptekin et al. (2022) "<strong><em>Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean</em></strong>", Current Biology <a href="https://doi.org/10.1016/j.cub.2022.11.034">https://doi.org/10.1016/j.cub.2022.11.034</a></p>
Thermal balance of forests in the Mediterranean-temperate ecotone
<p><strong>Thermal balance of forests in the Mediterranean-temperate ecotone.</strong></p> <p>This dataset comprises the data used in the manuscript "Disentangling the role of Forest structure and functional traits for the thermal balance in the Mediterranean–Temperate Ecotone", to be submitted to a scientific journal shortly after the publication date of this dataset. Data comprise 54 variables including case categorization, meteorological, climatic and forest structural variables and the thermal balance of the forest estimated from <a href="https://ecostress.jpl.nasa.gov">ECOSTRESS</a> remote sensing measurements.</p> <p> </p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) January 2014 - December 2014
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from January 2014 to December 2014 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 20 m [°C]; Salinity @ 20 m [psu].</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) March 2015 - December 2015
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from March 2015 to December 2015 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu].</p>
ScienceDex guides
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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.