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270 results for “climatology”
Monthly climatology of the upper Southern Ocean south of 60S
<p>Climatology of <em>in-situ </em>temperature and practical salinity south of 60S, from 10 to 380m, resolution 0.5x0.5 degrees.</p> <p>The maps are based on Argo, shipboard and seals CTD data. The raw profiles are decomposed in modes using a functional PCA (Pauthenet et al, 2019). The resulting principal components horizontal fields are interpolated for each months on a regular 0.5x0.5 degree grid, with the optimal interpolation of Schmidtko et al. (2013). Then climatological profiles are reconstructed at each grid points.</p> <p>Citation of the associated paper : Pauthenet, E., Sallée, J. B., Schmidtko, S., & Nerini, D. (2021). Seasonal Variation of the Antarctic Slope Front Occurrence and Position Estimated from an Interpolated Hydrographic Climatology. <em>Journal of Physical Oceanography</em>, <em>51</em>(5), 1539-1557.</p>
Monthly climatology of CMIP5 models historical run, for 1971-2000
<p>Monthly climatology (12 months, and the annual climatology), from CMIP5 historical run (1971 to 2000)</p>
Climatological global-mean Sea Surface Temperature (SST) in AWI-CM-1-1-MR simulations for CMIP6, in preindustrial, present-day, +2°C, +3°C, and +4°C climates
<p>Daily climatologies of global-mean sea surface temperature (SST, parameter 'tos') free-running simulations performed using the coupled climate models AWI-CM-1-1-MR. The unstructured grid-ocean component FESOM was conservatively remapped to the ERA5 grid. Data was averaged across the 5 ensemble members and temporally averaged over 10-year long time periods: 1850-1859 for preindustrial climate, 2015-2024 for present-day, 2034-2043 for +2°C climate, 2061-2079 for +3°C climate, and 2091-2100 for +4°C climate. </p><p>Data is provided in .nc files, one for each climate.</p>
Data for the publication "Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model" - part 1
<p>The data is split into two datasets, for each to be smaller than 50 GB.</p>
Bay of Plenty Climatology
<p>The Bay of Plenty Climatology is a daily climatology estimated from a 25+ years (1994-2020) high-resolution (~1 km horizontal resolution) hydrodynamic hindcast of Bay of Plenty, New Zealand.</p> <p>This repository contains 12 files, corresponding to each month, with the 3D daily climatology for sea surface elevation, temperature, salinity, velocity (u, v) and in z-coordinates. </p> <p>For practicality the time units are referenced to hours since day 0 and the calendar used is a 366 day-year. </p> <p>For more information about available datasets please visit the project webpage at https://www.moanaproject.org/</p> <p>For the sigma coordinates version of the climatology please access the <a href="http://thredds.moanaproject.org:6443/thredds/catalog/moana/BoP_1km/catalog.html" target="_blank" rel="noopener">Moana Project THREDDS server</a></p> <p> </p> <p>A description of the configuration of the simulation is provided by Montaño M. M., Suanda S. H., de Souza J. M. A. C. (2023). Modelled coastal circulation and Lagrangian statistics from a large coastalembayment: The case of Bay of Plenty, Aotearoa New Zealand. <em>Estuarine Coast. Shelf Sci.</em> 281, 108212. doi: <a href="https://doi.org/10.1016/j.ecss.2023.108212" target="_blank" rel="noopener">10.1016/j.ecss.2023.108212 </a> </p> <p> </p>
A new climatology of depth of nitracline in the Bay of Bengal for improving model simulations
<p>This link contains:</p> <p>Sensor_data.nc - This file contains Bioargo data with nitrate sensor used in the manuscript in figures 1, and 3 to 5</p> <p>NO3_Verical_Profile.xlsx - Contains the vertical profile of the nitrate used in figure 3</p> <p>Bottle_SurDensity+1_D26_Nitracline_depth.xlsx - contains the depths of surface density+1, D26, and Nitracline collected from various cruises/observational data mentioned in Table 1</p>
Fortnight Climatology Average of Corrected Chlorophyll-a Concentration from MODIS Level-2 Data in the Algerian Basin (2003-2018)
<p>We deposited the corrected MODIS Level-2 Chlorophyll data after a specific processing steps applied for the first time to the standard cloud-corrected Level-2 Chlorophyll fields to detect and remove spurious local patterns that affect data quality, even in time series averages.</p> <p>From a purely spatial point of view, the quality of the Chlorophyll field is improved more significantly by the outlier removal than the previous synthetic representation suggests.</p>
Supplementary Information for Coverage of in situ climatological observations in the world's mountains (Thornton et al.)
<p>Supplementary Information for "Coverage of in situ climatological observations in the world's mountains" (Thornton et al., Frontiers in Climate).</p>
Variability in Antarctic Surface Climatology Across Regional Climate Models and Reanalysis: Datasets
<p>This dataset includes output for snowfall, near-surface air temperature and melt from the following regional climate models (RCMs): Met Office Unified Model version 11.1 (MetUMv11.1), the Modèle Atmosphérique Régional version 3.10 (MARv3.10) and the Regional Atmospheric Climate Model version 2.3p2 (RACMOv2.3p2). The data is aggregated to monthly timesteps from initial 3/6hourly data. The code for aggregation is available here: https://github.com/Jez-Carter/Antarctica_Climate_Variability . Data goes from ~1971-2018 and includes two simulations from each RCM: 0.11° (12.25 km) and 0.44° (49 km) resolution simulations from the MetUM; ERA-Interim and ERA5 driven simulations from MAR and RACMO. The data used in the results for 'Variability in Antarctic Surface Climatology Across Regional Climate Models and Reanalysis Datasets' J.Carter et al, is included here and can be generated using the code available here: https://github.com/Jez-Carter/Antarctica_Climate_Variability . </p> <p><strong>Data usage notice:</strong><br> If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below.</p> <p>"We thank C. Kittel and the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations."</p> <p>In order to document MAR scientific impact and enable ongoing support of the model, users are encouraged to contact C. Kittel to add their works in the list of MAR-related publications.</p> <p>If you need other variables or output frequencies over Antarctica from: MAR, contact C.Kittel (c2kittel@gmail.com); RACMO, contact J.M. van Wessem; MetUM, contact A.Orr. </p>
High resolution maps of climatological parameters for analyzing the impacts of climatic changes on Swiss forests
<p>Assessing the impacts of climatic changes on forests requires the analysis of actual climatology within the forested area. In mountainous areas, climatological indices vary markedly with the micro-relief, i.e. with altitude, slope, and aspect. Consequently, when modelling potential shifts of altitudinal belts in mountainous areas due to climatic changes, maps with a high spatial resolution of the underlying climatological indices are fundamental. Here we present a set of maps of climatological indices with a spatial resolution of 25 by 25 m. The presented dataset consists of maps of the following parameters: average daily temperature high and low in January, April, July, and October as well as of the year; seasonal and annual thermal continentality; first and last freezing day; frost-free vegetation period; relative air humidity; solar radiation; and foehn conditions. The parameters represented in the maps have been selected in a knowledge engineering approach. The maps show the climatology of the periods 1961-1990 and 1981-2010. The data can be used for statistical analyses of forest climatology, for developing tree distribution models, and for assessing the impacts of climatic changes on Swiss forests.</p>
A historic global ground-based monthly seasonal aerosol climatology based in AERONET data: a database 1993-2013
<table class="ds-includeSet-table detailtable table table-striped table-hover"> <tbody> <tr class="ds-table-row odd "> <td class="metadata-key label-cell" title="dc.description.abstract"> </td> <td class="metadata-field word-break">We present an aerosol classification based upon AERONET level 2.0 almucantar retrieval products from the period 1993 to 2012. In the initial phase of this research we opto-physically identified five major types of Bulk Columnar Aerosol (BCA) - based solely upon intensive optical properties of spectral Single Scattering Albedo (SSA), spectral Indices of Refraction (real – RRI and imaginary - IRI), and two Angstrom Exponents (extinction – EAE and absorption - AAE). These BCA we classified as Maritime Aerosol, Dust Aerosol, Urban Industrial Aerosol, Biomass Burning Aerosol, and Mixed Aerosol. The classification of a particular observation as one of these aerosol types is determined by its five-dimensional Mahalanobis distance (MD) to the centroid of each reference cluster (itself a 5-D hyperellipsoid). To retain a greater number of AERONET sites in the study (200+), we kept the variable space to 5-D. To generate reference clusters, we only retained data points that lie within 2 MD from the data centroid. Our typology is based on AERONET retrieved quantities, which do not include low optical depth values (AOD=440nm < 0.4 as per AERONET criteria for almucantar scan inversion). The classifications obtained will be useful in interpreting aerosol retrievals from satellite borne instruments and as input for regional climate models. The result is a dataset describing the types of aerosol particles that are distinct from one another in optical properties, and a geographic distribution of those aerosol types. We used the typology scheme upon the qualifying AERONET data archive, and produced seasonal aerosol climatologies by aerosol type for each of the AERONET sites included in the study, regional aerosol climatology maps, and a time-integrated global aerosol climatology map based entirely upon ground-based photometric data. An internally hyperlinked compendium of the individual AERONET site aerosol climatologies was produced to contain the results of the first phase of this work [available at https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf]. Each of these five aerosol types can be further discriminated into specific sub-types by this same scheme. For example, optical discrimination into specific sub-types of Biomass Burning aerosol may provide insight into sources exhibiting spectrally distinct smoke properties. We then use the mathematical strategies to sort the global AERONET data retrievals into the aerosol type classified against the reference standards. We believe these strategies regarding aerosol differentiation using polarization data will be useful for analysis of the newer AERONET version 3 data retrievals, and data collected from the deployment of newer CIMEL sun-photometers (with enhanced polarization measurement capabilities) to the network. The resulting AERONET-based aerosol typology is useful for applications in aerosol optics, including forward modeling or radiative transfer for remote sensing algorithms, or evaluating radiative forcing calculations in atmospheric models.</td> </tr> </tbody> </table> <p>Necessary Reference Material:</p> <p><span><span><span><span><span><span><span><span><span><span>[1] Giordano, M. E.,<em> </em><em>On Interactions of Matter and Energy: Light and Particles in a Terrestrial Atmosphere Progress on Opto-Physical Recognition and Classification of Aerosols: </em>A PhD dissertation, University of Nevada, copyright M.E. Giordano, 294 pages, December 2019. URI: <a href="http://hdl.handle.net/11714/6686" title="http://hdl.handle.net/11714/6686">http://hdl.handle.net/11714/6686</a></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span> <a href="https://scholarworks.unr.edu/handle/11714/6686?show=full" title="https://scholarworks.unr.edu/handle/11714/6686?show=full">https://scholarworks.unr.edu/handle/11714/6686?show=full</a></span></span></span></span></span></span></span></span></span></span></p> <p>[2]<span><span><span><span><span><span><span><span><span><span> Giordano, M.E., Ward, C.S., and Hamill, P.: <em>A Compendium of Aerosol Types Based on Mahalanobis Distances and AERONET data. </em>[An internally hyperlinked compendium of seasonal aerosol and local aerosol compositions] Atmospheric Environment, 140, 213-233,2016. </span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><a href="https://doi.org/10.1016/j.atmosenv.2016.06.002" title="https://doi.org/10.1016/j.atmosenv.2016.06.002">https://doi.org/10.1016/j.atmosenv.2016.06.002</a></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span> <a href="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf" title="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf">https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf</a></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span>[3] </span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span>Hamill, P. J., Giordano, M. E., Ward, C.S., Giles, D., Holben, B.: <em>An AERONET - based aerosol</em></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><em> classification using the Mahalanobis distance,</em> Atmospheric Environment, Volume 140, September, pgs 213 -233, 2016. <a href="http://dx.doi.org/10.1016/j.atmosenv.2016.06.002">http://dx.doi.org/10.1016/j.atmosenv.2016.06.002</a>and also at</span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span> <a href="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf">https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf</a></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span>[4] Hamill, Patrick, Piedra, Patricio G., Giordano, Marco, E., 2020: <em>Simulated Polarization as a Signature of Aerosol Type</em>. Atmospheric Environment, Volume 224, 117348 article ATMENVD- 19-01763, 2020. </span></span></span></span></span></span></span></span></span></span><a href="https://doi.org/10.1016/j.atmosenv.2020.117348" title="Persistent link using digital object identifier">https://doi.org/10.1016/j.atmosenv.2020.117348</a></p> <p><span><span><span><span><span><span><span><span><span><span> </span></span></span></span></span></span></span></span></span></span></p>
Daily climatology of 3D ocean currents, SST and SSH based on a 22 year run of the SEA-COFS model
<p>This dataset is a daily climatology created based on a 22 year free run of the SEA-COFS model full domain (EAC 25.25 - 45.55 °S) forced with BARRA-R winds and tides, spanning from January 1994 to September 2016. The model has 30 sigma-stretch vertical levels and an across-shore resolution of 2.5 km (on shelf) – 6 km (off shelf) and 5 km along-shore. For a full description of this version of the model and its validation refer to the following papers:</p> <ul> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2021). <a href="https://doi.org/10.1029/2021GL094115%20">Dynamics of interannual eddy kinetic energy modulations in a Western Boundary Current</a>. <em>Geophysical Research Letters,</em>Vol 48, October 2021.DOI: <a href="https://doi.org/10.1029/2021GL094115">https://doi.org/10.1029/2021GL094115</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2021/2021GL094115.pdf">[PDF file]</a></li> <li> </li> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2022). <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">Variability and Drivers of Ocean Temperature Extremes in a Warming Western Boundary Current</a>. <em>Journal Of Climate,</em> February 2022. DOI: <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">https://doi.org/10.1175/JCLI-D-21-0622.1</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2022/Li_2022.pdf">[PDF file]</a></li> </ul> <p>This daily climatology of 365 days was created with NCO tools by taking the mean of the 22 daily average ROMS output files (one for each year) to generate each climatological day. Specific commands used are recorded in the NetCDF file history. Leap year days were excluded since its climatology was computed with only 5 instances.</p> <p>A sample file with the climatological data for January 1st is provided here as an example of the NetCDF format of the dataset. The entire dataset is one file of approximately 62GB and can be provided upon request. </p> <p><strong>NOTE: </strong>The ocean_time variable reflects the dates of the year 1994, but the values of the variables correspond to climatological values computed as described. </p> <p>The ROMS variables below are present in this daily climatology, as well as the S-coordinate stretching curves, grid defining variables and other time independent parameters:</p> <p>AKs = "time-averaged salinity vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKt = "time-averaged temperature vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKv = "time-averaged vertical viscosity coefficient" [meter2 seconds-1]</p> <p>bustr = "time-averaged bottom u-momentum stress" [newton meter-2]</p> <p>bvstr = "time-averaged bottom v-momentum stress" [newton meter-2]</p> <p>omega = "time-averaged S-coordinate vertical momentum component" [meter3 second-1]</p> <p>pvorticity = "time-averaged potential vorticity" [meter-1 second-1]</p> <p>pvorticity = "time-averaged 2D potential vorticity" [meter-1 second-1]</p> <p>rho = "time-averaged density anomaly" [kilogram meter-3]</p> <p>rvorticity = "time-averaged relative vorticity, vertical component" [second-1]</p> <p>rvorticity_bar = "time-averaged 2D relative vorticity" [second-1]</p> <p>salt: = "time-averaged salinity" [PSU]</p> <p>shflux = "time-averaged surface net heat flux" [watt meter-2]</p> <p>ssflux = "time-averaged surface net salt flux, (E-P)*SALT" [meter second-1]</p> <p>sustr = "time-averaged surface u-momentum stress" [newton meter-2]</p> <p>svstr = "time-averaged surface v-momentum stress" [newton meter-2]</p> <p>temp = "time-averaged potential temperature" [Celsius]</p> <p>u = "time-averaged u-momentum component" [meter second-1]</p> <p>u_eastward = "time-averaged eastward momentum component at RHO-points" [meter second-1]</p> <p>ubar = "time-averaged vertically integrated u-momentum component" [meter second-1]</p> <p>ubar_eastward = "time-averaged eastward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>uu = "time-averaged u-momentum times u-momentum" [meter2 second-2]</p> <p>uv = "time-averaged u-momentum times v-momentum" [meter2 second-2]</p> <p>v = "time-averaged v-momentum component" [meter second-1]</p> <p>v_northward = "time-averaged northward momentum component at RHO-points" [meter second-1]</p> <p>vbar = "time-averaged vertically integrated v-momentum component" [meter second-1]</p> <p>vbar_northward = "time-averaged northward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>vv = "time-averaged v-momentum times v-momentum" [meter2 second-2]</p> <p>w = "time-averaged vertical momentum component" [meter second-1]</p> <p>zeta = "time-averaged free-surface" [meter]</p>
TreeGOER Global Zones: Global atlas for the Climatic Moisture Index (CMI), Maximum Climatological Water Deficit (MCWD) and the number of months with average temperature > 10 degrees C (Tmo10)
<p>The <strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> database documents the environmental ranges for 48,129 tree species and is available from files archived at <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a>. Details on the preparation of this database from 30 arc-second global grid layers are provided by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>. The atlas from this archive was designed to be used together with TreeGOER and possibly also with the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database (Kindt et al. <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) to allow users to filter suitable tree species based on environmental conditions of the planting site.</p> <p><strong>TreeGOER</strong> includes a file (<em>TreeGOER_Tmo10_classes.txt</em>) that documents the distribution of species in zones defined by the number of months with average temperature > 10 degrees C. <strong>TreeGOER</strong> also includes a file (<em>TreeGOER_CMI_classes.txt</em>) that documents the distribution of species in zones defined by the Climatic Moisture Index (CMI). The atlas provided here shows the global distribution of the Tmo10 zones and CMI zones at high resolution on six sheets each, including three sheets in the northern hemisphere and three sheets in the southern hemisphere.</p> <p>The atlas also includes six sheets that show the global distribution of the Maximum Climatological Water Deficit (MCWD), another environmental variable covered by the <strong>TreeGOER</strong> database.</p> <p> </p> <table> <tbody> <tr> <td><strong>Zone</strong></td> <td><strong>Classes</strong></td> <td><strong>Comment</strong></td> </tr> <tr> <td>Tmo10</td> <td> Tmo10 = 12 + Bio06 >= 18</td> <td>tropical (minimum temperature of coldest month 18 degrees C or higher)</td> </tr> <tr> <td> </td> <td>Tmo10 = 12 + Bio06< 18</td> <td>tropical (minimum temperature of coldest month less than 18 degrees C)</td> </tr> <tr> <td> </td> <td>8 ≤ Tmo10 < 12</td> <td>subtropical</td> </tr> <tr> <td> </td> <td>4 ≤ Tmo10 < 8</td> <td>temperate</td> </tr> <tr> <td> </td> <td>1 ≤ Tmo10 < 4</td> <td>boreal</td> </tr> <tr> <td> </td> <td>Tmo10 < 1</td> <td>polar</td> </tr> <tr> <td>CMI</td> <td>CMI ≥ 0.5</td> <td>P >= 2 * PET</td> </tr> <tr> <td> </td> <td>0 ≤ CMI < 0.5</td> <td>PET <= P < 2 * PET</td> </tr> <tr> <td> </td> <td>−0.35 ≤ CMI < 0</td> <td>0.65 <= P/PET < 1</td> </tr> <tr> <td> </td> <td>−0.5 ≤ CMI < −0.35</td> <td>dry sub-humid</td> </tr> <tr> <td> </td> <td>−0.8 ≤ CMI < −0.5</td> <td>semi-arid</td> </tr> <tr> <td> </td> <td>−0.95 ≤ CMI < −0.8</td> <td>arid</td> </tr> <tr> <td> </td> <td>CMI < −0.95</td> <td> hyper-arid</td> </tr> <tr> <td>MCWD</td> <td>MCWD ≤ -100</td> <td> </td> </tr> <tr> <td> </td> <td>−200 ≤ MCWD < −100</td> <td> </td> </tr> <tr> <td> </td> <td>−400 ≤ MCWD < −200</td> <td> </td> </tr> <tr> <td> </td> <td>−600 ≤ MCWD < −400 </td> <td> </td> </tr> <tr> <td> </td> <td>−800 ≤ MCWD < −600</td> <td> </td> </tr> <tr> <td> </td> <td>−1000 ≤ MCWD < −800</td> <td> </td> </tr> <tr> <td> </td> <td>−1250 ≤ MCWD < −1000</td> <td> </td> </tr> <tr> <td> </td> <td>−1500 ≤ MCWD < −1250</td> <td> </td> </tr> <tr> <td> </td> <td>−1750 ≤ MCWD < −1500 </td> <td> </td> </tr> <tr> <td> </td> <td>−2000 ≤ MCWD < −1750 </td> <td> </td> </tr> <tr> <td> </td> <td>−2500 ≤ MCWD < −2000 </td> <td> </td> </tr> </tbody> </table> <p> </p> <p>A fourth map series in the atlas combines information from the Climatic Moisture Index with the distribution of 52,602 cities that were included in the CitiesGOER database, available from <a href="https://doi.org/10.5281/zenodo.8175429">https://doi.org/10.5281/zenodo.8175429</a><a name="_Hlk141002106"></a><br></p> <p>Maps were created from the environmental raster layers used to create the TreeGOER via the <a href="https://cran.r-project.org/web/packages/terra/">terra package</a> (Hijmans et al. 2022, version 1.7-46) in the <a href="https://cran.r-project.org/">R 4.2.1 environment</a>.</p> <p>Added country boundaries were obtained from <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/">Natural Earth</a> as <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip">Admin 0 – countries vector layers</a> (version 5.1.1). Also added after obtaining them from Natural Earth were <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_boundary_lines_disputed_areas.zip">Admin 0 – Breakaway, Disputed areas</a> (version 5.1.0, coloured yellow in the atlas), <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_roads.zip">Roads</a> (version 5.0.0, coloured red in the atlas) and <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/physical/ne_10m_lakes.zip">Lakes</a> (version 5.0.0, coloured darkblue in the atlas).</p> <p>For countries where the GlobalUsefulNativeTrees database included subnational levels, boundaries were added and depicted as dot-dash lines. These subnational levels correspond to level 3 boundaries in the World Geographical Scheme for Recording Plant Distributions. These were obtained from <a href="https://github.com/tdwg/wgsrpd">https://github.com/tdwg/wgsrpd</a>. Check <a href="https://github.com/tdwg/wgsrpd/blob/master/109-488-1-ED/2nd%20Edition/TDWG_geo2.pdf">Brummit 2001</a> for details such as the maps shown at the end of this document.</p> <p>When using the TreeGOER Global Zones atlas in your work, cite this depository and the following:</p> <ul> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> </ul> <p> </p> <p>The development of the TreeGOER Global Zones atlas (including development of version 2024.06) was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em>, by the <strong>Bezos Earth Fund</strong> to the <em>Bezos Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>.</p> <p> </p>
MAHTE Climatology Complete Dataset
<p>This dataset contains information for all Mesoscale Airmasses with High Theta-e (MAHTEs) collected from the Automated Surface Observing System (ASOS) for the period of January 2000 through June 2022. Data are provided in a .csv file, with each row corresponding to an individual MAHTE. Each column corresponds to one of the following data points:</p> <ul> <li>station: four letter code corresponding to the ASOS station where the MAHTE was identified</li> <li>index: line within the monthly ASOS text file where the MAHTE was identified (after erroneous/incomplete lines have been removed)</li> <li>datetime: date/time of MAHTE identification</li> <li>mahteflag: series of internal flags detailing MAHTE identification process, appearing in the following order <ul> <li>w1: indicates passage of wind test</li> <li>t1: indicates passage of thermodynamic tests</li> <li>r0/r1: indicates a retrograding/progressive MAHTE, respectively</li> <li>s1: indicates passage of size test</li> <li>w1: unused relic of previous algorithm version</li> </ul> </li> <li>dthte: change in theta-e across the boundary (cool side — warm side, regardless of MAHTE orientation)</li> <li>dtemp: change in temperature across the bounary (cool side — warm side, regardless of MAHTE orientation)</li> <li>u0: ground speed of boundary</li> <li>width: width of MAHTE</li> <li>duration: residence time of MAHTE over the ASOS station</li> <li>thermodelay: time required to obtain the required thermodynamic shift for passage of thermodynamic tests (0 = five minutes, 1 = ten minutes)</li> <li>envdwptdep: dew point depression on the warm side of boundary</li> <li>mahtedwptdep: dep point depression within the MAHTE</li> <li>mahtecape: Convective Available Potential Energy (CAPE) within the MAHTE</li> <li>envcape: CAPE on the warm side of the boudnary</li> <li>mahte06bwd: 0-6 bulk wind difference within the MAHTE</li> <li>env06bwd: 0-6 bulk wind difference on the warm side of the boundary</li> <li>ddwpt: change in dew point depression across the boundary (warm side — cool side, regardless of MAHTE orientation)</li> </ul> <p> </p>
Supplementary dataset for "Global Climatology of the Daytime Surface Cooling of Urban Parks Using Satellite Observations"
<p>This dataset supplements the paper "Global Climatology of the Daytime Surface Cooling of Urban Parks Using Satellite Observations" in Geophysical Research Letters by Agathangeldis et al.</p> <ul> <li>The file "parks.gpkg" contains the boundaries of the parks used in the study, along with the Surface Park Cool Island (SPCI) intensity and additional metadata as attributes.</li> <li>The file "parks.csv" includes the same information as "parks.gpkg" but without the geospatial information.</li> <li>The file "seasonal_values" provides the SPCI intensity for each park, broken down by season.</li> </ul>
Hydrodynamic daily climatology of South Eastern Australia based on a 22 year run of the SEA-COFS model
<p>This dataset is a daily climatology created based on a 22 year free run of the SEA-COFS model full domain (EAC 25.25 - 45.55 °S) forced with BARRA-R winds and tides, spanning from January 1994 to September 2016. The model has 30 sigma-stretch vertical levels and an across-shore resolution of 2.5 km (on shelf) – 6 km (off shelf) and 5 km along-shore. The original dataset can be access here:</p> <p>Li, J & Kerry C. (2021). A high-resolution, 22-year, free-running, hydrodynamic simulation of the East Australia Current System using the Regional Ocean Modeling System (Version 2.0). <a href="https://doi.org/10.26190/TT1Q-NP46">https://doi.org/10.26190/TT1Q-NP46</a></p> <p>For a full description of this version of the model and its validation refer to the following papers:</p> <ul> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2021). <a href="https://doi.org/10.1029/2021GL094115%20">Dynamics of interannual eddy kinetic energy modulations in a Western Boundary Current</a>. <em>Geophysical Research Letters,</em>Vol 48, October 2021.DOI: <a href="https://doi.org/10.1029/2021GL094115">https://doi.org/10.1029/2021GL094115</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2021/2021GL094115.pdf">[PDF file]</a></li> <li> </li> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2022). <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">Variability and Drivers of Ocean Temperature Extremes in a Warming Western Boundary Current</a>. <em>Journal Of Climate,</em> February 2022. DOI: <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">https://doi.org/10.1175/JCLI-D-21-0622.1</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2022/Li_2022.pdf">[PDF file]</a></li> </ul> <p>This daily climatology of 365 days was created with NCO tools by taking the mean of the 22 ROMS output files (one for each year) containing daily averages, to generate each climatological day. Specific commands used are recorded in the NetCDF file history. Leap year days were excluded since its climatology was computed with only 5 instances.</p> <p>A sample file with the climatological data for January 1st is provided here as an example of the NetCDF format of the dataset. The entire dataset is one file of approximately 62GB and can be provided upon request. </p> <p><strong>NOTE: </strong>The ocean_time variable reflects the dates of the year 1994, but the values of the variables correspond to climatological values computed as described. </p> <p>The ROMS variables below are present in this daily climatology, as well as the S-coordinate stretching curves, grid defining variables and other time independent parameters:</p> <p>AKs = "time-averaged salinity vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKt = "time-averaged temperature vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKv = "time-averaged vertical viscosity coefficient" [meter2 seconds-1]</p> <p>bustr = "time-averaged bottom u-momentum stress" [newton meter-2]</p> <p>bvstr = "time-averaged bottom v-momentum stress" [newton meter-2]</p> <p>omega = "time-averaged S-coordinate vertical momentum component" [meter3 second-1]</p> <p>pvorticity = "time-averaged potential vorticity" [meter-1 second-1]</p> <p>pvorticity = "time-averaged 2D potential vorticity" [meter-1 second-1]</p> <p>rho = "time-averaged density anomaly" [kilogram meter-3]</p> <p>rvorticity = "time-averaged relative vorticity, vertical component" [second-1]</p> <p>rvorticity_bar = "time-averaged 2D relative vorticity" [second-1]</p> <p>salt: = "time-averaged salinity" [PSU]</p> <p>shflux = "time-averaged surface net heat flux" [watt meter-2]</p> <p>ssflux = "time-averaged surface net salt flux, (E-P)*SALT" [meter second-1]</p> <p>sustr = "time-averaged surface u-momentum stress" [newton meter-2]</p> <p>svstr = "time-averaged surface v-momentum stress" [newton meter-2]</p> <p>temp = "time-averaged potential temperature" [Celsius]</p> <p>u = "time-averaged u-momentum component" [meter second-1]</p> <p>u_eastward = "time-averaged eastward momentum component at RHO-points" [meter second-1]</p> <p>ubar = "time-averaged vertically integrated u-momentum component" [meter second-1]</p> <p>ubar_eastward = "time-averaged eastward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>uu = "time-averaged u-momentum times u-momentum" [meter2 second-2]</p> <p>uv = "time-averaged u-momentum times v-momentum" [meter2 second-2]</p> <p>v = "time-averaged v-momentum component" [meter second-1]</p> <p>v_northward = "time-averaged northward momentum component at RHO-points" [meter second-1]</p> <p>vbar = "time-averaged vertically integrated v-momentum component" [meter second-1]</p> <p>vbar_northward = "time-averaged northward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>vv = "time-averaged v-momentum times v-momentum" [meter2 second-2]</p> <p>w = "time-averaged vertical momentum component" [meter second-1]</p>
Five day averaged sea surface salinity from MITgcm integration with climatological river discharge forcing
<p>The 5-day averaged sea surface salinity for experiment using climate river discharge, with file names like sss_clmflux_YEAR.mat for each year</p>
Five day averaged sea surface height from MITgcm integration with climatological river discharge forcing
<p>The 5-day averaged sea surface height for experiment using climate river discharge, with file names like ssh_dayflux_YEAR.mat for each year.</p>
Data used in "Climatology and variability of air mass transport from the boundary layer to the Asian monsoon anticyclone"
<p>The data presented here are needed to reproduce the analyses of the publication: Nützel, M., Brinkop, S., Dameris, M., Garny, H., Jöckel, P., Pan, L. L., and Park, M.: Climatology and variability of air mass transport from the boundary layer to the Asian monsoon anticyclone, Atmos. Chem. Phys., 22, 15659–15683, https://doi.org/10.5194/acp-22-15659-2022, 2022. A short explanation of the archived data is presented in the accompanying README.</p> <p>Note: In the previous data set version (<a href="https://doi.org/10.5281/zenodo.7275804">10.5281/zenodo.7275804</a>) one file was missing and is added here.</p> <p> </p> <p> </p>
Identification of high-wind features within extratropical cyclones using a probabilistic random forest - Part 2: Climatology - Video Supplement
<p>These videos provide examples of application of RAMEFI (RAndom-forest based MEsoscale wind Feature Identification) for winter storm cases between 2000 and 2019 and an animation of cyclone-relative occurrence (cf. Fig. 7 of 10.5194/wcd-2023-10).</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.