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62 results for “climate indices”
Mediterranean Sea Climatic Indices - Areal density of OHC/OSC
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_ArealDensity.tar.gz" contains : a) an ascii file named "inventory_ArealDensity.txt" which lists all the available indices, and b) a directory named "ArealDensity" with the indices under the following structure:</p> <ul> <li>ArealDensity/Annual_Decadal/Variable_decade_allmonths_z1z2.nc</li> <li>ArealDensity/Seasonal_Decadal/ Variable_decade_season_z1z2.nc</li> </ul> <p>Other naming:</p> <ul> <li>decade: 19501959 to 20062015</li> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>z1z2: vertical layer between z1 and z2 depths</li> </ul> <p> </p> <p>Example:</p> <ul> <li>OHCad_19501959_0112_5150.nc, is the areal density of the Ocean Heat Content anomaly for the decade 19501959 between 5 and 150 m</li> </ul>
Mediterranean Sea Climatic Indices - Differences of two 30-years averages of T/S, OHC/OSC Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_Climashift_30yrs.tar.gz" contains : a) an ascii file named "inventory_Climashift_30yrs.txt" which lists all the available indices, and b) a directory named "Climashift_30yrs" with the indices under the following structure:</p> <ul> <li>Climashift_30yrs/Anomalies/Annual/Variable_decade2_decade1_allmonths_z1_z2.nc</li> <li>Climashift_30yrs/Anomalies/Seasonal/ Variable_decade2_decade1_season_z1_z2.nc</li> <li>Climashift_30yrs/Vertical_Averages/Annual/Variable_decade2_decade1_allmonths_z1z2.nc</li> <li>Climashift_30yrs/Vertical_Averages/Seasonal/Variable_decade2_decade1_season_z1z2.nc</li> </ul> <p>The Variable naming is:</p> <ul> <li> Tanomclimashift: Temperature anomaly difference between decade2 and decade1</li> <li> Sanomclimashift: Temperature anomaly difference between decade2 and decade1</li> <li> Tvavgclimashift: Vertically averaged temperature anomaly difference between decade2 and decade1</li> <li> Svavgclimashift: Vertically averaged salinity anomaly difference between decade2 and decade1</li> <li> OHCclimashift: Ocean Heat Content anomaly difference between decade2 and decade1</li> <li> OSCclimashift: Ocean Salt Content anomaly difference between decade2 and decade1</li> </ul> <p> <br> Other naming:</p> <ul> <li>decade1: stands for 19501979</li> <li>decade2: stands for 19802015</li> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>z1z2: vertical layer between z1 and z2 depths</li> <li>z1, z2: standard depth levels</li> </ul> <p>Example:</p> <ul> <li>Sanomclimashift_19802015_19501979_0112_5_4000.nc, is the annual salinity anomaly difference between 1980-2015 and 1950-1970 from 5 to 4000 m.</li> </ul> <p><br> </p>
Mediterranean Sea Climatic Indices - T/S Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_Anomalies.tar.gz" contains : a) an ascii file named "inventory_Anomalies.txt" which lists all the available indices, and b) a directory named "Anomalies" with the indices under the following structure:</p> <ul> <li>Anomalies/Annual_Decadal/Variable_decade_allmonths_z1_z2.nc</li> <li>Anomalies/Seasonal_Decadal/Variable_decade_season_z1_z2.nc</li> </ul> <p>The Variable naming is:</p> <ul> <li>Tanom: Temperature anomaly</li> <li>Sanom: Salinity anomaly</li> </ul> <p>Other naming:</p> <ul> <li>decade: 19501959 to 20062015</li> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>z1, z2: standard depth levels</li> </ul> <p>Example:</p> <ul> <li>Sanom_19501959_0112_5_4000.nc, is the Salinity anomaly for the decade 19501959 from 5 to 4000 m</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Mediterranean Sea Climatic Indices - Linear trends of T/S, OHC/OSC Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_LinearTrends.tar.gz" contains : a) an ascii file named "inventory_LinearTrends.txt" which lists all the available indices, and b) a directory named "LinearTrends" with the indices under the following structure:</p> <ul> <li>LinearTrends/Annual/Variable_period_allmonths_z1z2.nc</li> <li>LinearTrends/Seasonal/ Variable_period_season_z1z2.nc</li> </ul> <p>The Variable naming is:</p> <ul> <li>Tlineartrend: Vertically averaged temperature anomaly linear trend</li> <li>Slineartrend: Vertically averaged salinity anomaly linear trend</li> <li>OHClineartrends: Ocean Heat Content anomaly linear trend</li> <li>OSClineartrends: Ocean Salt Content anomaly linear trend</li> </ul> <p> </p> <p>Other naming:</p> <ul> <li>period: stands for 19502015</li> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>z1z2: vertical layer between z1 and z2 depths</li> </ul> <p>Example:</p> <ul> <li>Tlineartrend_19502015_0112_6004000.nc, is the annual vertically temperature anomaly linear trend, for the period 19502015, between 600 and 4000 m</li> </ul>
Mediterranean Sea Climatic Indices - T/S Vertical Averages
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_VerticalAverages.tar.gz" contains : a) an ascii file named "inventory_VerticalAverages.txt" which lists all the available indices, and b) a directory named "VerticalAverages" with the indices under the following structure:</p> <ul> <li>VerticalAverages/Annual_Decadal/Variable_decade_allmonths_z1z2.nc</li> <li>VerticalAverages/Seasonal_Decadal/ Variable_decade_season_z1z2.nc</li> </ul> <p>The Variable naming is:</p> <ul> <li>Tanomvavg: Vertically averaged temperature anomaly</li> <li>Sanomvavg: Vertically averaged salinity anomaly</li> </ul> <p>Other naming:</p> <ul> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>decade: 19501959 to 20062015</li> <li>z1z2: vertical layer between z1 and z2 depths</li> </ul> <p>Example:</p> <ul> <li>Tanomvavg_20062015_1012_6004000.nc, is the autumn vertically averaged temperature anomaly, for the decade 2006-2015, between 600 and 4000 m</li> </ul>
Mediterranean Sea Climatic Indices - Time Series of T/S, OHC/OSC Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_TimesSeries.tar.gz" contains a directory named "TimeSeries" with the following 5 indices:</p> <ul> <li>TimeSeries_Annual.nc: annual time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_0103.nc: winter time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_0406.nc: spring time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_0709.nc: summer time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_1012.nc: autumn time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> </ul>
STORM Climate Indices Maps
<p>These data sets contains climate indices (for definitions please see README file) determined with the Climate Data Operator (cdo) software calculated based on EURO-CORDEX regional climate model projections using RCP4.5 and RCP8.5 for the period 2036-2065 as well as the time period 1971-2000 (baseline climate). The indices are based on an ensemble of EURO-CORDEX runs (see README file) after determining the indices based on each run individually, and are available for an area around the STORM pilot sites (located in Mellor, UK; Troia, Portugal; Rome, Italy; Rethymno, Greece; and Ephesus, Turkey). For more information and terms of use please consult the README file.</p>
Global Fire Weather Indices - supporting data for Jain et al. 2021, Nature Climate Change
<p>Daily fire weather indices (FWI and ISI, outputs of the Canadian Fire Weather Index System) from 1979-2020 at 0.25 deg resolution. This data supports the analysis in "Observed increases in extreme fire weather driven by atmospheric humidity and temperature", Jain et al. 2021, accepted for publication in Nature Climate Change.<br> <br> Variables were processed using inputs from the ERA5 Reanalysis (hourly surface data from 1979–2020, available from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a>). FWI System indices were calculated using the CFFDRS R package using the overwintering procedure outlined in McElhinny et al. 2020. </p> <p>References</p> <p>McElhinny, M., Beckers, J. F., Hanes, C., Flannigan, M., and Jain, P.: A high-resolution reanalysis of global fire weather from 1979 to 2018 – overwintering the Drought Code, Earth Syst. Sci. Data, 12, 1823–1833, https://doi.org/10.5194/essd-12-1823-2020, 2020.</p> <p> </p> <p> </p> <p> </p>
Data from: Microclimate-based species distribution models in complex terrain indicate widespread cryptic refugia under climate change
<p class="MsoNoSpacing"><i>Aim: </i>Species' climatic niches may be poorly predicted by regional climate estimates used in species distribution models (SDMs) due to microclimatic buffering of local conditions. Here, we compare SDMs generated using a locally validated below-canopy microclimate model to those based on interpolated weather station data at two spatial scales to determine the effects of scale, topography, and forest cover on potential future ground-level warming and species distributions.</p> <p class="MsoNoSpacing"><i>Location:</i> Great Smoky Mountains National Park (2090 km<sup>2</sup>; NC, TN, USA)</p> <p class="MsoNoSpacing"><i>Time period: </i>1970 – 2006</p> <p class="MsoNoSpacing"><i>Major taxa:</i> Vascular plant species of the Southern Appalachians</p> <p class="MsoNoSpacing"><i>Methods:</i> We compared the fit and predictions of SDMs generated using a database of plant occurrences and three climate models: macroclimate (1 km, WorldClim), fine-scale (30 m) interpolation of macroclimate with elevation, and fine-scale below-canopy microclimate from a ground-level sensor network.</p> <p class="MsoNoSpacing"><i>Results: </i>We found that, although SDM fit was similar across models, microclimate-derived SDMs predicted substantially greater species persistence with 4 °C of regional warming, with a difference of 50% of the species pool in some areas. Microclimate SDMs predicted higher stability of mid-elevation species, particularly in thermally buffered areas near streams, and critically, less change in species composition at high elevation. In contrast, predictions of macroclimate and interpolation models were similar despite improved resolution.</p> <p class="MsoNoSpacing"><i>Main conclusions:</i> Our results demonstrate that careful selection of climate drivers, including local near-ground validation rather than interpolation, is critical for projecting distributions. They also suggest that some species at risk from climate change might persist, even with 4 °C of macroclimate warming, in cryptic refugia buffered by microclimate, pointing to the roles of forest cover and topography in explaining slower-than-expected changes in understory communities. However, certain species, such as those currently occurring on low-elevation ridges that are sensitive to atmospheric changes, may be at more risk than macroclimate or interpolated SDMs suggest.</p> <p class="MsoNoSpacing"> </p>
CP_OdU (Climate Projections for Odesa, Ukraine): Climate indices and daily meteorological variables for Odesa (Ukraine) in 2021-2050 by different RCM simulations from Euro-CORDEX
<ol> <li>ODS-UA_RCM_outputs_day_20210101-20501231.zip file contains outputs from Euro-CORDEX RCM’s simulation for a land-located point closest to the Odesa meteorological site (46.44N, 30.77E).</li> <li>The RCM grids define the coordinates for this point (the gridpoint is mostly located in the city center (Kateryninska^Troitska) or near the 7-km market.</li> <li>The nomenclature of files and variables in these files are defined in http://is-enes-data.github.io/cordex_archive_specifications.pdf.</li> <li>Other files contain the so-called climate indices as described in <a href="https://knmi-ecad-assets-prd.s3.amazonaws.com/documents/atbd.pdf">https://knmi-ecad-assets-prd.s3.amazonaws.com/documents/atbd.pdf</a> and table in 0readme.pdf.</li> </ol>
Terrain- and climate-based habitat suitability indices for the reef-building coral Lophelia pertusa on the Blake Plateau (southeastern United States)
<p>Habitat suitability model outputs from Gasbarro et al. (2022) covering the Blake Plateau region of the southeastern United States margin. These include raster surfaces of ensemble mean, median, and standard deviation habitat suitability indices. Bathymetry-derived terrain variables were used to generate the terrain suitability layers, while climate model outputs were used to generate the climatic suitability layers. Rasters are in WGS84. See Gasbarro et al. (2022) for full modeling details.</p>
Climate change modelling indicates extensive range contractions for a scarce southern African endemic and minimal protected area network within its future climatically suitable range
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Data from: Microclimate-based species distribution models in complex terrain indicate widespread cryptic refugia under climate change
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Data from: Quantifying the climatic niche of symbiont partners in a lichen symbiosis indicates mutualist-mediated niche expansions
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Data from: Finding common ground: Toward comparable indicators of adaptive capacity of tree species to a changing climate
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Terrain- and climate-based habitat suitability indices for the reef-building coral Lophelia pertusa on the Blake Plateau (southeastern United States)
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Data from: Plant functional indicators of vegetation response to climate change, past present and future: I. Trends, emerging hypotheses and plant functional modality
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Data from: Molecular data and distribution dynamics indicate a recent and incomplete separation of manakins species of the genus Antilophia (Aves: Pipridae) in response to Holocene climate change
To determine a hypothetical scenario that accounts for the diversification of the two species of the genus Antilophia, we conducted multilocus molecular comparisons and species distribution modeling for the two taxa, which have distinct male plumage coloration patterns and allopatric geographic distributions, despite the high degree of genetic similarity indicated by recent studies. Three mitochondrial and three nuclear fragments were analyzed. The results indicate clear differences in the genetic diversity of the two species, but with ample sharing of haplotypes in all the markers analyzed, reflecting the absence of reciprocal monophyly, presumably due to the relatively recent and still incomplete separation of the two species. The paleoclimatic distribution models, together with the observed genetic profile indicate a recent process of divergence by geographic isolation in the ancestral populations of the two species. This scenario coincides with the recent climatic events of the South American dry diagonal, which involves the gallery forests of the Cerrado biome and the cloud forest enclaves of the seasonal tropical dry forest of the Caatinga between the late Pleistocene and the mid Holocene.
BioClim Austria: Gridded climate indicators for 1961-1990 and 1991-2020 at 250m resolution
<h2>Overview</h2> <p>This gridded meteorological data set consists of climatologies (climate indicators) on a 30-year average basis for Austria and covers two historical periods with a high spatial resolution of 250m. The two 30-year periods provided for the observations allow the analysis of the climate change that has already occurred. The selection of climate indicators is optimized for the needs of ecological models. </p> <p><strong>Resolution</strong>: 250x250m<br><strong>Projection</strong>: EPSG 31287<br><strong>Extent</strong>: Austria<br><strong>Periods</strong>: 1961-1990 and 1991-2020<br><strong>Format:</strong> GeoTIFF<br><strong>Data sources:</strong> Station data and derived products</p> <p><strong>List of climatologies (climate indicators) </strong></p> <table> <tbody> <tr> <td> <p><strong>#</strong></p> </td> <td> <p><strong>Short name</strong></p> </td> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Yearly (Y), monthly (M) or growing season (GS)<br></strong></p> </td> </tr> <tr> <td> <p><em>1</em></p> </td> <td> <p>tasmin</p> </td> <td> <p>Average daily minimum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>2</em></p> </td> <td> <p>tasmax</p> </td> <td> <p>Average daily maximum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>3</em></p> </td> <td> <p>tas</p> </td> <td> <p>Average temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>4</em></p> </td> <td> <p>tas_warmest_month</p> </td> <td> <p>Average temperature mean in the warmest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>5</em></p> </td> <td> <p>tas_coldest_month</p> </td> <td> <p>Average temperature mean in the coldest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>6</em></p> </td> <td> <p>tasmin_coldest_month</p> </td> <td> <p>Average temperature minimum in the coldest month</p> </td> <td> <p>Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>7</em></p> </td> <td> <p>tasmax_warmest_month</p> </td> <td> <p>Average temperature maximum in the warmest month</p> </td> <td> <p>Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>8</em></p> </td> <td> <p>thermal_continentality</p> </td> <td> <p>Average annual amplitude of monthly mean temperature (Thermal continentality)</p> </td> <td> <p>Climatological monthly mean temperature in the warmest month minus climatological monthly mean temperature in the coldest month (04_tas* minus 05_tas*). </p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>9</em></p> </td> <td> <p>FD</p> </td> <td> <p>Average number of frost days per year</p> </td> <td> <p>Defined by 0°C daily minimum temperature at 2m height.</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>10</em></p> </td> <td> <p>GSL</p> </td> <td> <p>Average length of the growing season</p> </td> <td> <p>The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>11</em></p> </td> <td> <p>tas</p> </td> <td> <p>Average temperature in the growing season</p> </td> <td> <p>Average temperature in the growing season defined as in climate indicator 10 (GSL). If a year does not have a growing season, this value is not defined. </p> </td> <td> <p>°C</p> </td> <td> <p>GS</p> </td> </tr> <tr> <td> <p><em>12</em></p> </td> <td> <p>GDD</p> </td> <td> <p>Average Growing Degree Days per year above 5°C</p> </td> <td> <p>Σ(Tmean – 5°C) per year. </p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>13</em></p> </td> <td> <p>FD_first</p> </td> <td> <p>Average date of the first frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>14</em></p> </td> <td> <p>FD_last</p> </td> <td> <p>Average date of the last frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>15</em></p> </td> <td> <p>HD35</p> </td> <td> <p>Average number of extremely hot days above 35°C per year</p> </td> <td> <p>Defined by 35°C daily maximum temperature at 2m height.</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>16</em></p> </td> <td> <p>FD_10</p> </td> <td> <p>Average number of days with hard frost below -10°C per year</p> </td> <td> <p>Defined by -10°C daily minimum temperature at 2m height.</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>20</em></p> </td> <td> <p>GLO_hori</p> </td> <td> <p>Average sum of global radiation on horizontal surface</p> </td> <td> <p>Also called irradiance or shortwave incoming radiation, taking cloud cover into account.</p> </td> <td> <p>kWh</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>21</em></p> </td> <td> <p>GLO_real</p> </td> <td> <p>Average sum of global radiation on the real surface</p> </td> <td> <p>Also called irradiance or shortwave incoming radiation, taking cloud cover into account.</p> </td> <td> <p>kWh</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>31</em></p> </td> <td> <p>vpd</p> </td> <td> <p>Average water vapor pressure deficit</p> </td> <td> <p>Calculated from daily dew point temperature and temperature. </p> </td> <td> <p>hPa</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>31</em></p> </td> <td> <p>hurs</p> </td> <td> <p>Average relative humidity</p> </td> <td> <p>Calculated from daily dew point temperature and temperature. </p> </td> <td> <p>%</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>32</em></p> </td> <td> <p>vpd</p> </td> <td> <p>Average water vapor pressure deficit</p> </td> <td> <p>Calculated from daily dew point temperature and temperature. </p> </td> <td> <p>hPa</p> </td> <td> <p>GS</p> </td> </tr> <tr> <td> <p><em>32</em></p> </td> <td> <p>hurs</p> </td> <td> <p>Average relative humidity</p> </td> <td> <p>Calculated from daily dew point temperature and temperature. </p> </td> <td> <p>%</p> </td> <td> <p>GS</p> </td> </tr> <tr> <td> <p><em>33</em></p> </td> <td> <p>pr</p> </td> <td> <p>Average precipitation sum</p> </td> <td> <p> </p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>34</em></p> </td> <td> <p>hygric_continentality</p> </td> <td> <p>Average hygric continentality according to Gams</p> </td> <td> <p>Defined as arctan of (elevation/annual_precip). Gams, H. (1931). Die klimatische Begrenzung von Pflanzenarealen und die Verteilung der hygrischen Kontinentalität in den Alpen. </p> </td> <td> <p>°</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>35</em></p> </td> <td> <p>pr1mm</p> </td> <td> <p>Average number of days with precipitation </p> </td> <td> <p>Daily precipitation of at least 1mm. </p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>36</em></p> </td> <td> <p>pr1mm</p> </td> <td> <p>Average number of days with precipitation in the growing season</p> </td> <td> <p>Daily precipitation of at least 1mm. </p> </td> <td> <p>days</p> </td> <td> <p>GS</p> </td> </tr> <tr> <td> <p><em>37</em></p> </td> <td> <p>DP_3days, DP_5days, DP_7days</p> </td> <td> <p>Average number of days in dry periods in summer half-year</p> </td> <td> <p>Number of days in periods of at least 3, 5, or 7 days with a daily precipitation total of less than 1mm. Summer half-year: April to September</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>39</em></p> </td> <td> <p>ET0</p> </td> <td> <p>Average annual potential evapotranspiration</p> </td> <td> <p>Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>40</em></p> </td> <td> <p>WBAL</p> </td> <td> <p>Average climatic water balance</p> </td> <td> <p>Precipitation minus potential evapotranspiration</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>41</em></p> </td> <td> <p>WBAL</p> </td> <td> <p>Average climatic water balance in the growing season</p> </td> <td> <p>Precipitation minus potential evapotranspiration in the growing season</p> </td> <td> <p>mm</p> </td> <td> <p>GS</p> </td> </tr> </tbody> </table> <p> </p> <h2>Data sources</h2> <p>The climate indicators were calculated using daily data from different sources:</p> <ul> <li><strong>Temperature</strong>: SPARTACUS v2.1 (Gridded data set, 1x1km, https://doi.org/10.1007/s00704-015-1411-4 )</li> <li><strong>Precipitation</strong>: SPARTACUS v2.1 (Gridded data set, 1x1km, https://doi.org/10.1007/s00704-017-2093-x )</li> <li><strong>Radiation</strong>: <br>APOLIS SHORT(Gridded data set, 100x100m, 2006-2020, https://adsabs.harvard.edu/abs/2012EGUGA..14.9705O)<br>APOLIS LONG (Gridded data set, 100x100m, 1981-2016)<br>SPARTACUS v2.1 (Gridded data set, 1x1km, daily sunshine duration, 1961-2020).</li> <li><strong>Wind</strong>: Daily station data (https://doi.org/10.60669/gs6w-jd70)</li> <li><strong>Humidity</strong>: Daily station data (https://doi.org/10.60669/gs6w-jd70)</li> <li><strong>Digital elevation model</strong>: © Kooperation Länder, Bund (BEV, BML), 2022</li> </ul> <p> </p>
Data from: Molecular data and distribution dynamics indicate a recent and incomplete separation of manakins species of the genus Antilophia (Aves: Pipridae) in response to Holocene climate change
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