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511 results for “climate effects”
The effects of changing climate on skull size in the common shrew
<p><span>We assesed the impact of the changes in climate on the overall skull size (the proxy of the overall body size) and the seasonal changes of skull height (Dehnel's phenomenon) in skulls of the common shrew, Sorex araneus, collected over 50 years in the Białowieża Forest, E Poland. Overall skull size decreased, along with increasing temperatures and decreasing soil moisture, which determined the availability of the shrews' main food source, earthworms. The magnitude of Dehnel's phenomenon increased over time, indicating an increasing selection pressure on animals in winter. Two files include the data on 1) the size of the skulls of Sorex araneus collected in the Białowieża Forest between 1953 and 2004; and 2) the meteorological data from Białowieża, from 1952 to 2004.</span></p>
Replication data for Chen and Khanna. (Global Environmental Change Advances, 2024), "Heterogeneous and Long-Term Effects of a Changing Climate on Bird Biodiversity"
<p>This dataset contains code and data to replicate the results for "Heterogeneous and Long-Term Effects of a Changing Climate on Bird Biodiversity" by Luoye Chen and Madhu Khanna.</p>
Figure 4 in Effects of Quaternary climatic oscillations over the Chacoan fauna: phylogeographic patterns in the southern three-banded armadillo Tolypeutes matacus (Cingulata: Chlamyphoridae)
Figure 4. Effective size change over time estimated from BSP analysis corresponding to all the localities for each marker. The y-axis represents the effective size (Ne) expressed on a logarithmic scale. The x-axis represents time in millions of years (Myr) before the present. The dark blue lines represent the median effective population size over time, and the lighter blue areas the range of Ne values with posterior densities higher than 95%. Expansion signals were highlighted in yellow. The analyses for the northern and southern groups are shown in the Supporting Information, Figure S1.
Figure 3 in Effects of Quaternary climatic oscillations over the Chacoan fauna: phylogeographic patterns in the southern three-banded armadillo Tolypeutes matacus (Cingulata: Chlamyphoridae)
Figure 3. Left panel (A) shows groups recovered by Bayesian inference in GENELAND. The middle and right panels present spatial distribution of posterior probability to belong to cluster north (B) and south (C).
Figure 2. Geographic distribution and haplotype networks for 12S in Effects of Quaternary climatic oscillations over the Chacoan fauna: phylogeographic patterns in the southern three-banded armadillo Tolypeutes matacus (Cingulata: Chlamyphoridae)
Figure 2. Geographic distribution and haplotype networks for 12S (top panels) and control region (bottom panels). The panels on the left plot the geographical distribution and frequency of haplotypes in the different localities analysed. Localities were labelled according to their ID (see Table 1). The right panels show the haplotype networks, where the dashes on the lines represent mutations, and the black circles represent intermediate variants not found. Principal Chacoan rivers are shown in light-blue labels. Capitalized labels indicate names of Argentinean provinces, and labels with all letters in uppercase refer to neighbouring countries.
Figure 1 in Effects of Quaternary climatic oscillations over the Chacoan fauna: phylogeographic patterns in the southern three-banded armadillo Tolypeutes matacus (Cingulata: Chlamyphoridae)
Figure 1. Possible range expansion scenarios for Tolypeutes matacus during glacial (A) and interglacial (B) periods. Yellow arrows denote scenarios proposed by Soibelzon (2019), while pink arrows indicate scenarios suggested by results from Ferreiro et al. (2022). Green polygon shows IUCN's current species distribution (Noss et al. 2014), and red and blue dots indicate fossil records from Holocene and Pleistocene periods, respectively (sensu Ferreiro et al. 2022).
Figure 5 in Effects of Quaternary climatic oscillations over the Chacoan fauna: phylogeographic patterns in the southern three-banded armadillo Tolypeutes matacus (Cingulata: Chlamyphoridae)
Figure 5. Spatiotemporal diffusion analysis of lineages for Tolypeutes matacus showing time slices for (A) 1.6 Mya, (B) 1.07 Mya, (C) 580 kya, and (D) the present. Red lines represent branches of the MCC tree and blue-shaded areas: indicate 80% HPD uncertainty in the location of ancestral nodes.
Effects of European emissions trading on the transformation of primary steelmaking: Assessment of economic and climate impacts in a case study from Germany
<p>Supplementary Information 1: This supporting information provides information on the modeling of material and energy flows for conventional and low-carbon steelmaking in an integrated steel mill. To this end, the production activities (unit processes) are provided. Also, information on the assessment of economic and climate impacts is included.</p> <p>Supplementary Information 2: This supporting information provides information on the main mechanisms of the European emissions trading system. Thereby, a focus is laid on regulations for the free allocation of EU allowances toward companies from the sectors with a high risk of carbon leakage. Additionally, the assessment and optimization models for designing favorable transformation pathways of integrated steel mills are provided. Also, the main assumptions and complementary results are provided.</p>
Data for "Climate outweighs human effects on vegetation properties during the early-to-mid Holocene"
<p>Data for analyses used in the PastHumanImpact project published in a manuscript "Climate outweighs human effects on vegetation properties during the early-to-mid Holocene". <br><br>All R code and workflows to reproduce the data analyses and figures are available at <a href="https://github.com/HOPE-UIB-BIO/PastHumanImpact" target="_blank" rel="noopener">HOPE-UIB-BIO/PastHumanImpact</a>.<br><br><strong>Usage:</strong><br>Each zip file should be extracted and placed into the `Data` folder, following the README file in the mentioned GitHub repo.<br><br>❗The data is under a CC BY-NC-ND licence, which requires approval from data owners before use (read <a href="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode" target="_blank" rel="noopener">here </a>for more details). Specifically, please contact Xianyong Cao (<a href="mailto:xcao@itpcas.ac.cn" target="_blank" rel="noopener">xcao@itpcas.ac.cn</a>) to share the Asian fossil pollen data.❗<br><br><strong>Citation</strong>:<br>If you want to use the data, please cite the mentioned paper "Felde et al. Climate outweighs human effects on vegetation properties during the early-to-mid Holocene". DOI: <a title="Climate outweighs human effects on vegetation properties during the early-to-mid Holocene" href="https://doi.org/10.21203/rs.3.rs-4692574/v1" target="_blank" rel="noopener">10.21203/rs.3.rs-4692574/v1</a></p>
The combined effect of climate change and building density on outdoor thermal comfort: a study in a southern middle sized Brazilian city)
<p>The files present all outputs (hourly data by sidewalk orientation) of physiological temperature equivalent from the simulations carried out in the research "The combined effect of climate change and building density on outdoor<br>thermal comfort: a study in a southern middle sized Brazilian city", classified according to the original ranges and Rosa's calibration.</p>
Continental scale α- and β-diversity patterns of terrestrial eukaryotic microbes: effect of climate and microhabitat on testate amoeba assemblages in Eurasian peatlands
Open the record for dataset details and reuse information.
Data for: Projected effects of climate-change-induced flow alterations on stream macroinvertebrate abundances
<p>Global change has the potential to affect river flow conditions which are fundamental determinants of physical habitats. Predictions of the effects of flow alterations on aquatic biota have mostly been assessed based on species ecological traits (e.g., current preferences), which are difficult to link to quantitative discharge data. Alternatively, we used empirically derived predictive relationships for species’ response to flow to assess the effect of flow alterations due to climate change in two contrasting central European river catchments. Predictive relationships were set up for 294 individual species based on (1) abundance data from 223 sampling sites in the Kinzig lower-mountainous catchment and 67 sites in the Treene lowland catchment, and (2) flow conditions at these sites described by five flow metrics quantifying the duration, frequency, magnitude, timing and rate of flow events using present-day gauging data. Species’ abundances were predicted for three periods: (1) baseline (1998–2017), (2) horizon 2050 (2046–2065) and (3) horizon 2090 (2080–2099) based on these empirical relationships and using high-resolution modeled discharge data for the present and future climate conditions. We compared the differences in predicted abundances among periods for individual species at each site, where the percent change served as a proxy to assess the potential species responses to flow alterations. Climate change was predicted to most strongly affect the low-flow conditions, leading to decreased abundances of species up to −42%. Finally combining the response of all species over all metrics indicated increasing overall species assemblage responses in 98% of the studied river reaches in both projected horizons and were significantly larger in the lower-mountainous Kinzig compared to the lowland Treene catchment. Such quantitative analyses of freshwater taxa responses to flow alterations provide valuable tools for predicting potential climate-change impacts on species abundances and can be applied to any stressor, species, or region.</p>
Data for: Global coupled climate response to polar sea ice loss: evaluating the effectiveness of different ice-constraining approaches
<p>This is the data for the paper titled "Global coupled climate response to polar sea ice loss: Evaluating the effectiveness of different ice-constraining approaches".</p>
Climate change has no apparent effect on debris flows in a supply-limited torrent
<h3><strong>This file contains all tree-ring data, growth disturbance data, final debris-flow chronology data and map background data used in the paper "A supply-limited torrent that does not feel the heat of climate change"</strong></h3> <p>Multetta-tree-ring raw data.rwl: Contains the raw measurement data from 761 tree-ring cores from 478 <em>P. mugo</em> trees.</p> <p>Growth Disturbances-Original data.xlsx: Contains detailed information on tree-ring cores, growth disturbances and their intensity. Zone area refers to the zonation (1-4 corresponds to SI-SIV) of the sampled trees in the study area. Tree ID refers to the name of the tree-ring core or cross section/wedge. The third and fourth columns refer to Y/latitude and X/longitude. Last ring refers to the year of the outermost ring and is mostly 2020, which is the year when the fieldwrok was carried out. In some cases, tree-ring cores were broken or they were taken from dead trees, so the year of the outermost ring is not 2020. Oldest ring refers to the year of the innermost available ring. In the age incomplete column, a value of 1 indicates that the oldest ring measured is not the innermost ring of the tree center. Age refers to the age of the trees, which is equals to the value of the last ring minus the value of the oldest ring. The Comments column indicates the wedge and the cross section. From the tenth column, the numbers 2020, 2019, 2018...... refer to different years corresponding to tree rings. Here, all values including 0, 1, 2, 3 and 4 indicate that there is a measured annual ring in the corresponding year. Blank indicates no data. GS refers to growth suppression. CW refers to compression wood. I refers to injury and CT refers to callus tissue. The number 0 means no growth disturbance. Numbers 1-4 mean intensity from weak to strong. For example, in the 2016 column, any core with '2GS' means the tree-ring showed growth suppression in 2016 and the corresponding intensity is 2. The growth disturbance (GDs) statistic is shown at the bottom, including all 1427 GDs, but GDs with intensity 1 were excluded from the analysis. Note: Samples mub74 and mul105 have data from both section and core, so 480 tree-ring series exist. </p> <p>Events-Final definition.xlsx: Contains all tree-ring based reconstructed debris-flow events for each zone (1-4 corresponds to SI-SIV) after careful examination of the spatial distribution of damaged trees. In the process of defining events, years were excluded from the analysis that (1) showed incoherent patterns of damaged trees (e.g. GDs evenly distributed on the cone, probably due to climatic extremes or insect pests), (2) were recorded in historical chronicles as snow avalanche years, or (3) were characterized by high mortality of <em>P. mugo</em> trees, as indicated by low tree-ring index (<1.5 average value) in the event cataster of the Canton of Grisons and the Swiss National Park (Bigler and Rigling, 2013).</p> <p><strong>Figures and the data used in figures are shown as below:</strong></p> <p>Information on tree location and age in fig.1D, GDs and sample depth (At) in fig.3A, reconstructed XXL events in fig.4A, GDs for affected regions (total of 16 regions) in fig.4B, GDs in different years for different zones in fig.S3 and GDs for different affected regions in fig.S4 is from the file: Growth Disturbances-Original data.xlsx.</p> <p>Information on the 56 defined events in fig.3B and the reconstructed debris-flow events in fig.5A, 5B is from the file: Events-Final definition.xlsx. Here, 75 events occurred in 4 zones, but actually all events occurred in 56 different years.</p> <p>The LiDAR DEM background in fig.1B, 1C and the hillshade view of the 2023 LiDAR DEM background in fig.S1, S2 are from Swisstopo (https://map.geo.admin.ch/).</p> <p>The R code used for the repose time pattern analysis in fig.5A, 5B is originally from the previously published paper (Heiser, M. et al., 2023) and is available on GitLab at https://gitlab.com/Rexthor/repose-time-patterns.</p>
Montreal climate data for building simulations with urban heat island effects and nature-based solutions
<p>As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Montreal airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.</p> <p>This dataset contains hourly historical and future weather files for use in building simulations for the city of Montreal, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned. </p> <p>The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R. </p> <p> </p> <p>The historical and future global warming thresholds and their corresponding time periods are as follows:</p> <table> <tbody> <tr> <td> <p><strong>Global Warming Scenario</strong></p> </td> <td> <p><strong>Time Period</strong></p> </td> </tr> <tr> <td> <p><strong>Historical</strong></p> </td> <td> <p>1991-2021</p> </td> </tr> <tr> <td> <p><strong>Global Warming 0.5ºC</strong></p> </td> <td> <p>2003-2033</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.0ºC</strong></p> </td> <td> <p>2014-2044</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.5ºC</strong></p> </td> <td> <p>2024-2054</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.0ºC</strong></p> </td> <td> <p>2034-2064</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.5ºC</strong></p> </td> <td> <p>2042-2072</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.0ºC</strong></p> </td> <td> <p>2051-2081</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.5ºC</strong></p> </td> <td> <p>2064-2094</p> </td> </tr> </tbody> </table> <p> </p> <p>The following variables are included in the files:</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>RUN</strong></td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td><strong>YEAR</strong></td> <td>Year associated with the record</td> </tr> <tr> <td><strong>MONTH</strong></td> <td>Month associated with the record</td> </tr> <tr> <td><strong>DAY</strong></td> <td>Day of the month associated with the record</td> </tr> <tr> <td><strong>HOUR</strong></td> <td>Hour associated with the record</td> </tr> <tr> <td><strong>YDAY</strong></td> <td>Day of the year associated with the record</td> </tr> <tr> <td><strong>DRI_kJPerM2</strong></td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DHI_kJperM2</strong></td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DNI_kJperM2</strong></td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the <em>HOUR</em> indicated)</td> </tr> <tr> <td><strong>GHI_kJperM2</strong></td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>TCC_Percent</strong></td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td><strong>RAIN_Mm</strong></td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>WDIR_ClockwiseDegFromNorth</strong></td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td><strong>WSP_MPerSec</strong></td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td><strong>RHUM_Percent</strong></td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td><strong>TEMP_K</strong></td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td><strong>ATMPR_Pa</strong></td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td><strong>SnowC_Yes1No0 </strong></td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td><strong>SNWD_Cm</strong></td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>
Toronto climate data for building simulations with urban heat island effects and nature-based solutions
<p>As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Toronto airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.</p> <p>This dataset contains hourly historical and future weather files for use in building simulations for the city of Toronto, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned. </p> <p>The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R. </p> <p> </p> <p>The historical and future global warming thresholds and their corresponding time periods are as follows:</p> <table> <tbody> <tr> <td> <p><strong>Global Warming Scenario</strong></p> </td> <td> <p><strong>Time Period</strong></p> </td> </tr> <tr> <td> <p><strong>Historical</strong></p> </td> <td> <p>1991-2021</p> </td> </tr> <tr> <td> <p><strong>Global Warming 0.5ºC</strong></p> </td> <td> <p>2003-2033</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.0ºC</strong></p> </td> <td> <p>2014-2044</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.5ºC</strong></p> </td> <td> <p>2024-2054</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.0ºC</strong></p> </td> <td> <p>2034-2064</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.5ºC</strong></p> </td> <td> <p>2042-2072</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.0ºC</strong></p> </td> <td> <p>2051-2081</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.5ºC</strong></p> </td> <td> <p>2064-2094</p> </td> </tr> </tbody> </table> <p> </p> <p>The following variables are included in the files:</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>RUN</strong></td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td><strong>YEAR</strong></td> <td>Year associated with the record</td> </tr> <tr> <td><strong>MONTH</strong></td> <td>Month associated with the record</td> </tr> <tr> <td><strong>DAY</strong></td> <td>Day of the month associated with the record</td> </tr> <tr> <td><strong>HOUR</strong></td> <td>Hour associated with the record</td> </tr> <tr> <td><strong>YDAY</strong></td> <td>Day of the year associated with the record</td> </tr> <tr> <td><strong>DRI_kJPerM2</strong></td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DHI_kJperM2</strong></td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DNI_kJperM2</strong></td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the <em>HOUR</em> indicated)</td> </tr> <tr> <td><strong>GHI_kJperM2</strong></td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>TCC_Percent</strong></td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td><strong>RAIN_Mm</strong></td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>WDIR_ClockwiseDegFromNorth</strong></td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td><strong>WSP_MPerSec</strong></td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td><strong>RHUM_Percent</strong></td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td><strong>TEMP_K</strong></td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td><strong>ATMPR_Pa</strong></td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td><strong>SnowC_Yes1No0 </strong></td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td><strong>SNWD_Cm</strong></td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>
Repository associated with "Quantifying CO2 forcing effects on lightning, wildfires, and climate interactions"
<p>Results of CESM2 simulation runs.<br>See readme file for details.</p>
Data for "Nonlinear and non-monotonic effect of ocean tidal mixing on exoplanet climates and habitability"
<p>Dataset analysed in order to obtain the results published in "Nonlinear and non-monotonic effect of ocean tidal mixing on exoplanet climates and habitability":</p> <ul> <li>IGCM_data: atmospheric data obtained by using the flux programme on the outcome of the standard FORTE2.0 climate simulations (instellation = 1.00 insolation)</li> <li>MOMA_standard_runs: oceaninc data of the standard FORTE2.0 climate simulations (instellation = 1.00 insolation)</li> <li>MOMA_reduced_runs: oceaninc data of the reduced FORTE2.0 climate simulations (instellation = 0.90, 0.85, 0.80 insolation)</li> </ul> <p>The FORTE2.0 code, compilation instructions and example run scripts, together with all necessary ancillary files, are accessible at <a href="https://doi.org/10.5281/zenodo.4108373">doi.org/10.5281/zenodo.4108373</a> (<a href="https://gmd.copernicus.org/articles/14/275/2021/#bib1.bibx6">Blaker et al.</a>, <a href="https://gmd.copernicus.org/articles/14/275/2021/#bib1.bibx6">2020</a>). </p>
Data from: Climate and competition effects on tree growth in Rocky Mountain forests
1. Climate is widely assumed to influence physiological and demographic processes in trees, and hence forest composition, biomass and range limits. Growth in trees is an important barometer of climate change impacts on forests as growth is highly correlated with other demographic processes including tree mortality and fecundity. 2. We investigated the main drivers of diameter growth for five common tree species occurring in the Rocky Mountains of the western United States using non-linear regression methods. We quantified growth at the individual tree level from tree core samples collected across broad environmental gradients. We estimated the effects of both climate variation and biotic interactions on growth processes and tested for evidence that disjunct populations of a species respond differentially to climate. 3. Relationships between tree growth and climate varied by species and location. Growth in all species responded positively to increases in annual moisture up to a threshold level. Modest linear responses to temperature, both positive and negative, were observed at many sites. However, model results also revealed evidence for differentiated responses to local site conditions in all species. In severe environments in particular, growth responses varied non-linearly with temperature. For example, in northerly cold locations pronounced positive growth responses to increasing temperatures were observed. In warmer southerly climates, growth responses were unimodal, declining markedly above a threshold temperature level. 4. Net effects from biotic interactions on diameter growth were negative for all study species. Evidence for facilitative effects was not detected. For some species, competitive effects more strongly influenced growth performance than climate. Competitive interactions also modified growth responses to climate to some degree. 6. Synthesis. These analyses suggest that climate change will have complex, species specific effects on tree growth in the Rocky Mountains due to non-linear responses to climate, differentiated growth processes that vary by location and complex species interactions that impact growth and potentially modify responses to climate. Thus, robust model simulations of future growth responses to climate trends may need to integrate realistic scenarios of neighborhood effects as well as variability in tree performance attributed to differentiated populations.
Data from: Interacting effects of wildlife loss and climate on ticks and tick-borne disease
Both large-wildlife loss and climatic changes can independently influence the prevalence and distribution of zoonotic disease. Given growing evidence that wildlife loss often has stronger community-level effects in low-productivity areas, we hypothesized that these perturbations would have interactive effects on disease risk. We experimentally tested this hypothesis by measuring tick abundance and the prevalence of tick-borne pathogens (Coxiella burnetii and Rickettsia spp.) within long-term, size-selective, large-herbivore exclosures replicated across a precipitation gradient in East Africa. Total wildlife exclusion increased total tick abundance by 130% (mesic sites) to 225% (dry, low-productivity sites), demonstrating a significant interaction of defaunation and aridity on tick abundance. When differing degrees of exclusion were tested for a subset of months, total tick abundance increased from 170% (only mega-herbivores excluded) to 360% (all large wildlife excluded). Wildlife exclusion differentially affected the abundance of the three dominant tick species, and this effect varied strongly over time, likely due to differences among species in their host associations, seasonality, and other ecological characteristics. Pathogen prevalence did not differ across wildlife exclusion treatments, rainfall levels, or tick species, suggesting that exposure risk will respond to defaunation and climate change in proportion to total tick abundance. These findings demonstrate interacting effects of defaunation and aridity that increase disease risk, and they highlight the need to incorporate ecological context when predicting effects of wildlife loss on zoonotic disease dynamics.
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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.