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2,837 results for “Climate Data”
Data for: Southern Europe is becoming climatically favourable for African birds: anticipating the establishment of a new species
<p><span><strong>Background</strong>:</span><span> The current modification of species distribution ranges, as a response to a warmer climate, constitutes an interesting line of work and a recent challenge for biogeography. This study aimed to determine if the climatic conditions of southern Europe are adequate to host a typical African species, the House Bunting, which is registered regularly during the last years, still in low numbers. To this end, the distribution of the species in its native range was modelled, both in the present and in future climate scenarios, using its current breeding distribution areas and a set of environmental variables. </span></p> <p><span><strong>Results</strong>:</span><span> The results showed that the southern half of the Iberian Peninsula exhibits high values of favourability to host this African species for the current climatic conditions. Furthermore, future forecasts indicated an increase in favourability for this area. The highly favourable areas we detected in the south of the Iberian Peninsula are already regularly receiving individuals of the species. These observations are very likely vagrant birds dispersing from recently colonised breeding areas in northern Morocco, which may indicate a continuous process of colonisation towards the north, as has occurred during the last decades in Northern Africa. </span></p> <p><span><strong>Conclusions</strong>:</span><span> We cannot anticipate when the House Bunting will establish on the European continent because colonisation processes are usually slow but, according to our results, we predict its establishment in the near future. We have also identified those areas hosting favourable conditions for the species in Europe. These areas are a potential focal point for the colonisation of this and other African birds if the climate continues to warm.</span></p>
Bold Park reptile species capture data for: Decadal abundance patterns in an isolated urban reptile assemblage: Monitoring under a changing climate
<p class="MsoNormal"><span>Fenced pitfall trapping in four sampling sites <span>representing different habitats and fire history</span> over the primary reptile activity period for 35 consecutive years with over 17000 individuals captured during 3300 days of sampling; the trapping regime was modified for the last 28 years.</span></p>
Data of Semi-Arid Climate and Environment Observatory Station of Lanzhou University (SACOL) (2019.12.09-2021.12.31)
<p><strong>Data Description</strong></p> <p>The PM<sub>2.5</sub> mass concentrations were observed by using a tapered element oscillating microbalance machine (TEOM, Model RP-1400A, Thermo Scientific, USA) with a temporal resolution of 1 minute. An aethalometer (Model AE31, Magee Scientific, USA) with a PM<sub>2.5</sub> inlet was used to measure the aerosol light absorption coefficients at 370, 470, 520, 590, 660, 880, and 950 nm. Aerosol scattering coefficients of PM<sub>2.5</sub> at 450, 550, and 700 nm wavelengths were observed by using an integrating nephelometer (Model 3563, TSI, USA) with a time resolution of 10 seconds. The micro-pulse lidar was installed approximately 50 m north of the tethered balloon observation site and used to observe atmospheric echoes within 20 km vertically and then invert parameters such as the aerosol σ and aerosol optical depth. The detailed parameters of lidar are shown in Table 1.</p> <p>A tethered balloon (volume: 10 m<sup>3</sup>; payload: 8 kg) was used to carry the following equipment: (1) MA200 micro-aethalometer (Magee Scientific, USA), (2) TSI 9306 optical particle counter (OPC, TSI, USA), (3) KZXLT-II sounding system from the Institute of Atmospheric Physics, and (4) GPSMAP 639sc GPS (GARMIN, USA). Sounding observations were made at 02:00, 08:00, 11:00, 14:00, 17:00, and 20:00 each day from December 9 to 31, 2019. A total of 91 vertical profiles were obtained for each of the available absorbing aerosols, particle number concentrations, and meteorological parameters.</p> <p>The MA200 measures light absorption at 5 wavelengths (375, 470, 528, 625 and 880 nm), and a dual-spot® compensation for loading effect correction is available. The MA200 observations have been converted to 550 nm in this data based on the AAE during the observation period. OPC was used to measure the particle number concentrations and particle number size distribution in the 0–10 μm size range. The temporal resolutions of MA200 and OPC were ~ 10 s, and their vertical resolutions were ~ 10 m. KZXLT-II sounding system was used to measure the vertical distribution of meteorological elements. GPS was used to measure real-time altitude.</p> <p> </p> <p><strong>Table 1.</strong> Main parameters of micro-pulse lidar</p> <table> <tbody> <tr> <td> <p>Projects</p> </td> <td> <p>Parameters</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Avalanche laser APD, photon counting model</p> </td> </tr> <tr> <td> <p>Observation band</p> </td> <td> <p>532 nm</p> </td> </tr> <tr> <td> <p>Pulse frequency</p> </td> <td> <p>2500 Hz</p> </td> </tr> <tr> <td> <p>Pulse energy</p> </td> <td> <p>3~4 µJ</p> </td> </tr> <tr> <td> <p>Maximum observation range</p> </td> <td> <p>15 km</p> </td> </tr> <tr> <td> <p>Time resolution</p> </td> <td> <p>1 min</p> </td> </tr> <tr> <td> <p>Vertical Resolution</p> </td> <td> <p>30 m</p> </td> </tr> </tbody> </table> <p> </p>
Output data for the article "Climate variability in 2030 European power systems"
<p>Model outputs for the paper "Climate variability on Fit for 55 European power systems"</p>
Supporting data for ''Nonlinearity of the cloud response postpones climate penalty of mitigating air pollution in polluted regions"
<p>Supporting data for our work on Nature Climate Change. You will find:</p> <p> - python codes for generating the plots in the manuscript.</p>
Climate Reanalyses Data from CESM-MSHea-CDA and CM2-MSHea-CDA systems
<p>We apply an identical coupled data assimilation algorithm and effective model error relaxing scheme to two widely-used IPCC coupled models, CESM and CM2, to establish two coupled data assimilation (CDA) systems. Then we use both CDA systems to assimilate historical atmospheric and oceanic observations to form two sets of climate reanalyses for the past four decades. Manuscript is written to report our efforts on convergent Climate Reanalysis, which is prepared to sumitted to <em>Journal of Climate. </em>Here we upload the mainly data of our reanalysis experiments.</p>
Data for the manuscript: Enabling Climate Change Adaptation in Coastal Systems. A Systematic Literature Review
<p>This dataset includes the list of publications, framework and dataset used for the paper "Enabling Climate Change Adaptation in Coastal Systems. A Systematic Literature Review"</p>
Data for the project: Climatic impacts on the availability and transfer of chemical elements through Arctic terrestrial food-chains: a pilot study
<p>Uploaded data contain trace element concentrations measured in various biological samples collected at Zackenberg Research station (northeast Greenland) in August 2021</p> <p>Fifty (50) sample plots were visited to collect invertebrates (using pitfalls) and sample soil, vegetation, muskox feces and wool.</p> <p>The sheet PlotInfo provides information on the sample plots, which were placed using a stratified random placement procedure across 5 vegetation types and the elevational gradient</p> <p>The sheet Soil contains trace element concentrations (ppm) as well as N and C (%) measured in the soil samples across all 50 sample plots</p> <p>The sheet Vegetation contains trace element concentrations (ppm) as well as N and C (%) measured in the vegetation samples across all 50 sample plots</p> <p>The sheet Insects contains trace element concentrations (ppm) measured in the various insect groups captured in pitfall traps in 9 sample plots</p> <p>The sheet Muskox feces contains trace element concentrations (ppm) measured in the feces samples collected at all 50 sample plots</p> <p>The sheet Muskox Wool contains trace element concentrations (ppm) measured in 25 wool samples collected throughout the sampling area (not at specific sample plots).</p> <p> </p> <p>Trace element concentrations were measured at the ICP-MS platform at the Observatoirie Midi-Pyrenees, Toulouse, France</p> <p>N and C analyses were done at Department of Biology, University of Copenhagen, Denmark</p> <p> </p> <p>For further information on the project, samples, methods used contact flbe@ecos.au.dk</p>
Stressed economies respond more strongly to climate extremes - Data and Code Supplement
<p>This repository provides data and code to reproduce the results of the publication "R. Middelanis, S. N. Willner, K. Kuhla, L. Quante, C. Otto, and A. Levermann (2023). Stressed economies respond more strongly to climate extremes. Environmental Research Letters."</p> <p> </p> <p><strong>dependencies:</strong></p> <ul> <li>a working environment is provided in environment.yml</li> <li>the Acclimate post-processing package can be downloaded from the respective GitHub repostory with <code>git@github.com:acclimate/post-processing.git</code>. Switch to the develop branch with <code>git checkout develop</code> and install the package with <code>conda develop .</code> from within the repository</li> </ul> <p> </p> <p><strong>data:</strong></p> <ul> <li>See <code>./data/README.md</code> for the required data and sources for those data that are not included in this repository.</li> </ul> <p> </p> <p><strong>Steps to reproduce the results:</strong></p> <p>1. Generate Acclimate input data</p> <ul> <li>Direct loss time series are obtained from "Kuhla et al. (2021). Ripple resonance amplifies economic welfare loss from weather extremes. <em>Environmental Reserach Letters</em>".</li> <li>Acclimate input data (cf. <code>./data/README.md</code>) are generated with <code>./code/forcing.py</code></li> <li>The input data used in the pubilcation are available at <code>./data/acclimate_input</code></li> </ul> <p>2. Run Acclimate</p> <ul> <li>the Acclimate model can be downloaded from the respective GitHub repository at <a href="https://github.com/acclimate/acclimate">https://github.com/acclimate/acclimate</a></li> </ul> <p>3. Run the analyses</p> <ul> <li>Acclimate output files of the calibration runs and the scenario runs are aggregated with functions <code>aggregate_calibration_ensemble </code>and <code>aggregate_ensembles</code> in <code>./code/utils.py</code>, respectively.</li> <li>Aggregated ready-to-use output data is located in <code>./data/acclimate_output</code></li> <li>All figures can be reproduced with <code>./code/plotting.py</code></li> </ul>
InTheMED WP3 Data Archive - Climate Projections in the Case Studies -
<p>The data archive InTheMED_WP3_DS_ClimateData is part of Task 3.3 “Downscaling of future climate projections at the case-study scale and their transfer to the Partners” and contains the climate projections at the five pilot sites under two emission scenarios (RCP4.5 and RCP8.5).</p>
Data from "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps"
<p>The following files were used as data and analysis in the article "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps" (<a href="https://doi.org/10.1029/2022EF003211">https://doi.org/10.1029/2022EF003211</a>). In the study, we applied self-organizing maps (SOMs) as an automated machine-learning approach to characterize the large-scale meteorological patterns (LSMP) and associated frequency and intensity of discrete extratropical cyclone (ETC) events over the northeastern U.S. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during 1980 - 2019. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMP and ETC events identified in the SOM. Please see the published paper for more details. Here we have archived: </p> <p>- data pre-processing scripts</p> <p>- code to run the self-organizing map analysis</p> <p>- code to calculate the SOM and ETC statistics</p> <p>- composites of 500-hPa geopotential height for each dataset as organized by the SOM</p> <p>- ETC tracking script and tracking output for each dataset</p> <p>- SOM output for each dataset </p>
Data for "Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China"
<p>The data contains some of the data necessary for this paper. and partly simulation results<br> </p>
Model outputs and species-level data for "Functional traits and climate drive interspecific differences in disturbance-induced tree mortality".V2
<p>A minor coding error was found in the pre-formatted data of <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16630">Barrere et al. (2023)</a>. This error did not affect the main results of the paper, but led to minor change in the value of the posterior estimates, stored in data/sensitivity/jags_dominance.Rdata. This repository contains the new version of the parameters. </p>
Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations (Data)
<p>README file for LBL-ERA5, LBL-GCM, and band datasets used in:<br> Raghuraman et al., 2023, Geophysical Research Letters,<br> "Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations "</p> <p>Point of Contact: Shiv Priyam Raghuraman, shivr@alumni.princeton.edu</p> <p> </p> <p>LBL-ERA5</p> <p>6 files (3 experiments with olr and olr_clr output separately):</p> <p>2003-2021.GFDL.all-2010-o3_*.nc - varying WMGHG,Ts,T,q,clouds, fixed o3</p> <p>2003-2021.GFDL.fo3_*.nc - varying WMGHG and fixed Ts,T,q,clouds,surface albedo,o3</p> <p>2003-2021.GFDL.ff_*.nc - fixed Ts,T,q,clouds,surface albedo, varying o3</p> <p> </p> <p>LBL-GCM (clear-sky only)</p> <p>4 AM4 files:</p> <p>AM4piclim-control.nc (for ERF)</p> <p>AM4piclim-4xCO2_1xCO2IRF.nc (for ERF)</p> <p>AM4piclim-control_STRAT.nc (for SARF)</p> <p>AM4piclim-4xCO2_1xCO2IRF_STRAT.nc (for SARF)</p> <p>4 CM3/AM3 files:</p> <p>CTLAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>EXPAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>CTLAM3CM3_P1_2003C.nc (for IRF)</p> <p>CTLAM3CM3_P1_2021C.nc (for IRF)</p> <p> </p> <p>Band-AM4/MERRA-Total: GFDL AM4 with prescribed SSTs and sea-ice (AMIP) and nudged with MERRA winds</p> <p>2 files:</p> <p>atmos.200001-202112.olr.nc - all-sky OLR</p> <p>atmos.200001-202112.olr_clr.nc - clear-sky OLR</p> <p> </p> <p>Observational and reanalysis files used in paper (AIRS, CERES EBAF and SSF, GISTEMP, ERA5 input data) can be downloaded from their respective websites (see paper's "Open Research" section).</p> <p> </p>
Data from: Coral reef state influences resilience to acute climate-mediated disturbances
<p>Aim: Understand the interplay between resistance and recovery on coral reefs, and investigate dependence on pre- and post-disturbance states, to inform generalisable reef resilience theory across large spatial and temporal scales.</p> <p>Location: Tropical coral reefs globally.</p> <p>Time period: 1966 to 2017.</p> <p>Major taxa studied: Scleratinian hard corals.</p> <p>Methods: We conducted a literature search to compile a global dataset of total coral cover before and after acute storms, temperature stress, and coastal runoff from flooding events. We used meta-regression to identify variables that explained significant variation in disturbance impact, including disturbance type, year, depth, and pre-disturbance coral cover. We further investigated the influence of these same variables, as well as post-disturbance coral cover and disturbance impact, on recovery rate. We examined the shape of recovery, assigning qualitatively distinct, ecologically relevant, population growth trajectories: linear, logistic, logarithmic (decelerating), and a second-order quadratic (accelerating).</p> <p>Results: We analysed 427 disturbance impacts and 117 recovery trajectories. Accelerating and logistic were the most common recovery shapes, underscoring non-linearities and recovery lags. A complex but meaningful relationship between the state of a reef pre- and post-disturbance, disturbance impact magnitude, and recovery rate was identified. Fastest recovery rates were predicted for intermediate to large disturbance impacts, but a decline in this rate was predicted when more than ~75% of pre-disturbance cover was lost. We identified a shifting baseline, with declines in both pre-and post-disturbance coral cover over the 50 year study period.</p> <p>Main conclusions: We breakdown the complexities of coral resilience, showing interplay between resistance and recovery, as well as dependence on both pre- and post-disturbance states, alongside documenting a chronic decline in these states. This has implications for predicting coral reef futures and implementing actions to enhance resilience.</p>
References and Climatic data of the different study sites of the Moroccan Mountains
<p>This Dataset present the Precipitation and Temperature of different study sites and the references used in the article: "Pollen indices of C. atlantica M. populations vary with climatic changes in the Moroccan Mountains" </p>
Data for "Dynamical Downscaling of Climate Simulations in the Tropics"
<p>Precipitation, radiation and vertical mass flux data. 'MPI' indicates conventional downscaling results. 'biascor' indicates bias-corrected downscaling results. 'sstcor' indicates SST-corrected downscaling results.</p>
Data for "Early planting adaptation makes the coupled food-water system more sustainable under climate change"
<p>Data for submission "Early planting adaptation makes the coupled food-water system more sustainable under climate change"</p><p>All data are in netcdf format and can be read in ncl, python, R code capacity.</p><p> </p><p><strong>geo_em.d01.conus.corn:</strong></p><p>Domain setup file for the Noah-MP crop model in the US corn belt. Lat/lon location specified by "XLAT_<i>M" and "XLONG_M</i>" variable and corn planting area specified by "CROPTYPE" variable.</p><p> </p><p>Three zip files are uploaded containing data from model simulations and county-level yield and irrigation record:</p><p><strong>Yield_data.zip:</strong></p><p>yield data from model simulations (denoted by three scenarios, CTRL, PGW, TAVE), with irrigation (irr), and from USDA NASS (NASS).</p><p><strong>Irrigation_data.zip:</strong></p><p>Irrigation amount data from three scenarios (CTRL, PGW, TAVE for early planting), and from USGS water use record (2005 and 2010).</p><p><strong>TempPrecPET.zip:</strong></p><p>temperature and precipitation and potential evapotranspiration data (PET) for the CTRL and PGW climate scenarios. </p>
Data from: The utility of climatic water balance for ecological inference depends on vegetation physiology assumptions
Open the record for dataset details and reuse information.
Physiology trait and growing region climate data compiled from the literature for 34 wine grape cultivars
Open the record for dataset details and reuse information.
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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.