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617 results for “Climate models”
Fig. 3 in Changes in the range of Pterostichus melas and P. fornicatus (Coleoptera, Carabidae) on the basis of climatic modeling
Fig. 3. Curves of dependence of the model of probable distribution of P. fornicatus on bioclimatic parameters: a – Bio11; along the abscissa axis – mean temperature of the coldest quarter of year; on the ordinate axis – index of suitability for the species, b – Bio14; on the abscissa axis – amount of precipitations in the driest month of the year; on the ordinate axis – index of suitability for the species, c – Bio 6; on the abscissa axis – minimum temperature
Fig. 2 in Changes in the range of Pterostichus melas and P. fornicatus (Coleoptera, Carabidae) on the basis of climatic modeling
Fig. 2. Model of potential (probable) range of P. fornicatus assessed in Maxent software basing on the data of WorldClim: in red the most suitable areas for living are indicated (70–100%), orange – 50–70%, yellow – 20–50%, blue – 0%.
Fig. 6 in Changes in the range of Pterostichus melas and P. fornicatus (Coleoptera, Carabidae) on the basis of climatic modeling
Fig. 6. Statistical analysis of the obtained model of the probable distribution of P. melas: a – omission and Predicted Area for P. melas: 1 – test data, 2 – training data, 3 – fraction of the initial data which were predicted, 4 – predicted emission; b – trend of the operative curve AUC: 1 – test data, 2 – training data, 3 – random prediction
Figure 3 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 3. Predicting of potential areas to the occurrence of Costalimaita ferruginea in the period of 2061-2080, in two climate change scenarios, Representative Concentration Pathways (RCP) 4.5 e 8.5 (W/m2), using the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Figure 2 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 2. Predicting of potential areas to the occurrence of Costalimaita ferruginea in the period of 2041-2060, in two climate change scenarios, Representative Concentration Pathways (RCP) 4.5 e 8.5 (W/m2), using the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Climate model and proxy input data for PaleoDA South America reconstruction
<p>This repository contains input data needed to run the paleoclimate reconstruction code for "A continental reconstruction of hydroclimatic variability in South America during the past 2000 years", submitted to Climate of the Past in February 2024 [https://egusphere.copernicus.org/preprints/2024/egusphere-2024-545/]. The Github repository is located here: https://github.com/mchoblet/paleoda_sa/tree/main</p> <p><strong>Structure:</strong></p> <p>model_data: One File for each Model (GISS, CCSM (isoGSM), CESM, ECHAM5, iHADCM3) and variable (prec,tsurf,d18O, SPEI). Monthly resolution.</p> <p>proxy_data: One File for each proxy record type (Trees and corals contain a separate file for annual and djf linear regression parameters, the proxy data as such is the same). The data has yearly resolution, and thus also contains NaNs for when a year is not covered by a proxy. Note, that these time series are resampled to a regular resolution in the multi-time scale PaleoDA code.</p> <p><strong>Climate Model Data:</strong></p> <p>The original data can be found in https://zenodo.org/records/6610684. The data in this repository here has been slightly modified and regridded for easier processing by the reconstruction algorithm. When using the data here, please also cite https://zenodo.org/records/6610684 and the publication </p> <p>"Investigating stable oxygen and carbon isotopic variability in speleothem records over the last millennium using multiple isotope-enabled climate models", by </p> <div>Janica C. Bühler, Josefine Axelsson, Franziska A. Lechleitner, Jens Fohlmeister, Allegra N. LeGrande, Madhavan Midhun, Jesper Sjolte, Martin Werner, Kei Yoshimura, and Kira Rehfeld (https://cp.copernicus.org/articles/18/1625/2022/cp-18-1625-2022.html)</div> <p><strong>Climate Proxy Data:</strong></p> <p>A regional proxy record subselection for South America. See References in Appendix A Choblet et al. (https://egusphere.copernicus.org/preprints/2024/egusphere-2024-545/). The DOI of each record is stored as Metadata.</p> <p><strong>How were these files created?</strong></p> <p>The steps are documented in the the Github repository https://github.com/mchoblet/paleoda_sa/tree/main (data_preprocessing). The SPEI drought index has ben computed from modeled precipitation and temperature using Thornthwaite's method (using the Climate Indices package, https://github.com/monocongo/climate_indices).</p> <p><strong>Manuscript revision in July 2024:</strong></p> <ul> <li>Added historical documentary indices time series and the Puyehue lake record. For technical reasons in the PaleoDA algorithm, it is kept apart from the other lake records. The reconstruction code on Github has been updated for including these datasets.</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <div> </div>
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</p>
Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model
<div> <div> <div> <p>Thermodynamic and microphysical data (matlab files) for NorESM2 simulations presented in the article "Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model" </p> </div> </div> </div>
Рис. 6. МоΔеΛирование экоΛогических ниш коΛораΔского жука ΔΛя ΔаΛьневосточного, европейского и североамериканского ареаΛов метоΔом метрического Δвухмерного шкаΛирования с применением коэффициента Жаккара Fig. 6. Models of ecological niches of the Colorado potato beetle for the Far Eastern, European, and North-American habitats (metric multidimensional scaling, Jaccard index) in Comparative characterization of the ecology of native (Henosepilachna vigintioctomaculata) and invasive (Leptinoatrsa decemlineata) species under the conditions of the monsoon climate in the southern part of the Russian Far East
Рис. 6. МоΔеΛирование экоΛогических ниш коΛораΔского жука ΔΛя ΔаΛьневосточного, европейского и североамериканского ареаΛов метоΔом метрического Δвухмерного шкаΛирования с применением коэффициента Жаккара Fig. 6. Models of ecological niches of the Colorado potato beetle for the Far Eastern, European, and North-American habitats (metric multidimensional scaling, Jaccard index)
Historical climate model output of ECHAM5-wiso from 1871-2011 at T106 resolution
<p>Historical climate model simulation of the isotope-enabled ECHAM5-wiso model from the years 1871 to 2011 at T106 (1 degree) resolution. The model code was provided by Martin Werner of AWI. The simulations were designed and run by Nathan Steiger on the Yellowstone supercomputer. The boundary conditions were interpolated HadISST fields. The simulations also included updated fractionation factors (an option within the ECHAM5-wiso Fortran code). All variables here are at monthly resolution in netcdf format. Please <a href="http://www.ldeo.columbia.edu/~nsteiger/contact.html">contact</a> Nathan Steiger if you have any questions about the simulation. In addition to the data citation, please also cite the following reference for where the data were first published: Steiger, N.J., E.J. Steig, S.G. Dee, G.H. Roe, and G.J. Hakim, (2017): <em>Climate reconstruction using data assimilation of water-isotope ratios from ice cores.</em> Journal of Geophysical Research: Atmospheres, doi:10.1002/2016JD026011.</p> <p>Standard variables include: ECHAM5 T106 orography, 2 m temperature, surface pressure, mean sea level pressure, vertically integrated water vapor, total precipitation, evaporation, soil moisture, relative humidity, specific humidity, atmospheric stream function at 200 hPa, geopotential height at 500 hPa, windspeed at 10 m, and u-velocity wind at 200 hPa. Moisture variables are given at the surface (lowest atmospheric level).</p> <p>Isotope variables include: d18O and dD of total precipitation, d18O and dD of evaporation, d18O and dD of snow fall, d18O and dD of seasonal snow cover, d18O and dD of snow on glaciers, d18O and dD of soil moisture, and specific humidity of water isotopes.</p>
Manipulation of netCDF data with R for climate change research: Multi-model analysis for CMIP5 models.
<p>Geoscientists now live in a world with an exponential growth in digital data and methods.<br> Climate change studies usually describe computational methods informally. Climate scientists seek to<br> share their information, the justification of reproducible research has received increasing attention in<br> geosciences. To have it in an open-source format makes it easier to interchange not only with fellow<br> scientists but also a variety of sources including funders, publishers, and journalists. R is a open-source<br> computer language powerful and highly extensible that can promotes reproductive science techniques in a<br> easier way. R is highly accessible for non-computational scientists when coupled with packages like<br> ‘raster', ‘netcdf', ´rgdal`and ‘rasterVis', R enables scientists to make sense of their data and to carry out<br> complex data analysis. In this paper we have assessed the power of R language for manipulating climate<br> data from a huge dataset: the Coupled Model Intercomparison Project Phase 5 (CMIP5). Moreover we<br> have proposed an example of best practices to handle model ensembles. This is the first study to our<br> knowledge to promote best practices for CMIP5 ensemble. The NetCDF data accessible to R via raster<br> package capabilities provides efficient access to the multi-model, with crucial applications in climate<br> change research. In recent years more than 100 peer-reviewed scientific publications have used the<br> CMIP5 data sets. We envision that in the near future (5-10 years), scientists will use radically new tools<br> to author papers and disseminate information about the process and products of their research.</p>
Antarctic surface mass balance with the regional climate model MAR (1979–2015)
<p>Outputs of the regional climate model MAR v3.6.41 for Antarctica, resolution 35km + source code</p> <p>===========================================</p> <p>Cécile Agosta, 23 Jan 2019 </p> <p>cecile.agosta@gmail.com</p> <p>===========================================</p> <p>Grid specifications are given in MAR-ant35km-grid.nc (projection : EPSG 3031).</p> <p>* State variables are averages of daily means:</p> <p> TT > temperature (°C)</p> <p> ZZ > height above sea level (m)</p> <p> UU, VV > x-wind and y-wind in the stereographic grid (m s-1)</p> <p> UV > wind speed (m s-1)</p> <p>State variables ending with z (e.g. UUz) are interpolated on fixed altitude levels above the ground.</p> <p>State variables ending with p (e.g. UUp) are interpolated on fixed pressure levels.</p> <p>* SMB components are summed: kg m-2 month-1 for montly files, kg m-2 year-1 for annual files, kg m-2 year-1 for clim files</p> <p> snf > snowfall</p> <p> rnf > rainfall</p> <p> rof > run-off</p> <p> sbl > sublimation/condensation</p> <p> smb = snf + rnf - sbl - rof</p> <p> mlt > snowmelt</p> <p> rfz > refreezing</p> <p>If you use this data, please cite the final accepted version of this article:</p> <p>Agosta C., Amory C., Kittel C., Orsi A., Favier V., Gallée H., van den Broeke M.R., Lenaerts J.T., van Wessem J.M., & Fettweis X. (in review, 2018). Estimation of the Antarctic surface mass balance using MAR (1979-2015) and identification of dominant processes. <em>The Cryosphere Discussions</em>, 1–22, <a href="https://doi.org/10.5194/tc-2018-76">doi:10.5194/tc-2018-76</a>.</p> <p>Please contact me if you need other outputs (variables/daily or hourly time steps)</p>
Ocean model fields shown in paper titled "E3SMv0-HiLAT: A Modifed Climate System Model Targeted for the Study of High Latitudes"
<p>These files contain ocean model climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format, with fields described within the file (and subsequently compressed).</p>
Sea ice model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes
<p>These files contain the full climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format, with fields described within the file (and subsequently compressed).</p>
More atmospheric model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes
<p>These files contain atmospheric climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format (subsequently compressed), with fields described within those files.</p>
Data for "COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees"
<p>In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the vegetated urban canopy model BEPTree and the mesoscale weather and climate model COSMO.</p> <p>The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June-July 2015.</p> <p>The data includes:</p> <p>1. <em>datasets</em><br> Datasets of building geometries (Shapefile, WGS84), trees (GeoTiff, WGS84), Landsat 7 scene (GeoTIFF, WGS84) and imperviousness (GeoTIFF, WGS84).</p> <p>2. <em>model outputs</em><br> The processed model outputs (.npy files, generated with Python v3) are provided for all the simulations, in terms of time series at the observation sites and spatial distributions. The full 3D model outputs, 1 TB) can be provided by request by contacting the author (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>3. <em>model inputs</em><br> Input namelists for the COSMO-BEP-Tree model and initial/static conditions. The full 3D boundary conditions (60 GB) can be provided by request (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>4. <em>observations</em><br> Measurement data (.txt).</p> <p>5. <em>post-processing scripts</em><br> Jupyter (Python 3) Notebook files used to generate the figures and to analyse model results. Tested in Python 3.6.5.</p>
IPSL-CM5A2. An Earth System Model designed for multi-millennial climate simulations: Boundary conditions and outputs.
<p>Inputs, boundary conditions, and ouputs files of the experiments described in Sepulchre et al. manuscript "<em>IPSL-CM5A2. An Earth System Model designed for multi-millennial climate simulations</em>" submitted for publication to Geoscientific Model Development:</p> <p><a href="https://www.geosci-model-dev-discuss.net/gmd-2019-332"><strong>https://www.geosci-model-dev-discuss.net/gmd-2019-332</strong></a></p> <p>The 90Ma_IPSLCM5A2_inputs.tar tarball contains the input and boundary files used to run the 3,000-year Cretaceous experiment.</p> <p>The output_files.tar tarball contains the netcdf output files of the preindustrial, historical and Cretaceous simulations analyzed in the manuscript. Diagnoses are presented through a Jupyter notebook that can be retrieved and played interactively <strong><a href="https://doi.org/10.5281/zenodo.3549652"><strong>here</strong></a>.</strong></p> <p><strong> </strong></p>
Model output used in the manuscript "Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (hereafter L84-S61; <a href="https://doi.org/10.3402/tellusa.v53i5.12229" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the manuscript "<em>Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective</em>", published by the Bulletin of the American Meteorological Society (<a href="https://doi.org/10.1175/BAMS-D-24-0064.1" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2025</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a> and <a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al. (2024)</a>.</p> <p>All files uploaded were generated from simulations run by the lead author.</p> <p>For specific information about each file uploaded, please refer to the README file. The details of each experiment are also presented in the supplementary materials of the manuscript. If you have any questions, please feel free to contact me.</p>
Figure 1 in Climatic preferences and distribution of 6 evolutionary lineages of Typhlops vermicularis Merrem, 1820 in Turkey using ecological niche modeling
Figure 1. Important mountain chains of Anatolia and ecological niche modeling of T. vermicularis in Turkey under current climatic conditions.
Figure 3 in Climatic preferences and distribution of 6 evolutionary lineages of Typhlops vermicularis Merrem, 1820 in Turkey using ecological niche modeling
Figure 3. Predicted models of lineages G, H, and I according to Last Interglacial (LIG) and Last Glacial Maximum (LGM; CCSM and MIROC) (4, 4A, 4B, 4C for lineage G; 5, 5A, 5B, 5C for lineage H; 6, 6A, 6B, 6C for lineage I).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.