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617 results for “Climate models”
Figure 2 in Climatic preferences and distribution of 6 evolutionary lineages of Typhlops vermicularis Merrem, 1820 in Turkey using ecological niche modeling
Figure 2. Predicted models of lineages B, C, and E according to Last Interglacial (LIG) and Last Glacial Maximum (LGM; CCSM and MIROC) (1, 1A, 1B, 1C for lineage B; 2, 2A, 2B, 2C for lineage C; 3, 3A, 3B, 3C for lineage E).
Figure 4 in Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricata (Blanford, 1874), in Iran: an insight into the impact of climate change
Figure 4. Overlay of Iranian Conservation Network with the habitat suitability map of the Mesopotamian spiny-tailed lizard.
Figure 3 in Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricata (Blanford, 1874), in Iran: an insight into the impact of climate change
Figure 3. Model of habitat suitability for the species based on the present climatic data (A) and 2.6 (B) and 8.5 (C) scenarios of the CCSM for the future.
Figure 1 in Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricata (Blanford, 1874), in Iran: an insight into the impact of climate change
Figure 1. The presence records (black dots) used for the development of a maximum entropy model for predicting the habitat suitability of the Mesopotamian spiny-tailed lizard.
Figure 7 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 7. Map of potential invasion range of S. woodiana in Europe under the RCP 8.5 climate change scenario at 2080-2100: green filling indicates areas defined as suitable using minimum presence (MP) threshold; orange filling indicates areas defined as suitable using 10th percentile presence (10P) threshold. Black dots indicate species record used for SDM.
Figure 6 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 6. Map of potential invasion range of S. woodiana in Europe under the RCP 4.5 climate change scenario at 2080-2100: green filling indicates areas defined as suitable using minimum presence (MP) threshold; orange filling indicates areas defined as suitable using 10th percentile presence (10P) threshold. Black dots indicate species record used for SDM.
Figure 4 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 4. Response curves of the environmental variables selected for prediction of S. woodiana distribution under the RCP 8.5 scenario. Each curve (green line) shows how the logistic prediction changes as each environmental variable is varied. The orange dashed line crosses the maximum value of the variable.
Figure 5 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 5. Map of potential invasion range of S. woodiana in Europe under the recent climate conditions: green filling indicates areas defined as suitable using minimum presence (MP) threshold; orange filling indicates areas defined as suitable using 10th percentile presence (10P) threshold. Black dots indicate species record used for SDM.
Figure 3 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 3. Response curves of the environmental variables selected for prediction of S. woodiana distribution under the RCP 4.5 scenario. Each curve (green line) shows how the logistic prediction changes as each environmental variable is varied. The orange dashed line crosses the maximum value of the variable.
Figure 1 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 1. Map of records of S. woodiana in Europe obtained from GBIF database and published sources (Vikhrev et al., 2024).
Modelling environmental suitability of sorghum, wheat and maize in Europe under climate change (Code & data)
<p><span>Wheat and maize play an important role as crops for human consumption and animal feed in Europe. To guarantee food security and the stability of the agricultural sector in Europe, it is crucial to determine how climate change will impact the environmental suitability and thus the potential geographic distribution of these crops. Sorghum, a crop that originates in Africa, has seen a recent increase in cultivation in Europe. Due to its tolerance to more extreme climate conditions and its versatility of use, it might inherit a high potential as an alternative crop. </span></p> <p><span>Occurrence data of sorghum, wheat and maize as well as several environmental variables were used as input data for an ensemble modelling approach that averages machine learning models for species distribution modelling (SDM). CHELSA served as a source for present bioclimatic conditions and future climate scenarios, namely SSP126 and SSP370 for the period 2041-2070, and HSWD supplied soil variables, since both climate and soil influence crop development. A set of models was evaluated to select the best performing models for the ensemble modelling. The ensemble models were extrapolated to the future scenarios to predict geographic shifts of suitable cultivation areas due to climate change and analyze sorghum’s potential as an alternative crop. </span></p> <p><span>Under the climate scenarios, the three crops saw a shift of suitability in Europe with losses in Southern Europe and expansions of suitable environmental conditions in the northeast of Europe. Sorghum was the crop with the highest potential to replace maize and wheat in Southern Europe in areas where they lose suitability under climate change. Therefore, sorghum confirmed its function as an alternative crop. It also was the crop that benefits consistently from climate change, growing its total suitable area in Europe under both climate scenarios. Maize loses total suitable area in Europe in both climate scenarios but kept the highest amount of total suitable area in Europe in all projected time periods. </span></p> <p><span>The outcome of this study is of high importance for European farmers and policy makers as it enables them to apply effective adaptation and mitigation strategies that will support crop production under future climate conditions. <br></span></p>
Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations
<p>This dataset contains NetCDF files necessary to replicate results from the 2024 paper "<em>Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations</em>"</p> <p>The dataset contains NetCDF files with 1 year of zonally-averaged NASA Ames Mars Global Climate Model (MGCM) fields with 5-sol binning for each of the simulations presented in the paper: </p> <ul> <li>a "low-resolution" simulation with no parameterization for gravity waves</li> <li>a "high-resolution" simulation with no parameterization for gravity waves</li> <li>a "low-resolution" simulation with parameterizations for orographic and non-orographic gravity waves</li> </ul> <p>Also included are:</p> <ul> <li>a file describing the coordinates for the MGCM's vertical grids used in the study </li> <li> atmospheric fields not provided in the other NetCDF files and necessary to replicate figures 3 and supplemental figure FS2 from the paper.</li> <li>a README.txt detailing the content of each file in the dataset</li> </ul>
FOCI model output used in the study by Ivanciu et al. - Twenty-first century Southern Hemisphere impacts of ozone recovery and climate change from the stratosphere to the ocean
<p>This dataset comprises the output from simulations with the coupled climate model FOCI (Flexible Ocean and Climate Infrastructure, Matthes et al., 2020) used in the analysis presented in the study by Ivanciu et al., 2021 “Twenty-first century Southern Hemisphere impacts of ozone recovery and climate change from the stratosphere to the ocean”. Four ensembles of three simulations each were performed: FixODS (II012, II014, II016), FixGHG (II013, II015, II017), INTERACT_O3 (SW128, II010, II011) and PRESC_O3 (JH027, II037, JH039). A detailed description of the simulations can be found in the above-mentioned manuscript.</p>
Fig. 4 in Modelling the change in the distribution of the black-shanked douc, Pygathrix nigripes (Milne-Edwards) in the context of climate change: Implications for conservation
Fig. 4. The predicted distribution of the black-shanked douc (P. nigripes) generated by the MaxEnt software under the RCP8.5 scenario. BGM = Bu Gia Map National Park; CYS = Chu Yang Sin National Park; CT = Cat Tien National Park; HB = Hon Ba Nature Reserve; KL-SM = Kalon-Song Mao Nature Reserve; KT = Krong Trai Nature Reserve; NK = Nam Ka Nature Reserve; NN = Nam Nung Nature Reserve; NC = Nui Chua National Park; NO = Nui Ong Nature Reserve; TK = Takou Nature Reserve; VC = Vinh Cuu Nature Reserve.
Fig. 2 in Modelling the change in the distribution of the black-shanked douc, Pygathrix nigripes (Milne-Edwards) in the context of climate change: Implications for conservation
Fig. 2. The predicted distribution of the black-shanked douc (P. nigripes) generated by the MaxEnt software under the RCP4.5 scenario. BGM = Bu Gia Map National Park; CYS = Chu Yang Sin National Park; CT = Cat Tien National Park; HB = Hon Ba Nature Reserve; KL-SM = Kalon-Song Mao Nature Reserve; KT = Krong Trai Nature Reserve; NK = Nam Ka Nature Reserve; NN = Nam Nung Nature Reserve; NC = Nui Chua National Park; NO = Nui Ong Nature Reserve; TK = Takou Nature Reserve; VC = Vinh Cuu Nature Reserve.
Fig. 3 in Modelling the change in the distribution of the black-shanked douc, Pygathrix nigripes (Milne-Edwards) in the context of climate change: Implications for conservation
Fig. 3. Response curve plots illustrate the dependence of predicted potential distribution on the three most important environmental variables for the black-shanked douc (P. nigripes). The curve shows the mean response of 10 replicates by MaxEnt (red line) and the standard deviation (blue band).
Fig. 1. The 472 in Modelling the change in the distribution of the black-shanked douc, Pygathrix nigripes (Milne-Edwards) in the context of climate change: Implications for conservation
Fig. 1. The 472 points recorded for the black-shanked douc (P. nigripes) that were used in this study. BGM = Bu Gia Map National Park; CYS = Chu Yang Sin National Park; CT = Cat Tien National Park; HB = Hon Ba Nature Reserve; KL-SM = Kalon-Song Mao Nature Reserve; KT = Krong Trai Nature Reserve; NK = Nam Ka Nature Reserve; NN = Nam Nung Nature Reserve; NC = Nui Chua National Park; NO = Nui Ong Nature Reserve; TK = Takou Nature Reserve; VC = Vinh Cuu Nature Reserve.
Upslope migration of snow avalanches in a warming climate: data and model source files
<p>Complete data and model source files corresponding to:</p> <p>Giacona, F., Eckert, N., Corona, C., Mainieri, R., Morin, S., Stoffel, M., Martin, B., Naaim, M. (2021). Upslope migration of snow avalanches in a warming climate. Proceedings of the National Academy of Sciences America, Nov 2021, 118 (44) e2107306118; DOI: 10.1073/pnas.2107306118</p>
Data, code and supplementary material for "A data integration framework for spatial interpolation of temperature observations using climate model data"
<p>Each zipped file contains code and data to reproduce the results in the paper and supplementary material. The Cyprus folder contains also the files to run the model, as well as the associated results. The Morocco folder only contains the results and the code used to manipulate it. </p>
Climate-ecosystem modelling made easy: the Land Sites Platform - simulation results
<p>Model data to reproduce the experiments and figures presented in "Climate-ecosystem modelling made easy: the Land Sites Platform" (Keetz & Lieungh et al., accepted; publication details to be added).</p> <p>The repository includes model input data for the BOR1 site, and model output for two cases (simulations) executed for that site. Case "bor1-1000y-allpfts" was run with default settings, whereas case "bor1-1000y-grasspfts" was run with C3 grass and Arctic C3 grass as the only plant functional types.<br> Concatenated versions of the model output (made by combining monthly history files) for each case are stored in the top folder as NetCDF files (.nc). The full case folders, including monthly output under /archive/lnd/hist, are stored under "cases/" as zipped directories.<br> The "data/" folder contains model input data for the site, and is identical for the two cases.</p> <p>Jupyter notebooks to analyse the data and create the plots in Figure 4 of the article are stored under "notebooks/technical_paper_results/" in the NorESM-LSP GitHub repository, which is accessible in the same version as the submitted manuscript here: 10.5281/zenodo.7310652</p> <p>Description: Model data to reproduce the experiments and figures presented in "Climate-ecosystem modelling made easy: the Land Sites Platform".<br> Coverage: Geographical coordinates 9.07876, 61.0355. Forcing data from the closest 0.5-degree model grid cell.<br> Format: NetCDF (.nc), zipped directories (.zip), shell scripts (.sh), XML files (.xml), text files (.txt), and others<br> Language: English<br> Relation: 10.5281/zenodo.7310652, <br> Source: GSWP3 for input data (Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T. and Hanasaki, N. (2006) GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface. Bulletin of the American Meteorological Society, 87(10), 1381–98.)</p>
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
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.