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2,837 results for “Climate Data”
Source code and data for Ou et al. (2021) Updates to Paris climate pledges improve chances of limiting global warming to well below 2°C
<p>There are two folders in this repository. The <strong>GCAM-model</strong> folder contains the version of GCAM5.3 used to estimate emission pathways for this analysis. The <strong>data</strong> folder contains source data for our main results. Please check readme.pdf and our original paper for details. </p> <p> </p> <p> </p>
Data from: Climate affects the outbreaks of a forest defoliator indirectly through its tree hosts
<p class="MsoNormal">Spatial variation in climate is known to affect the survival and reproduction of herbivorous forest insects and tree-species compositions, but the importance of indirect effects of climate on outbreaks of forest insects through its effects on forest composition is unclear. This data was compiled to examine the direct and indirect effects of climate, water capacity of the soil, host tree density, and non-host density on the spatial extent of <em>Lymantria dispar</em> outbreaks in the Eastern USA over a period of 44 years (1975-2018). Host species were subdivided into four taxonomic and ecologically distinct groups: red oaks (Lobatae), white oaks (Lepidobalanus), other preferred hosts, and intermediate (less preferred) hosts. The data offer quantitative evidence that geographic variation in climate can indirectly affect outbreaks of a forest insect through its effects on tree species composition.</p>
Data and climate variable selection from: Effects of density, species interactions and environmental stochasticity on the dynamics of British bird communities
<p>Our knowledge of the factors affecting species abundances is mainly based on time-series analyses of a few well-studied species at single or few localities, but we know little about whether results from such analyses can be extrapolated to the community level. We apply a Joint Species Distribution Model to long-term time-series data on British bird communities to examine the relative contribution of intra- and interspecific density dependence at different spatial scales, as well as the influence of environmental stochasticity, to spatio-temporal interspecific variation in abundance. Intraspecific density dependence has the major structuring effect on these bird communities. In addition, environmental fluctuations affect spatiotemporal differences in abundance. In contrast, species interactions had a minor impact on variation in abundance. Thus, important drivers of single-species dynamics are also strongly affecting dynamics of communities in time and space.</p>
the climatic data and socioeconomic data
<p>the climatic data and socioeconomic data in Xinjiang </p>
NZESM & UKESM data for JAMES study on climatic changes associated with a nested ocean model in the region around New Zealand.
<p>NZESM & UKESM data for JAMES study on climatic changes associated with a nested ocean model in the region around New Zealand.</p>
Data and Scripts used in "Analysis of Relations Between Solar Activity, Cosmic Rays and Earth Climate Using Machine Learning Techniques"
<p>This archive contains the data in relation to the work:</p> <p>Analysis of relations between solar activity, cosmic rays and earth climate using machine learning techniques<br> B. Belen, U. M. Leloglu, and M. B. Demirkoz </p> <p>See README file for more details.</p>
Supporting data for: Where and When Does Streamflow Regulation Significantly Affect Climate Change Outcomes in the Columbia River Basin?
<p>Unregulated and regulated streamflow statistics presented in: Where and When Does Streamflow Regulation Significantly Affect Climate Change Outcomes in the Columbia River Basin?</p>
Data for "Antarctic ice-sheet meltwater reduces transient warming and climate sensitivity through the sea-surface temperature pattern effect"
<p>Data of the Historical Hosing simulations presented in "Antarctic ice-sheet meltwater reduces transient warming and climate sensitivity through the sea-surface temperature pattern effect" submitted to Geophysical Research Letters</p> <p>Authors: Yue Dong, Andrew G. Pauling, Shaina Sadai, Kyle C. Armour </p> <p>Abstract:</p> <p>Coupled global climate models (GCMs) generally fail to reproduce the observed sea-surface temperature (SST) trend pattern since the 1980s. The model-observation discrepancies may arise in part from the lack of realistic Antarctic ice-sheet meltwater imbalance in GCMs. Here we employ two sets of CESM1-CAM5 simulations forced by anomalous Antarctic meltwater fluxes over 1980--2013 and into the 21st century. Both show a reduced global warming rate and an SST trend pattern that better resembles observations. The meltwater drives surface cooling in the Southern Ocean and the tropical southeast Pacific, in turn increasing low-cloud cover and driving radiative feedbacks to become more stabilizing (corresponding to a lower effective climate sensitivity). These feedback changes contribute more than ocean heat uptake efficiency changes in reducing the global warming rate. Accurately projecting historical and future warming thus requires improved representation of Antarctic meltwater and its impacts in models. </p>
Data used in the paper 'Climate change impact on fresh water balance of quasi-closed lagoons on the North-Western Black Sea coast' by Tuchkovenko et al.
<p>Find description within each file.</p>
Data for: Modeling climate-driven range shifts in populations of two bird species limited by habitat independent of climate
<p>Ranges of species around the world are expected to contract in response to climate change. Species distribution models (SDMs) are a powerful tool for predicting changes in habitat availability, but the variables selected to create SDMs influence their performance. In addition to climate, habitat characteristics and species traits can play a role in predicted species distribution. In this paper, we consider how variable selection influences the accuracy of SDMs when applied to isolated subpopulations of two widely distributed bird species: the great gray owl (<em>Strix</em> <em>nebulosa</em>) and the willow flycatcher (<em>Empidonax</em> <em>traillii</em>). In the Sierra Nevada of California, these species are restricted largely to discrete patches of meadow habitat within a forest matrix, providing the potential to identify specific locations to target conservation efforts. We contrast predictions made by SDMs that consider climatic variables alone with those that incorporate both climate and geophysical variables. Adding geophysical variables resulted in differing model predictions. For willow flycatchers, adding geophysical variables improved predictive performance. In the case of great gray owls, models with and without geophysical variables had nearly identical performance under historical conditions but differed starkly in their predictions. The full model (climatic and geophysical variables) predicted habitat availability to decrease moderately, whereas the climate-only model predicted nearly complete loss of favorable habitat by 2099. The climate-only model is consistent with expectations based on previous SDMs of birds across North America, but previous studies also assume homogeneity in species traits and range-wide habitat requirements. The full model appears more consistent with recent trends in great gray owl numbers in the Sierra Nevada specifically, where the population has remained relatively stable over recent decades. Given contradictions in our model predictions, care should be taken when trying to apply similar SDM models to other systems.</p>
MAgPIE model input data sets: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>These MAgPIE input data sets include harmonized crop yield projections from several crop models (9 crop models and 5 climate models). Additionally, regional, validation, and calibration data sets are also reported.</p>
Raynaud et al_Climate_of_the_Past2024_Data
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Attribution of daily ocean temperatures to climate change data
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Data for "Cost-effectiveness of natural forest regeneration and plantations for climate mitigation"
<p>All data associated with Busch et al. (2024) “Cost-effectiveness of natural forest regeneration and plantations for climate mitigation,” <em>Nature Climate Change</em>, doi: 10.1038/s41558-024-02068-1 are publicly available here. To facilitate reproducibility of our results and use of our datasets, we provide all intermediate datasets in addition to the final results. We provide brief dsecriptions of the published datasets in the 00_README.docx file, including contact information for individuals associated with each dataset. We encourage the use of our data and are happy to answer questions as needed.</p> <p>When using these data, please cite:</p> <p>Busch, J., Bukoski, J.J., Cook-Patton, S.C. Griscom, B., Kaczan, D., Potts, M.D., Yi, Y., and Vincent, J.R. Cost-effectiveness of natural forest regeneration and plantations for climate mitigation. <em>Nat. Clim. Chang.</em> <strong>14</strong>, 996–1002 (2024). https://doi.org/10.1038/s41558-024-02068-1</p>
Data from: Data transformations cause altered edaphic-climatic controls and reduced predictability on soil carbon decomposition rates
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Code and data for the article "Feasible deployment of carbon capture and storage and the requirements of climate targets"
<p>Here, we present data and code for our study “Feasible deployment of carbon capture and storage and the requirements of climate targets” (2024), where we project feasible ranges of carbon capture and storage (CCS) deployment in this century based on historical evidence of CCS deployment and other policy-driven technologies.</p>
Accompanying data to "Could an extremely cold central European winter such as 1963 happen again despite climate change?"
<h2>Accompanying data to "Could an extremely cold central European winter such as 1963 happen again despite climate change?"</h2> <div> </div> <div><strong>16.07.2024 This repository contains data that underlies the following publication:</strong></div> <div>Sippel, S., Barnes, C., Cadiou, C., Fischer, E., Kew, S., Kretschmer, M., Philip, S., Shepherd, T. G., Singh, J., Vautard, R., and Yiou, P.: Could an extremely cold central European winter such as 1963 happen again despite climate change? <em>Weather and Climate Dynamics</em> (accepted), 2024. Preprint: https://doi.org/10.5194/egusphere-2023-2523.</div> <div> </div> <div>This repository is a data collection, which contains simulated extremely cold Central European winter storylines. Climate model simulations use the technique of climate model boosting, and statistical generation using stochastic weather generators (SWG) empirical importance sampling. The repository contains the following data files:</div> <div> </div> <h3>(1) Climate model ensemble boosting for extremely cold winter storylines. </h3> <div> <ul> <li>Zip file BSSP370cmip6.0000013.zip: Contains all 750 files of the first-order boosting. First order boosting is based on ensemble member 21 in the CESM2-ETH ensemble, and with restart dates between 01.12 and 15.12.2022 (SSP3-70 scenario), with 50 members for each starting date. Example file: BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc</li> </ul> </div> <div>The boosting files follow a naming convention: </div> <div> <ul> <li> <ul> <li>BSSP370cmip6 all files based on CMIP6 SSP3-70 forcing.</li> <li><span>2022-12-06 starting date of the respective ensemble member.</span></li> <li><span>0000013 Ensemble member of CESM2-ETH that was used for boosting (i.e. member 13 of CESM2-ETH).</span></li> <li><span>ens023 Ensemble member of the boosted ensemble (i.e. member 23 with starting date 06.12.2022).</span></li> </ul> </li> </ul> </div> <div>The second-order boosting was branched off from first-order boosting file BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc.</div> <div> </div> <div> <ul> <li>Zip file BSSP370cmip6.0230013.zip: Contains all 750 files of the first set of second-order boosting simulations. All these simulations are based on first-order boosting file BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc. That is, the first-order boosting file started from ensemble member 13 of CESM2-ETH, starting date 06.12.2022 and ensemble member 23 of the first-order boosted ensemble. The second-order boosting file shown in Figs. 5-6 is the file BSSP370cmip6.0230013.2023-01-08.ens047.cam.h1.2023-01-09-00000.nc. That is, ensemble member 47 in second-order boosting ensemble from starting date 08.01.2023. </li> </ul> </div> <div> </div> <div> <ul> <li>Zip file BSSP370cmip6.0480013.zip: Contains all 750 files of the second set of second-order boosting simulations. All these simulations are based on first-order boosting file BSSP370cmip6.0000013.2022-12-15.ens048.cam.h1.2022-12-16-00000.nc. That is, the first-order boosting file started from ensemble member 13 of CESM2-ETH, starting date 15.12.2022 and ensemble member 48 of the first-order boosted ensemble. The second-order boosting file shown in Figs. 5-6 is the file BSSP370cmip6.0480013.2023-01-08.ens032.cam.h1.2023-01-09-00000.nc. That is, ensemble member 32 in second-order boosting ensemble from starting date 08.01.2023. </li> </ul> </div> <div> </div> <div> </div> <h3>(2) CESM2 maps of extremely cold winters (to generate Fig. 5)</h3> <div>* Zip file cesm2_maps.zip. Contains the following entries, all for DJF average anomalies (relative to the ensemble average climatology):</div> <div>- tas_ssp370_r2i1p1.2005-2035_anom.nc</div> <div>- tas_ssp370_r12i1p1.2005-2035_anom.nc</div> <div>Two members (r2i1p1 in 2008, r12i1p1 in 2007) from the CESM2-ETH ensemble, which produce very cold winters. Variables tas (surface air temperature), Z500 (geopotential height at 500 hPa), FSDS (surface downwelling shortwave radiation), and FSNS (surface net shortwave radiation) are available (FSDS and FSNS to calculate albedo). </div> <div>- tas_ssp370_0230013.2023-01-08.ens047_anom.nc</div> <div>- tas_ssp370_0480013.2023-01-08.ens032_anom.nc</div> <div>The two extremely cold boosted winters as described above, concatenated with their parent files from boosting. </div> <div> </div> <div> </div> <h3>(3) Storylines of extremely cold winters generated via Stochastic weather generator (SWG) empirical importance sampling</h3> <div>SWG-empirical-importance-sampling.zip Storylines of extremely cold winters generated via Stochastic weather generator (SWG) empirical importance sampling (Yiou and Jézéquel, 2020, https://doi.org/10.5194/gmd-13-763-2020). The available maps are seasonal average anomalies resampled from ERA5 (to generate Fig. 5):</div> <div> <ul> <li>Surface air temperature: t2m_WEGE_germany_1963_1972-2021_DJFmean.nc</li> <li><span>Albedo: fal_WEGE_germany_1963_1972-2021_DJFmean.nc</span></li> <li><span>z500: z500_WEGE_germany_1963_1972-2021_DJFmean.nc</span></li> </ul> </div> <div> </div>
Arctic and Antarctic sea ice thickness climate data record from ERS-1, ERS-2, Envisat and CryoSat-2
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FIGURE 11. A-C in Diversity and renewal of tropical elasmobranchs around the Middle Eocene Climatic Optimum (MECO) in North Africa: New data from the lagoonal deposits of Djebel el Kébar, Central Tunisia
FIGURE 11. A-C. Ouledia lacuna nov. sp. A.?Anterior tooth KEB 1-180, A1. Occlusal view, A2. Lingual view, A3. Magnificence of crown-root boundary in A2, A4. profile; B.?lateral tooth KEB 1-181, B1. Occlusal view, B2. labial view, C.?Anterior tooth KEB 1-182, C1. Labial view, C2. Basal view; D-H. Pachygymnura attiai nov. gen. D. Anterior tooth KEB 1-183, D1. Lingual view, D2. Occlusal view, D3. Labial view, D4. Basal view; E. Antero-lateral tooth KEB 1-184, E1. Lingual view, E2. Near labial view, F. lateral tooth KEB 1-185, F1. Occlusal view, F2. Profile, F3. Basal view; G. lateral tooth KEB 1-186, G1. Lingual view, G2. Occlusal view; H. Anterior tooth KEB 1-187, H1. Profile, H2. Occlusal view.
FIGURE 7. A-C in Diversity and renewal of tropical elasmobranchs around the Middle Eocene Climatic Optimum (MECO) in North Africa: New data from the lagoonal deposits of Djebel el Kébar, Central Tunisia
FIGURE 7. A-C: Hemipristis curvatus. A. Antero-lateral upper tooth KEB 1-133, A1. Lingual view, A2. Labial view; B. Lateral upper tooth of young specimen KEB 1-134, labial view; C. Anterior lower tooth of young specimen KEB 1-135, labial view; D-E: Moerigaleus sp. D. Lateral upper tooth KEB 1-136, D1. Lingual view, D2. Labial view; E. Lateral upper tooth KEB 1-137, E1. Lingual view, E2. Labial view; F-K: Leptocharias tunisiensis nov. sp. F. (HOLOTYPE) Antero-lateral lower tooth KEB 1-138, F1. Profile, F2. Lingual view, F3. Labial view; G. Antero-lateral upper tooth KEB 1-139, G1. Labial view, G2. Profile, G3. Lingual view; H. Anterro-lateral lower tooth KEB 1-140, labial view; I. Lateral lower tooth KEB 1-141, labial view; J. More lateral tooth KEB 1-142, J1. Labial view, J2. Lingual view, J3 occlusal view; K. Posterior tooth KEB 1-143, K1. Labial view, K2. Lingual view.
ScienceDex guides
Understand access before you commit
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.