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617 results for “Climate models”
Planning for Resilience: Incorporating scenario and model uncertainty and trade-offs when prioritizing management of climate refugia
<p>Climate change has become the greatest threat to the world's ecosystems. Locating and managing areas that contribute to the survival of key species under climate change is critical for the persistence of ecosystems in the future. Here we identify "Climate Priority" sites as coral reefs exposed to relatively low levels of climate stress that will be more likely to persist in the future. We present the first analysis of uncertainty in climate change scenarios and models, along with multiple objectives, in a marine spatial planning exercise and offer a comprehensive approach to incorporating uncertainty and trade-offs in any ecosystem. We first described each site using environmental characteristics that are associated with a higher chance of persistence (larval connectivity, hurricane influence and acute and chronic temperature conditions in the past and the future). Future temperature increases were assessed using downscaled data under four different climate scenarios (SSP1 2.6, SSP2 4.5, SSP3 7.0 and SSP5 8.5) and 57 model runs. We then prioritized sites for intervention (conservation, improved management or restoration) using robust decision-making approaches that select sites that will have a benign climate under most climate scenarios and models. The modeling work is novel because it solves two important issues. 1) It considers trade-offs between multiple planning objectives explicitly through Pareto analyses; and 2) It makes use of all the uncertainty around future climate change. Priority intervention sites identified by the model were verified and refined through local stakeholder engagement including assessments of local threats, ecological condition and government priorities. The workflow is presented for the Insular Caribbean and Florida, and at the national level for Cuba, Jamaica, Dominican Republic and Haiti. Our approach allows managers to consider uncertainty and multiple objectives for climate smart spatial management in coral reefs or any ecosystem across the globe.</p>
Model data of White et al. (2022, Climate of the Past)
<p>This contains the scripts and model and proxy data to reproduce Figures 1, 2, 3, 4, 7, 8, and 10 in White et al. (2022, Climate of the Past).</p> <p>Abstract. Paleoclimate reconstructions have identified a period of exceptional summer and winter cooling in the North Atlantic region following the eruption of the tropical volcano Huaynaputina (Peru) in 1600 CE CE3 . A previous study based on numerical climate simulations has indicated a potential mechanism for the persistent cooling in a slowdown of the North Atlantic subpolar gyre (SPG) and consequent ocean–atmosphere feedbacks. To examine whether this mechanism could have been triggered by the Huaynaputina eruption, this study compares the simulations used in the previous study both with and without volcanic forcing and this SPG shift to reconstructions from annual proxies in natural archives and historical written records as well as contemporary historical observations of relevant climate and environmental conditions. These reconstructions and observations demonstrate patterns of cooling and sea-ice expansion consistent with, but not indicative of, an eruption trigger for the proposed SPG slowdown mechanism. The results point to possible improvements in future model–data comparison studies utilizing historical written records. Moreover, we consider historical societal impacts and adaptations associated with the reconstructed climatic and environmental<br> anomalies.</p>
Validation Data used for manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model"
<p>those are the processed data that used for model-data comparison in the manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model", including Lake Surface Temperature and Lake Surface Ice Cover from Great Lakes Surface Environmental Analysis (GLSEA), Surface Air temperature and Precipitation from Climatic Research Unit (CRU). </p>
Model results based on COSMOS climate model (old version MPI-ESM1)
<p>Nino 3.4 SST of individual models (COSMOS-Nordemg and COSMOS-Tiedtke) and supermodel</p>
WRF model configuration and data used for the NHESS manuscript "Droughts in Germany: Performance of Regional Climate Models in reproducing observed characteristics"
<p>The file contains:</p> <ul> <li>the namelist.input document with the description of the WRF model configuration used in Warscher et al. (2019)</li> <li>WRF simulation outputs from the reanalysis run: monthly values for the time period 1980-2009 of precipitation, maximum and minimum temperature (needed for the SPEI calculation) from the innermost (5 km grid resolution) and second innermost (15 km) domain; from both domains the same section, relevant for the study, was taken; the data was bilineraily interpolated to 12.5 km horizontal grid resolution to match the EUR-11 CORDEX format</li> </ul> <p> </p>
Supporting Information: Vortex streets to the lee of Madeira in a km-resolution regional climate model
<p>This is the Supporting Information for the manuscript <em>Vortex streets to the lee of Madeira in a km-resolution regional climate model</em>.</p> <p>It is posted as a preprint on EGUsphere for the journal Weather and Climate Dynamics: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-965/.</p>
Model and input files for Iyer & Ou, et al. 2022 (Ratcheting of climate pledges needed to limit peak global warming )
<p>The GCAM model (GCAMv5.3 NDC) and input files used to conduct Iyer & Ou, et al. 2022 (Ratcheting of climate pledges needed to limit peak global warming )</p> <ol> <li>The model needs to be compiled using third-party libraries (see https://jgcri.github.io/gcam-doc/gcam-build.html). Source code has been included in the GCAMv5.3_NDC/csv</li> <li>Default GCAM input files have been included in GCAMv5.3_NDC/input/gcamdata/xml</li> <li>Additional input files for NDC scenarios have been included in GCAMv5.3_NDC/input/NDC_Ratchet_policy</li> <li>A sample configuration_NDC_sample.xml has been included in GCAMv5.3_NDC/exe with detailed setup instruction</li> </ol>
Climate data for Machine Learning based 100-year flood flow prediction model
<p>This study evaluates the application of ML technique over northeast United States regions and compares its performance to the U.S. Geological Survey (USGS) Streamflow Statistics (StreamStats)</p>
Model simulations for " Potential impacts of LUCC and climate change on evapotranspiration and gross primary productivity in the Haihe River Basin, China"
<p>Experiment_1.rar, Experiment_2.rar, and Experiment_3.rar are the model simulations from experiment 1, experiment 2, and experiment 3, respectively. All the simulations are original from the CLM5 model in netcdf format.</p> <p>More details on these data can be found in the paper "Potential impacts of LUCC and climate change on evapotranspiration and gross primary productivity in the Haihe River Basin, China". </p>
Model data - Impact of Ural blocking on early-winter climate variability under different Barents-Kara sea ice conditions
<p>Model data for JGR paper : Impact of Ural blocking on early-winter climate variability under different Barents-Kara sea ice conditions</p>
Output climate model parameters reported in Izquierdo et al., (2022)
<p>Data set containing an ensemble of model parameters for each of the candidate climate models in Table 1 of Izquierdo et al., (2022). These ensembles are stored as Python objects using the Pickle module, identified by the accumulation and lag sub models they contain and the number of steps used in the Markov chain Monte Carlo algorithm. From each Python object, it can be extracted the distribution of accumulation and retreat rates with time following the scripts and notebooks of the repository referenced in the open research section of the paper. </p> <p>The folders in this repository refer to the ensembles of all candidate models dependent of insolation values (insolation), ensembles of all candidate models dependent on obliquity values (obliquity, ensembles of best fit models (files that are not within a folder) and ensembles of best fit models with the addition of an age prior of 2 My. </p>
Data and software in support of article submitted to Journal Geophy. Res. Atmos., titled "Self lofting increases altitude of black carbon in a climate model""
<p>The collection provides data and software to support a journal article submission to the Journal of Geophysical Research Atmospheres. The contents will allow potential future investigators to explore the simulation data and reproduce the analyses in the submitted article.</p> <p>This dataset includes 7 tarred files that when expanded will contain a set of netcdf data files and a collection of python programs to read and analyze the data. The data provided in the netcdfs are model outputs from the UK Earth System Model (UKESM1) from a pair of simulations that explored the impact of black carbon aerosol on atmospheric motion.</p> <p> </p>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The code, scripts, and data used in the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>All Figures&Table and their corresponding NCL scripts are under the directory of Figs&Table. </li> <li>The modified model code, corresponding original model code, and model run scripts are under the directory of Mods_Scripts.</li> <li>The postprocessing NCL scripts, which select useful variables from simulation results, are under the directory of PostProcessing.</li> <li>The zonal mean data from model results used for making figures and corresponding data processing scripts are under the directory of Model_Results.</li> <li>The FORTRAN code used for offline tests is under the directory of Offline_Code.</li> <li>The code, data, and NCL scripts used for the figures and table in the Appendix are under the directory of Appendix.</li> </ul>
MAgPIE model runs outputs: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>Each folder contains the fulldata.gdx and the configuration files for each MAgPIE run based on the nine crop impact models and 5 gcms used in the paper.</p>
Urban Heat: Forward-Looking Climate Modelling for West-Africa : Togolese Cities
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. The study focuses on five cities in Togo: Tsevie, Dapaong, Kara, Sokode and Atakpame.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP2-4.5, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2031-2050, and 2051-2070</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>100 m</strong>, consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>E</strong><strong>PSG 32631</strong>. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. </li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_indicators.zip</strong>.</li> <li>Three representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (a hot day in 2020). The results are stored in WBGT_data.xlsx and visualized as WBGT_{date}.png. The shapefile is named as selected_locations.shp. These data together with the visualization of the land use map is compressed in <strong>{city}_landuse_wbgt.zip</strong>. </li> <li>More information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the <strong>Technical_Annex_Togo.docx</strong></li> </ul>
Urban Heat: Forward-Looking Climate Modelling for West-Africa: Guinea cities
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. The study focuses on two cities in Guinea: Conakry and Kankan.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP2-4.5, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2031-2050, and 2051-2070</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>100 m </strong>(Kankan) and <strong>200 m </strong>(Conakry), consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>E</strong><strong>PSG 32629 </strong>(Kankan) and <strong>EPSG 32628 </strong>(Conakry). The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. </li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_indicators.zip</strong>.</li> <li>Three representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (a hot day in 2020). The results are stored in WBGT_data.xlsx and visualized as WBGT_{date}.png. The shapefile is named as selected_locations.shp. These data together with the visualization of the land use map is compressed in <strong>{city}_landuse_wbgt.zip</strong>. </li> <li>More information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the <strong>Technical_description_Guinea.docx</strong></li> </ul>
Urban Heat: Forward-Looking Climate Modelling for West-Africa : Corridor Abidjan-Lagos
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. The study focuses on five cities at Corridor Abidjan-Lagos: Abidjan, Cotonou, Lome, Accra and Lagos.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator for the reference period <strong>(present: 2001 to 2020</strong>). For city Lome two more future scenarios (<strong>SSP2-4.5, SSP3-7.0</strong>) and with two twenty-year periods (<strong>2031-2050, and 2051-2070</strong>) were applied.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>100 m to 200 m</strong> (depending on the size of the city), consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. </li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_present_indicators.zip </strong>or <strong>{city}_indicators.zip </strong>(for Lome).</li> <li>Three representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (a hot day in 2020). The results are stored in WBGT_data.xlsx and visualized as WBGT_{date}.png. The shapefile is named as selected_locations.shp. These data together with the visualization of the land use map is compressed in <strong>{city}_landuse_wbgt.zip</strong>. </li> <li>More information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the <strong>Technical_description_corridor.docx</strong></li> </ul>
Urban Heat: Forward-Looking Climate Modelling for West-Africa: extra indicator
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. This dataset serves as a supplement to the previous two datasets: https://zenodo.org/doi/10.5281/zenodo.11085333 and https://zenodo.org/doi/10.5281/zenodo.11073297. It contains an extra indicator Heat Index (HI) based on the apparent temperature (AT) for 12 cities in west Africa. </p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for HI across three scenarios (<strong>present, SSP2-4.5, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2031-2050, and 2051-2070</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>The indicator is available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats. It is named as HIAT in the file name with extra information such as scenario, resolution, projection, etc. </li> <li>The indicator is calculated at a resolution from <strong>100 m </strong>to <strong>200 m </strong>(depending on the size of the city). Additionally, downscaled versions of the indicator is provided at a resolution of <strong>30 m</strong>.</li> <li>The Heat Index is calculated as the <strong>yearly average number of days when apparent temperature reaches 105 F</strong>. More information regarding the definition and calculation of HI can be found: Rohat, G., Flacke, J., Dosio, A., Dao, H., & Van Maarseveen, M. (2019). Projections of human exposure to dangerous heat in African cities under multiple socioeconomic and climate scenarios. <em>Earth's Future</em>, <em>7</em>(5), 528-546.</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format are available. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_HIAT.zip </strong>(for the cities with future projection) or <strong>{city}</strong><strong>_present_HIAT.zip </strong>(for those cities without future projection).</li> <li>More information about other indicators, including the simulation, methodology, all available data list, contact information, etc. can be found in the other two datasets.</li> </ul>
Data from Climate adaptability in hydrological models: variable storage capacity to improve performance under contrasting climates.
Open the record for dataset details and reuse information.
On the increase of climate sensitivity and cloud feedback with warming in the Community Atmosphere Models
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2020). On the Increase of Climate Sensitivity and Cloud Feedback With Warming in the Community Atmosphere Models. <em>Geophysical Research Letters</em>, <em>47</em>(18), e2020GL089143. <a href="https://doi.org/10.1029/2020GL089143">https://doi.org/10.1029/2020GL089143</a></p> <p> </p> <p><strong>Upadated on July 3, 2024</strong>: add more timeseries of precipitation and energy fluxes for analysis in Bonan, Schneider, & Zhu (2024).</p> <p>David Bonan, Tapio Schneider, Jiang Zhu. Precipitation over a wide range of climates simulated with comprehensive GCMs. <em>ESS Open Archive .</em> April 25, 2024.<br><span>DOI: <a href="https://doi.org/10.22541/essoar.171405864.45942692/v1" target="_blank" rel="noopener noreferrer">10.22541/essoar.171405864.45942692/v1</a></span> </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.