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617 results for “Climate models”
Simulations for pre-industrial climate using EC-Earth3-LR model — selected data for a study on AMOC
<p>A long-term control simulation of pre-industrial period (1850 CE) climates were performed by the EC-Earth3-LR climate model with a horizontal resolution of ~1.125°. The dataset contains selected output data from the simulations.</p> <p>In total, a 2000-year long control simulation was made, which has pre-industrial orbital boundary conditions, initialized by a pre-run steady restart file (the output of approximately 500-year pre-industrial control simulation). This dataset is used to investigate internal climate variability without external forcing changes under pre-industrial climate conditions.</p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Cao et al. (2022).</p> <p><strong>Model configuration</strong><br> Time periods: Pre-Industrial (2000-year time slice)<br> ESM configuration: EC-Earth3-LR<br> Horizontal resolution: ~1.125° (~125 km)</p> <p><strong>Available data</strong><br> Annual mean data for standard oceanographic and meteorological variables.</p>
Dataset of Last Interglacial climate from publication "Modeled storm surge changes in a warmer world: the Last Interglacial" by P. Scussolini et al.
<p>Results from the simulation of Last Interglacial (Eemian) climate with climate model CESM1.2. Variables are: sea-level pressure (PSL); meridional wind (V), and zonal wind (U). Time step is 6-hourly.</p> <p>Detailed description of the methods are in the original publication:</p> <p>Scussolini, P., Dullaart, J., Muis, S., Rovere, A., Bakker, P., Coumou, D., Renssen, H., Ward, P. J., and Aerts, J. C. J. H.: Modelled storm surge changes in a warmer world: the Last Interglacial, EGUsphere, 2022, 1-20, 10.5194/egusphere-2022-101, 2022.</p>
Dataset of pre-industrial climate from publication "Modeled storm surge changes in a warmer world: the Last Interglacial" by P. Scussolini et al.
<p>Results from the simulation of pre-industrial climate with climate model CESM1.2. Variables are: sea-level pressure (PSL); meridional wind (V), and zonal wind (U). Time step is 6-hourly.</p> <p>Detailed description of the methods are in the original publication:</p> <p>Scussolini, P., Dullaart, J., Muis, S., Rovere, A., Bakker, P., Coumou, D., Renssen, H., Ward, P. J., and Aerts, J. C. J. H.: Modelled storm surge changes in a warmer world: the Last Interglacial, EGUsphere, 2022, 1-20, 10.5194/egusphere-2022-101, 2022.</p>
Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios
<p><strong>Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios</strong></p> <p>This dataset contains output ice sheet model runs forced by climate boundary conditions provided by CMIP6 climate model output. Each experiment set is archived in separate compressed tar.gz files. </p> <p>Description of the experiment sets, including the model setup, key parameters, climate forcings, and their main objectives are documented in Table 1 of Li, DeConto, Pollard (2023) Climate model differences contribute deep uncertainty in future Antarctic ice loss, Science Advances.</p> <p>Two kinds of output are included in each ice sheet run: fort.22 files contain time series of several key variables for the Antarctic Ice Sheet (area, volume, sea-level equivalent, etc.); fort.92.nc files contain 2D and 3D fields such as ice thickness and velocity at specific time slices.</p> <p> </p> <p> </p>
cb-oura-1.0 : Generic climate scenarios from bias-adjusted CMIP5 global models
<p><strong>Context </strong></p> <p>The need to adapt to climate change is present in a growing number of fields, leading to an increase in the demand for climate scenarios for often interrelated sectors of activity. In order to meet this growing demand and to ensure the availability of climate scenarios responding to numerous vulnerability, impact, and adaptation (VIA) studies, <a href="https://www.ouranos.ca/">Ouranos</a> is working to create a set of operational multipurpose climate scenarios. The initial version of “Scénarios Génériques” (generic scenarios, acronym cb-oura-1.0) is used mainly in Ouranos’ work to provide a consistent image of the changing climate over the North East of North America, principally the province of Québec. Cb-oura-1.0 was produced in 2016 by downscaling and bias-adjusting a selection of global climate model simulations available through the CMIP5 program. </p> <p><strong>Climate simulations </strong></p> <table> <caption>Climate simulations in the ensemble</caption> <thead> <tr> <th scope="col">Modeling center</th> <th scope="col">Acronym</th> <th scope="col">Model</th> <th scope="col">RCP</th> <th scope="col">Status*</th> </tr> </thead> <tbody> <tr> <td><strong>College of Global Change and Earth System Science, Beijing Normal University</strong></td> <td>GCESS</td> <td>BNU-ESM</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Canadian Centre for Climate Modelling and Analysis</strong></td> <td>CCCMA</td> <td>CanESM2</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Centro Euro-Mediterraneo per I Cambiamenti Climatici</strong></td> <td>CMCC</td> <td>CMCC-CMS</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia</strong></td> <td>CSIRO-BOM</td> <td>ACCESS1.3</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>a</td> </tr> <tr> <td><strong>Institute for Numerical Mathematics</strong></td> <td>INM</td> <td>INM-CM4</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>a</td> </tr> <tr> <td><strong>Institut Pierre-Simon Laplace</strong></td> <td>IPSL</td> <td>IPSL-CM5A-LR</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td>IPSL-CM5B-LR</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Met Office Hadley Centre</strong></td> <td>MOHC</td> <td>HadGem2</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology)</strong></td> <td>MPI-M</td> <td>MPI-ESM</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Norwegian Climate Centre</strong></td> <td>NCC</td> <td>NorESM</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>NOAA Geophysical Fluid Dynamics Laboratory</strong></td> <td>NOAA-GFDL</td> <td>GFDL-ESM2M</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> </tbody> </table> <p>From the complete ensemble of RCP 4.5 and 8.5 driven CMIP5 climate simulations, a selection of 22 simulations (11 per RCP) was made using a clustering ensemble reduction methodology (Casajus et al. 2016). This objective selection method identifies a reduced number of simulations that best represent the overall ensemble. Input criteria for the reduction were the monthly changes between the present (1981-2010) and two future horizons (2041-2070 and 2071-2100), at 15 regions distributed across Canada, for three variables (mean daily maximum temperature, mean daily minimum temperature and total precipitation). An initial selection of 16 simulations shows a distribution projected changes for the 12 (months) x 2 (horizons) x 15 (regions) x 3 (variables) indices that is not statistically different from the complete ensemble. A small number of simulations were subsequently added to have a complete set with both RCPs represented equally (11 members for each emission scenario). </p> <p><strong>Reference dataset </strong></p> <p>The bias-adjustment reference (or target) is a gridded observation dataset produced by Natural Resources Canada (McKenney et al., 2011; et Hutchinson et al. ,2009). It uses the ANUSPLIN interpolation method over station observations to derive daily grids of minimum and maximum temperature, as well as total precipitation for the Canadian landmass. The grid has a resolution of 10 km x 10 km and cover the time period from 1950 to 2013. </p> <p>As this dataset is not available over the United States, it was merged with another observation interpolation dataset produced by Livneh et al. (2015) in order to enable the production of bias-corrected climate scenarios covering a portion of the northern United States. </p> <p><strong>Coverage </strong></p> <p>The final version of this dataset covers a region covering the Atlantic provinces, Québec, Ontario, Manitoba and Saskatchewan and part of the northern United States: From 120°W to 54°W and from 40°N to 62°N. </p> <p>It contains the daily minimum temperature, daily maximum temperature and daily precipitation flux, covering the period 1950 to 2100. </p> <p><strong>Bias-adjustment </strong></p> <p>The global simulations where downscaled to the reference grid using bilinear interpolation and then bias-adjusted with a 1-D quantile mapping method, as described by Gennaretti et al. (2015). A moving window of 31 days was used to adjust each day of the year, using 50 quantiles to define the statistical distributions to match. The long-term linear trends of the temperature variables were preserved explicitly. </p> <p><strong>Climate indicators </strong></p> <p>This dataset is used to in the first versions (up to 1.3) of Ouranos’ <a href="https://www.ouranos.ca/en/climate-portraits">Climate Portraits </a>website. A selection of 26 seasonal and annual climate indicators were computed from the daily scenarios, using the xclim software package (Logan et al. 2022). The "virtual indicator module" used for the computation is made available here in the "indicators.yml" file.</p> <p>On the Climate Portraits website, the information is presented from three aspects: spatial, temporal and summary. This repository stores the reduced ensemble data as shown on the website. Filenames are constructed as "{aspect}_{indicator}_{season}.nc".</p> <ul> <li> <p>Maps (files "spatial_*") : Climate indicators for each bias-adjusted climate simulation and for a given RCP emission scenario are averaged over 30-year horizons. Ensemble percentiles are computed in order to summarize climate model uncertainty. In particular the 10, 25, 50, 75 and 90th percentiles over the 11 members are calculated for each RCP. </p> </li> <li> <p>Timeseries (files "temporal_*") : Climate indicators for each bias-adjusted simulation are averaged spatially over each region for every time step (annual or seasonal). The ensemble statistics are computed by first pooling all regional average values for the 11 members using within a centred 30-year window and then calculating percentile values (same as above) on the pooled data. </p> </li> </ul> <ul> <li> <p>Summary (files "summary_*") : The indicators are averaged over each region and then over 30-year horizons. The ensemble statistics (same as above) are then computed. </p> </li> </ul> <p>In versions 2.x of the app, this data will be presented as "CMIP5".</p> <p><strong>Data availability </strong></p> <p>This repository stores the climate indicator ensemble statistics as shown on the Climate Portraits website and described above. The complete daily dataset is too large for this platform.</p> <p>The complete daily dataset is available through the public THREDDS server of the PAVICS platform maintained by Ouranos. This data might be removed in the future. When this is the case, please contact us for data requests.<br> <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip5/ouranos/cb-oura-1.0/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip5/ouranos/cb-oura-1.0/catalog.html</a> </p> <p>The annual and season indicators of the Climate Portraits website are available on the same server, along with a few more indicators not shown on the app. <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/ouranos/portraits-clim-1.3/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/ouranos/portraits-clim-1.3/catalog.html</a></p> <p><em>Terms of use</em>: Use of this dataset should be acknowledged as 'Data produced and provided by the Ouranos Consortium on Regional Climatology and Adaptation to Climate Change'. Furthermore, the modeling groups from which the bias-adjusted climate scenarios were constructed must also be acknowledged, please refer to: The Coupled Model Intercomparison Project <a href="https://pcmdi.llnl.gov/mips/cmip5/citation.html.">https://pcmdi.llnl.gov/mips/cmip5/citation.html.</a></p>
Regional climate model simulations (CCLM 15km) of profiles for the MOSAiC period
<p>The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic. The data set includes simulation data of profiles and derived data for the MOSAiC period (Oct. 2019-Sept.2020). The regional climate model CCLM was used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data (AMSR2) with around 6 km resolution, and sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead data with 1 km resolution for the winter period (Nov. 2019-April 2020). Model output is available every 1h. In the vertical, the model extends up to 22 km with 60 vertical levels. On data below 10km are used. In addition to profiles, integrated water vapour and temperature for the lowest 2km were calculated. Values are grid-box averages at the ship position. Geostrophic wind was computed from the pressure gradient of the four surrounding grid points.</p> <p>Reference: Heinemann, G., Schefczyk, L., Willmes, S., Shupe, M., 2022: Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period. Elem. Sci. Anth., 10 (1). DOI: 10.1525/elementa.2022.00033.</p> <p><strong>Project: </strong> Modelling the impact of sea-ice leads on the atmospheric boundary layer during MOSAiC (MISLAM)</p> <p><strong>Funding: </strong>Federal Ministry of Education and Research (BMBF), grant 03F0887A</p>
Challenges simulating the AMOC in climate models
<p>This is a data set for the article:</p> <p>Jackson, Laura C, Helene T. Hewitt, Diego Bruciaferri, Daley Calvert, Tim Graham, Catherine Guiavarc’h, Matthew B. Menary, Adrian L. New, Malcolm Roberts and David Storkey, 2023: Challenges simulating the AMOC in climate models, submitted to Philosophical Transactions of the Royal Society A.</p> <p>Fig 1 calculated correlations between AMOC strength or trend in 1% CO2 experiment (values given in CMIP6_AMOC.txt) and sea surface salinity fields in the controls (fields for each model given in CMIP6_SSS.nc)</p> <p>We also include data of the AMOC streamfunction (*amoc.nc), March mixed layer depth (MarchMLD.nc) and temperature and salinity fields (TS.nc) for the mean of years 20-30 of models HadGEM3-GC3-1ML, MM and MH. These models are documented here <a href="https://doi.org/10.5194%2fgmd-2019-148">doi:10.5194/gmd-2019-148</a>. This data was used for plotting figures 2-4. </p> <p>Data for Fig 5 is in HadGEM3-GC3-1MM-noGM-av-20-40_MarchMLD.nc and HadGEM3-GC3-1MM-withGM-av-20-40_MarchMLD.nc</p> <p>Data for Fig 6 is in HadGEM3-GC1_control_20390301_20780330_MLD.nc and HadGEM3-GC1_relax_20390301_20780330_MLD.nc</p> <p>Data for Fig 7 is included in <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F7764695&data=05%7C01%7Claura.jackson%40metoffice.gov.uk%7Cceeee8cf4c9d4401060e08db2bcc93f2%7C17f1816120d7474687fd50fe3e3b6619%7C0%7C0%7C638151929867577211%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=nkL88P4lDCDB5E4qPxvL6%2B6pu6nU5kDtCofZg5%2FAoDs%3D&reserved=0">https://zenodo.org/record/7764695</a> </p> <p> </p>
CESM and FOCI model data as supplementary data for Climate Index Collection based on model data (CICMoD)
<p>The Community Earth System Model (CESM) and the Flexible Ocean and Climate Infrastructure (FOCI) are both fully-coupled, global climate models that provide state-of-the-art computer simulations of the Earth's past, present, and future climate states.</p> <p>This dataset contains results from control runs with conditions of year 1850 without additional external forcing for 1000 years and 999 years for FOCI and CESM, respectively.</p> <p>Included features are:</p> <ul> <li>sea surface temperature</li> <li>surface air temperature</li> <li>sea level pressure</li> <li>sea surface salinity</li> <li>geopotential height (500mb)</li> <li>precipitation</li> </ul>
MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region. Article data
<p>Regional coupled and Earth System models are fundamental numerical tools for climate investigations, downscaling of predictions and projections, process-oriented understanding of regional extreme events, and many more applications. Here we introduce a newly developed coupled regional modeling framework for the Mediterranean region, called MESMAR (Mediterranean Earth System model at ISMAR) version 1, which is composed of the WRF atmospheric model, the NEMO oceanic 15 model, and the HD hydrological discharge model, coupled via the OASIS coupler. The model is implemented at moderate resolution (about 1/12° for the ocean and river routing, while twice coarser for the atmosphere) for long-term investigations.</p> <p>The gzipped tarball contains data files contained in the manuscript associated with the MESMARv1 description and submitted to Geoscientific Model Developments:</p> <p>MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region</p> <p>by Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino,<br> Rosalia Santoleri, Chunxue Yang</p>
SECURES-Met - A European wide meteorological data set suitable for electricity modelling (supply and demand) for historical climate and climate change projections
<p>For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.</p> <p> </p> <p>The SECURES-Met dataset provides variables visible in the table.</p> <table> <tbody><tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Aggregation methods</th> <th>Temporal resolution</th> </tr> </tbody><tbody> <tr> <th>Temperature (2m)</th> <td>T2M</td> <td> <p>°C</p> <p>°C</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th>Radiation</th> <td> <p>GLO (mean global radiation)</p> <p>BNI (direct normal irradiation)</p> </td> <td> <p>Wm-2</p> <p>Wm-2</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th><strong>Potential Wind Power </strong></th> <td>WP</td> <td>1</td> <td>normalized with potentially available area</td> <td>hourly</td> </tr> <tr> <th><strong>Hydro Power Potential</strong></th> <td> <p>HYD-RES (reservoir)</p> <p>HYD-ROR (run-of-river)</p> </td> <td> <p>MW</p> <p>1</p> </td> <td> <p>summed power production</p> <p>summed power production normalized with average daily production</p> </td> <td>daily</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (<strong>RCP 4.5 </strong>and <strong>RCP 8.5</strong>, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the <strong>spatial aggregation level</strong></p> <ul> <li>NUTS0 (country-wide),</li> <li>NUTS2 (province-wide),</li> <li>NUTS3 (Austria only),</li> <li>and EEZ (Exclusive Economic Zones, offshore only).</li> </ul> <p>The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As <strong>population weighted</strong> temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only.</p> <p>The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.</p>
Interaction matters: Bottom-up driver interdependencies alter the projected response of phytoplankton communities to climate change, links to model results
<p>This dataset provides the output of ten model simulations with the global ocean biogeochemical model FESOM-REcoM necessary to reproduce the findings of Seifert et al. (2023). In addition to information on the mesh, the dataset contains 5-year means of global phytoplankton biomass, chlorophyll, net primary production, growth rates, limitations, carbonate system parameters (dissolved inorganic carbon, CO2 partial pressure, total alkalinity), temperature, photosynthetically active radiation, and mixed layer depths.</p> <p>File names refer to the figures in the paper where the respective data are used. See “readme” for detailed information on the dataset and separate files.</p>
Forward-Looking Climate Modelling for Western Athens
<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 domain focus on the metropolitan area of Athens, Greece.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2021-2040, and 2041-2060</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>150 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>EPSG 32634</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>. Some indicators also have additional calculations for <strong>seasonal averages</strong>, including Spring (MAM), Summer (JJA), Autumn (SON), and Winter (DJF).</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. Present denotes the period 2001-2020; 2030 denotes the period 2021-2040; & 2050 denotes the period 2041-2060.</li> <li>The images are also clipped to focus on the ASDA municipalities, in which case the png files will be ended with ‘_clip.png’</li> <li>The NetCDF and GeoTiff data can be found in the <a href="https://zenodo.org/api/files/76dbc341-075d-49aa-9b73-8378f6410038/data.zip?versionId=df51b0aa-9cb0-4be0-afe6-46fa30372e91">data.zip</a>; The png files for quick viewing can be found in <a href="https://zenodo.org/api/files/76dbc341-075d-49aa-9b73-8378f6410038/quickview.zip?versionId=199b62b8-bba3-4ec0-a3f4-753d784972eb">quickview.zip</a>; more information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the <a href="https://zenodo.org/api/files/76dbc341-075d-49aa-9b73-8378f6410038/Technical_Annex_Athens_ver4.docx?versionId=695aac25-2b99-47ab-867f-721331eb9bfe">Technical_Annex_Athens_ver4.docx</a></li> </ul>
Holocene climate change in southern Oman deciphered by speleothem records and climate model simulations
<p>8 ka BP (thousand year, before present, 8K) and pre-industrial period (PI) paleoclimate equilibrium experiment, including precipitation (pr), near-surface relative humidity (hurs), evaporation (evspsbl) and 850hPa wind (u850 & v850).</p>
Model simulation data used in "The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)" (Righi et al., Earth Syst. Dynam., 2023)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Earth Syst. Dynam.</i>, 2023). For details see the README.md file.</p>
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model - model outputs
<p>This dataset contains the model outputs used in the figures in the paper entitled "Implementing the iCORAL (version 1.0) coral reef CaCO<sub>3</sub> production module in the iLOVECLIM climate model" submitted to GMD. For the description of the model and simulations we refer to this article.</p> <p>Provided files:</p> <ul> <li>Surface values of temperature (temp), salinity (salt), phosphate (opo4) and aragonite saturation state (omega) for:</li> </ul> <p>The modern period (mean of 2000-2010): <strong>temp_modern.nc</strong>, <strong>salt_modern.nc</strong>, <strong>opo4_modern.nc</strong>, <strong>omega_modern.nc</strong></p> <p>The pre-industrial (PI, mean of last 100 years of the simulation): <strong>temp_PI.nc</strong>, <strong>salt_PI.nc</strong>, <strong>opo4_PI.nc</strong>, <strong>omega_PI.nc</strong></p> <ul> <li>Coral location for:</li> </ul> <p>Imin=50 μE/m2/s: <strong>coral_location_Imin50.nc</strong></p> <p>Imin=300 μE/m2/s: <strong>coral_location_Imin300.nc</strong></p> <p>The values indicate:</p> <p>4 = presence of corals in the model simulation (coral area less or equal to 5% of the grid cell area) but not in observations</p> <p>3 = presence of corals in both model and observational data</p> <p>2 = presence of corals in observational data but not in the model simulation</p> <p>1 = presence of corals in the model simulation (coral area more than 5% of the grid cell area) but not in observations</p> <ul> <li>Global coral reef area (10<sup>3</sup> km<sup>2</sup>) and <em>I<sub>min</sub></em> (the minimum light intensity necessary for reef growth, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_area_vs_Imin.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>I<sub>min</sub></em> (the minimum light intensity necessary for reef growth, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_prod_vs_Imin.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>I<sub>k</sub></em> (the saturating light intensity, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_prod_vs_Ik.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>g<sub>max</sub></em> (the maximum production growth): <strong>Total_prod_vs_gmax.txt</strong></li> <li>Global carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and global coral reef area (10<sup>3</sup> km<sup>2</sup>):<strong> Total_prod_vs_total_area.txt</strong></li> <li>Root mean square error (RMSE, kg CaCO<sub>3</sub> m<sup>-2</sup> yr<sup>-1</sup>) between the simulations and the observational data of regional production (Perry et al., 2018) and global production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>): <strong>Total_production_vs_rmse_Perry.txt</strong></li> <li>Root mean square error (RMSE, kg CaCO<sub>3</sub> m<sup>-2</sup> yr<sup>-1</sup>) between the simulations and the observational data of regional production (Perry et al., 2018) and coral reef area (10<sup>3</sup> km<sup>2</sup>):<strong> Total_area_vs_rmse_Perry.txt</strong></li> </ul>
NPJ Climate Action AR6 scenarios database submission histograms by model family and project
<p>Based on submissions to the IPCC AR6 Scenarios Database, this datasets uses the metadata to construct histograms of the submitted scenarios by model family and project, noting the total submissions, vetted scenarios, and climate assessed scenarios. A total of 2304 scenarios were submitted to the global emissions database, of these, 618 did not passing vetting for sufficiently consistency with historical energy and emissions data, and a further 484 did not have sufficient data to perform a climate assessment, leaving a total of 1202 used in the primary assessment of scenarios. </p> <p>The database based on the scenario metadata. The ‘model family’ was determined by removing version numbers from the full model name. The ‘project family’ was obtained using the ‘Scenario family’ variable in metadata, supplemented by manually checking against cited literature. The classification of vetted scenarios was based on the variable ‘Historical vetting’ and the climate assessment on the ‘Climate Category’.</p> <p>This version is based on version 1.0 of the AR6 scenarios database.</p>
A dynamic von Mises-based model to evaluate the impact of urbanization and climate change on flood timing in Yangtze and Huaihe River Basins, China
<p>The daily streamflow data extracted from 8 selected stations from the Huaihe and Yangtze River Basins, China.</p>
Urban Heat: Forward-Looking Climate Modeling for Nis, Serbia
<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 <strong>present-day and future conditions</strong> under selected climate scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>). The study domain focuses on Nis, Serbia.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2021-2040, and 2041-2060</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>150 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 32634</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>. Some indicators also have additional calculations for <strong>seasonal averages</strong>, including Spring (MAM), Summer (JJA), Autumn (SON), and Winter (DJF).</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. Present denotes the period 2001-2020; 2030 denotes the period 2021-2040; & 2050 denotes the period 2041-2060.</li> <li>The NetCDF and GeoTiff data can be found in the data.zip; The PNG files for quick viewing can be found in quickview.zip; more information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the Technical_Annex_Nis.docx</li> </ul>
Data and model code for: Habitat use patterns suggest that climate-driven vegetation changes will negatively impact mammal communities in the Amazon (ACV)
Open the record for dataset details and reuse information.
Climate model experiments of regional-scale tree die-off replaced by shrubs (all monthly data fields): Part 3
Open the record for dataset details and reuse information.
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