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105 results for “cmip6”
Data for DynVarMIP postprocessing in CMIP6
<p>These netCDF files have been used to develop and test a diagnostic tool to compute the Transformed Eulerian Mean (TEM) variables. The calculation is done following the recommendations given in Gerber and Manzini, GMD, (2016).</p> <p>Data for January 2020 derived from the ERA5 reanalysis are provided, on pressure levels close to native levels and with reduced horizontal resolution. Note that ERA5 is distributed under the Copernicus <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">licence</a>.</p> <p>More details will be made available in due course.</p>
Results of ISMIP6 CMIP6 forced simulations: a multi-model ensemble of the Greenland and Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "Payne et al. 2021 Future sea level change under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets", published in GRL</p> <p>Contact: Tony Payne a.j.payne@bristol.ac.uk, Sophie Nowicki sophien@buffalo.edu, ismip6@gmail.com </p> <p><br> Further information on ISMIP6 can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Greenland</p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in the process producing this data set. Acknowledgements should have language similar to the below (if you only use CMIP5 forcing, remove CMIP6 and vice versa).</p> <p>“We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>For Greenland datasets </p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., Fettweis, X., and Nowicki, S.: Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution , The Cryosphere, 14, 985–1008, https://doi.org/10.5194/tc-14-985-2020, 2020.</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: <br> Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p>For Antarctica datasets</p> <p>Seroussi, H., Nowicki, S., Simon, E., Abe-Ouchi, A., Albrecht, T., Brondex, J., Cornford, S., Dumas, C., Gillet-Chaulet, F., Goelzer, H., Golledge, N. R., Gregory, J. M., Greve, R., Hoffman, M. J., Humbert, A., Huybrechts, P., Kleiner, T., Larour, E., Leguy, G., Lipscomb, W. H., Lowry, D., Mengel, M., Morlighem, M., Pattyn, F., Payne, A. J., Pollard, D., Price, S. F., Quiquet, A., Reerink, T. J., Reese, R., Rodehacke, C. B., Schlegel, N.-J., Shepherd, A., Sun, S., Sutter, J., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., and Zhang, T.: initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6, The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019, 2019.</p> <p>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p><br> Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p> </p>
CMIP6 tmax and tmin data for 50 most populous cities in USA
<p>These data were downloaded from Google Earth Engine. Each csv includes Year Month Day CM Scenario and other columns. CM includes three scenario (historical, ssp245 and ssp585). The scenario involves the parameter (tasmax or tasmin). Other columns means the city ID. The city ID and corresponding city name can be seen in the csv file named UID Abbreviation.csv above and the shapefile of cities were used from the open repository (Chen et al. 2022). This global inventory includes 247 cities within the contiguous US. This can be found at: <strong>Chen, B., Wu, S., Song, Y. <em>et al.</em> Contrasting inequality in human exposure to greenspace between cities of Global North and Global South. <em>Nat Commun</em> <strong>13</strong>, 4636 (2022). https://doi.org/10.1038/s41467-022-32258-4.<br><br><br></strong>And to select the 50 populous cities, 1 km X 1 km resolution imagery of population by Wang et. al (2022) was used.<strong>[Wang, X., Meng, X. & Long, Y. Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. <em>Sci Data</em> 9, 563 (2022). https://doi.org/10.1038/s41597-022-01675-x]. </strong>Based on this, we ranked 50 cities from those 247 US cities found in Chen et al. 2022.<br><br>Those data and real names of the cities can be studied from these papers.</p>
Scripts used for the analysis in the paper "How certain are ENSO frequency changes in CMIP6 models?"
<p>These are scripts used for the analysis of CMIP6 piCOntrol and 1pctCO2 simulations.</p> <p>They produce results and plots used in the paper "How certain are ENSO frequency changes in CMIP6 models, submitted to "International Journal of Climatology".</p> <p> </p> <p>Paper_ONIcontrol.sh, Prepare_CMIP.sh and PrepareCMIP_long.sh were used to process the original data.</p> <p>The notebooks and python scripts are used for analysis and plotting.</p>
CMIP6 HighResMIP HadGEM3-GC3.1 LL, MM and HH datasets for the heat transport in the lower limb of the AMOC
<p>These data include datasets for "Resolving mesoscale eddies is crucial for the heat transport in the lower limb of the AMOC", submitted to <em>Geophysical Research Letters</em>.</p> <p>Each file is prefixed with model resolution (LL, MM and HH) and experiment details (control, SSP5-8.5). Files ending with '_velocities' and '_age_pressure' contain diagnostics projected on the isopycnal surface (sigma2=36.95), depicted in Figures 2 and 3 of the submitted paper. </p>
Replication data for: "Revisiting the 'East African Paradox': CMIP6 models also struggle to reproduce strong observed Long Rain drying trends."
<h3><strong>Intro</strong></h3> <p>This repository contains replication data for Schwarzwald, Kevin and Richard Seager (2024), "'Revisiting the “East African Paradox': CMIP6 models also struggle to reproduce strong observed MAM long rain drying trends." (under revision at Journal of Climate). This includes the data necessary to replicate all main text figures and most figures in Supplementary Materials. Additional figures in Supplementary Materials require raw precipitation time series, detailed below. This repository also includes a copy of the code necessary to replicate the entire study; the latest version of the code, in addition to instructions on how to use it, is kept at <a href="https://github.com/ks905383/gha_trends">this GitHub archive</a>. </p> <h3><strong>Structure</strong></h3> <p>The repository is structured as follows: </p> <ul> <li><code>code</code>: Static / stable version of reproduction code for Schwarzwald and Seager (2024) that uses and creates these data (see <a href="https://github.com/ks905383/gha_trends">here</a> for more detailed instructions)</li> <li><code>figures</code>: Static / stable versions of main and supplemental figures for Schwarzwald and Seager (2024).</li> <li><code>climate_raw</code>: "raw" (often pre-processed) climate data files; only some files are included, see below for more details</li> <li><code>climate_proc</code>: Processed climate data files upon which the analysis is based, used by main and supplemental figure code</li> <li><code>aux_data</code>: Certain auxiliary data files (fonts, critical values) and intermediate files for long code processes. Created and used by the replication code. </li> </ul> <h3><strong>Notes on raw climate data</strong></h3> <p>Due to space limitations (and a desire to not create yet another cloud copy of CMIP6 data), this repository only contains processed CMIP6 data: the calculated linear trends of rainfall, sea surface temperatures, and 500 hPa geopotential height used in the analysis of Schwarzwald and Seager (2024). The raw data used to create these files can be downloaded from the <a href="https://aims2.llnl.gov/search/cmip6/">ESGF</a>, and consists of every available CMIP6 monthly rainfall, sea surface temperature (SST), and geopotential height file on the archive at the time of processing for the experiments detailed in the manuscript. For precipitation, only a bounding box (-3 S to 12.5 N, 32 E to 55 E) around East Africa was downloaded, and saved with the file suffix "_HoAfrica" (see replication code README for more details). One example precipitation file (one ensemble member of ACCESS-CM2 historical precipitation) is included in this repository for reference. </p> <p>Similarly, this repository only contains processed NMME data: calculated linear trends of rainfall used in the analysis of Schwarzwald and Seager (2024). The raw data used to create these files can be downloaded from the <a href="https://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/">IRI Data Library</a> and consists of every available monthly rainfall hindcast / forecast file from the NMME archive for the models used (see manuscript Table S1). As above, only a bounding box around East Africa was downloaded, and files were standardized to a partial CMIP* file format (one file per variable, with CMIP file and variable name conventions, but with forecast lead time as an additional dimension). Preprocessing is a bit more extensive than for CMIP6 models: first, hindcasts and forecasts were concatenated into a single file (since hindcasts are saved in the archive only up to the time when the model was operationalized), and then reindexed such that the "time" variable refers to the time <em>for which the forecast is made</em>, and not the time <em>at</em> which the forecast is made. One example precipitation file (CanCM4i rainfall hindcasts/forecasts) is included in this repository for reference. </p> <p>This repository does, however, contain preprocessed copies of the "raw" gridded observational rainfall data products used (in addition to the processed trends), since harmonizing and standardizing the data into a format easily compatible with the CMIP6 data was a nontrivial amount of work that would be tedious to replicate. For the ten gridded observational data products used, monthly rainfall was brought into the CMIP* file format (one file per variable, with CMIP file and variable name conventions), with one notable exception: all observational rainfall is saved in units of mm/day. </p> <p>Reanalysis and gridded ocean observations can be downloaded from each product's respective repositories. As before, the code assumes the data have been preprocessed into something akin to the CMIP* file format. </p> <h3><strong>Notes on processed climate data</strong></h3> <p>Note that the repository includes a file (<code>aux_data/pr_doyavg_CHIRPS_historical_seasstats_dunning_19810101-20141231_HoAfrica.nc</code>) containing CHIRPS seasonal characteristics in East Africa, created as part of Schwarzwald et al., 2023, <em>Climate Dynamics</em>. This file is primarily used to set the boundaries of the study region, see the manuscript for details of how it was calculated. </p> <h3><strong>Licensing, citing, questions</strong></h3> <p>These data are offered under a CC 4.0 license, which allows redistribution and reuse as long as they are correctly cited; note that for much of these data (especially for "raw" data), this requires citations to the original creators. </p> <p>For questions, please feel free to reach out to corresponding author Kevin Schwarzwald. </p>
The INMCM-4.8 Earth system model data used in the paper by Guryanov V.V. et al. entitled ''The present-day and future lightning frequency as simulated by four CMIP6 models'
<p>The INMCM-4.8 Earth system model data used in the paper by Guryanov V.V. et al. entitled ''The present-day and future lightning frequency as simulated by four CMIP6 models'</p>
PAMIP HadGEM3-LL CMIP6
<p>A selection of data from PAMIP HadGEM3-LL CMIP6 experiments (run at the University of Exeter). See README.txt for more details.</p>
Data belonging to 2022_Denissen_ELI_CMIP6_Nat_Clim_Change
<p>The Data and RData folders belonging to and is meant to reproduce the analysis and figures of 2022_Denissen_ELI_CMIP6_Nat_Clim_Change. </p> <p>Both of the folders can be unzipped with 'tar -xf Data.tar.gz' and 'tar -xf RData.tar.gz' in the main directory.</p>
Processed CMIP6 data for "The climate response to the Mt Pinatubo eruption does not constrain climate sensitivity"
<p>Processed CMIP6 output for Pauling et al. "The climate response to the Mt Pinatubo eruption does not constrain climate sensitivity" submitted to Geophysical Research Letters in 2023.</p> <p>Download this code repository: https://doi.org/10.5281/zenodo.7553024 and follow the instructions in the README to download data and reproduce the results of the paper.</p>
Pre-industrial, present and future atmospheric soluble iron deposition and the role of aerosol acidity and oxalate under CMIP6 emissions: DATASET
<p>This dataset provides output fields that were used in the research article titled "Pre-industrial, present and future atmospheric soluble iron deposition and the role of aerosol acidity and oxalate under CMIP6 emissions." The data includes the following fields presented as yearly means:</p> <ul> <li>Soluble Iron emissions (SFe_emi) in Tg/yr</li> <li>Total Iron emissions (TFe_emi) in Tg/yr</li> <li>Soluble Iron deposition (SFe_dep) (both wet and dry) in Tg/yr</li> <li>Total Iron deposition (TFe_dep) (both wet and dry) in Tg/yr</li> </ul> <p>The data covers the following periods:</p> <ul> <li>Pre-industrial period (PI), with the year 1850</li> <li>Present-day period (PD), with the climatology from 1985 to 2014</li> <li>Three future periods based on the SSP CMIP6 scenarios (SSP126, SSP245, SSP370), with the climatology from 2070 to 2099</li> </ul> <p>An additional file containing information on the cell areas of the output grid is also included (areacell_BergasMassoetal2023.nc).</p>
The Impact of Climate Change on Extreme Winds over Northern Europe According to CMIP6: data and codes
<p>Data and codes for the paper submitted to WES "The Impact of Climate Change on Extreme Winds over Northern Europe According to CMIP6"</p> <p>Data:</p> <p>1. U50*.nc: 50-year wind with spectral correction method from 18 CMIP6 models</p> <p>2. annual_max*.nc: annual wind maxima from 18 CMIP6 models</p> <p>3. spe*.dat: example of power spectrum from the original wind speed time series from a CMIP6 model and power spectrum with spectral correction</p> <p>Codes:</p> <p>1. AnnualMax*.py : calculate extreme winds from 18 CMIP6 models</p> <p>2. XFuture-code-sharing.nb: Mathematca code for analysis of data on the effect of climate change, and pack data for plotting using MATLAB</p> <p>3. *.m: MATLAB codes for plotting figures on climate change </p> <p> </p> <p> </p>
MAgPIE model runs csv for plotting: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>This .zip file contains the data used to create the figures for the paper. It includes .csv files and .nc files for maps. This version includes additional files like the mapping between countries and MAgPIE're economic regions.</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 8
<p>Future projections of precipitation by the BM10 model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 5
<p>Future projections of precipitation by the BM1 model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 3
<p>Future projections of 2-meter minimum temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 2
<p>Future projections of 2-meter maximum temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 11
<p>Future projections of 2-meter maximum, mean and minimum temperatures by the BMlinear model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 1
<p>Input data (ERA5 and the seven GCMs) used to train the CNN models (BMlinear, BM1, BM10 and BMdense) used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 9
<p>Historical projections of all predictands (2-meter maximum, mean and minimum temperatures, and precipitation) by all CNN models (BMlinear, BM1, BM10, BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia". Each CNN model architecture is available in the file "model.json" and its optimized weights for each case are available in the file "model_weights.h5".</p>
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