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105 results for “cmip6”
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 7
<p>Future projections of precipitation by the BMdense 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>
MPI-ESM1.2 data for Aerosol Forcing Does Not Sufficiently Explain the Suppressed Mid-20th Century Warming in CMIP6
<p>This dataset contains results from simulations with the MPI-ESM1.2-CR, created by Linnea Huusko and used in the following paper: Flynn, C. M., Huusko, L., Modak, A., and Mauritsen, T.: Strong aerosol cooling alone does not explain cold-biased mid-century temperatures in CMIP6 models, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1613, 2023.</p>
Evaluation and Projection of Surface Wind Speed over China Based on CMIP6 GCMs
<p>This file is for the upload of CN05.1 data for 2020JD033611RR.</p>
Dataset: Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models
<p>This dataset corresponds to the following publication:<br>Steinert, N., J., et al. 2023: <i>Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models</i>, Environmental Research Letters, 10.1088/1748-9326/ad10d7<br>https://iopscience.iop.org/article/10.1088/1748-9326/ad10d7</p><p>Global permafrost regions are undergoing significant changes due to global warming, whose assessments often rely on permafrost extent estimates derived from climate model simulations. These assessments employ a range of definitions for the presence of permafrost, leading to inconsistencies in the calculation of permafrost area. This dataset contains permafrost area calculations using 10 different definitions for detecting permafrost presence based on either ground thermodynamics, soil hydrology, or air-ground coupling from an ensemble of 32 Earth System Models.</p><p>This dataset includes two file archives:<br>1. 32 CMIP6 models, 10 permafrost definitions, historical period (1850-2014), annual data<br>2. 32 CMIP6 models, 10 permafrost definitions, SSP5-85 period (2015-2100), annual data</p><p>This dataset represents source data for the following publication. Please refer to this reference for a more detailed description of the definitions used in this dataset:<br>Steinert, N., J., et al. 2023: Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models, https://iopscience.iop.org/article/10.1088/1748-9326/ad10d7</p><p>The results show that variations between permafrost-presence definitions result in substantial differences of up to 18 million km2, where any given model could both over- or underestimate the present-day permafrost area. Ground-thermodynamic-based definitions are, on average, comparable with observations but are subject to a large inter-model spread. The associated uncertainty of permafrost area estimates is reduced in definitions based on ground-air coupling. However, their representation of permafrost area strongly depends on how each model represents the ground-air coupling processes. The definition-based spread in permafrost area can affect estimates of permafrost-related impacts and feedbacks, such as quantifying permafrost carbon changes. For instance, the definition spread in permafrost area estimates can lead to differences in simulated permafrost-area soil carbon changes of up to 28%. This dataset therefore supports an emphasis on the importance of consistent and well-justified permafrost-presence definitions for robust projections and accurate assessments of permafrost from climate model outputs.</p><p>For any questions regarding the dataset, please free feel to contact Norman J. Steinert (nste@norceresearch.no, normanst@ucm.es)</p>
Data for: The Precipitation Response to Warming and CO2 Increase: A Comparison of a Global Storm Resolving Model and CMIP6 Models
<p>Data to reproduce figures in manuscript "The Precipitation Response to Warming and CO$_2$ Increase: A Comparison of a Global Storm Resolving Model and CMIP6 Models" submitted to GRL</p>
X-SHiELD model verification figures, related to: "The Precipitation Response to Warming and CO$_2$ Increase: A Comparison of a Global Storm Resolving Model and CMIP6 Models"
<p>Figures comparing differrent fields in X-SHiELD with observations. Similar information can be recived at https://extranet.gfdl.noaa.gov/%7EAlex.Kaltenbaugh/verification/</p>
Number of Joint Precipitation and either Wind or Storm Surge Extremes Based on CMIP6 Simulations
<p>This data accompanies the manuscript ‘Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models’, Hermans et al. (2024). The dataset consists of the number of joint wind speed & precipitation or storm surge &precipitation extremes derived from CMIP6 models, for different time periods in the 20th and 21st centuries. </p> <p>The code used to produce this dataset can be accessed here: https://github.com/Timh37/CMIP6cex. </p> <p><em>For the production of this dataset, we acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid </em><em>Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.580. We also acknowledge the computing and storage resources provided by the ‘NSF Science and Technology Center (STC) Learning the Earth with Artificial intelligence and Physics (LEAP)‘ (Award #2019625).</em></p>
CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023
<p><span>Contains processed data to create all figures and tables from the publication Diamond et al. (2024) </span><span>CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023; Geophysical Research Letters </span><span>[Full reference available on publication]</span></p> <p><span> </span></p> <p><span>Directory structure and associated contents listed below. Folders contain all data required to make corresponding figure, apart from where indicated. Full description of analysis in Diamond et al. (2024). Unless otherwise indicated, 'SIE' refers to August Antarctic SIE.</span></p> <p><span> </span></p> <p><span>F1/</span></p> <p><span>f1a_obs_ts.csv: Timeseries of 1979-2023 SIE </span></p> <p><span>f1b_all_instances.csv: All instances (labelled by model, simulation, and simulation year) where an anomaly of magnitude SIE_2023 was detected. SIE timeseries from years -20 to +20 about each anomaly.</span></p> <p><span>f1b_quartiles.csv: Upper and lower quartiles, mean, and median, computed over all instances.</span></p> <p><span> </span></p> <p><span>F2/</span></p> <p><span>F2_a.csv: 5--95% range of SIE variability dataset, for all model simulations, and observations.</span></p> <p><span>F2_b.csv: T_aug23, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F2_c.csv: T_aug23, computed using GEVs, for all model simulations, and multi-model ensemble.</span></p> <p><span> </span></p> <p><span>F3/</span></p> <p><span>Data for all figures is already provided elsewhere:</span></p> <p><span>F3a: for observations in SF3/SF3_obs_dataset.csv, and for simulations in SF3/SF3_simulations.csv.</span></p> <p><span>F3b: for multi-model ensemble T_aug23 with associated errors in SF6/SF6_all.csv</span></p> <p><span>F3c: for GEV_estimated T_aug23 for all simulations and models in SF6/SF6_all.csv; and<span> </span>5--95% ranges of SIE variability datasets in F2/F2_a.csv</span></p> <p><span> </span></p> <p><span>~</span></p> <p><span>SF1/</span></p> <p><span>SF1_all.csv: SIE timeseries for all model simulations, and associated moving averages.</span></p> <p><span>SF2/</span></p> <p><span>sf2_all.csv: SIE decadal means and ranges for all model simulations.</span></p> <p><span>sf2_quartiles.csv: taken over all simulations above - mean decadal mean in each bin; and lower quartile, median and upper quartile of the decadal ranges in each bin.</span></p> <p><span>SF3/</span></p> <p><span>SF3_obs_dataset.csv: SIE variability dataset for 1979-2023 observations.</span></p> <p><span>SF3_simulations.csv: SIE variability datasets for all model simulations</span></p> <p><span>SF4/</span></p> <p><span>F4_a.csv: p_aug23, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_b.csv: p_aug22, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_c.csv: T_aug23, computed using GEVs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_d.csv: for all model simulations: p-value that simulation SIE variability dataset is drawn from same probability distribution as observations. Computed using Kolmogorov-Smirnoff 2-sample test.</span></p> <p><span>SF5/</span></p> <p><span>Data for both SF5a and b are already provided in F1/f1b_all_instances.csv.</span></p> <p><span>SF6/</span></p> <p><span>SF6_all.csv: for all model simulations, and multi-model ensemble, p_aug23 and T_aug23, with lower and upper errors (computed from 5--95% confidence interval using bootstrapping)</span></p> <p><span>SF7/</span></p> <p><span>Data for SF7a is already provided elsewhere: for observations in SF3/SF3_obs_dataset.csv, and for simulations in SF3/SF3_simulations.csv.</span></p> <p><span>SF7_all.csv: as for SF6_all.csv, but instead using subset of models.</span></p>
An evaluation dataset for the skills of CMIP5 and CMIP6 models in simulating climate of China
<p>General circulation model (GCM) simulations archived by the Coupled Model Intercomparison Project (CMIP) are crucial tools for climate science. However, with various GCM results simulated by different countries and institutions, researchers have difficulty in choosing appropriate models for their unique study area. To this end, this dataset provieds Tayler skill scores of 28 GCMs in simulating temperature and precipitation of 631 reference sites across China under daily, monthly and seasonally scales. These scores are calculated based on the observations of meteorological stations and historical simulations of GCMs during 1970-2005. </p> <p>The dataset is very important for researchers to select locally appropriate GCMs. For example, researchers concerned with climate change of Beijing could firstly download the GCMs with sound performance at station 54511 (i.e., NorESM2-LM, INM-CM5-0 and MPI-ESM1-2-LR for temperature and NorESM1-M, IPSL-CM5A-LR and INM-CM4 for precipitation), and then conduct the further works of downscaling.</p>
Data for the paper Robust expansion of extreme extratropical storms under global warming (CMIP6)
<p>Data for the paper "Robust expansion of extreme extratropical storms under global warming"</p>
The station-based error information of monthly snow depth, precipitation and air temperature for CMIP6 models in mainland China
<p>This dataset contains the data of monthly snow depth in terms of RMSD (cm), spatial correlation (R<sub>s</sub>), temporal correlation (R<sub>t</sub>), consistency index (CI), and Hotspot score (H-score) of the 1415 weather stations (only 342 stations with longterm observations were available for R<sub>t</sub>, CI and H-score) in China used for evaluating the snow depth simulated or estimated from 31 CMIP6 models, MERRA2 reanalysis and a remote sensing snow depth dataset (Che). It also includes the data of errors and accumulated errors of monthly precipitation (mm) and air temperature (℃) from the 342 stations of all the 31 CMIP6 models, which can be used for constructing the regression models for analyzing error sources of snow depth simulations. The NA values of monthly precipitation and temperature indicate that the effects of accumulated errors were ignored for the corresponding month and station.</p>
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>
Model simulation data used in "An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing" (Thor et al., GMD, 2022)
<p>This archive contains files that were used to produce the results published in the article "An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing" by Thor et al.</p> <p>The directory nml contains namelist setups (configuration files) used for each of the simulations that were performed for this study.<br> The used MESSy version is d2.54.0.3-pre2.55-02-2077-g6eca90858-dirty_6eca90858ecb4ee8fb8900681a94a79ac3d612af_2021-01-26T11:10:08+01:00_2021-02-18T09:14:01+0100 for the QCTM simulations and d2.54.0.3-pre2.55-02-1466-g1fb086944_1fb0869442bf4f1bb10a7dd5f36e4bde5c0cf6d7_2020-10-27T18:17:50+01:00_2020-10-27T18:24:16+0100 for the aerosol simulations (http://www.messy-interface.org).</p> <p>The directory figures contains ipython scripts that were used to produce the figures in the paper.</p>
CMIP6 scenarios' radiative forcing of non-CO2 greenhouse gases and aerosols for UVic ESCM simulations (1850-2500)
<h1>Overview</h1> <p>This repository contains the input files for the UVic Earth System Climate Model (ESCM) that are required to simulate the historical period (1850-2014) and the extended CMIP6 SSP-RCP scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, SSP5-3.4, SSP5-8.5 (2015-2500).</p> <p>For simulations of these scenarios, the model is forced with aggregated non-CO2 greenhouse gas radiative forcing, land use cover, aerosol radiative forcing, and either CO2 concentration or CO2 emissions. The radiative forcing of CO2 is calculated internally by the UVic ESCM.</p> <p>The following files are included in this repository:</p> <p><strong>CO2 concentrations (for concentration-driven simulations)</strong></p> <p>A_co2_hist.nc</p> <p>A_co2_119.nc</p> <p>A_co2_126.nc</p> <p>A_co2_245.nc</p> <p>A_co2_370.nc</p> <p>A_co2_434.nc</p> <p>A_co2_460.nc</p> <p>A_co2_534.nc</p> <p>A_co2_585.nc</p> <p> </p> <p><strong>CO2 emissions (for emission-driven simulations)</strong></p> <p>F_co2emit_119.nc</p> <p>F_co2emit_126.nc</p> <p>F_co2emit_245.nc</p> <p>F_co2emit_370.nc</p> <p>F_co2emit_434.nc</p> <p>F_co2emit_460.nc</p> <p>F_co2emit_534.nc</p> <p>F_co2emit_585.nc</p> <p> </p> <p><strong>Land use cover fractions (pasture and crops)</strong></p> <p>L_agricfra_hist_and_ssp119.nc</p> <p>L_agricfra_hist_and_ssp126.nc</p> <p>L_agricfra_hist_and_ssp245.nc</p> <p>L_agricfra_hist_and_ssp370.nc</p> <p>L_agricfra_hist_and_ssp434.nc</p> <p>L_agricfra_hist_and_ssp460.nc</p> <p>L_agricfra_hist_and_ssp534.nc</p> <p>L_agricfra_hist_and_ssp585.nc</p> <p> </p> <p><strong>Aggregated non-CO2 greenhouse gas forcing</strong></p> <p>A_aggfor_hist.nc</p> <p>A_aggfor_119.nc</p> <p>A_aggfor_126.nc</p> <p>A_aggfor_245.nc</p> <p>A_aggfor_370.nc</p> <p>A_aggfor_434.nc</p> <p>A_aggfor_460.nc</p> <p>A_aggfor_534.nc</p> <p>A_aggfor_585.nc</p> <p> </p> <p><strong>Aerosol optical depth</strong></p> <p>A_sulphod_hist.nc</p> <p>A_sulphod_119.nc</p> <p>A_sulphod_126.nc</p> <p>A_sulphod_245.nc</p> <p>A_sulphod_370.nc</p> <p>A_sulphod_434.nc</p> <p>A_sulphod_460.nc</p> <p>A_sulphod_534.nc</p> <p>A_sulphod_585.nc</p> <p> </p> <h1>Detailed description</h1> <h2>1. CO2 concentrations</h2> <p>The CO2 concentrations are provided here as the annual global mean mole fraction of CO2 in ppm and identical with the CMIP6 input data available at <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>.</p> <h2>2. CO2 emissions</h2> <p>The CO2 emissions are the same as provided by RCMIP (Meinshausen et al., 2020). Here the Agriculture, Forestry and Other Land Use (AFOLU) emissions are represented as “F_co2eland” emissions. Also, the sector based emissions from Aircraft, the Industrial Sector, International Shipping, Residential Commercial Other, Solvents Production and Application, the Transportation Sector, and Waste are aggregated into the Fossil and Industrial emissions and represented as “F_co2efuel” emissions. Both the F_co2eland and F_co2efuel emissions are finally aggregated into total CO2 emissions represented as “F_co2emit”. These aggregated CO2 emissions are likewise identical to globally averaged CMIP6 input data available at <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>. All three CO2 emission variables are included in the “F_co2emit*.nc” files. In addition to the SSP-RCP-scenario CO2 emissions also the historical CO2 emissions are included in all files (starting in year 1750).</p> <h2>3. Land use cover</h2> <p>The land-use forcing is provided as the pasture and cropland grid cell fraction (variable names: “L_cropfra” and “L_pastfra”; in file: “L_agricfra.nc”). The UVic ESCM translates pasture and cropland fractions internally into C3 grass or C4 grass fractions, depending on the local conditions. The land-use cover is based on LUH2v2f “states.nc” data (available at <a href="https://luh.umd.edu/data.shtml">https://luh.umd.edu/data.shtml</a>) and has been regridded and reaggregated for the UVic ESCM. The cropland fraction of the UVic ESCM input (“L_cropfra”) is the sum of all crop types given by LUH2v2f (“c3ann”, “c3nfxc”, “c3per”, “c4ann”, “C4per”), whereas the pasture fraction (“L_pastfra”) is the sum of LUH2v2f’s pasture fraction and rangeland fraction (“pastr”, “range”). The land-use forcing covers the period 850-2100.</p> <h2>4. Non-CO2 greenhouse gas radiative forcing</h2> <p>The aggregated radiative forcing of 44 non-CO2 greenhouse gases (GHG) was calculated from the respective atmospheric GHG concentrations (provided by RCMIP for CMIP6, see References), following the approach of Meinshausen et al. 2020 and Etminan et al. 2016. Radiative forcing of tropospheric ozone, stratospheric ozone, and stratospheric water vapor from methane oxidation was calculated as described in Smith et al. 2018.</p> <p>The following non-CO2 GHG are accounted for in the aggregated forcing files (“A_aggfor.nc”):</p> <p>N2O; CH4; CFC11; CFC12; HFC134a; C2F6; C6F14; CF4; HFC23; HFC32; HFC43_10; HFC125; HFC143a; HFC227ea; HFC245fa; SF6; CFC113; CFC114; CFC115; HCFC22; HCFC142B; HCFC141B; HALON1211; HALON1301; HALON2402; CH3BR; CH3CL; CCL4; CH2CL2; CH3CCL3; NF3; HFC365mfc; C3F8; C4F10; HFC236fa; C5F12; CHCL3; cC4F8; HFC152a; SO2F2; C7F16; C8F18; stratospheric and tropospheric O3; water vapor from CH4 oxidation.</p> <h2>5. Aerosol radiative forcing</h2> <p>Aerosol optical depth (AOD) 2D input data for the UVic ESCM was created using a UVic grid with the scripts and data provided by Stevens et al. (2017). The data provided describes nine different plumes globally which are scaled with time to produce monthly aerosol optical depth forcing for the years 1850-2018 (Stevens et al., 2017). For the future projection of the years 2018-2100, the same scripts were run with input data from Fiedler et al. (2019). To extend aerosol optical depth data from 2100 to 2500, the last year of available data (i.e. 2100) was repeated. </p> <p>Since the AOD input caused too great a negative forcing in the historical period, a scaling factor was implemented into the UVic ESCM, which allows to scale aerosol forcing from AOD data. The scaling factor was set to 0.7, which gives a globally averaged forcing of -1.03 Wm<sup>-2</sup> in 2011.</p> <p>Note that the file "A_sulphod_hist.nc" contains not only the data of the historical period (1850-2014) but also the data of the scenario SSP5-8.5 (extended until 2500).</p> <p> </p> <h2>References</h2> <p>Fiedler, S., Stevens, B., Gidden, M., Smith, S. J., Riahi, K., & van Vuuren, D. (2019). First forcing estimates from the future CMIP6 scenarios of anthropogenic aerosol optical properties and an associated Twomey effect. <em>Geoscientific Model Development</em>, <em>12</em>(3), 989-1007.Etminan, M., Myhre, G., Highwood, E., and Shine, K.: Radiative forcing of carbon dioxide, methane, and nitrous oxide: A significant revision of the methane radiative forcing, Geophys. Res. Lett., 43, 12614–12623,<a href="https://doi.org/10.1002/2016GL071930"> </a><a href="https://doi.org/10.1002/2016GL071930">https://doi.org/10.1002/2016GL071930</a>, 2016.</p> <p>Meinshausen, M., Nicholls, Z. R., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., ... & Wang, R. H. (2020). The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. <em>Geoscientific Model Development</em>, <em>13</em>(8), 3571-3605.</p> <p>Smith, C. J., Forster, P. M., Allen, M., Leach, N., Millar, R. J., Passerello, G. A., & Regayre, L. A. (2018). FAIR v1. 3: a simple emissions-based impulse response and carbon cycle model. <em>Geoscientific Model Development</em>, <em>11</em>(6), 2273-2297.</p> <p>Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433-452, https://doi.org/10.5194/gmd-10-433-2017, 2017</p> <p>RCMIP GHG concentration data:<a href="../record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv"> </a><a href="../record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv">https://zenodo.org/record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv</a></p> <p>RCMIP Emissions data:</p> <p><a href="https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv">https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv</a></p> <p>Input4mips CO2 concentration data: <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a></p> <p>LUH2 land-use cover data: <a href="https://luh.umd.edu/data.shtml">https://luh.umd.edu/data.shtml</a></p>
Fire Weather Index for Europe from Downscaled and Bias-Corrected CMIP6 Model Outputs
<p>This dataset contains the Canadian Forest Fire Weather Index (FWI) calculated from six downscaled and bias-corrected CMIP6 model outputs. The models included are:</p> <ul> <li>ACCESS-CM2 (Ziehn et al. 2020)</li> <li>CanESM5 (Swart et al. 2019)</li> <li>CNRM-ESM2-1 (Séférian et al. 2019)</li> <li>EC-EARTH3 (EC-Earth Consortium 2019)</li> <li>MPI-ESM1-2-HR (von Storch et al. 2017)</li> <li>MRI-ESM2-0 (Yukimoto et al. 2019)</li> </ul> <p>The dataset encompasses four Shared Socio-economic Pathway (SSP) projections:</p> <ul> <li>SSP1-2.6</li> <li>SSP2-4.5</li> <li>SSP3-7.0</li> <li>SSP5-8.5</li> </ul> <p>Each model output has been downscaled to a resolution of 0.0703135°, corresponding to approximately 9km×9km grids before the FWI calculation. The data covers Europe spatially and temporally spans from 1950 to 2080, offering comprehensive insights into past, present, and future fire weather conditions.</p> <p>This dataset supports the manuscript titled <strong>"The fire weather in Europe: large-scale trends towards higher danger" </strong>by Hetzer et al., currently under review in ERL. Detailed instructions for accessing the data can be found in the included README file. </p> <p>Note: Downloads are password protected. Please use "FWI_2024" for access. </p> <p>Funding: The authors acknowledge the financial support of the European Union’s Horizon 2020 research and innovation action for the FirEUrisk project under grant agreement ID: 101003890.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Investigating the "Too Bright" Issue Pertaining to Non-PBL Clouds over the South Pacific Trade-Wind Region in CMIP6 Global Climate Models
<p><a href="../api/records/13314147/draft/files/f09.C6.B-hist.SON_ANN.tar.gz/content" target="_blank" rel="noopener noreferrer">f09.C6.B-hist.SON_ANN.tar.g</a>z</p> <p>CESM2-CAM6 with falling ice radiative effects (FIREs), fully coupled run folloing CMIP6 historical run, same as CESM2-CAM6 in CMIP6 data port.</p> <p> </p> <p>The data includes with netcdf self description.</p> <p>f09.C6.B-hist.h01_AWNC_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLDHGH_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLDLOW_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDMED_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDTOT_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLOUD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLOUDFRAC_CLUBB_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CONCLD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_FREQL_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_ICWMR_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_NUMLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_OMEGA_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_PRECC_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_PRECL_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_SST_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_tauy_ANN_climo-CDO.nc</p> <p><a href="../api/records/13314147/draft/files/f09.C6.B-hist.SON_ANN.tar.gz/content" target="_blank" rel="noopener noreferrer">f09.C6.B-hist.NOS_ANN.tar.g</a>z</p> <p>CESM2-CAM6 without falling ice radiative effects (FIREs), fully coupled run folloing CMIP6 historical run, same as CESM2-CAM6 in CMIP6 data port.</p> <p><br>f09.C6.B-hist.nos81_AWNC_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CDNUMC_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDHGH_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CLDLOW_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDMED_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDTOT_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLOUD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CLOUDFRAC_CLUBB_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CONCLD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_FREQL_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_ICWMR_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_NUMLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_OMEGA_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_PRECC_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_PRECL_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_SST_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_taux_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_tauy_ANN_climo-CDO.nc</p>
CMIP6 SSP2-4.5 Aviation emissions with corrected geographical distribution after Thor et al., 2023
<p>The files contain the CMIP6 SSP2-4.5 emissions for the aviation sector with corrected geographical distribution, as described by Thor et al. 2023.</p> <p>1) cos²(lat)<br>2) a factor (uncorrected emissions / corrected emissions) to yield the same global total emissions in the corrected files. This corresponds to the 1.912 factor as described by Thor et al. 2023.</p> <p>The fix has been performed for:<br>BC, NOX, NMVOC, NH3, CO2, VOC, SO2, CO and OC</p> <p>Thor, R. N., Mertens, M., Matthes, S., Righi, M., Hendricks, J., Brinkop, S., Graf, P., Grewe, V., Jöckel, P., and Smith, S.: An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing, Geosci. Model Dev., 16, 1459–1466, https://doi.org/10.5194/gmd-16-1459-2023, 2023. </p>
Data for paper "Diagnosing the factors that contribute to the intermodel spread of climate feedback in CMIP6"
<p>This data deposit includes the model simulation data and intermediate data. All data is annunal mean to reduce the size.</p> <p>The globalmean.zip includes the global mean radiative repsonse data. </p> <p>The annualmean.zip includes the annual mean data of all model simulations done and have been regridded to 2x2.5 degree resolution. </p>
CMIP6_ISIMIP_GCM
<p>The climatic variables calculated with the outputs of CMIP6_ISIMIP five GCMs under SSP126, SSP245, SSP585. All the climate variables are calculated based on corn-specific indicators</p>
CMIP6 data for the analysis in the article "Storylines of Maritime Continent dry period precipitation changes under global warming"
<p>The files include the data required to generate the figures based on CMIP6 model simulation outputs</p>
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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.