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21 results for “cloud feedbacks”
Dataset for "Low-cloud feedback in CAM5-CLUBB: physical mechanisms and parameter sensitivity analysis"
<p>This repository contains the data of 512 perturbed-parameter ensemble experiments and CAM5-CLUBB default experiments for the paper "Low-cloud feedback in CAM5-CLUBB: physical mechanisms and parameter sensitivity analysis".</p> <p>In this paper, the quasi-Monte Carlo (QMC) sampling approach is applied to explore the high-dimensional space. 512 samples are generated with the 18 perturbed parameters. For each parameter sample, a pair of experiments is performed: the control one is based on the climatological sea surface temperature (SST), and the 4K experiment applies a uniform +4K SST perturbation to the control experiment. The total of 1024 simulations are then performed. In addition, CAM5-CLUBB default experiments that adopt the default values of the 18 selected parameters as in Bogenschutz et al. (2013) are performed to provide detailed model diagnostics for analyzing physical mechanisms of the cloud feedback, and they include both control and +4K simulations. Each simulation is run for 5 years and 4 months, forced by climatological SSTs. Monthly mean results from the last 5 years are analysed in this study.</p> <p>Note: data uploaded here is annual-mean and the dimension name 'time' in the files (CAM5-CLUBB_PPE_512*.nc) is the number of 512 PPE member.</p>
Supporting data for the manuscript: "The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback"
<p>This repository contains supporting data for the manuscript "The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback" in <em>Geophysical Research Letters</em>. Detailed descriptions of these datasets can be found in the manuscript text as well as in the file descriptions.</p>
Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" paper
<p>Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo<br>Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" by A. Ukhov, <br>G. Stenchikov, S.Osipov, N. Krotkov, N. Gorkavyi, C. Li, O. Dubovik, and A. Lopatin.</p> <p>Corresponding author: Alexander Ukhov, alexander.ukhov@kaust.edu.sa</p> <p>Contents<br>0. This 'README' file</p> <p>1. Emission profiles for ash and SO2<br> 1.1 In pickle and txt format, when radiative feedback is accounted for:<br> 1.1.1 Files 'ash_2d_emission_profiles_rad_on' [Mt/sec] and 'ash_2d_emission_profiles.txt' [Mt/(m sec)]<br> 1.1.2 Files 'so2_2d_emission_profiles_rad_on' [Mt/sec] and 'so2_2d_emission_profiles.txt' [Mt/(m sec)]</p> <p> 1.2 In pickle format, when radiative feedback is not accounted for:<br> 1.2.1 Files 'ash_2d_emission_profiles_rad_off' [Mt/sec]<br> 1.2.2 Files 'so2_2d_emission_profiles_rad_off' [Mt/sec]</p> <p>2. python script 'draw_supplementary_profiles.py' plots inverted emission profiles <br> (in pickle format) and their time integrated variants.</p> <p>3. WRF-Chem output file 'wrfout_d01_1991-06-16_00:00:00' in netcdf format contains <br> 3-D fields of ash, sulfate, and SO2 concentrations at 0000 UTC on 16 of June. <br> Instructions on how to process WRF-Chem output are available at the Appendix of [1].</p> <p>4. WRF-Chem domain grid description in the file 'wrf_small_grid.txt'. This file can be<br> used for conservative interpolation of 3-D fields to another grid, for example <br> using 'cdo remapcon'.</p> <p>There are two options: <br>1. Use inverted ash and SO2 emission profiles (see p.1 and p.2)<br>2. Use ash, sulfate, and SO2 concentrations from WRF-Chem output file (see p.3 and p.4)<br> as initial conditions for another run.</p> <p><br>References:<br>1. Ukhov, A., Ahmadov, R., Grell, G., and Stenchikov, G.: Improving dust simulations<br> in WRF-Chem v4.1.3 coupled with the GOCART aerosol module, <br> Geosci. Model Dev., 14, 473–493, https://doi.org/10.5194/gmd-14-473-2021, 2021.</p> <p>2. Ukhov et. al, Enhancing Volcanic Eruption Simulations with the WRF-Chem v4.7.x</p>
Cloud Feedbacks and Organized Convection in Radiative-Convective Equilibrium
<p>Derived data and scripts used in Stauffer and Wing (2024).</p> <p>The standardized RCEMIP output, including time and domain mean profiles, is hosted by the German Climate Computing Center (DKRZ) and is publicly available at <a href="http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e">http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e</a>.</p> <p>Descriptions of each of the data files and the variables contained within them are discussed in a README file. The jupyter notebook contains the scripts used to plot the figures used in Stauffer and Wing (2024).</p> <p>Updated 01 April 2025 to include corrected Index of Organization values.</p>
CESM1.2 simulation data for "Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks"
<p>CESM1.2 simulation data for Early Eocene</p> <p><strong>Citations:</strong></p> <p>Zhu, J., Poulsen, C. J., & Tierney, J. E. (2019). Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks. <em>Science Advances</em>, 5(9), eaax1874. <a href="https://doi.org/10.1126/sciadv.aax1874">https://doi.org/10.1126/sciadv.aax1874</a></p> <p>Zhu, J., Poulsen, C. J., Otto-Bliesner, B. L., Liu, Z., Brady, E. C., & Noone, D. C. (2020). Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction. Earth and Planetary Science Letters, 537, 116164. <a href="https://doi.org/10.1016/j.epsl.2020.116164" rel="nofollow">https://doi.org/10.1016/j.epsl.2020.116164</a></p> <p> </p> <ul> <li>Data set includes climatology (12 months) sea-surface temperature (TEMP), surface temperature (TS) and surface temperature at reference height (TREFHT) from four Eocene simulations with 1×, 3×, 6× and 9× preindustrial level of CO2 (284.7 ppmv), and a preindustrial simulation.</li> <li>Climatology was calculated from averaging data over the last 100 years of each simulation.</li> <li>TS and TREFHT are on the atmosphere grid of 1.9 × 2.5° (latitude × longitude).</li> <li>TEMP is on the POP ocean grid (~1°; see here: http://www.cesm.ucar.edu/models/cesm1.2/pop2/).</li> <li>NEW on July 09, 2024: restart files for the Eocene simulations.</li> </ul> <p>A case folder is available on GitHub: <a href="https://github.com/jiang-zhu/icesm1.2_eocene_cheyenne">https://github.com/jiang-zhu/icesm1.2_eocene_cheyenne</a></p> <p> </p>
Cloud feedback maps from CMIP5 and CMIP6 models
<p>This repository contains maps of cloud feedbacks computed using up to three methodologies from two types of experiments conducted across CMIP5 and CMIP6 models.</p> <p>Methods:</p> <ul> <li>The approximate partial radiative perturbation technique (shortwave cloud feedbacks only), denoted with 'APRP' in filename. </li> <li>The adjusted change in cloud radiative effect, denoted with 'adjCRE' in filename. </li> <li>The cloud radiative kernel method, in files without 'APRP' or 'adjCRE' in the name. </li> </ul> <p>Experiments:</p> <ul> <li>amip+4K experiments, known as amip4K and amip-p4K in CMIP5 and CMIP6, respectively</li> <li>abrupt CO2 quadrupling experiments, known as abrupt4xCO2 and abrupt-4xCO2 in CMIP5 and CMIP6, respectively</li> </ul> <p>Data formats:</p> <ul> <li>For cloud feedbacks derived using APRP and adjCRE methods, all models' maps are contained within single netcdf files.</li> <li>For cloud feedbacks derived using cloud radiative kernels, Individual netcdf files are provided for each model, which are compressed into a zip file.</li> </ul>
Cloud Feedbacks in Radiative-Convective Equilibrium
<p>Derived data and scripts used in Stauffer and Wing (2023).</p> <p>The standardized RCEMIP output, including time and domain mean profiles, is hosted by the German Climate Computing Center (DKRZ) and is publicly available at <a href="http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e">http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e</a>.</p> <p>Descriptions of each of the data files and the variables contained within them are discussed in a README file. The jupyter notebook contains the scripts used to plot the figures used in Stauffer and Wing (2023).</p>
Data for "First simulations of feedback algorithm-regulated marine cloud brightening"
<p>Data for the manuscript "First simulations of feedback algorithm-regulated marine cloud brightening," submitted for publication in the journal Geophysical Research Letters.</p>
Data for "Extreme Weather Variability on Hot Rocky Exoplanet 55 Cancri e Explained by Magma Temperature-Cloud Feedback"
<p>Data supporting "Extreme Weather Variability on Hot Rocky Exoplanet 55 Cancri e Explained by Magma Temperature-Cloud Feedback" by Loftus*, Luo*, Fan, & Kite (2025). </p> <p>* Note, these authors contributed equally.</p>
Importance of high-latitude sources for ice-nucleating particles in cold-air outbreaks and the implications for cloud-phase feedback
<p>This dataset contains FLEXPART results and codes for data analysis and visualization used in the MRes research project. </p> <p>Numpy arrays for FLEXPART data are in "data_flexpart.zip" with longitude and latitude coordination files.</p> <p>Codes for data analysis and visualization are in "ipynb.zip". Codes were written in Python language. Codes are categorized by figures in the article. </p>
Contribution of surface and cloud radiative feedbacks to Greenland Ice Sheet meltwater production during 2002-2023
<p>These datasets accompany the paper titled "Contribution of surface and cloud radiative feedbacks to Greenland Ice Sheet meltwater production during 2002-2023". </p>
Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record (Data)
<p>README file for ERA5-PRP datasets used in:</p> <p>Raghuraman et al., 2023, Journal of Climate,<br> "Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record"</p> <p>Point of Contact: Shiv Priyam Raghuraman, shivr@alumni.princeton.edu</p> <p>30 ERA5-PRP files:</p> <p>2 files: '2000_2020-allsky_small.nc' and '2000_2020-clearsky_small.nc'</p> <ul> <li>all input quantities varying</li> </ul> <p>2 files: 'clim-allsky-clim-var-all.nc' and 'clim-clearsky-clim-var-all.nc'</p> <ul> <li>all input quantities at climatology</li> </ul> <p>13 files: 'clim-var'</p> <ul> <li>Input quantity X at climatology, rest varying</li> <li>X = clouds, q, fal, skt, t, ghg, or o3. Fluxes computed in clear-sky and all-sky, apart from clouds which will only have all-sky.</li> </ul> <p>13 files: 'clim-except-var'</p> <ul> <li>Input quantity X varying, rest at climatology</li> <li>X = clouds, q, fal, skt, t, ghg, or o3. Fluxes computed in clear-sky and all-sky, apart from clouds which will only have all-sky.</li> </ul> <p>Other datasets:</p> <p>GFDL AM4 AMIP and Control<br> https://doi.org/10.5281/zenodo.4784726</p> <p>CMIP6 Control, Historical, RFMIP<br> Downloaded from ESGF</p>
The relationship between the present-day seasonal cycles of low clouds in the mid-latitudes and cloud-radiative feedback
<p>Supporting data for "The relationship between the present-day seasonal cycles of low clouds in the mid-latitudes and cloud-radiative feedback", by K. Furtado, Y. Tsushima and P. R. Field.</p>
Climate model data from "Changes in local and global climate feedbacks in the absence of interactive clouds: Southern Ocean-climate interactions in two intermediate-complexity models"
<p>This Dataset contains the model output described in the study<br> "Changes in local and global climate feedbacks in the absence of interactive clouds: Southern Ocean-climate interactions in two intermediate-complexity models"<br> by Pfister and Stocker 2020, published in Journal of Climate.</p> <p>The two zip files contain the model output of the two models Bern3D-LPX and LOVECLIM, in folder structures explained below.</p> <p>Bern3D-LPX:</p> <p>The 3 folders contain model simulations tuned to different ECS values (2, 3 and 6 Kelvin).<br> Each folder contains three subfolders corresponding to three simulations: Control, 2xCO2 and 4xCO2.<br> For each simulation, two netcdf model output files are given: a timeseries file for quick overview of various spatially averaged variables (e.g., global mean temperature), and a full output file for local analyses as done in the study.</p> <p>For the main simulations with an ECS of 3 Kelvin, annual mean output is provided for the first 500 years of each simulation. Thereafter, the full output is available only for selected years, which can be read out from the netcdf time dimension or, e.g., the netcdf variable "baseyear".</p> <p>Simulations with an ECS of 2 and 6 Kelvin are only used for Figure 8 and its discussion, therefore their full output file was written with less yearly outputs than the main simulation with ECS=3 Kelvin to reduce data load.</p> <p><br> LOVECLIM:</p> <p>The 2 folders contain the 2xCO2 and 4xCO2 simulations.<br> No separate Control simulations were made, but the first 1000 years of each simulation are unperturbed and used as a control reference (details in Pfister and Stocker 2020, J.Clim.).</p> <p>The two netcdf files for each simulation correspond to atmospheric variables (atmmmyl_cat.nc) and ocean variables (CLIO3m_cat_CO2_2_regridded.nc). Note that the spatial resolution of the atmosphere and ocean component of LOVECLIM are different. Monthly output is provided for the given variables of the full 2000-year-simulations.</p> <p> </p> <p>For a detailed description how these model outputs were analyzed, please refer to Pfister and Stocker 2020, J. Clim.</p> <p> </p> <p> </p>
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>
CESM1.2 simulation data for "Quantifying the cloud particle-size feedback in an Earth system model"
<p>CESM1.2-CAM5 simulation data for "Quantifying the cloud particle-size feedback in an Earth system model"</p> <p><strong>Citation: </strong>Zhu, J., & Poulsen, C. J. (2019). Quantifying the cloud particle-size feedback in an Earth system model. <em>Geophysical Research Letters</em>, <em>46</em>, 10910–10917. <a href="https://doi.org/10.1029/2019GL083829">https://doi.org/10.1029/2019GL083829</a></p> <p>Data include:</p> <p>(1) cloud liquid particle size for liquid (AREL) and ice (AREI), grid box averaged cloud liquid (CLDLIQ) and ice (CLDICE), fractional occurrence of liquid (FREQL) and ice (FREQI), and surface temperature (TS) in the preindustrial and 2xCO2 experiments; and<br> (2) the cloud feedback (lam_CLDTOT) and cloud particle-size feedback (lam_CLDEFR3L) from our PRP-based method.</p>
Supporting Data for Sagoo et al. GRL: Observationally Constrained Cloud Phase Unmasks Orbitally Driven Climate Feedbacks
<p>This dataset includes CESM climatologies relevant for Sagoo et al.: “Observationally Constrained Cloud Phase Unmasks Orbitally Driven Climate Feedbacks” in Geophysical Research Letters. Data is included for high (HI) and low (LO) obliquity experiments with the default model (OOTB) and with supercooled liquid fraction constrained to satellite observations (SLF1 and SLF2), as described in the manuscript. </p>
The Roles of Shallow Convection and Cloud Radiative Feedback in Convectively-Coupled Kelvin Wave: MPAS-aquaplanet Model configuration
<p>We provide the model configuration for the MPAS-A aquaplanet used in our journal article titled <em>“The Roles of Shallow Convection and Cloud Radiative Feedback in Convectively-Coupled Kelvin Waves”</em> by Hsu et al., submitted to the <em>Journal of Climate</em> in 2024.</p>
Data for the publication "Higher climate sensitivity and stronger cloud feedbacks in ECHAM6.3 with a prognostic cloud fraction scheme"
<p>This repository contains the data for the paper:</p><p>"Muench, S., Neubauer, D. and Lohmann, U.: Higher climate sensitivity and stronger cloud feedbacks in ECHAM6.3 with a prognostic cloud fraction scheme"</p><p>Each directory contains the model data to reproduce the figures in our paper.</p><p>Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.10057465)</p>
Cloud Regime for CRE Feedback Study
Cloud-only regimes (and 3 sub-regimes of CR15) originally derived from Terra and Aqua observations in 50S-50N, and corresponding "regime numbers on map" files assigned to 60S-60N domain are provided (see Jin et al. 2021, doi: 10.1175/JAMC-D-20-0253.1). The zip file also includes sample python codes to calculate regime mean SWCRE/LWCRE for the study of observational CRE feedback (Jin et al. 2023, J. of Climate, submitted).
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