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Abstract. Cloud processes constitute one of the key uncertainties for climate change projections. The fourth iteration of the Cloud Feedback Model Intercomparison Project, CFMIP4, contributes to the Coupled Model Intercomparison Project phase 7 (CMIP7), by providing a set of global climate model experiments aiming to enhance our understanding of clouds, circulation and climate sensitivity, thereby informing improved projections of future climate change. CFMIP4 targets four knowledge gaps: (1) Physical mechanisms of cloud feedback and adjustment; (2) Dependence of cloud feedback and adjustment on climate base state and on the nature of the forcing; (3) Coupled mechanisms of the sea-surface temperature pattern effect; and (4) Coupling of clouds with circulation and precipitation. CFMIP4 contributes four CMIP7 Assessment Fast Track experiments that are central to the quantification of climate feedback and sensitivity in past, present and future climates, essential for process understanding and model evaluation. Furthermore, CFMIP4 supports the joint analysis of models and observations through a data request that includes process and satellite simulator output. 

{"Abstract":["This folder contains the code needed to generate the plots and data for "Characteristics of a Multi-model Ensemble of Mock-Walker Simulations", a manuscript submitted to the Journal of Advances in Modeling Earth Systems (JAMES).\n\n \n\nAll code except for Figure 17 is run in Python.\n\n \n\nFor all codes, the variable "codeDirectory" should be set to the file location of the codes to run properly.  This is primarily to properly import code from "Utilities/"\n\n \n\nGeneral programs useful for handling RCEMIP data are given in \n\n"Utilities/".\n\n-Utilities/metFormulas.py: various meteorological formulas\n\n-Utilities/generalUtilities.py and Utilities/dsTools.py: basic processing tools\n\n-Utilities/extract*.py: imports RCEMIP data from directories specified in generalUtilities.py\n\n \n\nOrganization metric data is calculated by the codes in METRICS/A-*/.\n\nThe metric files are saved in METRICS/nc-mw/.  The current metric importation is designed to require the files in this folder rather than importing data directly from Utilities/aggRecalc/aggregation_metrics_mw.csv as several codes require timeseries of the values of the metrics.\n\n____________________________________________________________\n\n \n\nFigure Generation\n\nFigure 1: DemonstrateSST/demonstrateSST.py\n\n    -Saved as DemonstrateSST/demonstrateSST.png.\n\n \n\nFigure 2, 3, S1, S2: images/rlut_pr_mw_multimodel.py, function createImages().\n\n    -Stored in images/CRM/ and images/GCM/.\n\n \n\nFigure 4, S3-S6 [fourier, hovmoller]: ClassifyScenes/hovmollerWithClassifications.py\n\n    -Saved in ClassifyScenes/hovmollerWithPie/.\n\n    -4: 300dT1p25-crh-slice-sametime.pdf\n\n    -S3-S6: -crh-slice-sametime.pdf\n\n    A version of this code that omits the pie charts and does not require [fourier] is hovmoller/hovmoller.py.\n\n \n\nFigure 5, 8, 12, S7-S10: Metrics/multiplot.py.  \n\n    Files are saved as Metrics/Metrics-.pdf\n\n    -5 and S7-S10: SST is one of the five Simulations\n\n    -8: SST is T\n\n    -12: SST is DT\n\n \n\nFigure 6: ClassifyScenes/hovmollerClassFourierDiscretePoster.py.  \n\n    -Saved as ClassifyScenes/classFourierDemo/Plots/mw/SAM-CRM/SAM-CRM-300dT1p25-crh.png\n\n \n\nFigure 7, S11 [fourier]: ClassifyScenes/classDiscreteVsMetrics.py.  \n\n    -Saved as ClassifyScenes/metricViolinPlot/CRM/CRM_all_Lorg.pdf and ./CRM_all_Iorg.pdf.\n\n \n\nFigure 9: [fourier]: ClassifyScenes/plotPercentilesByCategory.py, method createFrequencyPieCharts().\n\n    -Saved as ClassifyScenes/pieCharts/CRM.pdf.\n\n \n\nFigure 10: [fourierContinuous]: ClassifyScenes/plotPercentilesByCategory.py, method createVarianceBarChartsPoster().\n\n    -Saved as ClassifyScenes/pieCharts/Bar-CRM-Variance.pdf.\n\n \n\nFigure 11, S12: Metrics/iorgBoxPlots.py, method changeMultiPanel().\n\n    -Saved as Metrics/BoxPlots/change/metricChangeCombined295305shared.pdf and ...all.pdf\n\n \n\nFigure 13, S13, S14: DomainStatistics/BoxPlots.py.\n\n    -13: DomainStatistics/boxplots-domainmean-T.pdf\n\n \n\nFigure 14-16, S15-S18 [isccp]: ISCCP/histograms.py, method plotHistMultimodel()\n\n    -Saved in histPlot/Multimodel-ISCCP-CRM__GCM.png\n\n    -14, S15-S18: is one of the Simulations\n\n    -15: is "changeT"\n\n    -16: is "changeDT"\n\n \n\nFigure 17: A-Statistics/plot_climatesensitivity.m.  \n\n    -This function requires some files from RCEMIP-I which are not included in this repository.\n\n    -Saved as A-Statistics/Fig_lambda_I_II.pdf.  \n\n    -Note: this is a MATLAB code.\n\n \n\nFigure 18 [percentiles]: Percentiles/plotPercentiles.py, method plotPercentileRatioVsAgg().\n\n    -Must be run twice for the two subpanels.\n\n    -Saved in percentilePlots/percentileRatioVsAgg/mw/pr/Ichange/, with the files corresponding to input parameters.\n\n \n\nFigure S19-S22 [percentiles]: Percentiles/plotSpaceTimeCorrelations.py, method correlatePercentileVsAggChanges().\n\n    -Must be run twice for the two subpanels.\n\n    -Saved in percentilePlots/LinearArithmetic/correlatePercentileAggChange/mw/pr/Change/.\n\n \n\nFigure S23 [scaling]: Scaling/BoxPlots.py\n\n    -Saved in scalingPlots/LinearArithmetic/BoxPlots/avgAbove/separateDyn2/mw/coarse15km/shift1/\n\n        File name matches *-def-*-99.9.pdf\n\n    -Generates two needed files, 305dT1p25-295dT1p25/ for S23a and 300dT2p5-300dT0p625/ for S23b\n\n \n\nFigure S24-S27 [scaling]: Scaling/ComponentsVsAgg.py, method componentsVsAggSubplots()\n\n    -Saved at scalingPlots/LinearArithmetic/componentsVsAgg/def-shift1/mw/coarse15km//Combined/\n\n    -S24 and S26: File name contains Ichange\n\n    -S25 and S27: File name contains Lchange\n\n__________________________________________________\n\n \n\n[fourier]: This code requires the discrete Fourier classification.  These classifications are generated by ClassifyScenes/hovmollerClassFourierDiscrete.py and saved in ClassifyScenes/classDS//.\n\n[fourierContinuous]: This code requires the continuous Fourier classification.  This is calculated by ClassifyScenes/hovmollerClassFourierContinuous.py and saved in ClassifyScenes/classDSContinuous//.\n\n[hovmoller]: This code requires hovmoller data.  This data is calculated by running hovmoller/createDatasets.py, with data stored in hovmoller/2D-Timeseries//.\n\n    -These data are created and saved in the 2D-Timeseries/ folder of the repository, which must be loaded in as hovmoller/2D-Timeseries/.\n\n[isccp]: This code requires use of the ISCCP data.\n\n    -CRMs: ISCCP data calculated via the Approximate ISCCP Simulator (Stauffer and Wing 2023) within ISCCP/tau2023.py.\n\nData is saved in two separate folders, isccp_mw/ and isccp_large/, which must be loaded in as ISCCP/isccpData/mw/ and ISCCP/isccpData/large/, respectively.\n\n    -GCMs: ISCCP data was saved by three models during running (CNRM-CM6, E3SM, UKMO-GA7.1)\n\n    From the ISCCP data, histograms are calculated by ISCCP/histograms.py method generateHistograms() (for CRMs) and generateHistogramsGCM() (for GCMs).\n\n        These are saved at ISCCP/hist//.\n\n[percentiles]: This code requires precipitation percentile tables.  These are calculated by Utilities/calcPercentiles.py and saved in  Percentiles/.\n\n[scaling]: This code requires the scaling JSON files in Scaling/scalingJsons.py. This code also requires [percentiles].\n\n    -To create these files, first run Scaling/calculatePercentileProfiles-accumulate-coarsen-parallel.py. This creates the composite profiles in Scaling/scalingParallel/.\n\n    -Then, run Scaling/separateComponentsDecomposeDyn.py to create the JSONs.\n\n    -The scaling profile creation code takes a long time to run and outputs large files.  Scaling/scalingParallel.py is uploaded as a separate folder within the repository to decrease the required file size."]} 

The RAPSODI (Radiosonde Atmospheric Profiles from Ship and island platforms during ORCESTRA, collected to Decipher the ITCZ) radiosonde dataset was collected during the ORCESTRA field campaign. It is designed to investigate the mechanisms linking mesoscale tropical convection to tropical waves and to air–sea heat and moisture exchanges that regulate convection and tropical cyclone formation. The campaign began at the Instituto Nacional de Meteorologia e Geofisica (INMG) on Sal on the Cape Verde Islands, continued with ship-based observations aboard the R/V Meteor across the Atlantic, and concluded at the Barbados Cloud Observatory (BCO) in the eastern Caribbean. During the campaign, a total of 624 radiosondes were launched, capturing high-resolution profiles of temperature, humidity, pressure, and winds. This radiosonde dataset, encompassing raw, quality-controlled, and vertically gridded data, is detailed in this paper and offers a valuable resource for investigating the atmospheric structure and processes shaping tropical convection and the intertropical convergence zone (ITCZ). The complete dataset is openly available at ipfs://bafybeid7cnw62zmzfgxcvc6q6fa267a7ivk2wcchbmkoyk4kdi5z2yj2w4. 

RCEMIP-II is a coordinated intercomparison of models configured to focus on "mock-Walker" simulations to investigate tropical convection and climate in an idealized setting. Unlike the first phase, which used uniform sea surface temperature (SST), RCEMIP-II introduces a prescribed sinusoidal SST pattern to resemble observed tropical circulations, allowing for the study of convective aggregation and its response to warming across various model types. For information on the first phase of the project, please refer to http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e. Model Experiment Description Paper: Wing, A.A., L.G. Silvers, and K.A. Reed (2024): RCEMIP-II: Mock-Walker Simulations as Phase II of the Radiative-Convective Equilibrium Model Intercomparison Project, Geosci. Model Dev., 17, 6195–6225, doi:10.5194/gmd-17-6195-2024. https://gmd.copernicus.org/articles/17/6195/2024/ 

This codebase contains data and code needed to generate plots for O'Donnell, G. L. and A. A. Wing (2024). Precipitation Extremes and their Modulation by Convective Organization in RCEMIP. Journal of Advances in Modeling Earth Systems, 16(11), e2024MS004535. https://doi.org/10.1029/2024MS004535   PrecipExtremesInRCEMIP/: Python code and most derived data, including tables of percentiles of precipitation for each model at numerous spatiotemporal scales scalingProfiles_{domain}/: Profiles of temperature, humidity, and vertical velocity conditioned on extreme precipitation, saved as .nc files   RCEMIP data can be found at http://hdl.handle731.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e or at https://swiftbrowser.dkrz.de/public/dkrz_70a517a8-039d-4a1b-a30d-841923f8bc7a/RCEMIP/   

{"Abstract":["Model configuration code and post-processed data for simulations with SAM6.11.2 (Khairoutdinov and Randall, 2003) and CAM6 (https://github.com/ESCOMP/CESM/releases/tag/release-cesm2.1.3) needed to reproduce figures in the protocol paper for RCEMIP-II (Wing et al., 2023):\n\nWing, A. A., Silvers, L. G., and Reed, K. A.: RCEMIP-II: Mock-Walker Simulations as Phase II of the Radiative-Convective Equilibrium Model Intercomparison Project, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-235, in review, 2023.\n\nSAM6.11.2 data (SAM6.11.2-lambda6000.zip and SAM6.11.2-lambda6144.zip):\n\n\n\nlambda6000: simulations with wavelength 6000 km\n\nlambda6144: simulations with wavelength 6144 km\n\nEach simulation, for a given mean SST ($SST) and delta SST ($$dT) has the following data files\n\n\n\ncrh_avg_$$SST_$$dT.mat: column relative humidity averaged over the short (y) dimension, as a function of x and time.\n\nmockwalker2048x128x74_3km_12s_$$SST_$$dT.nc: domain-averaged 0D (function of t) and 1D (function of z and t) data\n\n\n\nThe "long" simulations, which have a domain that is twice as long as normal, instead have files with names mockwalker4096x128x74_3km_12s_$$SST_$$dT.nc\n\nThe "wide" simulations, which have a domain that is twice as wide as normal, instead have files with names mockwalker2048x256x74_3km_12s_$$SST_$$dT.nc\n\nThe "longwide" simulations, which have a domain that is twice as long and twice as wide as normal, instead have files with names mockwalker4096x256x74_3km_12s_$$SST_$$dT.nc\n\n\n\nSAM_CRM_MW_$$SST_$$dT_1D_cldfrac_avg.nc: domain cloud fraction profile (function of z and t) following cfv2 definition of Stauffer and Wing (2022)\n\n\n\n\nSAM6.11.2 configuration files (SAM6.11.2-lambda6000-config.zip and SAM6.11.2-lambda6144.zip):\n\n\n\nlambda6000: simulations with wavelength 6000 km\n\nlambda6144: simulations with wavelength 6144 km\n\nEach simulation, for a given mean SST and delta SST has the following configuration files\n\n\n\nsnd: Initial sounding\n\nprm: Namelist parameters\n\ngrd: Vertical grid\n\ndomain.f90: Domain size and number of subdomains\n\nsimpleocean.f90: SST specification\n\n\n\n\nCAM6 data (CAM6.zip):\n\n\n\nEach simulation, for a given mean SST ($$SST) and delta SST ($$dT) has the following data files\n\n\n\nMockWalk54_humidity_HCF_$$dT_$$SST.nc: column relative humidity averaged over 4 longitude points, as a function of latitude and time.\n\nCAM6_MockW_$$dT_cos_$$SST_3_yr_HCF_0D_rlut_avg.nc: domain-averaged longwave flux at the top of the atmosphere\n\nCAM6_MockW_$$dT_cos_$$SST_3_yr_HCF_0D_rsut_avg.nc: domain-averaged upwelling shortwave flux at the top of the atmosphere\n\nCAM6_MockW_$$dT_cos_$$SST_3_yr_HCF_0D_rsdt_avg.nc: domain-averaged downwelling shortwave flux at the top of the atmosphere\n\nCAM6_MockW_$$dT_cos_$SST_3_yr_HCF_1D_cldfrac_avg.nc: domain-averaged cloud fraction profile (function of z and t)\n\n\n\n\nCAM6 configuration files (CAM6-MW295dT1p25-config.tar, CAM6-MW300dT1p25-config.tar, CAM6-MW305dT1p25-config.tar): Contains model initialization and configuration files for simulations with delta SST = 1.25 K. Simulations with other delta SST values need only change the delta SST parameter. "]} 

Derived data and scripts used in Stauffer and Wing (2022)