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{"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."]} 

Abstract Simulating the Earth system is crucial for studying Earth's climate and how it changes. Modeling approaches that simplify the Earth system while retaining key characteristics are important tools to advance understanding. The simplicity and flexibility of idealized models enables imaginative science and makes them powerful educational tools. Evolving scientific community needs and increasing model complexity, however, makes it challenging to maintain and support idealized configurations in cutting‐edge Earth system modeling frameworks. We call on the scientific community to re‐emphasize model hierarchies within these frameworks to aid in understanding the Earth system, advancing model development, and developing the future workforce. 

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. 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 We examine the influence of convective organization on extreme tropical precipitation events using model simulation data from the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP). At a given SST, simulations with convective organization have more intense precipitation extremes than those without it at all scales, including instantaneous precipitation at the grid resolution (3 km). Across large‐domain simulations with convective organization, models with explicit convection exhibit better agreement in the response of extreme precipitation rates to warming than those with parameterized convection. Among models with explicit convection, deviations from the Clausius‐Clapeyron scaling of precipitation extremes with warming are correlated with changes in organization, especially on large spatiotemporal scales. Though the RCEMIP ensemble is nearly evenly split between CRMs which become more and less organized with warming, most of the models which show increased organization with warming also allow super‐CC scaling of precipitation extremes. We also apply an established precipitation extremes scaling to understand changes in the extreme condensation events leading to extreme precipitation. Increased organization leads to greater increases in precipitation extremes by enhancing both the dynamic and implied efficiency contributions. We link these contributions to environmental variables modified by the presence of organization and suggest that increases in moisture in the aggregated region may be responsible for enhancing both convective updraft area fraction and precipitation efficiency. By leveraging a controlled intercomparison of models with both explicit and parameterized convection, this work provides strong evidence for the amplification of tropical precipitation extremes and their response to warming by convective organization. 

Abstract In simulations of radiative‐convective equilibrium (RCE), and with sufficiently large domains, organized convection enhances top of atmosphere outgoing longwave radiation due to the reduced cloud coverage and drying of the mean climate state. As a consequence, estimates of climate sensitivity and cloud feedbacks may be affected. Here, we use a multi‐model ensemble configured in RCE to study the dependence of explicitly calculated cloud feedbacks on the existence of organized convection, the degree to which convection within a domain organizes, and the change in organized convection with warming sea surface temperature. We find that, when RCE simulations with organized convection are compared to RCE simulations without organized convection, the propensity for convection to organize in RCE causes cloud feedbacks to have larger magnitudes due to the inclusion of low clouds, accompanied by a much larger inter‐model spread. While we find no dependence of the cloud feedback on changes in organization with warming, models that are, on average, more organized have less positive, or even negative, cloud feedbacks. This is primarily due to changes in cloud optical depth in the shortwave, specifically high clouds thickening with warming in strongly organized domains. The shortwave cloud optical depth feedback also plays an important role in causing the tropical anvil cloud area feedback to be positive which is directly opposed to the expected negative or near zero cloud feedback found in prior work. 

Abstract The early career stage for scientific researchers and faculty is fraught with challenges, including establishing professional relationships, securing funding, balancing work and personal life, and navigating job uncertainties. Early career professionals were among those especially impacted by the COVID-19 pandemic, in having limited opportunities for networking and professional development. Recognizing these challenges, the University Corporation for Atmospheric Research (UCAR) and the U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR) piloted a 1-day professional development workshop in Boulder, Colorado, on 8 October 2023 as a preworkshop for the biannual UCAR Members Meeting. We hoped to foster networking and peer learning among 122 attendees from the atmospheric sciences, 76 (62%) of whom were university faculty and 46 (38%) of whom were postdocs, researchers, and staff from NSF NCAR and UCAR. Participants, representing 58 universities across the U.S. and Canada, engaged in a program that included networking events, informational sessions, and hands-on workshops. Sessions covered topics such as active learning, mentoring graduate students, understanding tenure, time management, mental health, fostering welcoming environments, and grant proposal writing. Feedback from participants highlighted the value of networking opportunities and peer learning, emphasizing the importance of continued professional development tailored to early career scientists’ needs. The workshop also allowed us to learn more about challenges early career professionals are facing. This workshop serves as a model for future initiatives aimed at supporting early career researchers in Earth system science and related fields. 

Abstract The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) exhibits a large spread in the simulated climate across models, including in profiles of buoyancy and relative humidity. Here we use simple theory to understand the control of stability, relative humidity, and their responses to warming. Across the RCEMIP ensemble, temperature profiles are systematically cooler than a moist adiabat, and convective available potential energy (CAPE) increases with warming at a rate greater than that expected from the Clausius‐Clapeyron relation. There is higher CAPE (greater instability) in models that are on average moister in the lower‐troposphere. To more explicitly evaluate the drivers of the intermodel spread, we use simple theory to estimate values of entrainment and precipitation efficiency (PE) given the simulated values of CAPE and lower‐tropospheric relative humidity. We then decompose the intermodel spread in CAPE and relative humidity (and their responses to warming) into contributions from variability in entrainment, PE, the temperature of the convecting top, and the inverse water vapor scale height. Model‐to‐model variation in entrainment is a dominant source of intermodel spread in CAPE and its changes with warming, while variation in PE is the dominant source of intermodel spread in relative humidity. We also decompose the magnitude of the CAPE increase with warming and find that atmospheric warming itself contributes most strongly to the CAPE increase, but the indirect effect of increases in the water vapor scale height with warming also contribute to increasing CAPE beyond that expected from Clausius‐Clapeyron. 

Abstract This study seeks to characterize the development of atmospheric fronts during the extratropical transition (ET) of tropical cyclones (TCs) as a function of their evolution during ET. Composite histograms indicate that the magnitude of the lower atmospheric frontogenesis and average sea‐surface temperature is different based on the nature of the TC's structural change during ET. We find that the development of cold and warm fronts evolves as expected from conceptual models of extratropical cyclones. Composites of these fronts relative to the completion of ET show that azimuth, storm motion, and deep‐layer shear all appear to have equal influence on the frontal positions. TCs that have more fronts at the time of ET onset complete ET more quickly, suggesting that pre‐existing fronts before ET begins may contribute to a shorter ET duration. The orientations of fronts at ET completion in the North Atlantic and west Pacific align with the climatological distributions of the sea‐surface temperatures associated with the western boundary currents in each of those basins. These results provide a perspective on the locations of frontal development within TCs undergoing ET. 

Abstract Radiative‐convective equilibrium (RCE) is particularly well suited for studying tropical deep‐convection, a regime of clouds that contributes some of the highest uncertainties to the estimates of total cloud feedback. In order to perform a comprehensive calculation and decomposition of cloud feedbacks in cloud‐permitting models, previously primarily done in global climate models, the configuration of a satellite simulator for use with offline data was successfully implemented. The resultant total cloud feedback is slightly positive, primarily driven by the longwave effects of increases in cloud altitude. The high‐cloud altitude feedback is robustly positive and has a central value and uncertainty well‐matched with prior estimates. Reductions in high cloud amount drive a tropical anvil cloud area feedback that is on average negative, consistent with prior estimates. However, a subset of models with finer horizontal grid spacing indicate that a positive tropical anvil cloud area feedback cannot be ruled out. Even though RCE is only applicable to tropical deep‐convective clouds, the RCE total cloud feedback is within the range of prior comprehensive estimates of the global total cloud feedback. This emphasizes that the tropics heavily influence the behavior of global cloud feedbacks and that RCE can be exploited to learn more about how processes related to deep convection control cloud feedbacks.