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Abstract A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a secondposteriorconstrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top‐of‐atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many “unimportant” parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45‐dimensional parameter configurations are retained to generate radiatively‐balanced, auto‐tuned atmospheres that were used in two E3 submissions to CMIP6. 

Abstract Organized deep convective activity has been routinely monitored by satellite precipitation radar from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM). Organized deep convective activity is found to increase not only with sea surface temperature (SST) above 27°C, but also with low-level wind shear. Precipitation shows a similar increasing relationship with both SST and low-level wind shear, except for the highest low-level wind shear. These observations suggest that the threshold for organized deep convection and precipitation in the tropics should consider not only SST, but also vertical wind shear. The longwave cloud radiative feedback, measured as the tropospheric longwave cloud radiative heating per amount of precipitation, is found to generally increase with stronger organized deep convective activity as SST and low-level wind shear increase. Organized deep convective activity, the longwave cloud radiative feedback, and cirrus ice cloud cover per amount of precipitation also appear to be controlled more strongly by SST than by the deviation of SST from its tropical mean. This study hints at the importance of non-thermodynamic factors such as vertical wind shear for impacting tropical convective structure, cloud properties, and associated radiative energy budget of the tropics. Significance StatementThis study uses tropical satellite observations to demonstrate that vertical wind shear affects the relationship between sea surface temperature and tropical organized deep convection and precipitation. Shear also affects associated cloud properties and how clouds affect the flow of radiation in the atmosphere. Although how vertical wind shear affects convective organization has long been studied in the mesoscale community, the study attempts to apply mesoscale theory to explain the large-scale mean organization of tropical deep convection, cloud properties, and radiative feedbacks. The study also provides a quantitative observational baseline of how vertical wind shear modifies cloud radiative effects and convective organization, which can be compared to numerical simulations.