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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. Many passive remote-sensing techniques have beendeveloped to retrieve cloud microphysical properties from satellite-basedsensors, with the most common approaches being the bispectral andpolarimetric techniques. These two vastly different retrieval techniqueshave been implemented for a variety of polar-orbiting and geostationarysatellite platforms, providing global climatological data sets. Priorinstrument comparison studies have shown that there are systematicdifferences between the droplet size retrieval products (effective radius)of bispectral (e.g., MODIS, Moderate Resolution Imaging Spectroradiometer)and polarimetric (e.g., POLDER, Polarization and Directionality of Earth'sReflectances) instruments. However, intercomparisons of airborne bispectraland polarimetric instruments have yielded results that do not appear to besystematically biased relative to one another. Diagnosing this discrepancyis complicated, because it is often difficult for instrument intercomparisonstudies to isolate differences between retrieval technique sensitivities andspecific instrumental differences such as calibration and atmosphericcorrection. In addition to these technical differences the polarimetricretrieval is also sensitive to the dispersion of the droplet sizedistribution (effective variance), which could influence the interpretationof the droplet size retrieval. To avoid these instrument-dependentcomplications, this study makes use of a cloud remote-sensing retrievalsimulator. Created by coupling a large-eddy simulation (LES) cloud modelwith a 1-D radiative transfer model, the simulator serves as a test bed forunderstanding differences between bispectral and polarimetric retrievals.With the help of this simulator we can not only compare the two techniquesto one another (retrieval intercomparison) but also validate retrievalsdirectly against the LES cloud properties. Using the satellite retrievalsimulator, we are able to verify that at high spatial resolution (50m) thebispectral and polarimetric retrievals are highly correlated with oneanother within expected observational uncertainties. The relatively smallsystematic biases at high spatial resolution can be attributed to differentsensitivity limitations of the two retrievals. In contrast, a systematicdifference between the two retrievals emerges at coarser resolution. Thisbias largely stems from differences related to sensitivity of the tworetrievals to unresolved inhomogeneities in effective variance and opticalthickness. The influence of coarse angular resolution is found to increaseuncertainty in the polarimetric retrieval but generally maintains aconstant mean value.