Cloud fraction is a great source of uncertainty in current models. By utilizing cloudiness fields from CloudSat/cloud‐aerosol lidar and infrared pathfinder satellite observations (CALIPSO), the three widely used reanalyses including the Interim ECWMF Re‐Analysis (ERA‐Interim), Japanese 55‐yar Reanalysis (JRA‐55), and the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2) are assessed for their representation of cloudiness. Results show all three reanalyses can basically capture the cloud horizontal pattern and vertical structure as in CloudSat/CALIPSO, yet the magnitude is markedly underestimated, in particular for JRA‐55 and MERRA‐2. Besides, all reanalyses struggle to simulate the mid‐level clouds at low latitudes. In addition to these common deficiencies, the three reanalyses have their own distinctive behaviors and differ from one another. While ERA‐Interim and JRA‐55 show better performance for low‐level clouds in the tropics, they exhibit remarkable underestimation for high‐level clouds. On the contrary, MERRA‐2 succeeds in representing high‐level clouds but dramatically underestimates the low and mid‐level clouds at low latitudes. As a measure of subgrid‐scale variability of moisture, the derived “critical relative humidity (RH _c)” from CloudSat/CALIPSO exhibits distinctive vertical structures at different latitudes, it is thus speculated that poor specification or parameterization of RH _c is responsible for these bias behaviors.

The Philippine Sea anomalous anticyclone/cyclone (abbreviated to PSAC/PSCC) induced by El Niño/La Niña is a key atmospheric circulation system that connects El Niño/La Niña to the East Asian climate. The asymmetry between PSAC and PSCC is analyzed after El Niño–Southern Oscillation (ENSO) is divided into eastern Pacific (EP) ENSO and central Pacific (CP) ENSO. For EP ENSO, evident asymmetry exists between EP El Niño‐induced PSAC and EP La Niña‐induced PSCC, with the former being much stronger than the latter, caused by the nonlinearity of the convective anomalies over the western Pacific. Much stronger sea surface temperature (SST) anomalies for EP El Niño than for EP La Niña is responsible for this convective heating asymmetry.However, the PSAC and PSCC associated with CP ENSO show much weaker asymmetry compared with those associated with EP ENSO, which is related to the nearly symmetric distribution and intensity of the tropical SST anomalies between CP El Niño and CP La Niña. The resultant almost symmetric anomalous Walker circulation and the comparable convective anomalies over the western Pacific produce weak asymmetric responses of PSAC and PSCC to CP El Niño and CP La Niña. Furthermore, numerical experiments also confirm that the asymmetry in the anomalous convective activity over the western Pacific associated with EP (CP) ENSO is the main reason for the asymmetry between PSAC and PSCC.

The Tibetan Plateau (TP) and the Arctic are both cold, fragile, and sensitive to global warming. However, they have very different cloud radiative effects (CRE) and influences on the climate system. In this study, the effects of cloud microphysics on the vertical structures of CRE over the two regions are analyzed and compared by using CloudSat/CALIPSO satellite data and the Rapid Radiative Transfer Model. Results show there is a greater amount of cloud water particles with larger sizes over the TP than over the Arctic, and the supercooled water is found to be more prone to exist over the former than the latter, making shortwave and longwave CRE, as well as the net CRE, much stronger over the TP. Further investigations indicate that the vertical structures of CRE at high altitudes are primarily dominated by cloud ice water, while those at low altitudes are dominated by cloud liquid and mixed-phase water. The liquid and mixed-phase water results in a strong shallow heating (cooling) layer above the cooling (heating) layer in the shortwave (longwave) CRE profiles, respectively.

The critical relative humidity (RH_c), which approximately measures the subgrid‐scale variability of moisture, is important to cloud parameterization. Based on the diagnostics from CloudSat/CALIPSO satellite data, we propose an improved RH_c formula that incorporates geographic dependence and allows for non‐monotonic variations in the vertical. With the parameterized RH_c, a cloud macrophysics scheme is constructed in which fractional cloudiness and subgrid‐scale condensation are synergistically solved, with the latter being calculated using two different approaches. Results show the new scheme largely alleviates the underestimation of high‐ and mid‐level clouds in the default model. The performance is also superior to the simulations applying a globally uniform RH_c as conventionally used in the literature. Varying RH_c and the techniques for computing subgrid‐scale condensation leads to a marked diversity in cloud feedback, which nearly replicates the range of uncertainty found in Coupled Model Intercomparison Project Phase 6 models. Using smaller RH_c leads to larger spread than using larger ones. And the spread caused by different techniques for calculating subgrid‐scale condensation is larger than that caused by the choice of RH_c. While many previous studies have emphasized the diversity of cloud feedback due to low clouds, varying RH_c and its implementation leads to diversity of cloud feedback being mainly due to optically thick clouds. These results highlight the importance of RH_c in inducing uncertainty of cloud feedback, as well as notes of caution to modelers when using RH_c to tune models.

Using a variety of CloudSat/CALIPSO products, this study synergistically examines the performance of clouds and their radiative effects (CRE) for models participating in CMIP6. Results show virtually all models overestimate the net cooling effect of clouds, which is caused by the overestimation of shortwave CRE and the underestimation of longwave CRE. By dividing clouds into regimes jointly sorted by cloud water path and cloud cover, we found models commonly underestimate the relative frequency of occurrence (RFO) for clouds that are geometrically thick, and the bias of RFO is dominant over that of within‐regime CRE in an error decomposition of total CRE. This results in underestimations of CRE in geometrically thick clouds, which are partially offset by overestimations in the remaining cloud regimes, leading to the globally averaged CRE being less biased. The consideration of regime‐based CRE gives important information that could be used for correction of cloud parameterization in models.