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Abstract This study employs an explainable machine learning (ML) framework (XGBoost‐SHapley Additive exPlanations analysis) to investigate controlling factors on cloud liquid water path (LWP) using EPCAPE observations near the California coast. Aerosols are found to be the dominant factor explaining LWP variability, surpassing meteorological factors (MFs). By isolating aerosol effects from meteorological influences, the ML reveals a negative linear relationship between LWP and cloud droplet number concentration (N_d) in log space, likely driven by entrainment drying via evaporation‐entrainment feedback. This aligns with the negative regime of the inverted‐V relationship reported in previous studies, while no positive LWP responses are found due to a limited number of precipitating cases in EPCAPE. Furthermore, the sensitivity of LWP toN_dshows a non‐linear dependence on MFs like moisture contrast between surface and free troposphere and lower‐tropospheric stability. This occurs due to the interplay between the MFs' direct effects on entrainment drying and indirect effects through LWP adjustments. 

Key Points A preexisting stratocumulus deck is more persistent when experiencing warm‐air advection than cold‐air advection This persistence is due to reduced entrainment drying as a result of decoupling, which outweighs decreased cloud‐base moisture transport The mechanism is more notable when free‐tropospheric humidity is higher 

Abstract In this study, we evaluated the performance of machine learning (ML) models (XGBoost) in predicting low‐cloud fraction (LCF), compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. ML models show a substantial enhancement in predicting LCF regarding root mean squared errors and correlation coefficients. The good performance is consistent across the full spectrums of atmospheric stability and large‐scale vertical velocity. Employing an explainable ML approach, we revealed the importance of including the amount of available moisture in ML models for representing spatiotemporal variations in LCF in the midlatitudes. Also, ML models demonstrated marked improvement in capturing the LCF variations during the stratocumulus‐to‐cumulus transition (SCT). This study suggests ML models' great potential to address the longstanding issues of “too few” low clouds and “too rapid” SCT in global climate models. 

Abstract. The states of coupling between clouds andsurface or boundary layer have been investigated much more extensively formarine stratocumulus clouds than for continental low clouds, partly due tomore complex thermodynamic structures over land. A manifestation is a lackof robust remote sensing methods to identify coupled and decoupled cloudsover land. Following the idea for determining cloud coupling over the ocean,we have generalized the concept of coupling and decoupling to low cloudsover land, based on potential temperature profiles. Furthermore, by usingample measurements from lidar and a suite of surface meteorologicalinstruments at the U.S. Department of Energy's Atmospheric RadiationMeasurement Program's Southern Great Plains site from 1998 to 2019, we havedeveloped a method to simultaneously retrieve the planetary boundary layer(PBL) height (PBLH) and coupled states under cloudy conditions during thedaytime. The new lidar-based method relies on the PBLH, the liftedcondensation level, and the cloud base to diagnose the cloud coupling. Thecoupled states derived from this method are highly consistent with thosederived from radiosondes. Retrieving the PBLH under cloudy conditions, whichhas been a persistent problem in lidar remote sensing, is resolved in thisstudy. Our method can lead to high-quality retrievals of the PBLH undercloudy conditions and the determination of cloud coupling states. With thenew method, we find that coupled clouds are sensitive to changes in the PBLwith a strong diurnal cycle, whereas decoupled clouds and the PBL are weaklyrelated. Since coupled and decoupled clouds have distinct features, our newmethod offers an advanced tool to separately investigate them in climatesystems. 

Abstract Aerosol-boundary layer interactions play an important role in affecting atmospheric thermodynamics and air pollution. As a key factor in dictating the development of the boundary layer, the entrainment process in the context of aerosol-boundary layer interactions is still poorly understood. Using comprehensive field observations made at a superstation in Beijing, we gain insight into the response of the entrainment process to aerosols. We found that high aerosol loading can significantly suppress the entrainment rate, breaking the conventional linear relationship between sensible heat fluxes and entrainment fluxes. Related to aerosol vertical distributions, aerosol heating effects can alter vertical heat fluxes, leading to a strong interaction between aerosols and the entrainment process in the upper boundary layer. Such aerosol-entrainment coupling can inhibit boundary layer development and explains the great sensitivity of observed entrainment rates to aerosols than can traditional calculations. The notable impact of aerosols on the entrainment process raises holistic thinking about the dynamic framework of the boundary layer in a polluted atmosphere, which may have a significant bearing on the dispersion of air pollutants and the land-atmosphere coupling. 

Abstract Understanding interactions between low clouds and land surface fluxes is critical to comprehending Earth's energy balance, yet their relationships remain elusive, with discrepancies between observations and modeling. Leveraging long‐term field observations over the Southern Great Plains, this investigation revealed that cloud‐land interactions are closely connected to cloud‐land coupling regimes. Observational evidence supports a dual‐mode interaction: coupled stratiform clouds predominate in low sensible heat scenarios, while coupled cumulus clouds dominate in high sensible heat scenarios. Reanalysis data sets, MERRA‐2 and ERA‐5, obscure this dichotomy owing to a shortfall in representing boundary layer clouds, especially in capturing the initiation of coupled cumulus in high sensible heat scenarios. ERA‐5 demonstrates a relatively closer alignment with observational data, particularly in capturing relationships between cloud frequency and latent heat, markedly outperforming MERRA‐2. Our study underscores the necessity of distinguishing different cloud coupling regimes, essential to the understanding of their interactions for advancing land‐atmosphere interactions. 

Abstract This study leveraged a Lagrangian framework to examine the evolution of stratocumulus clouds under cold and warm advections (CADV and WADV) in the Community Earth System Model 2 (CESM2) against observations. We found that CESM2 simulates a too rapid decline in low‐cloud fraction (LCF) and cloud liquid water path (CLWP) under CADV conditions, while it better aligns closely with observed LCF under WADV conditions but overestimates the increase in CLWP. Employing an explainable machine learning approach, we found that too rapid decreases in LCF and CLWP under CADV conditions are related to overestimated drying effects induced by sea surface temperature, whereas the substantial increase in CLWP under WADV conditions is associated with the overestimated moistening effects due to free‐tropospheric moisture and surface winds. Our findings suggest that overestimated drying effects of sea surface temperature on cloud properties might be one of crucial causes for the high equilibrium climate sensitivity in CESM2. 

Abstract To enhance our understanding of cloud simulations over land, this study provides the first assessment of coupling between cloud and land surface in the Large‐Eddy Simulation (LES) Atmospheric Radiation Measurement Symbiotic Simulation and Observation (LASSO) activity for the shallow convection scenario. The analysis of observation data reveals a diurnal cycle of cloud‐land coupling, which co‐varies with surface fluxes. However, coupled (or decoupled) cumulus clouds are inadequately simulated, manifesting as a too‐high (or low) occurrence frequency during the afternoon. This discrepancy is mirrored by the overestimated cloud liquid water path and cloud‐top height. These overestimations are linked to the overpredicted boundary‐layer development and the easier trigger of shallow convection misrepresented in LES runs. Our study underscores the need to improve the representations of boundary‐layer processes and cloud‐land interactions within LES to better simulate shallow clouds in the future. 

Abstract Due to surface heating, the morning boundary layer transits from stable to neutral or convective conditions, exerting critical influences on low tropospheric thermodynamics. Low clouds closely interact with the boundary layer development, yet their interactions bear considerable uncertainties. Our study reveals that cloud‐surface coupling alters the morning transition from stable to unstable boundary layer and thus notably affects the diurnal variation of the boundary layer. Specifically, due to the reduction in surface fluxes, decoupled clouds can delay the process of eroding nocturnal inversion by 0.8‐hr and even prevent the transition of the boundary layer from happening for 12% of decoupled cases, keeping the boundary layer in a stable state during the noontime. On the other hand, when clouds are coupled with the surface, cloud‐top radiative cooling can directly cool the upper boundary layer to facilitate sub‐cloud convection, leading to an unstable boundary layer in the earlier morning.