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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. 

Abstract In order to improve the quality of products during additive manufacturing, we developed a novel freezing sublimation-based method for inkjet-based three-dimensional (3D) printing technology, which can significantly improve the uniformity of material distribution in printed products. In our previous studies, we used a laboratory prototype with single droplets of inkjet solution containing colloidal particles to prove the concept of this study. However, understanding the interaction between droplets on the printing substrate surface is also crucial for determining the printing resolution and accuracy of this method, which cannot be fully investigated through single droplet-based experimental studies. To fill this knowledge gap, we conducted a series of experiments on colloidal droplet impingement, freezing, and sublimation on substrates using dual droplets. The experimental setup allowed the release of two droplets in quick succession from a modified nozzle with two needles. These droplets coalesced on the substrate surface due to spreading during their impingement processes. Observations revealed that the coalescence pattern of these two droplets varied depending on the time interval between their release. When the second droplet was released immediately after the first, their coalescence was governed by fluid dynamics. However, when the second droplet was released after the first droplet had frozen on the substrate, it spread above the ice surface of the first droplet in a relatively slower process. This observation provides new insights for the continued study and optimization of the proposed novel freezing sublimation-based 3D printing method. 

Inkjet-based three-dimensional (3D) printing is widely used for fast and efficient non-contact manufacturing, yet it suffers from several drawbacks, such as coarse resolution, lack of adhesion, manufacturing inconsistency, and uncertain final part mechanical properties. These undesirable effects are related to complex flow phenomena in colloidal droplets in inkjet 3D printing, particularly the internal flows and droplet deformations during the deposition and drying processes. These challenges are due to the colloidal suspension droplets being kept in the liquid state during printing. To overcome these disadvantages, this paper presents a novel freezing-sublimation-based inkjet 3D printing concept that freezes the colloidal droplets upon impact followed by sublimation, eliminating the undesirable particle transport and fluid motions during deposition. A series of experiments were conducted to characterize the colloidal droplet behaviors during the impinging/freezing and sublimation processes and evaluate the effects of the freezing process on droplet impinging dynamics as well as the final deposition patterns through sublimation. It was demonstrated that the deposition patterns obtained from this new method are much more uniform than the conventional evaporation-based deposition method. Both qualitative and quantitative methods were applied to analyze the colloidal droplet profiles during the printing process (impinging, freezing, and sublimation), as well as the final deposition patterns. The study shows promising results of using this new method, providing a foundation for the development of the novel freezing-sublimation-based inkjet 3D printing technique. 

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 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.