Search for: All records

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 Aerosols are important environmental factors that can influence deep convective clouds (DCCs) by serving as cloud condensation nuclei. Due to complications in DCC dynamics and microphysics, and aerosol size distribution and composition, understanding aerosol‐DCC interactions has been a daunting challenge. Recently, the convective invigoration mechanisms through enhancing latent heating in condensation and ice‐related processes that have been proposed in literature are debated for their significance qualitatively and quantitatively. A salient issue arising from these debates is the imperative need to clarify essential knowledge and methodologies in investigating aerosol impacts on deep convection. Here we have presented our view of key aspects on investigating and understanding these invigoration mechanisms as well as the aerosol and meteorological conditions under which these mechanisms may be significant based on new findings. For example, the condensational invigoration is most significant under a clean condition with an introduction of a large number of ultrafine particles, and the freezing‐induced invigoration can be significant in a clean condition with a large number of relatively large‐size particles being added. We have made practical recommendations on approaches for investigating aerosol impacts on convection with both modeling and observations. We note that the feedback induced by the invigoration via the enhanced latent heating to circulation and meteorology can be an important part of aerosol impacts but is very complicated and varies with different convective storm types. This is an important future direction for studying aerosol‐DCC interactions. 

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 Knowledge of the planetary boundary layer height (PBLH) is crucial for various applications in atmospheric and environmental sciences. Lidar measurements are frequently used to monitor the evolution of the PBLH, providing more frequent observations than traditional radiosonde‐based methods. However, lidar‐derived PBLH estimates have substantial uncertainties, contingent upon the retrieval algorithm used. In addressing this, we applied the Different Thermo‐Dynamic Stabilities (DTDS) algorithm to establish a PBLH data set at five separate Department of Energy's Atmospheric Radiation Measurement sites across the globe. Both the PBLH methodology and the products are subject to rigorous assessments in terms of their uncertainties and constraints, juxtaposing them with other products. The DTDS‐derived product consistently aligns with radiosonde PBLH estimates, with correlation coefficients exceeding 0.77 across all sites. This study delves into a detailed examination of the strengths and limitations of PBLH data sets with respect to both radiosonde‐derived and other lidar‐based estimates of the PBLH by exploring their respective errors and uncertainties. It is found that varying techniques and definitions can lead to diverse PBLH retrievals due to the inherent intricacy and variability of the boundary layer. Our DTDS‐derived PBLH data set outperforms existing products derived from ceilometer data, offering a more precise representation of the PBLH. This extensive data set paves the way for advanced studies and an improved understanding of boundary‐layer dynamics, with valuable applications in weather forecasting, climate modeling, and environmental studies. 

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 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 The Amazon Basin, which plays a critical role in the carbon and water cycle, is under stress due to changes in climate, agricultural practices, and deforestation. The effects of thermodynamic and microphysical forcing on the strength of thunderstorms in the Basin (75–45°W, 0–15°S) were examined during the pre‐monsoon season (mid‐August through mid‐December), a period with large variations in aerosols, intense convective storms, and plentiful flashes. The analysis used measurements of radar reflectivity, ice water content (IWC), and aerosol type from instruments aboard the CloudSat and CALIPSO satellites, flash rates from the ground‐based Sferics Timing and Ranging Network, and total aerosol optical depth (AOD) from a surface network and a meteorological re‐analysis. After controlling for convective available potential energy (CAPE), it was found that thunderstorms that developed under dirty (high‐AOD) conditions were 1.5 km deeper, had 50% more IWC, and more than two times as many flashes as storms that developed under clean conditions. The sensitivity of flashes to AOD was largest for low values of CAPE where increases of more than a factor of three were observed. The additional ice water indicated that these deeper systems had higher vertical velocities and more condensation nuclei capable of sustaining higher concentrations of water and large hydrometeors in the upper troposphere. Flash rates were also found to be larger during periods when smoke rather than dust was common in the lower troposphere, likely because smoky periods were less stable due to higher values of CAPE and AOD and lower values of mid‐tropospheric relative humidity. 

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. 

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.