Using the large-eddy simulation framework, effects of an aerosol layer on warm cumulus clouds in the Korean Peninsula when the layer is above or around the cloud tops in the upper atmosphere are examined. Also, these effects are compared to effects of an aerosol layer when it is around or below the cloud bases in the low atmosphere. Simulations show that when the aerosol layer is in the low atmosphere, aerosols absorb solar radiation and radiatively heat up air enough to induce greater instability, stronger updrafts and more cloud mass than when the layer is in the upper atmosphere. As aerosol concentrations in the layer decrease, the aerosol radiative heating gets weaker to lead to less instability, weaker updrafts and less cloud mass when the layer is in the low atmosphere. This in turn makes differences in cloud mass, which are between a situation when the layer is in the low atmosphere and that when the layer is in the upper atmosphere, smaller. It is found that the transportation of aerosols by updrafts reduces aerosol concentrations in the aerosol layer, which is in the low atmosphere, and in turn reduces the aerosol radiative heating, updraft intensity and cloud mass. It is also found that the presence of aerosol impacts on radiation suppresses updrafts and reduces clouds. Aerosols affect not only radiation but also aerosol activation. In the absence of aerosol impacts on radiation, aerosol impacts on the droplet nucleation increases cloud mass when the layer is in the low atmosphere as compared to a situation when the layer is in the upper atmosphere. As aerosol impacts on radiation team up with those on the droplet nucleation, differences in cloud mass, which are between a situation when the layer in the low atmosphere and that when the layer is in the upper atmosphere, get larger. This is as compared to a situation when there is no aerosol impacts on radiation and only aerosol impacts on the droplet nucleation.
Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Other. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Contrasting impacts of forest on cloud cover based on satellite observationsAuthorsRuXuiDYanLiAdriaan JTeulingiDLeiZhaoDominick VSpracklenLuisGarcia-CarrerasiDRonnyMeierLiangChenYoutongZhengiDBojieFuSee all authors Ru XuiDBeijing Normal UniversityiDhttps://orcid.org/0000-0002-5203-5980view email addressThe email was not providedcopy email addressYan LiCorresponding Author• Submitting AuthorBeijing Normal Universityview email addressThe email was not providedcopy email addressAdriaan J TeulingiDWageningen University and ResearchiDhttps://orcid.org/0000-0003-4302-2835view email addressThe email was not providedcopy email addressLei ZhaoUniversity of Illinois at Urbana-Champaignview email addressThe email was not providedcopy email addressDominick V SpracklenUniversity of Leedsview email addressThe email was not providedcopy email addressLuis Garcia-CarrerasiDUniversity of ManchesteriDhttps://orcid.org/0000-0002-9844-3170view email addressThe email was not providedcopy email addressRonny MeierETH Zurichview email addressThe email was not providedcopy email addressLiang ChenUniversity of Illinois at Urbana-Champaignview email addressThe email was not providedcopy email addressYoutong ZhengiDUniversity of Maryland, College ParkiDhttps://orcid.org/0000-0002-5961-7617view email addressThe email was not providedcopy email addressBojie FuBeijing Normal UniversityChinese Academy of Sciencesview email addressThe email was not providedcopy email address
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.
Knowledge of vertical air motion in the atmosphere is important for both meteorological and climate studies due to its impact on clouds, precipitation and the vertical transport of air masses, heat, momentum, and composition. The vertical velocity (VV) of air is among the most difficult and uncertain quantities to measure due to its generally small magnitude and high temporal and spatial variability. In this study, a descending radiosonde system is developed to derive VV at the low and middle troposphere in north China during the summer months. The VV is estimated from the difference between the observed radiosonde descent speed and the calculated radiosonde descent speed in still air based on the fluid dynamic principle. The results showed that the estimated VV generally ranged from −1 m/s to 1 m/s, accounting for 80.2% of data points. In convective conditions, a wider distribution of the VV was observed, which was skewed to large values relative to those in nonconvective conditions. The average VV throughout the entire profile was close to 0 m/s under nonconvective conditions. In contrast, distinctive vertical air motions below 5 km above the ground were recorded under convective activities. Vigorous air motions with an absolute VV >2 m/s were occasionally observed and were often associated with the occurrence of cloud layers. Moreover, the detailed structure of the instant air motion near the cloud boundaries (i.e., top and base), with an absolute VV >10 m/s in convective weather systems, was clearly revealed by this technique. The uncertainty estimation indicated that this method has the potential to capture and describe events with vertical air motions >0.69 m/s, which is useful for a convective weather study. Further studies are required to carefully assess the accuracy and precision of this novel VV estimation technique.
Data from Large-eddy simulation data of stratocumulus advecting over cold and warm waters. The LES model used is the System for Atmospheric Modeling (SAM) model (http://rossby.msrc.sunysb.edu/~marat/SAM.html).
Abstract Marine stratocumulus clouds play a significant role in the Earth's radiation budget. The updrafts at cloud base ( W b ) govern the supersaturation and therefore the activation of cloud condensation nuclei, which modifies the cloud and precipitation properties. A statistically significant relationship between W b and cloud top radiative cooling rate (CTRC) is found from the measurements of the Department of Energy's Atmospheric Radiation Measurement Mobile Facility on board a ship sailing between Honolulu and Los Angeles. A similar relation was found on Graciosa Island but with greater scatter and weaker correlation presumably due to the island effect. Based on the relation, we are able to estimate the cloud base updrafts using a simple formula: W b = −0.44 × CTRC + 22.30 ± 13, where the W b and CTRC have units of cm/s and W/m 2 , respectively. This quantification can be utilized in satellite remote sensing and parameterizations of W b in general circulation models.
Abstract. General circulation models (GCMs), unlike other lines of evidence, indicate that anthropogenic aerosols cause a global-mean increase in cloud liquid water path (ℒ) and thus a negative adjustment to radiative forcing of the climate by aerosol–cloud interactions. In part 1 of this series of papers, we showed that this is true even in models that reproduce the negative correlation observed in present-day internal variability in ℒ and cloud droplet number concentration (Nd). We studied several possible confounding mechanisms that could explain the noncausal cloud–aerosol correlations in GCMs and that possibly contaminate observational estimates of radiative adjustments. Here, we perform single-column and full-atmosphere GCM experiments to investigate the causal model-physics mechanisms underlying the model radiative adjustment estimate. We find that both aerosol–cloud interaction mechanisms thought to be operating in real clouds – precipitation suppression and entrainment evaporation enhancement – are active in GCMs and behave qualitatively in agreement with physical process understanding. However, the modeled entrainment enhancement has a negligible global-mean effect. This raises the question of whether the GCM estimate is incorrect due to parametric or base-state representation errors or whether the process understanding gleaned from a limited set of canonical cloud cases is insufficiently representative of the diversity of clouds in the real climate. Regardless, even at limited resolution, the GCM physics appears able to parameterize the small-scale microphysics–turbulence interplay responsible for the entrainment enhancement mechanism. We suggest ways to resolve tension between current and future (storm-resolving) global modeling systems and other lines of evidence in synthesis climate projections.
Abstract. This study examines the role played by aerosols which act as cloud condensation nuclei (CCN) in the development of clouds and precipitation in two metropolitan areas in East Asia that have experienced substantial increases in aerosol concentrations over the last decades. These two areas are the Seoul and Beijing areas and the examination was done by performing simulations using the Advanced Research Weather Research and Forecasting model as a cloud system resolving model. The CCN are advected from the continent to the Seoul area and this increases aerosol concentrations in the Seoul area. These increased CCN concentrations induce the enhancement of condensation that in turn induces the enhancement of deposition and precipitation amount in a system of less deep convective clouds as compared to those in the Beijing area. In a system of deeper clouds in the Beijing area, increasing CCN concentrations also enhance condensation but reduce deposition. This leads to negligible CCN-induced changes in the precipitation amount. Also, in the system there is a competition for convective energy among clouds with different condensation and updrafts. This competition results in different responses to increasing CCN concentrations among different types of precipitation, which are light, medium and heavy precipitation in the Beijing area. The CCN-induced changes in freezing play a negligible role in CCN-precipitation interactions as compared to the role played by CCN-induced changes in condensation and deposition in both areas.
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