An official website of the United States government
Impacts of aerosol particles on clouds, precipitation, and climate remain one of the significant uncertainties in climate change. Aerosol particles entrained at cloud top and edge can affect cloud microphysical and macrophysical properties, but the process is still poorly understood. Here we investigate the cloud microphysical responses to the entrainment of aerosol-laden air in the Pi convection-cloud chamber. Results show that cloud droplet number concentration increases and mean radius of droplets decreases, which leads to narrower droplet size distribution and smaller relative dispersion. These behaviors are generally consistent with the scenario expected from the first aerosol-cloud indirect effect for a constant liquid water content (L). However, L increases significantly in these experiments. Such enhancement of L can be understood as suppression of droplet sedimentation removal due to small droplets. Further, an increase in aerosol concentration from entrainment reduces the effective radius and ultimately increases cloud optical thickness and cloud albedo, making the clouds brighter. These findings are of relevance to the entrainment interface at stratocumulus cloud top, where modeling studies have suggested sedimentation plays a strong role in regulating L. Therefore, the results provide insights into the impacts of entrainment of aerosol-laden air on cloud, precipitation, and climate.
more »
« less
Locally narrow droplet size distributions are ubiquitous in stratocumulus clouds
Marine stratocumulus clouds are the “global reflectors,” sharply contrasting with the underlying dark ocean surface and exerting a net cooling on Earth’s climate. The magnitude of this cooling remains uncertain in part owing to the averaged representation of microphysical processes, such as the droplet-to-drizzle transition in global climate models (GCMs). Current GCMs parameterize cloud droplet size distributions as broad, cloud-averaged gammas. Using digital holographic measurements of discrete stratocumulus cloud volumes, we found cloud droplet size distributions to be narrower at the centimeter scale, never resembling the cloud average. These local distributions tended to form pockets of similar-looking cloud regions, each characterized by a size distribution shape that is diluted to varying degrees. These observations open the way for new modeling representations of microphysical processes.
more »
« less
- PAR ID:
- 10575595
- Publisher / Repository:
- AAAS
- Date Published:
- Journal Name:
- Science
- Volume:
- 384
- Issue:
- 6695
- ISSN:
- 0036-8075
- Page Range / eLocation ID:
- 528 to 532
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract This study presents the first model intercomparison of aerosol‐cloud‐turbulence interactions in a controlled cloudy Rayleigh‐Bénard Convection chamber environment, utilizing the Pi Chamber at Michigan Technological University. We analyzed simulated cloud chamber‐averaged statistics of microphysics and thermodynamics in a warm‐phase, cloudy environment under steady‐state conditions at varying aerosol injection rates. Simulation results from seven distinct models (DNS, LES, and a 1D turbulence model) were compared. Our findings demonstrate that while all models qualitatively capture observed trends in droplet number concentration, mean radius, and droplet size distributions at both high and low aerosol injection rates, significant quantitative differences were observed. Notably, droplet number concentrations varied by over two orders of magnitude between models for the same injection rates, indicating sensitivities to the model treatments in droplet activation and removal and wall fluxes. Furthermore, inconsistencies in vertical relative humidity profiles and in achieving steady‐state liquid water content suggest the need for further investigation into the mechanisms driving these variations. Despite these discrepancies, the models generally reproduced consistent power‐law relationships between the microphysical variables. This model intercomparison underscores the importance of controlled cloud chamber experiments for validating and improving cloud microphysical parameterizations. Recommendations for future modeling studies are also highlighted, including constraining wall conditions and processes, investigating droplet/aerosol removal (including sidewall losses), and conducting simplified experiments to isolate specific processes contributing to model divergence and reduce model uncertainties.more » « less
-
Abstract Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance StatementA typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.more » « less
-
Deep convective clouds (DCCs) are associated with the vertical ascent of air from the lower to the upper atmosphere. They appear in various forms such as thunderstorms, supercells, and squall lines. These convective systems play important roles in the hydrological cycle, Earth’s radiative budget, and the general circulation of the atmosphere. Changes in aerosol (both cloud condensation nuclei and ice-nucleating particles) affect cloud microphysics and dynamics, and thereby influence convective intensity, precipitation, and the radiative effects of deep clouds and their cirrus anvils. However, the very complex dynamics and cloud microphysics of DCCs means that many of these processes are not yet accurately quantified in observations and models. This chapter outlines the main ways in which changes in aerosol affect the microphysical, dynamical, and radiative properties of DCCs. Aerosol interactions with DCCs depend on aerosol properties, storm dynamics, and meteorological conditions. When aerosol particles are light-absorbing, such as soot from industry or biomass burning, the aerosol radiative effects can alter the meteorological conditions under which DCCs form. These radiative effects modify temperature profiles and planetary boundary layer heights, thus changing atmospheric stability and circulation, and affecting the onset and development of DCCs. These large-scale effects, such as the effect of anthropogenic aerosol on the East and South Asian monsoons, can be simulated in coarse-resolution models. These processes are described in Chapter 13. This chapter is concerned with aerosol interactions with DCC systems ranging from individual clouds to mesoscale convective systems. Increases in cloud condensation nuclei (CCN) can enhance cloud droplet number concentrations and decrease droplet sizes, thereby narrowing the droplet size spectrum. For DCCs, a narrowed droplet size spectrum suppresses warm rain formation (rain derived from non-ice-phase processes), allowing the transport of more, smaller droplets to altitudes below 0°C. This may result in (i) freezing of more supercooled water, thereby enhancing latent heating from icerelated microphysical processes and invigorating storms (ice-phase invigoration); (ii) modification of ice-related microphysical processes, which changes cold pools, precipitation rates, and hailstone frequency and size; (iii) expansion of the mixed-phase zone and decreases in the cloud glaciation temperature; and (iv) slowing down of cloud dissipation, resulting in larger cloud cover and cloud depth in the stratiform and anvil regions due to numerous smaller ice particles. The increased cloud cover and cloud depth constitute an influence of aerosol on the cloud radiative effect. Reduced diurnal temperature variation has been observed and simulated as a result of enhanced daytime cooling and nighttime warming by expanded anvil cloud area in polluted environments. However, the global radiative effect of aerosol interactions with DCCs remains to be quantified.more » « less
-
Abstract Droplet growth due to stochastic condensation has been considered as one of the mechanisms to cause broadening of cloud droplet size distributions and jump the bottleneck between droplet growth due to diffusion and collision‐coalescence. Digital in‐line holography is used to measure variations in droplet number concentration and droplet size in marine boundary layer clouds. Distributions of phase relaxation times are quite broad for some clouds. Turbulence correlation times are estimated, and the comparison of these with phase relaxation times suggests that clouds exist in both fast and slow microphysical regimes. Presumed signatures of stochastic condensation, such as increasing relative size dispersion and increasing droplet size with decreasing number density, are observed.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1126/science.adi5550
