https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2017EA000346

Negative Aerosol‐Cloud re Relationship From Aircraft Observations Over Hebei, China

 Increased concentrations of aerosol can enhance the albedo of warm low-level cloud Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol statistics near clouds Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3-D radiative transfer) clouds To screen for this contamination we have developed a new cloud–aerosol pairing algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image The distance between each aerosol retrieval and nearest cloud is also computed in CAPA Results from two independent satellite imagers, the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS), show a marked reduction in the strength of the intrinsic aerosol indirect radiative forcing when selecting aerosol pairs that are located farther away from the clouds (−028±026 W m−2) compared to those including pairs that are within 15 km of the nearest cloud (−049±018 W m−2) The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol-loading, cloud albedo, and cloud fraction These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to the inclusion of retrieval artefacts in the aerosol located near clouds

/pdf/unveiling-aerosol-cloud-interactions-part-1-cloud-3tyetudaf0.pdf

Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate

Using 15‐year observations obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board both NASA Terra and Aqua from March 2003 to February 2018, this study investigated the spatio‐temporal variations of both macro‐ and micro‐physical cloud properties over China, including cloud fraction (CF), cloud top pressure (CTP), cloud top temperature (CTT), cloud optical thickness (COT), and effective radius (r e) of both liquid water and ice clouds. Multi‐year averaged CF is around 61% over whole China region. However, CF varies with both regions and seasons. The CFs are about 6–8% larger in summer and winter (~64–65%) than in spring and autumn (~58%). By classifying China into five regimes, which are northwestern China (NW), northeastern China (NE), Tibetan Plateau (TP), northern China (N), and southern China (S), there is a clear CF regional distribution pattern. In general, there are large amount of clouds in S and southeast of TP, and small amount in NE, N, NW and most TP. Moreover, there are generally more clouds over ocean than over land, and much more clouds over S than over N. The CFs are larger (smaller) in the afternoon than in the morning over most land (ocean) regions. Furthermore, the largest CF differences between afternoon and morning occur over the TP region in China. COT demonstrates almost the same regional distribution pattern as the CF for all four seasons. Specifically, COT is higher in S than in N, which is most likely associated with the type of clouds and the availability of water vapour. Cloud r e shows larger values in NW and TP than in eastern China regions in all seasons except for summer, which could be related to the heavy aerosol pollution in eastern China regions. Accompanying with the cold cloud tops over TP, a low CTP centre is often located there.

Fifteen-year statistical analysis of cloud characteristics over China using Terra and Aqua Moderate Resolution Imaging Spectroradiometer observations

[1] We quantitatively examine the aerosol first indirect effects (FIE) for non-precipitating low-level single-layer liquid phase clouds simulated by the Community Atmospheric Model version 5 (CAM5) running in the weather forecast mode at three DOE Atmospheric Radiation Measurement (ARM) sites. The FIE is quantified in terms of a relative change in cloud droplet effective radius for a relative change in accumulation mode aerosol number concentration under conditions of fixed liquid water content (LWC). CAM5 simulates aerosol-cloud interactions reasonably well for this specific cloud type, and the simulated FIE is consistent with the long-term observations at the examined locations. The FIE in CAM5 generally decreases with LWC at coastal ARM sites, and is larger by using cloud condensation nuclei rather than accumulation mode aerosol number concentration as the choice of aerosol amount. However, it has no significant variations with location and has no systematic strong seasonal variations at examined ARM sites.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2012GL051213

Aerosol first indirect effects on non‐precipitating low‐level liquid cloud properties as simulated by CAM5 at ARM sites

In situ aircraft measurements of cloud microphysical properties and aerosol during the 1st phase of the Cloud Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX-I) over the Indian sub-continent provided initial opportunities to investigate the dispersion effect and its implications for estimating aerosol indirect effects in continental cumuli. In contrast to earlier studies on continental shallow cumuli, it is found that not only the cloud droplet number concentration but also the relative dispersion increases with the aerosol number concentration in continental cumuli. The first aerosol indirect effect estimated from the relative changes in droplet concentration and effective radius with aerosol number concentration are 0.13 and 0.07, respectively. In-depth analysis reveals that the dispersion effect could offset the cooling by enhanced droplet concentration by 39 in these continental cumuli. Adiabaticity analysis revealed aerosol indirect effect is lesser in subadiabatic clouds possibly due to inhomogeneous mixing processes. This study shows that adequate representation of the dispersion effect would help in accurately estimating the cloud albedo effect for continental cumuli and can reduce uncertainty in aerosol indirect effect estimates.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2011JD016532

Aerosol effect on droplet spectral dispersion in warm continental cumuli

[1] The effect of increased aerosol concentrations on the low-level, non-precipitating, ice-free stratus clouds is examined using a suite of surface-based remote sensing systems. Cloud droplet effective radius and liquid water path are retrieved using cloud radar and microwave radiometer. Collocated measurements of aerosol scattering coefficient, size distribution and cloud condensation nuclei (CCN) concentrations were used to examine the response of cloud droplet size and optical thickness to increased CCN proxies. During the episodic events of increase in aerosol accumulation-mode volume distribution, the decrease in droplet size and increase in cloud optical thickness is observed. The indirect effect estimates are made for both droplet effective radius and cloud optical thickness for different liquid water path ranges and they range 0.02–0.18 and 0.005–0.154, respectively. Data are also categorized into thin and thick clouds based on cloud geometric thickness (Δz) and estimates show IE values are relatively higher for thicker clouds.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2009GL038451

Aerosol effect on cloud droplet size as monitored from surface‐based remote sensing over East China Sea region

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

K. Miyagi

Author Tools

Chat about Author

Papers

Aerosol effect on cloud droplet size as monitored from surface‐based remote sensing over East China Sea region