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Analyzing Aerosol–Cloud–Climate Interactions through Remotely Sensed Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 3963

Special Issue Editors


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Guest Editor
Bay Area Environmental Research Institute, Mountain View, CA 94035, USA
Interests: remote sensing; machine learning; aerosol; clouds; radiation
Department of Geosciences, University of Oslo, Blindernveien 31, 0371 Oslo, Norway
Interests: aerosol–cloud interactions; atmospheric chemistry
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Special Issue Information

Dear Colleagues,

The impact of aerosols on cloud properties is one of the largest uncertainties in the anthropogenic radiative forcing of the climate. Significant progress has been made in constraining this forcing using observations, but uncertainty remains, particularly around the magnitude of cloud rapid adjustments to aerosol perturbations. For example, the liquid water path (LWP) response to aerosol perturbation is often confounded by meteorological factors that are difficult to isolate. The cloud nucleus concentration’s dependency on cloud number concentration for both mixed-phase and cirrus clouds is also not well constrained and depends on the aerosol type and size distribution. Recent observational and modeling efforts found weak and average liquid–cloud–water responses to anthropogenic aerosols, while others found stronger relations under various conditions. To accurately quantify cloud responses to aerosols, there is a need for an improved detection of both spatial and temporal quantities of cloud water content, albedo, and cloud and aerosol particle numbers.

Our aim is to publish the state-of-the-art measurement capabilities of concurrent aerosol and cloud systems that use various remote sensing methodologies, including novel approaches. By advancing measurement capabilities, modeling and prediction capabilities can be advanced and better estimations of the radiative budgets of the Earth under different scenarios can be developed.

This Special Issue seeks papers dedicated to concurrent measurements of aerosols and clouds using either passive or active remote sensing sensors from space-borne, airborne, balloon-borne, and UAV platforms, as well as ground-based sensors, with a special emphasis on high temporal resolution measurements that can detangle meteorological effects from aerosol effects on clouds. We invite papers that combine measurement and modeling approaches that can further the understanding in this dynamic field and welcome papers covering scopes of liquid, mixed-phase, and cirrus cloud properties under various aerosol conditions (pristine and polluted) and at various geographical locations (equator and high latitudes).

Dr. Michal Segal-Rosenheimer
Dr. Haochi Che
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • radar and lidar measurements
  • passive remote sensing
  • LES
  • biomass burning aerosols
  • dust aerosols
  • stratocumulus clouds
  • cirrus clouds
  • mixed-phase clouds
  • Arctic and Antarctic environments

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Published Papers (2 papers)

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Research

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23 pages, 8260 KiB  
Article
Studying the Aerosol Effect on Deep Convective Clouds over the Global Oceans by Applying Machine Learning Techniques on Long-Term Satellite Observation
by Xuepeng Zhao, James Frech, Michael J. Foster and Andrew K. Heidinger
Remote Sens. 2024, 16(13), 2487; https://doi.org/10.3390/rs16132487 - 7 Jul 2024
Viewed by 885
Abstract
Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are [...] Read more.
Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models. Full article
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Review

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35 pages, 1603 KiB  
Review
Understanding Aerosol–Cloud Interactions through Lidar Techniques: A Review
by Francesco Cairo, Luca Di Liberto, Davide Dionisi and Marcel Snels
Remote Sens. 2024, 16(15), 2788; https://doi.org/10.3390/rs16152788 - 30 Jul 2024
Cited by 1 | Viewed by 2546
Abstract
Aerosol–cloud interactions play a crucial role in shaping Earth’s climate and hydrological cycle. Observing these interactions with high precision and accuracy is of the utmost importance for improving climate models and predicting Earth’s climate. Over the past few decades, lidar techniques have emerged [...] Read more.
Aerosol–cloud interactions play a crucial role in shaping Earth’s climate and hydrological cycle. Observing these interactions with high precision and accuracy is of the utmost importance for improving climate models and predicting Earth’s climate. Over the past few decades, lidar techniques have emerged as powerful tools for investigating aerosol–cloud interactions due to their ability to provide detailed vertical profiles of aerosol particles and clouds with high spatial and temporal resolutions. This review paper provides an overview of recent advancements in the study of ACI using lidar techniques. The paper begins with a description of the different cloud microphysical processes that are affected by the presence of aerosol, and with an outline of lidar remote sensing application in characterizing aerosol particles and clouds. The subsequent sections delve into the key findings and insights gained from lidar-based studies of aerosol–cloud interactions. This includes investigations into the role of aerosol particles in cloud formation, evolution, and microphysical properties. Finally, the review concludes with an outlook on future research. By reporting the latest findings and methodologies, this review aims to provide valuable insights for researchers engaged in climate science and atmospheric research. Full article
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