Unusual Aerosol Conditions in the Arctic

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Aerosols".

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 2676

Special Issue Editors

Institute of Oceanology of the Polish Academy of Sciences, 81-712 Sopot, Poland
Interests: oceanography; climate studies; sustainability; environmental studies
Special Issues, Collections and Topics in MDPI journals
GEMMA and POLARIS Centre, Università degli Studi di Milano-Bicocca, 20126 Milano, Italy
Interests: aerosol chemistry; arctic aerosols; aerosol measurements; aerosol–climate relations
Special Issues, Collections and Topics in MDPI journals
Institute of Oceanology PAN, Climate and Ocean Research and Education Laboratory, Powstańców Warszawy 55, 81-712 Sopot, Poland
Interests: atmospheric aerosols; climate and ocean changes; Arctic environment; science education
Alfred-Wegener-Institut, 27570 Bremerhaven, Germany
Interests: aerosol remote sensing; aerosol properties and forcing; Arctic climate; Arctic boundary layer

Special Issue Information

Dear Colleagues,

Typically, the Arctic environment (including atmosphere) has been regarded as pristine, which is related to its remoteness and relatively low anthropogenic impact. However, the situation is changing due to a number of stressors, with climate-change-related unusual aerosol events (including biomass burning), among many others. We have recently observed that beside the persistent and accelerated natural and anthropogenic processes (both local and regional) which modify the Arctic atmosphere, the extreme aerosol events are becoming a very serious large-scale source of adverse impact on the Arctic atmosphere. These aerosols and other pollutants are commonly transported from lower latitudes into the Arctic where they remain in the atmosphere, change their properties, and are deposited to the surface.

The physical, optical, and chemical properties of atmospheric aerosols are difficult to describe since they are of different origins (sources outside the Arctic or local) and also relate to meteorological conditions, which facilitate or prevent aerosol transport from distant sources or inhibit particle formation from local sources. Therefore, an increasing number of various aerosol events both local and regional (also extreme events) is an emerging issue, which we must now thoroughly study.

We invite research papers, inter- and transdisciplinary as well as review papers, contributing to the description of the Arctic climate issues related to aerosol studies (including extreme aerosol events), and which refer to the themes of the call.

Dr. Tymon P. Zielinski
Dr. Luca Ferrero
Dr. Paulina Pakszys
Dr. Christoph Ritter
Guest Editors

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Keywords

  • Aerosol pathways/sources into the Arctic
  • Aerosol chemical composition
  • Transformation of aerosol optical properties
  • Arctic haze and biomass burning effects
  • Long-range transport events
  • Aerosol optical properties
  • Radiative balance/radiative forcing
  • Remote sensing
  • Field and theoretical studies
  • AERONET (MAN)
  • Sustainable studies in the Arctic

Published Papers (1 paper)

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Research

9 pages, 1477 KiB  
Article
Deep Neural Networks for Aerosol Optical Depth Retrieval
Atmosphere 2022, 13(1), 101; https://doi.org/10.3390/atmos13010101 - 09 Jan 2022
Cited by 6 | Viewed by 1822
Abstract
Aerosol Optical Depth (AOD) is a measure of the extinction of solar radiation by aerosols in the atmosphere. Understanding the variations of global AOD is necessary for precisely determining the role of aerosols. Arctic warming is partially caused by aerosols transported from vast [...] Read more.
Aerosol Optical Depth (AOD) is a measure of the extinction of solar radiation by aerosols in the atmosphere. Understanding the variations of global AOD is necessary for precisely determining the role of aerosols. Arctic warming is partially caused by aerosols transported from vast distances, including those released during biomass burning events (BBEs). However, measuring AODs is challenging, typically requiring active LIDAR systems or passive sun photometers. Both are limited to cloud-free conditions; sun photometers provide only point measurements, thus requiring more spatial coverage. A more viable method to obtain accurate AOD may be found through machine learning. This study uses DNNs to estimate Svalbard’s AODs using a minimal set of meteorological parameters (temperature, air mass, water vapor, wind speed, latitude, longitude, and time of year). The mean absolute error (MAE) between predicted and true data was 0.00401 for the entire set and 0.0079 for the validation set. It was then shown that the inclusion of BBE data improves predictions by 42.167%. It was demonstrated that AODs may be accurately estimated without the use of expensive instrumentation, using machine learning and minimal data. Similar models may be developed for other regions, allowing immediate improvement of current meteorological models. Full article
(This article belongs to the Special Issue Unusual Aerosol Conditions in the Arctic)
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