Atmospheric Measurement Techniques (Aug 2020)

A global analysis of climate-relevant aerosol properties retrieved from the network of Global Atmosphere Watch (GAW) near-surface observatories

  • P. Laj,
  • P. Laj,
  • P. Laj,
  • A. Bigi,
  • C. Rose,
  • E. Andrews,
  • E. Andrews,
  • C. Lund Myhre,
  • M. Collaud Coen,
  • Y. Lin,
  • A. Wiedensohler,
  • M. Schulz,
  • J. A. Ogren,
  • M. Fiebig,
  • J. Gliß,
  • A. Mortier,
  • M. Pandolfi,
  • T. Petäja,
  • S.-W. Kim,
  • W. Aas,
  • J.-P. Putaud,
  • O. Mayol-Bracero,
  • M. Keywood,
  • L. Labrador,
  • P. Aalto,
  • E. Ahlberg,
  • L. Alados Arboledas,
  • L. Alados Arboledas,
  • A. Alastuey,
  • M. Andrade,
  • B. Artíñano,
  • S. Ausmeel,
  • T. Arsov,
  • E. Asmi,
  • J. Backman,
  • U. Baltensperger,
  • S. Bastian,
  • O. Bath,
  • J. P. Beukes,
  • B. T. Brem,
  • N. Bukowiecki,
  • S. Conil,
  • C. Couret,
  • D. Day,
  • W. Dayantolis,
  • A. Degorska,
  • K. Eleftheriadis,
  • P. Fetfatzis,
  • O. Favez,
  • H. Flentje,
  • M. I. Gini,
  • A. Gregorič,
  • M. Gysel-Beer,
  • A. G. Hallar,
  • J. Hand,
  • A. Hoffer,
  • C. Hueglin,
  • R. K. Hooda,
  • R. K. Hooda,
  • A. Hyvärinen,
  • I. Kalapov,
  • N. Kalivitis,
  • A. Kasper-Giebl,
  • J. E. Kim,
  • G. Kouvarakis,
  • I. Kranjc,
  • R. Krejci,
  • M. Kulmala,
  • C. Labuschagne,
  • H.-J. Lee,
  • H.-J. Lee,
  • H. Lihavainen,
  • N.-H. Lin,
  • G. Löschau,
  • K. Luoma,
  • A. Marinoni,
  • S. Martins Dos Santos,
  • F. Meinhardt,
  • M. Merkel,
  • J.-M. Metzger,
  • N. Mihalopoulos,
  • N. Mihalopoulos,
  • N. A. Nguyen,
  • N. A. Nguyen,
  • J. Ondracek,
  • N. Pérez,
  • M. R. Perrone,
  • J.-E. Petit,
  • D. Picard,
  • J.-M. Pichon,
  • V. Pont,
  • N. Prats,
  • A. Prenni,
  • F. Reisen,
  • S. Romano,
  • K. Sellegri,
  • S. Sharma,
  • G. Schauer,
  • P. Sheridan,
  • J. P. Sherman,
  • M. Schütze,
  • A. Schwerin,
  • R. Sohmer,
  • M. Sorribas,
  • M. Steinbacher,
  • J. Sun,
  • G. Titos,
  • G. Titos,
  • G. Titos,
  • B. Toczko,
  • T. Tuch,
  • P. Tulet,
  • P. Tunved,
  • V. Vakkari,
  • F. Velarde,
  • P. Velasquez,
  • P. Villani,
  • S. Vratolis,
  • S.-H. Wang,
  • K. Weinhold,
  • R. Weller,
  • M. Yela,
  • J. Yus-Diez,
  • V. Zdimal,
  • P. Zieger,
  • N. Zikova

DOI
https://doi.org/10.5194/amt-13-4353-2020
Journal volume & issue
Vol. 13
pp. 4353 – 4392

Abstract

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Aerosol particles are essential constituents of the Earth's atmosphere, impacting the earth radiation balance directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei. In contrast to most greenhouse gases, aerosol particles have short atmospheric residence times, resulting in a highly heterogeneous distribution in space and time. There is a clear need to document this variability at regional scale through observations involving, in particular, the in situ near-surface segment of the atmospheric observation system. This paper will provide the widest effort so far to document variability of climate-relevant in situ aerosol properties (namely wavelength dependent particle light scattering and absorption coefficients, particle number concentration and particle number size distribution) from all sites connected to the Global Atmosphere Watch network. High-quality data from almost 90 stations worldwide have been collected and controlled for quality and are reported for a reference year in 2017, providing a very extended and robust view of the variability of these variables worldwide. The range of variability observed worldwide for light scattering and absorption coefficients, single-scattering albedo, and particle number concentration are presented together with preliminary information on their long-term trends and comparison with model simulation for the different stations. The scope of the present paper is also to provide the necessary suite of information, including data provision procedures, quality control and analysis, data policy, and usage of the ground-based aerosol measurement network. It delivers to users of the World Data Centre on Aerosol, the required confidence in data products in the form of a fully characterized value chain, including uncertainty estimation and requirements for contributing to the global climate monitoring system.