Preprints
https://doi.org/10.5194/acp-2020-574
https://doi.org/10.5194/acp-2020-574
08 Jul 2020
 | 08 Jul 2020
Status: this preprint has been withdrawn by the authors.

Black Carbon Seasonal and Diurnal Variation in surface snow in Svalbard and its Connections to Atmospheric Variables

Michele Bertò, David Cappelletti, Elena Barbaro, Cristiano Varin, Jean-Charles Gallet, Krzysztof Markowicz, Anna Rozwadowska, Mauro Mazzola, Stefano Crocchianti, Luisa Poto, Paolo Laj, Carlo Barbante, and Andrea Spolaor

Abstract. Black Carbon (BC) is a major forcing agent in the Arctic but substantial uncertainty remains to quantify its climate effects due to the complexity of mechanisms involved. In this study, we provide unique information on processes driving the variability of BC mass concentration in surface snow in the Arctic. Two different snow-sampling strategies were adopted during spring 2014 and 2015, focusing on the refractory BC (rBC) mass Ny-Ålesund concentration daily/hourly variability on a seasonal/daily time scale (referred to as 80-days and 3-days experiments). Despite the low rBC mass concentrations (never exceeding 22 ng g−1), a daily variability of up to 4.5 ng g−1 was observed. Atmospheric, meteorological and snow-related physico-chemical parameters were considered in multiple statistical models to understand the factors behind the observed variation of rBC mass concentrations. Results indicate that the main drivers of the variation of rBC are the precipitations events, snow metamorphism (melting-refreezing cycles, surface hoar formation and sublimation) and the activation of local sources (wind resuspension) during the snow melting periods. The rBC in the snow seems de-coupled with the atmospheric BC load. Our results highlighted a common association of snow rBC with coarse mode particles number concentration and with snow precipitation events.

This preprint has been withdrawn.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Michele Bertò, David Cappelletti, Elena Barbaro, Cristiano Varin, Jean-Charles Gallet, Krzysztof Markowicz, Anna Rozwadowska, Mauro Mazzola, Stefano Crocchianti, Luisa Poto, Paolo Laj, Carlo Barbante, and Andrea Spolaor

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Michele Bertò, David Cappelletti, Elena Barbaro, Cristiano Varin, Jean-Charles Gallet, Krzysztof Markowicz, Anna Rozwadowska, Mauro Mazzola, Stefano Crocchianti, Luisa Poto, Paolo Laj, Carlo Barbante, and Andrea Spolaor
Michele Bertò, David Cappelletti, Elena Barbaro, Cristiano Varin, Jean-Charles Gallet, Krzysztof Markowicz, Anna Rozwadowska, Mauro Mazzola, Stefano Crocchianti, Luisa Poto, Paolo Laj, Carlo Barbante, and Andrea Spolaor

Viewed

Total article views: 996 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
763 190 43 996 121 43 50
  • HTML: 763
  • PDF: 190
  • XML: 43
  • Total: 996
  • Supplement: 121
  • BibTeX: 43
  • EndNote: 50
Views and downloads (calculated since 08 Jul 2020)
Cumulative views and downloads (calculated since 08 Jul 2020)

Viewed (geographical distribution)

Total article views: 1,052 (including HTML, PDF, and XML) Thereof 1,052 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 May 2024
Download

This preprint has been withdrawn.

Short summary
We present the daily and seasonal variability of Black carbon inferred from two specific experiment based on the hourly and daily time resolution sampling strategy. These unique datasets give us for the first time the opportunity to evaluate the associations between the observed surface snow rBC mass concentration and a set of predictors corresponding to the considered meteorological and snow physico-chemical parameters, via a multiple linear regression approach.
Altmetrics