論文

査読有り
2014年4月

Effect of dead leaves on early spring dust emission in East Asia

ATMOSPHERIC ENVIRONMENT
  • Jung-Yoon Kang
  • ,
  • Taichu Y. Tanaka
  • ,
  • Masao Mikami

86
開始ページ
35
終了ページ
46
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.atmosenv.2013.12.007
出版者・発行元
PERGAMON-ELSEVIER SCIENCE LTD

Dead leaves remaining after the growing season can suppress dust emission by wind erosion, but this has not been considered in numerical models of dust emission. We investigated the effect of dead leaves by analyzing surface meteorological data and numerical simulations for early spring Asian dust events, and propose a parameterization of the dead-leaf effect. World Meteorological Organization (WMO) synoptic data indicate that high vegetation cover from the previous year can increase the threshold friction velocity for early spring of the following year. The reduction rate of dead leaves is calculated using 10-year averaged monthly vegetation cover data derived from the Normalized Difference Vegetation Index (NDVI), assuming that the amount of dead leaves decreases linearly, and the amount of dead leaves is included in calculating threshold friction velocity. To investigate the effect of dead leaves in simulations of Asian dust events, case studies were carried out with and without the dead-leaf effect for early spring Asian dust events using a Weather Research and Forecasting with Chemistry (WRF-Chem) model with a new dust emission scheme. Including the dead-leaf effect improved the model results, and the modeled surface concentrations are similar to the observed PM10 concentrations. (C) 2013 Elsevier Ltd. All rights reserved.

リンク情報
DOI
https://doi.org/10.1016/j.atmosenv.2013.12.007
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000332143100005&DestApp=WOS_CPL
URL
http://orcid.org/0000-0003-0600-2122
ID情報
  • DOI : 10.1016/j.atmosenv.2013.12.007
  • ISSN : 1352-2310
  • eISSN : 1873-2844
  • ORCIDのPut Code : 24904470
  • Web of Science ID : WOS:000332143100005

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