Climate Dynamics

, Volume 26, Issue 7–8, pp 855–864 | Cite as

Asian summer monsoon anomalies induced by aerosol direct forcing: the role of the Tibetan Plateau

  • K. M. LauEmail author
  • M. K. Kim
  • K. M. Kim


In this paper we present results of a numerical study using the NASA finite-volume GCM to elucidate a plausible mechanism for aerosol impact on the Asian summer monsoon involving interaction with physical processes over the Tibetan Plateau (TP). During the pre-monsoon season of March–April, dusts from the deserts of western China, Afghanistan/Pakistan, and the Middle East are transported into and stacked up against the northern and southern slopes of the TP. The absorption of solar radiation by dust heats up the elevated surface air over the slopes. On the southern slopes, the atmospheric heating is reinforced by black carbon from local emission. The heated air rises via dry convection, creating a positive temperature anomaly in the mid-to-upper troposphere over the TP relative to the region to the south. In May through early June in a manner akin to an “elevated heat pump”, the rising hot air forced by the increasing heating in the upper troposphere, draws in warm and moist air over the Indian subcontinent, setting the stage for the onset of the South Asia summer monsoon. Our results suggest that increased dust loading coupled with black carbon emission from local sources in northern India during late spring may lead to an advance of the rainy periods and subsequently an intensification of the Indian summer monsoon. The enhanced rainfall over India is associated with the development of an aerosol-induced large-scale sea level pressure anomaly pattern, which causes the East Asia (Mei-yu) rain belt to shift northwestward, suppressing rainfall over East Asia and the adjacent oceanic regions.


Tibetan Plateau Black Carbon Asian Summer Monsoon Rainfall Anomaly Dust Aerosol 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported jointly by the Modelling, Analysis and Prediction (MAP) Program, and the Precipitation Measuring Mission (PMM) of the NASA Earth-Sun Exploration Division. M. K. Kim is supported by Climate Environment System Research Center (CES) sponsored by the Korea Science and Engineering Foundation. Part of the work was carried out by M. K. Kim during his visit to the Laboratory for Atmospheres, Goddard Space Flight Center under a Goddard Earth System Technology (GEST) visiting fellowship. The authors would like to acknowledge and thank Prof. G. Wu and an anonymous reviewer for their constructive comments and suggested revisions for this paper.


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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Laboratory for AtmospheresNASA Goddard Space Flight CenterGreenbeltUSA
  2. 2.Department of Atmospheric ScienceKongju National UniversityGongjuKorea
  3. 3.Science Systems and Applications, IncLanhamUSA

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