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.2017 Nov 22;7(1):16062.
doi: 10.1038/s41598-017-16338-w.

Declining pre-monsoon dust loading over South Asia: Signature of a changing regional climate

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Declining pre-monsoon dust loading over South Asia: Signature of a changing regional climate

Satyendra K Pandey et al. Sci Rep..

Abstract

Desert dust over the Indian region during pre-monsoon season is known to strengthen monsoon circulation, by modulating rainfall through the elevated heat pump (EHP) mechanism. In this context, an insight into long term trends of dust loading over this region is of significant importance in understanding monsoon variability. In this study, using long term (2000 to 2015) aerosol measurements from multiple satellites, ground stations and model based reanalysis, we show that dust loading in the atmosphere has decreased by 10 to 20% during the pre-monsoon season with respect to start of this century. Our analysis reveals that this decrease is a result of increasing pre-monsoon rainfall that in turn increases (decreases) wet scavenging (dust emissions) and slowing circulation pattern over the Northwestern part of the sub-continent.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
(a) The trend (year−1) of seasonal mean aerosol optical depth over the AERONET sites in the IGP for pre-monsoon season. The bold text indicates significance at 90% confidence level. (b) to (e) shows the spatial pattern of trends (year−1) in aerosol optical depth observed using different sensor/satellite platforms (b) MODIS Terra & (c) MODIS Aqua combined Deep Blue Dark Target, (d) MISR and (e) OMI-UV Aerosol Index. The black dots (circle) represent statistical significance at 90% (95%) confidence level. The map was generated using MATLAB 2015b,www.mathworks.com.
Figure 2
Figure 2
Inter annual trend (year−1) in (a) AOD from satellite and ground based data (2001 to 2015) for Stations over IGP, the dots represent statistical significance at the 90% confidence level (b) mean AOD (normalized with initial value) obtained from all the satellite data used in the present study over five sites, error bars represents the 95th percentile of inter-site and inter-sensor differences and (c) angstrom exponent (α440–870), bold font indicates statistical significance at the 90% confidence level. The map was generated using MATLAB 2015b,www.mathworks.com.
Figure 3
Figure 3
The changes in dust observed over the Northern part of the sub-continent (a) the percentage change in dust loading during pre-monsoon season (between the period 2011–2014 and 2006–2010) using an independent analysis to identify dust loading using AERONET retrieved aerosol size information and absorption. (b) MERRA2 reanalysis for the period 2002 to 2015 (year−1). The black dots (circle) represent statistical significance at 90% (95%) level. The map was generated using MATLAB 2015b,www.mathworks.com.
Figure 4
Figure 4
Spatial pattern of rainfall trends (year-1) from different datasets (a) TRMM (b) GPCP (c) UDel and (d) IMD. The black dots (large dots) indicate 90% (95%) confidence level. The map was generated using MATLAB 2015b,www.mathworks.com.
Figure 5
Figure 5
The spatial pattern of trends (year−1) in (a) 10 m wind speed (ms−1) (ECMWF-ERA-Interim) (b) Dust wet deposition (kg m−2s−1) (MERRA-2). The black dots (circle) represent statistical significance at 90% (95%) confidence level. (c) Trend in Extinction Coefficient (km−1) observed from the CALIPSO data over Jaipur (close to the dust source regions) during the period 2006 to 2015. The plots were generated using MATLAB 2015b,www.mathworks.com
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