Positive response of Indian summer rainfall to Middle East dust
Qinjian Jin
The Department of Geological Sciences, The University of Texas at Austin, Austin, Texas, USA.
Jiangfeng Wei
The Department of Geological Sciences, The University of Texas at Austin, Austin, Texas, USA.
Zong-Liang Yang
The Department of Geological Sciences, The University of Texas at Austin, 1 University Station
C1100, Austin, Texas 78712, USA. (Corresponding author: liang@jsg.utexas.edu)
Supplementary Material
Text S1
The area-averaged seasonal AOD over the AS and IP, and CEI is shown in Table S1. MISR
and MODIS have a very similar annual mean AOD over the AS and IP with value of about 0.29.
The JJA AOD is about 0.44/0.49 over the AP and IP, which is about twice of the values of other
seasons. This is also shown by very high standard deviations of 0.10 and 0.14 for MISR and
Aqua-MODIS, respectively. However, in CEI, Aqua-MODIS estimated AOD is higher than that
of MISR in MAM and JJA, but smaller in SON and DJF. Both MISR and Aqua-MODIS have
the highest AOD in JJA; the JJA AOD is much lower over the CEI than over the AS and IP. The
AOD difference between JJA and other seasons over CEI is smaller than over the AS and the AP,
as indicated by smaller annual standard deviation over CEI. The high standard deviation of AOD
over the AS and IP is attributed mainly to a high AOD in JJA resulting from the outbreak of dust
storms and the transport of dust aerosol from the AP (shown in Fig. 4). On the other hand, the
small AOD standard deviation over CEI may be attributed to anthropogenic emissions, which
usually exhibit smaller seasonal variations than natural dust storms. The higher JJA AOD over
the AS and IP compared to that over CEI implies the potential importance of aerosols over the
AS and IP on the ISM system.
Text S2
The relationship between the high JJA AOD over the AS and IP and ISM rainfall is studied
by correlation analysis. Figure S2 shows the correlation coefficients between JJA of years 2000
to 2013 ISM rainfall averaged over CEI and AOD at each grid. We used anomalies (relative to
the climatology of each month) of rainfall from TRMM and NOAA’s PREP/L and AOD
retrieved from MISR and Terra-MODIS level three datasets to perform the correlation analysis.
Figure S2a and S2b show the correlation coefficients between anomalies of TRMM and
NOAA’s PREP/L rainfall averaged over CEI and MISR AOD at each grid cell. Both TRMM and
NOAA’s PREP/L datasets show a significant correlation of 0.5 between rainfall averaged over
CEI and AOD over the southern AP, the AS, and the IP. Figures S2c and S2d are the same as
Figure S2a and S2b, but use Terra-MODIS AOD, showing a similar but higher positive
correlation coefficients between AOD and rainfall than using the MISR dataset over the AS. In
all four panels, CEI rainfall and AOD over the AS and IP are significantly correlated with a
correlation coefficient of about 0.5 at 95% confidence intervals. No significant lead-lag
correlations were found using monthly data, indicating that the JJA CEI rainfall has the strongest
correlation with AOD within a one-month timescales.
Text S3
Here we provide detailed information on the data sets used in this study and how users can
access them (in Table S2).
Table S1. Area-averaged AOD and its annual mean and annual standard deviation (σ) over the
AS and IP, and CEI from 2003 to 2013.
Region
MAM
JJA
SON
DJJ
Annual mean
Annual σ
0.29
0.44
0.23
0.18
0.29
0.10
Aqua-MODIS
0.30
0.49
0.19
0.17
0.28
0.14
MISR
0.33
0.33
0.23
0.33
0.29
0.05
Aqua-MODIS
0.37
0.46
0.21
0.29
0.33
0.09
Satellite
AS&IP (R2) MISR
CEI (R3)
Table S2. Description of datasets and how to access them.
Variable
Description
Data access
AOD
MISR: Monthly level-3
ftp://l5eil01.larc.nasa.gov/MISR/
MIL3MAEN.004
Terra- and Aqua-MODIS: Monthly or daily
http://ladsweb.nascom.nasa.gov/
level-3 and collection 5.1
data/search.html
MACC: Renalysis of AOD of various
http://apps.ecmwf.int/datasets/da
aerosol types on surface level
ta/macc_reanalysis/
Wind/
MERRA: Monthly IAU1 2d atmospheric
http://disc.sci.gsfc.nasa.gov/daac
geopotetial
single-level diagnostics for winds;
-bin/FTPSubset.pl
height
Monthly DAS2 3d analyzed state on
pressure for geopotential height.
ECMWF: Monthly means of daily means of
http://apps.ecmwf.int/datasets/da
2 m winds on surface level;
ta/interim_full_moda/
Monthly means of daily means of
http://apps.ecmwf.int/datasets/da
geopotential on pressure levels.
ta/interim_full_moda/?levtype=p
l
Rainfall
TRMM: Gridded 3B43 Monthly and 3B42
http://mirador.gsfc.nasa.gov/cgi-
daily version 007
bin/mirador/presentNavigation.pl
?project=TRMM&tree=project
SST
NOAA PREC/L: Monthly
http://www.esrl.noaa.gov/psd/dat
precip.mon.mean.1x1.nc
a/gridded/data.precl.html
NOAA Niño 3.4 monthly SST
http://www.esrl.noaa.gov/psd/gc
os_wgsp/Timeseries/Data/nino34
.long.data
1: IAU stands for Incremental Analysis Updates; 2: DAS stands for Data Assimilation System.