Synoptic weather conditions and aerosol episodes over Indo-Gangetic Plains, India

Synoptic weather conditions and aerosol episodesover Indo-Gangetic Plains, India

D. G. Kaskaoutis • E. E. Houssos • D. Goto • A. Bartzokas • P. T. Nastos •

P. R. Sinha • S. K. Kharol • P. G. Kosmopoulos • R. P. Singh • T. Takemura

Received: 29 May 2013 / Accepted: 13 January 2014

� Springer-Verlag Berlin Heidelberg 2014

Abstract The present study focuses on identifying the

main atmospheric circulation characteristics associated

with aerosol episodes (AEs) over Kanpur, India during the

period 2001–2010. In this respect, mean sea level pressure

(MSLP) and geopotential height of 700 hPa (Z700) data

obtained from the NCEP/NCAR Reanalysis Project were

used along with daily Terra-MODIS AOD550 data. The

analysis identifies 277 AEs [AOD500 [ AOD500 ? 1ST-

DEV (standard deviation)] over Kanpur corresponding to

13.2 % of the available AERONET dataset, which are

seasonally distributed as 12.5, 9.1, 14.7 and 18.6 % for

winter (Dec–Feb), pre-monsoon (Mar–May), monsoon

(Jun–Sep) and post-monsoon (Oct–Nov), respectively. The

post-monsoon and winter AEs are mostly related to

anthropogenic emissions, in contrast to pre-monsoon and

monsoon episodes when a significant component of dust is

found. The multivariate statistical methods Factor and

Cluster Analysis are applied on the dataset of the AEs

days’ Z700 patterns over south Asia, to group them into

discrete clusters. Six clusters are identified and for each of

them the composite means for MSLP and Z700 as well as

their anomalies from the mean 1981–2010 climatology are

studied. Furthermore, the spatial distribution of Terra-

MODIS AOD550 over Indian sub-continent is examined to

identify aerosol hot-spot areas for each cluster, while the

SPRINTARS model simulations reveal incapability in

reproducing the large anthropogenic AOD, suggesting need

of further improvement in model emission inventories.

This work is the first performed over India aiming to

analyze and group the atmospheric circulation patterns

D. G. Kaskaoutis (&)

Department of Physics, School of Natural Sciences,

Shiv Nadar University, Dadri Tehsil 203207, India

e-mail: [email protected];

[email protected]

E. E. Houssos � A. Bartzokas

Laboratory of Meteorology, Department of Physics,

University of Ioannina, 45110 Ioannina, Greece

D. Goto

National Institute for Environmental Studies (NIES),

Tsukuba, Ibaraki 305-8506, Japan

P. T. Nastos

Laboratory of Climatology and Atmospheric Environment,

Faculty of Geology and Geoenvironment, University of Athens,

15784 Zografou, Greece

P. R. Sinha

National Balloon Facility, Tata Institute of Fundamental

Research, ECIL Post 5, Hyderabad 500 062, India

S. K. Kharol

Department of Physics and Atmospheric Science,

Dalhousie University, Halifax, Canada

P. G. Kosmopoulos

Institute for Environmental Research and Sustainable

Development, National Observatory of Athens, Athens, Greece

R. P. Singh

School of Earth and Environmental Sciences, Schmid College

of Science and Technology, Chapman University, Orange,

CA 92866, USA

T. Takemura

Research Institute for Applied Mechanics, Kyusyu University,

Fukuoka, Japan

123

Clim Dyn

DOI 10.1007/s00382-014-2055-2

associated with AEs over Indo-Gangetic Plains and to

explore the influence of meteorology on the accumulation

of aerosols.

Keywords Aerosol episodes � Factor–cluster analysis �Weather clusters � Kanpur � India

1 Introduction

Several studies during the last decade have highlighted the

role of aerosols over northern India on radiative forcing,

regional climate, hydrological cycle and monsoon (Lau

et al. 2006; Dey and Tripathi 2007; Gautam et al. 2009a, b,

2010; Srivastava et al. 2011, 2012a). The aerosol climatic

effects over south Asia are still highly uncertain due to

variety of aerosol types, mixing processes in the atmo-

sphere and large spatio-temporal variation (Lawrence and

Lelieveld 2010; Srivastava et al. 2012b). A better under-

standing of the aerosol characteristics and their linkage to

meteorological conditions and atmospheric dynamics needs

further analysis from ground-based instruments, satellite

observations and model simulations as well as examination

of the role of meteorology and weather patterns (Lau et al.

2010; Krishnan et al. 2012).

The northern part of India, Indo-Gangetic Plains

(IGP), is one of the most aerosol laden, densely-popu-

lated and agriculturally productive areas (Kishcha et al.

2011), with a strong intra-annual and intra-seasonal aer-

osol variability (Singh et al. 2004; Dey and di Girolamo,

2010; Henriksson et al. 2011; Kaskaoutis et al. 2011) due

to changes in anthropogenic and natural emissions,

atmospheric and meteorological dynamics (Verma et al.

2012). Formation of fog and haze conditions over the

whole IGP region is common during winter (Gautam

et al. 2007), while in pre-monsoon and early monsoon

seasons IGP is under the influence of frequent dust

storms originated from Thar Desert or transported from

the Arabian Peninsula and Middle East (El-Askary et al.

2006; Prasad and Singh 2007a). During post-monsoon

season, crop residue burning takes place over north-

western IGP and smoke plumes cover the whole region

(Sharma et al. 2010). Natural coarse aerosols are effi-

ciently mixed with fine-mode anthropogenic ones (Dey

and Tripathi 2008; Srivastava and Ramachandran 2012)

forming a thick aerosol layer often called as ‘‘brownish

clouds’’ (Ramanathan et al. 2005) with serious implica-

tions on atmosphere, biosphere, climate and hydrological

cycle.

The significant interrelation of aerosols with the

monsoon system (Bollasina and Nigam 2009; Bhawar

and Devara 2010), drought conditions (Vijayakumar

et al. 2012), Quassi-Biennial Oscillation (QBO) in the

tropopause (Abish and Mohanakumar 2011), El-Nino

Southern Oscillation (Abish and Mohankumar 2013),

local anthropogenic emissions (Reddy and Venkatar-

aman 2002) and long-range transport over Indian sub-

continent (Guleria et al. 2011) motivated us to examine

the synoptic weather conditions that are associated with

aerosol episodes (AEs) over central IGP. Gkikas et al.

(2009) examined the frequency and intensity of AEs

along with their seasonal variability over Mediterranean

Basin by using daily Terra-MODIS observations during

the period 2000–2007. Recently, Gkikas et al. (2012)

classified the synoptic weather conditions that favor the

AEs over the Mediterranean identifying 8 characteristic

weather clusters. Local, regional and synoptic meteo-

rology are important factors for the atmospheric aerosol

lifetime, abundance and properties (Carmona and Alpert

2009; Kaskaoutis et al. 2012a), since they control the

aerosol emissions, convection, transport, dry and wet

deposition (Nastos 2012). Reversely, anthropogenic

aerosols over India have been recognized to have a

significant climatic response to the monsoon system

(Rotstayn and Lohmann 2002; Wang et al. 2009; Bo-

llasina et al. 2011; Ganguly et al. 2012). It is, therefore,

of significant importance to study the synoptic weather

systems that are associated with aerosol extremes over

northern India.

The present study focuses on grouping and analyzing the

synoptic circulation patterns that dominate over south Asia

during AEs over Kanpur (26.5�N, 80.2�E), central IGP, for

the period 2001–2010. The AEs correspond to daily values

of AOD500 [ AOD500 ? 1STDEV (standard deviation), as

obtained from Kanpur-AERONET site. The synoptic pat-

terns on the AE days are grouped objectively in discrete,

characteristic clusters, revealing, in this way, the main

modes of atmospheric circulation favoring accumulation of

aerosols over the region, either locally produced or long-

range transported. The clusters are defined by using the

multivariate statistical methods of Factor (S-mode) and

Cluster Analysis (K-means) on the meteorological maps at

700-hPa atmospheric pressure level over south Asia. This

is the first study conducted over India aiming at associating

severe aerosol episodes with synoptic meteorology clus-

ters. For each cluster the AOD distribution is analyzed via

MODIS observations and aerosol hot-spot areas are

revealed. Furthermore, Spectral Radiation-Transport

Model for Aerosol Species (SPRINTARS) model is applied

to examine the degree of satisfactory simulation of the AEs

over central IGP. The results will help in better under-

standing the strong variations in aerosol properties over

IGP associated with meteorological anomalies and

dynamics of weather.

D. G. Kaskaoutis et al.

123

2 Data set

2.1 AERONET data

The initial dataset used is the AOD500 values obtained from

Kanpur AERONET station (Level 2, cloud screened and

quality assured) during the period January 2001–December

2010 for the identification of the AEs, as the days with

daily-mean AOD500 above a critical threshold (AOD ?

1STD), i.e. AOD500 [ 0.928. Except of the AOD500, the

Angstrom exponent (a) and single scattering albedo (SSA)

values from direct and almucantar retrievals via Cimel

(CE-318) sun/sky radiometer are also used following the

uncertainties described elsewhere (Dubovik et al. 2000;

Smirnov et al. 2000). Details of the Kanpur AERONET

station, the atmospheric conditions over the region and

long-term aerosol variability and properties are discussed

by many (Singh et al. 2004; Eck et al. 2010, 2012; Giles

et al. 2011; Kaskaoutis et al. 2012b). The study period

includes 2,095 daily AOD500 values, with a mean of

0.627 ± 0.302, from which 277 have been identified as AE

days.

2.2 NCEP/NCAR re-analysis data

For the identification of the main atmospheric circulation

modes associated with the AEs over IGP, daily values (at

12:00 UTC, ?5:30 IST) of mean sea level pressure

(MSLP) and geopotential heights at 700 hPa (Z700)

obtained from the National Center for Environmental

Prediction/National Center for Atmospheric Research

(NCEP/NCAR) Reanalysis project (Kalnay et al. 1996) are

used. The data set corresponds to all AE days during the

period 2001–2010, at 2.5� 9 2.5� spatial resolution, cov-

ering a broad region of south-central Asia (45�–100�E and

5�–50�N).

The MSLP patterns are used to examine the influence of

near surface pressure systems to the accumulation of

aerosols over Kanpur and/or their transport from sources

near the city. The 700 hPa geopotential height has been

selected to investigate long-range dust transport in the

atmosphere, which although it may take place up to 5 km,

is more pronounced at about 3,000 m (Gautam et al. 2010;

Misra et al. 2012), the average height of this pressure level.

2.3 MODIS data

Terra-MODIS AOD data are available from March 2000

(under cloud free conditions) with good accuracy over dark

surfaces (Levy et al. 2010) by means of two separate

algorithms over land and ocean (Kaufman and Tanre

1998). The aerosol properties are derived by the inversion

of the MODIS-observed reflectance using pre-computed

radiative transfer look-up tables based on aerosol models,

on which cloud-screening procedure was initially applied.

Several validation studies over AERONET land locations

(including Kanpur) reveal that about 72 % of the retrievals

fall within expected uncertainty levels of ±0.05 ±

0.15*AOD (Remer et al. 2008; Levy et al. 2010; Shi et al.

2011), which is an improvement of the previous version

(collection 4). In the present study, the collection 5 (C005)

Level 3 (1� 9 1�) Terra-MODIS AOD550 is used on the AE

days over Indian sub-continent and adjoining oceanic

regions (3–32�N, 57–94�E) following the dark-target

approach with lack of data over the deserts (Levy et al.

2007) in order to reveal the aerosol hot-spot areas.

2.4 SPRINTARS model

The global three-dimensional aerosol model Radiation-

Transport Model for Aerosol Species (SPRINTARS)

Spectral has been used to simulate aerosols over Kanpur on

the AE days. SPRINTARS (Takemura et al. 2000, 2005,

2009) is implemented in both an atmospheric general cir-

culation model (AGCM) developed by the University of

Tokyo, National Institute for Environmental Studies and

the Japan Agency for Marine-Earth and Technology (K-1

Developers 2004; Watanabe et al. 2010) and, a radiation

transfer with a k-distribution scheme, MSTRN-8, (Nakaj-

ima et al. 2000; Sekiguchi and Nakajima 2008). The model

simulates AOD for various aerosol components (Black

Carbon (BC), Organic Carbon (OC), sulfate, soil dust and

sea salt) with prescribed optical properties, while the

simulations were conducted with nudged meteorological

fields from NCEP/NCAR reanalysis (wind field, water

vapor and temperature) with the T106 horizontal resolution

(1.1� 9 1.1�). Emission processes for dust with 6 bins

ranging from 0.1 to 10 lm are online-calculated in the

model according to an empirical relation by Gillette

(1978), depending on near-surface wind speed above a

threshold of 6.5 ms-1, vegetation, leaf area index, soil

moisture and snow amount (Takemura et al. 2009; Goto

et al. 2011a). For sea salt, emission process is a function of

wind speed using Monahan’s parameterization (Monahan

et al. 1986) and is online-calculated with 4 bins ranging

from 0.1 to 10 lm (Takemura et al. 2009). For the

anthropogenic components (BC, OC and sulfates) emission

processes and inventories are described by Diehl et al.

(2012) and are widely used in the AeroCom project (http://

aerocom.met.no/). Recently, SPRINTARS was used to

simulate aerosol emission and properties over India (Goto

et al. 2011b, c; Kaskaoutis et al. 2012c) and, in the present

work, model simulations of various aerosol parameters

(AOD, a, SSA, mass column loading, mass concentration)

have been performed over Kanpur aiming to investigate the

capability of the model in reproducing the AEs. The

Synoptic weather conditions

123

SPRINTARS AODs have been compared with various

satellites including MODIS under AeroCom project (Kinne

et al. 2006), while the dust loading, which dominates in the

current simulations, was also compared with various

measurements under the same project (Huneeus et al.

2011). Both studies revealed that SPRINTARS is well

within the uncertainty of the other global aerosol models

and have generally agreement with observations.

3 Factor and cluster analysis

At first, a data matrix of daily atmospheric circulation at

Z700 level is constructed. It consists of 437 columns cor-

responding to the 437 grid points, covering south-central

Asia and, 277 rows corresponding to the 277 AE days over

IGP during the period 2001–2010. In order to reduce the

dimensionality of the data matrix, Factor Analysis (Jolliffe

1986; Manly 1986) is carried out to group objectively time-

series highly correlated to each other. The original time-

series are substituted by new, fewer, characteristic and

uncorrelated ones, the Factors, consisting of the so-called

factor scores. It is found that the 437 initial grid point time-

series can be reduced to eight (8) Factors only, accounting

for 88 % of the total variance. Thus, a new matrix

(277 9 8) is constructed, in which the columns correspond

to the 8 Factors’ time-series, and the rows correspond to the

277 daily atmospheric circulations described by the 8

factor scores values only, instead of the 437 Z700 values.

In order to classify objectively the 277 atmospheric

circulations into homogeneous clusters K-Means Cluster

Analysis (Sharma 1996) is applied on the 277 9 8 factor

scores matrix. To decide the optimum number of clusters,

so that they are as much as possible homogeneous and

discrete, the ‘‘Jump’’ criterion (Sugar and James 2003) is

used. Thus, the 277 atmospheric circulations are classified

into 6 clusters. Detailed information about the Factor and

Cluster analysis techniques can be found elsewhere

(Houssos and Bartzokas 2006; Houssos et al. 2011). For

each cluster, the mean patterns of MSLP and Z700 are

drawn. Furthermore, MSLP and Z700 anomalies from the

30-year average (1981–2010, NCEP/NCAR) are calculated

and presented, in order to reveal the most significant fea-

tures of the synoptic patterns associated with high aerosol

loads over Kanpur.

It has to be mentioned that both multivariate statistical

methods are applied only to the Z700 data matrix and not

to the MSLP one in order to avoid problems resulting from

the high altitude of the Tibetan Plateau ([4,000 m). Spe-

cifically, the MSLP field over Tibet could be characterized

as inaccurate and this would lead to erroneous groupings in

the application of Factor Analysis. On the other hand, the

700 hPa level is relatively close to most places of the

Tibetan Plateau and the atmospheric circulation presented

is far more realistic. Thus, on the MSLP pattern of each

cluster we will focus on atmospheric circulation outside

Tibet.

4 Results and discussions

4.1 Temporal distribution of aerosol episodes

The frequency of occurrence of AE days over Kanpur

during the period 2001–2010 is shown in Fig. 1. A large

gap in AOD data series during pre-monsoon and monsoon

seasons of 2004 as well as during winter and monsoon of

2006 and monsoon to winter of 2007 occurs due to tech-

nical issues and calibration of the instrument; as a result,

low frequency of AE is observed. The 277 AEs (13.21 %

of the total 2,095 daily AOD observations) exhibit higher

frequency during post-monsoon (78 cases, 18.6 %) and

monsoon (76 cases, 14.7 %) seasons, while winter (65

cases, 12.5 %) and pre-monsoon (58 cases, 9.1 %) exhibit

lower absolute and percentage frequencies. The frequency

of occurrence of AEs presents considerable intra-annual

variations as a result of changes in atmospheric and

meteorological conditions, formation of hazy and foggy

conditions during winter, boundary-layer dynamics and

variability in dust outflows. The most important finding is

the frequent AEs during monsoon of 2002 (July) and 2003

(June) and during post-monsoon/winter of 2008 (Novem-

ber/December). The reason for these AEs is the drought

and prolonged dry conditions over the northwestern India

responsible for higher frequency in dust events and longer

aerosol lifetime due to deficit of rainfall (Kaskaoutis et al.

2012c). The second case corresponds to persistent dense

Fig. 1 Frequency of occurrence for AOD peaks for each season and

year at Kanpur AERONET station. There is lack of data during the

period pre-monsoon to monsoon of 2004, monsoon and winter of

2006 and monsoon to winter of 2007. [winter: Dec–Feb; pre-

monsoon: Mar–May; monsoon: Jun–Sep; post-monsoon: Oct–Nov]

D. G. Kaskaoutis et al.

123

smoke plumes originated from extensive agriculture crop-

residue burning in the northwestern IGP (Punjab state)

causing significant heating in the middle (700 hPa level)

troposphere over the region (Badarinath et al. 2009). In

general, the occurrence of extreme AODs over IGP during

pre-monsoon and monsoon is higher in the first half of the

study period, while during post-monsoon and winter in the

second half. The former influences the neutral-to-declining

AOD decadal trend observed over Kanpur (Kaskaoutis

et al. 2012b), Delhi (Lodhi et al. 2013) and northern India

(Dey and di Girolamo 2011; Kaskaoutis et al. 2011) in late

pre-monsoon and monsoon seasons, while the latter is

consistent with the significant increase in anthropogenic

AOD over IGP (Dey and di Girolamo 2011; Kaskaoutis

et al. 2011), in close consideration with the strong increase

in BC, SO2, and OC emissions (Lu et al. 2011).

4.2 Classification of the atmospheric circulation

patterns and the corresponding anomalies

Using the methodology discussed in Sect. 3, the 277

atmospheric circulation patterns have been classified into 6

clusters. Figure 2 shows the monthly variation of the fre-

quency of occurrence for each cluster. The mean composite

maps of MSLP and Z700 for each cluster (Figs. 3, 4, 5, 6,

7, 8) reveal the main characteristics of the atmospheric

circulation that are associated or being favorable for aer-

osol accumulation over IGP. For a further elucidation of

the results, the corresponding anomalies maps at MSLP

and Z700 from the 30-years (1981–2010) mean climato-

logical conditions are also presented. This is a common

approach for such climatological studies followed by many

researchers (Kaskaoutis et al. 2012a, c; Nastos 2012).

Furthermore, Kishcha et al. (2012) showed that the oscil-

lations (anomalies) in the wind field (direction, speed and

convergence) may be linked to aerosol trends over coastal

region of Bay of Bengal (BoB) without variations in aer-

osol emissions.

4.2.1 Cluster 1 (62 days—22 %)

Cluster 1 comprises 62 out of the 277 AE days (22 %).

Most of the days are distributed in the post-monsoon sea-

son but there are also a few in April (Fig. 2). The mean

circulation of Cluster 1 at Z700 (Fig. 3b) is characterized

by an intense ridge extended from Africa to the Arabian

Peninsula, while over Kazakhstan, northwestern China and

western Mongolia the atmospheric circulation is almost

zonal. Over India, the MSLP (Fig. 3a) patterns present

almost constant, relatively high values (above 1,010 hPa),

responsible for the lack of significant air flow and favoring

subsidence and atmospheric stability. The consequent dry

conditions, along with the intensification of the agriculture

burning in Punjab in October–November, contribute to

accumulation of fine-mode aerosols in the lower tropo-

sphere over IGP (Table 1). The corresponding anomalies

maps (Fig. 3c, d) show both negligible differences from the

30-year mean over India but higher than usual values over

Iran and the Caspian Sea. These positive anomalies indi-

cate an extension of the Arabian Peninsula ridge to the

north, increasing pressure gradient and winds there and not

to the east, over India, where calm conditions favor aero-

sols accumulation.

4.2.2 Cluster 2 (55 days—20 %)

This Cluster is more frequent in post-monsoon and winter

seasons, as well as during April and May (Fig. 2). Simi-

larly to Cluster 1, the constant geopotential height field

with relatively high values over India (Fig. 4b) favors the

accumulation of fine-mode aerosols over IGP (Table 1).

The main difference from Cluster 1 is the weakening of the

ridge over the Arabian Peninsula with a parallel extension

towards India and the appearance of another ridge over

western China and Kazakhstan. The latter is reflected in the

positive values of the Z700 anomalies map (Fig. 4d) over

the same area. On the surface (Fig. 4a), atmospheric

pressure is constant over India with higher values than in

Cluster 1. The anomalies (Fig. 4c) show positive values

over the northeast part of India and neutral-to-slight neg-

ative ones over the Arabian Peninsula and the Middle East.

Although the mean composite maps of Clusters 1 and 2

present similarities, especially over IGP, the stronger

anticyclonic circulation, dominating in the northern partsFig. 2 Monthly variation of the frequency of occurrence for each

atmospheric circulation cluster

Synoptic weather conditions

123

of the study region in Cluster 2, differentiates them sig-

nificantly. However these differences are found away from

IGP and do not seem to affect considerably the aerosol load

and aerosol type over Kanpur (Table 1).

4.2.3 Cluster 3 (38 days—14 %)

This atmospheric circulation occurs during winter, mainly

in January, while in some cases it may occur during late

pre-monsoon and post-monsoon seasons (Fig. 2). The main

characteristic that differentiates this Cluster from the pre-

vious two is the intense dipole of high Z700 values over

southern Arabian Peninsula and low ones over Kazakhstan

(Fig. 5b), and the consequent north–south high pressure

gradient, causing an intense westerly air flow over Iran,

Afghanistan and Turkmenistan, which however, does not

affect much IGP. Nevertheless a weak northeast-southwest

gradient over India could be responsible for aerosol outflow

over northern BoB (Kharol et al. 2011). In the Z700 field,

the high negative anomalies over central Asia and the

positive ones extending from Southern Arabia to Tibetan

Plateau via India (Fig. 5d), are coherent with the dipole of

low and high Z700 values. These positive anomalies are

associated with the extension of the ridge towards the

Arabian Sea (AS) and India causing subsidence of air

masses and favoring the accumulation of aerosols over

central IGP. The atmospheric circulation of Clusters 1, 2

and 3, with the calm conditions over IGP, favors the

accumulation of pollutants over Kanpur region.

4.2.4 Cluster 4 (48 days—17 %)

This Cluster is highly frequent during the beginning of the

monsoon season, mainly in June (Fig. 2), with its main

characteristic being the well-organized Indian Low near the

surface, centered over west India and eastern Pakistan and

Fig. 3 For Cluster 1, identified for the AE days over Kanpur during

the period 2001–2010, the composite mean maps of a Mean Sea Level

Pressure (hPa), b 700 hPa Geopotential Heights (gpm), and compos-

ite anomalies of c Mean Sea Level Pressure (hPa) and d 700 hPa

Geopotential Heights (gpm) from the mean climatology during the

period 1981–2010. [Green-to-red and blue-to-violet correspond to

positive and negative anomalies, respectively]

D. G. Kaskaoutis et al.

123

extended up to Iran and the Arabian Peninsula (Figs. 6a, c).

The cyclonic circulation over India, near the surface

(Fig. 6a), induces a strong southwesterly air flow, favoring

the transport of dust aerosols over IGP from the neigh-

boring Thar Desert and even from the southern parts of the

Arabian Peninsula mixed with marine particles. The

cyclonic anomaly over the Caspian Sea (Z700, Fig. 6d)

does not affect the south Asian conditions.

4.2.5 Cluster 5 (51 days—19 %)

The highest frequency in this Cluster is observed during the

late pre-monsoon and early monsoon seasons (Fig. 2). The

atmospheric circulation resembles that of Cluster 4, but the

Indian Low in this Cluster is weaker (Fig. 7a). Further-

more, a secondary low in MSLP centered over eastern

India is evident. Similarly to Cluster 4, the uplift and

transport of dust over IGP either from Thar Desert or, in a

lesser degree, from Arabia is favored. Positive MSLP and

Z700 anomalies influence most of the arid regions in

southwest Asia, the AS and the western Tropical Indian

Ocean, while negative anomalies cover the northern and

eastern parts of India and BoB (Fig. 7c, d). The dipole of

anomalies over Indian Ocean has been associated by

Gadgil et al. (2003) via anomalies in OLR (Outgoing Long-

wave Radiation) to deficit of monsoonal rainfall and

obviously to high AOD values. Furthermore, Ghude et al.

(2011) found westerly wind anomalies of 10 ms-1 at

850 hPa over IGP associated with anticyclonic vorticity,

which prevents convection and leads to the weakening of

the monsoon with a negative deviation in rainfall

(*10 mm/day) over western Ghats, central India and

western IGP in July 2002. Manoj et al. (2011) analyzed the

monsoon intra-seasonal oscillations via anomalies in OLR

and found that long break spells followed by active spells

correspond to atmospheric circulations that favor the

accumulation of aerosols over continental India. Some of

these spells (July 2002, June 2009) are associated with

persistent AE days over Kanpur belonging to Cluster 5.

Furthermore, Kaskaoutis et al. (2012c) examined the

anomalies in rainfall and atmospheric circulation associ-

ated with significant accumulation of aerosols over IGP

Fig. 4 As in Fig. 3, but for Cluster 2

Synoptic weather conditions

123

during May–June 2003 and July 2002, highlighting posi-

tive MSLP and Z700 anomalies implying the weakening of

cyclonic circulation, monsoon air flow and rainfall.

4.2.6 Cluster 6 (23 days—8 %)

This is the least frequent Cluster occurring mainly in Jan-

uary (Fig. 2). The almost constant MSLP field over India

(Fig. 8a) favors the accumulation of aerosols over IGP,

which are mainly generated by local sources and anthro-

pogenic activities (Kar et al. 2010). Although the synoptic

conditions over India resemble the ones of Clusters 1, 2 and

3, there is a difference at Z700; namely, a rather strong

zonal flow over IGP (Fig. 8b) favors the transport of

aerosols over eastern IGP and Bangladesh. The MLSP and

Z700 values are much lower in Cluster 6 than those in

Cluster 3 and, therefore, large negative anomalies in both

MSLP and Z700 are shown over the northern India and

Tibetan Plateau accompanied with positive ones over

central-western Asia (Fig. 8c, d). The negative anomalies

at Z700 over Tibetan Plateau enhance the westerly flow

over IGP, which accumulates aerosols over the easternmost

part of the Ganges Basin. The anomalous trough at Z700

over northwestern India associated with a small ridge over

eastern IGP and Bangladesh also helps in accumulation of

pollutants over this region. This is verified from the highest

AODs over central-eastern IGP (Table 1; Fig. 10) com-

pared to those found for the other late post-monsoon and

winter Clusters (1, 2 and 3).

4.3 Aerosol distribution

Aerosols over south Asia exhibit substantial spatio-tem-

poral variability, mainly controlled by population density

and local anthropogenic emissions, convection and

boundary-layer dynamics, transport pathways, local and

regional meteorology and apparently by duration and

intensity of the monsoon rainfall (Dey and di Girolamo

2010; Aloysius et al. 2011). The mean Terra-MODIS

AOD550 distribution over Indian sub-continent for each

cluster is shown in Fig. 9. Kanpur is denoted by a star and

exhibits AOD values ranging from 1.15 (Cluster 3) to 1.27

Fig. 5 As in Fig. 3, but for Cluster 3

D. G. Kaskaoutis et al.

123

(Cluster 4), which are in satisfactory agreement with those

of AERONET (Table 1).

Clusters 1, 2 and 3 show large similarities in the mean

AODs and spatial distribution due to similar synoptic

conditions (Figs. 3, 4, 5). The highest AODs are observed

in central IGP while in the rest of continental India low

values (below 0.4) are found. The oceans are low aerosol-

laden areas, except of the southeastern AS, coastal and

northern BoB, which are strongly influenced by the conti-

nental outflow (Kaskaoutis et al. 2011). Despite the general

similarities some differences can be detected, which could

be explained by the local emissions. The high AODs in

Cluster 1 are somewhat shifted towards northwestern IGP,

where significant crop residue burning occurs in post-

monsoon season contributing to the accumulation of fine-

mode aerosols, influencing the high a values for Cluster 1

(Table 1), and resulting to large negative forcing values at

surface and significant atmospheric heating (Sharma et al.

2010, 2012; Mishra and Shibata 2012). Clusters 2, 3 and 6

are mainly associated with AEs occurring during winter,

when aerosols are accumulated in the lower-elevated

Ganges basin (di Girolamo et al. 2004). Note that in Cluster

6 the prevailing zonal flow at Z700 (Fig. 8b) is responsible

for the transport of aerosols through central IGP up to

Bangladesh. Several studies over IGP (Komppula et al.

2012; Srivastava et al. 2012c; Misra et al. 2012) revealed

the presence of a thick aerosol layer near the ground during

late post-monsoon and winter seasons composed of a large

fraction of anthropogenic aerosols and BC from fossil and

bio-fuel burning. The aerosol properties over Kanpur are

similar for Clusters 2, 3 and 6; little higher AOD and lower

a are shown for Cluster 6 (Table 1).

The mean AOD550 spatial distribution for Clusters 4 and

5 reveals a large area of very high values, extending from

Pakistan and northernmost AS to eastern IGP. This is a

result of dust outbreaks in Thar Desert, Arabia and Middle

East (Kuhlmann and Quaas 2010; Gautam et al. 2011) and

the dust transport due to dominant wind flow from western

directions (Figs. 6b, 7b) in late pre-monsoon and early

monsoon season. Some extreme AODs shown over North

Indian Ocean (NIO) are attributed to intense dust outbreaks

from Arabian Peninsula in the beginning of 2000s

Fig. 6 As in Fig. 3, but for Cluster 4

Synoptic weather conditions

123

(i.e. 2001–2003) (Kaskaoutis et al. 2012c), while there is

lack of sufficient dataset after 2005. The enhanced dust

concentration leads to the highest AODs and lowest a over

Kanpur for Clusters 4 and 5 (Table 1).

The current analysis shows that the AEs over Kanpur

occur under favorable meteorological and atmospheric

circulation conditions, enhanced subsidence, increased

biomass or fossil fuel combustion and transport of dust

plumes. MODIS and AERONET AODs are satisfactory

correlated on cluster-mean basis (Table 1) and the same

has been found by numerous validation studies over Kan-

pur (Tripathi et al. 2005; Jethva et al. 2007; Prasad and

Singh 2007b; Shi et al. 2011).

A crucial aspect that needs the synergy of satellite

remote sensing is the investigation of how much repre-

sentative the AEs over Kanpur are for a greater area over

IGP and India. In this respect, the correlation coefficient

(r) between MODIS-AODs obtained over Kanpur (pixel

centered on 26.5�N, 80.5�E) and the AODs over the other

pixels in the spatial domain shown in Fig. 9 was calculated.

Such an analysis reveals the degree to which the temporal

variability of extreme AODs over Kanpur is related to that

of aerosol load over other regions in the Indian sub-con-

tinent. For neighboring to Kanpur regions, high positive r

values indicate, there too, AEs of similar type, sources and

transportation on the same day. For remote regions, high

positive r values do not necessarily imply AEs there;

nevertheless they do indicate an aerosol teleconnection

pattern, most probably due to similar atmospheric circu-

lation effects over both regions. High negative r values

(anti-correlation) identify a see-saw aerosol teleconnection

pattern, viz. an increase in extreme AOD values over

Kanpur corresponds to a decrease of AOD values over the

remote region and vice versa. Finally, neutral r values

suggest that the variability of aerosol load over Kanpur is

not related to the one over other regions. Possible reasons

for that may be inconsistency in the sources, emission

rates, long-range transport or deposition (wet and dry)

processes.

Figure 10 shows the spatial distribution of the r values

for each cluster, while the grey pixels correspond to

insufficient dataset for performing the regression analysis,

Fig. 7 As in Fig. 3, but for Cluster 5

D. G. Kaskaoutis et al.

123

i.e. data lesser than one-third of the AE days of a cluster.

The analysis revealed that the correlations are statistically

significant at 95 % confidence level (p \ 0.05) for values

of r above 0.45–0.50 depending on the degree of freedom

for each individual dataset. Independently of the cluster,

the results show that the AOD values during AEs over

Kanpur are well correlated (r [ 0.6) with those over the

whole IGP region (Fig. 10), indicating consistency in the

sources. On the other hand, some pockets of very high r

values are found at various regions for each cluster

indicating an aerosol teleconnection pattern. More specif-

ically, the high r values for Cluster 1 detected for pixels

located northwestern of Kanpur indicate the source con-

tribution of the Punjab agriculture fires in post-monsoon.

For Clusters 2 and 3 moderate-to-high correlation also

exists between Kanpur and eastern IGP/northern BoB,

which are in the downwind direction for pollution outflow

in the winter season (Kharol et al. 2011). Notable differ-

ences are observed for Clusters 4 and 5, where very high r

values ([0.6–0.7) cover nearly the whole northern India,

Fig. 8 As in Fig. 3, but for Cluster 6

Table 1 Frequency of the AE days for each cluster, along with AERONET-AOD500, MODIS AOD550 and Angstrom exponent values for

various wavelength ranges

Cluster Frequency AERONET AOD500 MODIS AOD550 a (440–870) a (380–500) a (675–870)

1 62 (22 %) 1.17 ± 0.22 1.02 ± 0.27 1.17 ± 0.32 0.81 ± 0.18 1.34 ± 0.44

2 55 (20 %) 1.17 ± 0.24 1.01 ± 0.30 1.02 ± 0.49 0.69 ± 0.31 1.17 ± 0.59

3 38 (14 %) 1.15 ± 0.19 0.91 ± 0.27 0.94 ± 0.46 0.69 ± 0.27 1.06 ± 0.58

4 48 (17 %) 1.27 ± 0.37 1.05 ± 0.70 0.40 ± 0.43 0.39 ± 0.34 0.42 ± 0.46

5 51 (19 %) 1.25 ± 0.32 1.17 ± 0.58 0.41 ± 0.47 0.38 ± 0.34 0.45 ± 0.52

6 23 (8 %) 1.23 ± 0.27 1.12 ± 0.34 0.90 ± 0.56 0.57 ± 0.36 1.07 ± 0.64

Synoptic weather conditions

123

D. G. Kaskaoutis et al.

123

northern AS and areas in Pakistan. This highlights the

consistency of the aerosol sources and dust transport

pathways from Arabia and Thar Desert over central IGP

during the May–June period. In contrast, large negative

correlations are found over southern AS, NIO and BoB,

revealing a see-saw teleconnection between these regions

and IGP. A comparison of Figs. 9 and 10 suggests that the

high AODs over southern latitudes are not consistent with

the AEs over Kanpur and are related to intensification/

weakening of dust transport from Arabia towards southern

AS and NIO and a synchronous weakening/intensification

of dust transport from Arabia and Pakistan to IGP. Finally,

the high r values over AS for Cluster 6 indicate a triple

aerosol teleconnection pattern between IGP, AS and east-

ern BoB, during winter.

4.4 Model simulations

This section provides an insight in SPRINTARS simula-

tions concerning aerosol properties over Kanpur

(1.1� 9 1.1� spatial resolution) and examines the degree

that SPRINTARS can reproduce the AERONET parame-

ters during severe AE days. On the other hand, there is a

compelling need for improving the aerosol inventories over

the region in order to make realistic assessment of the

impacts of aerosols on radiative forcing and the south

Asian monsoon. Figure 11 shows the SPRINTARS simu-

lated AOD550 time-series during the period 2001–2010

over Kanpur (total of 3,652 days) superimposed with the

AE days with different colored shape depending on cluster.

According to SPRINTARS, only 34 days (0.93 %) with

AOD550 [ 0.9 were found mostly during May–July period,

indicating a systematic underestimation in AOD compared

to AERONET and MODIS, especially during the winter

season. The results show that the majority of the AOD

peaks (except of years 2004 and 2008) correspond to AEs,

whereas nearly the whole AEs during post-monsoon and

winter are associated with very low SPRINTARS AODs

indicating incapability of the model to reproduce them.

Similarly, most GCMs (General Circulation Models) fail to

reproduce the high AODs over IGP, especially during

winter (Chin et al. 2009; Ganguly et al. 2009).

The analysis revealed that SPRINTARS underestimates

significantly the AERONET AOD values with their dif-

ferences being as high as 0.8–1.0 during the post-monsoon

and winter seasons; only on some few cases in late pre-

monsoon and monsoon SPRINTARS AODs are

comparable to those of AERONET. The low correlation

coefficient (r = 0.49, R2 = 0.24) is characteristic for the

model’s incapability in simulating the high AODs over

IGP. Similar results were obtained from the GOCART

simulations over Kanpur with R value of 0.18 (Chin et al.

2009). Significant differences were also found for a440–870

values, despite the qualitative agreement between AER-

ONET and SPRINTARS concerning the much lower val-

ues in Clusters 4 and 5 (Tables 1, 2). SPRINTARS SSA

simulations (Table 2) were not found to be correlated with

AERONET retrievals, suggesting an incapability of the

model in reproducing the absorbing nature of aerosols,

despite the consistency in the mean values (0.90 ± 0.03 for

AERONET and 0.91 ± 0.04 for SPRINTARS). It should

be noted that the SSA at 550 nm from AERONET was

obtained via interpolation from the values at 440 and

675 nm, which adds more uncertainty in the retrievals.

SPRINTARS was found to systematically overestimate aand SSA in Clusters 4 and 5 dominated by desert dust,

suggesting that the model assumes dust particles of lower

size and absorption.

The different aerosol components that contribute to the

total AOD, mass column loading and surface mass con-

centration over Kanpur are summarized in Table 2 for each

Cluster. For all the three parameters a clear dominance of

the dust component is shown, which is increased for the

AEs in Clusters 4 and 5. Thus, according to SPRINTARS

simulations, dust contributes 59–89 % to the total AOD

(depending on cluster), while its contribution to column

mass loading ranges from 75 to 94 % and to surface con-

centration from 35 to 85 %. Given the extremely low

contribution of sea salt to all of these parameters (see

Table 2), SPRINTARS results suggest that the anthropo-

genic contribution (BC ? sulfate ? OC) to total AOD,

mass loading and concentration is very low, even for the

winter season (Clusters 2, 3, 6), when anthropogenic

aerosols dominate over Kanpur (Singh et al. 2004; Dey and

Tripathi 2008). However, the model’s results show that

anthropogenic AOD is higher in Cluster 6, compared to

that in Clusters 1, 2 and 3 (Table 2), which is in agreement

with AERONET and MODIS (Table 1). It is worth to be

noted the very low contribution of BC to all aerosol

parameters (2.4 % for AOD550, 0.8 % for column loading,

3.7 % for mass concentration) compared to that found from

measurements (*9–11 % for AOD and *6–9 % for sur-

face mass concentration) over IGP (Dey and Tripathi 2007,

2008; Goto et al. 2011b; Kharol et al. 2012). The signifi-

cant model underestimation of the anthropogenic emissions

and their higher contribution to atmospheric aerosols in

post-monsoon and winter is the reason for the low AOD,

aerosol loading and concentration during these seasons.

Systematic SPRINTARS underestimation of particulate

matter (PM10 and PM2.5) and BC, especially during winter,

b Fig. 9 Spatial distribution of the mean Terra MODIS AOD550 over

Indian sub-continent and adjoining oceanic regions for the six

atmospheric circulation clusters. The white areas correspond to lack

of data (bright surfaces) or insufficient number of valid pixels. The

Kanpur site is denoted by a star

Synoptic weather conditions

123

D. G. Kaskaoutis et al.

123

was also found over Dibrugarh in Brahmaputra valley

(Pathak et al. 2013). Recent studies over India (Goto et al.

2011a, b) revealed better model simulations with measured

BC concentrations, AOD, SSA and a by using the

SPRINTARS assumed (Streets et al. 2003; Takemura et al.

b Fig. 10 Spatial distribution of the correlation coefficient values

obtained from the linear regression between the daily MODIS AODs

over Kanpur and the AODs over the remaining pixels for each

atmospheric circulation cluster. The luck of sufficient days for the

regression analysis, i.e. less than the one-third of the frequency for

each cluster, corresponds to gray

Fig. 11 SPRINTARS model

simulations of AOD550 time-

series over Kanpur during the

period Jan 2001–Dec 2010. The

colored shapes indicate the AE

days for each atmospheric

circulation cluster, as identified

from the AERONET AOD500

time-series

Table 2 SPRINTARS model simulations for various aerosol parameters over Kanpur for the AE days classified in the six atmospheric

circulation clusters

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6

AOD550 0.109 0.182 0.126 0.379 0.478 0.217

a(440–870) 1.210 1.248 1.076 0.632 0.639 1.324

SSA550 0.891 0.884 0.897 0.944 0.939 0.902

AOD BC 0.0054 0.0058 0.0049 0.0061 0.0057 0.0074

AOD Su 0.0202 0.0224 0.0198 0.0386 0.0319 0.0487

AOD OC 0.0167 0.0177 0.0168 0.0166 0.0154 0.0343

AOD SS 0.0002 0.0002 0.0002 0.0005 0.0006 0.0003

AOD Du 0.0665 0.1345 0.0852 0.3274 0.4329 0.1304

Load BC 1.95 2.10 1.78 2.22 2.06 2.67

Load Su 11.86 12.21 9.51 22.34 18.87 14.18

Load OC 10.32 10.70 9.56 10.11 9.47 13.63

Load SS 0.17 0.22 0.19 0.44 0.65 0.24

Load Du 75.01 164.31 99.15 402.60 524.37 173.88

Conc. BC 3.31 3.63 3.45 2.04 2.30 3.46

Conc. Su 5.88 6.43 5.06 9.15 8.36 5.85

Conc. OC 16.19 16.22 16.22 9.55 10.74 16.38

Conc. SS 0.01 0.03 0.01 0.09 0.16 0.02

Conc. Du 13.91 38.12 20.31 101.72 122.53 32.94

Mass column loading in lgm-2, mass concentration in lgm-3. Su sulfate, OC organic carbon, SS Sea salt, Du Dust

Synoptic weather conditions

123

2005) aerosol emission inventories rather than those used

in the AeroCom project. It is, therefore, needed to further

modify and improve the emissions, transport, mixing and/

or removal processes, especially for the anthropogenic

components, during aerosol life cycle over IGP to better

simulate the direct and indirect aerosol effects on local

monsoon system.

5 Conclusions

The present study classified the atmospheric circulations

(synoptic meteorological systems) associated with aerosol

episodes (AEs) over Kanpur, India into homogeneous and

discrete clusters and aimed to identify favorable conditions

for the accumulation and/or transport of aerosols over the

region. Days with daily mean AOD500 above the decadal

(2001–2010) mean plus 1STD, recorded at Kanpur AER-

ONET station, have been considered as AEs and for these

days the synoptic weather conditions over south Asia were

analyzed. A statistical methodology scheme comprising

Factor (S-mode) and Cluster (K-means) Analysis was

performed on the Z700 values over southern Asia obtained

from NCEP/NCAR re-analysis and six atmospheric circu-

lation clusters were identified. Clusters 1, 2, 3 and 6 pre-

sented their highest frequency in post-monsoon and winter

seasons, mainly associated with larger Z700 over Arabian

Peninsula extended over Arabian Sea and central India,

while Clusters 4 and 5 (higher frequency in May–July)

showed low MSLP over the arid regions of northwestern

India, Pakistan and Arabia thus, favoring convection and

uplift of dust. For each cluster the anomalies from the mean

climatological situation were used to reveal more regional

details of the atmospheric circulation. The mean composite

spatial distribution of MODIS-AOD550 for 4 of the clusters

showed increased aerosol loading only over IGP, consistent

with the synoptic conditions revealed by these clusters,

which favored the accumulation of aerosols. In the other 2

(Clusters 4 and 5) the atmospheric circulation characteris-

tics favored the long-range dust transport over IGP from

the neighboring Thar Desert and the Arabian Peninsula.

The AEs over Kanpur could be representative for the

neighboring regions over IGP and on certain circumstances

for the downwind areas, like northernmost BoB during

winter. Furthermore, for Clusters 4 and 5 the high AODs

over Kanpur were well correlated with AODs over northern

Arabian Sea, Pakistan and northwestern India suggesting

transport of dust aerosols from these areas during the AE

days. Therefore, the source regions and the strength of

advection depend on atmospheric circulation cluster that,

in turn, influences and modifies the aerosol properties over

central IGP. SPRINTARS simulations of aerosol properties

on the AE days over Kanpur revealed systematic

underestimation of AOD, especially in the winter season,

and very low anthropogenic aerosol (BC, sulfate, OC)

component, while the dust component was much higher for

AEs belonging in Clusters 4 and 5. These results, which are

in agreement with other model (GOCART, GFDL-AM2)

simulations over IGP, suggest necessity of improvement of

the emission inventories and/or aerosol life cycle in the

models in order to simulate better the aerosol impact on

monsoon system and regional climate.

Acknowledgments IIT Kanpur AERONET is operational since

January 2001; one of the authors (RPS) took lead to deploy Kanpur

AERONET after the joint agreement by IIT Kanpur and NASA. Our

sincere thanks to the Kanpur AERONET team and the current PIs

(S.N. Tripathi and B.N. Holben) for making the data available. The

NCEP/NCAR Reanalysis team is also gratefully acknowledged for

providing the meteorological maps. We also acknowledge the MO-

DIS scientists and associated NASA personnel for the production of

the data used in this research effort via Giovanni online data system.

The SPRINTARS calculations were performed by using National

Institute for Environmental Studies (NIES) supercomputer system

(NEC SX-8R/128M16). We would also like to thank many developers

for MIROC AGCM and SPRINTARS and the two anonymous

reviewers for helping us in improving the scientific quality of the

work.

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