Impacts of Aerosols and Climate Modes on Tropical Cyclone Frequency over
the North Indian Ocean: A Statistical Link Approach
MD.WAHIDUZZAMAN ,
a
MD.ARFANALI,
b
KEVINCHEUNG,
c
JING-JIALUO,
a
TANGSHAOLEI,
a
PRASADK. BHASKARAN,
d
CHAOXIAYUAN,
a
MUHAMMAD BILAL,
b
ZHONGFENG QIU,
b
AND
MANSOURALMAZROUI
e,f
a
Institute for Climate and Application Research/CICFEM/KLME/ILCEC, Nanjing University of Information Science and Technology,
Nanjing, China
b
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, China
c
Department of Climate Research, NSW Department of Planning Industry and Environment, Sydney, New South Wales, Australia
d
Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
e
Center of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia
f
Climate Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom
(Manuscript received 24 March 2021, infinal form 6 January 2022)
ABSTRACT: North Indian Ocean (NIO) tropical cyclone activity is strongly influenced by aerosols and climate modes. In
this study, we evaluated the impact of aerosols and climate modes on modulating tropical cyclone (TC) frequency over the
NIO. A statistical generalized additive model based on Poisson regression was developed to assess their relative impacts.
Aerosol optical depth for different compounds simulated by the Goddard Chemistry Aerosol Radiation and Transport model,
sunspot number (SN) as solar variability, and eight climate modes}Atlantic meridional mode (AMM), El Ni˜no–Southern
Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean dipole (IOD), Pacific decadal oscillation (PDO), Pacif-
ic–North American teleconnection pattern (PNA), Arctic Oscillation (AO), and Antarctic Oscillation (AAO), all based on
reanalysis datasets, were analyzed for the 40-yr period 1980–2019. A strong linkage was foundbetween TC activity and the
AMM, IOD, and ENSO over the NIO. In addition, black carbon, organic carbon, sea salt, and sulfate aerosols have a signifi-
cant impact on the cyclone frequency. Among these factors, black carbon, organic carbon, sea salt, and AMM account for the
most variance of TCs, and among the other climate modes, IOD contributes more than ENSO. This is thefirst attempt to
have identified this ranked set of aerosols and climate indices according to their relative ability to impact NIO TCs. Possible
linkages between the thermodynamic and dynamic effects of aerosols on the Indian monsoon environment and its modifica-
tions to the large-scale environmental parameters relevant to TC development, namely, sea surface temperature, vertical wind
shear, relative vorticity, and relative humidity during different phases of the climate modes are discussed.
SIGNIFICANCE STATEMENT: Aerosols and climate modes have enormous impact on tropical cyclones (TCs). In this
study, we evaluated the impact of aerosols and climate modes that modulate frequency of TCs over the north Indian Ocean.
To assess the impact, a statistical generalized additive model based on Poisson regression was developed. A strong linkage
was found between TC activity and Atlantic meridional mode, Indian Ocean dipole, and El Ni˜no–Southern Oscillation,
whereas other climate modes have no statistical significance. In addition, black carbon, organic carbon, sea salt, and SO4
aerosols have a strong linkage to cyclone frequency. The study postulates that most positive phases of these climate modes
are associated with more TCs, while the negative phases are associated with fewer.
KEYWORDS: Atmosphere-ocean interaction; Aerosols; Climate variability; Regression analysis; Statistical techniques;
Statistical forecasting
1. Introduction
Tropical cyclone (TC) induced disasters pose significant
risk to the countries surrounding the north Indian Ocean
(NIO) rim and is a topic of considerable importance having
wide socioeconomic implications. Compared to the global TC
count, the NIO makes up a relatively small percentage (7%)
(Mohapatra et al. 2012;Rajeevan et al. 2013;Balaguru et al.
2014;Sahoo and Bhaskaran 2016;Wahiduzzaman et al. 2020).
However, the socioeconomic impact of TCs around the NIO
rim is much greater than in the other ocean basins (Singh et al.
2000). This enhanced vulnerability may be attributed to sev-
eral factors, such as the prevalence of low-lying areas, high
population density along the NIO coastal belt, and physical
and socioeconomic conditions. TCs have short life cycles, and
their development in NIO tends to be relatively closer to the
coast than in other ocean basins, resulting in relatively little
time for emergency preparedness. Therefore, improvements
in the analysis and forecasting of TCs for these regions can
have significant beneficial value (Singh et al. 2012).
A number of dynamical and statistical models have been
used to improve the forecasting skill of TCs in the NIO
Denotes content that is immediately available upon publica-
tion as open access.
Supplemental information related to this paper is available at
the Journals Online website:https://doi.org/10.1175/JCLI-D-21-
0228.s1.
Corresponding author: M. Wahiduzzaman, [email protected]
DOI: 10.1175/JCLI-D-21-0228.1
Ó2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult theAMS Copyright
Policy(www.ametsoc.org/PUBSReuseLicenses).
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region. The Poisson statistical regression model is able to
count and predict TC frequency in the NIO (Wahiduzzaman
and Yeasmin 2019). Statistical modeling approaches have
been used to predict TC activity in various ocean basins and
subbasins since the 1980s (Klotzbach 2011). For example, hur-
ricane counts using the Poisson regression model were used
byElsner and Schmertmann (1993)andLehmiller et al.
(1997)in the North Atlantic, and byMcDonnell and Holbrook
(2004a,b) for the Australian region. A Bayesian approach was
used to investigate seasonal TC counts and landfall over the
United States (Elsner and Jagger 2004,2006), and Atlantic hur-
ricane activity (Elsner et al. 2008).Chand and Walsh (2010)
took a similar approach for Fiji, Samoa, and Tonga, whileChu
andZhao(2007)applied the method to the central North
Pacific.Nicholls (1979)andGray (1984)described thefirst sta-
tistical seasonal forecast model for TC activity in the Australian
and North Atlantic region.
A Poisson regression model is widely used for TC analysis
by considering a number of climate modes. For example,
Gray et al. (1992,1993,1994) used the model by considering
the quasi-biennial oscillation and African rainfall. Relation-
ship between hurricanes and the Sahel monsoon rainfall was
considered byLandsea and Gray (1992). Gray’s parameters
were used for the operational Atlantic seasonal TC forecasts
using a Poisson model (Owens and Landsea 2003;Saunders
and Lea 2005;Klotzbach 2007). Analogous TC predictands
were considered in the northwest Pacific and Australian
regions (Chan et al. 1998;Chan and Shi 1999;Chan et al.
2001;Liu and Chan 2012). El Ni˜no–Southern Oscillation–
related indices were used for predicting the annual number of
TCs in the South China Sea (Liu and Chan 2003;Goh and
Chan 2010). The Southern Oscillation index (SOI) was con-
sidered in the Australian region (Solow and Nicholls 1990),
and the September lead saturated equivalent potential tem-
perature gradient between 1000 and 500 hPa and SOI were
used for upcoming season (November to March next year).
TC genesis forecasting for the Australian region was explored
byMcDonnell and Holbrook (2004a,b)and for the eastern
Indian Ocean, northern Australia, and southwest Paci
fic
regions byMcDonnell et al. (2006). Also, Ni˜no-4, a trade
wind index, and the outgoing longwave radiation index were
used in the Australian region (Liu and Chan 2012). Recently,
meteorological variables and aerosols were considered in vari-
ous basins using the Poisson model (Chiacchio et al. 2017).
Aerosols are the miniature solid and liquid particles hov-
ering in the atmosphere. They are attributed to both natural
and man-made sources. Once emitted, they are transported
horizontally and vertically by atmospheric currents. Atmo-
spheric aerosols are released in the form of mineral dust
and volcanic dust and ash and by biomass burning. Mist,
fog, smoke, sea salt, and particulate pollution may all be
caused by both natural and anthropogenic activities (Ali
et al. 2020;Ali and Assiri 2019). Aerosol particles are
acknowledged as crucial parameters in Earth’s climate sys-
tems. They affect Earth’s climate and radiative balance
directly by absorbing and scattering solar radiation, and
indirectly by changing the microphysical properties of
clouds. According to the Intergovernmental Panel on
Climate Change, the forcing of atmospheric aerosols on
Earth’s climate system is uncertain due to the large spatio-
temporal unevenness of their physiochemical attributes (Ali
et al. 2017;Islam et al. 2019). Aerosols have adverse impacts
on TCs under global warming (Evan et al. 2011).Evan et al.
(2011)found a significant relationship between aerosols and
TC intensity over the Arabian Sea.
The Indian Ocean climate is strongly influenced by wider
ocean variability at various spatial and temporal scales. To
simulate or predict TC activity, it is necessary to understand
the various controlling factors. TC activity over the NIO is
strongly influenced by El Ni˜no–Southern Oscillation (ENSO)
(Girishkumar and Ravichandran 2012;Girishkumar et al.
2014;Albert et al. 2021), and the Indian Ocean dipole (IOD)
(Saji et al. 1999). ENSO is the dominant interannual mode of
ocean–atmosphere variability in the Pacific and affects the
large-scale climate all over the globe (Girishkumar et al.
2015). Its effects on TCs are long observed in the North
Atlantic (Gray 1984), the western North Pacific(Saji et al.
1999), and northern Australia (Nicholls 1979). Many earlier
studies investigated the relationship between ENSO and sea-
sonal TC activity in NIO basins (Schott and McCreary 2001;
Girishkumar and Ravichandran2012;
Kikuchi and Wang 2010;
Mohapatra and Adhikary 2011;Philander 1985;Chan 1985;Ho
et al. 2006;Albert et al. 2021). Another strong climate mode is
the IOD, which is a coupled ocean–atmosphere phenomenon in
the Indian Ocean, described by anomalously cold or anoma-
lously warm sea surface temperature (SST) in the southeast-
ern equatorial Indian Ocean and western equatorial Indian
Ocean. IOD is a local mode of the Indian Ocean that can
exist independently of the Pacific(Saji et al. 1999) and affects
TCs in the NIO basin. These are also affected by the Pacific
decadal oscillation (Kikuchi and Wang 2010), the boreal
summer intraseasonal oscillation (Mohapatra and Adhikary
2011), and the Madden–Julian oscillation (Ho et al. 2006;
Kuleshov et al. 2008;Camp et al. 2015).
Considering the importance of the remote forcing effects
associated with various climate indices, statistical models
were developed to address TC activity for the countries sur-
rounding the NIO. A Poisson regression model was devel-
oped byfitting a generalized additive model tofind the
relationships between climate modes, aerosols, and TC fre-
quency over this region. A previous study byChiacchio et al.
(2017)considered the linear relationship among variables
using Poisson regression byfitting a generalized linear model.
In this study, we used a Poisson regression byfitting a general-
ized additive model that considers both linear and nonlinear
relationships amongfive aerosol types: black carbon (BC),
organic carbon (OC), sulfate (SO
4), sea salt (SS), and dust
(DU) along with sunspot number (SN) and eight climate
modes, namely, Atlantic meridional mode (AMM), ENSO,
North Atlantic Oscillation (NAO), IOD, Pacific decadal oscil-
lation (PDO), the Pacific–North American (PNA) telecon-
nection pattern, Arctic Oscillation (AO), and Antarctic
Oscillation (AAO).
This paper discusses the statistical relationship among TC
frequency, types of aerosols, solar variability and the eight cli-
mate modes, and it quantifies their relative contribution to
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modulate TC activity under the same modeling framework. It
also attempts tofind a possible link between TC variability
and the large-scale geographical distribution of the TC gene-
sis-related parameters, namely, SST, vertical wind shear, vor-
ticity, and relative humidity.
The paper is organized as follows: details of the data, kernel
density estimation, Poisson regression approach, and general-
ized additive model are described in section 2. Section 3 pre-
sents the results pertaining to TC activity and their relationship
using statistical techniques. Discussion and conclusions are pro-
vided insection 4.
2. Data and methods
a. Sources of data
The Joint Typhoon Warning Centre (JTWC) TC dataset is
a contributed subset within the International Best Track
Archive for Climate Stewardship (IBTrACS) and commonly
used by researchers worldwide. We have used the TC loca-
tions (latitude and longitude), year, serial number, time, and
wind speed from the JTWC data. Eight climate modes}
AMM, ENSO, NAO, IOD, PDO, PNA, AO, and AAO }
and sunspot number were obtained from the National
Centers for Environmental Research–National Center for
Atmospheric Research (NCEP–NCAR) reanalysis project.
Monthly AMM, ENSO, and IOD SST time series were
downloaded from the NOAA Physical Sciences Laboratory
(https://psl.noaa.gov/data/timeseries/monthly/AMM/;https://
psl.noaa.gov/gcos_wgsp/Timeseries/Nino34/;https://psl.noaa.
gov/gcos_wgsp/Timeseries/DMI/).
The positive (above 0.5 standard deviation) and negative
(below20.5 standard deviation) phases of AMM, ENSO, and
IOD are determined by the March–May (MAM), Decem-
ber–February (DJF), and September–November (SON)-aver-
aged AMM, ENSO, and IOD SST anomaly (SSTA) series,
respectively. The 850-hPa pressure level vorticity, relative
humidity, wind data were collected from NCEP–NCAR rean-
alysis. Note that except for the maximum potential intensity,
these environmental parameters make up the TC genesis
parameters (Sattar and Cheung 2019). The vertical wind shear
(VWS) is calculated based on
fifififififififififififififififififififififififififififififififififififififififi
u
2002u850()
2
1y2002y850()
2
fl
(1)
Here, (u
200,y200) and (u 850,y850) represent the zonal and
meridional wind anomalies at 200 and 850 hPa, respectively.
Aerosol type data were collected from both the Modern-
Era Retrospective Analysis for Research and Applications
(MERRA-2) and Copernicus Atmospheric Monitoring Ser-
vice (CAMS) project. The specific aerosol products (BC, DU,
OC, SO
4, and SS) are not available from observations, there-
fore, we applied the reanalysis aerosol optical depths (AODs)
data from both MERRA-2 and CAMS. MERRA-2 includes
an interactive analysis of aerosols that feed back into the
circulation, uses NASA’s observations of stratospheric ozone
and temperature (when available), and takes steps toward
representing cryogenic processes. The CAMS provides the
reanalysis of atmospheric composition datasets (e.g., aero-
sols, chemical species, greenhouse gases) produced by the
European Centre for Medium-Range Weather Forecasts
(ECMWF). The global CAMS models combine satellite-
based observations with aerosol chemistry modeling using
the four-dimensional variational (4D-VAR) data assimila-
tion technique to attain aerosol mass concentrations and
trace gases. For anthropogenic emissions of chemical spe-
cies, CAMS uses the Monitoring Atmospheric Composition
and Climate–CityZen (MACCity) inventory at a spatial res-
olution of 0.5830.58from 1960 to 2010 (Granier et al.
2011). More details about the model and emission inventory
can found in previous studies (Flemming et al. 2017,2015).
b. Methodology
In this study, we used Cramer’sVcorrelation coefficient to
find the relationship between TC frequency on one hand and
eight climate modes and sunspot number as the solar variabil-
ity on the other. Cramer’sVis a statistical quantity used to
measure the strength of association between two nominal var-
iables, and it considers the symmetric measure (Cramer
1946). It is measured asV
fifififififififififififififififififi
c
2

nk21()


, wherec
2
is the
chi-square,nis the sample size, andkis the number of rows
or columns in the table. We discretized the climate indices
into categories, for example, positive or negative, and use
the counts in each category for the Cramer’sVcalculation.
Scores are interpreted to reflect relationships that are either
very strong (0.25 or higher), strong (0.15–0.25), moderate
(0.11–0.15), weak (0.06–0.10), or negligible/no correlation
(0–0.05).
Poisson regression through a generalized additive model
was used to estimate the strength of the relationship between
TCs on one hand and aerosols, and climate modes on the
other. A Poisson regression model specifies the logarithm of
TC rates annually and is an alternative to linear regression.
The TCs are independent in the sense that the arrival of one
TC will not make another one more or less likely, but their
rates vary from year to year because of the covariates.
The regression is expressed in the form
log
l()b
01b
1x11···1 b
nxn (2)
The model uses the logarithm of the rate (
l) as the response
variable and model coefficients are determined by the method
of maximum likelihood. To quantify the influence of climate
modes, sunspot number, and types of aerosols, on the number
of TCs in NIO, we used Poisson regression model. Details are
available inChiacchio et al. (2017).
We used the kernel density estimation for TC genesis distri-
bution. Kernel density is a method for estimating the proba-
bility density function of TCs in a nonparametric way. The
TC distribution is defined by a smoothing function and a
plug-in bandwidth value (length scale) that controls the
smoothness. Details of kernel density estimation are available
inWahiduzzaman et al. (2017). In the Poisson regression
model, we used the generalized additive model (GAM) func-
tion to consider the linear and nonlinear nature of the
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datasets. The GAM is an extension of the generalized linear
model, in which the linear terms are replaced by smooth
transformations of the predictors. Standard regression models
assume the responseyis normally distributed about its mean
mwith variances
2
:
y∼N
m,s
2

: (3)
In the GAM case, the mean can be modeled as a linear
combination of predictor variables,X
1,X
2,…,X
n
mb
01b
1X11b
2X21···1 b
nXn, (4)
where
b
0andb
nare the regression coefficients to be estimated.
There are two key elements in the generalized linear
model. First, the GAM assumes the response may be
distributed about its expected value according to any distri-
butionffrom any exponential family of distributions
(including the Poisson, binomial, and normal families). Sec-
ond, the predictors enter the model through the linear
predictor.
The GAM further relaxes the functional relation through a
number of smooth transformations expressed in the form
mf1X1()1f 2X2()1···1f nXn(): (5)
Where the regression model seeks to estimate the regression
coefficients
b
02b
n, the additive model seeks to estimate
these smooth transformationsf
1,…,f
n.
More details about GAM are available inWahiduzzaman
et al. (2017,2019,2020).
3. Results
On average, about 7 TCs per year formed over the NIO
region during the past 40 years. A total of 33 out of 40 years
(82%) experienced four or more TCs (Fig. 1). The highest
(lowest) number of TCs were seen during the decade of
1990–99 (1980–89) (Fig. 1).
TC activity in the NIO region exhibits a bimodal character-
istic (Yanase et al. 2012;Li et al. 2013;Akter and Tsuboki
2014). Distinct bimodal characteristics in the NIO occur during
the premonsoon (March–May) and postmonsoon (September–
November) periods (Akter and Tsuboki 2014;Wahiduzzaman
et al. 2017,2019;Wahiduzzaman and Yeasmin 2020), with the
primary (secondary) peak in TC frequency occurring during
November (May). These seasons are also characterized
by distinct summer and winter prevailing wind directions.
Figure 2illustrates the density distribution of cyclogenesis
during the premonsoon and postmonsoon periods using ker-
nel density estimation. These two seasons contributes almost
three-quarters of the annual total, consistent with thefind-
ings byWahiduzzaman et al. (2017)andWahiduzzaman and
Yeasmin (2020).
Previous studies have claimed that TCs over the NIO are
strongly influenced by a number of climate modes. This study
considered eight climate modes (supplementary Figs. 1–4),
including the AMM, ENSO, NAO, IOD, PDO, PNA, AO,
AAO, and SN, to establish possible relationship with
TC frequency, using Cramer’sVcorrelation and fraction
of explained log likelihood (pseudoR
2
) from the Poisson
regression. ENSO, IOD, PNA, and PDO values are mea-
sured using SST anomalies, whereas the NAO (AAO and
AO) are measured using sea level pressure (geopotential
height) anomalies. Positive (negative) values indicate the
positive (negative) phase of the climate modes. The positive
(negative) phase of climate modes is mostly associated with
more (fewer) TCs over the NIO (supplementary Figs. 1–4).
The correlations between TCs and the climate modes
(including SN) are shown inFig. 3(see also supplementary
Fig. 5). As described insection 2(data and methods) below,
using Cramer’sVtechnique,Fig. 3shows a very strong (0.25
FIG. 1. Tropical cyclone frequency for the north Indian Ocean
during 1980–2019.
FIG. 2. Distribution of tropical cyclone genesis during (left) premonsoon and (right) postmon-
soon over the north Indian Ocean region (08–308N, 508–1008E) using kernel density estimate.
Green colors show the highest density (concentration of TC count per square kilometer) area of
genesis.
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or higher) relationship with AMM, a strong (0.15–0.25) rela-
tionship with IOD, a moderate (0.11–0.15) relationship with
AAO, a weak (0.06–0.10) relationship with ENSO and NAO,
and negligible or no correlation (0–0.05) with AO, PDO,
PNA, and SN. Accordingly, the dominant modes that influ-
ence TC variability are AMM and IOD, the physical interpre-
tation of which will be discussed insection 4.
This study also focused on AOD for BC, DU, OC, SO
4, SS,
and their total contribution (TOT). The distribution of AOD
during the premonsoon and postmonsoon periods, as well as
annually, are shown inFigs. 4–6. AOD is an indicator of the
columnar aerosol mass and is a measure of the spatial distri-
butions of aerosols (i.e., BC, DU, OC, SO
4, SS). Several pre-
vious studies (Shi et al. 2019;Gueymard and Yang 2020;
Rizza et al. 2019;Zhang et al. 2020) evaluated MERRA-2
based aerosol products and reported a good degree of accu-
racy with respect to observations (i.e., satellite onboard
MODIS-based AOD).Figure 4shows the mean annual spa-
tial distribution aerosol AOD from MERRA-2 for the period
1980–2018. BC aerosols are maximum over the Indo-Gangetic
Plains (IGP) as compared to other land areas (Fig. 4a). Simi-
lar results are also evident for OC and SO
4(Figs. 4c,d). IGP is
impacted mainly by natural and anthropogenic aerosols,
which are generated primarily as a consequence of a large
number of residents and high air pollution emissions (Tiwari
et al. 2015). The maximum dust aerosol is found over the
desert areas such as Oman and the Thar Desert rather than
the urban and vegetated areas (Fig. 4b). On the other hand,
the maximum SS aerosols are seen over the coastal regions
(Fig. 4e). In addition, the maximum total AOD is found over
the desert (Oman and the Thar Desert), coastal and IGP (Fig.
4f) areas. With relevance to TC development, the total AOD
has a meridional gradient over the oceans.
InFig. 5, seasonal variations of BC, OC, and SO
4are evi-
dent over the study region. During premonsoon periods
(MAM), the maximum BC and OC aerosols are found
over large areas of Bangladesh and Myanmar, whereas
the maximum SO
4aerosol is seen over Bangladesh, Myan-
mar, and the southern and eastern coastal areas of India
(Figs. 5a,c,d).Figure 5bshows that the distribution of Dust
FIG. 3. Relationship between eight climate modes, sunspot
number, and tropical cyclone during 1980–2019. A significantly
high correlation (5% significance level using the chi-square
test) is seen for AMM and IOD. Correlation using Cramer’sV
is classified as very strong (0.25 or higher), strong (0.15–0.25),
moderate (0.11–0.15), weak (0.06–0.10), and negligible/no cor-
relation (0–0.05).
FIG. 4. Mean spatial distribution of MERRA-2-derived AOD for the period 1980–2018 (a) BC, (b) DU, (c) OC, (d) SO 4, (e) SS, and
(f) TOT AOD.
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aerosol is high over the desert areas (Oman and the Thar
Desert). SS aerosols are also high over the southern and
eastern coastal areas of India and Bangladesh, while higher
level of total AOD is evident over the entire NIO rim
(Figs. 5e,f).Evan et al. (2011)found that the increase of
anthropogenic aerosols (e.g., BC, OC, and SO
4) during pre-
monsoon season can influence the increase in intensity of
Arabian Sea TCs.Chiacchio et al. (2017)also reported that
BC and OC aerosols have a significant impact on TC activ-
ity over the NIO region.
During postmonsoon periods, the levels of BC, OC, and
SO4 aerosols over the IGP region are higher (Figs. 6a,c,d).
This is attributable to excessive anthropogenic activities and
biomass burning (Ojha et al. 2020). Fine mode aerosols (e.g.,
BC, OC, SO
4) are dominant over the IGP during the post-
monsoon and winter season (Kedia et al. 2014). The maxima
of Dust aerosols are found over the desert areas as compared
to urban and vegetated areas, whereas the SS aerosols are
dominant over the southern coastal areas of India (Figs. 6d,e).
Total AOD amounts are concentrated over the IGP regions
during the postmonsoon period (Fig. 6f).
The relationship between TCs and different types of aero-
sols are shown inFig. 7. This (see also supplementary Fig. 7)
shows a very strong (0.25 or higher) relationship with BC,
OC, and SS, a strong (0.15–0.25) relationship with total aero-
sol, a moderate (0.11–0.15) relationship with SO
4, and negligi-
ble or no correlation (0–0.05) with DU. The low correlation
with DU is expected since the highest concentration of dust is
located over land. We also considered aerosol type data
(2003–18) from CAMS and compared them with MERRA-2.
Both CAMS and MERRA-2 datasets showed consistent spa-
tial pattern of aerosol components over the study area and
there is difference in the AODs that might be due to using
two different emission inventories and satellite observation
(supplementary Figs. 8–13).
From our GAM model, the explained log likelihoods of BC
and OC are approximately 25% (Fig. 8and supplementary
Fig. 14). During extreme weather events, large sea salt par-
ticles entering the atmosphere under high-wind conditions
can lead to intensification of TCs (Chiacchio et al. 2017),
although in that study SS has not been identified as a signifi-
cant factor for TC development over the NIO (comparatively
BC and OC are significant). For the climate modes considered
in the present study, AMM and IOD showed the highest
pseudo-R
2
values. They are statistically significant with
pseudo-R
2
values of 18%–20% in the NIO and can be attrib-
uted to large-scale circulation features discussed in this study.
4. Discussion and summary
Tropical cyclones over the NIO are influenced by climate
modes and aerosols. A significant positive relationship was
found between three climate modes (AMM, ENSO, and
IOD) and TC frequency over this region. We observed about
15%–25% explained log likelihoods for AMM, IOD, BC,
OC, and SS in the NIO. The influence of aerosols has
increased due to global warming and population density along
the NIO rim countries (Ali and Assiri 2019). Further, we
examined the influence of sea surface temperature (SST), vor-
ticity, vertical wind shear, and relative humidity on TC activ-
ity over the NIO.
Our results on the contributions from BC, OC, and SS to
the TC variability are mostly consistent with previous studies
carried out for the same region. Using a Poisson regression
FIG.5.AsinFig. 4, but for the premonsoon period.
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approach,Chiacchio et al. (2017)also identified BC and OC
as significant factors influencing TC frequency in the NIO.
However, SS was only significant for TCs in the North Atlan-
tic and South Pacific. As discussed byChiacchio et al. (2017),
SS emission is favorable for TC intensification and there is
more SS emission under the high winds of intense TCs, thus a
positive feedback exists. However, high winds are often asso-
ciated with high VWS depending on the upper-level circula-
tion. Therefore, there is not a simple relationship between TC
development and SS. The high correlation with SS identified
in this study needs further investigation.
For BC and OC, there are multiple links from the thermo-
dynamic and dynamic conditions to ultimate TC develop-
ment. Thermodynamically, the warming in the troposphere
due to solar energy absorption by the aerosols and the surface
cooling due to blocking of solar irradiance (i.e., dimming
effect) would stabilize the atmosphere. This stabilization
effect would reduce the maximum potential intensity of TC,
albeit by just a small amount (Chiacchio et al. 2017). The
larger effect is the cooling of SST and the resulted changes in
the monsoon circulation and VWS. Such SST cooling has
been demonstrated byEvan et al. (2011)using a specially
designed numerical experiment to identify aerosol effects.
They considered the Arabian Sea only, but the mechanism
should apply to the entire NIO. When the monsoon circula-
tion is weakened, the associated VWS is also reduced and
FIG.7.AsinFig. 3, but for BC, DU, OC, SO 4, SS, and TOT.
FIG. 8. PseudoR
2
for Poisson regression through a generalized
additive model for the TC frequency, aerosol, and climate modes
over the north Indian Ocean. Significant climate modes and aero-
sols shown in magenta color through the Student’sttest.
FIG.6.AsinFig. 4, but for the postmonsoon period.
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FIG. 9. Regression of the aerosol types (using MERRA-2) onto SST and VWS over the NIO. The stippled areas
indicate statistical significance exceeding the 95% confidence level.
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becomes more conducive to TC development. In fact, the
total AOD of the aerosols in our results (Fig. 4f) has a similar
meridional gradient to that reported byEvan et al. (2011),
with the highest values north of about 108N where most TCs
develop. From the geographical distribution of aerosols, it is
evident that during premonsoon periods, BC, OC, and SS,
contribute the most to the total forcing, especially over the
Arabian Sea (Fig. 5). During postmonsoon periods, SS has
lower concentration, and because SO
4concentrates over the
northeast Indian subcontinent, it makes large contributions to
the AOD meridional gradient over the Bay of Bengal
(Fig. 6). There is a significant reduction of SST by OC, SS,
and SO
4in Bay of Bengal around 108N(Fig. 9and supple-
mentary Fig. 15).Evan et al. (2011)have shown the effect
only in Arabian Sea and we have shown that it applies to
more Bay of Bengal more than Arabian Sea (AS). The reduc-
tion effect to VWS is also clear by all four aerosol types, and
for BC, OC, and SS the results over Arabian Sea are more sig-
nificant (Fig. 9). Overall, the effect on TC development is
then in the entire north Indian Ocean. The reduced VWS
associated with such an AOD pattern was demonstrated in
the model ofEvan et al. (2011). Nevertheless, it is only when
the VWS reduction by aerosol forcing is in phase with the
changes due to other modes of climate variability would the
impact on TC frequency become most apparent, as discussed
next. An additional note is thatWang et al. (2014)did empha-
size a distinct effect of aerosol onto TC development that is
different from greenhouse gases forcing. However,Wang et al.
(2014)focused on the microphysical effects of aerosols, lead-
ing to modified convection near the TC eyewall versus the
outer rainbands, rather than the long-term variability of TC
frequency.
AMM is a dynamic mode of climate variability on interan-
nual and decadal time scales over the North Atlantic. The
impacts of AMM on Atlantic hurricane activity have been
studied in detail (Vimont and Kossin 2007;Kossin et al.
2010), including the relationship with ENSO (Ng and Chan
2012). On a multidecadal time scale, AMM is considered to
be closely related to the Atlantic multidecadal oscillation
(AMO), and the latter is defined only by its SST pattern
(Vimont and Kossin 2007;Kossin et al. 2010). It was found
that AMM influences both the thermodynamic and dynamic
factors relevant to hurricane development and explains more
variance of hurricane activity than the local SST. When the
global composite patterns of SST, RH, relative vorticity, and
VWS for the positive and negative phase of AMM are exam-
ined (not shown), the remote anomaly patterns of these fac-
tors via teleconnections over the Pacific and Indian Oceans
are clear. The following discussion focuses on the NIO.Figure
10shows the regression pattern of the AMM index with RH,
relative vorticity, and VWS, respectively. It can be seen that
significant correlations exists between AMM and RH, and
negative correlation with VWS. Relative vorticity has also
show regions that has significant correlation with AMM
though the pattern is subsynoptic. In other words, there are
favorable factors for TC development in the NIO during the
positive phase of AMM like RH is enhanced while the VWS
is lowered. This is consistent with the statistical model results
in the last section. Note thatChiacchio et al. (2017)identified
AMM as a significant factor for the NIO TC development,
but not VWS. This is likely due to the fact that aerosols also
affect the monsoon circulation and VWS, and thus the signal
from VWS was complicated under their modeling framework.
When AMM is explicitly regressed against these environmen-
tal parameters as in our study, VWS was clearly lowered dur-
ing the positive phases of AMM.
AMM is known to possess maximum variability during
boreal spring (Kossin et al. 2010). This is evident in the com-
posite patterns of the environmental factors, both globally
and over the NIO. They are different during the premonsoon
and postmonsoon seasons. In general, the statistical signifi-
cance is higher for the premonsoon season, most likely
because of stronger AMM variability. For example, during
the premonsoon season, RH is enhanced in southwest Bay of
Bengal (BoB) where a lot of TCs develop (Fig. 11). The SST
in the entire NIO is warmer during positive AMM phases.
Also, VWS is reduced at the low latitudes where cyclogene-
sis occurs. Nonetheless, VWS is also low during the nega-
tive phase of AMM over most regions of NIO. The
observation in postmonsoon season is also consistent with
premonsoon season but not significant (see the supple-
mentary Fig. 16).
Comparatively, the impacts from ENSO on TC activity in
the NIO have been more extensively studied (Girishkumar
and Ravichandran 2012;Ng and Chan 2012;Felton et al.
2013;Girishkumar et al. 2015). SST is one of the parameters
FIG. 10. Regression of the AMM index onto (a) RH, (b) relative vorticity, and (c) VWS over the NIO. The stippled areas indicate statisti-
cal significance exceeding the 95% confidence level.
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FIG. 11. Composite anomalies of SST, relative vorticity, vertical wind shear, and relative humidity during the pre-
monsoon period for the (left) positive and (right) negative phases of AMM. The stippled area indicates the difference
between the positive phase and negative phase exceeding the 95% confidence level using the Student’sttest.
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FIG. 12. As inFig. 10, but for ENSO phase in the postmonsoon period.
WAHIDUZZAMAN ET AL.
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that potentially contributes to the difference in TC activity
between ENSO phases, due to a cold tongue of SST especially
during postmonsoon periods associated with the seasonal
phase lock of ENSO (Fig. 12). In the eastern Pacific, upward
motion was weakened through the modulation by the zonal
Walker circulation. Due to warm SST anomalies, the Walker
circulation is diffuse and weak during El Ni˜no and strong dur-
ing La Ni˜na events over the tropical Indian Ocean rim
countries.
During El Ni˜no years, the low vertical wind shear and
enhanced low-level vorticity (Fig. 12) provide a more favor-
able environment for TC development, while during La Ni˜na
years the opposite is the case in the Pacific Ocean. It is note-
worthy that over the NIO, VWS is lowered during the ENSO
warm phases, but relative vorticity is larger during La Ni˜na
years. RH is another key factor that influences TC develop-
ment. During negative phases of ENSO, positive humidity
anomalies are found over the NIO, while negative humidity
anomalies occur during positive ENSO phases. This is related
to a strengthened Walker circulation during La Ni˜na, and
larger moisture convergence over Indo-China. Considering
relative vorticity and RH together, the cold phase of ENSO
seems to be more favorable for TC development in the
NIO. In fact, this has been reported by previous studies
(Girishkumar and Ravichandran 2012;Ng and Chan 2012;
Felton et al. 2013;Girishkumar et al. 2015) when TC clima-
tology was examined and/or correlation analysis with
individual factors was performed, especially for the post-
monsoon periods and the more intense TCs (severe
cyclonic storm and very severe cyclonic storm categories).
This does not necessarily contradict the result here that
indicates a generally positive correlation with ENSO when
all cyclone intensities are included. It is also clear from
Fig. 12that during the positive phase of ENSO, SST over
NIO is significantly warm, and the low VWS is favorable
for TC development. During the positive phase of ENSO,
premonsoon SST is not significant as like postmonsoon sea-
son and VWS is much higher than that in postmonsoon (see
also supplementary Fig. 17).
Considering the SST distribution over the NIO, the IOD
has direct impact on TC development in that region (Yuan
and Cao 2013;Li et al. 2016). During positive IOD phases,
both BoB and AS are warm, and it has been established that
TC annual frequency is higher and the general storm motion
is more westward then, especially during postmonsoon peri-
ods (Yuan and Cao 2013).Li et al. (2016)reported on the
impact of IOD on TC development through midtropospheric
RH, long-term mean states of absolute vorticity, VWS and
potential intensity. Positive IOD TC frequency is reduced in
BoB when the eastern Indian Ocean is cold.Li et al. (2016)
also showed that there is an anticyclone in BoB during posi-
tive IOD phases. During negative IOD, there is cyclonic
anomaly in BoB. The TC correlation with IOD is much higher
than with ENSO, which is consistent withLi et al. (2016).As
shown inFig. 13, during positive (negative) IOD post mon-
soon seasons the maximum positive (negative) SST anoma-
lies, negative (positive) low-level vorticity, vertical wind shear
and relative humidity are more favorable environments for
TC development. During the premonsoon season, the SST,
VWS, RH show same characteristics but less significant and
vorticity is positive in IOD negative phase (see the supple-
mentary Fig. 18).
To conclude, the modeling framework developed in this
study has identified the ranked contributions from both
anthropogenic aerosols and natural climate variability
modes to TC variability in the NIO. In the order of
explained TC frequency variance, the most important fac-
tors are BC, OC, SS, and AMM followed with the IOD,
SO
4, and ENSO. The long-term trends of BC and OC emis-
sions have persistent effects onto the pre and postmonsoon
environment in the NIO where most TCs develop. Aerosol
influenceonTCactivityislikelytobeviathereduced
VWS within the monsoon circulation. When this dynamic
factor is coherent with the impact from the climate modes
such as the AMM, IOD, and ENSO, the effects are com-
bined, bringing substantial changes to TC frequency. Cer-
tainly, from our analysis of the TC-related environment
parameters under the influence of AMM, IOD, and ENSO,
it is also apparent thermodynamic factors such as RH
would have significant changes. For statistical models on
seasonal/interannual time scales andeven climate projec-
tions, aerosols and their radiative effects must be included
as predictors. For dynamical models, especially on climate
time scales, aerosol effects must be part of the modeling
framework to generate robust simulations of future TC
activity.
While AMM has been well studied for its influence on
hurricane activity over the Atlantic Ocean (e.g.,Vimont
and Kossin 2007), the physical link to TC development over
the NIO has not yet been clearly identified. ENSO and
IOD influences on TCs in the NIO have been extensively
studied. In this study, wefind ENSO explains a much
smaller amount of TC frequency variance than IOD. Two
aspects are relevant to this issue. First, only the canonical
ENSO index (i.e., the eastern PacifictypeofENSO)has
been analyzed. It is well known that ENSO behavior
changes in terms of the increasing occurrence frequency
of the central-PacifictypeofENSO( Pascolini-Campbell
et al. 2015), which must be considered in further research.
Second, ENSO and IOD are mutually interacting systems
(e.g.,Luo et al. 2010;Zhang et al. 2015;Le et al. 2020).
Improved statistical frameworks must be developed to
study the impacts of such interactive systems on TC
development.
Acknowledgments.This work is supported by National
Natural Science Foundation of China (Grant 42088101 and
42030605). M.W was supported by a China Postdoctoral
Special Funding and Start-Up Funding. Md. Arfan Ali by
China Scholarship Council, China. PK Bhaskaran was sup-
ported by the Department of Science and Technology
under the Climate Change Programme (CCP), Government
of India [Reference Number DST/CCP/CoE/79/2017(G)]
through a sponsored project.
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FIG. 13. As inFig. 11, but for IOD phase.
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Data availability statement.The datasets generated during
and/or analyzed during the current study are openly available
in a general repository (IMAS Data Portal;https://data.imas.
utas.edu.au/static/landing.html), and MERRA-2-based aero-
sol products were obtained fromhttps://giovanni.gsfc.nasa.
gov/giovanni/.
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