Impacts of Aerosols and Climate Modes on Tropical Cyclone Frequency over the North Indian Ocean: A Statistical Link Approach

Md. Wahiduzzaman aInstitute for Climate and Application Research/CICFEM/KLME/ILCEC, Nanjing University of Information Science and Technology, Nanjing, China

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https://orcid.org/0000-0002-8974-8247
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Md. Arfan Ali bSchool of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, China

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Kevin Cheung cDepartment of Climate Research, NSW Department of Planning Industry and Environment, Sydney, New South Wales, Australia

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Jing-Jia Luo aInstitute for Climate and Application Research/CICFEM/KLME/ILCEC, Nanjing University of Information Science and Technology, Nanjing, China

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Tang Shaolei aInstitute for Climate and Application Research/CICFEM/KLME/ILCEC, Nanjing University of Information Science and Technology, Nanjing, China

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Prasad K. Bhaskaran dDepartment of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

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Chaoxia Yuan aInstitute for Climate and Application Research/CICFEM/KLME/ILCEC, Nanjing University of Information Science and Technology, Nanjing, China

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Muhammad Bilal bSchool of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, China

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Zhongfeng Qiu bSchool of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, China

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Mansour Almazroui eCenter of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia
fClimate Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom

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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 Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean dipole (IOD), Pacific decadal oscillation (PDO), Pacific–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 found between TC activity and the AMM, IOD, and ENSO over the NIO. In addition, black carbon, organic carbon, sea salt, and sulfate aerosols have a significant 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 the first 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 modifications 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 Niño–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.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: M. Wahiduzzaman, wahid.zaman@nuist.edu.cn

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 Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean dipole (IOD), Pacific decadal oscillation (PDO), Pacific–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 found between TC activity and the AMM, IOD, and ENSO over the NIO. In addition, black carbon, organic carbon, sea salt, and sulfate aerosols have a significant 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 the first 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 modifications 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 Niño–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.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: M. Wahiduzzaman, wahid.zaman@nuist.edu.cn

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 several 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 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, hurricane counts using the Poisson regression model were used by Elsner and Schmertmann (1993) and Lehmiller et al. (1997) in the North Atlantic, and by McDonnell 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 hurricane activity (Elsner et al. 2008). Chand and Walsh (2010) took a similar approach for Fiji, Samoa, and Tonga, while Chu and Zhao (2007) applied the method to the central North Pacific. Nicholls (1979) and Gray (1984) described the first statistical 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. Relationship between hurricanes and the Sahel monsoon rainfall was considered by Landsea 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 Niño–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 considered in the Australian region (Solow and Nicholls 1990), and the September lead saturated equivalent potential temperature 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 by McDonnell and Holbrook (2004a,b) and for the eastern Indian Ocean, northern Australia, and southwest Pacific regions by McDonnell et al. (2006). Also, Niño-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 various basins using the Poisson model (Chiacchio et al. 2017).

Aerosols are the miniature solid and liquid particles hovering in the atmosphere. They are attributed to both natural and man-made sources. Once emitted, they are transported horizontally and vertically by atmospheric currents. Atmospheric 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 systems. 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 spatiotemporal 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 Niño–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 seasonal TC activity in NIO basins (Schott and McCreary 2001; Girishkumar and Ravichandran 2012; 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 anomalously warm sea surface temperature (SST) in the southeastern 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 surrounding the NIO. A Poisson regression model was developed by fitting a generalized additive model to find the relationships between climate modes, aerosols, and TC frequency over this region. A previous study by Chiacchio et al. (2017) considered the linear relationship among variables using Poisson regression by fitting a generalized linear model. In this study, we used a Poisson regression by fitting a generalized additive model that considers both linear and nonlinear relationships among five aerosol types: black carbon (BC), organic carbon (OC), sulfate (SO4), 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 oscillation (PDO), the Pacific–North American (PNA) teleconnection 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 climate modes, and it quantifies their relative contribution to modulate TC activity under the same modeling framework. It also attempts to find a possible link between TC variability and the large-scale geographical distribution of the TC genesis-related parameters, namely, SST, vertical wind shear, vorticity, and relative humidity.

The paper is organized as follows: details of the data, kernel density estimation, Poisson regression approach, and generalized additive model are described in section 2. Section 3 presents the results pertaining to TC activity and their relationship using statistical techniques. Discussion and conclusions are provided in section 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 locations (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 (below −0.5 standard deviation) phases of AMM, ENSO, and IOD are determined by the March–May (MAM), December–February (DJF), and September–November (SON)-averaged AMM, ENSO, and IOD SST anomaly (SSTA) series, respectively. The 850-hPa pressure level vorticity, relative humidity, wind data were collected from NCEP–NCAR reanalysis. 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
(u200u850)2+(υ200υ850)2
Here, (u200, υ200) and (u850, υ850) 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 Service (CAMS) project. The specific aerosol products (BC, DU, OC, SO4, and SS) are not available from observations, therefore, 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., aerosols, 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 assimilation technique to attain aerosol mass concentrations and trace gases. For anthropogenic emissions of chemical species, CAMS uses the Monitoring Atmospheric Composition and Climate–CityZen (MACCity) inventory at a spatial resolution of 0.5° × 0.5° from 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’s V correlation coefficient to find the relationship between TC frequency on one hand and eight climate modes and sunspot number as the solar variability on the other. Cramer’s V is a statistical quantity used to measure the strength of association between two nominal variables, and it considers the symmetric measure (Cramer 1946). It is measured as V=c2/[n(k1)], where c2 is the chi-square, n is the sample size, and k is 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’s V calculation. 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(λ)=β0+β1x1++βnxn
The model uses the logarithm of the rate (λ) 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 in Chiacchio et al. (2017).
We used the kernel density estimation for TC genesis distribution. Kernel density is a method for estimating the probability 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 in Wahiduzzaman et al. (2017). In the Poisson regression model, we used the generalized additive model (GAM) function to consider the linear and nonlinear nature of the 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 response y is normally distributed about its mean μ with variance σ2:
yN(μ,σ 2).
In the GAM case, the mean can be modeled as a linear combination of predictor variables, X1, X2, …, Xn
μ=β0+β1X1+β2X2++βnXn,
where β0 and βn are 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 distribution f from any exponential family of distributions (including the Poisson, binomial, and normal families). Second, 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
μ=f1(X1)+f2(X2)++fn(Xn).
Where the regression model seeks to estimate the regression coefficientsβ0βn, the additive model seeks to estimate these smooth transformations f1, …, fn.

More details about GAM are available in Wahiduzzaman 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).

Fig. 1.
Fig. 1.

Tropical cyclone frequency for the north Indian Ocean during 1980–2019.

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

TC activity in the NIO region exhibits a bimodal characteristic (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 2 illustrates the density distribution of cyclogenesis during the premonsoon and postmonsoon periods using kernel density estimation. These two seasons contributes almost three-quarters of the annual total, consistent with the findings by Wahiduzzaman et al. (2017) and Wahiduzzaman and Yeasmin (2020).

Fig. 2.
Fig. 2.

Distribution of tropical cyclone genesis during (left) premonsoon and (right) postmonsoon over the north Indian Ocean region (0°–30°N, 50°–100°E) using kernel density estimate. Green colors show the highest density (concentration of TC count per square kilometer) area of genesis.

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

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’s V correlation and fraction of explained log likelihood (pseudo R2) from the Poisson regression. ENSO, IOD, PNA, and PDO values are measured 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 in Fig. 3 (see also supplementary Fig. 5). As described in section 2 (data and methods) below, using Cramer’s V technique, Fig. 3 shows a very strong (0.25 or higher) relationship with AMM, a strong (0.15–0.25) relationship 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 influence TC variability are AMM and IOD, the physical interpretation of which will be discussed in section 4.

Fig. 3.
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’s V 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 correlation (0–0.05).

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

This study also focused on AOD for BC, DU, OC, SO4, SS, and their total contribution (TOT). The distribution of AOD during the premonsoon and postmonsoon periods, as well as annually, are shown in Figs. 46. AOD is an indicator of the columnar aerosol mass and is a measure of the spatial distributions of aerosols (i.e., BC, DU, OC, SO4, SS). Several previous 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 accuracy with respect to observations (i.e., satellite onboard MODIS-based AOD). Figure 4 shows the mean annual spatial 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). Similar results are also evident for OC and SO4 (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.

Fig. 4.
Fig. 4.

Mean spatial distribution of MERRA-2-derived AOD for the period 1980–2018 (a) BC, (b) DU, (c) OC, (d) SO4, (e) SS, and (f) TOT AOD.

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

Fig. 5.
Fig. 5.

As in Fig. 4, but for the premonsoon period.

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

Fig. 6.
Fig. 6.

As in Fig. 4, but for the postmonsoon period.

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

In Fig. 5, seasonal variations of BC, OC, and SO4 are evident 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 SO4 aerosol is seen over Bangladesh, Myanmar, and the southern and eastern coastal areas of India (Figs. 5a,c,d). Figure 5b shows that the distribution of Dust 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 SO4) during premonsoon 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 activity 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, SO4) are dominant over the IGP during the postmonsoon 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 aerosols are shown in Fig. 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 aerosol, a moderate (0.11–0.15) relationship with SO4, and negligible 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 spatial 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).

Fig. 7.
Fig. 7.

As in Fig. 3, but for BC, DU, OC, SO4, SS, and TOT.

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

From our GAM model, the explained log likelihoods of BC and OC are approximately 25% (Fig. 8 and supplementary Fig. 14). During extreme weather events, large sea salt particles 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 significant 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-R2 values. They are statistically significant with pseudo-R2 values of 18%–20% in the NIO and can be attributed to large-scale circulation features discussed in this study.

Fig. 8.
Fig. 8.

Pseudo R2 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 aerosols shown in magenta color through the Student’s t test.

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

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), vorticity, vertical wind shear, and relative humidity on TC activity 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 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 Atlantic and South Pacific. As discussed by Chiacchio 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 associated with high VWS depending on the upper-level circulation. 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 thermodynamic and dynamic conditions to ultimate TC development. 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 by Evan 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 circulation is weakened, the associated VWS is also reduced and 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 by Evan et al. (2011), with the highest values north of about 10°N 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 SO4 concentrates 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 SO4 in Bay of Bengal around 10°N (Fig. 9 and supplementary 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 reduction effect to VWS is also clear by all four aerosol types, and for BC, OC, and SS the results over Arabian Sea are more significant (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 of Evan 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 that Wang et al. (2014) did emphasize 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, leading to modified convection near the TC eyewall versus the outer rainbands, rather than the long-term variability of TC frequency.

Fig. 9.
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.

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

AMM is a dynamic mode of climate variability on interannual 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 examined (not shown), the remote anomaly patterns of these factors via teleconnections over the Pacific and Indian Oceans are clear. The following discussion focuses on the NIO. Figure 10 shows 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 that Chiacchio 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 environmental parameters as in our study, VWS was clearly lowered during the positive phases of AMM.

Fig. 10.
Fig. 10.

Regression of the AMM index onto (a) RH, (b) relative vorticity, and (c) VWS over the NIO. The stippled areas indicate statistical significance exceeding the 95% confidence level.

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

AMM is known to possess maximum variability during boreal spring (Kossin et al. 2010). This is evident in the composite patterns of the environmental factors, both globally and over the NIO. They are different during the premonsoon and postmonsoon seasons. In general, the statistical significance 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 cyclogenesis occurs. Nonetheless, VWS is also low during the negative phase of AMM over most regions of NIO. The observation in postmonsoon season is also consistent with premonsoon season but not significant (see the supplementary Fig. 16).

Fig. 11.
Fig. 11.

Composite anomalies of SST, relative vorticity, vertical wind shear, and relative humidity during the premonsoon 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’s t test.

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

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 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 Niño and strong during La Niña events over the tropical Indian Ocean rim countries.

Fig. 12.
Fig. 12.

As in Fig. 10, but for ENSO phase in the postmonsoon period.

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

During El Niño years, the low vertical wind shear and enhanced low-level vorticity (Fig. 12) provide a more favorable environment for TC development, while during La Niña years the opposite is the case in the Pacific Ocean. It is noteworthy that over the NIO, VWS is lowered during the ENSO warm phases, but relative vorticity is larger during La Niña years. RH is another key factor that influences TC development. 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 Niña, 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 climatology was examined and/or correlation analysis with individual factors was performed, especially for the postmonsoon 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. 12 that 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 season 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 periods (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 positive 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 with Li et al. (2016). As shown in Fig. 13, during positive (negative) IOD post monsoon seasons the maximum positive (negative) SST anomalies, 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 supplementary Fig. 18).

Fig. 13.
Fig. 13.

As in Fig. 11, but for IOD phase.

Citation: Journal of Climate 35, 8; 10.1175/JCLI-D-21-0228.1

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 factors are BC, OC, SS, and AMM followed with the IOD, SO4, and ENSO. The long-term trends of BC and OC emissions have persistent effects onto the pre and postmonsoon environment in the NIO where most TCs develop. Aerosol influence on TC activity is likely to be via the reduced 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 combined, bringing substantial changes to TC frequency. Certainly, 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 and even climate projections, 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, we find 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 Pacific type of ENSO) has been analyzed. It is well known that ENSO behavior changes in terms of the increasing occurrence frequency of the central-Pacific type of ENSO (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 supported 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.

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 aerosol products were obtained from https://giovanni.gsfc.nasa.gov/giovanni/.

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