Southern and Tropical Indian Ocean SST: A Possible Predictor of Winter Monsoon Rainfall over South India

Ravi P. Shukla, Shailendra Rai, Avinash C. Pandey

Centre for Ocean-Land-Atmosphere Studies, Calverton, USA.
K. Banerjee Center of Atmospheric & Ocean Studies, Institute of Interdisciplinary Studies.

DOI: 10.4236/acs.2013.34045                

Abstract

The complexities in the relationship between winter monsoon rainfall (WMR) over South India and Sea Surface temperature (SST) variability in the southern and tropical Indian Ocean (STIO) are evaluated statistically. The data of the time period of our study (1950-2003) have been divided exactly in two halves to identify predictors. Correlation analysis is done to see the effect of STIO SST variability on winter monsoon rainfall index (WMRI) for South India with a lead-lag of 8 seasons (two years). The significant positive correlation is found between Southern Indian Ocean (SIO) SST and WMRI in July-August-September season having a lag of one season. The SST of the SIO, Bay of Bengal and North Equatorial Indian Ocean are negatively correlated with WMRI at five, six and seven seasons before the onset of winter monsoon. The maximum positive correlation of 0.61 is found from the region south of 50⁰ S having a lag of one season and the negative correlations of 0.60, 0.53 and 0.57 are found with the SST of the regions SIO, Bay of Bengal and North Equatorial Ocean having lags of five, six and seven seasons respectively and these correlation coefficients have confidence level of 99%. Based on the correlation analysis, we defined Antarctic Circumpolar Current Index A and B (ACCIA (A) & ACCIB (B)), Bay of Bengal index (BOBI (C)) and North Equatorial Index (NEI (D)) by averageing SST for the regions having maximum correlation (positive or negative) with WMRI index. These SST indices are used to predict the WMRI using linear and multivariate linear regression models. In addition, we also attempted to detect a dynamic link for the predictability of WMRI using Nino 3.4 index. The predictive skill of these indices is tested by error analysis and Willmott’s index.

Keywords

Winter Monsoon Rainfall over South India; Southern/Tropical Indian Ocean; Multivariate/Linear Regression Models

1. Introduction

Indian summer monsoon, which is a part of the Asian monsoon system, is a regular annual phenomenon which brings heavy rainfall to India and adjacent countries during summer monsoon season (June to September; JJAS). It contributes about 70% - 90% of rainfall in most parts of country whereas, the rainfall during October-November-December (OND) over south India which is commonly referred as winter monsoon rainfall. It contributes about 50% of annual rainfall in the east cost of Indian Peninsula. The winter monsoon is highly variable both spatially and temporally. During winter monsoon season the prevailing wind becomes north-easterly and the zone of maximum rainfall migrates to southern India and Sri Lanka. Over the years, there are many instances of the years with flood (strong monsoon) or drought (weak monsoon) during which South India as a whole receives excess or deficient seasonal rainfall, respectively [1]. In an agricultural country like India, the success or failure of the summer/winter monsoon and its effects on agricultural production and water scarcity on regional basis are always of great concern. Even small fluctuation in the seasonal rainfall can have devastating impacts on agricultural sector. Winter monsoon rainfall (WMR) is comprised of five meteorological subdivisions namely Coastal Andhra Pradesh (CAP), Tamilnadu (TAM), Kerala (KER), South Interior Karnataka (SIK) and Rayalaseema (RAY) of South India. The water received during the winter monsoon season is utilized for various purposes viz, hydroelectric power production, irrigation of farm lands and drinking water in these subdivisions. Hence, the amount of rainfall during winter season over South India is very significant. Accurate long range prediction of winter monsoon rainfall can improve planning to mitigate the adverse impacts of rainfall variability and will be beneficial to policy makers and farmers both.

Further, many researchers have studied the predictability/trends of the Indian summer monsoon [2-16] using dynamical as well as statistical models. For more than one century, the prediction of Indian Monsoon Rainfall has been based on empirical models [2,3,17-21]. The prediction and predictability of WMR is still in early stage in spite of its potential societal and economical impact for South India. There are few studies which try to predict WMR using India ocean dipole index, Southern oscillation Index [22-27] and applying statistical technique.

Gradients of Sea Surface Temperature (SST) are important in determining the position of precipitation over the tropics including monsoon regions. It is assumed that the SST Anomaly (SSTA) over Indian Ocean vitally determines the monsoon rainfall variability. Shukla and Fennessy [28] pointed out that the annual cycle of SST in the Indian Ocean is significant in establishing the monsoon circulation and rainfall using General Circulation Model (GCM). The association of WMR on global climate including ENSO and Indian Ocean SST is still in early stage. Influence of ENSO and IOD on WMR has been investigated by Bhanu Kumar et al. [29] and interesting relationships were traced out. Kripalani and Kumar [30] extended the study between the northwest monsoon rainfall and IOD. Furthermore, an influence of monsoon upper-air temperatures over India on WMR was thoroughly studied by Bhanu Kumar et al. [31]. In all the above studies related to WMR, no attempt was made to understand the effect of sea Surface temperature variability in the Southern as well as tropical Indian Ocean (STIO) on the WMR monsoon on lead-lag of 8 seasons (2 years).

In the present study, attempt has been made to understand the Indian Ocean SST variability over STIO region with WMRI for 53 years (1950-2003) of extended Reynolds Smith reconstructed version 2 data [32]. The quality of the Reynolds SST version 1.0 data was very poor in southern ocean region (below 50˚S) but version 2.0 has improved much due to the inclusion of new schemes.

The variability of STIO SST and WMRI relationship is examined under the lead-lag time scales of 08 seasons for providing some insight into the possibility of early prediction of winter monsoon rainfall. This relationship will enable us to understand dependency of winter monsoon conditions on Southern Indian Ocean (SIO) SSTA, and in turn will provide important clues to oceanic system memory, which is still poorly understood.

Correlation analysis is done to see the effect of STIO SST and Niño-3.4 index [33,34] on WMR with a lead-lag of 8 seasons. Based on this analysis, we have defined SST index of those seasons which have confidence level of more than 99%. The regression and multiple regression technique have been used to study the predictability of the WMRI with above indices individually as well as in various combinations.

2. Data Used

The winter monsoon rainfall amounts for 5 meteorological subdivisions namely CAP, RAY, TAM, SIK and KER have been collected from the Indian Institute of Tropical Meteorology (IITM) website for the period 1950-2003. The mean of these meteorological subdivisions have computed and termed as Winter Monsoon Rainfall index over South India.

The extended reconstructed SST (ERSST, version 2.0, 2˚ ´ 2˚ resolutions) is used in the present study, which employs improved high frequency analysis and error analysis, use to sea ice to SST conversion algorithm and adjustment of the bias correction [32]. This data set contains global record of monthly SST from 1871 to 2003 but due to the uncertainties and data scarcity in the earlier data set of 19th century the temporal coverage has been considered only from 1949 to 2003.

3. Results and Discussion

3.1. Correlation Analysis between STIO SST and Winter Monsoon Rainfall Index (WMRI)

The correlation coefficients between SST variability in the STIO region for the seasons January-March (JFM), April-June (AMJ), July-September (JAS), and OctoberDecember (OND) with WMRI have been computed and presented in Figure 1 with a lead-lag of 0 - 7 seasons. We have used the leading correlation of WMRI up to 2 years which will enable us to study the long-term effect of STIO SST on winter monsoon. Positive correlation between STIO SST in JAS season with a lead of one season w.r.t. WMRI is found in Figure 1(a) having a maximum value of 0.61 which is significant above 99% confidence level near 90˚E, 55˚S. The positive significant correlations are similar for the region 50˚S to 60˚S by leading SST for 2 (AMJ) and 3 (JFM) seasons respectively. We have extended our analysis by leading SST with respect to WMRI for more than a year. The interesting finding is that the correlations for the domain of our study are negative by leading SST more than a year. The correlations are found to be low for most of the domain although significant at few scattered places by lagging SST for 2 to 5 seasons in the STIO region. For JAS season (6 season before onset the winter monsoon) (Figure 1(b)), the negative correlation value reaches a maximum of −0.5 with a confidence level of 99% near 50˚S and 110˚E (Figure 1(b)). For AMJ and JFM (7 and

(a)(b)

Figure 1. (a) (b) Correlation coefficient (r) between sea surface temperature (SST) variability in the Southern/Tropical Indian Ocean (STIO) and Winter Monsoon Rainfall Index (WMRI) over South India lead-lag by 1 - 8 season(s) (The quantity in bracket indicates the lead-lag in years).

8 seasons before the onset of the monsoon), negative correlation is high over Bay of Bengal and near southern tip of India respectively. The correlation between Nino 3.4 index and WMRI with a lead lag of 8 seasons have been computed and shown in Figure 2. It is found that Nino 3.4 index with a lead of 2 - 3 season have a positive correlation of 0.29 and 0.24 respectively whereas a negative correlation of 0.49 has been found with a lead of 7 seasons.

Based on the correlation analysis we have selected predictors by taking area averaged SST anomaly for the regions having highest positive or negative correlations and is tabulated in Table 1. The indices, defined thus are ACCIA (A), ACCI B (B), BOBI (C) and NEI (D) and were correlated with WMRI shown in Figures 3(a)-(d) and its value is 0.61, −0.60, −0.53 and −0.57 respectively.

Figure 2 shows the correlation coefficients of the Niño 3.4 index with WMRI. The following predictor has been found to have a correlation with the WMRI for period 1950-1976 with a confidence level of 99%: Niño3.4 index (seven seasons before onset of Monsoon) and its value is −0.49 (Figure 3(e)).

Conflicts of Interest

The authors declare no conflicts of interest.

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