Nat Hazards DOI 10.1007/s11069-016-2148-9 ORIGINAL PAPER
Precipitation extremes during Indian summer monsoon: role of cyclonic disturbances J. V. Revadekar1 • Hamza Varikoden1 • B. Preethi1 M. Mujumdar1
•
Received: 7 June 2014 / Accepted: 1 January 2016 Ó Springer Science+Business Media Dordrecht 2016
Abstract The Indian summer (JJAS) shows high variability in both space and timescales. Changes in precipitation extremes play an important role on the regional scale due to their serious socio-economic consequences. This study, therefore, is mainly focused on understanding the variation of precipitation extremes during summer monsoon season in the presence of cyclonic disturbances forming over the Bay of Bengal (BOB), Arabian Sea, Land Area (LA) and Total. For this, several indices of observed precipitation extremes, in terms of frequencies, intensities and spell duration have been computed for the period 1951–2007 using daily APHRODITE data of 0.5° latitude 9 0.5° longitude resolution. Correlation analysis reveals that a large part of the country exhibits positive relationship between the indices of precipitation extremes and frequency of cyclonic disturbances. Correlations with the indices of frequencies defined as seasonal count of days when rainfall exceeds 30, 20 and 10 mm show that spatial extent and strength of the positive relationship decreases with increase in threshold values. Disturbances forming over BOB play dominant role in precipitation during Indian summer monsoon. Keywords
Summer monsoon Precipitation extreme Cyclonic disturbances
1 Introduction The air–sea interaction plays an important role in modulating the structure and dynamics of summer monsoon (JJAS) circulation over the Asian monsoon domain. Cyclonic disturbances are generated due to the tightly coupled air–sea interaction during summer monsoon season (Mohapatra and Mohanty 2007). These cyclonic disturbances are mainly
& J. V. Revadekar
[email protected] 1
Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Pashan, Pune 411008, India
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formed over tropical warm waters where the sea surface temperatures are more than 26 °C (Henderson-Sellers et al. 1998). Intensities of cyclonic disturbances are classified using wind strength over sea. However, over the land and adjoining sea area, this classification is mainly based on the number of closed isobars (Saha et al. 1981; Raghavan and Rajesh 2003; Joseph 1976). These cyclonic disturbances are obviously important to society, and they produce widespread havoc over their passage and adjoining areas of the track in terms of loss of human life and economy (Anthes 1982; Peilke et al. 1998). In the scenarios of global warming, the long-term linear trend of cyclonic storms in terms of its frequency and intensity has been found significantly decreasing over the north Indian Ocean (Srivastava et al. (2000). Rajeevan et al. (2000) also found similar results during summer monsoon period over the Bay of Bengal (BOB) for the period 1901–1998 with a decrease of 1 storm per decade. However, Webster et al. (2005) observed that based on Saffir–Simpson scale, tropical storms of the category 4 and 5 are increased during 1970–2005 (http://www.aoml.noaa.gov/general/lib/laescae.html). Number of cyclone days has also shown considerable decrease over Indian Ocean. On the global perspective, increasing trend in tropical cyclones is seen in the back drop of warming tropical oceans (Emanual 2005). Rajendra Kumar and Dash (2001) noticed the coherence of epochal variability of tropical disturbances with the epochal variability of Indian summer monsoon rainfall (ISMR). Understanding the role of these tropical disturbances in the precipitation extremes during summer monsoon is of paramount importance. Earlier studies (Joseph 1981a, b; Mooley and Parthasarathy 1984; Verma et al. 1985; Sontakke et al. 1993; Parthasarathy et al. 1994) indicate high variability of ISMR with an epochal nature, and it also reported that ISMR did not have any significant trend during last more than 100 years (Rupa Kumar et al. 1992). Various weather systems such as tropical cyclones, depressions and weak disturbances contribute to monsoon rainfall (Ramage 1971; Krishnamurty 1979). Numerous studies have been made to understand the possible mechanism in inter-annual variability of the ISMR (Chen and Weng 1999). In addition to monthly and seasonal rainfall, extreme climate events in terms of the frequency, intensity and duration assume profound importance on the local, regional and national scales due to the severe consequences (Revadekar et al. 2011). Revadekar and Preethi (2012) have shown significant positive relationship between yield and the precipitation extremes; however, heavy rainfall events are less useful. Adaptation and mitigation measure are required due to changes in the precipitation extreme events. Goswami et al. (2006) have done the analysis on extreme rainfall events over Central Indian region during the summer monsoon season over the period 1951–2003. Their analysis indicates the increase in frequency and magnitude of extreme rain event, and this increase has been compensated by decrease in frequency of moderate and weak rainfall events. Other studies also indicate similar results over different parts of India (Sinha Ray and Srivastava 2000; Joshi and Rajeevan 2006). For the Indian region, Alexander et al. (2006) have shown the largest declining trends in the annual number of consecutive dry days and increasing trends in precipitation extremes. Roy and Balling (2004) also have shown that indices of precipitation extremes exhibit increasing trends. In view of all the above, the variability in precipitation extremes during Indian summer monsoon is examined in the presence of cyclonic disturbances forming over the BOB, the Arabian Sea (AS) and Land Areas (LA).
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2 Data and methodology 2.1 Data Monthly frequencies of cyclonic disturbances (depressions and above) forming over the BOB, AS and LA during summer monsoon season (June to September) are obtained from India Meteorological Department (www.imd.gov.in) for the period 1891–2012 to examine their role in precipitation extremes over India. This study is mainly focused on understanding the variability of precipitation extremes during the summer monsoon season in the presence of cyclonic disturbances. For this, several objectively defined indices of observed precipitation extremes, in terms of frequencies, intensities and spell duration have been computed at each grid point using daily APHRODITE rainfall data for the period 1951–2007 at 0.5° latitude 9 0.5° longitude resolution.
2.2 Method of analysis The joint World Meteorological Organization (WMO) Commission for Climatology (CCl)/ World Climate Research Programme (WCRP) project on Climate Variability and Predictability (CLIVAR)/Joint WMO-Inter-governmental Oceanographic Commission (IOC) Technical Commission on Oceanography and Marine Meteorology (JCOMM) Expert Team on Climate Change Detection and Indices (ETCCDI) coordinated the development of a suite of climate change indices that primarily focus on extremes (Peterson et al. 2001). These indices are derived from daily temperature and precipitation data. The development of the indices is described at the link http://cccma.seos.uvic.ca/ETCCDMI/, including a user-friendly software package, which is freely available to the international research community. In all, 27 indices of precipitation and temperature extremes were defined which have been widely used for global and regional analyses of climate extremes (Alexander et al. 2006; Klein Tank et al. 2006).
Table 1 List of indices of precipitation extremes used in the study Index (unit)
Description
Definition
R10mm (days)
Frequency
Number of days in summer monsoon (1 June to 30 September) with rainfall [10 mm
R20mm (days)
Frequency
Same as above but for [20 mm
R30mm (days)
Frequency
Same as above but for [30 mm
RX1day (mm)
Intensity
1-day maximum precipitation
RX5day (mm)
Intensity
5-day maximum precipitation
R95p (mm)
Intensity
Seasonal rainfall due to heavy rainfall event [95th percentile
R99p (mm)
Intensity
Seasonal rainfall due to very heavy rainfall event [99th percentile
CWD (days)
Spell duration
Continuous wet days when rainfall [1 mm
SDII (mm/rainy days)
Intensity
Average seasonal daily precipitation due to rainy days
Percentile values are based on daily precipitation data for the period 1961–1990
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A study of spatial variations of precipitation extremes during the summer monsoon season of 1951–2007 over the country has been made by computing various objectively defined indices of precipitation extremes (Table 1). These indices are computed at each grid individually using the daily rainfall data. A day is considered as a rainy day when rainfall of the day exceeds 1 mm. Frequency of moderate rainfall day, heavy rainfall day and very heavy rainfall days is computed as number of rainy days during the season exceeds a fixed threshold values of 10, 20 and 30 mm, respectively, at each grid. An extreme event at one place may be treated as a normal event at another place and vice versa based on the rainfall condition; therefore, in addition to the fixed threshold values heavy and very heavy rainfall events are also studied using percentile threshold values. It is quite obvious that the percentile threshold value varies from grid to grid depending on the precipitation climatology of a particular grid. One of the most significant threats of cyclonic disturbances is heavy rainfall. Large, slow-moving and non-sheared system produces heaviest rainfall. Therefore, study also uses intensity indices computed as 1- and 5-day maximum precipitation at each grid. Role of cyclonic disturbances during the Indian summer monsoon season on spatial distribution of precipitation extremes over the country has been studied with the correlation analysis done between seasonal time series of the indices at each grid and time series of cyclonic disturbances forming over BOB, AS and LS. Correlation coefficients are calculated using the Spearman rank correlations method. Only correlation coefficients that are significant with p \ 0.05 are shown in spatial plots.
3 Results and discussion 3.1 Variability in cyclonic disturbances The main measures in quantitative statistics are mean which indicates central tendency, standard deviation which indicates spread of the data and trends to understand the direction of change. These statistical properties of the cyclonic disturbances formed over BOB, AS and LA during summer monsoon are described in Table 2. Based on the data for the period 1891–2013, seven cyclonic disturbances formed over Indian Ocean and Land per year. The Bay of Bengal contributes maximum towards it with five disturbances per year. Disturbances formed over the BOB normally move westward (IMD 1979; 1996). Cyclonic disturbances formed over the AS and LA are much less (Jadhav and Munot 2009). Table 2 also shows similar feature with only one disturbance per year over LA. Variability of cyclonic disturbances is high over the BOB and lowest over the AS. Figure 1 shows yearto-year variation in frequency of cyclonic disturbances formed over the BOB, AS and LA.
Table 2 Mean, standard deviation and trend in cyclonic disturbances formed during 1891–2013 over the BOB, AS, LA and Total
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Region
Mean
SD
Trend
BOB
5.0
2.23
-2.6/100 years
AS
0.6
0.71
?0.5/100 years
LA
1.0
0.99
-0.17/100 years
Total
6.6
2.63
-2.2/100 years
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Fig. 1 Time series of frequency of cyclonic disturbances forming over Bay of Bengal (top left), Arabian Sea (top right), Land (bottom left) and Total (bottom right). Thick continuous line represents 5-year moving average and dash line indicates trend line
Cyclonic disturbances formed over the BOB show decreasing trend of 2.6 cyclonic disturbances per 100 years with 1 % significant level. This decreasing trend significantly contributes towards the total number of disturbances formed over the Indian Oceans and LA. Therefore, decreasing trend can also be seen in total number of cyclonic disturbances with slightly lesser magnitude (2.2 cyclonic disturbances per 100 years), which is coherent with the findings of Rajeevan et al. (2000). Disturbances forming over the AS show slight increasing trend of 0.5 disturbances per 100 years. In addition to linear trend, cyclonic disturbances show epochal variation. The peak epochs in cyclonic disturbances formed during 1930–1950 resemble with the epochs of ISMR flood period (Parthasarathy et al. 1994). The most pronounced monsoon winds of the world are those that flow over the Indian subcontinent from June to September every year. The summer monsoon accounts for *80 % of the annual rainfall for most of the India. The highest precipitation for India as a whole occurs during the months of July and August. Similarly, high frequency of cyclonic disturbances formed over the BOB is also seen during these 2 months (Fig. 2). Lower frequencies are very common in the months of June and September during which monsoon onset and withdrawal take place, respectively. The figure also indicates the many incidences without any cyclonic disturbance during month of June.
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Fig. 2 Frequency of distribution of cyclonic disturbances formed over BOB during June (top left), July (top right), August (bottom left) and September (bottom right)
3.2 Impact of cyclonic disturbances on rainfall 3.2.1 Genesis of cyclonic disturbances The space–time variation of rainfall often occurs in association with the genesis and movement of synoptic scale systems, such as cyclonic disturbances (Patwardhan and Bhalme 2001). Large-scale spatio-temporal variation of precipitation over Orissa, an eastern coastal state of India, caused by the interaction of the basic monsoon circulation with the monsoon disturbances over the Bay of Bengal in addition to the orography over the region (Mohapatra and Mohanty 2007). A slight increase in number and intensity of disturbances is more dangerous and causes widespread disasters (Niyas et al. 2009). Many studies attempted analysis on the long-term trends and variability in the frequencies of cyclonic disturbances forming over the Indian Ocean (Shyamala and Iyer 1996; Singh et al. 2000; Singh 2001; Srivastava et al. 2000; Singh and Rout 1999). The cyclonic disturbances are 5–6 times more frequent over the BOB than the AS (Niyas et al. 2009). Joseph and Xavier (1999) studied the time series of ISMR and the frequencies of monsoon depressions and tropical cyclones using harmonic analysis and found that a large number of lowpressure systems (LPS) clustered along the monsoon trough are associated with active phase. During break phases, very few LPS are formed, and they clearly avoid the monsoon trough region, and they form either near the foothills of Himalayas or off the western coast and move westward (Kazi and Bhamare 2013). Therefore, genesis of cyclonic disturbance is important in the spatial distribution of rainfall over India. In this view, spatial
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distribution of rainfall is studied in relation to the disturbances forming over the BOB, AS, LA and Total (TOT) separately (Fig. 3). For this purpose, rank correlations are computed between number of cyclonic disturbances formed and summer monsoon rainfall at each grid for the period 1951–2007. Spatial distribution of correlation coefficients significantly changes with the genesis of cyclonic disturbances. Disturbances formed over the BOB show positive correlations with rainfall over large part of the country indicating
Fig. 3 Spatial distribution of Spearman’s rank correlations between summer monsoon rainfall and frequency of cyclonic disturbances form over Bay of Bengal (top left), Arabian Sea (top right), Land (bottom left) and Total (bottom right) for the period 1951–2007 based on the APHRODITE data set (shade represents significant correlation coefficients with p \ 0.05)
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enhancement in rainfall. Strong correlations are seen along the monsoon trough, west coast and north-eastern parts of India. However, these systems do not contribute towards the rainfall in leeward side of the Western Ghats. Disturbances formed over the AS show large impact on enhancement of rainfall in western side of India. Strong correlations are seen over Northwest India, Western Ghat and Peninsular regions, where as the parts of northeastern region experience subdued rainfall activities. Thus, the driving atmospheric circulation in the presence of disturbances is important in rainfall patterns. As the contribution of number of cyclonic disturbances formed over the BOB towards the total is higher, spatial distribution of correlations for the total number of disturbances with rainfall resembles with correlation patterns of rainfall during disturbances in the BOB.
3.2.2 Variability in cyclonic disturbances The onset of summer monsoon season over India is expected around the end of May or at beginning of June, and it fade away by the end of September or at the beginning of October. However, large part of the country receives rainfall during the peak monsoon months (July and August). The ISMR shows large year-to-year variability in its spatiotemporal distribution. The monsoon rainfall is very important for food security, agricultural production and water availability (Mishra et al. 2012). Also the variability in rainfall may differ from month to month; therefore, the spatial patterns of correlations between frequency of cyclonic disturbances and rainfall are examined for the individual summer monsoon months. Rainfall during June and September is less compared with the rainfall during July and August; therefore, correlation patterns are shown for peak monsoon months July and August (Fig. 4). During July and August, the monsoon is set over the country and rainfall activities are higher due to strong low-level jet stream (Joseph and
Fig. 4 Spatial distribution of Spearman’s rank correlations between frequency of cyclonic disturbances and rainfall during the months of July (left) and August (right) for the period 1951–2007 using APHRODITE data set (shade represents significant correlation coefficients with p \ 0.05)
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Sijikumar 2004). Though the frequencies of cyclonic disturbances are higher during these months, rainfall activity in response to disturbances is not well distributed. Significant correlations are seen along the monsoon trough and the parts of Western Ghat. Northwestern India receives low rainfall during summer monsoon with high rainfall variability. The figure indicates that the region receives good amount of rainfall during cyclonic disturbances formed during July and August. Correlations are stronger and widespread during the month of August than the month of July.
3.2.3 Impact on precipitation extremes The detection of the occurrence and changes in extreme climate events assumes profound importance on the regional scale. Changes in the frequency, intensity as well as spell duration of extreme climate events would have intense impacts on human society, infrastructure, natural resources and ecosystem. They are expressions of the variability; therefore, the nature of variability in spatial scales in the presence of cyclonic disturbances is important to our understanding of extremes. Therefore, various indices of extremes are computed using daily APHRODITE rainfall data at each grid as listed in Table 1. Spatial patterns of correlation coefficient between these indices of precipitation extremes and frequencies of cyclonic disturbances are also examined. Spatial patterns of the correlation coefficients of these indices show resemblance with the spatial patterns of correlations with seasonal precipitation (Figs. 5, 6, 7, 8). Correlations with the indices of frequencies of precipitation extremes defined as seasonal count of days when the rainfall exceeds 30, 20 and 10 mm show that strength of the positive relationship and its spatial extent decreases with increase in threshold values (Fig. 5). It indicates that the frequencies of precipitation extremes increase with increase in frequencies of cyclonic disturbances over large part of the country. It is well known that the rainfall distribution over India varies considerably from day to day. Over major parts, rain occurs in spells under the influence of favourable circulation conditions. The country receives heavy rainfall along the path of cyclonic disturbances. Large parts of the country experience increase in rainfall during its presence. Therefore, 1-day maximum precipitation (RX1), 5-day maximum precipitation (RX5), rainfall due to heavy rainfall event (R95) and rainfall intensity (SDII) also exhibit positive
Fig. 5 Spatial distribution of Spearman’s rank correlations between frequency of cyclonic disturbances and precipitation extremes: (1) R10: seasonal count of days exceeding rainfall 10 mm (left). (2) R20: seasonal count of days exceeding rainfall 20 mm (middle) and (3) R30: seasonal count of days exceeding rainfall 30 mm (right) for the period 1951–2007 using APHRODITE data set (shade represents significant correlation coefficients with p \ 0.05)
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Fig. 6 Spatial distribution of Spearman’s rank correlations between cyclonic disturbances and precipitation extremes (1) RX1: 1-day maximum precipitation (left) and (2) RX5: 5-day maximum precipitation (right) for the period 1951–2007 using APHRODITE data set (shade represents significant correlation coefficients with p \ 0.05)
relationship with frequency of cyclonic disturbances (Figs. 6, 7, 8, respectively). Higher impact of disturbances is seen on 5-day maximum precipitation and R95p. All indices of precipitation extremes show widespread positive correlations over large part of country. The enhancement in precipitation extremes contributes towards the seasonal precipitation. Therefore, an attempt is also made to examine the impact of frequency of cyclonic disturbances on summer precipitation over All India and Central India for the period 1951–2007 (Fig. 9). Significant correlation is seen for the both All India and Central India summer monsoon rainfall with the frequency of cyclonic disturbance. Impact of cyclonic disturbances on rainfall is mainly over monsoon trough region and western Ghat. They have very short duration compared with seasonal length of summer monsoon, and also they follow certain track along which high rainfall activities occur. Therefore, higher correlation is seen for the central Indian region. Rainfall distribution over India varies considerably from day to day. Over major parts, rain occurs in spells under the influence of favourable circulation conditions. This intermittent behaviour of rainfall is associated with a hierarchy of quasi-periods (Kripalani et al. 2004). A substantial component of this variability over the region arises from the fluctuations on the intra-seasonal scale between active spells with good rainfall and weak spells or breaks with little rainfall (Rajeevan et al. 2010). When monsoon trough shifts south, plenty of rain occurs over the main land, which is known as ‘active’ monsoon. Monsoon trough intensifies as monsoon westerlies, the convergence of water vapour flux and monsoon rainfall intensifies during an active phase of the monsoon (Yoon and Chen 2005). There have been several studies on impact of ENSO on changes in summer monsoon rainfall and extremes over the Indian region (e.g. Pant and Rupa Kumar 1997; Krishna Kumar et al. 1999).
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Fig. 7 Spatial distribution of Spearman’s rank correlations between frequency of cyclonic disturbances and precipitation extremes (1) R95p: rainfall due to heavy rainfall event exceeding 95th percentile and (2) R99p: rainfall due to heavy rainfall event exceeding 99th percentile for the period 1951–2007 using APHRODITE data set (shade represents significant correlation coefficients with p \ 0.05)
Summer monsoon rainfall is also contributed by the westward moving synoptic systems generated over the high SST regions of the Bay of Bengal (Joshi and Pandey 2011). The monsoon intercepts the rain-bearing winds and consequently yields high rain over the region. Therefore, though cyclonic disturbances may not explain entire rainfall variability, it has strong link with rainfall patterns over India.
4 Summary and conclusions The Indian summer monsoon accounts for more than 75 % of annual precipitation over most parts of the country. Precipitation is a meteorological variable of the most importance, as it conditions the availability of water at the surface. This most precious resource is sometimes scarce, sometimes plentiful and always unevenly distributed in space and time. Numerous studies examined the variability of summer monsoon rainfall to explain the mechanisms responsible for this inter-annual variation of the ISMR. The cyclonic disturbances developing over the BOB, the AS and LA play a vital role in enhancement in
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Fig. 8 Spatial distribution of Spearman’s rank correlations between frequency of cyclonic disturbances and simple daily intensity measured as rainfall per rainy day (SDII) (shade represents significant correlation coefficients with p \ 0.05)
rainfall activities over Indian region. Limited studies explore the analysis on role of such systems on precipitation over Indian region. Therefore, an attempt is made in the present study to understand the role of frequencies of cyclonic disturbances on spatial distribution of indices of precipitation extremes over India. For this purpose, several indices of observed precipitation extremes, in terms of frequencies, intensities and spell duration have been computed for the period 1951–2007 using daily APHRODITE data of 0.5° latitude 9 0.5° longitude resolution. Correlation analysis reveals that a large part of the country exhibits positive relationship between the indices of precipitation extremes and frequency of cyclonic disturbances. Correlations with the indices of frequencies defined as seasonal count of days when rainfall exceeds 30, 20 and 10 mm (99th and 95th percentile) show that spatial extent and strength of the positive relationship decreases with increase in threshold values. Disturbances formed over the BOB, AS, LS and TOT show similar results; however, disturbances formed over the BOB play a dominant role over precipitation extremes during Indian summer monsoon.
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Acknowledgments The authors are thankful to Director, IITM, Pune, for providing the necessary facilities to pursue this study. The authors are grateful to Dr. R. Krishnan, Head, CCCR, IITM, Pune, for kind encouragements. Thanks are also due to Dr. Ramesh Vellore for comments and suggestions to improve the analysis.
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