Performance of all-india southwest monsoon seasonal rainfall when monthly rainfall reported as deficit/excess

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1 METEOROLOGICAL APPLICATIONS Meteorol. Appl. 21: (14) Published online 12 March 13 in Wiley Online Library (wileyonlinelibrary.com) DOI:.2/met.138 Performance of all-india southwest monsoon seasonal rainfall when monthly rainfall reported as deficit/excess D. R. Kothawale and J. R. Kulkarni Indian Institute of Tropical Meteorology, Pune, India ABSTRACT: Performance of all-india summer monsoon rainfall (June to September) has been studied when June or July or June + July rainfall is reported as being in excess/deficit, using monthly and seasonal rainfall data for the period The study shows that seasonal rainfall was above/below the long term mean in almost all the years when rainfall of June or July or June + July was in excess/deficit. The probabilities of occurrence of seasonal rainfall below the long term mean are 77, 83, and 92% respectively when June, July, and June + July rainfalls are in deficit. The years in which the rainfalls of June and July were deficit, were mostly Niño years and their corresponding seasonal rainfalls were below the mean. The deficiencies in June/July or June + July rainfall during Niño years thus give an indication of seasonal rainfall being below the mean, and vice versa for La Niña years. The study has brought out an important result that rainfall during June gives a first signal of the performance of seasonal rainfall. In order to explain the relationship between June excess/deficit rainfall and seasonal above/below mean rainfall, an hypothesis is proposed which states that the circulation anomalies developed over the Indian region during an excess/deficit June persist throughout the season. The hypothesis has been proved by examining the composites of excess/deficit June wind anomalies at 8 and hpa and observing their subsequent persistence from July to September. KEY WORDS Southwest monsoon rainfall; probabilities; excess/deficient; seasonal prediction Received 23 May 12; Revised 9 November 12; Accepted December Introduction Abnormalities in the performance of southwest monsoon (June to September) rainfall (hereafter termed as seasonal rainfall) over India have significant impacts on agriculture, industry and generation of hydroelectric power, causing severe strain on the national economy. Large parts of the country are severely affected due to deficit monsoon rainfall. The government of India spends large sums of money on providing relief in the affected areas. Occurrences of droughts/floods are inherent components of monsoon variability and therefore have been the topic of numerous studies (Mooley et al., 1981; Pant et al., 1988; Parthasarathy et al., 1988). Parthasarathy et al. (1984) estimated probabilities of droughts and floods over India during the monsoon season. Their results showed that areas prone to droughts and floods were nearly the same. Further, Parthasarathy et al. (1987) studied occurrences of droughts/floods over different meteorological subdivisions of India for the period They observed a higher frequency of droughts/floods over Saurashtra and Kutch, Punjab, West Rajasthan and a lower frequency over the west coast and north eastern part of India. Kothawale and Munot (1998) computed the probabilities of occurrence of excess, deficit and normal seasonal rainfalls when monthly (June, July and August) rainfalls are in excess/deficient, and reported that when the rainfall of any monsoon month is in excess/deficient for any subdivision, then the probability of seasonal rainfall to be deficient/excess is almost nil. Gadgil et al. (2) reported that the Correspondence: D. R. Kothawale, Indian Institute of Tropical Meteorology, Pune 418, India. kotha@tropmet.res.in years in which rainfalls in the first half of the season (June to July) were below the mean, the rainfalls in the second half of those years (August to September) were also below the mean. The Indian monsoon rainfall variability is strongly linked to the planetary scale phenomena such as Niño and La Niña, which affect the large scale circulation over the globe (Rasmusson and Carpenter, 1982). The decrease in the monsoon seasonal rainfall associated with Niño is due to an anomalous regional Hadley circulation with descending motion over the Indian continent and ascending motion near the equator sustained by the ascending phase of the anomalous Walker circulation in the equatorial Indian Ocean (Krishnamurthy and Goswami, ). Many researchers have studied the relationship between Niño and seasonal monsoon rainfall. Kripalani and Kulkarni (1997) showed that there are more Niño related droughts in the decades when the Indian monsoon is generally below the mean than during the decades when the Indian monsoon is generally above the mean. Further, Krishnamurthy and Goswami () reported that the interdecadal variation of the Indian monsoon rainfall is strongly correlated with the interdecadal variations of various indices of ENSO. However, Krishna Kumar et al. (1999) have shown that the relation between the Indian monsoon and ENSO weakened in recent decades. Ashrit et al. (1) reported that anomalous warming over the Eurasian landmass as well as enhanced moisture conditions over the Indian monsoon region in the global warming scenario have possibly contributed to the weakening of the impact of warm ENSO events on the monsoon. From all these studies, the general conclusion is that there is a tendency for less Indian monsoon rainfall in Niño years and above 13 Royal Meteorological Society

2 D. R. Kothawale and J. R. Kulkarni Rainfall Percentage Departure (mm) Mean (M) = 82.1 mm Std. Dev. (SD) = 83.4 mm CV = 9.9 Excess years - Deficient years Normal Deficient Excess Trend 11 year moving average M+SD M-SD YEAR Figure 1. All-India southwest monsoon monsoon rainfall interannual variability during Figure updated from Kothawle et al. (8), Journal-Therotical. Appl. Climatology. Rainfall (%) departure (mm) (a) June rainfall Deficit (b) July rainfall Deficit (c) June+July rainfall Deficit M+SD M-SD Rainfall (%) departure (mm) (d) (e) (f) June rainfall Excess July rainfall Excess June+july rainfall Excess Below mean seasonal rainfall above mean seasonal rainfall M+SD M-SD Figure 2. (a f) Performance of all-india monsoon (June to September) rainfall when monthly rainfall is reported as excess/deficient ( Niño year,. La Niña years, M, mean and SD, standard deviations of the series). normal (long term mean) rainfall in La Niña years which has been also corroborated in the present study. Improvement of monsoon rainfall prediction has been perpetually urged to alleviate the problems associated with vagaries of monsoon. Even some small new insight into monsoon variability may find potential application towards improving its prediction. With this view, an attempt is made here to understand performance of seasonal rainfall on the basis of June, July (JJ) and June + July rainfalls. 2. Data and methodology In the present study, monthly and seasonal area weighted rainfall series of all-india as 1 unit and 3 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

3 Performance of all-india southwest monsoon rainfall 621 (a) June+July deficit rainfall Rainfall (%) departure Niño year (b) 3 June+July excess rainfall La La La La La La La La Rainfall (%) departure La- La Niña year June+July rainfall August+September rainfall June-September rainfall 3 Figure 3. (a) Performance of August + September and seasonal (June to September) rainfall and (b) when June + July rainfall is reported as deficit/excess. Table 1. Probability of occurrence of seasonal rainfall excess, deficit and normal when monthly rainfall is in deficit/excess. Probability (%) when monthly rainfall deficit Excess Deficit Normal June July June + July Probability (%) when monthly rainfall excess June July 3 6 June + July meteorological subdivisions for the period 1871 are used. The data sets are taken from Indian Institute of Tropical Meteorology (IITM) Pune web site Mooley and Parthasarathy (1984) and Parthasarathy et al. (1987, 199) derived the time series of all-india rainfall on monthly and seasonal time scales as weighted average of rainfall data of 36 stations, for the period Data from these stations are obtained from the India Meteorological Department, Pune. Subdivisional monthly and seasonal rainfall series were also prepared by taking weighted average of station data from respective subdivisions. The data sets have been extended to in IITM and series are available on-line at Monthly mean horizontal wind data at 8 and hpa levels for the monsoon months have been 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

4 622 D. R. Kothawale and J. R. Kulkarni 4 N 3 N 3 N 2 N N N N N 1. Andaman & Nicobar Islands 2. Arunachal Pradesh 3. Assam & Meghalaya 4. Naga., Mani., Mizo. & Tripura. Sub-Him. W. Bengal & Sikkim Gangetic West Bengal 7. Orissa CHINA 8. Jharkhand 9. Bihar 14. East Uttar Pradesh West Uttar Pradesh Uttaranchal 11 BHUTAN Haryana, Chandigarh & Delhi 14. Punjab Himachal Pradesh 16. Jammu & Kashmir West Rajasthan East Rajasthan West Madhya Pradesh 26. East Madhya Pradesh Gujarat 22. Saurashtra, Kutch & Diu Konkan & Goa 24. Madhya Maharashtra Marathwada BAY OF 26. Vidarbha 32 3 BENGAL 27. Chattisgarh ARABIAN Coastal Andhra Pradesh SEA 29. Telangana Rayalaseema Tamil Nadu & Pondicherry 32. Coastal Karnataka 3 SRI 33. North Interior Karnataka 34. South Interior Karnataka LANKA 3. Kerala 36. Lakshadweep 7 E 8 E 9 E E PAKISTAN NEPAL Figure 4. Meteorological subdivisions of India (source: http//: used to examine persistency of the circulation patterns throughout the monsoon season. The wind data from 1949 to at resolution of (lat. long.) available on the IRI website NCAR/CDAS-1/ have been used. In addition ERSST V3.4 ( sea surface temperature (SST) during the period 1871 was also used All-India seasonal rainfall (June to September) (R) of each year has been classified into three categories viz. Excess, Deficit and Normal as. 1 Excess: R M + SD 2 Deficit : R M SD 3 Normal: M SD < R < M + SD where M and SD are mean and standard deviation of the seasonal time series respectively. The criterion used in this study is taken from Parthasarathy et al. (1992). The excess/deficit categories for the monthly rainfall have been defined similarly considering monthly mean and standard deviation. Similar categorization has been done for the subdivisional rainfalls. The rainfall has been termed as above the mean if R > M and below the mean if R < M. Probabilities of occurrences of seasonal rainfall in the categories: (a) excess/deficient (b) above/below the mean when (1) June (2) July and (3) June + July rainfalls are in excess/deficit have been calculated. These are computed by taking the ratio of number of seasonal (a) excess/deficit (b) above/below mean rainfall years to the number of June, July and June + July excess/deficit years. The Chi-square test (χ2) is applied to examine the dependency of seasonal rainfall on monthly rainfall: χ 2 = (O E) 2 (1) E O: observed frequency, E: expected frequency. Here, frequency means number of years. χ2 value greater than table value suggests there is significant association between monthly and seasonal rainfall The influence of monthly rainfall on the performance of seasonal rainfall has also been examined in Niño/La Niña years. These Niño and La Niña years are taken from Halpert and Ropelewski (1992) and the list is extended to include recent events. 3. Discussions 3.1. All-India summer monsoon rainfall To provide the necessary background to understand the performance of seasonal rainfall in relation to monthly rainfall, the interannual variability of all-india monsoon rainfall has been studied using the data for the period of The all- India monsoon rainfall series is almost trendless for the period and has shown a very little trend,.4 mm year 1 (Kothawale et al., 8). On the regional scale, Goswami et al. (6) reported that in spite of considerable interannual variability in the monsoon rainfall, there has been an increase in heavy and moderate rainfall events in central India during the period 191. The interannual variability of the all-india monsoon rainfall is here expressed as a percentage departure from long term mean (Figure 1). M, SD and co-efficient of variation (CV )of all-india monsoon rainfall are 82.1 mm, 83.4 mm and 9.9% respectively. There are 2 deficits, excess and 9 normal years in the all-india monsoon rainfall series, during the study period. The year-to-year rainfall variability shows that majority of the deficit rainfall years are in the two 3 year periods, (7) and (). These periods are called below mean epochs (Parthasarathy et al. 1992). Similarly, more than half of excess rainfall years are in another two 3 year periods, (6) and (). These periods are called above mean epochs. During the period 1961, the frequency of deficit monsoon rainfall is higher than that of excess monsoon rainfall, with 13 deficit and 6 excess monsoon years (Figure 1). In the recent two decades (1991 ), the seasonal rainfall was below the mean for 11 years and above the mean for 9 years. 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

5 Performance of all-india southwest monsoon rainfall 623 (a) Years 3 2 June excess rainfall above the mean below the mean (b) Years 3 2 June deficit rainfall (c) Years 3 2 July excess rainfall (d) Years 3 2 July deficit rainfall (e) Years (f) Years Assam and Megha Naga.Mani.Mizo. and Tri S.H.W. Bengal G.W. Bengal June+July excess rainfall June+July deficit rainfall Jharkhand Orissa Bihar East U. P. West U. P. Hariyana Punjab West Rajasthan East Rajasthan West Madhy Pradesh East Madhy Pradesh Gujarat Saurashtra and Kutch Konkan and Goa Madhya Maharashtra Marathawada Vidarbha Chattisgarh Co. Andhra Pradesh Telangana Rayalseema Tamil Nadu Coastal Karnataka Subdivisions South Int. karnataka North Int. Karnataka Kerala All-India Figure. (a f) Number of years when seasonal rainfall is above and below the mean with monthly rainfall reported as being in excess/deficit during Performance of seasonal rainfall based on monthly rainfall June rainfall reported as deficit/excess Long term mean and SD of all-india June, July, August, September, June + July and seasonal rainfalls are 163. mm (36.6 mm), 274. mm (36.9 mm), mm (38.8 mm), mm (37.9 mm), 437. mm (1.2 mm) and 82.1 mm (83.4 mm) respectively (number in the bracket is the SD). The percentage contributions of June, July, August, September, and June + July to the seasonal rainfall are 19.1, 32.2, 28.6,.1, 1.3% respectively. The data period used for the mean and SD is (Parthasarathy et al., 199). Figure 2 shows the performance of seasonal rainfall (percentage 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

6 624 D. R. Kothawale and J. R. Kulkarni 4 June July June+July Chi-square value.1% level 1 % level Assam and Megha Naga.Mani.Mizo. and Tri S.H.W. Bengal G.W. Bengal Jharkhand Orissa Bihar East U. P. West U. P. Hariyana Punjab West Rajasthan East Rajasthan West Madhy Pradesh East Madhy Pradesh Gujarat Saurashtra and Kutch Konkan and Goa Madhya Maharashtra Marathawada Vidarbha Chattisgarh Co. Andhra Pradesh Telangana Rayalseema Tamil Nadu Coastal Karnataka Subdivisions South Int. karnataka North Int. Karnataka Kerala All-India Figure 6. Chi-square values computed for testing dependability between monthly and seasonal rainfall, when monthly rainfall is reported as excess/deficit. departure) when June, July and June + July rainfalls are in deficit/excess. Percentage departure greater/lesser than zero indicates above/below the mean rainfall. During the period 1871, rainfall of June was in deficit in 22 years. Out of these 22 years, the seasonal rainfall was below the mean in 17 years and above the mean in years (Figure 2(a)). Out of 17 below mean years, 8 years were in deficit. Besides, it seems important to note that out of above mean years, 3 years fall in the above mean epochs of , and one of these years coincides with the La Niña year 1924 (shown by filled circle in Figure 2(a)). Many studies have shown that during La Niña years seasonal rainfall is above the mean (Bhalme and Jadhav, 1984; Kripalani and Kulkarni, 1997). The other year (1926) is a positive Indian Ocean Dipole (IOD) year (Meyers et al., 7). The positive IOD helps to strengthen monsoon activity over India (Ashok et al., 4). However, after 196, seasonal rainfall was never above the mean when June rainfall was deficient. The probabilities of occurrence of seasonal rainfall in the categories of excess, deficit and normal when June rainfall is in deficit are 4.6% (1/22 ), 36.4% (8/22 ) and 9.1% (13/22 ) respectively (Table 1). In the normal rainfall category, 3.8% (4/13 ) years are above the mean and 69.2% (9/13 ) years are below the mean. The analysis reveals that after the above mean epoch of , seasonal rainfall was below the mean when June rainfall was deficient in all the years. Based on the entire data, the probability of occurrence of seasonal rainfall below the mean when June rainfall is deficient is 77% (17/22 ). It may be further noted that more than % of June deficit rainfall years were -Niño years (shown by star) and their seasonal rainfalls were below the mean. Joseph et al. (1994) reported that after an Niño event, most of the onsets of summer monsoon over Kerala (India) tend to be delayed, which causes below normal June rainfall. However, in none of these years was June rainfall deficient. Boschat and Terray (11) also reported that the onset of the Indian summer monsoon tends to be delayed during the decaying Niño years, and it may cause below mean June rainfall. However, in the present study, the June deficient rainfall condition has been considered. Further, it is seen that out of 22 June deficit rainfall years, 13 years are associated with ongoing Niño events. The rainfalls in these years are either deficit or below the mean. It is also observed that though the rainfall of June was deficit during the La Niña year 1924, its seasonal rainfall was above the mean. Thus, it is revealed that deficit June rainfall during Niño years gives an indication of seasonal rainfall to be below the mean, and vice versa in La-Niña years. An overall conclusion is that during a majority of years when June rainfall is in deficit, the seasonal rainfall is generally below the mean. On the other hand, during the entire period, June rainfall was excess in 2 years (Figure 2(d)). Out of these 2 years, the seasonal rainfall was above the mean in 19 years and below the mean in 4 years, and in 2 years it was near to the mean (slightly on the negative side in 1871 and 1883) (Figure 2(d)). Out of these 2, years (4% (/2 )) are La-Niña years, and in these years seasonal rainfalls were above the mean. It is interesting to note that there are two Niño years in these 2 years and the seasonal rainfall was above the mean in these 2 years, even becoming excess in 1 year, Overall, when June rainfall is in excess (Figure 2(d)), the probabilities of occurrence of seasonal rainfall in the categories excess, deficit and normal are 36% (9/2 ), % (/2 X), 64% (16/2 ) respectively (Table 1). The dependency of seasonal rainfall on monthly rainfall was then examined using the Chi-square test. The Chi square value was found to be statistically significant at the.1% level (Figure 6). Thus, June rainfall is a good indicator of the performance of seasonal rainfall July rainfall reported as deficit/excess July is the peak month of the monsoon season, as its contribution to the seasonal rainfall is 32.2%, which is the highest 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

7 Performance of all-india southwest monsoon rainfall 62 Figure 7. (a d) Spatial patterns of composite of wind anomalies (m s 1 ) at 8 hpa during years of excess June rainfall. among all the four monsoon months. Hence, the performance of July rainfall has vital importance for the prediction of seasonal rainfall. Keeping this in mind, a total of 18 July deficit rainfall years during 1871 study period was identified (Figure 2(b)). Out of these 18 years, the seasonal rainfall of years was below the mean, out of which 12 years were in deficit (Figure 2(b)). There were only 3 years when the rainfall was above the mean, of which two are La Niña years, 19 and 197. The analysis shows that even though July rainfalls were in deficit in La Niña years, their corresponding seasonal rainfalls were above the mean. Thus, it can be said that during La Niña years the expected deficiency in seasonal rainfall due to weak performance of July rainfall is recovered by good performance of monsoon in the remaining monsoon months of August and September (AS). The results are in agreement with those of Boschat and Terray (12), who showed that during Niño years, monsoons are dry and characterized by deficient rainfall in June and July, while in La Niña years, most of the wet monsoons are characterized by enhanced rainfall in August and September. All the Niño years with their July deficit rainfall lead to seasonal rainfall below the mean. The probability of seasonal rainfall below the mean is 83% (/18 ). The probabilities of occurrences of seasonal rainfall in the categories excess, deficient and normal are % (1/18 ), 67% (12/18 ) and 28% (/18 ) respectively when July rainfall is in deficit (Table 1). Finally, July rainfall was in excess for a total of years (Figure 2(e)). The probability of seasonal rainfall in the categories excess, deficit and above the mean being 3% (7/ ), % (/ ) and 6% (13/ ) (Figure 2(e)). Note that out of three below mean years (Figure 2(e)), two were -Niño years (1912, 1932) and the remaining 1 year, 1937, had a seasonal rainfall just below the mean. Overall, this analysis reveals that there is strong positive association between July rainfall and the corresponding seasonal rainfall. The Chi-square value is also highly significant, above the.1 level (Figure 6). 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

8 626 D. R. Kothawale and J. R. Kulkarni Figure 8. (a d) Spatial patterns of composite of wind anomalies (m s 1 ) at hpa during years of excess June rainfall June + July rainfall reported as deficit/excess Rainfall received in the first 2 months of the monsoon season (June + July) constitutes 1.3% to the seasonal rainfall. During the entire period 1871, there were 24 years when June + July rainfall was in deficit (Figure 2(c)). It is interesting to see the contribution of each month to the total June + July deficit (Figure 2(a) (c)). Out of these 24 years, June was in deficit in 9 years, July in years and both June and July in 4 years. In the year 1992, neither June nor July was in deficit but the total June + July was in deficit. The contributions of June and July towards the June + July deficit are 4% (13/24 ) and 8% (14/24 ) respectively. This indicates that the deficiency in any one of the 2 months is capable of generating the total deficiency on the 2 months total in more than % of the years. When the total June + July rainfall is in deficit (Figure 2(c)), the probability of occurrence of seasonal rainfall below the mean is 92% (22/24 ) which includes 71% (17/24 ) probability of deficit rainfall, whereas the probability of occurrence of seasonal rainfall above the mean is only 8% (2/24 ) (Figure 2(c)). Approximately 6% of the deficit June + July rainfall years are Niño years. On the other hand, there are 19 years in which June + July rainfall was in excess during 1871 (Figure 2(f)). In this case the probability of occurrence of seasonal rainfall above the mean is 9% (18/19 ) and below the mean is % (1/19 ) (Figure 2(f)). The Chi-square value for June + July rainfall being in excess/deficit and its corresponding seasonal rainfall above/below the mean is shown in Figure 6. The Chi-square value is highly statistically significant, above the.1% level. The performance of rainfall during AS has been examined when June + July rainfall was in deficit as well as in excess. There are 24/19 years when June + July rainfalls were in deficit/excess. Here, the percentage departures (PDs) of June + July and PDs of the corresponding August + September and seasonal rainfall have been computed and shown in Figures 3(a) and (b). It is seen that the PDs of August + September are higher than the PDs of 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

9 Performance of all-india southwest monsoon rainfall 627 Figure 9. (a d) Spatial patterns of composite of wind anomalies (m s 1 ) at 8 hpa during June rainfall was in excess in La Niña years. June + July rainfall in almost all the years except 4 (1941, 196, 1979 and 9) in which the PDs of August + September are just lower than PD s in June + July and only in 1 year (1899, a severe drought year, Figure 1), in which the PD of August + September rainfall was much lower than that of June + July. It may be noted that out of these 24 years, are Niño years (Figure 3(a)). Boschat and Terray (12) also reported that during Niño years the performance of Indian monsoon rainfall during AS is better than that in JJ. They explained the mechanism of better monsoon rainfall in AS as the onset of Niño during boreal spring causes deficit monsoon rainfall in JJ and in response to weaker monsoon winds, warm SST anomalies appear in the west equatorial Indian Ocean (IO), generating favourable conditions for the development of positive IOD conditions during AS. Local airsea processes strengthened by the SST anomalies in eastern node of IOD result in active rainfall during AS. It may be concluded that the performance of rainfall during second half of the season (AS) is generally better than in the first part of the season (JJ), when the first part of season (JJ) is in deficit. It is also observed that the PDs of AS rainfall years are comparatively less than that of when June + July rainfall years are in excess (Figure 3(b)). 4. Performance of subdivisional seasonal rainfall The performance of subdivisional seasonal rainfalls has been examined when June, July and June + July rainfalls are in excess/deficit. In this analysis, monthly rainfall of June through September and seasonal rainfall of 3 meteorological subdivisions have been used. The subdivisions are shown in Figure 4. The number of years in which subdivisional seasonal rainfalls were above/below the mean when June, July and June + July rainfalls were in excess/deficit have been identified and shown in Figure (a) (f). It is observed that the probability of occurrence of seasonal rainfall above/below the mean is about 9/% when June + July rainfall is in excess and vice versa. The Chi-square test is also used to determine whether there is 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

10 628 D. R. Kothawale and J. R. Kulkarni Figure. (a d) Spatial patterns of composite of wind anomalies (m s 1 ) at hpa during June rainfall was in excess in La Niña years. a significant association between June, July and June + July rainfalls and their corresponding seasonal rainfalls. In this test, the number of years when June, July and June + July rainfall were in excess and deficit are identified along with the corresponding and expected frequencies of above/below the mean seasonal rainfall years. For June excess/deficit rainfall, the Chi-square values are found statistically significant at 1% and above level for 24 subdivisions except 6 subdivisions: West Uttar Pradesh, Haryana, Punjab, West Rajasthan, East Rajasthan and West Madhya Pradesh (Figure 6). By this month, the monsoon circulations are not well established over these subdivisions, which may be the reason for absence of relationship. However, in the case of July excess/deficit rainfall, the monsoon is well established over the entire country and the influence of the monsoon circulation during this month is found in the seasonal rainfall. The statistically significant Chi-square values indicate that the performance of subdivisional seasonal rainfall is highly associated with the initial month s (i.e. June and July) rainfall.. Spatial patterns of composite monthly wind anomalies In Section 3.2.1, it was shown that when June rainfall is in excess/deficit, then there is a propensity of seasonal rainfall being above/below the mean, and similarly for July and June + July rainfalls. In order to explain the strong relationships between June and July rainfalls and seasonal rainfalls, the circulation patterns set in June and their persistence throughout the season have been examined. In a recent paper, DelSole and Shukla (12) showed that June to September sea surface temperatures (SSTs) over the tropical Indo-Pacific (3 Sto3 N and 3 Eto6 W region) are significantly related to Indian summer monsoon rainfall. The SSTs vary slowly with time and determine the boundary conditions for the seasonal rainfall, which indicate that the large scale circulation set in June due to SST forcings may retain its characteristics throughout the season. It is suggested here that the deficit (excess) rainfall in June may be related to weak (strong) monsoon circulation in June. Weak circulation then persists throughout the season, 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

11 Performance of all-india southwest monsoon rainfall 629 Figure 11. (a d) Spatial patterns of composite of wind anomalies (m s 1 ) at 8 hpa during years of deficit June rainfall. taking monsoon to the negative side of the mean. In order to test this hypothesis, the spatial patterns of composite wind anomalies during the years of excess/deficit June rainfall as well as excess/deficit June rainfall in La Niña / Niño years are examined for June through September. Hence, the June excess/deficit rainfall as well as its corresponding La Niña/ Niño years are selected during the period 1949, and composite wind anomalies are prepared for 8 and hpa levels. Spatial patterns of composite wind anomalies at the 8 and hpa levels represent lower and upper tropospheric circulations. In the case of June excess rainfall years, at 8 hpa, strong westerly wind anomalies are observed south of N and strong easterlies north of 24 N, during JJ, indicating a southward shift of the monsoon trough (Figure 7(a) and (b). During AS (Figure 7(c) and (d)), the anomalous cyclonic circulations are seen over some area of the head Bay of Bengal i.e. west of 84 E and Peninsular India (south of N). These anomalous cyclonic circulations indicate the favourable conditions for formation of monsoon low pressure systems, which can intensify monsoon activity over India. At hpa, (Figure 8(a) (c)), strong easterly anomalies are seen during June to August over the domain, and weaken by September. These anomalies indicate strong easterly wind at the upper level. The analysis reveals that when June rainfall is in excess the wind circulations are favourable for good rainfall activity during the entire season. In La Niña years, the spatial patterns of wind anomalies of June and July at 8 hpa (Figure 9(a) and (b)) are almost similar to the spatial patterns of June excess rainfall years. A trough is seen between and 24 N latitude which persisted through AS. At hpa both (La Niña and June Excess) spatial patterns are almost similar with higher wind anomalies for La Niña (Figure ). In the case of June deficient rainfall years, the wind anomalies at 8 hpa in June are easterlies over southern India and westerlies over north India (Figure 11(a). This indicates weak westerlies over south India and weak easterlies over north India, implying a feeble monsoon trough. This weak circulation is seen to persist through July and August (Figure 11(b) and (c)). 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

12 63 D. R. Kothawale and J. R. Kulkarni Figure 12. (a d) Spatial patterns of composite of wind anomalies (m s 1 ) at hpa during years of deficit June rainfall. The positive anomalies in July over south India suggest strong westerlies, but are displaced south of the normal position, which is an indication of unfavourable conditions for the monsoon. In September, the wind anomalies are northerlies over the west coast and westerlies over the Bay of Bengal (Figure 11(d)). At the hpa level, westerly anomalies are seen in all the 4 months indicating weak easterlies (Figure 12). The spatial patterns of composite wind anomalies at 8 and hpa of June deficient in Niño years are shown in Figures 13 and 14 respectively, the patterns are almost similar to spatial patterns of wind anomalies during June deficit rainfall years (Figures 11 and 12). The wind analysis clearly shows that the circulation pattern which developed as either weak or strong in the deficit or excess June month persists throughout the season. This proves the proposed hypothesis and explains the link between deficit/excess June rainfall and the below/above mean seasonal rainfall. Similar anomalies exist during the deficit/excess July months also (not shown)..1. Spatial patterns of composite seasonal SST anomalies Composites of SST anomalies have been prepared for the seasons pre-monsoon (March + April + May) and monsoon when June rainfall was excess/deficit during the period 1871, and patterns are shown in Figure. Pronounced colder composite anomalies are observed over the Indian Ocean and the Arabian Sea during pre-monsoon and monsoon seasons in the case of June excess rainfall. This is a favourable situation for performance of a good monsoon. Indeed, prior to the monsoon, the cross hemispheric reversal of winds brings considerable amount of water vapour from colder Indian Ocean and Arabian Sea to the low pressure system over the heated landmass area of India. The almost opposite SST anomalies are observed over the Indian Ocean when June rainfall is deficit. This is consistent with Reason et al. () who reported that when an Niño occurred during summer, the Indian Ocean was characterized by a slight warming of SST compared to normal conditions which was associated with weaker wind magnitudes 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

13 Performance of all-india southwest monsoon rainfall 631 Figure 13. (a d) Spatial patterns of composite of wind anomalies (m s 1 ) at 8 hpa when June rainfall was in deficit in Niño years. than normal and reduced cloudiness. While opposite configuration occurred during the La-Niña events. Besides, most of the Niño/La Niña events are associated with deficit/excess Indian monsoon rainfall (Bhalme and Jadhav, 1984). Hence, these results support the present analysis. 6. Forecasting implication Thus, knowledge of June rainfall may be cleverly incorporated in the design of experiment related to monsoon seasonal rainfall prediction using coupled or simple General Circulation Models (GCM). GCMs are run in the ensemble mode with different initial conditions. Operationally, IMD issues a first forecast at the end of April with April initial conditions. The second modified forecast is issued in July. Present GCM forecasts (including coupled GCMs) are far from attaining the required skill in the prediction so there is always urge for improvement of forecast (Gadgil et al., ). Results of rainfall analysis in this paper may help in designing the GCM integrations for improving the forecast. The forecast experiment may be run in the first week of July. GCM ensemble integration may be started with June initial conditions. Depending upon the performance of June rainfall, appropriate ensemble members may be selected for averaging, in order to accurately forecast the seasonal rainfall. To elaborate this point, let us consider the case in which June rainfall is deficit. In this case, our analysis has clearly shown that in the present epoch, seasonal rainfall has a large probability of going to the negative side of the mean. After the completion of the GCM model seasonal run, only those ensembles members which predict the rainfall on the negative side of the mean may be selected to generate the seasonal forecast. The negative circulation anomalies persisting throughout the season will preclude the rainfall going on the positive side of the mean. It may be argued that the ensembles members showing positive rainfall departures might have come purely due to phenomenon of 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

14 632 D. R. Kothawale and J. R. Kulkarni Figure 14. (a d) Spatial patterns of composite of wind anomalies (m s 1 ) at hpa during June rainfall was in deficit in Niño years. sensitivity to initial conditions because of inherent existence of chaos in the nonlinear dissipative monsoon dynamical system. A similar argument and procedure hold in the case of excess June rainfall. The important conclusion is that June rainfall gives a first and unambiguous signal of the performance of seasonal rainfall. Clever use of the June rainfall signal has thus a potential in improving the monsoon seasonal forecast. Further work is required to test this concept using GCM forecast data. Another potential application of the results is for the decision making for implementation of cloud seeding for rain enhancement. The rain-shadow subdivisions in the north peninsular India are most vulnerable to monsoon vagaries. Cloud seeding programs have been carried out over these subdivisions to alleviate the water problems. The June rainfall conditions will provide the probabilities of occurrences for the different categories of seasonal rainfall over each subdivision. This will help in taking early decision for planning for operational cloud seeding programs for rainfall enhancement in those subdivisions. 7. Summary The seasonal rainfall variability can be understood on the basis of performance of monthly rainfall. When June, July and June + July rainfall of all-india are deficit then the probability of occurrence of seasonal rainfall below the mean are 77, 83 and 92% respectively. When Niño years coincide with years of deficit rainfalls in June or July, their corresponding seasonal rainfalls are inherently below the mean. Chi-square test strongly suggests that the seasonal rainfalls of all-india and meteorological subdivision depend on the performance of the initial month s rainfall (i.e. June and July). The performance of rainfall during the second half of season, i.e. August and September, is generally better than the first part of the season, June and July, when this first part of the season is deficit and vice versa when this first part of the season is excess. The present study reveals that seasonal rainfall over India depends on June rainfall. This suggests a new strategy for 13 Royal Meteorological Society Meteorol. Appl. 21: (14)

15 Performance of all-india southwest monsoon rainfall 633 Figure. Composites of SST standardized anomalies ( C) when June rainfall was excess/deficit during 1871 (light/dark shading indicates significant positive/ negative anomalies). Significant anomalies are ± 1 SD. This figure is available in colour online at wileyonlinelibrary.com/journal/met monsoon seasonal rainfall prediction using GCM approach. The forecasts experiment may be run in the first week of July. A selection of appropriate ensemble members will produce better seasonal forecast than that carried out by taking all ensemble members. June rainfall will be useful in taking decisions for cloud seeding operations over the rain-shadow subdivisions of north peninsular India. Acknowledgements The authors are thankful to Prof. B. N. Goswami, Director, Indian Institute of Tropical Meteorology (IITM), Pune for providing the necessary facilities for this study. References Ashok K, Guan Z, Saji NH, Yamagata T. 4. On the individual and combined influences of the ENSO and the Indian Ocean dipole on the Indian summer monsoon. J. Clim. 17: Ashrit R, Rupa Kumar K, Krishna Kumar K. 1. Enso-monsoon relationship in greenhouse warming scenario. Geophys. Res. Lett. 28: Bhalme HN, Jadhav SK Southern oscillation and its relation to the monsoon rainfall. J. Climatol. 4: 9. Boschat G, Terray P. 11. Interannual relationships between Indian summer monsoon and Indo-pacific coupled modes of variability during recent decades. Clim. Dyn. 37: Boschat G, Terray P. 12. Robustness of SST teleconnections and precursory patterns associated with Indian summer monsoon. Clim. Dyn. 34: Royal Meteorological Society Meteorol. Appl. 21: (14)

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