el niño timings and rainfall extremes in india, southeast asia and china

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INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 19: 653–672 (1999) EL NIN 0 O TIMINGS AND RAINFALL EXTREMES IN INDIA, SOUTHEAST ASIA AND CHINA R.P. KANE* Instituto Nacional de Pesquisas Espacias (INPE), Caixa Postal 515, 12201 -970, Sa ˜o Jose ´ dos Campos, SP, Brazil Recei6ed 5 February 1998 Re6ised 14 October 1998 Accepted 21 October 1998 ABSTRACT Whereas some El Nin ˜ o years are known to be associated with droughts in some parts of the globe, notably India, other El Nin ˜ os do not seem to be effective. Recently, it was observed that Unambiguous ENSOW (El Nin ˜o years, in which the Southern Oscillation Index minima and Pacific sea surface temperature maxima occurred in the middle of the calendar year) were better associated with droughts. This association was checked for rainfalls in South Asia and China. Singapore, Brunei, Indonesia and East Asia (comprising of the People’s Republic of China and adjacent regions, including India) showed a good association of Unambiguous ENSOW events with droughts. Thailand, Malaysia and the whole Philippines showed some association; but the northwest Philippines showed opposite results. To find a rational for this criterion, it was checked whether such events were in any way related to the timings of the El Nin ˜ o events. In general, El Nin ˜ os active during the main rainy season (June – September for all India’s summer monsoon rainfall) were better associated with droughts. But some events did not fit this pattern. Also, many years not having El Nin ˜ os were associated with droughts. Thus, the El Nin ˜ o relationship is not clear-cut and predictions based on the same alone are likely to go wrong more often than not, as in the case of the recent El Nin ˜ o (1997). Copyright © 1999 Royal Meteorological Society. KEY WORDS: ENSO; drought; India; Southeast Asia; China 1. INTRODUCTION Southeast Asia is the mainland east of India and south of China, with the islands to the south and east (Figure 1, from Kripalani and Kulkarni, 1997a) and consists of Myanmar (formerly Burma), Thailand, Indo-China (Vietnam, Laos, Cambodia or Kampuchea), Malaysia, Singapore, the islands forming the Republic of Indonesia, Borneo, Brunei, the Philippine islands, Portuguese Timor and the western New Guinea. Several workers have reported links between the El Nin ˜ o – Southern Oscillation (ENSO) phe- nomenon and rainfall extremes in India (e.g. Rasmusson and Carpenter, 1983; Kiladis and Diaz, 1989; Ropelewski and Halpert, 1987, 1989; Mooley and Paolino, 1989) and Australia (e.g. Ropelewski and Halpert, 1987, 1989; Nicholls and Wong, 1990; Evans and Allan, 1992). For the Southeast Asian region, Quinn et al. (1978) reported an El Nin ˜ o relationship with Indonesian droughts, particularly in the east monsoon season May – October. Recently, investigations have been reported of relationships between India summer monsoon rainfall and rainfalls in China (Kripalani and Singh, 1993), Thailand (Kripalani et al., 1995) and Bangladesh and Nepal (Kripalani et al., 1996a), which indicate that variations over central India, north China and northwest Thailand are in-phase with Indian monsoon rainfall (IMR), while variations from northeast India up to southeast China are out of phase with IMR (Kripalani, 1997). Chinese meteorologists (Chen Longxun et al. 1991; Ding Yihui, 1994) suggest the existence of an East Asian monsoon circulation system relatively independent of the Indian monsoon. The Southeast Asian * Correspondence to: INPE, Caixa Postal 515, 12201-970, Sa ˜o Jose ´ dos Campos, SP, Brazil. E-mail: [email protected] Contract/grant sponsor: FNDCT, Brazil; Contract/grant number: FINEP-537/CT CCC 0899–8418/99/060653 – 20$17.50 Copyright © 1999 Royal Meteorological Society

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Page 1: El Niño timings and rainfall extremes in India, Southeast Asia and China

INTERNATIONAL JOURNAL OF CLIMATOLOGY

Int. J. Climatol. 19: 653–672 (1999)

EL NIN0 O TIMINGS AND RAINFALL EXTREMES IN INDIA,SOUTHEAST ASIA AND CHINA

R.P. KANE*Instituto Nacional de Pesquisas Espacias (INPE), Caixa Postal 515, 12201-970, Sao Jose dos Campos, SP, Brazil

Recei6ed 5 February 1998Re6ised 14 October 1998

Accepted 21 October 1998

ABSTRACT

Whereas some El Nino years are known to be associated with droughts in some parts of the globe, notably India,other El Ninos do not seem to be effective. Recently, it was observed that Unambiguous ENSOW (El Nino years, inwhich the Southern Oscillation Index minima and Pacific sea surface temperature maxima occurred in the middle ofthe calendar year) were better associated with droughts. This association was checked for rainfalls in South Asia andChina. Singapore, Brunei, Indonesia and East Asia (comprising of the People’s Republic of China and adjacentregions, including India) showed a good association of Unambiguous ENSOW events with droughts. Thailand,Malaysia and the whole Philippines showed some association; but the northwest Philippines showed opposite results.To find a rational for this criterion, it was checked whether such events were in any way related to the timings of theEl Nino events. In general, El Ninos active during the main rainy season (June–September for all India’s summermonsoon rainfall) were better associated with droughts. But some events did not fit this pattern. Also, many yearsnot having El Ninos were associated with droughts. Thus, the El Nino relationship is not clear-cut and predictionsbased on the same alone are likely to go wrong more often than not, as in the case of the recent El Nino (1997).Copyright © 1999 Royal Meteorological Society.

KEY WORDS: ENSO; drought; India; Southeast Asia; China

1. INTRODUCTION

Southeast Asia is the mainland east of India and south of China, with the islands to the south and east(Figure 1, from Kripalani and Kulkarni, 1997a) and consists of Myanmar (formerly Burma), Thailand,Indo-China (Vietnam, Laos, Cambodia or Kampuchea), Malaysia, Singapore, the islands forming theRepublic of Indonesia, Borneo, Brunei, the Philippine islands, Portuguese Timor and the western NewGuinea. Several workers have reported links between the El Nino–Southern Oscillation (ENSO) phe-nomenon and rainfall extremes in India (e.g. Rasmusson and Carpenter, 1983; Kiladis and Diaz, 1989;Ropelewski and Halpert, 1987, 1989; Mooley and Paolino, 1989) and Australia (e.g. Ropelewski andHalpert, 1987, 1989; Nicholls and Wong, 1990; Evans and Allan, 1992). For the Southeast Asian region,Quinn et al. (1978) reported an El Nino relationship with Indonesian droughts, particularly in the eastmonsoon season May–October. Recently, investigations have been reported of relationships betweenIndia summer monsoon rainfall and rainfalls in China (Kripalani and Singh, 1993), Thailand (Kripalaniet al., 1995) and Bangladesh and Nepal (Kripalani et al., 1996a), which indicate that variations overcentral India, north China and northwest Thailand are in-phase with Indian monsoon rainfall (IMR),while variations from northeast India up to southeast China are out of phase with IMR (Kripalani, 1997).Chinese meteorologists (Chen Longxun et al. 1991; Ding Yihui, 1994) suggest the existence of an EastAsian monsoon circulation system relatively independent of the Indian monsoon. The Southeast Asian

* Correspondence to: INPE, Caixa Postal 515, 12201-970, Sao Jose dos Campos, SP, Brazil. E-mail: [email protected]

Contract/grant sponsor: FNDCT, Brazil; Contract/grant number: FINEP-537/CT

CCC 0899–8418/99/060653–20$17.50Copyright © 1999 Royal Meteorological Society

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R.P. KANE654

summer monsoon (Indian monsoon) has its low level branch of the local Hadley circulation, withsubstantial convergence, pronounced updraft and vigorous rain, around (10°–20°N, 85°–95°E) over theBay of Bengal, whereas the western North Pacific summer monsoon (East Asian monsoon) has its activeconvection and updraft centre over the key domain (10°–20°N, 130°–150°E) of world’s highest seasurface temperature (SST) (Kripalani and Kulkarni, 1997a). The boundary between these two monsoonregimes appears somewhere over the South China Sea, where relatively dry weather persists and where thedowndraft portion of both these monsoons occurs. Hence, the rainfalls at various locations around theSouth China Sea may show different characteristics and may not necessarily correlate positively with theIndian monsoon. Recent simulations of the East Asian monsoon with general circulation models indicatethat the monsoon over the Indian region (6°–22°N, 60°–90°E) and the East Asian region (12°–22°N,110°–125°E) may be negatively correlated (Wang and Xun-Qiang, 1995).

For relationship with El Ninos, the listing given by Quinn et al. (1978, 1987) is often used. Kane(1997a,b, 1998) noticed that not all El Ninos in this list were associated with droughts in India, andattempted a finer classification in which Unambiguous ENSOW-type events (explained later in this paper)were found to be overwhelmingly associated with droughts in India. In the present communication, thebehaviour of rainfalls in the Southeast Asian region is examined to see whether Unambiguous ENSOWhave any special significance. Also, it examines whether the timing of the El Nino (starting month andduration) has any bearing on rainfall extremes. Some other aspects are also examined.

2. DATA

The All India summer monsoon (IMR) rainfall data for 1871–1990 were obtained from Parthasarathy etal. (1992). Rainfall data for Thailand (Tha, 1911–1989, monsoon period), Malaysia (Mal, 1898–1975,annual), Singapore (Sin, 1872–1980, annual), Brunei (Bru, 1908–1980, annual), Indonesia (Ind, 1898–1975, annual), Philippines (Ph1, 1903–1975, monsoon period for the northwest sector) and Philippines(Ph2, 1903–1975, annual) were extracted from Kripalani and Kulkarni (1997a). For East Asia as a whole(15°–60°N, 70°–140°E), which includes the whole of the People’s Republic of China and the surrounding

Figure 1. Map showing the region of Southeast Asia (30°N–10°S, 90°–140°E, Kripalani and Kulkarni, 1997a)

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territories, including India, Hulme and Zhao (1994), have given annual and seasonal precipitation timeseries, from which the summer season (EAS, 1880–1992, June–August) series is considered here. Data forPuerto Chicama (8°S, 80°W, Peru coast) SST (sea surface temperature) anomalies (for 1925 onwards)were obtained from private sources and those for El Nino 1+2 region (0°–10°S, 90°–80°W) nearPeru–Ecuador coast and El Nino 3 region (5°N–5°S, 150°–90°W) in the eastern Pacific and for Tahiti(18°S, 150°W) minus Darwin (12°S, 131°E) pressure difference (T−D) (for 1950 onwards, representingSouthern Oscillation Index, SOI) from Parker (1983) and the monthly Climate Diagnostic Bulletins ofCPC (Climate Prediction Centre of NOAA’s National Centre for Environmental Prediction). The SOIobtained by Wright (1975) based on pressure at a wide spread of stations (Cape Town, Bombay,Djakarta, Darwin, Adelaide, Apia, Honolulu, Santiago) was also examined and found to be very similarto the Tahiti minus Darwin pressure difference. For Pacific SST, the Wright (1984) Index was also usedand refers to the region 6°N–6°S, 180–90°W (central and eastern equatorial Pacific). A similar SST indexdeveloped by Angell (1981), and a further private communication, was also used, for comparison.

During 1925–1950, only SST anomalies at Puerto Chicama (Peru coast) were available. Plots of these(not shown here) were compared with those of El Nino 1+2 region, El Nino 3 region, the Wright (1984)SST index and the Southern Oscillation Index (SOI) (12-monthly running means). The following featureswere noted:

(i) The largest SST fluctuation was at Puerto Chicama. In 1982–1983, the temperature anomaly reachedalmost 10°C.

(ii) The El Nino 1+2 region SST fluctuation was almost similar to that of Puerto Chicama butsmoother and smaller in magnitude (about half, being the average over an extended region), withalmost similar commencement, to within a month. The El Nino 3 region SST fluctuation was roughlysimilar but with still smaller magnitudes (about one third, averaged over a very large region) andcommenced generally with a lag of 1–2 months, though 1982–1983 was an exception, with the ElNino 3 region commencing earlier. The plots of Wright SST were very similar to those of El Nino3, as expected. In a few cases (1968, 1986, 1991), El Nino 3 and Wright SST showed anomalies notaccompanied by El Nino 1+2.

(iii) The SOI evolution was occasionally out of phase with respect to the El Nino evolution, startingearlier (1982) and/or lasting longer or occurring separately (1959, 1974). SOI is the atmosphericcomponent of the general ENSO phenomenon, while SST is the oceanic component and the two canbe out of phase (Deser and Wallace, 1987; Trenberth, 1997). This explains why ENSO events chosenby different workers on the basis of El Ninos (Rasmusson and Carpenter, 1983, following Quinn etal.’s list), or on the basis of Southern Oscillation Index (T−D) (Kiladis and Diaz, 1989) or on thebasis of eastern equatorial Pacific SST (Mooley and Paolino, 1989), do not always tally. Across-correlation analysis between (T−D) and El Nino 1+2 SST yielded a maximum correlationcoefficient (0.6090.06) at a phase shift of �2 months.

The designations (ENSOW etc.) for each year, used in Kane (1997a,b, 1998) are restated below.

EN=Presence of an El Nino at Puerto Chicama and El Nino region 1+2, (list of Quinn et al., 1978,1987, updated).SO=Presence of minimum in the Southern Oscillation Index, or Wright Index or Tahiti minus Darwinatmospheric pressure difference.W=Presence of maximum (positive anomalies) in the sea surface temperature in the eastern equatorialPacific (El Nino 3 region).C=Presence of minimum (negative anomalies) in the sea surface temperature in the eastern equatorialPacific region (El Nino 3 region). These are La Ninas.NON=Non-events, i.e. years not falling into any particular category.

The terms EN, SO, etc., are used here in their literal sense. (Thus, ENSO does not imply here the generalphenomenon, but only the presence of El Nino and SOI minima in the same year). Some years wereENSOW, i.e. El Nino (EN) existed (Quinn et al.’s list), SOI minima (SO) also existed and, easternequatorial Pacific SST were higher (W). The ENSOW were further subdivided into two categories, as

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follows. The 12-monthly running means of (T−D) and Pacific SST were used to check whether the SOIminima or SST maxima occurred in the middle of the calendar year (May–August). If so, the events weretermed as ENSOW-U, i.e. Unambiguous ENSOW. If the extremes were in the earlier or later part of theyear (not in the middle), the events were termed as ENSOW-A, i.e. Ambiguous ENSOW. In other years,combinations observed were: ENSO, ENW, ENC (EN in the early part of the year, C in the later part),SOW, SO, W only, and finally, C (cold events, La Ninas). Years having neither an EN nor SO nor W norC were termed as non-e6ents.

3. RELATIONSHIP OF RAINFALLS IN YEARS OF DIFFERENT CATEGORIES

Table I indicates the relationship between the rainfalls at the various locations for three categories ofyears involving El Nino events, 6iz. (a) Unambiguous ENSOW, (b) Ambiguous ENSOW, (c) other types,ENSO, ENW, EN, ENC. The rainfalls are indicated as broad categories. Thus, symbols + and −represent positive and negative deviations within 0 and 0.5s ; d (mild droughts) and f (mild floods)represent negative deviations between −0.5s and −1.0s and positive deviations between +0.5s and+1.0s ; D (severe droughts) and F (severe floods) represent deviations exceeding 1.0s.

In Table I, the following may be noted:

(i) There is a profusion of (− , d, D) in Unambiguous ENSOW in IMR, Thailand, Singapore, Brunei,Indonesia and EAS, to a lesser extent in Malaysia and no preferences for positive or negativedeviations in the Philippines. Thus, events of this type are o6erwhelmingly associated with droughts inIndia and some of the regions of Southeast Asia, but not all.

(ii) In contrast, events of Ambiguous ENSOW are associated more with floods rather than with droughtsin IMR, Malaysia, Philippines (northwest) and East Asia, while Singapore, Brunei, Indonesia and thewhole Philippines show bias for droughts.

(iii) In other events, IMR, Thailand, Malaysia, Philippines (northwest) and East Asia show biases fordroughts, while Singapore, Brunei, Indonesia and whole Philippines show biases for floods.

(iv) If all the 49 El Nino events are considered together (see numbers at the bottom of Table I), there isa tendency for droughts in all locations (+/− ratios 0.43–0.83) except the northwest Philippines,where there is a tendency for floods (ratio 1.27).

This paper now examines whether the El Nino effects are dependent on the month of commencement andthe duration of the El Nino events.

4. EL NIN0 O COMMENCEMENT AND DURATION

Since plots of the SST anomalies at Puerto Chicama (data available from 1925 onwards only) were similarto those of El Nino 1+2 region, El Nino 3 region and Wright SST for periods for which data for all thesewere available during major El Nino events, only Puerto Chicama SST plots were used to locate thecommencements and endings of the El Nino events. For double events, i.e. events that continued in thenext year (1957–1958, etc.), each year was considered as a separate event, the first year event consideredas ending in December of the first year and the second year event as commencing in January of the secondyear. For events having a structure (El Nino active for a few months, extinct for the next few months, andreappearing again), both the parts were one event. The list tallied exactly with the list of Trenberth (1997).In the present list, there were 24 Q events (present in the list of Quinn et al., 1978, 1987) and seven othersequivalent to Q events (SST anomaly at Puerto Chicama positive). In all, there were 31 events.

(a) For India:There were 14 events (1925, 1927, 1929, 1930, 1939, 1940, 1941, 1951, 1957, 1965, 1972, 1987, 1991,1992), when El Nino was strong during June–September and droughts (d or D) were expected andwere observed.

Copyright © 1999 Royal Meteorological Society Int. J. Climatol. 19: 653–672 (1999)

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Table I. Rainfall deviationsa for Unambiguous ENSOW, Ambiguous ENSOW and other types ofEl Ninos, with the numbers of F, f, +, −, d, D and the ratio (F, f, +)/(D, d, −) given at the

bottom

Event IMR Tha Mal Sin Bru Ind Ph1 Ph2 EAS

Unambiguous ENSOWS 1877 I D DM 1888 I d D DM 1896 I − D +S 1899 I D F − DM 1902 d f D D DM 1905 D − D − f D −S 1911 I D − − d D d F + −S 1918 I D d − f F d F + DM 1930 I d d d d D d − d DS 1941 II D − D d dM 1951 D − f + − d − + dS 1957 I d f + d d − d D dM 1965 D D − D d d d d DS 1972 I D d D D D D F f −S 1982 I D + −M 1987 D D −

16 eventsF 0 0 2 0 1 0 3 0 0f 0 1 1 1 0 0 1 1 0+ 0 1 1 1 0 0 0 3 1− 1 3 4 0 1 3 2 0 5d 4 3 1 5 2 5 2 2 3D 11 2 2 6 3 2 0 2 6

(F, f, +)/(−, d, D) 0/16 2/8 4/7 2/11 1/6 0/10 4/4 4/4 1/14Ratio 0.00 0.25 0.57 0.18 0.17 0.00 1.00 1.00 0.07

Ambiguous ENSOWS 1878 II F +M 1914 f d f − D D F D −M 1919 II + D + + D f − +M 1923 − D d d f d f f −S 1925 I d d F F F D F F DS 1926 II f + f − + f f f −M 1931 II + d f f d − f − fS 1940 I − d D d − d − D dW 1948 + F d f d D d D +M 1953 f F f D − D − + +S 1958 II + f D d D F − d +W 1963 + + D D f d f D −W 1969 − f + − − D + D fM 1976 + − d d +S 1983 II F F +

15 eventsF 2 3 1 1 1 1 2 1 0f 3 2 4 2 2 1 5 2 2+ 6 2 2 2 1 0 1 1 6− 3 1 0 3 3 1 3 2 4d 1 4 2 5 3 3 1 1 1D 0 2 3 1 3 5 0 5 1

(F, f, +)/(−, d, D) 11/4 7/7 7/5 5/9 4/9 2/9 8/4 4/8 8/6Ratio 2.75 1.0 1.40 0.55 0.44 0.22 2.00 0.50 1.33

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Table I. (continued)

Event IMR Tha Mal Sin Bru Ind Ph1 Ph2 EAS

Other types of El Nino1871 ENSO −1873 ENSO D f1880 ENSO − f D1891 ENSO d F D1900 ENSO II + − f D1912 ENSO II d f + f D − d −1884 ENW f d D1897 EN II + F −1929 EN − D d d − D f + d1932 EN II d D − d f f f +1939 EN d d − + − + − F D1943 EN + d D d F −1874 ENC F d1887 ENC f f −1889 ENC II f − F1907 ENC d d D + d − D1917 ENC F f f F F F + F F1973 ENC II f − f F + F d F

18 eventsF 2 0 0 4 1 3 0 2 3f 4 2 2 4 1 2 1 1 0+ 3 0 1 1 1 2 1 1 1− 3 1 2 2 2 1 1 1 4d 5 2 2 5 0 0 2 1 1D 1 2 1 1 1 1 0 0 6

(F, f, +)/(−, d, D) 9/9 2/5 3/5 9/8 3/3 7/2 2/3 4/2 4/11Ratio 1.00 0.40 0.60 1.13 1.00 3.50 0.67 2.00 0.36

All 49 events(F, f, +)/(−, d, D) 20/29 11/20 14/17 16/28 8/18 9/21 14/11 12/14 13/30Ratio 0.69 0.55 0.82 0.57 0.44 0.43 1.27 0.86 0.43

I and II indicate first and second years of double El Nino events (1957–1958, etc.). Symbols S (strong), M(moderate), W (weak) indicate strengths of the El Nino involved.a +, −, positive, negative, between 0 and 0.5s ; f, d, mild floods and droughts, between 0.5s and 1.0s ; F, D,severe floods and droughts, exceeding 1.0s.

There were five events (1931 II, 1949, 1958 II, 1969, 1973 II) when the El Ninos occurred beforeJune–September and hence were not likely to give droughts in India and did not give droughts. In1963, El Nino occurred after August and hence, did not give droughts. There were 11 events whenexpected and observed values differed (droughts were expected in seven cases but normal or excessrainfall occurred. Normal rainfall was expected in four cases but droughts or excess rainfalloccurred).Thus, in case of India, expectations based on El Nino timings alone were not fulfilled in a substantialnumber of events (11 out of 31, �35%).For India, the number of events conforming were 20, non-conforming were 11 and the percentage ofnon-conforming events was 35%.

(b) For south-Asia:The number of non-conforming events were: Thailand (41%), Malaysia (35%), Singapore (33%),Brunei (32%), Indonesia (32%) and East Asia (33%); but Philippines northwest monsoon season(45%) and Philippines annual (60%) showed larger non-conformity (values near 50%). If any of thesenumbers would have been near 0%, it could have indicated reversed effects (El Nino associated withfloods rather than droughts).

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EL NIN0 O TIMINGS AND RAINFALL EXTREMES 659

In conclusion, it seems that the relationship between El Nino onsets and droughts is not perfect. In thecase of the Philippines, the relationship was the poorest, probably because of the influence of the EastAsian monsoon, which is said to be mostly independent of the Southeast Asian monsoon (Ding Yihui,1994). On the other hand, East Asia as used by Hulme and Zhao (1994) had 160 Chinese locations andonly about two dozen Indian locations; yet, the response was similar to that of India. Hence, the effectof the East Asian monsoon should be restricted to the South China Sea only. However, Shi and Zhu(1996) report that the relationship between the East Asian summer monsoon and rainfall over Chinashows remarkable regional features. For the East Asian summer monsoon, they define an index MI, basedon standardized sea level air pressure differences (110°E minus 160°E) at seven latitudes between 50°Nand 20°N. For rainfall, the ‘National Meteorological Centre’ of the Meteorological Administration ofChina has divided the summer rain belts in East China into three main patterns, 6iz. (I) rain over and tothe north of the Huanghe River, (II) rain between Huanghe River and Changjiang River, (III) rain overand to the south of Changjiang River. For the 40 years 1950–1989, they show that when the monsoonindex MI is positive (weak East Asian summer monsoon), mostly rainfall patterns III and II are seen, andwhen MI index is negative (strong East Asian summer monsoon), only rainfall patterns I or II are seen.In short, a powerful East Asian summer monsoon pushes the rain belt further north. Wet summers overthe middle and lower reaches of the Changjiang River are related to a weak summer monsoon, and, yearswith dry summers are related to a strong monsoon. Thus, the rain patterns inside China are highlyvariable from one region to another and are strongly associated with the East Asian MI. Kripalani andSingh (1993) also examined the spatio-temporal variability of the rainfall over India and China. TheirEOF analysis showed a coherent pattern over a large part of India, in phase with North China, while overChina, rainfall occurred in the form of east–west oriented bands, suggesting two different climatic regimesoperating above and below �35°N. In this study, data over the whole of the People’s Republic of Chinaare averaged, and hence the regional effects of the East Asian summer MI have probably been wiped out,leaving only the El Nino effects similar to those for India and Southeast Asia.

5. DROUGHTS

Let us examine what are the characteristics of the years when mild and severe droughts occur. For India,droughts (negative deviations of −0.5s or more) occurred in the following years:

For 1871–1924, when only Wright SST index was available:

Year Deviation ENSO characteristics Year Deviation ENSO characteristics

1873 −1.18 ENSO (W not seen) 1905 −1.64 ENSOW-U(January–December)

1876 −0.91 Non-event 1907 −0.91 ENC (W not seen)1877 −2.98 ENSOW-U ENSOW-U1911 −1.43

(August–December)(February–December)1888 −0.50 ENSOW-U ENSO1912 −0.58

(January–December) (January–February)1891 −0.76 ENSO SOW (No data for W)1913 −0.84

(April–August)1899 −2.69 ENSOW-U Non-event1915 −0.86

(July–December)1901 −1.60 Non-event 1918 −2.45 ENSOW-U (No data for W)1902 −0.73 ENSOW-U W1920 −1.62

(January–December) (June–October)1904 −1.24 SOW

(July–December)

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For 1925 onwards, when Puerto Chicama SST data were available:

Year Deviation ENSO characteristics Year Deviation ENSO characteristics

1925 −0.59 ENSOW-A Non-event1966 −1.40(January–December)

1928 −1.03 C 1968 −1.18 W1930 −0.62 ENSOW-U ENSOW-U1972 −2.39

(March–December) (February–December)1932 −0.61 EN 1974 −1.26 SO

(February–June)1939 −0.76 EN (January) 1979 −1.73 SOW1941 −1.48 ENSOW-U ENSOW-U1982 −1.40

(October–December)(January–June)1951 −1.38 ENSOW-U Non-event1985 −0.88

(March–December)1952 −0.72 Non-event 1986 −1.34 (EN)W1957 −0.82 ENSOW-U ENSOW-U1987 −1.86

(February–December) (January–November)1962 −0.52 Non-event (C?) 1991 −0.80 ENSOW-A

(July–September)1965 −1.74 ENSOW-U 1992 −0.80 ENSOW-A

(March–December) (January–August)

For the period 1871–1924, from the 17 drought events (nine severe, eight mild), seven were associatedwith ENSOW-U; but ten were associated with other types of events. Thus, whereas ENSOW-U was afavourable combination, it was not exclusive.

For 1925 onwards, from the 22 droughts (ten mild, 12 severe) in All India summer monsoon, eight wereassociated with Unambiguous ENSOW and three with Ambiguous ENSOW, indicating that ENSOW(especially ENSOW-U) is a combination favourable for droughts. However, it is neither necessary norsufficient. In 11 cases (50%), droughts occurred in other categories (three non-events, two EN, one SO,one SOW, one W, one ENW, two C) and among these, the two El Ninos of 1932 and 1939 did not remainactive after June. On the other hand, during the ENSOW-A of 1926, 1948, 1953, 1976, 1983 and theENSOW-U of 1976, El Nino timings were suitable for droughts and yet, droughts did not occur. Incontrast, 1982 ENSOW-U had El Nino starting very late (October) and yet, a severe drought occurred.Thus, whereas some relationship with El Nino timings is indicated, this is by no means the decidingfactor. Many other complicating factors must be involved.

Table II shows the drought severity and ENSO characteristics in years when droughts occurred atdifferent locations.

In Table II, many droughts are widespread, occurring in four or more locations. From these commonevent types, seven are Ambiguous ENSOW, eight Unambiguous ENSOW, one EN, two SOW, one SO,two W, two ENC, one SOC, one C and two non-events. Thus, whereas a large number is in the ENSOWcategory, some can occur in other categories also, including non-events. Figure 2(a) shows the occurrencefrequency of these droughts. The following may be noted:

(i) For India (IMR, top plot), the largest occurrence (15) is for Unambiguous ENSOW. AmbiguousENSOW are only three; but droughts do occur in other categories also. Out of the 39 droughts, forcategories expected to be favourable to droughts (ENSOW-A, ENSOW-U, ENSO, ENW, EN, SOW,SO, W), the score is 30 (77%). Thus, any manifestation of the ENSO phenomenon (EN and/or SO

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and/or W) is favourable for droughts; but the combination Unambiguous ENSOW is more effective.However, from the other eight events, only one is an ENC (EN would give drought) and the other sevenare C and non-events, categories not expected to give droughts.

Table II. Years of occurrence of drought (d, mild; D, severe) at different locations intovarious categories

Bru Ind Ph1Category Ph2 EASIMR Tha Mal Sin

1914D 1914D 1948d 1914D1923d 1925D1923d1914d1925dENSOW-A1940D 1940d 1919D 1923d 1 1940D 1940d1991d 1919D

1931d 1925D 1948D 21953d1948d1923D1992d1958d 1948d 1940d 1958D3 1925d 1958D

1963D 1963D 1958D 1948D 1963D1931d1976d 1953d 1969D1976d1940d 5

6 6 1963d 661969D8

ENSOW-U 1877D 1911D 1902D 1957d 1905D 1888D1877D 1918d 1930d1941D 1888D 1930D 1911d 1965d 1930d 1899D1888d 1930d

1957d 1918d 2 1957D 1902D1896d1972D1965D1899D1902D 1965d 1930d 1965d 1918D1902d 1972d 3

1972D 1951d 4 1930D1905D 1987D 1905D5 1965d 1941D1911d51911D

1930d 1972D 1951d1918D7 1957d1930d 1941d

1965D1957d1941D1965D 91951D

1957D 1972D111965D

1972D1982D1987D15

1912D 1912d 1880D1873dENSO1891D1891d1900D1912d

ENW 1884d 1884D1986D 1986D1986D

1929D 1969d1929d 1929d1932d 1929D 1929dEN1932d 1971D 1939D1939d 1932D 1943D

1975D1939d 1943d1943d

SOW 1944D 1913d 1913d 1904D1904D 1977D 1904d1944D 1977D 1944D 1913D1913d 1979D

1944D1979D1979D

SO 1885D 1959d 1959D 1959d1974D 1974D 1974D1974d

1920D 1968D1920D 1920DW 1968d1920D1920D1968d1968D

ENC 1907d 1927d 1907D1907d 1927D 1907d 1874d1927d 1927D1907D1973d

1935DSOC 1946d 1946D 1936d1936d 1935D1946D 1949D 1949D1946D

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Table II. (continued)

Category IMR Tha Mal Sin Bru Ind Ph1 Ph2 EAS

C 1928D 1922d 1898d 1872d 1909D 1921d 1903D 1903D 1893D1962d 1928D 1903D 1882d 1922d 1950d 1906d 1960d 1894D2 1961d 1916d 1894d 1961d 1961D 1909d 2 1942d

1967d 1928d 1908d 3 3 1910d 1950d4 1938d 1960D 1938D 1975d

1942d 1961D 1950D 51955d 1971D 1954d7 1975d 1955D

8 8

Non-events 1876d 1915D 1901d 1876D 1978d 1901d 1915D 1915D 1883D1901D 1984d 1952d 1883D 1 1966D 1947D 1 1895D1915d 1985d 2 1901d 1967D 1966d 1898D1952d 1989D 3 3 3 1915d1966D 4 1978D1985d 1989D6 6

Events common to four or more locations are underlined.

(ii) In the plots for other locations, only East Asia (bottom plot) has a preponderance (9) for UnambiguousENSOW only (Ambiguous ENSOW are only 2) and is thus similar to IMR, except that the numberof droughts occurring in C and non-events is also large (11). Singapore also has a preponderance (11)for Unambiguous ENSOW; but Ambiguous ENSOW also contribute considerably (6) and C andnon-events contribute largely (11). In Thailand, Malaysia, Brunei, Indonesia and the whole Philippines,both Unambiguous and Ambiguous ENSOW contribute considerably (3,5; 5,6; 7,8; 4,6). But at all theselocations a considerable number of droughts occur during C (La Nina) or non-events also (9, 4, 6,3), a highly disconcerting aspect. A considerable influence of other factors must be involved, includingan effect of the East Asian summer monsoon.

(iii) For northwest Philippines (Ph1, third plot from bottom), the pattern is opposite to IMR, few droughtsduring Unambiguous or Ambiguous ENSOW and many droughts during C and non-events. Thus, thisregion seems to be affected more by the East Asian monsoon, indicated to be negatively correlatedto the Indian monsoon (Wang and Xun-Qiang, 1995).

6. FLOODS

Table III shows the years of occurrence of floods at the various locations.In Table III, many events are widespread, three being Ambiguous ENSOW, one Unambiguous ENSOW,

one SOW, two ENC, three SOC, 15 C and one non-event. Thus, a tendency towards C-type events (LaNina) is indicated. Figure 2(b) shows the occurrence frequency of the floods. The following may be noted:

(i) ForIndia(IMR,topplot), the largestoccurrence(22) is fortheCevents, indicatingafairlygoodassociationof La Nina with floods (22 out of 44 events, 50%). If the ENC (5) and SOC (3) are considered as affectedby C, the score would be 30 (68%) out of 44. But ten events fall in the ENSO group (EN and/or SOand/or W). Thus, in these cases, ENSO could not cause a drought or could not prevent floods.

(ii) In the plots for other locations, East Asia had fewer floods (24) as compared with IMR (44), thoughthe data length is almost the same. Also, many floods occur during events of other types. This probablyindicates a diluting effect, when a very large area is considered, 6iz. the whole People’s Republic ofChina, where heterogeneous rainfall regimes may be involved. In Thailand, Singapore, Brunei, Indonesia,the pattern is similar to India. In the whole Philippines and Malaysia, many droughts occur outsidethe C category.

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Figure 2. Occurrence frequency of (a) droughts and (b) floods, at different locations (IMR, Thailand, etc.), during different typesof years (ENSOW, etc.)

(iii) In the northwest Philippines, a larger number is outside the C category, indicating behaviour oppositeto India.

Overall, Indian monsoon seems to have the best relationship with the ENSO phenomenon (though by nomeans perfect) and some regions in Southeast Asia show almost similar relationship; but some others showlarger dissimilarities, probably due to interference from the East Asian monsoon and/or other local factors.

7. ASSOCIATION WITH EAST ASIAN MONSOON

The author does not know how representative the MI index of Shi and Zhu (1996) is for the East Asianmonsoon. In any case, using that for East Asian monsoon and using Wright SST values (12-monthly mean)as an ENSO index, a correlation analysis was carried out. Only common data for 1911–1940 and 1946–1975were used. Most of the correlations were low. Reasonably good correlations (+0.4 or more) were: IMRwith Thailand (+0.42) and with East Asia rainfall (EAS) (+0.58); Thailand with EAS (+0.47);

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Table III. Years of occurrence of floods (f, mild; F, severe) at different locations into variouscategories

Tha Mal Sin Bru IndCategory Ph1 Ph2 EASIMR

1948F 1925F 1925F 1923fENSOW-A 1926f1878F 1914F 1923f 1931f1914f 1953F 1926f 1931f 1925F 1958F 1919f 1925F 1969f

1958f 1931f 1948f 1963f 21926f 1923f 1926f 21953f 1969f 1953f 3 3 1925F 3

1983F 4 1926f1983F55 1931f

1963f7

1975f 1899F 1918f 1918F 1905f 1918fENSOW-U1902F 1911F 1972f1914f 1918F1951f 1972F

1912f 1873fENSO 1900f1880f1912f

1884fENW

1897F 1932fEN 1932f 1929f 1932f1943f 1939F

1944f 1944f 1913F 1913F 1913f 1904F 1904f 1904fSOW1977f1994F 1913f

1959F 1959f 1959f 1974f 1974F 1885fSO1959F

W 1920f 1920f1968F 1968f

ENC 1874F 1917f 1917f 1887f 1917F 1917F 1917F 1889F1927f 1917F 1973F1887f 1917f1973f 1927f 1973F1889f

1973F1917f1973f

1949F 1936f 1946F 1935fSOC 1935f 1935f 1946f1936f1946f 1946F 1936F 1936f 1949f1949f 1949F

1916F 1906F 1879F 1916FC 1903F1872f 1961F 1906f 1892f1875f 1924f 1910f 1890F 1924f 1906f 1962F 1908F 1903f

1933F 1922f 1893F 1928f 1908f 1967f 1910f 1908F1879f1934f 1924F 1898f 1933f 1909F1882f 3 1916F 1910f

1890f 1938F 1934f 1903f 1934F 1910F 1921F 1938f1942F 1967F 1906F 1938F 1916F 1922f 1954F1892f1950f 6 1910f 1942F 1933f1893F 1928f 1956F1954f 1921f 1954F 1934f 1934F 1964f1894F1955F 1922f 1955f 1938f1908f 1938f 8

1910F 1960F 1924f 1956f 1942f 1956F1962F 1928F 1962f 1954F1916F 1971F

1933F 1964F 1934F 1971f 1955F 111970F 1954F 12 1956F1934f1971f 1956f 1962f1938f

1942F 1988F 1964F 1971f15 1967F 151955f

161956F1961F

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Table III. (continued)

Category IMR Tha Mal Sin Bru Ind Ph1 Ph2 EAS

1964f1970F1975F1988F22

Non-events 1945f 1937F 1915f 1895F 1936f 1937F 1947f 1937f1947f 1945F 1947F 1952F 1980F 1952f 1947f1978f 1966f 1966F 1978F1990f 1978F

1980F 1981F

Events common to four or more locations are underlined.

Malaysia with Singapore (+0.47); Brunei with whole Philippines (+0.47). All other correlations were lowerthan +0.4. Northwest Philippines had a negative correlation with all other rainfalls except with the wholePhilippines (+0.16). All rainfalls had very low correlations (0.16 or less) with the MI index. However, theWright SST index had reasonably good negative correlations (implying droughts) with rainfalls at IMR(−0.56), Brunei (−0.46) and Indonesia (−0.43) and low negative correlations with all other rainfalls exceptthe northwest Philippines (+0.35). The correlation between the MI index and the Wright SST index was+0.15, which indicates that these two are relatively independent of each other (Chen Longxun et al., 1991;Ding Yihui, 1994) rather than negatively correlated as mentioned by Wang and Xun-Qiang (1995).

If two effects act simultaneously, the direct correlations may be low; but partial correlations can be high.In the present case, the correlation between IMR and Wright SST was −0.56, between IMR and MI indexwas −0.11 and between MI and Wright SST was +0.15. A partial correlation analysis changed the −0.56to −0.55. Thus, the correlations remained moderate. Obviously, neither the Wright index nor the MI indexnor their combination gives good results. For a longer period 1873–1989, a bivariate analysis in which IMRrainfall was considered as the independent variable and the MI index and the Wright Index as the twodependent variables yielded a multiple correlation coefficient of only 0.58, accounting for only �35% ofthe total variance.

In Table II many droughts occurred in the category C when only floods are expected (seven at Malaysia,eight at Singapore and eight at the northwest Philippines). Was the East Asian summer monsoon indexMI particularly suitable for droughts for these years? The standardized MI index series for 1873–1989 asgiven in Shi and Zhu (1996) ranges from −2.53 to +2.92. Positive values indicate weak summer monsoonand negative values indicate strong summer monsoon. The MI values for drought years in the C categorywere as follows:

Malaysia Singapore North-west Philippines

Year MI Year MI Year MI

1898 +1.89 1882 +0.87 1903 +0.621903 +0.62 1894 +0.65 1906 −0.251916 +0.14 1908 −0.62 1909 +0.561928 −0.98 1960 −1.25 1910 −0.981938 −0.81 1961 −1.16 1938 −0.811942 +0.33 1971 +0.35 1950 −0.821955 −0.20 1975 −0.99 1954 −0.09

1955 −0.20

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Figure 3. Plots of the various rainfall series, for (a) 1870–1940, (b) 1940–1994. Major floods are indicated by dots and majordroughts by triangles. The thick lines are 11-year running averages. A and B see text

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EL NIN0 O TIMINGS AND RAINFALL EXTREMES 667

As can be seen, there is no preponderance of large positive or negative MI values in any column. Thus,the East Asian summer MI does not seem to have any strong influence on the rainfall extremes at Malaysia,Singapore and the northwest Philippines.

8. EPOCHAL BEHAVIOUR

Figure 3 shows the interannual variability of the various time-series during (a) 1870–1940 and (b)1940–1994. Dots indicate major floods and triangles indicate major droughts. These are sometimes in thesame years for more than one location. The thick lines represent 11-year running averages and show whatKripalani and Kulkarni (1997a,b) term as the epochal behaviour, i.e. there are long epochs when the rainfallis either above normal (A) or below normal (B). For example, for IMR (All India summer monsoon),1880–1895 (16 years) and 1930–1963 (34 years) were epochs of above normal rainfall, while 1895–1930(36 years) and 1963–1990 (28 years) were years of below normal rainfall (indicated in Figure 3, top plot,as A and B). For other locations, there were other epochs and the average length of the epochs over theequatorial region (e.g. Singapore, Indonesia) was about a decade (oceanic influence), whereas over thetropical regions away from the equator (e.g. India, Thailand), the length was about three decades. It wasalso noticed by these authors that the impact of El Ninos was more se6ere during the below normal epochs.This might as well be because of recent changes in the frequency and intensity of El Nino versus La Ninaevents (Trenberth and Hurrel, 1994). To check this, Kripalani and Kulkarni (1997b) examined the long-termchanges, omitting the El Nino and/or La Nina events and concluded that the epochal behaviour was slightlymodified but not fundamentally forced by the El Nino/La Nina frequency. Hence, El Nino/La Nina eventscan be considered as external forcing and the epochal behaviour as internal variability. Table IV combinesthe results presented by Kripalani and Kulkarni (1997a,b) to illustrate this feature.

Thus, for IMR, the mean values for the above normal epoch A and the below normal epoch B are−0.3690.31s and −1.2690.27s, respectively. The two are significantly different at a 5% level. The samelevel of difference is seen at some other locations also, substantiating the conclusion of Kripalani andKulkarni (1997a,b) that the external forcing by El Ninos is more effective in causing droughts when theepoch is of below normal rainfall (B).

However, there are some unsatisfactory features. Thus,

(i) Even in the above normal epoch A, some deviations are negative, indicating that droughts are notruled out. The average value in A is less negative (or even positive) as compared with epoch B, becausein some years, the deviations are large positive, much more so in epoch A than in epoch B.

(ii) In the case of Indonesia, all the deviations in epochs A and B are negative. Hence, their averages−1.1390.21s (for B) and −0.6790.20s (for A) are not significantly different and, as mentionedby Kripalani and Kulkarni (1997a), the external forcing (El Nino effect) plays a more dominant rolethan the internal epochal behaviour (A or B).

(iii) If the years in which the deviations in A or B in Table IV are positive are considered as conforming,it is interesting to note that many of these years are common to many locations. Ignoring Phillipines(monsoon) and to a lesser extent, the whole Phillipines, which show results differing from those ofother locations, the years 1914 (ENSOW-A), 1925 (ENSOW-A), 1951 (ENSOW-U), 1953 (ENSOW-A)seem to have failed to give droughts at more than one location.

(iv) Even in the group B, some deviations are very small (within 0 and 0.5s), indicating normal rainfall.Thus, whereas the observation that El Ninos are more effective in low rainfall epochs is correct onan average basis, it may be risky to use it for predicting the impact of any given El Nino.

9. EURASIAN SNOW COVER EFFECT

As mentioned earlier, some droughts and floods occurred in India without any ENSO connection. Apossible effect (droughts) due to the excess Himalayan snow cover was hypothesized and used for

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Table IV. Rainfall deviations (normalized, units of 0.1s) during El Nino events occurring during A, epochs of above normal rainfalls andB, epochs of below normal rainfall, different at different locations

BIMR Sin B Bru B Ind B Ph1 B Ph2 BB Tha B Mal

−4 1877 −20 1911 −15 1923 −6 19411939 0 1951 1−91918−30187719411896 −23 1884 −7 1914 −36 1925 −16 1951 −1 1953 1−3 1923 −13

1902 −12 1965 −8 1930 −6 1953 −1−5 1957 −121899 −27 19251905 −11 1969 −2 1951 −8 1957 −51902 1965 −5−7 1930 −91939 4 1972 −14 1965 −8−121932−1619051941 −6 1969 −151911 −141957 −8 1972 −201914 5

1918 1965 −12−241923 1969 −1−3

1972 −14−619251965 −171969 −31972 −24

119761982 −141987 −19

Mean −13.5 −8.7 −15.0 −11.3 −1.8 −3.8−12.6 −9.69.3 2.1 5.7 2.1 1.1 3.1S.D. 1.42.7

A A A A A AA A−2 1891 0 1923 6 1902 −13 19111905 28−9 1918 61884 10 1939−4 1911 −6 1925 19 1905 −2 1914 271887 1923 106 1941 −4 1911

7 1914 −4 1930 −13 1911 −5 19181914 14 1925 12−11951−71891−3 1918 7 1932 7 1914 −12 1923 101932 1930 −8−6 1953 13 1918−5 1923 −8 1939 −1 1918 −6 19251923 161939 1932 871957−7

19251941 22 1925 22 1941 0 1957 −2 1930 −3−15 1965 −1019511951 6 1951 3 1932 0−13 1969 7

8 19651953 −6819531957 −8

3.6 2.0 3.0 −6.7 10.8Mean 5.6−3.6 0.43.2 3.9 4.3 2.0 4.6 3.5S.D. 3.1 3.3

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Figure 4. Plots of (a) IMR anomalies and (b) ESC spring anomalies (March, dots; April, crosses)

correlation with Indian rainfall by Blanford (1884) and later by Walker (1910). Up to 1920, a negativecorrelation was observed; but it became uncertain in later decades and the effort was abandoned. Whensatellite data became available in 1966, Hahn and Shukla (1976) and later Dey and Bhanukumar (1983),Dickson (1984), Verma (1990) and others pursued snow studies and concluded that extensive (little) Eurasiansnow cover (ESC) in winter/spring was followed by deficient (excess) Indian summer monsoon rainfall.Recently, Kripalani et al. (1996b) reported that the January snow mass over two regions of the former USSRwas inversely related to IMR and the ESC in April also had a similar relationship. Direct correlations havebeen shown to be negative; but there are complications due to the effect of El Ninos. Yang (1996) andSankar-Rao et al. (1996) show that the ESC–IMR negative relationship improves if El Nino years areomitted. The correlation between SOI and ESC is rather low. Nevertheless, an impact of snow cover onENSO phenomenon has been investigated. In a simple way, excess (deficit) Eurasian snow acts to keep theland and the overlying atmospheric column colder (warmer), reducing (enhancing) the land–oceantemperature contrast and weakening (strengthening) the monsoon and hence, rainfall. However, Barnettet al. (1989) used a coupled atmosphere–ocean model and showed that the negative relationship was notdue to increased snow cover alone, but by increased snowfall rate also, which provided large amount ofsnow for melting and evaporation. A perturbation induced by doubling of Eurasian snowfall rate couldtrigger El Ninos in equatorial Pacific 1–2 seasons following the India monsoon deficit. Yasunari et al. (1991)and Vernekar et al. (1995) performed experiments with MRI and COLA GCM and came to similarconclusions. Khandekar (1991) made a phase lagged correlation analysis and hypothesized that a lighter(heavier) than normal ESC, followed by an excess (deficit) IMR, could trigger El Ninos 4–5 seasons (12–15months) after the IMR season. Yang (1996) made a similar analysis and found that heavy winter ESCoccurred during El Nino winters, while the SOI led the winter ESC by 2–3 seasons and, whereas ESC wasnegatively related to IMR, the relationship was considerably disrupted by El Ninos. Sankar-Rao et al. (1996)also came to a similar conclusion and found that in years when there was no El Nino, excess ESC in winterwas followed by lower temperatures over Asia in the following summer. Thus, ESC–IMR relationship canbe greatly disturbed by ENSO phenomena, which, in turn may be triggered or modified by ESC.

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Table V. The staus of ESC (+, n, −) and ENSO phenomenon, the expected effects on rainfall(+, n, −), the observed IMR (D, d, −, +, f, F) and comments

Year Status Expected effects Observed Comments

ESC ENSO ESC ENSO IMR

1973 n ENC n + or − f C prevailed1974 n SO n − D SO prevailed1975 − C, La Nina + + F ESC and C worked*1976 + ENSOW-A − n or − n ESC failedx

1977 − SOW + − n ESC cancelled SOW*1978 n Non-event n n f Doubtful1979 + SOW − − D ESC and SOW worked*1980 + Non-event − n n ESC failedx

1981 + Non-event − n n ESC failedx

1982 n ENSOW-U n D D ENSO worked1983 n ENSOW-A n n or − F ENSOW failed1984 n Non-event n n n All as expected1985 + Non-event − N d ESC worked*1986 n ENW n − D ENSO worked1987 + ENSOW-U − − D ESC and ENSO worked*1988 − C, La Nina + + F ESC and C worked*1989 − Non-event + N n ESC failedx

1990 − Non-event + N f ESC worked*1991 − ENSOW-A + n or − d ESC failedx

1992 − ENSOW-A + n or − d ESC failedx

1993 − ENW + − n ESC cancelled ENSO*1994 − SOW + − F ESC prevailed*

* Expectations fulfilled; x not fulfilled.

Reliable ESC data are available from NOAA since about 1972. Figure 4(a) shows a plot of IMR for1973–1994. For each year, the classification status (ENSOW, SOW, C, etc.) is also indicated. When theIMR values were correlated with ESC values for January, February, March, April and May, thecorrelation coefficients were −0.27, −0.07, −0.30, −0.34 and −0.02. Thus, the ESC values of Marchand April showed the largest correlations. Figure 4(b) shows a plot of the March (dots) and April(crosses) anomalies (deviations from the average pattern) of ESC. A comparison of Figure 4(a) and (b)shows that in many years, ENSO is active i.e. there is either an El Nino of some kind or a La Nina (C).From the 22 years 1973–1974, only 7 (1978, 1980, 1981, 1984, 1985, 1989, 1990) are non-events, duringwhich ESC effects could be seen exclusively. Also, from the 22 years data, 7 years have normal ESC, 6years have ESC well above average (exceeding +0.5) and 9 years have ESC well below average.Assuming that El Nino and ESC above average would give droughts in IMR (− , d, D), C and ESCbelow average would give floods in IMR (+ , f, F) and non-events and normal ESC would give normalrainfalls (n), Table V lists the expected and observed effects for each of the 22 years, and comments asto whether the expected effects were observed.

In Table V, from the 22 years, 7 years had normal ESC (n) and for these, the IMR would be relatedto ENSO phenomenon only. For the other 15 years, ESC either worked in conjunction with ENSO orworked alone. In nine (60%) cases, ESC gave expected results (*) while in six (40%) cases, ESC failed togive expected results (x). On the whole, the results are satisfactory; but, for prediction purposes,uncertainties would be large. A similar study for other regions in SE Asia needs to be conducted.

10. CONCLUSIONS

The data for rainfalls in India, Southeast Asia (Thailand, Malaysia, Singapore, Brunei, Indonesia,Philippines) and East Asia (mainly the whole of China, with some adjacent regions, including India) were

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examined for association with El Nino events. For All Indian summer monsoon rainfall (IMR),Unambiguous ENSOW (El Nino years, in which the SOI minima and equatorial eastern Pacific SSTmaxima occurred in the middle of the calendar year) had an overwhelming bias for droughts. Similargood association was seen for rainfalls at Singapore, Brunei, Indonesia and East Asia and to a lesserextent, with Thailand, Malaysia and whole Philippines. The northwest Philippines showed almostopposite results.

A check was made to see whether this relationship was in any way related to the timings of the El Ninosalso. Out of 31 events, 14 El Ninos occurred during the Indian summer season and gave droughts and sixwere not active during the Indian summer and did not give droughts. However, in seven cases when theEl Nino timing was suitable for droughts, they did not occur. In four cases, El Nino timings were notsuitable for anything; but droughts or floods occurred.

In C-type years (La Ninas), excess rains occurred overwhelmingly in IMR and to lesser extent at otherlocations, except the northwest Philippines where almost opposite results were seen.

In general, excess Eurasian snow cover in spring was associated with droughts in IMR. But the presenceof El Ninos or C events complicated the snow effects. In years when ENSO effects were absent, the snoweffect was not seen invariably, indicating the interference of factors unrelated to ENSO or snow.

Even though Unambiguous ENSOW show an overwhelming association with droughts in India and, toa lesser extent in Southeast Asia, the relationship of rainfall with El Ninos in general is not clear cut, asthere are other contributory factors to be taken into account. Predictions based on the ENSO phenomenaalone should be made with great caution and reservation.

ACKNOWLEDGEMENTS

Thanks are due to Dr Todd Mitchell and Dr Don Garrett for supplying Puerto Chicama SST data, to DrRoland Schweitzer for the Reynolds SST data for Indian Ocean, to Dr P.B. Wright and Dr J.K. Angellfor SST and SO data and to Climate Data Center, Washington, for several other data. This work waspartially supported by FNDCT, Brazil under contract FINEP-537/CT.

REFERENCES

Angell, J.K. 1981. ‘Comparison of variations in atmospheric quantities with sea surface temperature variations in the equatorialeastern Pacific’, Mon. Weather Re6., 109, 230–243.

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