1. Seasonal Prediction of South Asian Monsoon:
Sulochana Gadgil
SASCOF-1
IITM, 13April 2010

2. Work done in collaboration with
M Rajeevan, Lareef Zubair & Priyanka Yadav

3. Focus on understanding interannual variation of seasonal rainfall
Why?
We are supposed to have achieved spectacular success in prediction of seasonal to interannual scales (TOGA, CLIVAR etc.) and hence have IRI, CLIPPS etc.
Variation of seasonal rainfall is known to have a large impact on parts of South Asia and its prediction is, therefore, supposed to be useful.
Consider first the Indian case.

4. Summer monsoon: June-September
mean 85cm, std. dev 8.5cm

5. Interannual Variation of the anomaly of ISMR
(as % of the mean); (std dev is about 10% of mean)
Drought: ISMR anomaly <-10% of the mean
Excess rainfall seasons: ISMR anomaly >10% of the mean
Monsoon of 2009 is the one of the top five most severe droughts during

7. However, the skill of atmospheric and coupled models in predicting the Indian summer monsoon rainfall is not satisfactory, and the problem is particularly acute as these models fail to predict the extremes.
For example, almost none of these models could predict the droughts of 2002 and 2004.

8. Monsoon prediction – Why yet another failure?
Sulochana Gadgil*, M. Rajeevan and Ravi Nanjundiah
CURRENT SCIENCE, VOL. 88, NO. 9, 10 MAY 2005 p
showed systematic bias in model simulations

9. Almost no model predicted the most recent severe drought of 2009
Almost no model predicted the most recent severe drought of Is this true for the other countries as well?
Nanjundiah 2009: ‘A Quick Look Assessment of Forecasts for the Indian Summer Monsoon Rainfall in 2009’
Understanding how the models behave and why, is important for improving them

12. Precipitation Data used
Indian summer monsoon rainfall from
Bangladesh, Pakistan and Sri Lanka :
PREC data based on gauges from
“Global Land Precipitation: A 50-yr Monthly Analysis Based on Gauge Observations” M Chen, P Xie, J E. Janowiak and P A. Arkin (2002) Jrnl of Hydromeorology vol 3 p249-66 :
GPCP data for Bangladesh and Pakistan but
Lareef data for Sri Lanka :

13. PREC data

15. Rainy season: July-August
Pakistan Rainfall: monthly mean
Rainy season: July-August
Means 11.24, cm ;std. dev 4.16, 4.66cm

17. Summer monsoon: June-September
Bangladesh Rainfall: monthly mean
Summer monsoon: June-September
Means 166.5, cm, std. dev 17.5, 22.3 cm

19. Sri Lanka Rainfall: monthly mean
October-November
Means 55.3, cm std. dev 13.3, 12.1 cm

20. All-India summer monsoon rainfall

21. Pakistan July-August rainfall

22. Bangladesh June-Sept rainfall

23. Sri Lanka October-November rainfall

24. Impact of the Interannual variation of seasonal rainfall:
Consider first the Indian case.
Quantitative assessment of the impact of the monsoon on GDP and agriculture:
The Indian Monsoon, GDP and Agriculture, Gadgil, Sulochana and Siddhartha Gadgil, (2006)
Economic and Political Weekly, XLI,

25. A significant finding is the observed asymmetry in the response to monsoon variation, with the magnitude of the impact of deficit rainfall on GDP and FGP being larger than the impact of surplus rainfall.
The impact of severe droughts has remained between 2 to 5% of GDP throughout, despite a substantial decrease in the contribution of agriculture to GDP over the five decades.

26. Prediction of the interannual variation of ISMR and particularly for the occurrence/nonoccurrence of the extremes (i.e. droughts and excess rainfall seasons) continues to be extremely important even in the modern era.
Will discuss this at the end if time permits

27. Since the major success story in tropical prediction, which triggered the seasonal-interannual prediction programmes, is the prediction of ENSO, it would be of interest to determine the extent to which prediction of ENSO could contribute to the prediction of the interannual variation of the rainfall over the major countries of the region coming under the sway of the South Asian monsoon.

28. The strong link of the Indian summer monsoon rainfall (ISMR) with ENSO, manifested as an increased propensity of droughts during El Nino and of excess rainfall during La Nina, is well known (Sikka 1980, Pant and Parthasarathy 1981, Rasmusson and Carpenter 1983 and several subsequent studies).

29. There is also a significant relationship of the autumn rainfall over Sri Lanka with ENSO (Rasmusson and Carpenter, 1983, Suppiah, 1989), which leads to a propensity of enhanced rainfall during El Nino and reduction in the seasonal rainfall during La Nina (Suppiah, 1997). The link of Sri Lankan rainfall during October-November with ENSO appears to be strengthening (Zubair and Ropelewski, 2006, Pankaj Kumar et al. 2007).

30. No droughtsWhen ENSO ind. >1
No excess rainfall seasons when ENSO ind.< -1
But several extremes for ENSO
Ind bet -1 and +1

31. In addition to ENSO, another mode is important-EQUINOO
Correlation (x 100) between OLR and ENSO Index (JJAS)
In addition to ENSO, another mode is important-EQUINOO

32. Recent studies (Gadgil et al. 2004, Ihara et al
Recent studies (Gadgil et al. 2004, Ihara et al. 2007) have shown that, in addition to ENSO, Equatorial Indian Ocean Oscillation (EQUINOO) plays a role in determining the extremes of the ISMR.

33. Equatorial Indian Ocean Oscillation (EQUINOO).

34. Equatorial Indian Ocean Oscillation (EQUINOO).
When the convection over WEIO is enhanced, convection over EEIO is suppressed. Associated with this, equatorial wind anomalies also changes direction; which suggests changes in sea level pressure gradient.
We call this oscillation as Equatorial Indian Ocean Oscillation (EQUINOO). We use EQWIN an index of EQUINOO, defined as the negative of the anomaly of the surface zonal wind averaged over 60E-90E:2.5S-2.5N, normalized by its standard deviation.

35. Correlation (x 100) between EQWIN and OLR (JJAS).
EQWIN: negative of anomaly of zonal surface wind over E, 2.50S N (normalized by its std. deviation)

36. During El Nino (La Nina) the convection over the entire equatorial Indian Ocean gets suppressed (enhanced) whereas during negative (positive) phases of EQUINOO the convection over the EEIO is enhanced (suppressed) and WEIO suppressed (enhanced).

37. Correlation (x 100) between OLR (JJAS) and ISMR.

38. Extremes of ISMR during 1979-2003 and 1997
Thus the extremes of the Indian Monsoon are determined by the intensity and phases of two modes: ENSO and EQUINOO .

39. Red blobs droughts, brown: severe droughts; Blue blobs :excess;dark blue:large excess ( after Gadgil et al GRL2004)

40. ISMR for all the June- September seasons between 1958-2004
ISMR Anomaly
<-1.5 Maroon
-1.5 to -1.Red
-1. to-.5Orange
-0.5 to .5 Grey
0.5 to 1.0 Green
!.0 to 1.5 Blue
> Dark blue
Corr of ENSO and EQWIN=-.08
a) ISMR in the phase plane of June-September average values of the ENSO
Index and EQWIN for all the June- September seasons between Red (dark red)
represents seasons with ISMR deficit greater than 1 and 1.5 standard deviation respectively;
whereas blue (dark blue) represents seasons with ISMR excess of magnitude greater than
1 and 1.5 standard deviation respectively. . Green (orange) represents moderate positive
(negative) ISMR anomaly of magnitude between 0.5 and 1 standard deviation. b) Same as
Fig 12 a, but for July-August all India rainfall.

41. Assn. also for Jul-Aug
Corr of ENSO and EQWIN=-.13

42. Prediction?

43. Will explore the links with these two modes of the seasonal rainfall of Bangladesh, Pakistan and Sri Lanka

45. Summer monsoon rainfall over Pakistan and Bangladesh
Bangladesh lies near the heart of the south Asian summer monsoon system and receives about 140cm of rainfall in the summer monsoon season.
On the other hand, Pakistan lies along the western periphery of the Indian monsoon zone and receives only a little over 12 cm of rainfall in the summer monsoon season on an average.
Since different data sets are used for the periods and 1980 onwards, the relationship of the seasonal rainfall with ENSO and EQUINOO is analyzed separately for the two periods

46. Pakistan July-August rainfall
Fig 3a
Fig 3b
Fig 3c

47. From the scatter plots of the Pakistan rainfall with ENSO index and EQWIN, it is clear that the relationship of the Pakistan seasonal rainfall with ENSO and EQUINOO is similar to that of ISMR with these two modes, with positive values of ENSO index and EQWIN being favourable.
However, the correlation coefficients have to be interpreted with caution because of the small sample size.

48. It is interesting that like ISMR, the years with positive anomaly of rainfall when ENSO is unfavourable are 1997 and 1994, of which 1994 recorded the highest rainfall during
The relationship of the July-August rainfall over Pakistan with that over the Indian region is much stronger than the relation of the Pakistan rainfall with ENSO or EQWIN. Thus a prediction for the July-August rainfall over India would be useful for predicting that over Pakistan as well.

49. Bangladesh June-September rainfall

51. We find that average monsoon rainfall over Bangladesh is neither related with ENSO nor with EQWIN. It is interesting that for El Nino years of 1987, 1997 as well as for the La Nina years of 1988, 1998 the rainfall anomaly is positive.

53. There is a negative correlation between Bangladesh and Indian summer monsoon rainfall. The major outliers are 1988, 1997 (in which both the regions had significant positive anomalies) and 1982 in which there was deficit rainfall in both.

54. The second intermonsoon over Sri Lanka

57. The relationship of the Sri Lankan Rainfall in the October- November (intermonsoon season) with ENSO index is opposite to that of the ISMR with ENSO index but that with EQUINOO is similar to that of ISMR.

58. Corr bet ENSO index and EQWIN for ON=-0.67

60. It should be noted that the ENSO index and EQWIN for June-September are not significantly correlated (correlation coefficient -.08) and can be considered to be independent. However, for October-November, they are highly correlated (correlation coefficient -.67).
Even then, there are a few years in which both the ENSO index and EQWIN are negative suggesting that EQUINOO does not adjust to ENSO in every year. In fact one moderate drought and one excess rainfall year are in the ‘wrong’ quadrant.

61. We consider next Sri Lankan rainfall during October-November in the phase plane of the ENSO and EQWIN during August –September.
Note that corr bet ENSO index and EQWIN for AS is not significant ( =-0.17)

66. It is seen that when the point is above the line there is no history of excess and below the line there is no history of droughts. Thus on the basis of the August –September values of the ENSO and EQUINOO indices, it should be possible to predict the nonoccurrence of one type of extreme.

67. The behaviour of the models: systematic bias

68. AMIP results
1988

69. 1994

70. Why is the prediction of nonoccurrence of extremes important?

72. a possible reason for the relatively low response of FGP to average or above average monsoon rainfall post 1980 is that the strategies which would allow farmers to reap benefits the rainfall in good monsoon years are not economically viable in the current milieu.

74. Thus prediction of the interannual variation of ISMR and particularly for the occurrence/nonoccurrence of the extremes (i.e. droughts and excess rainfall seasons) continues to be extremely important even in the modern era.