CLIMAG Challenges ahead: an Indian Perspective

1. CLIMAGChallenges ahead: an Indian Perspective • Sulochana Gadgil, CAOS, Indian Inst. of Science, Bangalore • CLIMAG 2005 • WMO, Geneva

2. The beginning: Major advances in capability of predicting ENSO (important from an Indian perspective because of the known link of the Indian Monsoon with ENSO) • Given the large impact of monsoon variability on agriculture, there were high expectations of using ENSO predictions for enhancing agricultural production. • Development of crop models made it possible to explore the yields associated with different farming strategies for different climate scenarios and hence identify the appropriate strategies for the predicted scenario-El Nino, La Nina etc. In this talk, I briefly discuss my perspective on what has been achieved in the last decade, what are the challenges ahead and how do we address them?

3. The beginning: Major advances in capability of predicting ENSO (important from an Indian perspective because of the known link of the Indian Monsoon with ENSO) • Given the large impact of monsoon variability on agriculture, there were high expectations of using ENSO predictions for enhancing agricultural production. • Development of crop models made it possible to explore the yields associated with different farming strategies for different climate scenarios and hence identify the appropriate strategies for the predicted scenario-El Nino, La Nina etc. In this talk, I briefly discuss my perspective on what has been achieved in the last decade, what are the challenges ahead and how do we address them?

7. Mean monthly all-India rainfall Indian summer monsoon: June-September

8. Mean June-September rainfall in cm

9. Interannual Variation of the anomaly of All-India summer monsoon rainfall (as % of the mean) std dev about 10% of mean; Droughts and excess rainfall seasons-amplitude of the anomaly > 10%

10. Interannual Variation of ISMR during1979-2002

11. Interannual Variation of the Monsoon • Link with ENSO: high propensity of droughts during El Nino, excess rainfall during La Nina (Sikka 1980, Rasmusson and Carpenter 1983) e.g. El Nino events of 1982, 87 were droughts (ISMR anomalies -14%,-18%) and during the La Nina of 1988 the rainfall was in excess (ISMR anomaly +12%).

12. INDIAN MONSOON AND ENSO Note Several droughts in the absence of El Nino e.g. 1979 1985,1986 ISMR above average (+2%) in the strongest El Nino of the century in 1997 Very large deficit in 2002 (-19%) although the El Nino was weak Excess rainfall in 88 associated with La Nina, but not that of 1994

13. Numberof years with … Deficient monsoon Deficit>10% Normal monsoon(-ve) Normal monsoon (+ve) Excess monsoon excess>10% Total El Nino 11 11 4 0 26 La Nina 0 1 9 8 18 Other 11 23 42 11 87 Total 22 35 55 19 131 • El Nino/La Nina association with all-India summer monsoon rainfall anomalies during 1871-2001 (after Rupakumar et al 2002) NOTE:As many droughts with El Nino as without!

14. Weakening link of the Indian Monsoon with ENSO • During the strongest El Nino of the century in 1997 the rainfall was above average. • Kumar et al (1999) suggested that the link with ENSO had weakened in the recent decades. The monsoon is also supposed to be linked with Himalayan/Eurasian Snow cover. Kumar et. al suggested that increase in surface temperatures over Eurasia favoured a stronger monsoon and hence the smaller response to El Nino events of the nineties.

15. However, MONSOON 2002 turned out to be a drought All-India summer monsoon (June-September) 2002 rainfall anomaly -19%

16. MONSOON 2002 The failure of the monsoon in 2002 was not anticipated, even though it was known that a weak El Nino was developing. This drought was not predicted either by empirical models or GCMs. From the experience of 1997 and 2002 it is clear that we are yet to understand completely the impact of El Nino on the monsoon.

17. Challenges • Recent El Nino events: 1997 and 2002 • Wake-up call

18. June-Sept 1997 ISMR anom. +2% June-Sept 2002 ISMR anom -19% excess (>+20%) normal (-19 to+19%) deficit (-20% to-59%) scanty (--60% to -99%) Nino3.4 anom =1.93 , SOI= - 4.9 Nino3.4 anom =1.02, SOI= -1.00

19. Experience of the 1997 El Nino- Zimbabwe “Following the evolution of the strong El Nino event of 1997 a forecast for a high probability of low rainfall was issued for the whole of eastern and southern Africa as early as September 1997.Memories of the devastating droughts associated with the El nino events of 1982-83 and 1991-92 resulted in most people preparing for the worst possible drought in southern Africa.

20. Strategies appropriate for low rainfall worked for the southern parts of the country where the rainfall was low.However, for the northern areas, the season turned out average to above average and some opportunities were missed……….Feedback from farmers shows that there is intense regret for a loss incurred because one changed decisions as a result of the forecast.” • Unganai, 2000

21. Australia also experienced a far more severe drought in 2002 than in 1997 a aa

23. from

24. Thus, there are major differences in the impact of different El nino events on rainfall (and hence agriculture) over Australia, India. • Over Australia, the differences in impact of wheat yields (bio-indicator) arise from differences in spatial patterns of rainfall anomalies and time of onset of the event (Pottgeiter, Hammer, Meinke, Stone and Goddard J Climate (under review). • They have identified three types of El Nino events (with 1997,2002 in separate groups). • Hence need to predict not only the occurrence and intensity of the El Nino but the type as well.

25. Impact of the variability of the MONSOON RAINFALL still significant despite the Green revolution (has become more in the last decade due to the fatigue of the green revolution)

26. Impact of monsoon of 2002

27. Impact of the monsoon of 2002

28. COMPARISON WITH 2003 Fortunately, the monsoon of 2003 turned out to be far better (all India monsoon rainfall 2% above average). In particular, whereas there was an unprecedented deficit of 49% in all-India rainfall, in July 2003 there was excess of 7%. Comparison of the OLR anomaly patterns for July 2002,2003 is revealing.

29. Impact of the Monsoon of 2003

30. OLR and OLR anomaly patterns for July 2002 and 2003

31. METEOSAT image at 00z15Jun2003 Convection over eastern Arabian Sea and western parts of Indian Ocean is linked toconvection over the western equatorial Indian Ocean

32. OLR and OLR anomaly patterns for August 1986 and July 1994

33. LINK TO EQUATORIAL INDIAN OCEAN • CONVECTION OVER EASTERN ARABIAN SEA AND WESTERN PART OF THE INDIAN REGION IS : • POSITIVELY CORRELATED .W.R.T. CONVECTION OVER THE WESTERN EQ. IND (WEIO :50-70E,10S-10N) AND • NEGATIVELY CORRELATED WITH EASTERN EQ IND OCEAN (EEIO:90-110E, 0-10S);

34. Convection and Wind anomaly patterns suggest • 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). EQUINOO is the atmospheric component of Indian Ocean Dipole also called the Indian Ocean Zonal mode (Saji et al. 1999, Webster et al. 1999) • 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. We focus on the links between the monsoon and atmospheric convection/circulation rather than SST • In the coupled system SST, OLR, wind are all interrelated. However, often the SST responds to changes in OLR, wind and there are lags.

36. Extremes (i.e. with magnitude of the anomaly> one std. dev which is 10%of the mean) of the Indian Summer Monsoon Rainfall during 1979-2003 EQWIN: Index of EQUINOO defined as anomaly of the zonal wind averaged over central equatorial Indian Ocean (60-90E, 2.5S-2.5N); ENSO index is the negative of Nino 3.4 index

39. Strong relationship between large anomalies of ISMR and a composite index which is a linear combination of the indices for ENSO and EQUINOO with all seasons with large deficits (excess) characterized by small (large) values of the index • Gadgil et al 2004 GRL

40. 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 ). Extremes of the Indian Monsoon are thus determined by the intensity and phases of two modes: ENSO and EQUINOO . Thanks to the efforts over the TOGA-CLIVAR period, simulation of ENSO and its links with the Indian monsoon is now possible e.g. AMIP results for the 1987 /88 El Nino /La Nina events.

41. OLR anomaly patterns for El Nino (July 1987) and La Nina (August 1988) El Nino La Nina

42. AMIP results 1988

43. AMIP results • However when the extremes are not associated with ENSO (e.g. 1994) what happens?

44. 1994

45. Prediction of all-India summer monsoon rainfall-ISMR 1. With GCMs/coupled models: a lot of work went into making realistic simulations of the 1987/88 El Nino (drought) and La Nina (excess rfl) events. Now a large number of models can simulate these, if observed SST is used as boundary condition-AMIP results. However the same cannot be said about EQUINOO events. Need more R&D in modelling to achieve that. 2. Empirical models

46. Can we predict the evolution of EQUINOO? • Observations of the evolution of the equatorial wind before two major EQUINOO events suggests that it may be possible.

47. Anomalies of the zonal component of surface wind along the eq.

48. Empirical models • Generally based on correlations with various factors including those related to ENSO . Most models assume linear relationships. However, the relationships are seldom linear. e.g. with SOI, NINO3.4 SST, Eurasian snow cover

49. Note that when the SSTanom. Over NINO3.4< -1, there are no droughts; and when Nino3.4 > 1 there are no floods However for values in between very little can be said.

50. Signal clear only for SOI>1 clear Signal clear