1. Seasonal prediction of the Indian Monsoon (SPIM)
Barcelona, June 2007

2. Seasonal prediction of the Indian Monsoon
Why SPIM? Impact of variation of Indian summer monsoon rainfall
What kind of forecasts are useful
Value of forecasts
Social pressure for seasonal prediction: Birth of the project SPIM
Challenges of prediction of interannual variation and particularly extremes: Links with ENSO and EQUINOO

4. Monsoon
Zone (approx) within red dashed lines

6. Impact of the drought of 2002
Need for quantitative estimates

8. Note: In the variation during 1871-2004, no long term trends, dominated by interannual variation

9. Note the increase is by a factor of 4

11. $2*1010
GDP at factor cost
With as ref

12. Basic premise: While the monsoon (and factors dependent on the monsoon) fluctuate from year to year the other factors leading to the change growth of agricultural production, GDP etc. vary on a longer time-scale.
We expect the deviations of FGP/GDP in any year from the long period trends to be related to the impact of the monsoon of that year.
However, it must be noted that other special events which have time-scales of about an year such as wars, economic crises etc. will also contribute to these deviations.

15. Trends: best fit for GDP
year = 1951 to 81
Log GDPf = * (year-1950);
year= 1981 to 2003
Log GDPf = *(year -1981);.
GDPf = exp (Log GDPf)

16. GDP per Capita
British Raj: , License Raj: , Green revolution : ; Economic reforms : 1991-present
From ‘End of Poverty” Sachs 2005,p181

18. Trends: best fit for FGP
year =1951 to 1994,
Log FGPf = *(year-1950);
year = 1994 to 2004.
Log FGPf = *(year );
FGPf = exp (Log FGPf).

20. The growth rate of FGP has increased steadily from 1951 (at about 2
The growth rate of FGP has increased steadily from 1951 (at about 2.7 %) up to the early 90s.
Consistent with analysis of Kurosaki (1999) which showed reversal of decreasing trends occurred with independence from colonial rule

23. 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.

25. 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.

27. 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.

28. Social pressure in monsoonal regions personal experience Monsoon 2005 and its aftermath
Two predictions available in the public domain: from the official agency: IMD and from CMMACS
IMD: normal monsoon

29. Problems created by publicizing predictions of the monsoon rainfall (on monthly/ seasonal scales) based on atmospheric general circulation models (AGCMs) without any indication of the magnitude of errors associated with such predictions by the model used.

30. :Monthly rainfall predicted by
CMMACS,Bangalore for the summer monsoon of 2005
Rainfall Predicted for
Standard deviation/mean (obs)
Predicted rainfall
anomaly (% of mean)
June
22.5%
-34%
July
13.5%
-12%
August
16%
+13%

31. Contrasting monsoon forecast ‘confusing’ farmers
The India Meteorological Department (IMD) and Bangalore based Centre for Mathematical Modelling and Computer Simulation (CMMACS) are at variance on the monsoon prospects that are crucial for the agriculture
sector.
Commenting on the discrepancy, Secretary
General of Confederation of Indian Farmers
Association, P. Chengal Reddy said ‘such forecasts by two government departments lead to confusion amongst farmers down the line’.
—New Delhi, June 3 (PTI)

32. R. Ramachandran The Hindu, 29 June 2005
Should the IMD monopolise monsoon forecasts?
‘The Department of Science and Technology’s directive that no agency other than the IMD can make public the annual long-range monsoon forecast could set a dangerous precedent.’
R. Ramachandran
The Hindu, 29 June 2005

33. :Monthly rainfall predicted by CMMACS and observed during the summer monsoon of 2005
Rainfall Predicted for
Standard deviation/mean
Predicted rainfall
anomaly (% of mean)
obs. rainfall anomaly (% of mean)
Error
(% of mean)
June
22.5%
-34%
-12%
-22%
July
13.5%
+14%
-26%
August
16%
+13%
-24%
+37%

35. “The best course before us would appear to be, to make the predictions by all the models public and let the users exercise their own judgments. The problem is that while information on predictions is readily available, critical information on the expected error levels, which is essential for making an appropriate decision, is seldom available for the dynamical models.”

36. Announcing predictions for droughts
in the forthcoming season, based on models that have not been shown to generate reliable predictions of the monsoon, is as unacceptable as arbitrary suppression of information about research results. In this, meteorological predictions are more akin to results of research on impact of various factors (including drugs) on health, than those in mathematics and particle physics in which the
preprint culture has thrived.

37. “In order to ensure accountability and transparency, it is necessary to stipulate that forecasts from models can be made public, if and only if, information about the performance of the model and the objectively assessed error levels is included.”
“After all, when a company makes a public offering, it is obliged to state the potential risks. Once the responsibility of including such information is accepted, freedom to make the predictions public, irrespective of whether they are generated by an agency of the government or private enterprise, should not be curtailed.”

38. We believe that organizing an inter-comparison of all the atmospheric general circulation models used in the country for generating predictions on the monthly/seasonal scales, should be given a very high priority by DST.
DST in fact gave a high priority and SPIM was launched.

39. A programme
“Seasonal Prediction of Indian Monsoon (SPIM)”
Was launched to assess skills of the AGCMs used for generating monthly/seasonal predictions
with hindcasts for 20 seasons
All models run on the CDAC Param-Padma.

40. Operational details:
All the runs to be made on the Param-Padma at CDAC
Coordinators
Sulochana Gadgil and J Srinivasan from CAOS, IISc and S Purohit from CDAC
With collaboration from the different modelling groups in the country
Independent Assessment Team: Drs. Rajeevan (IMD) and Rupakumar (IITM)

41. Problems of seasonal prediction of the monsoon
“It has not been possible to make dynamical predictions of seasonal rainfall because of various limitations in models and modelling strategies” Shukla and Kinter 2006

42. Wang et al.(2005) attribute the failure of the AGCMs to simulate the interannual variation of the monsoon even when observed SSTs are specified as boundary condition, to the special nature of the SST-rainfall relationship over parts of the tropics such as the Bay of Bengal, South China Sea and the Western Pacific.

43. However, in Kang and Shukla ’s (2006) assessment “The current generation of AGCMs have such large systematic errors in simulation of both the mean and variance of the monsoon rainfall that it is not possible to conclude whether our current inability to make useful dynamical prediction is due to a lack of boundary forced predictability or the inadequacy of the current models

44. Would like to suggest that there is a systematic bias in most AGCMs, which, if removed, would lead to substantial improvements in simulation of the interannual variation of the Indian monsoon

45. Observed and simulated variation of all-India rainfall: 1985-2004
Note:1985,1994 where all are wrong and 1988 where all are okay
SYSTEMATIC BIAS

46. AMIP results
1988

47. 1994

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

49. Role of 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). EQUINOO is considered to be the atmospheric component of Indian Ocean Dipole also called the Indian Ocean Zonal mode.
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.

50. 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 ). In fact, the extremes of the Indian Monsoon are determined by the intensity and phases of two modes: ENSO and EQUINOO .

51. Extremes (i. e. with magnitude of the anomaly> one std
Extremes (i.e. with magnitude of the anomaly> one std. dev which is 10%of the mean) of the Indian Summer Monsoon Rainfall during
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

52. Note that 1985 deficit was due to negative phase of EQUINOO inspite of positive phase on ENSO whereas 1994 excess was due to positive phase of EQUINOO, despite negative phase of ENSO.
These are the seasons in which all models appear to get the sign of the anomaly wrong.

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

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

55. Monsoon of 1997 While all the models simulated realistic rainfall
anomalies over the eastern and central Pacific, none of the models could simulate realistically
the observed response of the Indian monsoon to the 1997 El Nino event (Kang et al., 2002; Wang et al., 2004)).
Analysis of five member ensemble simulation for the period with the Seoul National University model showed that the skill in simulating the Indian monsoon is higher
than that of the simulation of by the same model, suggesting that the models experience unusual difficulties in simulating the Indian monsoon of 1997 (Wang et al., 2004).

56. Problems with AGCMs: 1985,94,97

57. Conclusions
While AGCMs are able to simulate the response to ENSO they are not able to simulate the link with EQUINOO.
This systematic bias suggests that the potential of two tier prediction is yet to be achieved. R & D aimed at improving the link to EQUINOO should lead to a substantial improvement in the skill of simulation and hence prediction of the Indian summer monsoon rainfall.