CLIMAG Challenges ahead: an Indian Perspective Sulochana Gadgil, CAOS, Indian Inst. of Science, Bangalore CLIMAG 2005 WMO, Geneva The beginning:
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CAOS, Indian Inst. of Science, Bangalore
Major advances in capability of predicting ENSO (important from an Indian perspective because of the known link of the Indian Monsoon with ENSO)
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)
Indian summer monsoon: June-September
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%
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%).
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
Normal monsoon (+ve)
(after Rupakumar et al 2002)
NOTE:As many droughts with El Nino as without!
All-India summer monsoon (June-September) 2002 rainfall anomaly
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.
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
“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.
Thus, there are major differences in the impact of different El nino events on rainfall (and hence agriculture) over Australia, India.
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)
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.
Convection over eastern Arabian Sea and western parts of Indian Ocean is linked toconvection over the western equatorial Indian Ocean
We focus on the links between the monsoon and atmospheric convection/circulation rather than SST
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
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.
OLR anomaly patterns for El Nino (July 1987) and La Nina (August 1988)
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
However, the relationships are seldom linear. e.g. with SOI, NINO3.4 SST, Eurasian snow cover
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.
What are the decisions of stakeholders which depend on information/prediction of climate variability and which facets of climate variability (which events) are involved in these decisions? In other words, what are the events that need to be predicted?
Can we predict them?
Yield gap –gap between the yield at the agri. station and the yield on farms or the district average yield
Knowledge/prediction of climate variability could be usefulif and only ifit has an impact on decision-making of stakeholders (farmers,policymakers etc)
The approach used - SYSTEM (END-TO-END) APPROACHfor linking decisions to climate variability involves crop models
Farm level decisions
Scenario of different options
Note that farm-level decisions come on top
(validated for the crop variety and the region )
Variation of yield/profit with choice of option
Simulated gross margin (average for 1887-1992) for cotton grown using different row configurations at Dalby, Queensland fromHammer 2000
The large impact of monsoon variability on Indian agricultural production (and hence the economy) has been known for decades and is generally mentioned as the reason for studies aimed at understanding monsoon variability ( and funding).
However, until the last decade, there have been hardly any attempts to figure out how this understanding could be used for mitigating the impact or enhancing agricultural production.
Need for identifying strategies that are appropriate for the Climate Variability experienced
Since the 70s large number of new varieties and crops were introduced.Unlike for the traditional crops, the farmers do not have enough experience to understand the impact of climate variability on these crops/varieties and evolve the optimum strategies.
MODEL VALIDA-TION FOR ANATAPUR
Singh et al 1994
Critical stage at which lack of rain has a large impact on the yield-pod filling stage which is 30-50 days after planting
This result can also be verified using observations