 So, today we continue our discussions about ENSO, the El Nino Southern Oscillation Phenomena in try and take stock of the understanding of the phenomenon. Until the 60s, the data was so scanned that it seemed reasonable to consider El Nino as an occasional departure from normal conditions as a special event which occurs now and then you know. So, the pertinent question then was what causes the El Nino, what are the triggers for the development of the event and which factors lead to the subsequent decay of this anomalous atmospheric and oceanic conditions. So, it was ok to consider with the data available then to consider El Nino as an event that occurs and disappears so that conditions return to normal. So, this is some kind of anomalous atmospheric oceanic conditions that occur which is called El Nino. This is what was thought. Availability of new data sets in the 80s revealed the presence of Westerly windbursts along the equator for periods as long as a month. Since then, Westerly windbursts have been considered an important trigger of El Nino. Now, such bursts were indeed of central importance in the initiation of the intense El Nino of 1997. We saw this was the most intense El Nino of the century. However, there are several occasions when Westerly bursts such as those in 97 were not succeeded by El Nino. So, this is the problem because it appears to be that you may get bursts which do not later on lead to an El Nino. On the other hand, major El Nino events may be preceded by such bursts. Studies in the 80s and 90s provided a new perspective of an El Nino. The new perspective involved viewing El Nino not as an event which is departure from normal such as a hurricane. We talk of a hurricane being generated, moving, dying and then conditions return to normal. So, that is the kind of event which it was considered for as an event which is a departure from normal conditions earlier. But now, a new perspective was to consider El Nino rather as a part of a continual oscillation what they call the Southern Oscillation. So, the Southern Oscillation comprises oscillation between El Nino and La Nina. So, El Nino is a part of this kind of an oscillation, something like a pendulum, but it is not regular like a pendulum. By the 1980s, scientists had developed ocean models that could reproduce the observed time dependent fields of SST when driven by the observed time dependent fields of tropical surface fluxes of heated momentum. In other words, see when I talk of models and I have not discussed them at any length in this course, but basically models of the ocean or the atmosphere are models which have governing equation which are just the basic laws of physics. And if it is an ocean model, then conditions have to be specified on the top, the atmosphere, the surface of the ocean. If it is an atmospheric model, conditions have to be specified on land or on ocean depending on whether the atmosphere is on land or ocean. And then these equations are integrated with time to give a solution of the equation. So, when we have ocean models, if the ocean models are driven by the kind of winds that are observed and by the fluxes, you have seen that radiation is one important flux driving the ocean. So, by surface fluxes of heat and momentum, momentum being wind, if these are specified realistically from the atmosphere, then ocean models could reproduce the observed fields of sea surface temperature and their evolution with time. So, the models had enough physics in them that ocean models when driven by appropriate conditions of the atmosphere could reproduce evolution of SST as observed. In other words, also could reproduce El Nino and La Nina which are major anomalies of SST fields. Similarly, atmospheric models could reproduce the local ENSO fields that is to say fields corresponding to oscillation of sea level pressure and precipitation, surface fluxes etc. As well as the remote teleconnections, you have seen earlier that ENSO is in fact has connections with many parts of the tropics. For example, El Nino is associated with deficit rain over India and so on and so forth. So, these atmospheric models could reproduce a lot of this if sea surface temperature was specified as a bottom boundary condition realistically. So, atmospheric sea surface temperature was specified as a boundary condition. So, we had a situation in which if we had the right boundary condition for the atmosphere, it could produce the southern oscillation. If we had the right boundary conditions for the ocean surface in terms of fluxes of momentum and heat, it could produce El Nino and La Nina. But we have seen that the two are linked because sea surface temperature itself is determined to a large extent the pressure distribution and the wind we have seen that that is a walker circulation is connected with the sea. So, the outstanding question was if coupled together would the ocean and atmosphere components spontaneously reproduce oscillations between El Nino and La Nina. Because the real atmosphere and ocean are coupled and what we need to start is with an initial condition and let the coupled system evolve with time obeying the laws of motion obeying the dynamical equations. Now, question is would they then generate spontaneously an oscillation between El Nino and La Nina the kind of SSD anomaly is associated with it and also the oscillation that you see in the atmosphere between the sea level pressure and so on and so forth. So, question is when coupled together can these be generated spontaneously can the oscillations be generated spontaneously in a run of the coupled model. In fact, the first simple coupled model which showed that inter-annual variability could arise spontaneously solely from the coupling of the atmosphere and ocean was developed by Philander of who who has written two excellent books on the theme to which I have referred to before. Now and this was in the Philander's book was in 90 a paper was in 1984. Now in early 80s Mark Kane and Steve Zibiak developed an elegant and relatively simple coupled model which incorporated all the known processes important for Enso. So you know the models vary in complexity coupled models vary in complexity also you can make many simplifying assumptions to make them simpler, but the most realistic coupled models which involve a realistic incorporation of all the physics are extremely complex. The game is to try and see whether a simple model can generate some of the features that are important because it is much easier to analyze and understand simple models than the complex model. So what Mark Kane and Steve Zibiak developed was an elegant and relatively simple coupled model which incorporated all the known processes important for Enso. So given the SST patterns in low latitudes their simulated atmosphere reproduces that which matters most for Enso the tropical winds without introducing extraneous features of the mid latitudes such as jet streams etcetera. Because a genuine general circulation model which is a coupled model has weather climate and so on for the entire atmosphere over the earth that is not required for understanding the basic physics of Enso. So their model actually could simulate realistic features of the tropics given the SST pattern their simulated ocean is similarly focused it regards the thermocline as given thereby excluding the circulation that maintains this feature. Instead the model concentrates on the rise and fall of thermocline in response to the changing of the winds and on the associated SST patterns. So there were lot of things that were built into the model and this model in fact reproduced spontaneous oscillations between El Nino and La Nina. So this is the simplest coupled model which can rather realistically reproduce oscillations between El Nino and has been extensively used to gain insight into the various facets of Enso. So it still produce rather realistic oscillations. Now when I said given SST fields what did I mean see the major simplification in this model is that the formulation of the model is an anomaly model where the anomalies are calculated related to an annual cycle specified from observations. So you have the annual cycle both of the atmosphere and ocean which is specified from observations and what the model is predicting is the departures from these mean fields. So it is what is called an anomaly model it only works with anomalies. Now this simplification removes the necessity for simulating the mean climate state and mean annual cycle. So this whole problem of how to get the mean cycle right and climate right and so on is just wished away in this model it does not come into the picture at all. Instead it requires that the mean and the annual cycle in both the atmosphere and ocean be specified from observations. So mean and annual cycle are specified from observations and the model then simulates the anomalies which are departures from these mean. So the model also simulates the other processes for the atmosphere and ocean that determine the SST anomaly at the surface. In particular the ocean includes an explicit if highly simplified surface mixed layer which allows the mixed layer processes of wind driven convergence and divergence to be captured. So although the model is simple the critical physical processes are included in the reasonable way. So it includes an explicit boundary layer or a surface mixed layer so that you get an interaction between the atmosphere and the mixed layer and depending on the wind you will get convergence or divergence as we discussed in the lecture before the Ekman convergence and divergence and so on. So the model has to realistically get the convergence or divergence. The response of the thermocline to the winds are modeled by linear dynamics and an approximate relation between the thermocline depth and the temperature of the water entrained into the mixed layer is included. Because once we have the thermocline there and if the wind is such that upwelling is introduced then we need to know what is the temperature of the water entrained. So that is also specified the temperature of the water is specified by assuming an approximate relation between the thermocline depth. In the atmosphere the effects of SST anomalies and the changes of surface winds are included. Remember this whole feedback, Birkenness's feedback implies that SST differences in the SST gradient which are directly responsible for differences in pressure. Surface pressure will actually have an impact on the winds to which the surface pressure is directly related. So effects of SST anomalies on changes of the surface winds have to be included if the coupling has to be properly incorporated. So that has also been included. The magnitude of the coupling of the wind to the stress is taken at a conventional value and the magnitude of the convergence for a given SST anomaly is adjusted to give reasonable magnitudes of the resulting winds surface winds. So here again some parameters have to be brought in and they have just made sure that those are not unreasonable and completely close the links. So the links are as we know them then the model is able to simulate realistically the evolution of an Elnino and I will just show you a sample. See the model is simple enough that it is it was possible even in the 80s the computers available even in the 80s to make runs like 1000 year run and what did they find. So this is a 1000 year run from the model and this is temperature of the Nino 3 region. Nino 3 is the region which goes all the way from Central Pacific to East Pacific. So this is the temperature of the Nino 3 region SST of the Nino 3 region and what you see is an irregular oscillation there is definitely oscillation between Elnino and La Nina and these oscillations occur at a certain with a certain period up to a point and then simply disappear not much oscillation. Then they pick up again they appear when in which there are not much oscillation and this is what is seen. So this is a typical irregular oscillation that the model produced. So what they found was that the oscillation that the model simulates is irregular with decades of little activity interspersed with decades with regular Fourier cycles of warm and cold phase which is what we saw here. So these are it happens to be Fourier cycles of warm phase cold phase warm phase cold phase and so on. Now this period is quite regular but the amplitude you can see there is hardly anything here and then it picks up and so on and so forth. Now this model has been has taught us a great deal. So let us see what are the lessons learned from the model. First of all ENSO is an oscillation of the coupled atmosphere upper ocean system. The very fact that the model could produce such realistic ENSO, Elnino, La Nina and oscillations suggests that what they did in the model was enough to simulate ENSO. That is to say it is an oscillation of the coupled atmosphere and upper ocean system because that is all the coupling was with. The interactions essential to creating and maintaining the cycle all take place in the tropical Pacific. No extra tropical influences need to be in work. Now this is a very important thing to say that is to say it is an entirely tropical phenomena. To generate ENSO we do not need mid-latitudes at all and that the surface layer of the ocean can respond strongly and swiftly to the atmosphere profoundly influences the character of ENSO. So it is important to remember that the surface layer of the ocean can respond very strongly and swiftly to the atmosphere. This is a very important element and which is incorporated in the model and this profoundly influences the character of ENSO. However the basin wide response of the upper ocean down to the thermocline is at the core of the inter-annual variability that defines the phenomena. So it is not enough that you have a quick response of the ocean to the atmospheric forcing. The basin wide response of the upper ocean down to the thermocline is very important to capture and that is at the core of the inter-annual variability that defines the phenomena because you remember during El Nino the thermocline is lifted up is forced down in the specific during La Nina it is lifted up and this is a basin wide phenomena and therefore it is very important to capture this as well. So to sum up in the Zibia-Kane model and by implication nature the ENSO cycle is a combination of the Birkenness hypothesis which you remember involved interaction between the SSDs and the winds and so on and so forth and linear equatorial ocean dynamics. So as Birkenness envisaged it a warm El Nino event results from a positive feedback. So these positive feedbacks are actually incorporated in the model and that is what lead to an El Nino. You remember his positive feedback involved in the following. Warm SSD anomalies in the eastern equatorial Pacific reduce the east-west temperature gradient and thus the atmospheric sea level pressure gradient decreasing the strength of the trades. Now weakening of the winds reduces the upwelling of cold water reduces the eastward advection of cold water and deepens the thermocline in the east making the upwell water warmer than before. All this increases the warm SSD anomaly and a positive food feedback loop is complete. So this is the Birkenness feedback that we had talked of. So you have warm SSD anomalies via generating sea level pressure gradients and thereby impacting the wind actually again have an impact on the upwelling and hence on the SSD itself. So the loop is complete. So this is the feedback. A cold event has the same feedbacks but with opposite sign. So colder SSD results in strength and strength trades because colder SSD implies stronger pressure gradient from east to west which means stronger trade winds which will mean further cooling right because of stronger upwelling further cooling of the SSDs that is to say enhancing the negative SSD anomalies. So we have these are the feedbacks that Birkenness talked about and they are very much in the model. The significant addition to Birkenness's original hypothesis is the inclusion of the non-local modes of the thermocline response that are part of the equatorial oceans basin wide response to the winds. So this is the new element that had to come in because you remember Birkenness with his feedbacks could say why an initial anomaly can intensify and lead to an elnino or lead to a lanina depending on the sign of the anomaly. He actually elucidated the these feedbacks that lead to it but what he could not get is the oscillation between the two. He could not say why once an elnino system is set up why it does not stay that way forever. Why does it decay and then finally lead to a lanina. To incorporate that part it is necessary to look at basin wide response of the non-local modes of thermocline response that are part of the equatorial oceans basin wide response to the winds. So that factor has to come in only then can we get an oscillation. So given the model structure it is clear from the ZC model results that the interannual oscillation the feature Birkenness could not account for is a consequence of the equatorial ocean dynamics that control the displacement of the thermocline from its climatological state. So this is the element that was not there and that is why one could not get the oscillation. This is the additional factor which leads to the oscillation in the ZBK model that the feature that Birkenness could not account for is a consequence of the equatorial ocean dynamics that control the displacement of the thermocline from its climatological state. So what is the summary of this Birkenness's mechanism explains why the system has two favoured states but not why it oscillates between them. That part of the story relies on the understanding of the equatorial ocean dynamics that developed in the two decades since Birkenness wrote the key variable is the depth of the thermocline or equivalently the amount of warm water above the thermocline. The depth changes in this warm layer associated with ENSO are much too large to be due to exchanges of heat with the atmosphere they are a consequence of wind driven ocean dynamics. While the wind and SST changes in the ENSO cycle are tightly locked together the sluggish thermocline changes are not in phase with the winds driving them. See this is an important point because the ocean dynamics equatorial ocean dynamics which is the new element that has come into this model is such that while the SST and winds are strongly coupled and SST response immediately to wind the thermocline responses are sluggish. Now every oscillation must contain some element that is not perfectly in phase otherwise everything would occur instantaneously and so every oscillation must contain some element that is not perfectly in phase with the other and for ENSO it is the tropical thermocline. In particular it is the mean depth of the thermocline or equivalently the heat content in the equatorial region. So the most widely accepted account of the underlying dynamics emphasizes wave propagation which is the ocean dynamics I was talking about ocean dynamics of the equatorial ocean and is referred to as the delayed oscillator. Now of crucial importance is the rapid response of the atmosphere to changes in SST and the slow oceanic adjustment to the changes in winds. To the 0th order the state of the atmosphere at any time depends on the SST at that time but the state of the ocean depends not only on the state of the wind at that time but also on the state of the wind at earlier times because the ocean being sluggish it kind of integrates the forcing by wind over time. This memory of the ocean is in terms of undulations of the thermocline which are most rapid and coherent at the equator. So with K and NGBF model in fact a lot of physics got unraveled it is the 0th order model of ENSO in many ways and it has been used extensively to gain further insight into ENSO into things even like predictability and so on and so forth. I will not dwell on that topic but now let us consider the dynamics of this coupled ocean atmosphere system which gives rise to ENSO. Now ENSO cycle in a coupled atmosphere ocean system can be either one in which LNO event is triggered but the magnitude of the anomalies decreases with time and the system returns to the normal state. Remember this is what I was talking of earlier the more traditional perspective in which LNO is considered as an event that is born that lives its life and it dies and with its death things go back to quote unquote normal. The that is one possibility that ENSO cycle may be a series of events like this of LNO and Lanina each of which are individual events which are triggered by some other events and there are processes which lead to their growth development and eventually to their decay. So this is one possibility. Second may be regular oscillations between LNO and Lanina this could be like swings of a pendulum with one point of where the swing ends being Lanina the other being LNO and you could have regular oscillations between the two if that was the case then you would get strictly periodic occurrence of LNO or Lanina and the third may be an irregular oscillation. Now with a relatively simple coupled model in which there is no atmospheric weather which is run from an initial condition with a westerly wind burst near the date line. Philander has shown that these three types arise as the strength of the coupling increases this is a very very interesting study. So the same model it is kicked initially by a westerly wind burst which you know has been thought of as an important trigger for LNO. So it is been put in there as a initial trigger and what happens with varying strength of coupling is shown. This is the case in which you have weak coupling this is an LNO which has occurred followed by a Lanina followed by much weaker LNO much much weaker Lanina and that is the end this is the case of weak coupling. So here one could think of LNO as an isolated event or rather isolated pairs of events isolated pair of events. Now if the coupling is of medium strength what you get is a nice regular oscillation but in that case prediction would be trivial and half the fun of the challenge would have gone. So we get weak coupling coupling of medium thing and if you have a strong coupling then you get an unstable mode which is slightly chaotic. So you have irregular oscillation they will not be strictly repeated but you see patterns are somewhat repeated. So this is what you get with strong coupling. So the oscillation is strongly damped in the first case. In the case of weak coupling the oscillation is very very strongly damped which you can see this is the amplitude for the first and it is hardly there for the second. So it is a very strongly damped oscillation it is self sustaining in the second this is obviously a self sustaining oscillation. So given that trigger of the vessel is afterwards triggers and all are irrelevant for this it just keeps on oscillating in a very regular manner and unstable in the third case. Now since this study there have been a lot of studies and I will not go into details of this what is interesting is the final result. Now it is believed that the southern oscillation is neither strongly damped nor highly unstable. It is weakly damped and sustained by random disturbances which is very interesting. So the effect of a trigger such as the wind burst depends on the timing or when during the cycle the burst appears. In other words triggers will lead to El Nino only if they occur when the system is ready. So this is a very interesting concept and this is why you know triggers Westerly winds like the ones that occurred for the 97 El Nino occur at other times too but are not succeeded by El Nino probably because the system is not ready. So this kind of understanding of the dynamical system is very important of the phenomena is extremely important if you want to use this understanding translate it into making reasonable model for predicting this event because we have to then that tells us we have to then monitor and figure when the system is ready and then when a trigger occurs then you expect the thing to happen. The El Nino actually to materialize or La Nina actually to materialize. Now and so prediction has been one of the major successes on which have been built huge international programs of observation modeling and so on. I will not dwell on that here but let me just say that the first models used for predicting and so was statistical models these were in the old times. The first forecast by a dynamically coupled dynamical coupled at ocean atmosphere model was made by K and Zibyac at Dallan in 1986 using the Zibyac K and model and what is amazing is they predicted the onset of the 86-87 warm phase from initial conditions for the spring of 86. So they actually predicted that an El Nino would occur during 86-87 and this prediction proved to be correct. And the era of ENSO prediction and short range climate prediction was launched. This success of this simple elegant model has such a major impact on our field on the meteorology and oceanography done in the subsequent period in the 90s and 2000s that it is remarkable that one began to think that now we could generate with dynamical models predictions short range predictions of climate short range from climate view point means seasonal predictions could be of the monsoon could be of the occurrence of an El Nino and so on and so forth. In fact, Keynes concludes that the degree of forecasting skill obtained despite the crudeness of the model is telling. It suggests that the mechanism responsible for the generation of El Nino events and by extension the entire ENSO cycle is large scale robust and simple. If it were complex, delicate or dependent on small scale details this model would not succeed. So this is a very encouraging thought that the mechanisms involved are in fact large scale robust and simple and that is why the model has succeeded. Now I think there are a lot of lessons to be learned for modeling the monsoon from this. I believe that the monsoon system is also large scale robust not as not very simple. Of course, things appear simple only after all the physics has been elucidated that has not been the case for the monsoon as yet. But therefore, we should be able to succeed in unraveling this complexity and actually predicting. Now after the success of Keynes Zebac models, a lot of more complex models have been developed and now at all the international centers ENSO forecasts are routinely generated. I am not going to discuss in detail about ENSO prediction in this set of lectures. But let me just say that getting an accurate prediction of ENSO is extremely important for our getting a reasonable prediction of the monsoon as well. But I will come to that when I talk of monsoon prediction. Now I would like to shift to a connected but a somewhat different topic. So far we have been looking at what is ENSO? How is it defined? What is the physics of ENSO? What are the basic elements of a model? A model must incorporate if ENSO is to be simulated and so on and so forth. And you can see that right from Berkness onwards up to Keynes Zebac and Philander and others we have got considerable insight into ENSO and are in a situation where we are able to generate predictions with reasonable skill of ENSO with existing models. Now we saw that ENSO has teleconnections with many important phenomena and one of them is the monsoon. So now let me talk about monsoon ENSO link. You know Sir Gilbert Walker discovered the Southern Oscillation during his quest for predictors of the Indian monsoon. He was looking for how to predict Indian monsoon and for that he analyzed a lot of data and discovered the Southern Oscillation. Now link to Indian rainfall was specific in Walker's original definition of Southern Oscillation that is tendency of pressure at stations in the Pacific and the rainfall in India and Java and presumably also in Australia and Abyssinia to increase while the pressure in the Indian Ocean region decreases. This is what he is talking of the Southern Oscillation. However Sir Gilbert Walker's efforts to translate the relationship to skillful prediction of the Indian monsoon were unsuccessful. So the subject was kind of dropped. Southern Oscillation was certainly considered as an extremely interesting phenomena in the atmosphere but as a useful tool for predicting the monsoon it was dropped. Now this link between monsoon and ENSO was rediscovered by Sikha in 1980. The way it was rediscovered was the following. He pointed out that writer's finding on the basis of Line Island Precipitation Index that there were three distinct epochs of frequency of El Nino in the period 1910 to 1975. You remember I talked about how the rainfall over Central Pacific increases during El Nino. So Central Pacific Precipitation Index these are Line Islands of six silence in the Central Pacific. So this was the index that writer used and using it he identified three epochs of different frequency in which the frequency of El Nino differed very much and earlier epoch in which it was frequent then low frequency then high again. Now Sikha pointed out that in the two epochs 1911 to 1920 and 63 to 75 which is a total period of 23 years there were eight years of major failures of the monsoon whereas in between that is to say the epoch of 29 to 62 droughts were rare and occurring only in two years. So you had frequent droughts in these two epochs whereas relatively less frequent droughts in the period 29 to 62. The Sikha showed that epochs of high or low frequency of droughts generally coincided with epochs of higher low frequency of El Nino. Now it is also seen from the interannual variation of ISMR that the frequency of droughts was high in the first and third epoch and low in the second epoch so here you have it. Now I have drawn here the epochs of El Nino droughts on the top here which were recognized by writer this is the period in which El Nino epoch this was the epoch of high frequency of El Nino then this is the major epoch of low frequency of El Nino in which you see only two droughts have occurred and again there is a high frequency of El Nino in this period in which you see several droughts have occurred. Similarly before this epoch also before this you see within this high epoch also several droughts have occurred so this is the El Nino frequency and the drought frequency is given here. So there is a correspondence between low frequency of El Nino occurrence and low frequency of droughts and high frequency of El Nino occurrence and high frequency of droughts this in terms of epochs and this is what Sikha first pointed out. Now so frequency of droughts so frequency of El Nino and droughts from 1911 to 28 frequency of El Nino was high 29 to 62 it was low and 63 to 75 it was high. Now droughts if you look at there were 3 in 18 years in this case and in this case 5 in 13 years so you can see chance of drought is rather high when the frequency of El Nino is high. On the other hand when the frequency of El Nino is low chance of drought is only 2 in 34. So indeed there is some correspondence in the frequencies of occurrence of El Nino over the Pacific and droughts over India this is what Sikha pointed out. Now you can see this is only up to 62 but after that many years have passed so we can see what happened in the subsequent years. So if we look at now we know how many El Ninos have occurred because of that ONI index that I showed you and we can see that from 62 to 87 there were 8 El Ninos this is a period of 26 years in which there were 8 El Ninos and 10 droughts whereas 88 to 2001 which is a period of 14 years slightly over half of this 2 El Ninos occurred and no droughts at all. So Sikha's identification of correspondence between epochs of low frequency of El Nino and low frequency of drought and high frequency of El Nino and high frequency of drought seems to have held in the period subsequent to what he had analyzed also. Now in addition to that Sikha also examined the El Nino events during 75 to 1909 on the basis of Southern Oscillation Index prior to the writer data set based on Lines Island Precipitation Index of 1910. So he went actually there was not data on Lines Island Precipitation so he went to another data set and Southern Oscillation Index and with that he could analyze over the entire period for which monsoon rainfall is available as I said now this was done by IITM scientists and one could identify monsoon droughts and excess monsoon years on the basis of the data in India or monsoon. So when he examined it for this very long period 1875 onwards then he found that El Nino years which were monsoon failures were 15 years, years of monsoon failure which were not El Nino were only 3 years and years of El Nino which were not monsoon failure was 7 years. So you could have cases in which you had an El Nino but monsoon did not fail but out of the years in which monsoon did fail which is 18 years 15 were El Nino years. So this was another way in which he showed that the propensity for droughts of the Indian monsoon is very high during El Nino years. So what did he conclude? He says that the preliminary relationship presented above with respect to the number of El Nino years associated with a large number of monsoon failures over India points to the desirability of further work in this area. The very indication that in some years or epochs the out of phase relationship exists between the poor performance of the monsoon rain over India and abnormal rain over eastern and central Pacific which is the El Nino suggests very large teleconnections and he used the word teleconnections as early as in 1980 which operate through the displacement of the east-west circulation resulting from the changes in the thermal forcing in the equatorial regions on the planetary scale. He also addressed the important question of whether the El Nino event precedes the monsoon failure or vice versa. However the fragmentary observational evidence and a few experiments with models available at that time could not provide an answer to this. So the important question monsoon and so seem to be linked there are no two views on it anymore but the question is what leads which of the event leads the other. Does monsoon lead to El Nino or El Nino lead to monsoon because whether we can use things for prediction of the monsoon depends very much on the answer to this question. Now Sikha's thought provoking study was followed by that of Panth and Parsathi who showed that correlation of the area average June to August rainfall over India and a contemporaneous southern oscillation index developed by Wright in 1977 was 0.59. So this is very interesting that in fact there was a very strong correlation that they showed this was certainly I am sure significant at 95 90 percent and so they showed that what Sikha had shown simply by comparing epochs and comparing number of years actually stood the test of time when we looked at correlations. Now after Sikha's and Panth and Parsathi's studies came this paper by Rasmussen and Carpenter because that came in the paper that I talked about earlier in which a great deal of data about El Nino was synthesized by Rasmussen and Carpenter that was a paper in 1982 and they followed it up with a paper on impact of El Nino over other regions and particularly Asia. So Rasmussen and Carpenter as I mentioned before based their definition of El Nino on SST anomalies of the coast of South America which corresponds to the region Nino 1 plus 2 in the earlier graph and on the basis of that they identified what can be considered as warm episode years. Now these are given here and they give different sources for this the last few from 50s onwards are their own in fact even in 23 and so on. Queen is the source for a large number of this identification and these are identified basically from sea surface temperature data of the South American coast. So they have identified all these warm episode years which are called WEY 0 and they show that there is a strong tendency for below normal summer monsoon precipitation on the all India scale during such El Nino years with negative departures in 19 out of 25 years. So what they are doing is looking at El Nino years and whereas you can see Sikha looked at all years in that period which were monsoon failures. He did not look at only El Nino years but there he looked at El Nino years which were monsoon failures. He also looked at years of monsoon failure which were not El Nino years and years of El Nino which were not monsoon failure. Now what Rasmussen and Carpenter did was to focus on El Nino years that is to say take this 15 years in which there were monsoon failures and 7 years in which it was not monsoon failure. Those two categories first and third category only were considered by Rasmussen and Carpenter and what they say is that whereas Sikha got I think 15 out of 22 El Nino's were deficits. They are getting 19 out of 25 years rather comparable statistics I would say because they are talking of only negative departures how large the deficit they are considering is not known and what they showed was and this is now very clear solid bars indicate El Nino years. So, this is data on all India rainfall here and this is a drought area index which is a measure of the how deficit the rainfall is by actually looking at over what how much area the deficit is substantive. This is the drought area index and what you find here let us look at this one first you will find that a large number of deficit here certainly the major ones this is the famous one of 1876 and then these are in the earlier part of the epoch see this is where you had quite a few deficits and quite a few El Nino years. This is the epoch where you had relatively less El Nino's as you can see and relatively less droughts as well and then of course you have this very big El Nino 65 was an El Nino which was a drought 66 was a monsoon failure which was not an El Nino and then the major El Nino of 72 major drought of 72 which coincided with an El Nino. So, you can see that there is a close correspondence but a large number of deficit monsoon are not El Nino years at all. So, Rasmussen and Carpenter also derived the special pattern of rainfall anomalies for the El Nino years and what they find is that by and large you have very very large anomalies here and towards here which is really the heart of the monsoon towards Bangladesh and these other regions there is hardly any impact in fact, but the large impact is on this part and this is the part particularly here which determines the all India monsoon rainfall. So, it is not surprising that you will have a large impact on this. Now, so this was the second study. Now, the question was should we only look at all India rainfall or is there a better index of the monsoon and I will start discussing that in the next lecture. Now, let us just summarize what we have done today we have tried to gain an understanding of what people think of as the basic physics of El Nino, what is the Enso system dynamical system that leads to Enso and we showed that depending on the strength of the coupling you could get a strongly damped system a regular oscillation or an irregular oscillation and that now people believe that it is not strongly damped, but a weakly damped system which is not highly unstable. So, whether you get an El Nino or not depends on whether the appropriate triggers occur when the system is ready for it. So, this is the basic understanding of the dynamics that has come in the last decade or so. Then we started looking at the monsoon Enso link which actually was first discovered by Sir Gilbert Walker in his search for predictors of the monsoon, but was rediscovered in A.T. by Sikha who started looking at epochs of high and low frequency of El Nino and showed that they more or less coincided with epochs of high and low frequency of Indian monsoon droughts and this was followed by work by Panthan Parasarthi and then by Rasmussen and Carpenter who reinforced Sikha's conclusions that droughts over India and El Nino have a tendency to go together. Now, we will continue from this to see the links between monsoon and Enso in the next lecture. Thank you.