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Okay. I think we'll we'll kick off. So welcome, everybody, to this year's Halloween lecture,

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a lecture which is certainly one of the most prestigious in the physics department and is a history going back over 100 years.

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It shared between astrophysics and atmospheric oceanic and planetary physics and many of the great and

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the good I've spoken in the lecture series over the years in the atmospheric and climate sciences,

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people may have remembered Susan Solomon, who was chairman of the IPCC Working Group One Rapier Humbert,

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who actually soon become the Halley professor in atmospheric physics.

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Carl Winch, one of the most renowned oceanographers.

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And this year, keeping up the standards. We have Peter Webster from the Georgia Institute of Technology.

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Let me just say a few words about Peter. I think in physics, we're all used to the sort of dichotomy between theorists and experimenters.

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And in atmospheric sciences, I think the divisions are even more nuanced than that.

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We have out of out theorists, we have people that develop and use the big computer models, and we have experimenters.

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And then we actually have people that interface with society and trying to get them to utilise the forecasts that are made by the models.

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And on the whole, people tend to focus on one of these four areas.

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Occasionally you might get somebody who spills over from one into two areas virtually unheard of to get somebody who's become experts in all four.

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But so we have the exception to the rule here with Peter.

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So Peter did his Ph.D. at M.I.T. some years ago and became very quickly a renowned figure in the dynamics of AC coupling in the

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tropics and the wave modes that determine tropical atmospheric variability and links to to the monsoons and to the El Nino event.

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He's become, over the years, an expert in the predictability of these of these phenomena,

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making use of these very large climate models to study interactions between El Nino and

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monsoons and the so-called intra seasonal oscillations of the atmosphere and so on.

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Now in the early nineties, Peter also masterminded what probably was the most important field experiment in in the tropics, the so-called tiger cause,

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tropical ocean tropics, tropical ocean, global atmosphere, coupled ocean atmosphere, research experiment, something like that.

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But this was an absolute tour de force of, of, of measurements in the,

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in the basically what we call the warm pool parts of the tropical western Pacific where sea temperatures are warmest and where the

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coupling between the atmosphere and the ocean is absolutely critical for understanding how the whole global climate system works.

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And as I say, Peter masterminded that.

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It involved I don't know exactly the statistics, but but many hundreds of ship hours, maybe thousands of ship hours,

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many hundreds of aircraft flights, many tens of thousands of of balloon measurements.

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And all this produced a completely, completely revolutionised our understanding of sea interaction in that part of the world, and importantly,

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led to new developments in the way in which the fluxes of heat and momentum and moisture and so on are represented in the in these large scale models,

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which ultimately are used for weather and climate prediction.

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And then finally, Peter, in recent years has become particularly interested and involved in actually showing how society in developing countries

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primarily can actually make use of all the science and the technology that's involved in weather prediction.

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And he's he's had many trips to countries like Bangladesh.

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And what I find remarkable is that he's been able to engage with the local farming

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community in getting them to understand things like probability forecasts.

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How I wish the BBC could understand such things.

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And I. Perhaps, Peter. Next time you over, I'll. I'll sign you up for a trip up to the BBC to educate them too.

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And famously,

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he published a paper in Nature showing how the Pakistan floods from a few years ago in the Indus Valley were actually well predicted two weeks ahead.

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And so this has had a big influence in how people, you know,

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just ordinary members of society in these very floods and disaster prone areas of the world

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can make use of the advance advances in science and technology and weather prediction.

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So Peter, as I say, Matty, he's had various academic appointments in Australia where he was born, not born,

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but he was born in the UK but raised and then more recently working in the US,

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founding the program in Atmospheric Sciences at the University of Colorado.

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And now he's a professor in Earth and Atmospheric Sciences at the Georgia Institute of Technology.

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Peter's won top prizes from the American Metrological Society and the Royal Meteorological Society here in the UK.

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The Rossby and Mason Prize is he's a fellow of the Royal Maths of the American SOC, the American Geophysical Union.

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And in fact, until very recently,

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he was president of the Atmospheric Sciences Division of the American Geophysical Union and made some important innovations in in that in that field,

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particularly bringing on new prices for for for young and developing scientists.

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So Peter is an ideal candidate for our Halley lecture series,

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even more so because I think he will very much focus on a topic that was of interest to Sir Edmund Halley, how the atmosphere works.

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And I invite Pesnell to give us this year's Halley lecture.

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So Peter Webster understanding the monsoon, Halley and beyond.

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Thank you. You don't know what a great honour it is to do this for a whole heap of reasons.

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One because of Sir Edmund Halley, who has always been my hero.

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I'll explain why in a few minutes.

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Four To give a lecture at Oxford, a special lecture, and to talk about something we sort of understand, but not quite.

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We're on the brink of doing all types of interesting things, but we're not quite there.

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So we'll tell you the things we know. I'll tell you what Halley did, and then we can move on.

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But, you know, the the monsoon is a very strange animal and it's viewed enormously differently by people who live in India.

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It's almost a religious thing. It's special.

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And that has certain social problems in working in India, too, to bring basic science.

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But there's an interesting words by a man called Freja, and if you want to find out about the monsoon, one might want to to read this book.

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It's chasing the monsoon. He had this idea from being a child that he was going to get to suddenly India of India.

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And he's going to walk all the way up to the end of the monsoon season. It took him two years.

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And and he found out very quickly that Prime Minister Ruth found out when he was a little boy.

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He went also down to try and hide in the southern part of Kerala and he waited and wait for the monsoon to come.

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And then in the newspapers it said the monsoon is arrived in Bombay.

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And he said it came like a thief in the night and often the same type of thing.

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So you see, this is attributed to a person who is director of meteorology in the Southern.

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But the difference between our weather and the weather in Europe is the difference between the poor men are the millionaires.

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Europe is the poor fellow. His habits are predictable, his move is restricted.

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Each day he will follow the same routine, taking morning coffee in the same restaurant, trudging off to a tedious job, going home to a bored wife.

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This is written some time ago. Indian weather.

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Those extreme are a wilful, fast moving and wholly unpredictable.

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Indian weather is the millionaire. The sort of sort who would jump and possibly jump into a plane and fly off to London for lunch?

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It's a marvellous book. I recommend you read that.

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So one of the reasons we think about trying to predict the behaviour of the monsoon on short term and long term can be summarised in this map.

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It's looking down on the Himalayan Tibetan plateau here and what we have all

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these river basins and this is in terms of population and monsoon climate,

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there is all the way through here and involves all types of activities of people who are 60, 70, 80% still subsistence form of living.

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And I'll mention this a little bit later on. This is what people do.

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If they know something about what's happening with the monsoon, you can do you can take steps.

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So of you like to give some early thoughts on the physics of monsoons,

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a little bit about signatures of monsoons, elements of the monsoons and what elevated heating does.

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And finally, especially in terms of the the definition of transient.

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So in the monsoon, they talk about long term interannual variations of the monsoon.

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And then we have one of the great problems, one of the great problems of of atmospheric physics.

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So who are the monsoon giants? And, of course, is Devon Halley.

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A little later on, George Hadley, Sir Gilbert Walker.

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And each of these did something very special.

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Haley invoked differential buoyancy, the different effect on the atmosphere of of being over a cooler ocean and over a warmer land.

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Hadley added rotation to this. The planners rotating Hadley, it turns out, got rotation quite wrong.

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Gilbert Walker, who spent a lot of time in India, is the director general of the metallurgical department.

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He tried to predict the monsoon and invoked very, very large scale circulation features.

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So I know it's sort of a long time tradition.

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Forgive me for this to talk about seeing things from the shoulders of giants.

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And this is something to do with Oxford and a couple of certain gentlemen.

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So I'm not involved here. But anyhow, I thought about this for a while and one of the great giants, I think, is Edmund Halley.

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But the picture on the left is sort of interesting. It's a Ryan with his servant, a sad alien from Greek mythology.

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And Orion had been blinded and he had the help of his.

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Of his. Served to guide him around, to eventually got his sight response returned.

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And so he sees things somewhat better and merely from his blind eyes.

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And so I was amazed by this picture because it looks like he has a cell phone here.

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But that aside, we see things further now, I think because of the early intuition of many of the people like Hadley and Haley.

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But I think that we have so much more data, we have better models.

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But has all this led to a greater understanding?

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We had Brian Hoskins seminar the other day.

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There was a big discussion about, Oh, well, okay, we can simulate, but do we understand more and more of this very,

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very old fashioned belief that simulation is fun, but understanding is useful?

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And so Hayley's contribution I just talked a bit about it.

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He did many, many things besides to invoke the initial idea of the monsoon,

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and he invented the diving bell, which you can see on the left hand side here.

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He sailed around in the Atlantic Ocean in particular, and mapped the magnetic field of the of the planet and thought about this for a while.

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I said, oh, my goodness. One of the great problems of maritime and maritime at that stage was the determination of longitude and latitude.

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Longitude is very difficult. So he thought perhaps if the new sphere field was constant, you could work out where you are in terms of longitude.

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And I don't think it quite worked out. The observations were good enough, but it was a brilliant idea.

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Oh, he also was the original person to to be able to draw isolates.

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It's a bit controversial about this if it was a first, but until then, people don't use isomers.

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But one of the most amazing things he did was this. He wrote a paper sometime 75, 76 on compound words and annuities.

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And as this nice people discuss this these this data is from a Polish city and it's age versus number of people the number of deaths.

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And so what he worked out was it's there in the bottom here.

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He could work out for a given age how long the person could be expected to live.

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All his insurance mates want to know this so they could set the premiums.

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And so in a sense, he was the original actuary and he spawned, of course,

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$1,000,000,000,000 industry in selling insurance by showing you could do that and not lose money.

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You know, something else, of course, which was the he he explained comets.

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Newton, of course, had thought that comets were ahead probably a parabolic orbit and came and went and disappeared off, never to be seen again.

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But Halley thought, well, my goodness, they are made of mass and therefore maybe they have an elliptical orbit.

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And so but until then, there are all types of concerns that that if you saw a comet,

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it was all types of terrible things would happen to you and see here people festering diseases and so on and so on.

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And but he he came up with the idea is that in the upper left diagram that we have in fact an elliptical orbit and the long term he

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went back to the records and managed to show that the the occurred every 80 odd years and so on which is just about if you're lucky,

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you see it twice in your lifetime.

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This cartoon here, a marvellous book by Adrian and Sagan and one of the great accomplishments in astrophysics or astronomy, I should say,

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was predicting when the next appearance of Halley's Comet would be turned out, that they only had, of course, circular slide rules in those days.

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No computers, but they took into account the they knew it was the basic time of the year it was going to come and unfortunately not exactly sure why,

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because they didn't know the impact of Saturn and Jupiter and they managed to get it within a week was a great accomplishment,

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a good book or comet but here so they wouldn't talk about this.

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And this is Halley's original map with my lettering on it.

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And this was a cumulative from millions upon millions of of logs of thousands anyhow around the sea.

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And, and he looked at what the prevailing winds were like.

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And it's remarkably accurate if you were to do a seasonal map or an annual map, this is an annual map.

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You get something like this. He showed, for example, that A, B and A are the big trade winds.

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They vary very little from time to time, from season to season.

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But he showed that there were these these are the doldrums. But in the doldrums, there's a change of wind from from time.

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And there's this where the it exists.

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And you can. Explain that in terms of different types of physics now.

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Both of these things did hit these green areas of the monsoons where there's a reversal of winds and

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just blow up one particular area here and it's down here and it's hard to see on the original map.

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But he shows alternate columns of vectors, lines of vectors showing that this varies every six months.

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So he made this great hypothesis and it was that, my goodness, during the warm season, winds flow towards land.

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During the cold season, they flew off and he sees here action of sun's beams upon air and water.

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According to the law of statics, which is less rarefied and extended by heat,

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must have a motion towards those which are rarefied to bring its own equilibrium.

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So this was the, the, the differential buoyancy that he brought on.

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Now this is sort of like the global or the very large scale monsoons that he was so big sea breezes.

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And the trouble is there was no rotation.

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If you go back to that chart here,

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you can see that the impact of rotation here in terms of the bending of the winds and so on, he wasn't aware of that.

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So he tried to understand what the trade winds were made of, why these tended to to go from a to towards the equator,

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from a northeasterly direction or from south east direction.

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It was a great problem. People can understand that. So you had this idea that every day the sun would essentially, in effect, rotate around the Earth.

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He knew it didn't, of course, but he thought the heating from the sun moved from east to west.

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And what happened? This invoked differential buoyancy and the winds would follow that.

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Well, we know now that that can't happen because we know on those timescales nowhere near a resonance, although we do have thermal tides of things.

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But they they don't explain the trades.

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And we know now that that the most basic idea of the trade is that one has an accumulated heating with the sun essentially

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going around around heating and and creating a pressure gradient by virtue of the the the shape of the sun of the earth.

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So are a number of other things he didn't do.

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And one is not critical of Haley because when you think that he was the first person to look at observations in a global sense,

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but this is the type of monsoons that one gets.

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And there are lots of interesting arguments about the African monsoon, the the monsoon here of North America.

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But this is one called by Chao and Chen, an oceanic monsoon.

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And and here also in the southern this is precipitation, by the way.

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And this is the the ice in the winter time now winter time in the southern India Ocean.

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And when you now move to here, you see this something enormously different.

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The precipitation in these regions extends far, far further north than all the other monsoon regions.

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Now we have very good understanding, I think, of why this particular precipitation is occurring in this particular latitude band here and here.

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But here it's a little more awkward because precipitation is occurring far further from the equator and the monsoon varies enormously also.

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This is a time series of precipitation over all of India.

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And what you see, the average is, oh, roughly 90 millimetres per year.

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This is going over a 100 year period, but it varies a lot from time to time.

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It's very the centre deviation is quite small actually only about ten millimetres.

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But the going from here to here I can on one sense because you have an agricultural system which depends upon the rain coming every year.

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At the same time, roughly a small change or large change will give you either famine or give you times of plenty.

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And also, when you look at the in one particular year, say, in central India,

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and this is just for particular years, and what one sees is the annual cycle here.

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But now we see within a season that the rainfall tends to vary enormously on about 20 to 30 day period.

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These are the active in the break periods and it's thought that the great advantage that the greatest

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thrust food can make is if we can forecast these particular undulations of the sub seasonal monsoon.

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And then when we look on to higher timescales, more frequent timescales, this is a mean precipitation and this is the spectra for you spectra.

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And what one can see is an annual cycle that notice enormous amount of various in the about 5 to 14 days in each of those regions.

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It doesn't matter particularly which ones they are, they all have the same characteristic.

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I'm going to spend a little time explaining what they are.

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One of the fascinating things is that these are independent and the spectra tend to overlap.

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This is the for the Bay of Bengal. I showed you a minute ago the diagonal variability.

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And this is the what I call synoptic.

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These are the ten, 14, 15 day timescales and this is the longer term activity break sequence, intra seasonal oscillation.

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But what look at this. This is now a particular time. We will then pass filtered and just plotted up each of these bands.

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And what one find is the ISO the active in the break and within that maximum amplitude of this and maximum amplitude of the tidal variation.

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So these are related in many ways.

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Now, why do we want to know? Because within every one of those variations is the possibility of extreme events such as floods, heat waves and so on.

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And so at the end of this talk, I'll spend a little bit of time talking about how we can say some things on the 1 to

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15 day time scale that we probably can't say with confidence and a longer timescale.

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So Halley's theory does not include rotation.

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You don't take a look at most processes, no deep mixing of heat in the ocean.

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So the manner in which the solar radiation is taken up by the ocean doesn't take into account the implication that all monsoon should be the same,

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but they're not. And the monsoons are very, very variable.

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So Hadley spoke about rotation and he gave, of course, nice explanation of the trade winds.

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This diagram is very misleading. One must be very careful because you'll finish up with if you have east please

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everywhere you finish up with a a slowing down earth and an accelerating planet.

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You have to have westerlies, too. But it doesn't matter. The point is, it was important.

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And so but even so, the monsoon has long been thought to be a giant sea breeze.

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And if you look in most textbooks, elementary textbooks, now you see pictures like this.

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This is the Hadley circulation and the same type of.

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If you put a continent that. Now, if you were to solve the equations of motion,

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in the simplest sense you can predict the depth of the monsoon and it will be very shallow, about 2 to 4 kilometres deep.

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It's very difficult to. But when you look at observations, the monsoon is very deep.

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It's up to about 15, 16, 17 kilometres.

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And the only way you can explain that difference is if you have a moist parcel from that releases latent heat and gives you a very,

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very large, not only geometric extent, but the other thing that is left out is the manner in which land and ocean take up heating and land.

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You go from spring to summer. This is the balance here. Lots of solar radiation and so on.

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But you see, now that land, the temperature becomes very, very large, but it doesn't go very deep.

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So you finish up with a very, very large increase of temperature in the ocean.

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On the other hand, you tend to mix heat down and the winds tend to stir heat down.

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So there's a big two or three month lag that isn't taking into account.

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Now, finally, the monsoon as a solar collector, our monsoon winds, let's assume that are are forced by the Haley mechanism.

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And you can see here that this is the the vectors of wind, but it's also the vectors of the of the moisture flux.

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The moisture flux. This is the wind here in the vertical. This is the moisture in the atmosphere.

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So you multiply this two together and you finish up with almost all the moisture is

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being evicted in the lower levels because the atmosphere dries out very quickly.

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And one of the things to notice here is enormous convergence of moisture in the in the Asian monsoon region.

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So one question we're going to have to address, my goodness, why is the South Asian monsoon so different from all the others?

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One of the things, too, that doesn't take into account less the local ocean dynamics, the ocean moves and and this multi variability.

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This is the again the picture of the precipitation. Who knows the wind through here.

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Now, if you draw a plan of that, this is the wind. And you can you can work out what what the geostrophic analysis like.

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But notice now that we have these little vectors here, these are the Ekman transport vectors.

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If you blow on the ocean this way, you, you get a flux of mass and heat to the right and in the southern hemisphere to the left.

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And you can argue very nicely that that the flux of the ocean has to be orthogonal

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to the direction of the wind from from angular momentum considerations.

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In order for the annual momentum of the whole system to remain the same, you have to have this orthogonal flux.

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So interesting thing when you look at that, my goodness, this is summer and the wind is towards Asia.

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But now the the ocean flux is towards the south.

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So that means that the ocean tends to cool the northern hemisphere,

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whereas they are the winds are bringing more and more moisture, which is releasing latent heat and heating it.

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So the ocean and the atmosphere work at absolutely opposite directions and you can see that in the next picture.

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This here is the the this is Peter Watts.

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Ten of the 15 watts. And. And here you can see the transport by the ocean.

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And this is the transport by the atmosphere. And when you add those two together, this is this is the surface heating.

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What you see is that the the the amount of push of heat towards the north is being balanced by the push of heat towards the south, by the oceans.

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And what is even more staggering about this, but in fact, this is a very simple model,

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what it shows in springtime, you get this little overturning here.

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It's called meridional overturning. But then as you move to summertime, it's a reverse.

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So in springtime, in winter time, the summer hemisphere is heating the winter hemisphere and vice versa or during our summer.

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Well, one of the staggering things about this is that it gives you a two, two year oscillation.

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This is when you look at use models or observations, you find spectra where you get about a two year variation in this flux.

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And when you look at the rainfall over the months in region,

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through the same spectral analysis, you find about a 2 to 3 year variation in the monsoon.

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So the strongest variability the monsoon turns out to be biennial and even more staggering.

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And forgive this diagram that when you plot the year by year variations this time versus latitude,

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putting once again the the variability of the of the north south flux of heat.

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And then take out the mean.

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And what you find is that one year, two years, three years, four years, five years, six years, they are varying one year to the next.

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And this is what we call the regulation of the monsoon. So in pictorial form, we find that during the boreal summer, the winds are roughly like this.

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The wind transport is like that during the winter, the the atmospheric mass flow is like that and the ocean flows that way.

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So the idea being that the atmospheric flux of heat and the ocean flux of the heat tend to compensate.

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But because of the regulation, it means also that if you have a strong monsoon,

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all of whatever reason, that your flux of heat to the to the northern hemisphere is very strong.

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But that means the winds are much stronger and the flux of heat by the ocean through the south is much stronger.

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And therefore that will cool the northern hemisphere so that next year the monsoon will be weak.

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And the Met, the Ekman fluxes are less. And you finish up with a warmer northern hemisphere, so you go from one season to the other.

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And so this is referred to as the biennial regulation of the South Asian monsoon.

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Previously, Jerry Meehl tried to explain this in terms of, well, if a strong monsoon,

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you get deeper mixing, therefore we get cooler and therefore the next monsoon will be weaker.

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But this this idea I have here invokes the invokes the the ocean as well.

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So an interesting thing about this, though, is when you look this is not these two degrees of strong monsoon looks like enhanced westerly winds here.

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And this this is the weak one. You get weaker winds here.

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This also changes the upwelling of the ocean.

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And so you find in the core, if you take this strong minus weak monsoon, you finish in the west, you get increased upwelling, therefore cooler.

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And in the in the east, you get decreased upwelling and therefore it's warmer.

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You change the sign of this. And now we remember going from one year to the next, one year to the next, the strong monsoon,

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that weak monsoon will give you decrease upwelling here and increase here.

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So we might expect that from east to west, there's going to be a temperature gradient invoked by this north south temperature gradient.

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And indeed, that's what happens. This is some work that we did many years ago trying to explain the ocean, the oceanography,

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ocean atmosphere, variation of what happens when you, for example, have anomalous heating here.

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And one can argue that you you can invoke some nice wave dynamics to be able to

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transmit that signal all the way across and eventually create what we call this dipole,

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which Sardesai shows very nicely here in this particular phase.

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Warmer temperatures here, colder temperatures here.

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And so without going into much detail, you also go back and forward between a dipole, a warm cold, a warm, very warm.

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And. Getting back to the cold. And so every two years, you finish up with variations back and forth.

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And these are very important variations of the monsoon. So you bring all these things together, which I will spend more time on.

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Well, you start off with a cold spring.

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This gives you a weak monsoon that gives you a weakened dipole and so on.

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And you finish up going through all of these transitions. So you might say, well, why is it so important?

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Well, this gives us probably the strongest physics of the strongest variation of the monsoon we have, which is the biannual oscillation.

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So the conclusion so far.

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Monsoon is a couple of ocean atmosphere system that highly like driven winds produce strong moisture convergence and latent heat release.

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And we can conjure up mechanisms for the regulation. So why the monsoon doesn't get stronger and stronger and stronger.

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Now, I'll show you a picture of the while of the successive.

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Well, I, I just passed it of this very rarely that you get to monsoon is very, very weak in a row or two monsoons very strong in a row.

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This is wiped out by this regulation mechanism.

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So if things are bad one year, I guess there's consolation that they're going to be much better next year.

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But what really drives the mean monsoon circulation, we've shown that the in the in this region of South Asia,

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that we get enormous precipitation far more than we get elsewhere on the globe, at least in the monsoon regions.

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Well, a man called Hermann Flown, who was a German geographer,

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I wrote some startling papers back in the 1950s and seventies where he said that the the the

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anomalous monsoon in the Indian Ocean was caused by the elevated heating of the Tibetan plateau.

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And he showed some observations of of precipitation and heating.

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There's a marvellous book, a of recent review by Ashwin Wu very recently.

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And it's a beautiful demonstration of of of the intuition of flown and so on.

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So just to show you once again, we have this is the do it where we have the maximum precipitation, the Asian monsoon.

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It's opposite where we have great deserts. And this is the same way if you go further east,

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this is the region of the Asian monsoon precipitation far north of the other regions of of of precipitation is the North Australian one.

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This is the latitude of the north of the Asian monsoon.

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And you can see it's in a region of desert.

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So something very special should be happening in that region to cause precipitation to be so far from the equator.

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Now, why should that be an important thing?

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Well, it turns out that one would think that if you had heating, you're always going to get convergence and rising motion and divergence at top.

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And that, of course, will give you precipitation.

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The problem is that the further you go away from the equator, the greater you heating you need in order to finish up with the meridional circulation.

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And this beautiful paper by beautiful paper by Plumb Alan Ploughman, who in 1993 and they showed something very, very special.

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I said, as you go further away from the equator,

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the heating to create a marine circulation and his rising motion of precipitation has to be stronger and stronger.

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And so something must be happening in the monsoon regions that's different from elsewhere.

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And so, in other words, this is the the cross section north of south.

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And this is the monsoon. The reaction, rising motions here, 30, 35 degrees.

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And so a nice hypothesis came forward by Peter Molnar and he is a geologist and became enchanted with looking at many things,

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but spent a lot of time looking at the geology of the Tibetan Plateau.

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And out of the Tibetan plateau comes all types of rivers.

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And so I guess what Peter did was taking sedimentation cores, where the way the rivers came to the to the sea.

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And you could look at them and say, oh, my goodness, they didn't occur until about 6 million years ago.

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And all of a sudden they started and off they went. And then you say, well, what about that?

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So when you look at the the growth of the Himalayan Tibetan plateau only do you get a, uh, the sedimentation or precipitation if you'd like.

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It occurs when the mountains are so high. And so this was a to me, a revolutionary paper, geological evidence.

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The mountains are growing, growing, growing, growing, and boom, all of a sudden you get the monsoons.

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So when you now look at the cross-sections in the present climate, and this is ten degrees north, 20 degrees north.

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And this shows you the change in. A jet in the atmosphere between May and September, October.

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And you see this is the mountains, the Himalayas, and we have something like a 10 to 14 degree anomaly.

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And so the argument was to go back to to whole plum.

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Only when you have an elevated heat source can the heating be big enough to give you a meridional circulation so far north.

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And if you can play games, if you'd like, of calculating how warm and elevated heat will be,

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and the idea being that the temperature of the Tibetan plateau here, the surface is very similar to the surface of and along the Ganges plain.

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So here is an enormous temperature difference. And this is the.

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These are the heat fluxes.

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They're almost the same between the Ganges Valley and the Tibetan plateau, except they're elevated over the Tibetan plateau.

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So and in fact, this gives you two things what we call the Himalayan Tibetan summer hot tower.

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And this is winter and this is some of this is the mean temperature of the upper troposphere.

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And you can see here enormous temperature difference.

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And here and during the winter time, a tiny one here, but not particularly much.

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The biggest thing, the and we'll see how important this is in the minute, but that the Himalayan region is heating the atmosphere enormously.

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I would argue in a few minutes that this has a global effect.

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At the same time, sometimes called the Himalaya Tibet Summit Water Tower, the lower diagram.

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This is the anomalous amount of water that exists in the monsoon region.

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You can see that it sends it very, very much over the Himalayan plateau.

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There's one other important mountain range for the monsoons, and that's the East Asian Highlands, the East African Highlands.

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And here's a cross-section. This is the Somali jet stream.

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And you can see that's about ten, 14 metres per second.

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And that, of course, has the the the way of ducting that moist air towards towards the continent.

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So what happens in the upper troposphere? And so this is a winter time.

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This is latitude and this is longitude. This is now up at about 100 millibars, 17 kilometres in the atmosphere.

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And you can see that the vorticity is is very bland.

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There are very few gradients during the winter time going from strongly positive to negative.

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But when you now look in the summer time, you get this big yellow blob here and this enormous trough through here.

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Now, one of the interesting things is that the temp, the gradient on this ice and trope, this tends to change sine.

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And this is troublesome because that means you had the potential instabilities.

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This is known. I call it the great yellow I rhinoceros.

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Cause the yellow spot. You'll see why I call it the yellow eye in just a minute.

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So it's unstable. Hugh and Plumb showed that in 1998.

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What's the consequences of that instability? This is picture from Wu.

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Upper level heating. You have a change in in heating as a function of altitude.

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You finish up with an anti cyclone. So. Is if this if if the system would be unstable, would you not expect an eye to be blinking?

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Well, actually, it does. And how these blinks relate to rain bearing systems, I'll show you now.

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So this is the eye. So you don't know how high tech this is to be able to do that.

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And his the the evolution of of these fields and these have been shown a Brian was

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showing these earlier in the week and what you're going to see this is the date here and

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now as the heating over here becomes largest can start now you start seeing these

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filaments moving in through here and this is going to develop into this big anti cyclone.

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There you see. This is very special.

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And the point I want to make, too, is that this is global.

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And and this will come back right at the end of the talk when I talk about interannual variability.

386
00:42:35,780 --> 00:42:41,890
So here we are. Just go one more cycle and that goes back here.

387
00:42:41,900 --> 00:42:49,310
But interesting, Brian was also talking in particular about filaments from the Southern Hemisphere influence in the Northern Hemisphere.

388
00:42:49,580 --> 00:42:55,760
But today we will concentrate on this here. And this is now April.

389
00:42:59,610 --> 00:43:03,870
May things always go so slowly. And here we see the filaments.

390
00:43:04,170 --> 00:43:07,500
And this we get this big, the negative side.

391
00:43:08,160 --> 00:43:13,049
But it's blinking is important. And what you can do is very simple experiment.

392
00:43:13,050 --> 00:43:16,860
You can take the simplest model you have, which is a shallow water model.

393
00:43:17,310 --> 00:43:23,160
And you can this is the equator here, north and south and put an anti cyclone in here.

394
00:43:23,280 --> 00:43:28,890
Haiti cyclones are very strange because as you know, that you can have an infinitely deep low,

395
00:43:28,920 --> 00:43:33,480
but you have a finite amplitude on how high pressure can be.

396
00:43:33,780 --> 00:43:39,150
So this is just a very simple example of the dynamics of what happens if I meet a going.

397
00:43:41,340 --> 00:43:49,510
I want to. I. Well, sneak up on it.

398
00:43:52,640 --> 00:43:56,150
I got flu. There we are.

399
00:43:57,260 --> 00:43:59,510
And you see that now? It's growing and growing.

400
00:43:59,510 --> 00:44:08,930
It starts to shed all types of wood and now you'll see along the equator, these things are developing all the way.

401
00:44:09,830 --> 00:44:11,420
And so, you know, it's itself.

402
00:44:11,420 --> 00:44:24,079
The question, is this just a model result or is it really global in extent if you go to these are the the the the IPV distribution of September,

403
00:44:24,080 --> 00:44:32,930
July and May and then these these are all the diagrams for April all the way for the rest of the year.

404
00:44:33,290 --> 00:44:38,420
And you see that during the time when you have this enclosed that it's like load shedding, it's more disease.

405
00:44:38,840 --> 00:44:47,780
You get east to west propagation is very, very different from where you have don't have that anti cyclical.

406
00:44:48,860 --> 00:44:54,769
Well you know are these you can define these events relative to what happens over here.

407
00:44:54,770 --> 00:45:04,370
So what we can do is look at the variations in this region through here and see what the they look like as you move through.

408
00:45:04,910 --> 00:45:15,469
And so these. If you go to that square and you look at the the the perturbations in in a

409
00:45:15,470 --> 00:45:22,670
potential vorticity and then use that as a a counter and you can composite them.

410
00:45:23,060 --> 00:45:31,070
If you do that, here's what you get. This is ten days before you get maximum IPV here and you see this coming down.

411
00:45:32,380 --> 00:45:39,060
There's. The streamer going through and going around and this occurs time after time.

412
00:45:39,510 --> 00:45:44,130
So is this related to low level fields? And it turns out that it is.

413
00:45:44,310 --> 00:45:48,060
And now we look at the same thing, at the same composer.

414
00:45:48,230 --> 00:45:53,010
Now we look at highs and lows here potential. This is the IPV field here.

415
00:45:53,430 --> 00:45:58,350
But what we're going to see things moving through here like this, highs and lows moving through.

416
00:45:59,100 --> 00:46:12,020
So. And so this is sort of important because it say something about the we've

417
00:46:12,020 --> 00:46:17,780
developed this this this heating this anomaly and this enormous anti cyclone.

418
00:46:18,290 --> 00:46:26,810
And not only is it controlling a large amount of the variance of rainfall over the Asian region, it's also influencing other parts of the globe.

419
00:46:27,610 --> 00:46:40,999
And so the other thing you can do is take the line now across here and see what the variations of of of the weather look like.

420
00:46:41,000 --> 00:46:46,430
And so we're going to look in the in the vertical. And so here's what's happening in the upper troposphere.

421
00:46:46,970 --> 00:46:50,390
And the the shaded is the precipitation.

422
00:46:50,900 --> 00:46:54,469
And the other is going to be the potential to see what you're going to see is that

423
00:46:54,470 --> 00:46:59,150
these are going to move along and they're going to invoke low level precipitation here.

424
00:46:59,600 --> 00:47:06,050
This is the the field of humidity. And as you move through, you see this moving along.

425
00:47:06,350 --> 00:47:11,600
And notice here that leading the precipitation fields is this big IPV field.

426
00:47:11,810 --> 00:47:17,960
There's some theoretical problems involved in trying to understand this, but at least I think the observations are fairly good.

427
00:47:18,890 --> 00:47:22,990
So what we can do is my winking person did.

428
00:47:26,240 --> 00:47:30,500
This is transient monsoon. What you can say is that the elevated heating creates planetary scale.

429
00:47:31,400 --> 00:47:35,690
And you saw it is 180 degrees of of longitude.

430
00:47:36,020 --> 00:47:45,379
It extends completely over Africa and extends way into the Central Pacific Ocean, where that large trough comes through Arizona, you know,

431
00:47:45,380 --> 00:47:49,760
stable breaking waves or ventilated cyclonic going around the eye and the gyre

432
00:47:50,090 --> 00:47:54,650
originating in the tropics and they extending deeply into the tropics near the equator.

433
00:47:55,310 --> 00:48:00,530
Well, these upper tropospheric disturbance appear to be associated with westward propagating rainfall events.

434
00:48:01,010 --> 00:48:07,460
And this means in the sense that at least we have an understanding of why we might be getting a.

435
00:48:08,560 --> 00:48:14,410
Well, we might be getting certain events. I mean, they probably predictability limit has been extended somewhat.

436
00:48:14,890 --> 00:48:22,480
So what we can say now is that if, for example, reason W of model doesn't show these 10 to 14 day variations,

437
00:48:22,920 --> 00:48:26,499
we can ask questions about why it doesn't and therefore change the model.

438
00:48:26,500 --> 00:48:30,760
Maybe the heating of the animal is done well enough or the resolution isn't enough.

439
00:48:30,760 --> 00:48:33,040
At least we can ask questions from a theoretical point of view.

440
00:48:34,630 --> 00:48:39,130
Well, finally, I'd like to just talk about the inter annual variability of the monsoon.

441
00:48:40,030 --> 00:48:48,700
Is it a driver or is it driven? Now, we know from early on that we get variations of precipitation in all of the monsoon regions,

442
00:48:49,240 --> 00:48:55,090
East Asia and South Asia, Africa and so on over northern Australia.

443
00:48:55,660 --> 00:49:07,959
And there is interannual. So we go back to this has been the how can I put it the course to live for a long,

444
00:49:07,960 --> 00:49:18,730
long time of trying to understand the variation of number one, long term climate in the in the tropics and extending to mid-latitudes.

445
00:49:19,240 --> 00:49:23,680
And so much of it depends upon our understanding of El Nino, some oscillation.

446
00:49:24,370 --> 00:49:29,050
The second thing is, can this also give us forecasts for the monsoon rainfall?

447
00:49:30,190 --> 00:49:33,549
Well, there's been great hope in that.

448
00:49:33,550 --> 00:49:41,260
And I am come to a stage where this prediction, panacea as it would be, has probably not been realised.

449
00:49:42,010 --> 00:49:51,940
So let me give you an example of that. I don't want to for those who are not familiar with wave analysis, if I were to take data from here,

450
00:49:53,050 --> 00:49:57,850
this is now this is a period this is like, let's say, to choose ten years of time.

451
00:49:58,240 --> 00:50:04,960
I might get some type of spectra like this and then I move it down to here and look at a different spectra and so on.

452
00:50:05,230 --> 00:50:12,640
So a wave analysis is nothing more than an evolving forecast for the transport system.

453
00:50:13,000 --> 00:50:19,570
And so what you can find here is that there are lots of variants in the 2 to 4 year bed, not much here and quite a lot back there.

454
00:50:20,290 --> 00:50:24,910
And so this gives you a visualisation of what has occurred over time.

455
00:50:25,360 --> 00:50:30,440
Now, if I were to add up from the 2 to 8 year, then all the variants.

456
00:50:30,460 --> 00:50:38,740
I get this red curve here. This is smooth a little bit. And you see that during this particular time there's been a lot of variables this Nino.

457
00:50:38,740 --> 00:50:42,010
This is the sea surface temperature in the middle of the Pacific Ocean.

458
00:50:42,730 --> 00:50:53,140
This is the sea surface temperature. Trying to predict that was the the the reason we had the tiger experiment that we've spoken about before.

459
00:50:54,490 --> 00:51:06,220
Now, you might remember also that glucose exhibit, Walker said, are if we know this, we know what the monsoon rainfall is going to be.

460
00:51:06,340 --> 00:51:12,520
He didn't know anything about El Nino. All he knew about was the southern oscillation, which was a an oscillation of sea level pressure.

461
00:51:13,300 --> 00:51:16,640
And so. This is the all India rainfall.

462
00:51:16,640 --> 00:51:26,270
And one might expect that you would expect a correspondence between this if the Nino El Nino was the forcing factor and the all-india rainfall.

463
00:51:26,480 --> 00:51:35,930
Well, this is the same type of analysis you see in the average 2 to 8 year band is how the any variance here is very little variance here.

464
00:51:36,380 --> 00:51:39,680
And this is the spectra. This is the average spectra. You can see here.

465
00:51:39,680 --> 00:51:43,190
We have a 2 to 3 year full year. This is Nino.

466
00:51:43,190 --> 00:51:49,970
So it's about four year. And here in the India we have a two year variance is a strong thing.

467
00:51:50,810 --> 00:51:59,120
In fact, I summarise that by saying we have a period of weak variance in the El Nino and

468
00:51:59,120 --> 00:52:03,829
here we have a very strong variance as a 3 to 5 year and here we have weak,

469
00:52:03,830 --> 00:52:07,580
we have strong. The correspondence between the weak and strong occurs.

470
00:52:07,580 --> 00:52:14,210
But, but there are different, uh, different bands and this is troublesome somewhat.

471
00:52:14,570 --> 00:52:25,250
Now we can be very sneaky and do what we call a cross wavelet modulus, which looks at the interaction of these two.

472
00:52:26,410 --> 00:52:33,700
And if we do that, as we finish up with a strong, weak, strong, we expect that.

473
00:52:34,360 --> 00:52:39,250
But now the code response is this 3 to 6 year ban.

474
00:52:39,910 --> 00:52:43,120
The trouble is, you can't work out what leads what.

475
00:52:44,270 --> 00:52:49,330
What are the mechanisms to force El Nino forcing the monsoon or monsoon forcing in the year?

476
00:52:50,230 --> 00:52:55,389
And so we're sort of stuck a little bit there. And then one of the problems is it's very,

477
00:52:55,390 --> 00:53:03,190
very hard to deconvolution a variation on the 2 to 3 year timescale from something on a 3 to 4 year timescale.

478
00:53:03,640 --> 00:53:04,660
It's a lot of data.

479
00:53:05,800 --> 00:53:16,180
But, you know, when we look at the strength of the anti cyclone, a big yellow I, it varies from year to year by about plus or -20 to 30 per.

480
00:53:17,230 --> 00:53:25,690
And we just did this a few days ago, so I'm not quite sure the implications of what it means if you have a very large eye or a very small eye,

481
00:53:25,690 --> 00:53:33,700
what that means in terms of global consequences or what it means in terms of of what happens over the monsoon region.

482
00:53:34,810 --> 00:53:36,700
So a lot to learn about these.

483
00:53:37,360 --> 00:53:46,310
Now, when you look at the El Nino or the southern oscillation and its warm sea surface to recall sea surface temperature there.

484
00:53:46,540 --> 00:53:48,250
What does it do for rainfall in this region?

485
00:53:48,940 --> 00:53:57,700
And if you plot all those things up, this is now the variation from year to year, the anomaly of precipitation over India.

486
00:53:58,060 --> 00:54:10,960
And the interesting thing you find is that you that most wet periods are associated with La Nina that's cold in the sea surface temperature,

487
00:54:11,290 --> 00:54:15,550
and most drought years are when you have El Nino.

488
00:54:16,630 --> 00:54:21,880
So you may think, terrific. That means I have no cause and effect.

489
00:54:22,780 --> 00:54:28,389
The trouble is, because El Nino doesn't develop until April,

490
00:54:28,390 --> 00:54:37,960
May and the monsoon is becoming its maximum in April, May, June, you're not quite sure what's leading what.

491
00:54:38,500 --> 00:54:50,440
And so the other possibility is that the El Ninos in London is of forced by anomalous monsoon circulation, as Norman suggested that long time ago.

492
00:54:50,440 --> 00:54:58,510
And we played that game a few years ago, too. So it's a sort of showing the same things again.

493
00:54:58,930 --> 00:55:07,930
So the other consequence possibility is for a covering ENSO or El Nino monsoon situation.

494
00:55:08,800 --> 00:55:16,960
Now when you look at the strong and weak, the surface winds and the strong and weak monsoons, these are anomalies in strong monsoon.

495
00:55:17,770 --> 00:55:24,430
Increased westerly sea. But now, look, you get enhanced trade winds or vice versa.

496
00:55:24,760 --> 00:55:28,120
You get weak winds in the monsoon region here.

497
00:55:28,360 --> 00:55:31,480
But but stronger east lives here.

498
00:55:31,960 --> 00:55:41,170
So we hit this how to face thing. Could it be that the strong monsoon is what is the cartoon of these two things is here and here.

499
00:55:41,950 --> 00:55:48,219
So one possibility is that we have a variation of the monsoon heating that changes the strength of

500
00:55:48,220 --> 00:55:56,020
the Pacific Ocean traits which drives and the manner in which pupils thought the the Pacific Ocean.

501
00:55:56,500 --> 00:56:02,830
And this gives you ENSO, which then invokes this influence on the Indian Ocean.

502
00:56:02,860 --> 00:56:08,230
Now, this might seem a little fanciful, but it turns out there's some evidence for that.

503
00:56:08,920 --> 00:56:16,240
One of the things that we did do and also there's a certain degree of of nonlinearity in response.

504
00:56:16,360 --> 00:56:20,970
This is a very, very simple model. We like simple models.

505
00:56:20,980 --> 00:56:25,540
This is Asia and Africa. This is an Indian Ocean, and this is the Pacific Ocean.

506
00:56:26,260 --> 00:56:32,530
And what we did, you can run the model with some start up winds.

507
00:56:32,530 --> 00:56:37,150
And this is the sea surface temperature that you will get this dark line here.

508
00:56:38,380 --> 00:56:45,880
And if you do that, then you can say, well, what about if I vary the strength of the monsoon just slightly here?

509
00:56:46,540 --> 00:56:50,860
And what I find is I start to get a spread of the chaotic response.

510
00:56:51,460 --> 00:56:58,310
So the monsoon is this changing the form of the of the of the of the El Nino.

511
00:56:58,330 --> 00:57:00,459
And you can see here, this is a bit hard to see.

512
00:57:00,460 --> 00:57:06,310
But between the weak, the strong, you can see the sea surface temperature vary and also the propagation characteristics change.

513
00:57:07,390 --> 00:57:15,090
Of course, you could do the reverse experiment and say, what about if I hold this diff and look at the the monsoon force?

514
00:57:15,100 --> 00:57:15,850
We have done that yet.

515
00:57:16,630 --> 00:57:25,870
But it's a troublesome thing because I think we'd all like to be able to say the monsoon varies because because if we can say that, then we can also.

516
00:57:25,900 --> 00:57:29,500
To say that next monsoon is going to be stronger or weaker or so on.

517
00:57:30,310 --> 00:57:34,310
Well, all is not lost. Monsoon circulation.

518
00:57:36,970 --> 00:57:44,680
And so it appears strongly coupled with with with the monsoon and certain what leads one during the growing phase of the monsoon,

519
00:57:44,680 --> 00:57:46,360
the Pacific Ocean has a weaker stability.

520
00:57:46,480 --> 00:57:54,910
This is a very important thing as the monsoon is growing at the east west, temperature gradient of the Pacific Ocean is weaker.

521
00:57:55,000 --> 00:57:56,170
It's almost homogeneous.

522
00:57:56,650 --> 00:58:04,120
And so you might think that means that the communication between the atmosphere and the thermocline waves on the thermocline is weakest.

523
00:58:04,780 --> 00:58:11,919
So therefore, any forcing that you do that particular time will invoke the strongest response

524
00:58:11,920 --> 00:58:18,820
during the whole year before the water circulation becomes stronger again. So this is all occurring at the same time, just to make things even worse.

525
00:58:19,690 --> 00:58:24,850
So it may not be possible to decouple the influence path between the monsoon and external factors.

526
00:58:26,080 --> 00:58:29,950
It may not be external factors. We may be talking about one particular thing.

527
00:58:30,100 --> 00:58:34,710
And I ask you to remember the size of the summer monsoon gyre.

528
00:58:37,120 --> 00:58:41,370
So. This seems that perhaps a little bit unfortunate.

529
00:58:41,370 --> 00:58:46,409
Maybe we can't do anything if we've shown that we can forecast the forecast,

530
00:58:46,410 --> 00:58:53,370
we can understand low frequency variations in precipitation due to instabilities.

531
00:58:53,610 --> 00:58:59,940
That's very helpful. But the other thing we can do, too, is that we say, okay, what do we know better?

532
00:59:00,090 --> 00:59:04,079
We know the 1 to 15 day predictions particularly well.

533
00:59:04,080 --> 00:59:14,280
So we have spent a lot of time talking about using forecasts in order to be able to predict things like this for the Bangladesh floods,

534
00:59:15,300 --> 00:59:20,520
for heatwaves over northern India, the Pakistan floods, and so on and so on.

535
00:59:20,910 --> 00:59:26,660
And what we have this is the we have a model which I won't bother to show you.

536
00:59:27,150 --> 00:59:34,170
And this is the response. This is a swath of 51 forecast per day, ten days ahead of time.

537
00:59:34,650 --> 00:59:40,290
The actual observed flow is the black line here and the southern being is white.

538
00:59:40,290 --> 00:59:49,590
But you will see that this is the threshold level that we are forecasting ten days in advance, the the floods.

539
00:59:49,860 --> 00:59:52,710
And we've done this for year after year after year. It works out very well.

540
00:59:53,670 --> 01:00:04,680
But the important thing is understanding about why do you bother to use ensembles if anybody is in doubt.

541
01:00:06,030 --> 01:00:14,850
If you take a forecast here, which is forecasting for there and then you plot that PDF over here, this is what it looks like.

542
01:00:15,690 --> 01:00:20,640
There is the flood level. If I decide that Ensemble 17 is the one I believe in.

543
01:00:20,940 --> 01:00:24,600
Oh my goodness. It's a. No flood.

544
01:00:25,230 --> 01:00:31,290
But what about here? Oh, it's the biblical flood. 20 days is 20 days, 40 days of rain, whatever.

545
01:00:31,650 --> 01:00:35,880
And so the point is that you have to do ensembles to be able to work out the probability.

546
01:00:36,480 --> 01:00:40,379
And as Tim said, that the greatest surprise to me.

547
01:00:40,380 --> 01:00:47,370
But when you think about it, it's not really a surprise that people who live on the edge understand probability very well.

548
01:00:48,480 --> 01:00:52,890
There are two attitudes is one, it's a sad one, which is we are born to suffer.

549
01:00:53,460 --> 01:01:01,650
And the other is that any information can only be useful because floods used to come with no warning whatsoever.

550
01:01:02,440 --> 01:01:10,560
So when you look at the advantage of a good successful forecast, there are seven cattle on that hill.

551
01:01:10,950 --> 01:01:14,010
They were moved ahead of that particular 62,007 flood.

552
01:01:14,790 --> 01:01:24,270
That is, let me see, seven. That's 12 men years women is working to be able to recoup those animals that would normally have been drowned.

553
01:01:25,110 --> 01:01:36,120
So here is the great thing. I love this and we would be briefing people in 2008 and this is a priest,

554
01:01:36,120 --> 01:01:42,270
a muslim priest, and here's what he said and how would they interpret the forecast thing?

555
01:01:42,720 --> 01:01:45,720
He doesn't know which house of news reduces.

556
01:01:46,080 --> 01:01:47,219
The forecast was made.

557
01:01:47,220 --> 01:01:56,850
It is rendered at Georgia Tech, tuned to a hydrological model sent off to Bangladesh and disseminated to this guy over about 8 hours.

558
01:01:57,630 --> 01:02:01,860
And so the earth is flat type of analogy with the communications.

559
01:02:02,010 --> 01:02:05,700
He said, look, we disseminate the forecast information and how to read.

560
01:02:06,090 --> 01:02:09,570
What's their interpretation? The flag of flood pillow.

561
01:02:09,810 --> 01:02:17,700
That gives you an idea of what the rather than a number you know what the flags tell you, how far the floods are going to go to understand the risk.

562
01:02:17,880 --> 01:02:24,540
During the prayer time, my field he was a seething and translator you say to use the flood forecasts

563
01:02:24,540 --> 01:02:30,630
information for harvesting crops and making decisions for seedling and transplantation.

564
01:02:31,410 --> 01:02:34,440
We also saved household assets.

565
01:02:34,950 --> 01:02:39,330
I think I, you know, looked at this this fisherman and he saved $230.

566
01:02:40,140 --> 01:02:44,430
This guy saved 190. This family kept their cow and so on.

567
01:02:45,330 --> 01:02:47,850
But the annual incomes only $450 a year.

568
01:02:48,390 --> 01:02:56,010
And you think about what would happen without that forecast, any of those families and this priest or anybody, they would have nothing.

569
01:02:56,960 --> 01:02:59,900
They'd have to start all over again on the treadmill of poverty.

570
01:03:00,350 --> 01:03:06,560
But using the forecast at places like Eastern W of produce, getting them to the people is really useful.

571
01:03:06,740 --> 01:03:11,840
So this is the end of the theory. There's still some theories and a result,

572
01:03:12,560 --> 01:03:21,740
but we can understand from an intellectual point of view why the flood occurred and we can make the the model better by understanding those plots.

573
01:03:22,160 --> 01:03:28,370
So this is picture here of. So this is development hazard.

574
01:03:29,900 --> 01:03:36,230
No warning. You lose resilience completely. At best you can with warning, you can maintain the resilience.

575
01:03:36,290 --> 01:03:40,190
These are all pictures of the things that people did ahead of the floods occurring.

576
01:03:41,000 --> 01:03:43,190
So that's all I have to say. So thank you very much.