Jagadish Shukla Maniac Lecture, May 2, 2016

Updated : Aug 30, 2019 in Articles

Jagadish Shukla Maniac Lecture, May 2, 2016


-Good afternoon.
And, as usual, welcome to
today’s Maniac Talk. And we are all so fortunate to have
Professor Jagadish Shukla. So please, let’s give him
a warm welcome with a round of applause. [ Applause ] As is, of course,
usual with these talks, we don’t do
a formal introduction. But I want to make a few
remarks about Shukla. Now, as a boy —
Of course, we all know he was born in India, a small village called
Ballia district of India. Shukla walked barefeet
all the way to school, and also
while he was — was grazing cows. He learned Sanskrit and math
under a kerosene lamp. Some of you may not know
what that means. I know about it.
So, kerosene lamp, you are using it
as a source of light. It’s also producing gases
which also are not comfortable. So you can actually imagine
somebody reading and the smoke coming from
this kerosene lamp. So it’s pretty interesting. Also, he used to use
the bullock carts and elephants for transportation and somehow ended up at MIT, and also at Goddard. So what a —
what a journey, you know. Now, his lecture today
will include a personal retrospective of the origins
of the idea of predictability. And of course, I must say that some of that work
has been recognized by the international — by World Meteorological
Organization. He got an IMO Prize. Also, the AMS also
has recognized that work. So, very prestigious work. And so he’s gonna
talk about that, the idea of predictability
in the midst of chaos and also
the evolution of numerical weather prediction
to numerical climate prediction. Today, he’s also joined
by his wife, Anastasia, and daughter, Sonia. So please, help me
welcome Jagadish Shukla! [ Applause ] -Good afternoon. This is
a great pleasure for me, to be here at Goddard. Goddard is probably the unique center of excellence
among all NASA centers, which has excellence both
in the earth science and also in the space science. I think their science group
is one of the most distinguished in the — in the world. And of course,
you also are the home of the James Webb
Space Telescope. So this is really
an amazing place. And I worked here many,
many years ago. I worked here from ’79 to ’83. So I’m still happy
to see a lot of faces that I had actually met
when I was here. But I’m also happy to see
a lot of very young faces. These are the students from Maryland
and George Mason University, who have come and joined
in this. And of course, Charles,
thank you for inviting me. This is an amazing series. You know, we as humans
like to talk about ourselves. So — so it just —
it gives you a blank check. You can talk about what journey you have been through. And he told me
the key word was “journey.” So, what you do
when you go on a journey? You take pictures, right? When you go on a trip,
you take pictures. So my talk is really
going to be mostly a slide show
through this journey — through this journey,
space-time of life, you know, just think of it. But I thought
that would be too boring for many of you scientists. So I’ll also add a — an intellectual part
of the journey. And I’ll really describe
some of the scientific ideas that I have been
involved with and how the ideas develop, how in the scientific
research we — Once you get an idea,
what do you do next? How does it lead into a
sort of a real contribution to science
and advancing society? So I’ll try to give
a few examples of that. But in a talk like that, there is also possibility
of appearing to be bragging about
what you have done and so on. Please, just want
to make sure that — My life is mostly
a random walk, okay? There was nothing — I mean, as you mentioned
about the village, I’ll show you some things
for my early life. It was a pure random walk. You have no idea
what is going to happen next, next week, next year,
so you just go. And during this journey,
I mean, I have met so many people
who have helped me. Many of you are here,
by the way, in this room. And so, rather than
identifying everybody, I’m just going to
say thanks to everybody. But I will take
the time to recognize, I mean, you know,
you really need the love and affection
of your family to be able to do anything
worthwhile in your life. And you know, my wife
and my two daughters, Puja and Sonia,
have been great. Puja and her family
are not here, but my wife and Sonia are here. Thank you very much.
You already saw them. Thank you for doing that.
Thank you. What I like to do in
lectures like that is first simply walk through
the outline just orally so — so in a way, I’m going
to give you the — the oral journey before I really jump
into the actual PowerPoint. And I think Charles
already mentioned, you know, I was born
in a very small and primitive village in India. How primitive? Well, I’ll show you
some pictures. But just remember,
nobody had soap. There were no soap
in my village, actually. The soap was only once a year
on some very special occasions. Of course, no roads, no power, no drinking water
and no school. So my elementary school
was under a banyan tree. And every time the monsoon rain
will come, we run into the — So there was a shed for cows. So you just go there
once the thing is there. And yes, the next school
away was three miles. You walked barefoot. I didn’t have a shoe till I was
about 12 years old. So that’s one
consequence of that, and my wife reminds me
all the time, every time we go to buy shoes, I need wide shoes
because my feet are so flat. Okay. So, how do you get
from that to MIT? So, you’ll see
these kind of several transitions in my talk. And, you know, when you have
so many transitions, it’s a question which one —
which stories you want to tell. So I have chosen a few. Although the college has no school and no science, I managed somehow
to get into science. And I’ll tell you this story. Ended up being
in oil prospecting, to MIT, then from MIT to Goddard. Each one, I mean, is just quite a random story,
or just luck. I have to use the word luck.
I mean, I have not studied meteorology. And I ended up arguing with a man
whose name was Charney. And then later on I found out
he’s a very famous man. Well, that’s how I ended up
at MIT. I’ll tell you about that.
I came to Goddard. Of course, I was very lucky. Goddard was so, so nice to me. I was not even a U.S. citizen. But they gave me
a civil service job thanks to Milt and David and Meredith
and so on, so forth. So, in addition
to the professional work, then I will also give you
a little bit of story about Gandhi College. That’s something
that my mother just shamed me into asking. “You are doing all over
the world, you are doing this. What have you done
for the village?” And I thought,
“That’s a good question.” So my wife and I
are really working hard to try to help the girls
in the village. I’ll tell you
a little bit about that. And after all that, I thought we should talk
a little bit about the scientific ideas. And I chose these
three scientific ideas just to give you
a little bit of background at how you could get exposed
to them. What do you do next? And then, I will mention
a few words about the science and politics of global change. Because in the month of April,
I had a symposium. There were 200 scientists
all over the world. And we had a wonderful time
talking about this symposium. In month of September, I was labeled by Fox News and Wall Street Journal
as the most dangerous man in the world. One of the — one of the — Out of 20. I was in the list of 20.
I was number 3. Anyway, but then I’ll look — I would like to end by
some ideas about the future. Because I have been
thinking about — Some of the ideas are old. And I’ll tell you this story
of my failures, also. I have tried to create
a world center for modeling, and I have failed. And I was so delighted
to see the display into the lobby
of this high-resolution model. That’s something that we have
been really trying to argue for, a kilometer-scale global model. And then, of course, I’ll give
a few concluding remarks. So this is a kind of journey. As I say, I’ll go
one step at a time. I think I have already
told you about village. This is India. This is UP, okay? That’s the state
where I was born, one of the most backward, one of the most poor
and most populated. If my state was a country, it would be the fifth
most populous country in the world,
200 million people. And that state
has 70 districts. I am from one
of those districts. And that’s where — And I went recently
and took a picture. And you can see that
you feel like you are in some kind of
a Biblical village. The only thing
I feel quite good about is that I left the village
to come to MIT 45 years ago, and every year I have gone
to my village, sometimes once,
sometimes twice, because all the things,
because I love my village. Turns out villagers love me. And I turn out to be
a villager when I go. It has been wonderful journey. And I’ll tell you
a little bit about that. As Charles said, yeah,
this was my first vehicle. Well, not my vehicle,
but source of transportation. I’m going from the village
to the city 15 miles away. But of course, nowadays people are not using it
for transportation. Now they’re just
using it cruelly, cruelly to carry sort of food, you know, and vegetables
and stuff and so on. I think that this entertainment,
this is interesting. So last time I went to my village with many
of my friends from the universities. And my friends in the village put up an entertainment
for them. What do you do
for entertainment? You bring some elephants, bring some horses
and let them run around. That sort of — I myself was
the beneficiary of quite significant transportation
by the elephant, especially when you have
some wedding, like, 10 or — or, you know, 15 miles away. And I think that —
It’s so — so funny. I was a student at MIT.
I went to the village. And the principal
of the high school found out I’m in the village. And he said, “Please come
and give a lecture. You know, the students
wants to see.” But there was monsoon rains. And there was just
small kind of river that — 2 or 3 feet. And I says, “Look,
I can’t walk through that. And my bicycle will not
go through that.” And he said, “Don’t worry.
I’ll send you an elephant.” And so then, you know,
they have no excuse. You just go. All right. This is the… Now, this is not
the kerosene lamp that I used. But this is a kerosene lamp. I actually sent an e-mail
to somebody in the village — Sent it, I say,
“I’m giving this lecture.” So they sent it. And
Charles was absolutely right. There’s so much smoke comes, but this is the only source
of power when I was in the primary school
and high school. And in the elementary school,
this is the notebook. I don’t know how many of you
are from the village. It’s a wooden plate. And you write it by chalk. And you have to erase it at the end of the day
and fresh start again. That’s it.
Because there were no paper, there were no pens
in the village, okay? So, and you do that. Of course, nowadays
they don’t use it. Some of them
are still using it. So, the question is:
So what happened? How did from this
elementary school, I ended up studying science? And there, you know,
you have to recognize the great role of your parents and your family
and your society that plays. My father was determined
that I have to study science. So there was no science
in the village. There was no science
up to high school. So I studied economics,
Sanskrit, Hindi and math. And luckily, I did get good
grades into Sanskrit and math, very high grade. And after high school,
my father goes to a college 15 miles away,
which has science. And he said, “You have to get
my son into your science class.” And the principal says,
“Come on. He has no science up
to 10th grade.” And my father says, “Come on.
You are a Brahmin. I am a Brahmin.
You got to do something.” [ Chuckles ] The principal said, “Listen. We can take a test,
entrance test. And if you’ll pass…” And my father comes and he buys
the science books for 6, 7, 8, 9, 10 and says, “This summer you
are not grazing cows. You are reading these books.
And there will be a test.” And there was a test,
and, again, thanks, God, I passed the test. And that’s how, actually,
I got into science. This is the unfortunate part
of the village in India is that things are
still not changed much. This is a picture
of the village. I just went recently. So, the banyan tree has been
replaced by two rooms. And actually,
I had another picture which I have lost it. I went one day, in the morning,
to take the picture. And there were two
or three cows in the classroom, because there’s no door,
you know. So things have actually — have not changed much. And that’s the story about —
you read in the newspapers — India is one of
the emerging powers and one of this
and that economic powers. But 60%, 70% of the village — And I have to say,
I felt sorry. I have been trying
to do something about the village
primary school. But I can’t.
The bureaucracy. Because they are run
by the state government. So the only thing you can
do is totally private. So that’s why we worked
on the college. And I’ll tell you. So, the story going from this
elementary school to MIT — This is an MIT building, okay? I think that this is a — I will be very fast, because I don’t want
to really bore you with the — the details. But this is what happened. My father made sure that I study
into this university 100 miles away from the village. And you can take a train
and go there. And I studied geophysics
and oil prospecting. But the way randomness
controls your life in India — But I ended up getting a job
in a meteorological institute that I had never heard, in Pune. Now, actually, it is considered
one of the distinguished institutes of
tropical meteorology. So I was working there in Pune, learning meteorology
for the first time. I bought a — I took a glossary
from the meteorology. And that’s how I started
learning meteorology. I used to learn — But I think that
I like to do programming. So I learned how to run
a better tropic model. And there are punch cards. So you have to learn
how to punch, which I didn’t know very well. But you learn to punch cards. And there was a great motivation
to learn that and punch. Because the only room
in that institute which was air-conditioned
was the computer room. So you just go there
and you learn how to punch. Anyway, I was kind of happy
with what I was doing. And they nominated me
to come to United States on a fellowship,
United Nations fellowship. United Nations
used to give money to the people
developing countries. And they gave it. And that was, by the way,
quite an experience. I skipped some classes
in the primary school. So I actually did my master’s when I was only less than 19. So by the time I had this job
in Pune, I was actually — No, by the time I was sent
to USA, I was 22. It was the most
unbelievable experience. The only places I’ve been before
is my village, then to Pune. And those days — It was Washington, actually. I came to Washington. It was called NMC. This used to be called NMC. And, you know, I still —
I can’t find a picture. But you could just
walk into White House back lawn and go. That’s how little
the security was. Because two of us had come. Anyway, during that trip, they also sent me to Japan. So it was fellowship six months, four months USA,
two months in Japan. In Japan, I met two
great scientists: Gambo and Nita. Those who are in the numerical
weather prediction might know. Gambo was one of the very
senior Japanese scientists who was at the Institute
of Advanced Studies, along with Charney
and von Neumann. And it was pure —
that’s what I say — pure luck. And he told me to do
some numerical experiment. And I didn’t know
what I am doing. I tell you, but they had supercomputer and ran this two-layer model. And the experiment was
basically try to see if there is a vertical coupling
in the tropical atmosphere. And Charney has written
a paper a few years ago that tropical atmosphere is not vertically coupled,
is barotropic. And this paper had got
a lot of attention. Gambo told me,
“Let’s do an experiment. Let’s change
the vertical coupling.” How do you do that? Just change
the research number, low to high. So we did the experiment.
The result came out. If the research number is low,
it is a vertical coupling. And luckily, next year — So this was in ’67, ’68. There was a symposium in Japan. And Gambo got me to come
to the symposium. Well, there’s a long story how, in the Indian bureaucracy,
I got to the symposium. You don’t get to
foreign travel in a very bureaucratic country. And I’m told that
the only reason I did it is because the director general,
who really controls everything, was trying to decide between two
people who really wanted to go. They have one. And he didn’t like any of them. And he told his assistants, “I don’t like any of them to go.
Is there any third way?” And his assistant said, “There’s this fellow in Pune,
you know, he has a paper.” “Ah, great! Let’s send him!”
So — so I end up in Pune — in international symposium
as the only person from India representing numerical
weather prediction. And, again, I had no idea
what is going on. I go there and then I realize that I’m going
to criticize a work that was done by Charney. So I asked people,
“Who’s Charney?” And they said,
“That guy is Charney.” So they said, “Okay. If you want to study
meteorology, oil prospecting,
you will know who Charney is.” And I said —
actually, I just showed him. I found that every time
he raises his hand, the whole room becomes
totally quiet. And if he gets up,
10 Japanese cameras taking his picture — boom! And I tell you, by that time,
I was so scared. I says, “I’m going
to actually say something that is critical of this guy?” But, again, I don’t know where
from that nervous energy comes. And I always sort of give credit to my parents and to my village
and my friends. I actually went back
and forth with him. I was very nervous. So the chairman
came and told me, said, “Look.
You are talking too fast. Japanese people
are not following it. Can you please slow down?” He didn’t know
how nervous I am. That’s why I was
talking too fast. Anyway, he actually — At the end of the lecture,
“Any questions?” Nobody raised their hand.
Nobody. And I said, “This is over. This is great.”
Then Charney raises his hand. “I have four questions.”
By the way, this is all in
the NWP symposium proceedings. And he said one, two, three. And his last point was, “What you have done proves
my paper, not — not that there was
anything wrong with it. And anyway, that would have
an effect,” okay? So here is the thing
you have to know, how events change your life,
you know? He just came to me at the end
of this session. There was a coffee break. And he came to me.
And he says, “You know…” And he started
explaining me again. I did say that when
research number is small, you know, this happens. And I was referring to — And I told him, “But look.
But I’m just in the monsoon. And in the monsoon season, there’s a lot of
vertical coupling.” You know, the lower
atmosphere is very — “Yeah. Yeah. Yeah.” Then he says, “Okay,
let me give you another paper.” He has written another paper, a further note on vertical
coupling in the atmosphere. And that scene, I will never
forget, from the lecture hall. I am walking behind him. And behind us,
12 people are walking. Why? Because he was the chairman
of the Global — GARP — Global
Atmosphere Research Project. And all these committee members were trying to get
hold of him for a meeting. And they couldn’t. And their plan was to meet him. Anyway, I go into his room,
and he gave me another paper. And all of these people
are standing at the door. The reason I’m
telling you that story is because that day,
when I went back to India, I decided I wanted
to study with this guy, if he’ll ever take me
as a student at MIT. So I went back to India. I wrote a letter to him
that I really — “Remember, Professor Charney,
we met in Tokyo?” Which he never replied. I know what happens, right?
We get these letters like this. I get a lot of letters
like that now. But he did give the letter
to Norm Phillips, who was the chairman
of the department. And, anyway, to cut
a long story short, I get admission
and came to MIT as a graduate student. So that’s sort of the long story of how I went from my village to MIT, okay? Now, look: luck.
Again, what happens to the luck? These four — Now, those of you
are in meteorology, you will recognize these names. Why am I showing you this? They’re all my thesis advisors. How lucky you have to be
that when I went — Charney and Phillips
became the thesis advisor. And many of you know,
Phillips left MIT and went to ANSEP. And then Charney and Lorenz
became the thesis advisor. And then Charney went
on sabbatical to Israel. So they shipped me off
to Princeton as a visiting student. And Manabe became
my thesis advisor. So these were sort of — And this period at MIT
was so wonderful. I mean I came to know
very well, you know, professors like Henry Stommel,
Victor Starr, Manabe at GFDL, Namias, Eliasson, Mintz — We’ve become, actually,
good friends. Piers Sellers and I worked
with him so closely on this. And so many friends. My classmates,
we’re on the same floor: Mark King, Inez Fung,
we are all — Antonio Moura, who is now the head of the Brazilian
weather service. They were all classmates. It was a wonderful time. So I think that that journey and that experience
was really wonderful. I don’t want to really detour,
but I have to — to tell you, honest,
I have to tell you that for a moment
after all that, I felt meteorology
is not a good field. And I was thinking that
maybe I should switch and go back to India
and run some elections. But I didn’t do that. There was many other —
other things happen. But from Goddard,
I managed to come — from MIT to Goddard. And, as I say, some of you
probably recognize yourself. I can see you into this. I tell you, the Goddard thing
was really so interesting. So Charney and Halem
and Gestro managed to get me this joint position
between MIT and NASA. And I used to commute
between MIT and Goddard — and GISS in New York. And then the GISS group moved
to here at Goddard. So Milt Halem’s group
was called — named as Modeling
and Simulation Facility. Joe, were you in New York, and you came here
or you were here? -I was his first civil servant.
-Right, right. Anyway, we moved. And by the way,
so Milt was my first boss, I told him,
also my last boss. I never —
I didn’t have a boss after that. I just changed the field. Which was so wonderful, that all these — And, you know,
it was the most wonderful time. Because you have — And those friendship are
actually containing — In fact, Yuhenia and me
were the — Milt was the branch chief. And Yuhenia used to say she and I are the twigs
in his branch, okay? So I was taking care
of the climate. She was taking care
of the weather. Oh, and then I looked
at your Maniac Talks. And I says,
“My God, look at that! How many Maniacs
are my friends?” [ Laughs ] And Milt — Marvin, actually, Marvin
was there in the other branch. And Milt always used
to bring up a lot of visitors. Really, we had a lot
of visitors. And we got
to make some really, really long-term friends. I’ll show you one guy
that you recognize. See on the left side? This guy is cooking, you know, in my kitchen, chicken korma. He was very good cook, okay?
[ Chuckles ] And then, of course,
with my wife, we went — She became a Frenchman
for the other. But the reason I want
to show you that is because these friendships
are enduring, that are just enduring. This is a picture taken
only a few weeks ago. So these are really
the Goddard experience. But anyway, let me
just run through this. I really wanted to thank
Goddard one more time, because Goddard did
give me their — their medal, which is really one
of the wonderful things to — to receive, along with it. So, that’s the second part of journey I wanted
to tell you about. I do have some time left
for the science. But anyway, it’s more fun,
right, to talk about stories. So I think that
the most interesting — And I’ll give you a little bit
of science, is that
what I learned at Goddard — See, before, is that,
you know, at that time, people used to think
that weather prediction limit is 10 days, 15 days. Because the butterfly effect
of Lorenz was the most dominant
scientific theme. Now, as I showed you before,
Lorenz was one of my advisors. He’s a great genius. I somehow didn’t
quite feel comfortable that nature would be
so cruel to us that there’s nothing predictable
beyond certain number of days. So I actually started a set
of numerical experiments to see, is there any predictability
beyond weather? And I kept finding evidence
that, actually, long waves are more
predictable than short waves. And all this limit of 5 days
is really based on short waves. And long waves are the one
who has a lot of energies there. And then I kept finding
that the boundary conditions are really a very
important influence — boundary conditions of
temperature, soil wetness, sea ice, snow, vegetation. And we did some experiments. And we came up with this idea that if these
boundary conditions can be measured and modeled,
we can have more prediction. And this is something
that was wonderful, because right here in NASA — Because, you know, till that
time, everybody was saying, “We need all these measurements so that we can describe climate. We can describe the nature. We can describe
the full dimensional, the structure and variability
of atmosphere, ocean, land.” And we said, “You need
these also for prediction! Because the only source
of predictability is in these boundary
conditions.” So that was actually
the fundamental scientific outcome of this. The only downside of that is that you need
a room of 10 people where you have an expert
of atmosphere, ocean, land. So I just wanted
to be on the record, tell you that
I didn’t leave Goddard because there was
anything wrong with it. I loved my time.
I have still my friends. We came up with the idea that we need a collection
of scientists. You have such
a collection in Goddard. The only problem was you have
to go through three divisions: atmosphere division, ocean
division and land division. And about six branch chiefs. So that was sort of
a proposal to the university, and that’s what we got,
because it was the — That was the whole kind of
the basis for creating a center
of the ocean, land, atmosphere. For Jim Kinter, director of COLA and business manager and I, our primary mission became to create the best
possible research environment for COLA scientists. And I tell you, that’s another
wonderful story. Because it takes, actually,
quite a good luck, right, to be able to have people
that you can work with. I mean, we have been
working together — this group have been
working together for more than 35 years. This group
is almost 15 to — to 25 years. And I’ll show you. We have sort of moved
to George Mason. And it was the most
wonderful, scientific, exciting experience because we did
a whole bunch of numerical experiments from AGCM. Then we learned how to do the ocean modeling
in the process and go from atmosphere model
to the couple modeling. We worked actually
very close with Goddard, just at the formation of GEMA. And it’s wonderful
to be able to go to work every day and be excited
about going there. We never had
really a bad day, I will say. We got a lot
of students to do PhD. We managed to work with a lot of
international programs: MONIX, DOGA, G-Rex, Glass…
just the whole acronyms. We got to do that. So it was really
a wonderful experience. And I think that, that led to the creation of a whole new PhD program at George Mason University. This is, again, a wonderful — I used to say, it’s like
a marriage made in heaven. COLA is an independent
nonprofit. There’s no tenure positions. George Mason was willing
to give 10 faculty tenure positions. So we sort of all moved there. It is a — a department we have created. I had to pay the price of,
you know, creating a new department.
It’s not easy. But once you go
to the university, you can imagine
a state university, as you have the usual rules
and regulations. But we do have now
a PhD program. And we do have a department. So I think that we — the whole COLA group
has moved there. I showed you. And now we have
a full-fledged department. Oh, I have to — I have to, again, acknowledge
Goddard’s help. So when I was there teaching,
in the beginning, classes in climate, they said, “You know, we really want something more
than just the atmosphere. Ocean, we want ocean.”
I says, “But where do you get a sonographer? There’s none.” And then I heard that
Paul Schoff was retiring or taking retirement
from Goddard. So he was my second
faculty hire at GMU. And we have now a department. So, I think that I have still told you
the journey by pictures. And I will take a detour to tell you a little bit
about my village. But before that, I’m going
to show you some pictures. I’m telling you bragging photos because they’re really
bragging photos. But they’re also, hopefully,
there is something to laugh about them
behind these pictures, okay, without offending anybody. So, this is
a picture, of course, with you know,
Pope St. John Paul II. And there were only —
so anyway, so, I took to my village. And the questions
I was asked is that, how come you are hanging around with people in long skirts? So this is how different
cultures are. I take that — The other bragging photo
is with India prime minister. And this one I wanted to show
to my younger people. So somehow,
I was asked to meet him when he came to White House. And I was asked,
he wants to know what’s going on in India
in the weather and climate department
and how to do it. I met with him
in the Blair House. And I was so nervous. Should I tell him the truth,
or should I just tell him what normally people
tell the prime ministers? And I told him that, “Sir,
I think that really a lot of things
have to be done.” And I was thinking this would
be my last meeting with him. And you are not going
to believe what he did. As his aides come to remind him that he has another meeting with
the chief of the world bank, he was waiting another room. And he gets up, and he says, “Dr. Shukla,
do you have a card?” I says, “Oh, my God.
I don’t carry cards.” I said, “Sir,
you don’t need my card. Just tell me.”
He said, “All right. Next time you come to India,
you will meet with me.” I met with him.
Anyway, what he does, he makes me the chairman
of India’s Advisory Committee for Weather and Climate. He creates the new Ministry
of Weather and Climate and all that. This is my picture with the
the governor of Virginia. And the reason
I want to show you is because I thought
this would be a great chance to make changes
in Virginia’s policy about climate change. Well, guess what?
No luck, absolutely no luck. The Virginia Climate — I’m a member
of Climate Commissions — has absolutely no
recommendations. You’ll fall
asleep if you read them. And I really appreciate
Maryland, has really a very
aggressive policy. This one I like to show
simply for one reason is that, when I took this picture
to George Mason, it was new assistant. And she says, “Dr. Shukla,
is this your wedding picture?” I said, “Come on.
I didn’t marry her. No.” She has never met my wife. She had just
joined it and so on. Well, I have to
explain to her “no.” This is the president of India. This lady is
the president of India. And it is one
of the things the — that Indians like to really do:
big ceremonies. It’s one of those
presidential awards and honors. And they learn from the British,
do it very well. But the picture I’m very proud
of is with my mother. So this is the picture
in my village. By the way, this is what we use. This is a cot
that you sit down. And I just say —
When I look back, I feel that my father
was very clear. I need to get good grades.
And my mother was very clear. “You have to do good things
to other people. You have to help people
who are actually doing…” And she actually asked me that, “What have you done
for the village?” And then my wife
and I basically decided, “Okay. We are going to start
a college in the village.” And the biggest problem in that part of India
is cheating. Nobody actually
follows the rules. The teachers don’t teach.
Students don’t go to the class. And end of the examination
time, they cheat. This is a picture
that was on the web. Many of you might
have seen this. This is very near. These are the people
who are helping the students inside by throwing chips
and so on. And they have kept
a person few miles away with a squad car. And because before,
the police used to fly. You know, they used
to call flying squad — not fly but take a Jeep
and fast drive and catch them. Nowadays, because of cellphone,
they can’t do that because there’s
one guy’s watching when the police car is coming. Anyway, so we decided
we’re going to build a college. So this is the beginning,
the same place where I used to graze cows, we really started a college. And, again, I’m happy to say
that we have a college. We named it Gandhi College
for the simple reason that it has to follow
the Gandhian principles. And by the way,
there are a lot of people have actually helped us. And Gandhi’s sort of,
you know, message is what we try to use
to inspire these people. We give girls bicycles
’cause most of them walk. And some of them
come on bicycle. And there have been
a whole bunch of people we try to build a library. And you know,
it’s such a nice thing to see, these kids from the village,
you know, sort of all excited. We have — So it has — Not that their economic life
has improved but tremendous change
in their self-esteem. And, you know,
because we made this. A lot of people
have come and visit, thank you to many a few that have a whole group called
Friends of Gandhi College. A lot of people come and visit. So, that’s the part that I was hoping
that I can do in 30 minutes. But I have taken more
than the 30 minutes. Let me just tell you
the three signs story. Now, what I’m planning to do now
is that these three ideas… And in each case,
what happens — And I think
that the reanalysis is the — is the simplest one. And I have to simply thank — See, if you have an idea,
just say it. That’s my — the message
for the young people. My office was next
to Milt’s office. Yuhenia’s office is next door. NASA came up with this
global habitability project. Come up with some idea.
What do you want to do? And because I was influenced
by Milt and Yuhenia, I said, “Listen. How about
reanalysis for 10 years?” This wasn’t this. And everybody said, “Oh, no. You want to go back 10 years? Who will collect the data?
Who will do?” Anyway, as you can see
from this history, I think that greenhouses
have just exploded on the field. In fact, you cannot
do climate research. But the reason I want to show
you is that it was not easy. It was very challenging
to convince people. And you have to just —
If you — if you believe in something,
go and tell your boss, “I think this is something
that should be done.” The more interesting
story is, of course, of the land-atmosphere. You know, the way
our food has grown — National Center for Atmospheric
Research, NCAR, right? Why? Because atmosphere
is very important. Then you have Institute
for Ocean and Atmosphere because ocean and atmosphere
are very important. We are the first one to say
land is very important. And it happened in in the most
kind of unusual way. I’ll just tell you
the very simple story. Johnny had done
the albedo experiment. You guys must be aware
of how the albedo affects the hail drought. And he gave me his paper
to read as a student. I was his greatest student. And the paper
was written so badly, but I had no courage
to tell him that. So I went to Yale Mintz. I said, “Yale,
this is so badly written. But how can I tell to Johnny?”
He had it. He says, “This paper
is written the way he teaches in his classes.
It’s awful.” And I said,
“You are absolutely right. That’s the way his classes are.” Anyway, we had the courage,
finally, to tell him that the most important result in your paper you were
just not even talking. That was how the soil wetness affects rainfall,
not the albedo. Albedo part was sort of — Anyway, to cut
a long story short, and thanks to all
these heroes in this. I mean, I learned land.
I learned. And eventually, we ended up having a model called
simple biosphere models. This guy, Piers Sellers,
came up with it. It was his model, sig. model. And then we had
a Japanese visitor, Sato. He just sort of will
work during the night and sleep during the day. And he implemented it. So, I think that, just to be able to think something sort of
outside the box, that, you know, maybe
land can be just as important, not just for variability
but also for predictability. So Yale and I actually
did this experiment. And I’m just showing you just
for the historical purpose. We basically — we basically changed the whole world
into a parking lot. Beta is equal to zero.
Whole world is a parking lot. And then again,
to change the whole world into a wet swale,
in, like, a swamp, just due to experiments. What happens? This was, like, you know,
absolutely the largest change you can make in land. And we are amazed that, everywhere,
the rainfall decreased by 50%, everywhere. Except a few places,
it increased. And that was quite puzzling
to us and gave us a lot of clue about how the land
and dynamics interact. But what was really amazing is that the temperature
change was 30 degrees. So if you change your globe to a parking lot according
to this old NASA glass model, by the way, you are — So it just sent this message so loud and clear that, look, there is actually a major factor
that we have to — and we were really amazed
how this whole field exploded
with the field experiment, satellite measurement, you know,
the measurements of land, measure of albedo, vegetation. I haven’t got slides for —
This guy, Piers Sellers, took me to fly
into the airplanes measuring land surface
in Kansas. Do you remember that?
[ Laughs ] Anyway, it was actually
a wonderful experience. So I think
my last scientific point, which I really need
to go fast on this, is that this idea of predictability
in the midst of chaos. So the dominant factor was that there’s no
predictability beyond. And this was the idea of Lorenz. And I did talk to him. I talked to Charlie.
And I had some ideas. And they both encouraged me
to pursue these ideas. That’s only the great thing
about great scientists, I said, because he did — he makes it clear that,
look, we only said that it’s not predictable
if you’re looking into instantaneous state
of the weather. But if you’re looking
into the monthly or seasonal means,
it is predictable. That was really
the understanding, that we’re really
trying to predict this. And as I say, Lorenz was great. He came to NASA. We’ll study under
the feet of the master. And basically,
this was our hypothesis, that, yes, weather
cannot be predicted, but maybe averages
can be predicted. How do you prove it? By the way, you couldn’t find
a room filled with people, 20 people, who are willing
to accept that, that things are predictable
beyond 10 days, 15 days. And so we did this experiment.
And we said, “Okay. Let’s take the atmosphere
in completely different years and integrate them and see what happens
because, in predictability, they diverge, right?” That’s what you lose
is the predictability. Our trick was we said,
“Look, the solutions converge.” So what was the trick? The reason the solutions,
they converge, is because they have
the same boundary condition, not because of
the initial condition. So that was the basic experiment which really proved that,
not only — Look at this.
This is the 100-day mean rainfall
based on initial condition in ’88 and ’82,
as large as you can. This really had — So in fact, that’s the title
of the paper — “Predictability
in the Midst of Chaos.” Look.
They’re almost identical, that when solutions
really converged, and not only that
they converged, end up — Well, yeah. [ Laughs ] We talked. You know what? We really have to sell
this idea that, look, if you have a huge
boundary effect, these fluctuations in the
weather will not affect. That’s where the predictability
has to come from. And not only
they are converged, but they’re very close
to observations. So, that basically
started the — I’ll just up that. You can get very good simulation
of the boundary conditions. So these are really the three
ideas that I want to tell you. And I think that the — the last idea about the — the science and politics —
here’s what I was telling you. In April — Pierce was there.
Many of you are there. You had a huge impact of signs. I was having a wonderful time.
A lot of people came. And look at what happened
in January. So, what happened? So you express your opinion. And this is what I just wanted
to leave you with a message — So as a scientist, we really have to defend
the integrity of science. There is — Because it’s —
it’s unbelievable how, at least in this country, there’s far more controversy. There’s 20 scientists. We wrote the letters
to President Obama. And just total —
just a constant harassment and intimidation
of the scientists. Many of you here
in the room who have — I’m sure you have felt that. So I didn’t want
to dwell on it. But this is something that,
you can see, it’s a major scientific,
societal issue that — that we are facing with. But I did want
to really tell you a few things about the future
because where do we go? And this is something
that I believe that — So, the picture
in your lobby is fantastic. The GEOS-5 picture.
Okay. We have been proposing
for a number of years that — And this, in fact,
we did it firmly at the World Modeling Summit
first 7 years ago. But as I say,
we haven’t succeeded. There is still
a lot of predictability that can be realized. But why? It’s not the limit
of predictability. It is that we do not
have sufficient observations to define the initial condition
of the whole climate system, ocean, land,
atmosphere, cryosphere, and the chemical composition. And we don’t have good models. So this is what we
have been proposing, that we probably need
a major international effort. But how major? Just like other
international efforts we have. It takes many, many countries. So, I mean, we ask ourselves, “How come we don’t have
a CERN for climate, two or three CERNs?”
This is our proposal. And we actually made
the point that, look, you can’t say that, “Oh, I have a modeling
group at Princeton. I have modeling group
at Goddard. I have modeling group at NCAR, and modeling group in England. And therefore,
we don’t need any more.” Our argument is that, look,
all these countries have huge accelerators
that they had. And still, none of them
had enough power — 250 gigavolts. So they created
a 7-teravolts machine. Why can’t you do
the same thing? This is an old picture. I didn’t have time
to really update it. Why can’t you do the same thing
for our field? So we actually have gone ahead,
put down a proposal. This we have published
in the BAMS. And we are saying that,
“Look, while all these centers at GFDL, Goddard, England, France
are flourishing, let’s continue
to support them because you need a lot
of scientific research. But in addition, let us create a few truly
international efforts, which are much bigger
than we can actually have. Normally, a model development group for climate model
has 20 people, 30 people who are working on boundary layer radiation,
convection. We’re saying,
that’s not good enough. We need 200 people together, working on developing the next generation
Earth system model, and get young people and guarantee their career
support for next 10 years. You can’t ask a post doc to work on a model development
Earth system model and say, “After 2 years, by the way,
we don’t have funding for you,” or, “You are not going
to get a tenure,” or, “You are not going”… So we really need
a fundamental change in our — in our sort of thinking if we want to make
next generation model. And we’ll have to. Such a huge problem
society’s facing, right, about the future of humankind. So this is our proposal. As I say, we haven’t
really succeeded. So I want to tell you, it doesn’t stop me
from talking about it because, hopefully… My friend Kip Palmer — he had wrote a paper
in the World Society two weeks ago, still asking, “Let’s try to do that.” Our suggestion is that you have
to have three of them, okay. So it has to be a truly international
sort of collaboration. Again, as I say, considering the politics
in the world — By the way, can you imagine
how unrealistic it is? But I, again, I feel, and that’s what I tell
the young people, if you have an idea,
go ahead and say it, no matter how unrealistic
it is and no matter what will happen. We really think that
the only way for society to answer some of
the big questions that we have to ask
about our future, you really need observations
and very good models. So, I think that
I would like to come to some concluding remarks. I would like to make sure that we have
some time to discuss and ask questions. So if you look at — I mean, I don’t want to be
either bragging, or as I said,
I don’t want to be a preacher because I am not
a good preacher. But if you look at it,
I felt that — Okay. Charles said,
“Leave a message to the young scientists
you have to say.” I say, “Okay. I’ll tell you a message
which also, hopefully, reflected into
some of the things.” So I have two messages,
one as a scientist. And I think that this topic and the group
that I showed you — As you know, if you are — if you are working with a lot
of group scientists, it’s a question that you
are faced with all the time. The next paper
I’m going to write, is it going to go on my list
of publications in my resume? Or is it really
advancing the science in some way, number one? Now, please remember
that I’m totally aware of the challenges in society. I have been in many selection
committees and promotions of tenure and so on. And this is the way
society is now. How many papers
she has published or he has published? She’s first author
or second author. How many citation index? So we are faced
with this challenge. At the same time, it seems to me that one has
to be conscious that we really do. But the second question, which has been
very important for me, is that, all I’m doing, is it
helping society in any way? [ Speaks indistinctly ] It was instability of a flow, which has horizontal sheer,
vertical sheer and convection. And you needed to do that
for getting a PhD from MIT. And later, I find, you know, I’d really rather like to see
if I can predict the next drought because, after all, that’s one of
the biggest problem, monsoon forecasting in India. So that’s my question
as a scientists. As a citizen of the world, I think that I still feel
very much inspired by my mother,
that, okay, have anything changed because of what you have done
or what you are doing? Okay. Have you left the place
better when you came, whether it is a meeting or whether or it is a village
or whether it is a city or whether it is the world
or whatever it is? So I think that my last comment is to the — to the — to the young people. Let me just say,
because it is quite possible that you find that
our field has controversy. There are not
enough jobs and so on. But let me just show you that you are basically seeing the dawn of the golden age
of Earth system science. It is developing. I mean, look at couple
ocean atmosphere model. It was not there. I just saw it for the first time
when I was a student, at GFDL. There is so much unrealized
predictability in the system that you can
explore and harvest for the benefit of society. I think that society’s
counting on you to give science-based advice about how to manage
the planet. I mean, look what
happened in Paris. 200 countries got together.
Why? Because the scientists have said
that global warming is real. Can you imagine
200 governments taking the conclusions
of a scientific community? That is how important
your field is. I think you have
chosen a great field. And I think there is
a great future ahead of you. Go and change the world. Thank you very much. [ Applause ] Yes?
-What’s the science problem you’re working on now? -I’m working on, well, the predictability of — I mean, oh, I didn’t
tell you another story is that, while monsoon is the interest
what I started with, one of the worst simulations in all the models
now is a monsoon. And if I showed you the CEMA-3 and CEMA-5 simulations, you can figure the difference
between the others. That’s how it is. Of course, the other thing
we are working on that, can we actually — How do you improve
model fidelity? Because predictability completely depends
upon the fidelity. But of course,
I’m also interested in the academic part
as a more — You know, we have
a climate dynamics program, of the PhD program. And beginning
to get interested in how poverty
and climate is related, because here’s the thing.
Look at that. Who will be affected
the worst by climate change? The poorest people,
the poorest nation. So my interest
has also moved from climate to climate and poverty. And then, ultimately,
to, really, inequality in general
because — So climate change becomes really
an exasperating factor into this. -Thank you for the great talk. I think you mentioned about
the incorrect knowledge [ Speaks indistinctly ] Do you happen to have some [ Speaks indistinctly ] …we have to understand
better to improve predictability? -Yes. I think that,
as I told you, one of the major barriers
of predictability now, in my opinion,
is the fidelity of the models. And why the fidelity
of the models is not good, because we are not able
to treat the small convection, okay, the sort of the small
scales and their — How do they affect
the large scale? We’re able to do very well
with the large scales. By the way, the entire progress in numerical prediction
in last 25, 30 years is because we’re able
to handle the potential diversity
dynamics much better. But that, we can do it
with a large scale. That’s why our prediction
of cyclones moving from here to there,
much better. Our prediction
of five days weather, the long waves,
their propagation and their amplification
is very good. But we are not able
to handle the deep convection. We are not able to handle
interaction between the deep
convection radiation and the precipitation
processes, and its interaction
with the boundary layer. So, unless we make some
fundamental advances in also understanding the basic physics
of the smallest scale, we are not able to improve
the fidelity of the models. And if we cannot improve
the fidelity of the models, we cannot make the prediction. You know, predictability
is not the property of the atmosphere. Predictability
is the property of a model. You just use the model
to estimate this predictability. However, if your model
is not good, you’ve estimated, but it might be irrelevant
to the actual nature. That’s why
our constant endeavor has to be to produce
a hypothetical, perfect model,
which we’ll never do. But that has to be —
we strive towards there. What is a hypothetical,
perfect model? A model that can capture
all the means, variances and covariances that we have
observed in nature. You don’t have
to really predict, show you what happened
after 10 days. But you must be able to capture
the statistics of fluctuations. How many hurricanes were formed,
how many El Ninos? What is their PDF? We are nowhere there yet. So in my opinion, we need — That’s why my proposal was to improve
the fidelity of the models. And if we do that, we would need then improve
our parameterizations of the unresolved scales. And we then have to go and estimate the predictability. Does that answer your question? -Yeah. -Sounds more like — My question is that,
do we have — do we have enough knowledge
to model the convection? You would have to go out
and measure more? -Oh. I think I was
just trying to tell you that there are some processes where we have enough knowledge,
and we’re doing it well. But the processes
of convection, process of deep convection,
interactions — we have to do a lot
more research to be able to bring their
parameterizations up to that. In other words, I’m saying that
we’re starting out with a goal of building a model. That’s why I’m asking, okay. Understanding
a physical process, then going to the model
is very fundamental. And we don’t of this process,
which we need to do research on. And by the way,
then we can say, what is the limit
of predictability? Now, it is quite possible
that we never be able. That would be
a fundamental limit, okay, which we can’t. All I’m telling you is that we
haven’t reached that limit yet. Right now, we are basically insufficient knowledge
of this very small scale and their science
and our inability to prioritize them to put
into the large-scale model is the big barrier. [ Applause ]

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