0:10

Hello, and welcome to the NVIDIA

0:12

AI Podcast. I'm your host, Noah

0:14

Kravitz. Climate change is a defining

0:17

issue of the 21st century. Can machine learning

0:19

help us model climate even though the future

0:21

will not be like the past? Can it

0:24

help us plan for coming new extremes of

0:26

heat, flood, drought, and rising sea levels? My

0:28

guest today is here to help us break it all down. Chris

0:31

Brotherton is the Senior Director of Climate

0:33

Modeling at the Allen Institute for Artificial

0:35

Intelligence, also known as AI2. And Chris

0:37

is here at GTC 2024 in San

0:41

Jose, California to talk about machine

0:43

learning and the future of climate

0:45

modeling. Chris, thanks so much for

0:47

joining the NVIDIA AI Podcast. Thanks,

0:49

Noah. So, let's start with

0:51

the basics. Before we get into your work

0:54

specifically, can you tell us a bit about

0:56

AI2? AI2 is

0:58

a group of about

1:00

200 people now who are

1:03

primarily supported philanthropically

1:05

through the Paul

1:08

Allen estate, working

1:10

on a diverse range of AI

1:13

tasks. Some of

1:15

the most important tasks include open

1:18

source, large language modeling, machine

1:20

reasoning, and knowledge

1:22

distillation. Environmental AI

1:24

is a newer focus for

1:27

AI2, but now in addition

1:29

to climate modeling, AI2

1:31

is using artificial

1:34

intelligence to detect

1:36

illegal fishing, track wildlife,

1:39

and other really interesting and

1:41

diverse AI challenges. So

1:43

we're here to talk about climate change, and everybody

1:46

is aware of it. We're in

1:48

California right now. You traveled

1:50

down from the Pacific Northwest,

1:52

so we're both pretty aware

1:54

of droughts, wildfires, record-blaking snowfall

1:57

and rainfall, just these

1:59

extraordinary things. extreme weather events seem to be

2:01

coming one after another. Can

2:03

you tell the audience what climate modeling

2:05

is, how it relates to climate change,

2:08

and how it's helping us all prepare

2:11

for whatever's to come in the future? In

2:13

the Western United States, there's always been

2:16

extreme weather. We've always had floods

2:18

and droughts. We've always had fires.

2:22

But as I think everyone who lives

2:24

here realizes, something is

2:26

changing. We're getting a

2:28

lot more smoke. It seems

2:30

like the floods are stronger. Of course,

2:33

snowpack is going down on average, but

2:35

the extremes are getting more

2:37

severe. And we

2:39

wonder what's going to happen in the future. Well,

2:42

a lot of what is going to happen

2:44

is a consequence of basic

2:46

physics, which we can encode

2:49

into mathematics. And

2:52

at the most basic level, we can

2:54

understand it on the back of a

2:56

napkin. But if we're interested in understanding

2:58

what's going to happen in places like

3:00

California, for instance, we need to

3:02

go a lot further. And

3:05

60 years ago, Suki Manabe

3:07

at a NOAA lab called the

3:09

Geophysical Fluid Dynamics Lab built

3:12

the first climate model with that in mind.

3:15

And what a climate model is, a physics-based

3:18

climate model is, is it's a

3:20

representation of the Earth, of

3:22

the atmosphere, of the ocean, of

3:25

other land surface components like land and ice

3:27

and so on, on

3:29

a grid, encoding mathematical

3:31

algorithms that represent our best

3:33

guess at how different kinds

3:35

of processes work. Atmospheric

3:38

winds, ocean currents, more

3:40

complicated things like how clouds are

3:43

formed, even how

3:45

soil decomposes vegetation

3:48

by microbes into CO2 and so on.

3:51

So there are all these things going on

3:53

in a climate modeling model, and

3:55

there's the 60 years of evolution

3:57

of complexity and using... finer

4:00

and finer grids that have made them more

4:02

sophisticated. But they are both

4:05

expensive to run. They

4:08

can only be run and developed

4:10

at major climate modeling centers. And

4:14

there's still uncertainties. They don't tell

4:16

us the whole answer. So there's

4:18

a lot further to go. And a

4:21

few years ago, I got interested in

4:23

trying to think about how machine learning

4:25

might help with that. And so climate

4:27

modeling used to be done with FORTRAN? Climate

4:29

modeling is still done with FORTRAN. OK. Even

4:31

if I said used to, I was like,

4:33

oh, no, I'm setting myself up. All right.

4:35

No. In fact, the heart

4:38

of many climate models still dates back to

4:40

the 1970s. And

4:43

in fact, it's hard to hire people

4:45

to do climate model development because they

4:47

have to learn FORTRAN. Although

4:49

the FORTRAN of today is not the FORTRAN of the

4:52

1970s. It

4:54

is a much more modern language. But

4:56

that is a hindrance. Because,

4:59

for instance, now we have computer

5:01

languages like JAX, which naturally

5:04

produce code which is friendly to

5:06

using with machine learning. But

5:08

no operationally

5:11

used climate model is written without friendliness

5:13

in mind. Right. And so that means

5:15

that the field is going to

5:18

have to be transformed in a somewhat

5:20

revolutionary as opposed to evolutionary

5:22

way because of

5:25

this legacy technology that it's based on. And

5:27

so you said a couple of years ago

5:29

you started thinking about how to use machine

5:32

learning in climate modeling. And so

5:34

what's that been like? What have you been

5:36

able to do or started thinking about doing?

5:39

And you said it's going to

5:41

be more of a revolutionary than

5:44

evolutionary process. Where both you specifically

5:46

and the field in general, where

5:49

are things at with bringing machine

5:51

learning into climate modeling? Yeah, well,

5:53

let's start five years ago

5:55

in, well, actually a little over five years

5:57

ago, I think in 2007. when

6:01

I was actually standing in front

6:03

of Paul Allen and proposing a

6:06

project on using machine learning

6:08

for climate modeling to

6:11

him and a committee of

6:13

experts in both climate

6:15

and actually in computational

6:17

science. At that time, there was an

6:19

extreme degree of skepticism that machine learning

6:22

could be helpful at all in the

6:24

near future. In fact,

6:26

so much so that our project

6:28

was originally rejected and only later

6:31

got revitalized after Paul

6:33

Allen's passing and turned into a

6:35

small venture with initially a two-year

6:40

pilot period only

6:43

to demonstrate some success. Wow. Things

6:46

have changed quickly. Things have changed pretty quickly. And

6:48

they haven't just changed in our field. There have

6:50

actually been a lot of other groups

6:52

that have suddenly got interest in this too for

6:56

the same reason that basically the

6:58

public and almost every

7:00

scientist or technically-minded person is now

7:02

thinking about ways they could possibly

7:04

apply AI to that work. The

7:07

tools are just so much more powerful

7:10

that the computational technology

7:12

is powerful. And I

7:15

think we've all seen how transformative it can

7:17

be in some examples

7:19

like large language models. So

7:21

what were some of the limits or I guess I should

7:23

say are some of the limits of

7:26

FORTRAN based modeling and

7:28

how are you able to overcome them or

7:30

might be able in the future to overcome

7:32

them using machine learning? Yeah, well, many of

7:34

those limits don't actually have to do with

7:36

FORTRAN, but they have to do with the

7:39

algorithms that we use to, for

7:41

instance, represent the flow of air

7:44

and the atmosphere, the wind basically.

7:47

They basically have to do fundamentally with

7:49

how you numerically solve

7:52

equations on grids. And

7:54

without describing any details, basically they require

7:57

a limit on how fast a model

7:59

like that can be stepped forward and

8:03

what size stops it can be stopped.

8:05

And for a climate model, that means

8:07

that climate models written and phrased

8:09

in the traditional way require time

8:12

steps of a few minutes. With

8:15

machine learning, we are able to

8:17

actually sidestep that limitation because our

8:19

algorithms are not formulated in the

8:22

same way. And so

8:24

we can take much longer steps of many hours.

8:26

And that by itself allows the

8:29

machine learning to accelerate the model

8:31

by a factor of 20 to

8:33

50. Wow, okay. Even

8:36

with no improvement in computation. Right. Furthermore,

8:39

machine learning runs very efficiently

8:41

on GPUs. And

8:43

so we're able to much more efficiently use

8:45

the computational resources that are

8:48

available. Right. And so

8:50

that actually adds to

8:52

the efficiency yet further. I

8:54

don't know if this is a good question

8:56

to ask per se. So tell me if

8:59

I'm off base here. But can you compare

9:01

the size of a climate model to something

9:03

like a large language model just to kind

9:05

of give the offer me but

9:08

also to give the audience a sense of, you

9:10

know, in my head, describing

9:13

the wind in an algorithm seems much

9:15

more complicated than describing a sentence of

9:17

text. But I really have no idea. Yeah.

9:21

And of course, what we're not we're

9:23

not comparing describing the globe

9:25

to describing a single word or single

9:27

sentence, we're sort of thinking about it,

9:30

describing all the sentences that you might

9:32

make. And so it's

9:34

more like, you know, the

9:37

encyclopedia Britannica versus representing the

9:39

Earth on a global grid. And of course, an

9:42

issue is that it depends on how detailed

9:44

the grid is. If you

9:46

represent the earth on a hundred

9:48

kilometer grid, you might be talking

9:50

about hundreds of thousands

9:52

of different grid points and parameters that

9:54

you're trying to advance in a model

9:56

at each time step. If you're

9:59

talking about a kilometer scale model

10:01

of the kind that we're actually

10:03

hoping to train our machines learning

10:05

on, you're talking about

10:08

hundreds of millions to a

10:10

billion different points. But

10:12

that's still small compared to the number

10:15

of parameters in a

10:17

very large language model where there might be

10:19

a trillion parameters in where you're definitely training

10:21

on trillions of tokens of

10:24

data. And so when we're talking about

10:26

a climate model, how does

10:28

it work kind of for the

10:30

layperson to understand, is it a

10:32

single model of the Earth? Are

10:34

there different models for different geographical

10:36

regions? Is it something totally different?

10:39

How do these models represent what

10:42

someone like me might think of right, like

10:44

the planet and like climate change over time?

10:48

So we modularize the model

10:50

into different components representing parts of

10:52

the climate system. And in fact,

10:55

part of the climate system we're

10:57

working to improve by machine learning

10:59

in our project is the atmosphere.

11:02

So we represent the entire three

11:04

dimensional structure of the global atmosphere

11:07

by breaking it into about a

11:09

hundred layers and representing

11:13

each of those layers on a horizontal grid.

11:16

There are also somewhat

11:18

similarly constructed models of the

11:20

ocean for representing ocean currents,

11:22

which are much

11:24

slower but are complicated too and

11:27

have a lot of detail. And

11:30

those models interact with each other

11:32

through what's called a coupler, which

11:34

allows information to be exchanged between

11:36

these models. Similarly, there are

11:38

models of

11:41

ice, of sea ice, for instance, of

11:44

chemical processes, such as

11:48

create the little particles

11:50

that actually cause

11:52

clouds to nucleate when

11:54

water vapor condenses And

11:57

a whole variety of other processes. So.

12:00

Climate model consists of all of these.

12:02

Are. Different components interacting through

12:05

on. A couple are some

12:07

time. It's kind of the it's if

12:09

you want the conductor as the orchestra,

12:11

freight rates and then what are you

12:13

able to see from the model? What

12:15

kind of information is it for testing?

12:17

Is it current conditions? Is it what?

12:19

What kind of information are you getting

12:21

back from the law? So to think

12:23

about it's a traditional climate model and

12:25

actually it but were trying to achieve

12:28

and machine learning as being. An

12:31

Earth System simulator? So it

12:33

is. I'm it's trying to

12:35

predict climate. and climate is

12:37

by definition. Ah, you know

12:39

how. How to the

12:41

long term conditions look like? How

12:43

are. Things. Like out. Extremes.

12:46

Of precipitation Changing that it's

12:48

statistics of climates is a

12:50

way it's getting at that

12:52

is it's actually simulating. The

12:54

system is basically making artificial

12:56

weather and the ocean equivalents

12:58

in a changing environments. Ah

13:00

that allows the different model components to talk

13:03

to each other, the atmosphere to talk to,

13:05

the ocean to talk to us, the biology

13:07

and all the others, The chemistry, insulin, Or

13:10

but it's a simulator And so as

13:12

a result, when we do machine learning,

13:15

we actually retain that start of trying

13:17

to make a simulator which is just

13:19

predicting a mirror on artificial or pretend.

13:22

Likely our evolution of how atmosphere month

13:24

and yes might go and I'm I'm

13:27

a little hesitant or perhaps ah circles

13:29

the asked by out what are the

13:31

models saying what is what is the

13:33

future look like for for all of

13:35

us on earth right? Well I a

13:38

I conventional climate models have told us

13:40

everything we need to know s to

13:42

know that's ah you know the earth

13:44

is getting warmer. That has many consequences

13:47

which a very robust warms nora land

13:49

at Worms especially in the polar regions

13:51

basically in order to. Turn that around.

13:53

Regained has to get to a carbon

13:55

neutral power system and economy as fast

13:57

as we town. The effects of emitting.

14:00

The dioxide a cumulative so we know that

14:02

and we have all the information and which

14:04

we need to act and so you might

14:06

ask. Okay, well, why do we need climate

14:09

models? It's there Any better than that. And

14:11

the reason is that you know as you

14:13

think about society and how we generate. Our.

14:16

Power. ah it's kind of my cab.

14:18

It's a huge battleship. It has many

14:20

components and still it's also like a

14:22

balance it without one captain. As as

14:24

hundred sit ups and semester the ships

14:27

they have a lot of other priorities

14:29

undermined other than climate change. no matter

14:31

how hard they are how much they're

14:33

aware that that's in this year and

14:36

so so we are going to be

14:38

dealing with a warming climate and all

14:40

the yes extreme heat and more extreme

14:42

precipitation saw on that is going to

14:44

creates we are going. To have to

14:46

plan thought were going to have to adapt

14:49

to that as well as try to mitigate

14:51

it by switching away from. I.

14:53

Greenhouse dependency on and so

14:55

it's not as adaptation that

14:57

we made the able to

14:59

inform better with better climate

15:01

models because for instance if

15:03

you. Say.

15:05

Are are a homeowner in the

15:08

suburbs of Allies eyes enough to

15:10

an area which was recently as

15:12

affected by atmospheric river that was

15:15

quite destructiveness. Ah you are wondering?

15:17

Okay well in my water said

15:19

are we likely to see in.

15:22

Rainfall. Events that are twice destroying thirty

15:24

years from now. And if so, what do I

15:26

have to do for a new How do I?

15:28

dogs? Are. How to we as

15:31

a society build our infrastructure or

15:33

keep people from loving in certain

15:35

places or plant different crops in?

15:37

All Those things are very local

15:39

decisions and so for many of

15:42

those decisions we need very local

15:44

information. Because climate change might

15:46

be, you know it might affect the

15:48

windward slopes of Hawaii. Different. An ugly

15:50

words. First phone since his house I'm

15:53

so that's where I. That's why we

15:55

really can help to do better to

15:57

is localized modeling. One of the things

15:59

that. The top of the less

16:01

that machine learning eyes able to help

16:03

us? Yeah, so of that's that's certainly

16:05

part of our vision. So the idea

16:07

really is is two fold with what

16:10

we can do with machine learning. The

16:12

first part of it has to do

16:14

and localized modeling but rather and directly

16:16

And that is that. Climate models

16:18

are typically run on a grid

16:20

as I'm sixty two one hundred

16:23

kilometers or south in. The reason

16:25

to that is that. You.

16:27

Have to be able to compute for

16:29

hundreds of years. So you need to

16:32

be able to simulate the earth for

16:34

hundreds or thousands of years with a

16:36

model. You can computationally afford to do

16:39

that. On the other hand, we can

16:41

simulate the earth much better with a

16:43

model with a much finer grads say,

16:45

two or three kilometers sept two miles

16:48

or something like that's that kind of

16:50

his neighbourhood scale. practically. nice. And so

16:52

it represents all the details of the

16:55

earth's surface. and ah, and the atmosphere

16:57

and individual. Storm Systems and South's and

16:59

sometimes it's a fundamentally better model. So

17:01

if we can train or machine learning

17:03

it on models like S which we

17:05

can't afford to run for hundreds of

17:07

years. But. The machine learning

17:09

version or emulate or of the model

17:12

ten run for hundreds of years. Many

17:14

times we can get the information with

17:16

a lover's information that have that find

17:19

good model to help a and or

17:21

productions such one of the goals. But

17:23

the other thing is that nevertheless when

17:26

we train the machine learning model we

17:28

don't train it on a very fine

17:30

bread. Me trying to have actually typically

17:33

under fifty or hundred kilometer grads but

17:35

then another machine learning tool called Jenner

17:37

Dovey I'm particular. something called the season

17:40

but lengths can be used to take

17:42

the information we have i done hundred

17:44

condor scale and downscale at noon two

17:46

or three kilometer model to scale with

17:48

the help of this find good model

17:51

the we used for training and and

17:53

so these two parts kind of sit

17:55

together lights hand in glove it's existence

17:57

of these time for models allows

18:00

us to develop ML

18:02

to emulate them and then also allows

18:04

us to downscale that information back to

18:06

that very fine scale which people wanted

18:08

to help people in figuring out

18:11

what to do with that bridge in the neighborhood that

18:13

they're afraid is going to wash out. Right.

18:17

You took me back to my own neighborhood. They're doing

18:19

work on the bridge right now, in fact. Yeah, right.

18:22

So, I have two questions. One is

18:24

sort of about your work

18:26

specifically, but before that, since you

18:28

mentioned talking about, we're talking about

18:30

localized modeling and things that people

18:32

and your neighborhoods and local governments

18:34

might be able to do with

18:36

some of the forecasting information, what

18:39

if anything, can individuals, can

18:42

small communities be doing now

18:44

to prepare for what climate

18:47

change has in store for us in

18:49

the near and kind of medium future? Right. Well,

18:52

I mean, I think the first thing to realize is every

18:55

culture, every region, everywhere is

18:57

going to have to change

19:00

the way that it lives in

19:02

many ways. We have to move to EVs,

19:04

but we have to generate concrete. We

19:07

have to make concrete differently. We have

19:09

to farm in ways that release less

19:11

nitrogen oxides to the atmosphere. It's another

19:13

greenhouse gas. We have to release less

19:15

methane from fracking. And so, this is

19:18

a problem where we have a death

19:20

by a thousand cuts. And so, don't

19:23

pretend that someone else's technology is going

19:25

to solve the problem for you. But

19:28

this is a problem where we

19:30

have to consistently support

19:32

policies that take

19:34

a holistic view of the problem.

19:37

Carbon taxes, policies that

19:39

basically say, you know, we will

19:41

emit 50% less CO2 by 2035 than

19:45

today. If we don't do that,

19:48

we're not going to get to the end game. And

19:50

so, this is an urgent problem. What we

19:52

do now matters a huge amount for the

19:54

future. So that's one thing for people to

19:56

realize. And we know everything about that right

19:59

now. And I can't

20:01

stress more how important it is to

20:04

work on the prevention, because

20:06

that is in the end the way we'll get to

20:09

the end of this problem. But then

20:11

thinking more about, okay, and what will

20:14

more AI do for people's view of

20:16

this problem? As I said, we

20:18

are going to have to adapt. And

20:20

the role of AI here is to

20:24

basically make the amount of computational

20:27

power and the technological prowess you

20:29

need to have in order to

20:31

get a climate prediction, climate

20:34

change prediction for your backyard, it's

20:36

gonna bring it down to the

20:39

scale where individual communities potentially can,

20:42

I won't say they can run their own model, but

20:44

they can find someone who can do it for a

20:46

reasonable price using cloud

20:49

resources, using things that

20:51

are within their capability to go

20:53

after. And so it will

20:55

be the case that no one will be able

20:57

to afford or want to make

20:59

planning decisions without accounting for

21:01

climate information. And

21:04

right now there's a lot of friction in that process,

21:07

because there's a long way from the

21:09

huge climate modeling centers somewhere else to

21:11

the decision you have to make next

21:13

week, but that doesn't have to be

21:15

the case. And really, ML

21:17

is a way to get there. And

21:20

a feature of ML that's particularly important

21:22

there is that I

21:24

think we'll be able to combine these

21:26

kinds of models that will tell you about

21:29

climate with language models so that you can

21:31

even query them with the questions you

21:33

wanna ask and the way you want to ask them

21:35

and they'll be able to give you sensible answers. That's

21:38

really the sort of biggest vision here. Yeah, and

21:40

so kind of along those lines, I was gonna

21:42

ask what's next

21:44

for the work that you're specifically doing,

21:46

or maybe kind of fold it into

21:49

that, are there problems

21:51

right now from kind of a

21:53

science and technology standpoint that

21:55

you're working on that,

21:58

not that you're waiting for... for someone else's advancement to

22:01

help you get over the hump. But

22:03

are there kind of hurdles right now that you're

22:05

trying to get past that, you know, can kind

22:07

of pave the way for the next part of

22:10

the vision? Right,

22:13

so there are sort of two different kinds of

22:15

hurdles. One of them is I mentioned that the

22:17

earth system is complex and the way that we

22:20

model it is modularized. And

22:23

in machine learning, we

22:26

find that you can get a much

22:28

better answer if you can put

22:30

your entire model under the machine

22:32

learning hood and sort of optimize

22:34

everything at once. Sure, right. But

22:37

when you have a very modular starting

22:39

model and some of the components are

22:41

not available in some kind of machine

22:44

learning form, that's just to be

22:46

difficult. So one thing

22:48

we're trying to do is work with our

22:50

other colleagues

22:53

doing machine learning, say of

22:55

the ocean, to

22:57

try and at least build emulators of

22:59

the ocean, couple them to

23:01

emulators of the atmosphere, try to have

23:04

them emulate sort of coupled atmosphere ocean

23:06

models, because that's the minimal configuration that

23:08

I think we need in order to

23:12

talk about 21st century

23:14

climate change. So this

23:16

coupling of the atmosphere and the ocean is one

23:18

big challenge that we're working on right now with

23:21

colleagues like Laura Zana, who's

23:24

talking later in this GTC. And

23:28

forgive my ignorance in asking this, but

23:30

is that a software problem? Is it

23:32

a sort of infrastructure and hardware problem?

23:34

Is it a, you know,

23:37

humans kind of getting together and figuring

23:39

out how to best work together problem?

23:41

It's a, you know, it's a people

23:43

power problem in that machine learning software

23:45

doesn't design itself from scratch. And

23:49

an emulator of either the atmosphere

23:51

or the ocean is

23:54

a piece of software which starts

23:56

with something you might be

23:58

able to take from another field. like

24:00

a vision model or a video

24:02

model or something like that. But

24:07

it has to be adapted to your particular

24:09

situation and that takes experts and it takes

24:12

a lot of decisions and it

24:14

actually takes a lot of

24:16

playing around with the model. And so

24:18

it's really people power. So in this

24:20

particular case, we have an

24:22

atmospheric emulator from AI2 that

24:25

I think is an appropriate atmospheric component

24:27

for such a model. But

24:29

the oceanic equivalent doesn't really exist yet.

24:32

An ocean that you can play for

24:34

a long time in this emulator mode

24:36

and have it stay stable and

24:39

accurate and represent meaningfully

24:41

a climate. And so

24:43

that's a real, it's

24:46

a development challenge to sort of a

24:48

mixture of science and technology. It's not

24:51

really being limited by the hardware that

24:53

we have right now. But

24:55

once we start running this model, we'll

24:57

get limited by the hardware because we'll

24:59

be wanting to run the emulator for

25:01

hundreds and thousands of years. And

25:04

I should mention that actually our

25:06

climate model is based on the

25:08

SFNO architecture of

25:11

a product called ForecastNet, which

25:13

an open source piece of

25:15

software that was developed in

25:17

video. And SFNO and developed

25:20

by Boris Bonov here has

25:22

been totally instrumental in making

25:24

our project successful. But

25:27

another thing that we're also trying

25:29

to start collaborating with NVIDIA on

25:31

is this downscaling issue. How do

25:33

you take information from an

25:35

emulator that might work natively at 125

25:37

kilometer scale and

25:41

really reduce it accurately to a

25:44

local scale using machine

25:46

learning based statistical techniques that are

25:48

very efficient and that we can

25:51

sort of apply on

25:53

as little as a single GPU. And

25:55

so that's also a challenge that

25:57

we're currently working on. How do

25:59

you... measure the accuracy

26:02

is the right word, the kind of the

26:04

performance of an emulator

26:06

that's doing something like climate modeling

26:09

out into the future? Yeah, that's

26:11

a great question. So when people

26:13

have developed weather emulators, which have

26:15

actually weather forecast emulators have been developed,

26:18

and at this point, they're actually more

26:20

skillful than our most conventional weather forecast

26:22

model in the world after

26:24

10 days, which is remarkable since

26:26

these emulators have only been developed

26:28

for the last few years. The

26:30

emulators are better at that. They

26:34

make better forecasts than the original models. The way

26:37

that they do it is they're trained on the

26:40

last 50 years of historical

26:42

data blended together using these

26:44

physically based forecast models. So

26:47

they still are actually still relying on the

26:49

physically based model for

26:52

their initial guess. But from then on, they

26:54

can make forecasts that are

26:56

more skillful simply by

26:58

training on observed data. Now

27:01

the problem is if you take these same

27:03

models and you try to run them out

27:05

for longer, a lot of them drift away

27:07

or they go unstable. They're not actually suitable

27:09

for climate directly. But nevertheless,

27:12

so one obvious way to train a climate

27:14

model would be try and train it the

27:16

same way. But the only problem is the

27:19

future is not the present, is not

27:21

the past. And one

27:24

of the adages of machine learning is

27:26

you should never expect your model to

27:28

generalize too much out of sample. So

27:30

we don't do that. And instead, our

27:32

approach to emulation is we

27:35

are just going to try and emulate physically

27:37

based climate models. So right

27:39

now, the physically based climate model is one that

27:42

a major climate modeling center could

27:44

affordably run for themselves. So we're

27:46

doing nothing that is of interest

27:48

to them, except that we can

27:51

do it many, many times faster. But

27:53

in future, our goal then, as I

27:55

mentioned before, is to emulate

27:57

these three kilometer global models, which are

28:00

are inherently more accurate and believable, not

28:02

just in the present climate, but also

28:04

in future climates. And

28:07

at that point, we're doing something which you can't do

28:10

right now. And

28:12

so that's sort of the

28:15

aim. But nevertheless, it

28:18

is important to recognize we're just trying to

28:20

emulate another model. So we inherit all

28:23

of the pros and cons of

28:25

the other model. You're still extrapolating,

28:27

but now you're taking advantage of

28:29

the physically-based model to

28:31

use the physical reasoning

28:34

and physical laws that we can build into

28:36

that. And we're using

28:38

that then to train

28:40

our machine learning in multiple climates

28:42

so that it can also do

28:44

that same job of being able

28:47

to predict the cross-changing climates. Right.

28:49

Are there certain regions where it's harder

28:51

or easier to forecast

28:53

the climate and climate change than

28:55

others? Yeah, actually, regions

28:58

like California are quite difficult.

29:00

I think you could ask

29:02

hard versus, well,

29:05

what's the problem? So in

29:07

some sense, the problem

29:10

in places like the

29:12

Southwest US is there's

29:14

a lot of variability, but it seems like

29:16

the climate is getting drier. And is it

29:18

getting drier? And it turns out if you

29:21

query something like 20 or 30 of

29:23

the world's leading climate models

29:26

today about that, they don't all

29:28

agree. And so some

29:30

of them say, okay, the Southwest is drying

29:32

out. Others of them say, well, it's not

29:34

really drying out. It's not getting any wetter,

29:36

but it's not getting any wetter. And so

29:38

there are regions of the world, and especially

29:40

these are kind of in the edge of

29:42

the tropics in regions sort of

29:45

at the latitude of California, where it's actually hard

29:47

to predict what some of the trends are going

29:50

to be like. And

29:52

which are also subject to a lot of extreme

29:55

events. And so those

29:57

actually are complicated regions. issues,

30:00

though, are things like there's

30:02

some areas which are very

30:04

susceptible to, say, tropical cyclones,

30:06

hurricanes and typhoons, where

30:09

one extreme event can wipe the place

30:11

out. And so they care a

30:13

lot about something that's very rare and not

30:16

so easy to simulate. And so in some sense,

30:18

those are also hard areas to

30:20

forecast. Right. That makes

30:22

sense. Chris, for listeners who would like to

30:25

find out more about climate modeling, about the

30:27

work you're doing, about the work AI2 is

30:29

doing more broadly, where

30:31

would you direct them to look online? Well,

30:33

we have a website. So

30:36

AI2 has a website,

30:38

lnai.org is our name.

30:42

On there, there is a link

30:44

to climate modeling that talks about

30:46

our group's activities. I should

30:48

emphasize, though, that again, there are many groups

30:50

other than our own that are now working

30:52

in this area. And so hopefully

30:54

you'll read about this in places like The

30:57

New York Times, The Economist, and Washington Post,

30:59

which already actually have been doing a very

31:02

good job of covering

31:05

the explosion of weather forecast emulators and how

31:07

they're transforming the field. So I'm thinking you'll

31:09

read about this area of research in the

31:12

news in the not too different future as

31:14

well. I hope so. And I hope it

31:16

moves us to act. Chris

31:19

Brutherson, thank you so much for taking the

31:21

time out of GTC to join the podcast.

31:23

Best of luck with everything you're doing and

31:25

your colleagues. We're all, I was

31:27

gonna say we're all counting on you, but that's a lot of pressure

31:29

for a podcast. But we appreciate the

31:31

work you're doing is an understatement. It's vital. Okay,

31:34

well, thanks very much, Noah. It's a pleasure to be here. www.mooji.org