complexity and dynamism

我們活在非常複雜的環境裡：

and patterns of evidence

複雜性及動力系統論

from satellite photographs, from videos.

及各式氣候變化形態的證據

You can even see it outside your window.

從衛星照相、從影片得之

It's endlessly complex, but somehow familiar,

你甚至能從你的窗戶外面看到

but the patterns kind of repeat,

無止盡的複雜又具某種程度的熟悉

but they never repeat exactly.

但形態的確有一定程度的重複

It's a huge challenge to understand.

但從來沒有一模一樣過

The patterns that you see

要了解它是很大的挑戰

are there at all of the different scales,

你所看到的形態

but you can't chop it into one little bit and say,

都以不同的尺度存在著

"Oh, well let me just make a smaller climate."

但你不能切下一小塊然後說

I can't use the normal products of reductionism

「喔，那我做個小一點的氣候。」

to get a smaller and smaller thing that I can study

我不能以一般的化約論產品

in a laboratory and say, "Oh,

得到愈來愈小的東西

now that's something I now understand."

使我能在實驗室裡研究且說

It's the whole or it's nothing.

「喔，這是我能理解的了。」

The different scales that give you

這是全有或全無

these kinds of patterns

這些氣候形態

range over an enormous range of magnitude,

以不同的尺度呈現

roughly 14 orders of magnitude,

其範圍幅度非常大

from the small microscopic particles

大約是 14 數量級的差距

that seed clouds

從最小的顯微粒子

to the size of the planet itself,

以造雨

from 10 to the minus six

到這個星球本身的大小

to 10 to the eight,

從 10 的負六次方到

14 orders of spatial magnitude.

10 的八次方

In time, from milliseconds to millennia,

空間數量級的差距為 14

again around 14 orders of magnitude.

在時間上，從毫秒到千年

What does that mean?

同樣也是 14 數量級

Okay, well if you think about how

這意味著什麼？

you can calculate these things,

好，如果你想一想

you can take what you can see,

你要如何計算這些東西

okay, I'm going to chop it up

你會把你見到的事物

into lots of little boxes,

好，我要把它切碎

and that's the result of physics, right?

成這些小方塊

And if I think about a weather model,

這就是物理學的結果，對吧？

that spans about five orders of magnitude,

如果我想到一個氣象模型

from the planet to a few kilometers,

尺度橫跨五數量級

and the time scale

也就是從地球的大小到幾公里

from a few minutes to 10 days, maybe a month.

時間尺度則是

We're interested in more than that.

從幾分鐘到十天或者一個月

We're interested in the climate.

我們感興趣的不只這些

That's years, that's millennia,

我們對氣候感興趣

and we need to go to even smaller scales.

那是以年計的，是千年

The stuff that we can't resolve,

我們還需要看更小尺度的

the sub-scale processes,

我們無法解決的東西

we need to approximate in some way.

次網格尺度過程

That is a huge challenge.

我們必須想辦法得到近似值

Climate models in the 1990s

那是很大的挑戰

took an even smaller chunk of that,

1990 年代的氣候模式

only about three orders of magnitude.

是拿更小塊的規模來看

Climate models in the 2010s,

大約只有三數量級

kind of what we're working with now,

2010 年代的氣候模式

four orders of magnitude.

就像我們現在正在使用的

We have 14 to go,

是四數量級

and we're increasing our capability

我們還要繼續擴展到 14

of simulating those at about

而我們的

one extra order of magnitude every decade.

模擬能力

One extra order of magnitude in space

每十年大約增加一數量級

is 10,000 times more calculations.

以空間而言每增加一數量級

And we keep adding more things,

就是增加一萬倍的計算

more questions to these different models.

而我們還繼續加東西上去

So what does a climate model look like?

加更多問題到這些不同的模式上

This is an old climate model, admittedly,

所以氣候模式是甚麼樣子？

a punch card, a single line of Fortran code.

這是老式的氣候模式，無可否認

We no longer use punch cards.

打孔卡，單行福傳語言

We do still use Fortran.

我們不再使用打孔卡了

New-fangled ideas like C

我們還是用福傳語言

really haven't had a big impact

新的想法像使用 C 語言

on the climate modeling community.

還沒有什麼大的影響力

But how do we go about doing it?

在氣候模式族群裡

How do we go from that complexity that you saw

但這是怎麼做出來的？

to a line of code?

我們如何把你所看到的複雜

We do it one piece at a time.

變成一行的程式？

This is a picture of sea ice

我們一次做一件

taken flying over the Arctic.

這是一張海冰圖

We can look at all of the different equations

飛越北極上空時照的

that go into making the ice grow

我們可以看所有不同的方程式

or melt or change shape.

使結冰量增加

We can look at the fluxes.

或融化或改變形狀

We can look at the rate at which

我們可以看看各種通量

snow turns to ice, and we can code that.

我們可以看雪變成冰的速率

We can encapsulate that in code.

我們可以為之編寫程式

These models are around

我們可以封裝在程式裡

a million lines of code at this point,

這些模式目前大約要以

and growing by tens of thousands of lines of code

一百萬行程式才做的出來

every year.

每年還要以上萬行的程式

So you can look at that piece,

成長

but you can look at the other pieces too.

所以你看這件是這樣

What happens when you have clouds?

別件也是如此

What happens when clouds form,

有雲的時候怎麼辦？

when they dissipate, when they rain out?

雲形成的時候怎麼辦？

That's another piece.

雲散了呢？下雨了呢？

What happens when we have radiation

這是一件

coming from the sun, going through the atmosphere,

有太陽輻射怎麼辦？

being absorbed and reflected?

輻射穿過大氣層

We can code each of those very small pieces as well.

被吸收及反射又怎麼辦？

There are other pieces:

我們也能為這些非常小的東西寫程式

the winds changing the ocean currents.

還有其他的

We can talk about the role of vegetation

風改變洋流

in transporting water from the soils

我們也能談植被

back into the atmosphere.

從土壤中輸送水分

And each of these different elements

回到大氣層的角色

we can encapsulate and put into a system.

每一種不同的要素

Each of those pieces ends up adding to the whole.

我們都可以封裝寫進系統內

And you get something like this.

每一件最後都會加在整體上

You get a beautiful representation

那你就得到一個像這樣的東西

of what's going on in the climate system,

你會得到漂亮的圖表

where each and every one of those

告訴你氣候系統發生什麼事

emergent patterns that you can see,

每一個像這樣

the swirls in the Southern Ocean,

你看到的突現形態

the tropical cyclone in the Gulf of Mexico,

南冰洋的旋渦

and there's two more that are going to pop up

墨西哥灣的熱帶颶風

in the Pacific at any point now,

還有兩個隨時都要跑出來

those rivers of atmospheric water,

在太平洋形成

all of those are emergent properties

那些大氣水氣形成的河流

that come from the interactions

這些都是突現性質

of all of those small-scale processes I mentioned.

從我剛剛談到的次網格尺度過程

There's no code that says,

交互作用而來

"Do a wiggle in the Southern Ocean."

沒有什麼程式會說

There's no code that says, "Have two

「在南冰洋擺動一下。」

tropical cyclones that spin around each other."

也沒有程式會說：「讓兩個

All of those things are emergent properties.

熱帶颶風互相繞著旋轉。」

This is all very good. This is all great.

這些都是突現性質

But what we really want to know

這很好，這很棒

is what happens to these emergent properties

但我們真的想知道的

when we kick the system?

是這些突現性質會怎麼辦

When something changes, what happens to those properties?

在我們系統改變的時候

And there's lots of different ways to kick the system.

當情況改變了那些性質會怎麼辦？

There are wobbles in the Earth's orbit

有很多方法會讓系統改變

over hundreds of thousands of years

地球的軌道

that change the climate.

在過去數萬年的擺動

There are changes in the solar cycles,

會改變氣候

every 11 years and longer, that change the climate.

太陽週期的改變

Big volcanoes go off and change the climate.

每 11 年或更長的時間 也會改變氣候

Changes in biomass burning, in smoke,

大的火山爆發會改變氣候

in aerosol particles, all of those things

生質燃燒的改變，煙霧

change the climate.

氣膠粒子，這些東西

The ozone hole changed the climate.

都會改變氣候

Deforestation changes the climate

臭氧洞會改變氣候

by changing the surface properties

森林除伐會改變氣候

and how water is evaporated

因為這改變了地表性質

and moved around in the system.

也改變水分如何蒸發

Contrails change the climate

並在系統內移動

by creating clouds where there were none before,

凝結尾會改變氣候

and of course greenhouse gases change the system.

因為會在以前無雲的地方產生雲

Each of these different kicks

當然溫室氣體也會改變系統

provides us with a target

這些不同的改變因素

to evaluate whether we understand

提供我們一個目標

something about this system.

以評估我們是否瞭解

So we can go to look at

這個系統

what model skill is.

所以我們可以去看

Now I use the word "skill" advisedly:

模式預測技巧是什麼

Models are not right or wrong; they're always wrong.

那我非常審慎的用「技巧」這個字

They're always approximations.

模式沒有對錯；它們永遠是錯的

The question you have to ask

它們永遠是近似值

is whether a model tells you more information

你該問的問題是

than you would have had otherwise.

模式能否告訴你更多的資訊

If it does, it's skillful.

比你沒用模式時所得的還多

This is the impact of the ozone hole

如果是，那它就是技巧很好

on sea level pressure, so low pressure, high pressures,

這是臭氧洞

around the southern oceans, around Antarctica.

對海平面氣壓的影響 所以低氣壓高氣壓

This is observed data.

在南冰洋四周，在南極洲四周

This is modeled data.

這是觀測數據

There's a good match

這是模式推測出的數據

because we understand the physics

這兩者匹配度很高

that controls the temperatures in the stratosphere

因為我們瞭解

and what that does to the winds

控制平流層溫度的物理

around the southern oceans.

及其對

We can look at other examples.

南冰洋四周的風的作用

The eruption of Mount Pinatubo in 1991

我們還可以看看其他例子

put an enormous amount of aerosols, small particles,

1991 年皮納土波火山爆發

into the stratosphere.

將大量的氣膠，微粒

That changed the radiation balance of the whole planet.

噴入平流層中

There was less energy coming in than there was before,

那件事改變了整個地球的輻射平衡

so that cooled the planet,

與之前相比，較少的能量進入地球

and those red lines and those green lines,

導致地球變冷

those are the differences between what we expected

而那些紅線及那些綠線

and what actually happened.

那些是我們所預期

The models are skillful,

及實際狀況的差別

not just in the global mean,

這些模式很有技巧

but also in the regional patterns.

不僅在全球平均上很準確

I could go through a dozen more examples:

在區域形態上也如此

the skill associated with solar cycles,

我還可以講上打的例子：

changing the ozone in the stratosphere;

與太陽週期

the skill associated with orbital changes

平流層臭氧變化相關的預測技巧

over 6,000 years.

與六千年來地球軌道變化

We can look at that too, and the models are skillful.

相關的預測技巧

The models are skillful in response to the ice sheets

我們也可以看那個 而模式的技巧也很好

20,000 years ago.

對二萬年前的冰層

The models are skillful

這些模式的技巧也很好

when it comes to the 20th-century trends

這些模式在過去幾十年

over the decades.

談到二十世紀的趨勢時

Models are successful at modeling

其技巧很好

lake outbursts into the North Atlantic

模式很成功地

8,000 years ago.

將八千年前北極冰湖潰決

And we can get a good match to the data.

模式化

Each of these different targets,

我們在數據上的匹配度很高

each of these different evaluations,

每一個不同的目標

leads us to add more scope

每一個不同的評估

to these models,

都導致我們加大範圍

and leads us to more and more

到這些模式中

complex situations that we can ask

導致日益加增的

more and more interesting questions,

複雜情況，使我們不禁要問

like, how does dust from the Sahara,

更多有意思的問題

that you can see in the orange,

像是撒哈拉塵

interact with tropical cyclones in the Atlantic?

也就是這些橘色的東西

How do organic aerosols from biomass burning,

與大西洋的熱帶颶風如何交互作用？

which you can see in the red dots,

生質燃燒所產生的有機氣膠

intersect with clouds and rainfall patterns?

也就是這些紅點

How does pollution, which you can see

與雲及雨型如何交互作用？

in the white wisps of sulfate pollution in Europe,

這些汙染，就是你看到

how does that affect the temperatures at the surface

在歐洲上方，一縷縷的白色硫酸

and the sunlight that you get at the surface?

這些如何影響地面溫度

We can look at this across the world.

以及你在地表上得到的太陽光量？

We can look at the pollution from China.

我們可以看看世界各地的狀況

We can look at the impacts of storms

我們可以看從中國來的汙染

on sea salt particles in the atmosphere.

我們可以看暴風

We can see the combination

對大氣層內海鹽粒子的影響

of all of these different things

我們可以看

happening all at once,

這些同時發生的

and we can ask much more interesting questions.

不同東西的組合

How do air pollution and climate coexist?

我們可以問更有意思的問題

Can we change things

空氣汙染與氣候如何共存？

that affect air pollution and climate at the same time?

我們是否能改變

The answer is yes.

對空氣汙染及氣候 同時產生影響的事物？

So this is a history of the 20th century.

答案是肯定的

The first one is the model.

這是二十世紀的歷史

The weather is a little bit different

第一個是模式

to what actually happened.

天氣與實際狀況

The second one are the observations.

有一點不同

And we're going through the 1930s.

第二個是觀察

There's variability, there are things going on,

我們來看 1930 年代的情況

but it's all kind of in the noise.

總是有變數，總是有狀況發生

As you get towards the 1970s,

但是都有點像是雜音

things are going to start to change.

然後時間接近 1970 年代

They're going to start to look more similar,

事情開始有了變化

and by the time you get to the 2000s,

它們開始看起來愈來愈接近

you're already seeing the patterns of global warming,

而到了 2000 年代

both in the observations and in the model.

你已經可以看到全球暖化的型態

We know what happened over the 20th century.

觀察及模式預測兩者皆是

Right? We know that it's gotten warmer.

我們知道二十世紀發生了什麼

We know where it's gotten warmer.

對吧？我們知道一定會更熱

And if you ask the models why did that happen,

我們還知道哪裡一定會更熱

and you say, okay, well, yes,

如果你問模式為什麼這種情形會發生

basically it's because of the carbon dioxide

然後你說，對，嗯，沒錯

we put into the atmosphere.

基本上就是因為二氧化碳

We have a very good match

我們把它排放到大氣層

up until the present day.

我們的匹配度

But there's one key reason why we look at models,

到今天為止都很高

and that's because of this phrase here.

但我們為什麼要看模式 有個關鍵的理由

Because if we had observations of the future,

而那就是因為這句話

we obviously would trust them more than models,