Gavin Schmidt: The emergent patterns of climate change

譯者: Regina Chu 審譯者: Tsz Ying Choi

We live in a very complex environment:

我們活在非常複雜的環境裡：

complexity and dynamism

複雜性及動力系統論

and patterns of evidence

及各式氣候變化形態的證據

from satellite photographs, from videos.

從衛星照相、從影片得之

You can even see it outside your window.

你甚至能從你的窗戶外面看到

It's endlessly complex, but somehow familiar,

無止盡的複雜又具某種程度的熟悉

but the patterns kind of repeat,

但形態的確有一定程度的重複

but they never repeat exactly.

但從來沒有一模一樣過

It's a huge challenge to understand.

要了解它是很大的挑戰

The patterns that you see

你所看到的形態

are there at all of the different scales,

都以不同的尺度存在著

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

但你不能切下一小塊然後說

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

「喔，那我做個小一點的氣候。」

I can't use the normal products of reductionism

我不能以一般的化約論產品

to get a smaller and smaller thing that I can study

得到愈來愈小的東西

in a laboratory and say, "Oh,

使我能在實驗室裡研究且說

now that's something I now understand."

「喔，這是我能理解的了。」

It's the whole or it's nothing.

這是全有或全無

The different scales that give you

這些氣候形態

these kinds of patterns

以不同的尺度呈現

range over an enormous range of magnitude,

其範圍幅度非常大

roughly 14 orders of magnitude,

大約是 14 數量級的差距

from the small microscopic particles

從最小的顯微粒子

that seed clouds

以造雨

to the size of the planet itself,

到這個星球本身的大小

from 10 to the minus six

從 10 的負六次方到

to 10 to the eight,

10 的八次方

14 orders of spatial magnitude.

空間數量級的差距為 14

In time, from milliseconds to millennia,

在時間上，從毫秒到千年

again around 14 orders of magnitude.

同樣也是 14 數量級

What does that mean?

這意味著什麼？

Okay, well if you think about how

好，如果你想一想

you can calculate these things,

你要如何計算這些東西

you can take what you can see,

你會把你見到的事物

okay, I'm going to chop it up

好，我要把它切碎

into lots of little boxes,

成這些小方塊

and that's the result of physics, right?

這就是物理學的結果，對吧？

And if I think about a weather model,

如果我想到一個氣象模型

that spans about five orders of magnitude,

尺度橫跨五數量級

from the planet to a few kilometers,

也就是從地球的大小到幾公里

and the time scale

時間尺度則是

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

從幾分鐘到十天或者一個月

We're interested in more than that.

我們感興趣的不只這些

We're interested in the climate.

我們對氣候感興趣

That's years, that's millennia,

那是以年計的，是千年

and we need to go to even smaller scales.

我們還需要看更小尺度的

The stuff that we can't resolve,

我們無法解決的東西

the sub-scale processes,

次網格尺度過程

we need to approximate in some way.

我們必須想辦法得到近似值

That is a huge challenge.

那是很大的挑戰

Climate models in the 1990s

1990 年代的氣候模式

took an even smaller chunk of that,

是拿更小塊的規模來看

only about three orders of magnitude.

大約只有三數量級

Climate models in the 2010s,

2010 年代的氣候模式

kind of what we're working with now,

就像我們現在正在使用的

four orders of magnitude.

是四數量級

We have 14 to go,

我們還要繼續擴展到 14

and we're increasing our capability

而我們的

of simulating those at about

模擬能力

one extra order of magnitude every decade.

每十年大約增加一數量級

One extra order of magnitude in space

以空間而言每增加一數量級

is 10,000 times more calculations.

就是增加一萬倍的計算

And we keep adding more things,

而我們還繼續加東西上去

more questions to these different models.

加更多問題到這些不同的模式上

So what does a climate model look like?

所以氣候模式是甚麼樣子？

This is an old climate model, admittedly,

這是老式的氣候模式，無可否認

a punch card, a single line of Fortran code.

打孔卡，單行福傳語言

We no longer use punch cards.

我們不再使用打孔卡了

We do still use Fortran.

我們還是用福傳語言

New-fangled ideas like C

新的想法像使用 C 語言

really haven't had a big impact

還沒有什麼大的影響力

on the climate modeling community.

在氣候模式族群裡

But how do we go about doing it?

但這是怎麼做出來的？

How do we go from that complexity that you saw

我們如何把你所看到的複雜

to a line of code?

變成一行的程式？

We do it one piece at a time.

我們一次做一件

This is a picture of sea ice

這是一張海冰圖

taken flying over the Arctic.

飛越北極上空時照的

We can look at all of the different equations

我們可以看所有不同的方程式

that go into making the ice grow

使結冰量增加

or melt or change shape.

或融化或改變形狀

We can look at the fluxes.

我們可以看看各種通量

We can look at the rate at which

我們可以看雪變成冰的速率

snow turns to ice, and we can code that.

我們可以為之編寫程式

We can encapsulate that in code.

我們可以封裝在程式裡

These models are around

這些模式目前大約要以

a million lines of code at this point,

一百萬行程式才做的出來

and growing by tens of thousands of lines of code

每年還要以上萬行的程式

every year.

成長

So you can look at that piece,

所以你看這件是這樣

but you can look at the other pieces too.

別件也是如此

What happens when you have clouds?

有雲的時候怎麼辦？

What happens when clouds form,

雲形成的時候怎麼辦？

when they dissipate, when they rain out?

雲散了呢？下雨了呢？

That's another piece.

這是一件

What happens when we have radiation

有太陽輻射怎麼辦？

coming from the sun, going through the atmosphere,

輻射穿過大氣層

being absorbed and reflected?

被吸收及反射又怎麼辦？

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

我們也能為這些非常小的東西寫程式

There are other pieces:

還有其他的

the winds changing the ocean currents.

風改變洋流

We can talk about the role of vegetation

我們也能談植被

in transporting water from the soils

從土壤中輸送水分

back into the atmosphere.

回到大氣層的角色

And each of these different elements

每一種不同的要素

we can encapsulate and put into a system.

我們都可以封裝寫進系統內

Each of those pieces ends up adding to the whole.

每一件最後都會加在整體上

And you get something like this.

那你就得到一個像這樣的東西

You get a beautiful representation

你會得到漂亮的圖表

of what's going on in the climate system,

告訴你氣候系統發生什麼事

where each and every one of those

每一個像這樣

emergent patterns that you can see,

你看到的突現形態

the swirls in the Southern Ocean,

南冰洋的旋渦

the tropical cyclone in the Gulf of Mexico,

墨西哥灣的熱帶颶風

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

還有兩個隨時都要跑出來

in the Pacific at any point now,

在太平洋形成

those rivers of atmospheric water,

那些大氣水氣形成的河流

all of those are emergent properties

這些都是突現性質

that come from the interactions

從我剛剛談到的次網格尺度過程

of all of those small-scale processes I mentioned.

交互作用而來

There's no code that says,

沒有什麼程式會說

"Do a wiggle in the Southern Ocean."

「在南冰洋擺動一下。」

There's no code that says, "Have two

也沒有程式會說：「讓兩個

tropical cyclones that spin around each other."

熱帶颶風互相繞著旋轉。」

All of those things are emergent properties.

這些都是突現性質

This is all very good. This is all great.

這很好，這很棒

But what we really want to know

但我們真的想知道的

is what happens to these emergent properties

是這些突現性質會怎麼辦

when we kick the system?

在我們系統改變的時候

When something changes, what happens to those properties?

當情況改變了那些性質會怎麼辦？

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

有很多方法會讓系統改變

There are wobbles in the Earth's orbit

地球的軌道

over hundreds of thousands of years

在過去數萬年的擺動

that change the climate.

會改變氣候

There are changes in the solar cycles,

太陽週期的改變

every 11 years and longer, that change the climate.

每 11 年或更長的時間 也會改變氣候

Big volcanoes go off and change the climate.

大的火山爆發會改變氣候

Changes in biomass burning, in smoke,

生質燃燒的改變，煙霧

in aerosol particles, all of those things

氣膠粒子，這些東西

change the climate.

都會改變氣候

The ozone hole changed the climate.

臭氧洞會改變氣候

Deforestation changes the climate

森林除伐會改變氣候

by changing the surface properties

因為這改變了地表性質

and how water is evaporated

也改變水分如何蒸發

and moved around in the system.

並在系統內移動

Contrails change the climate

凝結尾會改變氣候

by creating clouds where there were none before,

因為會在以前無雲的地方產生雲

and of course greenhouse gases change the system.

當然溫室氣體也會改變系統

Each of these different kicks

這些不同的改變因素

provides us with a target

提供我們一個目標

to evaluate whether we understand

以評估我們是否瞭解

something about this system.

這個系統

So we can go to look at

所以我們可以去看

what model skill is.

模式預測技巧是什麼

Now I use the word "skill" advisedly:

那我非常審慎的用「技巧」這個字

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

模式沒有對錯；它們永遠是錯的

They're always approximations.

它們永遠是近似值

The question you have to ask

你該問的問題是

is whether a model tells you more information

模式能否告訴你更多的資訊

than you would have had otherwise.

比你沒用模式時所得的還多

If it does, it's skillful.

如果是，那它就是技巧很好

This is the impact of the ozone hole

這是臭氧洞

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

對海平面氣壓的影響 所以低氣壓高氣壓

around the southern oceans, around Antarctica.

在南冰洋四周，在南極洲四周

This is observed data.

這是觀測數據

This is modeled data.

這是模式推測出的數據

There's a good match

這兩者匹配度很高

because we understand the physics

因為我們瞭解

that controls the temperatures in the stratosphere

控制平流層溫度的物理

and what that does to the winds

及其對

around the southern oceans.

南冰洋四周的風的作用

We can look at other examples.

我們還可以看看其他例子

The eruption of Mount Pinatubo in 1991

1991 年皮納土波火山爆發

put an enormous amount of aerosols, small particles,

將大量的氣膠，微粒

into the stratosphere.

噴入平流層中

That changed the radiation balance of the whole planet.

那件事改變了整個地球的輻射平衡

There was less energy coming in than there was before,

與之前相比，較少的能量進入地球

so that cooled the planet,

導致地球變冷

and those red lines and those green lines,

而那些紅線及那些綠線

those are the differences between what we expected

那些是我們所預期

and what actually happened.

及實際狀況的差別

The models are skillful,

這些模式很有技巧

not just in the global mean,

不僅在全球平均上很準確

but also in the regional patterns.

在區域形態上也如此

I could go through a dozen more examples:

我還可以講上打的例子：

the skill associated with solar cycles,

與太陽週期

changing the ozone in the stratosphere;

平流層臭氧變化相關的預測技巧

the skill associated with orbital changes

與六千年來地球軌道變化

over 6,000 years.

相關的預測技巧

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

我們也可以看那個 而模式的技巧也很好

The models are skillful in response to the ice sheets

對二萬年前的冰層

20,000 years ago.

這些模式的技巧也很好

The models are skillful

這些模式在過去幾十年

when it comes to the 20th-century trends

談到二十世紀的趨勢時

over the decades.

其技巧很好

Models are successful at modeling

模式很成功地

lake outbursts into the North Atlantic

將八千年前北極冰湖潰決

8,000 years ago.

模式化

And we can get a good match to the data.

我們在數據上的匹配度很高

Each of these different targets,

每一個不同的目標

each of these different evaluations,

每一個不同的評估

leads us to add more scope

都導致我們加大範圍

to these models,

到這些模式中

and leads us to more and more

導致日益加增的

complex situations that we can ask

複雜情況，使我們不禁要問

more and more interesting questions,

更多有意思的問題

like, how does dust from the Sahara,

像是撒哈拉塵

that you can see in the orange,

也就是這些橘色的東西

interact with tropical cyclones in the Atlantic?

與大西洋的熱帶颶風如何交互作用？

How do organic aerosols from biomass burning,

生質燃燒所產生的有機氣膠

which you can see in the red dots,

也就是這些紅點

intersect with clouds and rainfall patterns?

與雲及雨型如何交互作用？

How does pollution, which you can see

這些汙染，就是你看到

in the white wisps of sulfate pollution in Europe,

在歐洲上方，一縷縷的白色硫酸

how does that affect the temperatures at the surface

這些如何影響地面溫度

and the sunlight that you get at the surface?

以及你在地表上得到的太陽光量？

We can look at this across the world.

我們可以看看世界各地的狀況

We can look at the pollution from China.

我們可以看從中國來的汙染

We can look at the impacts of storms

我們可以看暴風

on sea salt particles in the atmosphere.

對大氣層內海鹽粒子的影響

We can see the combination

我們可以看

of all of these different things

這些同時發生的

happening all at once,

不同東西的組合

and we can ask much more interesting questions.

我們可以問更有意思的問題

How do air pollution and climate coexist?

空氣汙染與氣候如何共存？

Can we change things

我們是否能改變

that affect air pollution and climate at the same time?

對空氣汙染及氣候 同時產生影響的事物？

The answer is yes.

答案是肯定的

So this is a history of the 20th century.

這是二十世紀的歷史

The first one is the model.

第一個是模式

The weather is a little bit different

天氣與實際狀況

to what actually happened.

有一點不同

The second one are the observations.

第二個是觀察

And we're going through the 1930s.

我們來看 1930 年代的情況

There's variability, there are things going on,

總是有變數，總是有狀況發生

but it's all kind of in the noise.

但是都有點像是雜音

As you get towards the 1970s,

然後時間接近 1970 年代

things are going to start to change.

事情開始有了變化

They're going to start to look more similar,

它們開始看起來愈來愈接近

and by the time you get to the 2000s,

而到了 2000 年代

you're already seeing the patterns of global warming,

你已經可以看到全球暖化的型態

both in the observations and in the model.

觀察及模式預測兩者皆是

We know what happened over the 20th century.

我們知道二十世紀發生了什麼

Right? We know that it's gotten warmer.

對吧？我們知道一定會更熱

We know where it's gotten warmer.

我們還知道哪裡一定會更熱

And if you ask the models why did that happen,

如果你問模式為什麼這種情形會發生

and you say, okay, well, yes,

然後你說，對，嗯，沒錯

basically it's because of the carbon dioxide

基本上就是因為二氧化碳

we put into the atmosphere.

我們把它排放到大氣層

We have a very good match

我們的匹配度

up until the present day.

到今天為止都很高

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

但我們為什麼要看模式 有個關鍵的理由

and that's because of this phrase here.

而那就是因為這句話

Because if we had observations of the future,

因為「假設我們能直接觀察未來，

we obviously would trust them more than models,

與其相信模式， 我們顯然會更相信觀察數據。

But unfortunately,

但不幸的是…

observations of the future are not available at this time.

…對未來的觀察目前行不通。」

So when we go out into the future, there's a difference.

所以當我們預測未來 就會產生差異

The future is unknown, the future is uncertain,

未來是未知的；未來是不確定的

and there are choices.

但我們有選擇

Here are the choices that we have.

以下是我們的選擇

We can do some work to mitigate

我們能做點什麼以減少

the emissions of carbon dioxide into the atmosphere.

二氧化碳排放入大氣層

That's the top one.

這是最重要的

We can do more work

我們還能做更多

to really bring it down

以真正減少排放量

so that by the end of the century,

所以到了本世紀末

it's not much more than there is now.

排放量不會比現在更多

Or we can just leave it to fate

或者我們就看天命

and continue on

並繼續著

with a business-as-usual type of attitude.

一切如常的態度

The differences between these choices

這兩種選擇的差異

can't be answered by looking at models.

看模式是回答不了的

There's a great phrase

有句名言

that Sherwood Rowland,

是弗蘭克‧羅蘭說的

who won the Nobel Prize for the chemistry

他是諾貝爾化學獎得主

that led to ozone depletion,

他的研究發現了臭氧耗竭

when he was accepting his Nobel Prize,

他在領取他的諾貝爾獎時

he asked this question:

他問了這個問題

"What is the use of having developed a science

「這到底有甚麼用呢？ 某項科學發展的很好，

well enough to make predictions if, in the end,

好到能做出預測，但最後

all we're willing to do is stand around

我們只願意袖手旁觀，

and wait for them to come true?"

冷眼看著它們成真？」

The models are skillful,

模式的預測技巧很好

but what we do with the information from those models

但我們要怎麼使用 模式預測出來的數據

is totally up to you.

就全看你們了

Thank you.

謝謝

(Applause)

（掌聲）