Gavin Schmidt: The emergent patterns of climate change

翻译人员: Peipei Xiang 校对人员: Li Li

We live in a very complex environment:

（掌声）

我们生活在一个非常复杂的环境里，

complexity and dynamism

我们有来自卫星照片和影像的 复杂性、动态性和模式数据。

and patterns of evidence

from satellite photographs, from videos.

You can even see it outside your window.

你甚至能从你的窗外看到。

It's endlessly complex, but somehow familiar,

无止尽的复杂但又具某种程度的熟悉，

but the patterns kind of repeat,

模式有一定的重复性，

but they never repeat exactly.

但从来不会重复。

It's a huge challenge to understand.

要了解它是一个很大的挑战。

The patterns that you see

你所看到的气候模式

are there at all of the different scales,

都以不同的尺度存在着，

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

但你不能切下一小块然后说：

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

“喔，那我做个小一点的气候。”

I can't use the normal products of reductionism

我不能以一般的简化方法

to get a smaller and smaller thing that I can study

得到愈来愈小的东西

in a laboratory and say, "Oh,

使我能在实验室里研究并且说

now that's something I now understand."

“喔，现在我懂了。”

It's the whole or it's nothing.

这是全有或全无。

The different scales that give you

这些气候模式以不同的尺度呈现，

these kinds of patterns

range over an enormous range of magnitude,

其范围幅度非常大，

roughly 14 orders of magnitude,

大约是 14 数量级的差距，

from the small microscopic particles

从最小的造雨的显微粒子

that seed clouds

to the size of the planet itself,

到这个星球本身，

from 10 to the minus six

从 10 的负六次方到

to 10 to the eight,

10 的八次方，

14 orders of spatial magnitude.

空间数量级的差距为 14。

In time, from milliseconds to millennia,

在时间上，从毫秒到千年，

again around 14 orders of magnitude.

同样也是 14 数量级。

What does that mean?

这意味着什么？

Okay, well if you think about how

好，如果你想一想

you can calculate these things,

你要如何计算这些东西，

you can take what you can see,

你会拿你眼前的东西，

okay, I'm going to chop it up

好，我要把它切碎成这些小方块，

into lots of little boxes,

and that's the result of physics, right?

这就是物理学的结果，对吧？

And if I think about a weather model,

以一个天气模型为例，

that spans about five orders of magnitude,

尺度横跨五数量级，

from the planet to a few kilometers,

也就是从地球的大小到几公里

and the time scale

时间尺度则是

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

从几分钟到十天或者一个月。

We're interested in more than that.

我们感兴趣的不只这些。

We're interested in the climate.

我们感兴趣的是气候，

That's years, that's millennia,

那是以年计的，是千年，

and we need to go to even smaller scales.

我们还需要看更小尺度的。

The stuff that we can't resolve,

那些我们无法解决的东西，

the sub-scale processes,

次网格尺度过程，

we need to approximate in some way.

我们必须想办法模拟。

That is a huge challenge.

那是很大的挑战。

Climate models in the 1990s

1990 年代的气候模型

took an even smaller chunk of that,

是拿更小块的尺度来看，

only about three orders of magnitude.

大约只有三数量级。

Climate models in the 2010s,

2010 年代的气候模型

kind of what we're working with now,

就像我们现在正在使用的

four orders of magnitude.

是四数量级。

We have 14 to go,

而我们要扩展到 14 数量级。

and we're increasing our capability

而我们的模拟能力

of simulating those at about

每十年大约增加一数量级

one extra order of magnitude every decade.

One extra order of magnitude in space

空间一数量级等同于1万次的计算。

is 10,000 times more calculations.

And we keep adding more things,

而我们还继续加东西上去，

more questions to these different models.

加更多问题到这些不同的模型上。

So what does a climate model look like?

气候模型是什么样的？

This is an old climate model, admittedly,

无可否认这是旧的气候模型，

a punch card, a single line of Fortran code.

打孔卡，单行福传语言。

We no longer use punch cards.

我们不再使用打孔卡了，

We do still use Fortran.

我们还是用福传语言。

New-fangled ideas like C

新的想法像 C 语言

really haven't had a big impact

在气候模型研究上 还没有什么大的影响力。

on the climate modeling community.

But how do we go about doing it?

但这是怎么做出来的？

How do we go from that complexity that you saw

我们如何把你所看到的复杂现象

to a line of code?

变成一行行的代码？

We do it one piece at a time.

我们一次做一块。

This is a picture of sea ice

这是一张海冰图，

taken flying over the Arctic.

是飞越北极上空时照的。

We can look at all of the different equations

我们可以看所有不同的方程式

that go into making the ice grow

或使结冰量增加

or melt or change shape.

或融化或改变形状。

We can look at the fluxes.

我们可以看各种通量，

We can look at the rate at which

我们可以看雪变成冰的速率，

snow turns to ice, and we can code that.

我们可以为之编写代码。

We can encapsulate that in code.

我们可以用代码封装。

These models are around

这些模型目前大约要以

a million lines of code at this point,

一百万行代码才做的出来，

and growing by tens of thousands of lines of code

每年还在以上万行代码的速度增长。

every year.

So you can look at that piece,

所以你可以看这块，

but you can look at the other pieces too.

你也可以看那块。

What happens when you have clouds?

有云的时候怎么办？

What happens when clouds form,

云形成的时候怎么办？

when they dissipate, when they rain out?

云散了呢？下雨了呢？

That's another piece.

这是另一块。

What happens when we have radiation

有太阳辐射怎么办？

coming from the sun, going through the atmosphere,

辐射穿过大气层

being absorbed and reflected?

被吸收及反射又怎么办？

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

我们也能为这些非常小的东西写代码。

There are other pieces:

还有其他的，

the winds changing the ocean currents.

风改变洋流，

We can talk about the role of vegetation

我们也能谈植被的角色，

in transporting water from the soils

它从土壤中输送水分

back into the atmosphere.

回到大气层。

And each of these different elements

每一种不同的要素

we can encapsulate and put into a system.

我们都可以封装写进系统内。

Each of those pieces ends up adding to the whole.

每一块最后都会加在整体上，

And you get something like this.

你就会得到一个像这样的东西。

You get a beautiful representation

你会得到漂亮的图表，

of what's going on in the climate system,

告诉你气候系统的状况，

where each and every one of those

每一个像这样的

emergent patterns that you can see,

你看到的新气候形势，

the swirls in the Southern Ocean,

南冰洋的漩涡，

the tropical cyclone in the Gulf of Mexico,

墨西哥湾的热带飓风，

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

还有两个在太平洋随时可能爆发的，

in the Pacific at any point now,

those rivers of atmospheric water,

那些大气水气形成的河流，

all of those are emergent properties

这些都是来自我刚刚谈到的 次网格尺度过程交互作用的新特性。

that come from the interactions

of all of those small-scale processes I mentioned.

There's no code that says,

没有什么代码会说

"Do a wiggle in the Southern Ocean."

“在南冰洋晃一下。”

There's no code that says, "Have two

也没有代码会说：“让两个

tropical cyclones that spin around each other."

热带飓风互相绕着旋转。 ”

All of those things are emergent properties.

这些都是新特性，

This is all very good. This is all great.

这很好，这很棒。

But what we really want to know

但我们真的想知道的是

is what happens to these emergent properties

这些新特性在我们系统改变的时候会怎样？

when we kick the system?

When something changes, what happens to those properties?

当情况改变的时候，那些特性会怎样？

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

有很多方法可以让系统改变，

There are wobbles in the Earth's orbit

地球的轨道在过去数万年的摆动中

over hundreds of thousands of years

that change the climate.

改变着气候。

There are changes in the solar cycles,

太阳周期的改变，

every 11 years and longer, that change the climate.

每 11 年或更长的时间也会改变气候。

Big volcanoes go off and change the climate.

大的火山爆发会改变气候，

Changes in biomass burning, in smoke,

生物质燃烧、烟雾、气胶粒子， 所有这些东西的改变

in aerosol particles, all of those things

change the climate.

都会改变气候。

The ozone hole changed the climate.

臭氧洞会改变气候，

Deforestation changes the climate

森林除伐会改变气候，

by changing the surface properties

因为这改变了地表性质，

and how water is evaporated

也改变了水分如何蒸发

and moved around in the system.

并在系统内移动。

Contrails change the climate

凝结尾气会改变气候，

by creating clouds where there were none before,

因为它会在以前无云的地方产生云。

and of course greenhouse gases change the system.

当然温室气体也会改变系统。

Each of these different kicks

这些不同的影响因素

provides us with a target

给我们提供了一个目标，

to evaluate whether we understand

就是评估我们是否理解这个系统。

something about this system.

So we can go to look at

那么让我们来看看

what model skill is.

模型预测技巧是什么。

Now I use the word "skill" advisedly:

那我非常审慎地用“技巧”这个字，

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

模型没有对错，它们永远是错的。

They're always approximations.

它们永远是模拟情况。

The question you have to ask

你该问的问题是

is whether a model tells you more information

一个模型能否给到 你反之不会得到的信息。

than you would have had otherwise.

If it does, it's skillful.

如果是，那就是“有技巧”的。

This is the impact of the ozone hole

这是臭氧洞对海平面气压的影响，

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

around the southern oceans, around Antarctica.

围绕南冰洋南极洲的低气压高气压。

This is observed data.

这是观测数据，

This is modeled data.

这是模型推测出的数据。

There's a good match

这两者匹配度很高，

because we understand the physics

因为我们理解控制平流层温度的物理

that controls the temperatures in the stratosphere

and what that does to the winds

及其对南冰洋四周的风的影响。

around the southern oceans.

We can look at other examples.

我们还可以看看其他例子。

The eruption of Mount Pinatubo in 1991

1991 年皮纳土波火山爆发

put an enormous amount of aerosols, small particles,

将大量的气胶，微粒

into the stratosphere.

喷入平流层中。

That changed the radiation balance of the whole planet.

那件事改变了整个地球的辐射平衡。

There was less energy coming in than there was before,

与之前相比，较少的能量进入地球，

so that cooled the planet,

导致地球变冷，

and those red lines and those green lines,

而那些红线及那些绿线

those are the differences between what we expected

那些是我们所预期的与实际状况的差别。

and what actually happened.

The models are skillful,

这些模型很有技巧，

not just in the global mean,

因为它们不仅在全球平均上预测很准确，

but also in the regional patterns.

在区域形态上也如此。

I could go through a dozen more examples:

我还可以举上一打的例子：

the skill associated with solar cycles,

与太阳周期、 平流层臭氧变化相关的预测技巧，

changing the ozone in the stratosphere;

the skill associated with orbital changes

与六千年来地球轨道变化相关的预测技巧。

over 6,000 years.

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

我们也可以看那个，模型的技巧也很好。

The models are skillful in response to the ice sheets

对二万年前的冰层，这些模型的技巧也很好。

20,000 years ago.

The models are skillful

这些模型对预测二十世纪的气候趋势，技巧也很好。

when it comes to the 20th-century trends

over the decades.

Models are successful at modeling

模型很成功地模拟了八千年前北极冰湖溃决。

lake outbursts into the North Atlantic

8,000 years ago.

And we can get a good match to the data.

我们在数据上的匹配度很高。

Each of these different targets,

每一个不同的目标，

each of these different evaluations,

每一个不同的评估，

leads us to add more scope

都使我们能够扩展这些模型，

to these models,

and leads us to more and more

使我们能够看日渐复杂的情况，

complex situations that we can ask

使我们能够问更多有意思的问题，

more and more interesting questions,

like, how does dust from the Sahara,

比如，撒哈拉尘，

that you can see in the orange,

也就是这些橘色的东西，

interact with tropical cyclones in the Atlantic?

与大西洋的热带飓风如何交互作用？

How do organic aerosols from biomass burning,

生物质燃烧所产生的有机气胶，

which you can see in the red dots,

也就是这些红点，

intersect with clouds and rainfall patterns?

与云和雨如何交互作用？

How does pollution, which you can see

这些污染，就是你看到的

in the white wisps of sulfate pollution in Europe,

欧洲上方一缕缕的白色硫酸，

how does that affect the temperatures at the surface

这些如何影响地面温度

and the sunlight that you get at the surface?

以及你在地表上得到的太阳光量？

We can look at this across the world.

我们可以看世界各地的状况，

We can look at the pollution from China.

我们可以看来自中国的污染，

We can look at the impacts of storms

我们可以看暴风

on sea salt particles in the atmosphere.

对大气层内海盐粒子的影响。

We can see the combination

我们可以看

这些不同东西同时发生的整体情况，

of all of these different things

happening all at once,

and we can ask much more interesting questions.

我们可以问更有意思的问题。

How do air pollution and climate coexist?

空气污染与气候如何共存？

Can we change things

我们是否能同时改变

that affect air pollution and climate at the same time?

影响空气污染及气候的事物？

The answer is yes.

答案是肯定的。

So this is a history of the 20th century.

这是二十世纪的历史。

The first one is the model.

第一个是模型，

The weather is a little bit different

天气与实际状况有一点不同。

to what actually happened.

The second one are the observations.

第二个是观察结果。

And we're going through the 1930s.

我们来看 1930 年代的情况。

There's variability, there are things going on,

总是有差异，总是有状况发生，

but it's all kind of in the noise.

但都不是很清楚。

As you get towards the 1970s,

接近 1970 年代，

things are going to start to change.

事情开始有了变化。

They're going to start to look more similar,

它们开始变得愈来愈接近。

and by the time you get to the 2000s,

而到了 21 世纪，

you're already seeing the patterns of global warming,

你已经可以看到全球变暖的形势，

both in the observations and in the model.

可以观察到，也可以在模型中看到。

We know what happened over the 20th century.

我们知道二十世纪发生了什么，

Right? We know that it's gotten warmer.

对吧？我们知道气候变暖了。

We know where it's gotten warmer.

我们还知道哪里变暖了。

And if you ask the models why did that happen,

如果你问模型为什么会发生这种情形，

and you say, okay, well, yes,

然后你说，对，嗯，没错，

basically it's because of the carbon dioxide

基本上就是因为 我们排放到大气层中的二氧化碳。

we put into the atmosphere.

We have a very good match

我们的模型匹配度

up until the present day.

到今天为止都很高。

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

但我们看模型的一个关键理由

and that's because of this phrase here.

就是这句话。

Because if we had observations of the future,

因为“如果我们能直接观察未来，

we obviously would trust them more than models,

与其相信模型， 我们显然会更相信观察数据。

But unfortunately,

但不幸的是…

observations of the future are not available at this time.

…目前我们无法观察未来。 ”

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

所以当未来真正到来的时候，会有所不同。

The future is unknown, the future is uncertain,

未来是未知的，未来是不确定的，

and there are choices.

但我们有选择。

Here are the choices that we have.

以下是我们的选择。

We can do some work to mitigate

我们能做点什么以减少

the emissions of carbon dioxide into the atmosphere.

二氧化碳排放入大气层。

That's the top one.

这是最重要的。

We can do more work

我们还能做更多来减少排放量，

to really bring it down

so that by the end of the century,

使我们到本世纪末的时候，

it's not much more than there is now.

排放量不比现在多。

Or we can just leave it to fate

或者我们就听天命

and continue on

并以一切如常的态度继续着。

with a business-as-usual type of attitude.

The differences between these choices

这两种选择的差异

can't be answered by looking at models.

看模型是回答不了的。

There's a great phrase

有句名言，

that Sherwood Rowland,

是舍伍德•罗兰说的，

who won the Nobel Prize for the chemistry

他是诺贝尔化学奖得主，

that led to ozone depletion,

他的研究发现了臭氧耗竭。

when he was accepting his Nobel Prize,

他在领取他的诺贝尔奖时，

he asked this question:

他问了这个问题：

"What is the use of having developed a science

“科学发展得再好再能预测有什么用，

well enough to make predictions if, in the end,

如果到头来我们愿意做的只是袖手旁观，

all we're willing to do is stand around

and wait for them to come true?"

冷眼看着它们成真？”

The models are skillful,

模型的预测技巧很好，

but what we do with the information from those models

但我们要怎么使用模型预测出来的数据

is totally up to you.

则完全取决于你。

Thank you.

谢谢。

（掌声）

(Applause)