(掌声)
We live in a very complex environment: complexity and dynamism and patterns of evidence from satellite photographs, from videos. You can even see it outside your window. It's endlessly complex, but somehow familiar, but the patterns kind of repeat, but they never repeat exactly. It's a huge challenge to understand. The patterns that you see are there at all of the different scales, but you can't chop it into one little bit and say, "Oh, well let me just make a smaller climate." I can't use the normal products of reductionism to get a smaller and smaller thing that I can study in a laboratory and say, "Oh, now that's something I now understand." It's the whole or it's nothing.
我们生活在一个非常复杂的环境里, 我们有来自卫星照片和影像的 复杂性、动态性和模式数据。 你甚至能从你的窗外看到。 无止尽的复杂但又具某种程度的熟悉, 模式有一定的重复性, 但从来不会重复。 要了解它是一个很大的挑战。 你所看到的气候模式 都以不同的尺度存在着, 但你不能切下一小块然后说: “喔,那我做个小一点的气候。” 我不能以一般的简化方法 得到愈来愈小的东西 使我能在实验室里研究并且说 “喔,现在我懂了。” 这是全有或全无。 这些气候模式以不同的尺度呈现,
The different scales that give you these kinds of patterns range over an enormous range of magnitude, roughly 14 orders of magnitude, from the small microscopic particles that seed clouds to the size of the planet itself, from 10 to the minus six to 10 to the eight, 14 orders of spatial magnitude. In time, from milliseconds to millennia, again around 14 orders of magnitude.
其范围幅度非常大, 大约是 14 数量级的差距, 从最小的造雨的显微粒子 到这个星球本身, 从 10 的负六次方到 10 的八次方, 空间数量级的差距为 14。 在时间上,从毫秒到千年, 同样也是 14 数量级。 这意味着什么?
What does that mean? Okay, well if you think about how you can calculate these things, you can take what you can see, okay, I'm going to chop it up into lots of little boxes, and that's the result of physics, right? And if I think about a weather model, that spans about five orders of magnitude, from the planet to a few kilometers, and the time scale from a few minutes to 10 days, maybe a month. We're interested in more than that. We're interested in the climate. That's years, that's millennia, and we need to go to even smaller scales. The stuff that we can't resolve, the sub-scale processes, we need to approximate in some way. That is a huge challenge. Climate models in the 1990s took an even smaller chunk of that, only about three orders of magnitude. Climate models in the 2010s, kind of what we're working with now, four orders of magnitude. We have 14 to go, and we're increasing our capability of simulating those at about one extra order of magnitude every decade. One extra order of magnitude in space is 10,000 times more calculations. And we keep adding more things, more questions to these different models.
好,如果你想一想 你要如何计算这些东西, 你会拿你眼前的东西, 好,我要把它切碎成这些小方块, 这就是物理学的结果,对吧? 以一个天气模型为例, 尺度横跨五数量级, 也就是从地球的大小到几公里 时间尺度则是 从几分钟到十天或者一个月。 我们感兴趣的不只这些。 我们感兴趣的是气候, 那是以年计的,是千年, 我们还需要看更小尺度的。 那些我们无法解决的东西, 次网格尺度过程, 我们必须想办法模拟。 那是很大的挑战。 1990 年代的气候模型 是拿更小块的尺度来看, 大约只有三数量级。 2010 年代的气候模型 就像我们现在正在使用的 是四数量级。 而我们要扩展到 14 数量级。 而我们的模拟能力 每十年大约增加一数量级 空间一数量级等同于1万次的计算。 而我们还继续加东西上去, 加更多问题到这些不同的模型上。 气候模型是什么样的?
So what does a climate model look like? This is an old climate model, admittedly, a punch card, a single line of Fortran code. We no longer use punch cards. We do still use Fortran. New-fangled ideas like C really haven't had a big impact on the climate modeling community.
无可否认这是旧的气候模型, 打孔卡,单行福传语言。 我们不再使用打孔卡了, 我们还是用福传语言。 新的想法像 C 语言 在气候模型研究上 还没有什么大的影响力。
But how do we go about doing it? How do we go from that complexity that you saw to a line of code? We do it one piece at a time. This is a picture of sea ice taken flying over the Arctic. We can look at all of the different equations that go into making the ice grow or melt or change shape. We can look at the fluxes. We can look at the rate at which snow turns to ice, and we can code that. We can encapsulate that in code. These models are around a million lines of code at this point, and growing by tens of thousands of lines of code every year.
但这是怎么做出来的? 我们如何把你所看到的复杂现象 变成一行行的代码? 我们一次做一块。 这是一张海冰图, 是飞越北极上空时照的。 我们可以看所有不同的方程式 或使结冰量增加 或融化或改变形状。 我们可以看各种通量, 我们可以看雪变成冰的速率, 我们可以为之编写代码。 我们可以用代码封装。 这些模型目前大约要以 一百万行代码才做的出来, 每年还在以上万行代码的速度增长。 所以你可以看这块,
So you can look at that piece, but you can look at the other pieces too. What happens when you have clouds? What happens when clouds form, when they dissipate, when they rain out? That's another piece. What happens when we have radiation coming from the sun, going through the atmosphere, being absorbed and reflected? We can code each of those very small pieces as well. There are other pieces: the winds changing the ocean currents. We can talk about the role of vegetation in transporting water from the soils back into the atmosphere. And each of these different elements we can encapsulate and put into a system. Each of those pieces ends up adding to the whole.
你也可以看那块。 有云的时候怎么办? 云形成的时候怎么办? 云散了呢?下雨了呢? 这是另一块。 有太阳辐射怎么办? 辐射穿过大气层 被吸收及反射又怎么办? 我们也能为这些非常小的东西写代码。 还有其他的, 风改变洋流, 我们也能谈植被的角色, 它从土壤中输送水分 回到大气层。 每一种不同的要素 我们都可以封装写进系统内。 每一块最后都会加在整体上, 你就会得到一个像这样的东西。
And you get something like this. You get a beautiful representation of what's going on in the climate system, where each and every one of those emergent patterns that you can see, the swirls in the Southern Ocean, the tropical cyclone in the Gulf of Mexico, and there's two more that are going to pop up in the Pacific at any point now, those rivers of atmospheric water, all of those are emergent properties that come from the interactions of all of those small-scale processes I mentioned. There's no code that says, "Do a wiggle in the Southern Ocean." There's no code that says, "Have two tropical cyclones that spin around each other." All of those things are emergent properties.
你会得到漂亮的图表, 告诉你气候系统的状况, 每一个像这样的 你看到的新气候形势, 南冰洋的漩涡, 墨西哥湾的热带飓风, 还有两个在太平洋随时可能爆发的, 那些大气水气形成的河流, 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。 没有什么代码会说 “在南冰洋晃一下。” 也没有代码会说:“让两个 热带飓风互相绕着旋转。 ” 这些都是新特性, 这很好,这很棒。
This is all very good. This is all great. But what we really want to know is what happens to these emergent properties when we kick the system? When something changes, what happens to those properties? And there's lots of different ways to kick the system. There are wobbles in the Earth's orbit over hundreds of thousands of years that change the climate. There are changes in the solar cycles, every 11 years and longer, that change the climate. Big volcanoes go off and change the climate. Changes in biomass burning, in smoke, in aerosol particles, all of those things change the climate. The ozone hole changed the climate. Deforestation changes the climate by changing the surface properties and how water is evaporated and moved around in the system. Contrails change the climate by creating clouds where there were none before, and of course greenhouse gases change the system.
但我们真的想知道的是 这些新特性在我们系统改变的时候会怎样? 当情况改变的时候,那些特性会怎样? 有很多方法可以让系统改变, 地球的轨道在过去数万年的摆动中 改变着气候。 太阳周期的改变, 每 11 年或更长的时间也会改变气候。 大的火山爆发会改变气候, 生物质燃烧、烟雾、气胶粒子, 所有这些东西的改变 都会改变气候。 臭氧洞会改变气候, 森林除伐会改变气候, 因为这改变了地表性质, 也改变了水分如何蒸发 并在系统内移动。 凝结尾气会改变气候, 因为它会在以前无云的地方产生云。 当然温室气体也会改变系统。 这些不同的影响因素
Each of these different kicks provides us with a target to evaluate whether we understand something about this system. So we can go to look at what model skill is. Now I use the word "skill" advisedly: Models are not right or wrong; they're always wrong. They're always approximations. The question you have to ask is whether a model tells you more information than you would have had otherwise. If it does, it's skillful. This is the impact of the ozone hole on sea level pressure, so low pressure, high pressures, around the southern oceans, around Antarctica. This is observed data. This is modeled data. There's a good match because we understand the physics that controls the temperatures in the stratosphere and what that does to the winds around the southern oceans.
给我们提供了一个目标, 就是评估我们是否理解这个系统。 那么让我们来看看 模型预测技巧是什么。 那我非常审慎地用“技巧”这个字, 模型没有对错,它们永远是错的。 它们永远是模拟情况。 你该问的问题是 一个模型能否给到 你反之不会得到的信息。 如果是,那就是“有技巧”的。 这是臭氧洞对海平面气压的影响, 围绕南冰洋南极洲的低气压高气压。 这是观测数据, 这是模型推测出的数据。 这两者匹配度很高, 因为我们理解控制平流层温度的物理 及其对南冰洋四周的风的影响。 我们还可以看看其他例子。
We can look at other examples. The eruption of Mount Pinatubo in 1991 put an enormous amount of aerosols, small particles, into the stratosphere. That changed the radiation balance of the whole planet. There was less energy coming in than there was before, so that cooled the planet, and those red lines and those green lines, those are the differences between what we expected and what actually happened. The models are skillful, not just in the global mean, but also in the regional patterns.
1991 年皮纳土波火山爆发 将大量的气胶,微粒 喷入平流层中。 那件事改变了整个地球的辐射平衡。 与之前相比,较少的能量进入地球, 导致地球变冷, 而那些红线及那些绿线 那些是我们所预期的与实际状况的差别。 这些模型很有技巧, 因为它们不仅在全球平均上预测很准确, 在区域形态上也如此。 我还可以举上一打的例子:
I could go through a dozen more examples: the skill associated with solar cycles, changing the ozone in the stratosphere; the skill associated with orbital changes over 6,000 years. We can look at that too, and the models are skillful. The models are skillful in response to the ice sheets 20,000 years ago. The models are skillful when it comes to the 20th-century trends over the decades. Models are successful at modeling lake outbursts into the North Atlantic 8,000 years ago. And we can get a good match to the data.
与太阳周期、 平流层臭氧变化相关的预测技巧, 与六千年来地球轨道变化相关的预测技巧。 我们也可以看那个,模型的技巧也很好。 对二万年前的冰层,这些模型的技巧也很好。 这些模型对预测二十世纪的气候趋势,技巧也很好。 模型很成功地模拟了八千年前北极冰湖溃决。 我们在数据上的匹配度很高。
Each of these different targets, each of these different evaluations, leads us to add more scope to these models, and leads us to more and more complex situations that we can ask more and more interesting questions, like, how does dust from the Sahara, that you can see in the orange, interact with tropical cyclones in the Atlantic? How do organic aerosols from biomass burning, which you can see in the red dots, intersect with clouds and rainfall patterns? How does pollution, which you can see in the white wisps of sulfate pollution in Europe, how does that affect the temperatures at the surface and the sunlight that you get at the surface?
每一个不同的目标, 每一个不同的评估, 都使我们能够扩展这些模型, 使我们能够看日渐复杂的情况, 使我们能够问更多有意思的问题, 比如,撒哈拉尘, 也就是这些橘色的东西, 与大西洋的热带飓风如何交互作用? 生物质燃烧所产生的有机气胶, 也就是这些红点, 与云和雨如何交互作用? 这些污染,就是你看到的 欧洲上方一缕缕的白色硫酸, 这些如何影响地面温度 以及你在地表上得到的太阳光量?
We can look at this across the world. We can look at the pollution from China. We can look at the impacts of storms on sea salt particles in the atmosphere. We can see the combination of all of these different things happening all at once, and we can ask much more interesting questions. How do air pollution and climate coexist? Can we change things that affect air pollution and climate at the same time? The answer is yes.
我们可以看世界各地的状况, 我们可以看来自中国的污染, 我们可以看暴风 对大气层内海盐粒子的影响。 我们可以看 这些不同东西同时发生的整体情况, 我们可以问更有意思的问题。 空气污染与气候如何共存? 我们是否能同时改变 影响空气污染及气候的事物? 答案是肯定的。 这是二十世纪的历史。
So this is a history of the 20th century. The first one is the model. The weather is a little bit different to what actually happened. The second one are the observations. And we're going through the 1930s. There's variability, there are things going on, but it's all kind of in the noise. As you get towards the 1970s, things are going to start to change. They're going to start to look more similar, and by the time you get to the 2000s, you're already seeing the patterns of global warming, both in the observations and in the model.
第一个是模型, 天气与实际状况有一点不同。 第二个是观察结果。 我们来看 1930 年代的情况。 总是有差异,总是有状况发生, 但都不是很清楚。 接近 1970 年代, 事情开始有了变化。 它们开始变得愈来愈接近。 而到了 21 世纪, 你已经可以看到全球变暖的形势, 可以观察到,也可以在模型中看到。
We know what happened over the 20th century. Right? We know that it's gotten warmer. We know where it's gotten warmer. And if you ask the models why did that happen, and you say, okay, well, yes, basically it's because of the carbon dioxide we put into the atmosphere. We have a very good match up until the present day.
我们知道二十世纪发生了什么, 对吧?我们知道气候变暖了。 我们还知道哪里变暖了。 如果你问模型为什么会发生这种情形, 然后你说,对,嗯,没错, 基本上就是因为 我们排放到大气层中的二氧化碳。 我们的模型匹配度 到今天为止都很高。
But there's one key reason why we look at models, and that's because of this phrase here. Because if we had observations of the future, we obviously would trust them more than models, But unfortunately, observations of the future are not available at this time.
但我们看模型的一个关键理由 就是这句话。 因为“如果我们能直接观察未来, 与其相信模型, 我们显然会更相信观察数据。 但不幸的是… …目前我们无法观察未来。 ”
So when we go out into the future, there's a difference. The future is unknown, the future is uncertain, and there are choices. Here are the choices that we have. We can do some work to mitigate the emissions of carbon dioxide into the atmosphere. That's the top one. We can do more work to really bring it down so that by the end of the century, it's not much more than there is now. Or we can just leave it to fate and continue on with a business-as-usual type of attitude. The differences between these choices can't be answered by looking at models.
所以当未来真正到来的时候,会有所不同。 未来是未知的,未来是不确定的, 但我们有选择。 以下是我们的选择。 我们能做点什么以减少 二氧化碳排放入大气层。 这是最重要的。 我们还能做更多来减少排放量, 使我们到本世纪末的时候, 排放量不比现在多。 或者我们就听天命 并以一切如常的态度继续着。 这两种选择的差异 看模型是回答不了的。 有句名言,
There's a great phrase that Sherwood Rowland, who won the Nobel Prize for the chemistry that led to ozone depletion, when he was accepting his Nobel Prize, he asked this question: "What is the use of having developed a science well enough to make predictions if, in the end, all we're willing to do is stand around and wait for them to come true?" The models are skillful, but what we do with the information from those models is totally up to you.
是舍伍德•罗兰说的, 他是诺贝尔化学奖得主, 他的研究发现了臭氧耗竭。 他在领取他的诺贝尔奖时, 他问了这个问题: “科学发展得再好再能预测有什么用, 如果到头来我们愿意做的只是袖手旁观, 冷眼看着它们成真?” 模型的预测技巧很好, 但我们要怎么使用模型预测出来的数据 则完全取决于你。 谢谢。
Thank you.
(掌声)
(Applause)