Climate Physicists Face the Ghosts in Their Machines: Clouds
Clouds are a tricky mix of ephemeral and ethereal, making them challenging to simulate.
云是短暂和空灵的微妙混合体，这使得模拟它们具有挑战性。
In October 2008, Chris Bretherton lifted off from the coast of northern Chile in a C-130 turboprop plane.
2008 年 10 月，克里斯·布雷瑟顿 (Chris Bretherton) 乘坐一架 C-130 涡轮螺旋桨飞机从智利北部海岸升空。
It was too dark to see the sandy hills of the Atacama Desert below, but the darkness suited Bretherton just fine.
天太黑了，看不清下面阿塔卡马沙漠的沙山，但黑暗很适合布雷瑟顿。
The researcher wasn’t going sightseeing.
研究人员没有去观光。
Seated directly behind the pilots, he kept his focus entirely on the sky.
他坐在飞行员的正后方，将注意力完全集中在天空上。
The plane was stuffed with instruments, and its wings bristled with sensors and other devices.
飞机上装满了仪器，机翼上布满了传感器和其他设备。
Bretherton’s immediate mission was to help the pilots collect information about the ice, water vapor, and air pressure around them.
布雷瑟顿的当前任务是帮助飞行员收集有关他们周围的冰、水蒸气和气压的信息。
His longer-term goal was to use that data — as well as data he would collect over California, Hawai‘i, and Antarctica — to deal with one of the most challenging factors in climate science: clouds.
他的长期目标是利用这些数据以及他在加利福尼亚州、夏威夷和南极洲收集的数据来应对气候科学中最具挑战性的因素之一：云。
The plane passed a fluffy cumulus, and Bretherton spotted a rainbowlike prism of colors.
飞机飞过一个蓬松的积云，布雷瑟顿发现了一个彩虹般的色彩棱镜。
This was strange; the cloud seemed too thin to host the large droplets required to refract light in this way.
这很奇怪。云层似乎太薄，无法容纳以这种方式折射光线所需的大水滴。
“The six-to-nine-hour flights rarely get boring,” Bretherton said, “because we always run into surprising cloud structures that rattle our scientific preconceptions.” He would later conclude that the air must have been so pristine that the cloud’s vapor was condensing into unusually large droplets on an unusually small number of particles.
“六到九小时的飞行很少会感到无聊，”布雷瑟顿说，“因为我们总是会遇到令人惊讶的云结构，这些结构扰乱了我们的科学先入之见。”他后来得出结论，空气一定非常纯净，以至于云中的蒸气在异常少量的颗粒上凝结成异常大的液滴。
In the nearly two decades since Bretherton boarded that plane, the globe has warmed by roughly half a degree Celsius.
自从布雷瑟顿登上这架飞机以来的近二十年里，全球变暖了大约半摄氏度。
And clouds, which both reflect sunlight and trap heat, are still the biggest source of uncertainty in climate predictions.
云既能反射阳光，又能吸收热量，仍然是气候预测不确定性的最大来源。
The world’s top supercomputers aren’t nearly super enough to include tiny digital clouds in the gigantic digital Earths they simulate.
世界顶级的超级计算机还不够强大，无法在它们模拟的巨大数字地球中包含微小的数字云。
So climate scientists are developing workarounds, techniques for coaxing relatively cloudless climate simulations to swirl, storm, and warm as if they contained a full portfolio of realistic clouds.
因此，气候科学家正在开发解决方法和技术，以诱导相对无云的气候模拟产生漩涡、风暴和温暖，就好像它们包含了完整的真实云彩组合一样。
Over the last few years, a competition has broken out among physicists to build the next generation of these crystal balls for climate.
在过去的几年里，物理学家之间爆发了一场建造下一代气候水晶球的竞赛。
Bretherton , now working at the Allen Institute for Artificial Intelligence (Ai2), is one prominent entrant.
目前在艾伦人工智能研究所 (Ai2) 工作的 Bretherton 是一位杰出的参赛者。
Tapio Schneider at the California Institute of Technology is another.
加州理工学院的 Tapio Schneider 是另一个例子。
Chris Bretherton of the Allen Institute for Artificial Intelligence pivoted after spending decades studying clouds to developing AI techniques that can implicitly learn how clouds behave from real data.
艾伦人工智能研究所的克里斯·布雷瑟顿 (Chris Bretherton) 在花了数十年时间研究云之后，转向开发人工智能技术，可以从真实数据中隐式了解云的行为方式。
Galvanizing these new efforts is the rise of machine learning techniques categorized as artificial intelligence.
人工智能机器学习技术的兴起推动了这些新的努力。
Schneider leans on AI to better incorporate the effects of clouds into climate models that use physics equations to see what’s ahead.
施耐德依靠人工智能更好地将云的影响纳入气候模型中，该模型使用物理方程来预测未来的情况。
Bretherton, worried that these equations will never fully capture clouds’ behavior, is developing new AI tools that can predict the future directly from real-world data, barely relying on physics equations at all.
布雷瑟顿担心这些方程永远无法完全捕捉云的行为，因此正在开发新的人工智能工具，可以直接根据现实世界的数据预测未来，几乎不依赖物理方程。
While Schneider, Bretherton, and other physicists differ in their approach, they share a sense of urgency.
尽管施奈德、布雷瑟顿和其他物理学家的方法不同，但他们都有共同的紧迫感。
“Climate is changing fast,” Bretherton said.
“气候变化很快，”布雷瑟顿说。
“Having a perfect model in 100 years will not be useful for solving the climate crisis.”
“100年后拥有一个完美的模型对于解决气候危机并无帮助。”
If humanity continues to fill the atmosphere with carbon at its current rate, some simulations predict that over the next 50 or so years, the climate is headed for 2 degrees Celsius of warming.
如果人类继续以目前的速度向大气中注入碳，一些模拟预测在未来 50 年左右的时间里，气候将变暖 2 摄氏度。
Others say 6.
别人说6。
The first possibility would lead to a future of increased severe weather events and amplified food and water scarcity — a dangerous situation for many communities, but one that the global population may be able to adapt to.
第一种可能性将导致未来恶劣天气事件增多，粮食和水短缺加剧——这对许多社区来说是一个危险的局面，但全球人口可能能够适应。
The latter possibility, however, could give rise to enough disaster and famine to fully destabilize human civilization.
然而，后一种可能性可能会引发足够的灾难和饥荒，从而彻底破坏人类文明的稳定。
“Six degrees would be pretty frightening,” Schneider said.
“六度是相当可怕的，”施奈德说。
Modern climate simulations account for the influence of the planet’s atmosphere, its ocean, its land, its ice, and more, with each model handling these components in its own way.
现代气候模拟考虑了地球大气、海洋、陆地、冰等的影响，每个模型都以自己的方式处理这些组成部分。
But more than half of the variation between predictions comes from how the simulations treat clouds.
但预测之间一半以上的差异来自于模拟处理云的方式。
“If you are off by a few percent — 2 or 3% — of cloud cover, you will get warming that is several degrees Celsius different,” said George Matheou , a physicist studying clouds at the University of Connecticut.
康涅狄格大学研究云层的物理学家乔治·马修说：“如果云量减少百分之几——2%或3%——，气温就会相差几摄氏度。”
Tapio Schneider, a climate physicist at the California Institute of Technology, has been leading the construction of CLIMA, a next-generation climate model that fuses traditional physics tools with modern AI tools.
加州理工学院气候物理学家 Tapio Schneider 一直在领导 CLIMA 的构建，这是一个融合了传统物理工具与现代人工智能工具的下一代气候模型。
In 2022, the Department of Energy tasked Frontier, then the world’s most powerful supercomputer, with running a new flagship climate model.
2022 年，能源部责成当时世界上最强大的超级计算机 Frontier 运行新的旗舰气候模型。
The model was based on the physics of fluid dynamics, as calculated via a set of equations called Navier-Stokes.
该模型基于流体动力学物理学，通过一组称为纳维-斯托克斯的方程进行计算。
Developing the model marked, in some sense, the culmination of a six-decade enterprise of improving the accuracy of climate models by increasing the resolution of the computer simulation.
从某种意义上说，该模型的开发标志着六年来通过提高计算机模拟分辨率来提高气候模型准确性的事业的顶峰。
Simulations had gone from thousands of kilometers per pixel, to hundreds, to — in this case — three.
模拟已经从每个像素数千公里，增加到数百公里，在本例中增加到三公里。
But even this state-of-the-art model couldn’t directly account for the subtle cumulative effects of clouds, which can span just meters and be shaped by even tinier zephyrs of air.
但即使是这种最先进的模型也无法直接解释云的微妙累积效应，云的跨度只有几米，并且受到更小的空气和风的影响。
“To get to the low clouds, you need something like 100 billion times the compute power we have,” Schneider said, “so that’s not going to happen in my lifetime.”
施耐德说：“要到达低云层，你需要的计算能力是我们现有计算能力的 1000 亿倍，所以这在我有生之年是不可能实现的。”
Unable to add clouds to their models directly, physicists have effectively resorted to estimating their influence.
由于无法直接将云添加到模型中，物理学家只能有效地估计它们的影响。
They add extra, nonphysical terms, called parameters, to the Navier-Stokes equations that indirectly capture the effects of clouds.
他们在纳维-斯托克斯方程中添加了额外的非物理项（称为参数），间接捕获云的影响。
These alternative equations are engineered to produce digital atmospheric currents that bend and curl in the ways that a truly cloudy model would.
这些替代方程被设计用来产生数字大气流，这些大气流以真正多云模型的方式弯曲和卷曲。
In a laborious process, researchers tweak these factors until the models produce accurate predictions based on past data.
在一个费力的过程中，研究人员调整这些因素，直到模型根据过去的数据产生准确的预测。
But data is patchy, so physicists also let their intuition guide them.
但数据并不完整，因此物理学家也让直觉来指导他们。
In the end, it’s tough to know whether one model’s parameters are better than another’s.
最后，很难知道一个模型的参数是否优于另一个模型的参数。
“You have to guess a little bit,” Matheou said.
“你必须猜测一下，”马修说。
The need to turn parameter picking from an art into a science was one of the reasons Schneider established the Climate Modeling Alliance, CLIMA, in 2019.
将参数选择从艺术转变为科学的需要是施耐德于 2019 年成立气候建模联盟 CLIMA 的原因之一。
He hoped to automate the process and make it less subjective by training machines to pick the best parameters possible.
他希望通过训练机器来选择可能的最佳参数，使该过程自动化并减少主观性。
But to do that, researchers would need a lot more data about different types of clouds: California clouds, mid-Pacific clouds, winter clouds, summer clouds, and so on.
但要做到这一点，研究人员需要更多关于不同类型云的数据：加利福尼亚云、中太平洋云、冬季云、夏季云等等。
Researchers like Bretherton can afford to fly planes through real clouds only so often.
像布雷瑟顿这样的研究人员只能经常驾驶飞机穿越真实的云层。
So cloud physicists turn to the next best thing: a Navier-Stokes simulation called a large-eddy simulation.
因此，云物理学家转向了下一个最好的方法：称为大涡模拟的纳维-斯托克斯模拟。
“LES is the best model we have for cloud turbulence, for a limited area and a short time,” said Zhaoyi Shen , a CLIMA researcher at Caltech.
加州理工学院 CLIMA 研究员沈兆义表示：“LES 是我们在有限区域和短时间内研究云湍流的最佳模型。”
The catch is that generating an LES also doesn’t come cheap: It takes a formidable amount of computational power.
问题是生成 LES 的成本也不菲：它需要大量的计算能力。
Until somewhat recently, Shen said, researchers had produced just a few dozen high-quality cloud simulations — not enough to give physicists a comprehensive view of cloud behavior, and certainly not enough to teach a machine how clouds work.
沉说，直到最近，研究人员只制作了几十个高质量的云模拟——不足以让物理学家全面了解云的行为，当然也不足以教会机器云是如何工作的。
So a few years ago, Schneider approached scientists at Google for help.
因此，几年前，施耐德向谷歌的科学家寻求帮助。
A large-eddy simulation (LES) generates digital clouds so detailed that they can almost pass for real, as seen here in this graphical rendition of LES data.
大涡模拟 (LES) 生成的数字云非常详细，几乎可以视为真实的，如 LES 数据的图形再现所示。
George Matheou, University of Connecticut
乔治·马修，康涅狄格大学
Sheide Chammas and his collaborators at Google coded an LES algorithm from scratch to run on custom computer chips called tensor processing units.
Sheide Chammas 和他在 Google 的合作者从头开始编写了 LES 算法，并在称为张量处理单元的定制计算机芯片上运行。
They ran the code on thousands of these chips, churning out simulation after simulation.
他们在数千个这样的芯片上运行代码，不断地进行模拟。
Ultimately, they developed a library of over 8,000 digital clouds native to 500 locations in the Pacific Ocean during all four seasons.
最终，他们开发了一个包含 8,000 多个数字云的库，这些数字云在所有四个季节来自太平洋 500 个地点。
“Sheide’s library will be game-changing,” Schneider said.
“谢德的图书馆将改变游戏规则，”施奈德说。
“We’ve never had anything like it.”
“我们从来没有遇到过这样的事情。”
Schneider and other CLIMA researchers have now trained an algorithm on this digital menagerie and used it to configure new cloud parameters.
Schneider 和其他 CLIMA 研究人员现在已经在这个数字动物园上训练了一种算法，并用它来配置新的云参数。
That was one of a number of improvements that Schneider believes will make CLIMA’s global climate model the leading next-generation model.
施耐德认为，这是众多改进之一，将使 CLIMA 的全球气候模型成为领先的下一代模型。
As of this winter, the model is finally up and running.
截至今年冬天，该模型终于启动并运行。
The collaboration will unveil it at a conference in Japan in March, but Schneider says preliminary testing suggests that they are well on their way to achieving their main goal: building a model twice as accurate as any other.
此次合作将于三月份在日本举行的一次会议上揭晓，但施耐德表示，初步测试表明他们正在顺利实现主要目标：建立一个比其他模型准确两倍的模型。
“It is more accurate than other models in key metrics — and with room for further improvement,” he said.
“它在关键指标上比其他模型更准确，而且还有进一步改进的空间，”他说。
But even as CLIMA researchers celebrate the results of a decade of work, other physicists are championing an alternative vision for the next generation of climate models — one that skips the thorny issues of cloud parameters by largely abandoning the equations of fluid dynamics.
但就在 CLIMA 研究人员庆祝十年工作成果的同时，其他物理学家也在倡导下一代气候模型的另一种愿景——一种通过很大程度上放弃流体动力学方程来跳过云参数的棘手问题的模型。
Marveling through the windows of propeller planes, Bretherton developed an appreciation for — and apprehension about — the complexity of clouds.
布雷瑟顿透过螺旋桨飞机的窗户惊叹不已，对云的复杂性产生了欣赏和担忧。
He would spend decades trying to grapple with clouds through LES techniques, but he became frustrated as climate models seemed to hit an accuracy ceiling.
他花了几十年的时间试图通过 LES 技术来解决云问题，但当气候模型似乎达到了精度上限时，他感到沮丧。
Perhaps, he eventually concluded, clouds contained too much richness to be imitated with parameters, even ones based on high-resolution large eddy simulations.
他最终得出结论，也许云包含太多丰富的参数，无法用参数来模拟，即使是基于高分辨率大涡流模拟的参数也是如此。
In 2017, he wondered whether there might be a way for climate scientists to bypass the tyranny of the Navier-Stokes equations and go straight to the ultimate source: real data describing the real atmosphere with real clouds.
2017年，他想知道气候科学家是否有办法绕过纳维-斯托克斯方程的束缚，直接找到最终来源：用真实云描述真实大气的真实数据。
Soon after, he found validation in climate’s sister field of weather.
不久之后，他在气候的姊妹领域天气中找到了验证。
Weather simulations resemble climate simulations.
天气模拟类似于气候模拟。
Until recently, the best weather forecasts relied on the Navier-Stokes equations to calculate how heat, pressure, and moisture in the air would interact to produce rain, sleet, and snow.
直到最近，最好的天气预报仍然依赖纳维-斯托克斯方程来计算空气中的热量、压力和水分如何相互作用产生雨、雨夹雪和雪。
In 2018 and 2020 , however, physicists and computer scientists teamed up to pioneer a new strategy.
然而，在 2018 年和 2020 年，物理学家和计算机科学家联手开创了一项新策略。
The Energy Exascale Earth System Model, one of the most detailed climate simulations to date, was developed over the course of a decade.
百亿亿次能源地球系统模型是迄今为止最详细的气候模拟之一，历经十年的发展。
Hyun Kang/ORNL, E3SM, U.S. Department of Energy
Hyun Kang/ORNL、E3SM、美国能源部
They were inspired by video generation, a task already mastered by computer scientists.
他们受到视频生成的启发，这是计算机科学家已经掌握的一项任务。
The process consisted of training a type of machine learning algorithm called a neural network on a corpus of videos so that the network would learn to take in a frame of a new video and output a plausible next frame.
该过程包括在视频语料库上训练一种称为神经网络的机器学习算法，以便网络能够学习接收新视频的帧并输出合理的下一帧。
By looping this process, the algorithm could generate whole videos.
通过循环这个过程，算法可以生成整个视频。
Weather forecasters wondered if they might do the same.
天气预报员想知道他们是否也会这样做。
If they could train a neural network on historical data about the weather in Kansas and then feed it the status of the atmosphere in Kansas at noon, could they generate an accurate guess as to what would happen in the state that evening — no Navier-Stokes equations required?
如果他们能够利用堪萨斯州天气的历史数据训练一个神经网络，然后向其提供堪萨斯州中午的大气状况，他们是否可以准确猜测该州当晚会发生什么——不需要纳维-斯托克斯方程？
At first, the answer was “not really.” But by 2022, multiple groups were proving that answer wrong.
起初，答案是“不是真的”。但到 2022 年，多个团体证明这个答案是错误的。
“No one expected the weather enterprise to transform as rapidly as it has,” said Mike Pritchard , director of climate simulation research at Nvidia.
英伟达气候模拟研究总监迈克·普里查德 (Mike Pritchard) 表示：“没有人预料到气象企业会像现在这样迅速转型。”
Today AI weather forecasts are roughly 10% more accurate than physics-based weather simulations.
如今，人工智能天气预报比基于物理的天气模拟准确率大约高 10%。
Throughout this weather revolution, Bretherton and his collaborators were building similar tools for forecasting the climate.
在这场天气革命中，布雷瑟顿和他的合作者正在构建类似的气候预测工具。
In 2024, they released the Ai2 Climate Emulator version 2 (ACE2) , a neural network trained on how the atmosphere has behaved over the past 50 years (bolstered, where data is patchy, with fill-in-the-gap simulations).
2024 年，他们发布了 Ai2 气候模拟器版本 2 (ACE2)，这是一个针对过去 50 年大气变化进行训练的神经网络（通过填充模拟，在数据不完整的情况下得到支持）。
This data incorporates the effects of real clouds on the real atmosphere, and so ACE2 makes forecasts that also reflect that influence.
该数据包含了真实云对真实大气的影响，因此 ACE2 做出的预测也反映了这种影响。
Similar to CLIMA, with its improved parameters, ACE2 smuggles in clouds indirectly.
与 CLIMA 类似，ACE2 具有改进的参数，间接在云中走私。
This simulation, produced with the Energy Exascale Earth System Model, shows a hurricane approaching North America from over the Atlantic Ocean.
该模拟由百亿亿次级能源地球系统模型生成，显示了一场飓风从大西洋上空逼近北美。
Mathew Maltrud/Los Alamos National Laboratory
马修·马尔特鲁德/洛斯阿拉莫斯国家实验室
Scientists can feed ACE2 a snapshot of the atmosphere at one moment, then use it to predict how it might look six hours later, then six hours after that, and so on.
科学家可以向 ACE2 提供某一时刻的大气快照，然后用它来预测六小时后、六小时后的情况，依此类推。
The futures it sees have many of the rich atmospheric events seen in the real world: cyclones, abrupt warming events in the stratosphere, and other familiar phenomena.
它所看到的未来有许多在现实世界中看到的丰富的大气事件：气旋、平流层突然变暖的事件，以及其他熟悉的现象。
But are these machine visions actually useful as mid-term forecasts?
但这些机器视觉真的有用作为中期预测吗？
Recent work suggests that they are.
最近的研究表明确实如此。
Last year, the United Kingdom’s national meteorological service tasked ACE2 with peering one full season into the future.
去年，英国国家气象局责成 ACE2 展望未来一个完整的季节。
They found that, for a 23-year period beginning in 1993, ACE2 could start with the sea surface temperature in one season and predict global temperatures and precipitation three months later nearly as well as the best Navier-Stokes–based simulation.
他们发现，从 1993 年开始的 23 年期间，ACE2 可以从一个季节的海面温度开始，预测三个月后的全球气温和降水，几乎与基于 Navier-Stokes 的最佳模拟一样。
Moreover, where a traditional physics-based simulation might take hours to run on a supercomputing cluster, the ACE2 simulations took two minutes on a single machine.
此外，传统的基于物理的模拟在超级计算集群上可能需要几个小时才能运行，而 ACE2 模拟在单台机器上只需要两分钟。
What’s thus far unproved — and the topic of fierce debate — is whether algorithms like ACE2 can keep up over the long term.
迄今为止尚未得到证实的——也是激烈争论的话题——是像 ACE2 这样的算法是否能够长期跟上。
A weather forecast might focus on how a cold front will move over North America in the next 10 days, but climate forecasts must ultimately predict how temperatures will change over the whole planet in the next century.
天气预报可能会关注未来 10 天内冷锋将如何在北美移动，但气候预测最终必须预测下个世纪整个地球的温度将如何变化。
Physicists have good reasons for skepticism.
物理学家有充分的理由对此表示怀疑。
Unlike fluid dynamics equations, neural networks only approximate the laws of physics.
与流体动力学方程不同，神经网络仅近似物理定律。
If you run them for a long time, their small errors can begin to pile up.
如果你运行它们很长时间，它们的小错误就会开始堆积。
Another problem is that neural networks excel at reproducing complicated patterns in their training data, but climate predictions deal with events no one has seen before.
另一个问题是，神经网络擅长在训练数据中再现复杂的模式，但气候预测处理的是前所未见的事件。
“These are things that are trying to predict the future,” said Sarat Sreepathi , a computer scientist at Oak Ridge National Laboratory who coordinated the development of the Department of Energy’s flagship climate model.
“这些都是试图预测未来的东西，”橡树岭国家实验室的计算机科学家萨拉特·斯里帕蒂（Sarat Sreepathi）说，他负责协调能源部旗舰气候模型的开发。
“How much confidence do you have?
“你有多少信心？
If [your predictions are] based on physical principles, you might have a bit more.”
如果[你的预测]基于物理原理，你可能会得到更多。”
While individual researchers compete to develop their preferred methods of predicting the climate precisely, they recognize that theirs is a collective effort, in which progress on one side spurs progress on another.
尽管个别研究人员竞相开发自己喜欢的精确预测气候的方法，但他们认识到，这是一项集体努力，一方的进步会刺激另一方的进步。
In addition to training neural networks on real atmospheric data, as scientists did with ACE2, scientists are training neural networks on predictions from physics models — then using those neural networks to make new predictions at lightning speed.
除了像科学家对 ACE2 所做的那样，根据真实大气数据训练神经网络之外，科学家们还根据物理模型的预测来训练神经网络，然后使用这些神经网络以闪电般的速度做出新的预测。
Compared to the physics models themselves, “it’s 100 to 1,000 times faster,” Pritchard said.
与物理模型本身相比，“速度快了 100 到 1,000 倍，”Pritchard 说。
Pritchard sees this speedup as the killer AI climate app.
Pritchard 将这种加速视为杀手级人工智能气候应用程序。
That’s because the goal of climate forecasting isn’t to predict the exact state of the Earth in 30 years.
这是因为气候预测的目标不是预测 30 年后地球的确切状态。
Rather, the goal is to get a statistical sense of which possible futures are likely, which ones are rare, and how our fossil fuel habit disturbs that distribution.
相反，我们的目标是从统计上了解哪些可能的未来是可能的，哪些是罕见的，以及我们的化石燃料习惯如何扰乱这种分布。
While innovations aren’t likely to have a noticeable impact on the average person anytime soon, they feel transformative to researchers working in the field.
虽然创新不太可能很快对普通人产生明显影响，但对于该领域的研究人员来说，它们具有变革性的意义。
Even incremental improvements to how well we can anticipate future temperatures, rainfall patterns, and storms can add up to big benefits over time.
即使我们对未来气温、降雨模式和风暴的预测能力不断提高，随着时间的推移，也会带来巨大的好处。
And researchers are making more than incremental improvements.
研究人员所做的不仅仅是渐进式的改进。
“We’ll have a different notion for how predictable the Earth system is in 10 years,” Pritchard said.
“十年后我们会对地球系统的可预测性有不同的看法，”普里查德说。
“All my climate-scientist colleagues are buzzing with the art of the possible.”
“我所有的气候科学家同事都在谈论可能性的艺术。”
Of course, what we do with that information is up to us.
当然，我们如何处理这些信息取决于我们自己。
Current climate models clearly show that our carbon emissions are making the planet a hotter, more dangerous place.
目前的气候模型清楚地表明，我们的碳排放正在使地球变得更热、更危险。
The next generation of models, with their improved implicit handling of clouds, will tell us with greater precision how much hotter, and how much more dangerous.
下一代模型通过改进对云的隐式处理，将更精确地告诉我们有多热，有多危险。
But no simulation can tell us whether gaining that knowledge will spur us to aim for a different future.
但没有任何模拟可以告诉我们获得这些知识是否会激励我们瞄准不同的未来。
“It’s a question that goes beyond just the science of it,” Schneider said.
“这是一个超越科学范畴的问题，”施奈德说。
“It’s hard to predict.”
“这很难预测。”
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