内容来源：https://www.quantamagazine.org/climate-physicists-face-the-ghosts-in-their-machines-clouds-20260220/

内容总结：

气候物理学家直面模型中的“幽灵”：云

2008年10月，气候物理学家克里斯·布雷瑟顿乘坐一架满载仪器的C-130涡轮螺旋桨飞机，从智利北部海岸起飞，深入云层收集数据。他的长期目标，是应对气候科学中最棘手的难题之一：云。

近二十年过去，全球气温已上升约0.5摄氏度，而云——既能反射阳光又能捕获热量——依然是气候预测中最大的不确定性来源。世界上最顶尖的超级计算机，其算力仍不足以在模拟的“数字地球”中直接生成微小的数字云朵。因此，科学家们正竞相开发“变通方案”。

目前，两大技术路径正在竞争。以加州理工学院的塔皮奥·施耐德为代表的团队，致力于利用人工智能优化传统物理模型中的“云参数”，间接体现云的影响。他们借助与谷歌合作建立的、包含8000多个数字云的庞大数据库训练算法，显著提升了模型的准确性。其新一代全球气候模型已于今冬开始运行，初步测试显示其关键指标精度有望达到现有模型的两倍。

与此同时，布雷瑟顿所在的艾伦人工智能研究所团队则采取了更激进的策略。他们开发的ACE2模型，直接利用过去50年真实大气数据训练神经网络，绕过复杂的流体动力学方程来预测气候。该模型在短期季节预测上已媲美传统物理模型，且计算速度极快。然而，其长期预测的可靠性仍受质疑，因为神经网络可能累积误差，且难以应对训练数据中未曾出现的气候状态。

尽管方法不同，科学家们共享着紧迫感。“气候正在快速变化，”布雷瑟顿强调，“一百年后拥有完美模型对解决气候危机毫无用处。”当前模型已清晰表明碳排放正使地球变得更热、更危险。下一代模型将通过更精准地刻画云的影响，告诉我们具体会热多少、危险几何。

然而，正如施耐德所言，如何运用这些知识来塑造不同的未来，“是一个超越科学本身的问题”。模型的进步照亮了前路，但选择走向何方，依然取决于人类自己。

中文翻译：

气候物理学家直面机器中的幽灵：云

引言

2008年10月，克里斯·布雷瑟顿乘坐一架C-130涡轮螺旋桨飞机从智利北部海岸起飞。当时天色太暗，看不见下方阿塔卡马沙漠的沙丘，但黑暗正合布雷瑟顿之意。这位研究者并非来观光。他坐在飞行员正后方，将全部注意力集中在天空上。

飞机上塞满了仪器，机翼上布满传感器和其他设备。布雷瑟顿的当务之急是帮助飞行员收集周围冰、水蒸气和气压的信息。他的长期目标则是利用这些数据——以及他将在加利福尼亚、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。

飞机掠过一朵蓬松的积云，布雷瑟顿看到了一道彩虹般的色散光谱。这很奇怪；这朵云看起来太薄，无法容纳足以如此折射光线的大水滴。"六到九小时的飞行很少会无聊，"布雷瑟顿说，"因为我们总会遇到令人惊讶的云结构，动摇我们的科学成见。"他后来得出结论，空气一定异常洁净，以至于云中的水蒸气在异常少的颗粒上凝结成了异常大的水滴。

自布雷瑟顿登上那架飞机以来的近二十年里，全球气温已升高了约0.5摄氏度。而既能反射阳光又能捕获热量的云，仍然是气候预测中最大的不确定性来源。世界上最顶尖的超级计算机还远不足以在它们模拟的巨大数字地球中包含微小的数字云。因此，气候科学家正在开发变通方法，即通过技术手段，诱使相对无云的气候模拟像包含了全套逼真云层一样，产生涡旋、风暴和变暖效应。

过去几年里，物理学家之间展开了一场竞赛，旨在为气候预测打造下一代"水晶球"。布雷瑟顿，现在艾伦人工智能研究所工作，是其中一位杰出的参赛者。加州理工学院的塔皮奥·施耐德是另一位。

激励这些新努力的，是被归类为人工智能的机器学习技术的兴起。施耐德借助人工智能，更好地将云的影响纳入使用物理方程来预测未来的气候模型中。布雷瑟顿则担心这些方程永远无法完全捕捉云的行为，他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，几乎完全不依赖物理方程。

尽管施耐德、布雷瑟顿和其他物理学家在方法上存在分歧，但他们都有一种紧迫感。"气候正在快速变化，"布雷瑟顿说，"一百年后拥有一个完美的模型，对于解决气候危机将毫无用处。"

虚假云库

如果人类继续以目前的速度向大气中排放碳，一些模拟预测，在未来大约50年内，气候将走向升温2摄氏度。另一些模拟则预测升温6摄氏度。第一种可能性将导致未来严重天气事件增加，粮食和水资源短缺加剧——这对许多社区来说是危险的情况，但全球人口或许能够适应。然而，后一种可能性则可能引发足够的灾难和饥荒，足以彻底动摇人类文明。"六度会相当可怕，"施耐德说。

现代气候模拟考虑了地球大气、海洋、陆地、冰层等的影响，每个模型都以自己的方式处理这些组成部分。但预测之间超过一半的差异来自于模拟如何处理云。"如果你的云量预测偏差几个百分点——2%或3%——你得到的升温幅度就会相差几摄氏度，"康涅狄格大学研究云的物理学家乔治·马修说。

2022年，美国能源部委托当时世界上最强大的超级计算机"前沿"运行一个新的旗舰气候模型。该模型基于流体动力学物理原理，通过一组称为纳维-斯托克斯的方程进行计算。从某种意义上说，开发该模型标志着通过提高计算机模拟分辨率来改进气候模型准确性的六十年事业达到了顶峰。模拟的分辨率从每像素数千公里，到数百公里，再到——在这个案例中——三公里。

但即使这个最先进的模型也无法直接解释云的微妙累积效应，因为云的跨度可能只有几米，并且由更微小的气流塑造。"要模拟低层云，我们需要的计算能力大约是现有的一千亿倍，"施耐德说，"所以这在我有生之年是不会发生的。"

由于无法直接将云添加到模型中，物理学家实际上不得不转而估算其影响。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，以间接捕捉云的影响。这些替代方程经过设计，能够产生数字大气流，其弯曲和旋转的方式与真正包含云的模型相同。在一个繁琐的过程中，研究人员调整这些因素，直到模型能够基于过去数据做出准确的预测。

但数据是零散的，所以物理学家也依靠直觉来指导。最终，很难判断一个模型的参数是否优于另一个。"你必须稍微猜测一下，"马修说。

将参数选择从一门艺术转变为一门科学的需求，是施耐德于2019年建立气候建模联盟（CLIMA）的原因之一。他希望通过训练机器挑选最佳参数来自动化这一过程，并减少主观性。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：加利福尼亚的云、中太平洋的云、冬季的云、夏季的云等等。

像布雷瑟顿这样的研究人员，只能偶尔负担得起驾驶飞机穿越真实云层的费用。因此，云物理学家转向次优选择：一种称为大涡模拟的纳维-斯托克斯模拟。"LES是我们拥有的针对有限区域和短时间的云湍流的最佳模型，"加州理工学院CLIMA研究员沈兆毅说。

问题在于，生成LES模拟成本也不低：它需要巨大的计算能力。沈兆毅说，直到不久前，研究人员只生成了几十个高质量的云模拟——这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。因此，几年前，施耐德向谷歌的科学家寻求帮助。

谢德·沙马斯和他在谷歌的合作者从头开始编写了一个LES算法，以在称为张量处理单元的定制计算机芯片上运行。他们在数千个这样的芯片上运行代码，不断生成一个又一个模拟。最终，他们建立了一个包含8000多个数字云的库，这些云原生存在于太平洋500个地点，覆盖所有四个季节。"谢德的库将改变游戏规则，"施耐德说，"我们从未有过这样的东西。"

施耐德和其他CLIMA研究人员现在已在这个数字"动物园"上训练了一种算法，并用它来配置新的云参数。这是施耐德认为将使CLIMA的全球气候模型成为领先的下一代模型的众多改进之一。

截至今年冬天，该模型终于启动并运行。该合作项目将于三月在日本的一次会议上公布该模型，但施耐德表示，初步测试表明他们正在顺利实现其主要目标：构建一个比其他任何模型都精确两倍的模型。"它在关键指标上比其他模型更准确——并且还有进一步改进的空间，"他说。

然而，就在CLIMA研究人员庆祝十年工作成果的同时，其他物理学家正在倡导下一代气候模型的另一种愿景——一种通过基本放弃流体动力学方程来绕过云参数棘手问题的方法。

预测一个世纪的天气

透过螺旋桨飞机的舷窗惊叹，布雷瑟顿对云的复杂性既欣赏又忧虑。他花了数十年时间试图通过LES技术来理解云，但随着气候模型似乎遇到了准确性的天花板，他感到沮丧。也许，他最终得出结论，云包含的丰富性太多，无法用参数来模仿，即使是基于高分辨率大涡模拟的参数。2017年，他思考气候科学家是否有可能绕过纳维-斯托克斯方程的"暴政"，直接诉诸终极来源：描述带有真实云的真实大气的真实数据。不久之后，他在气候学的姊妹领域——天气预报中找到了验证。

天气模拟类似于气候模拟。直到最近，最好的天气预报都依赖纳维-斯托克斯方程来计算空气中的热量、压力和湿度如何相互作用产生雨、雨夹雪和雪。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种新策略。

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。该过程包括在大量视频上训练一种称为神经网络的机器学习算法，使网络学会接收新视频的一帧并输出一个合理的下一帧。通过循环这个过程，算法可以生成整个视频。

天气预报员想知道他们是否也能这样做。如果他们能在堪萨斯州天气的历史数据上训练一个神经网络，然后输入堪萨斯州中午的大气状态，他们能否生成关于该州当晚将发生什么的准确猜测——而无需纳维-斯托克斯方程？

起初，答案是"不太行"。但到了2022年，多个团队证明这个答案是错误的。"没有人预料到天气预报领域会如此迅速地转变，"英伟达气候模拟研究主任迈克·普里查德说。如今，人工智能天气预报的准确性比基于物理的天气模拟高出约10%。

在这场天气革命期间，布雷瑟顿和他的合作者正在为气候预测构建类似的工具。2024年，他们发布了Ai2气候模拟器第二版（ACE2），这是一个神经网络，训练数据是过去50年大气的行为方式（在数据零散的地方，用填补空白的模拟数据加以补充）。这些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。与拥有改进参数的CLIMA类似，ACE2间接地"偷运"了云的影响。

科学家可以向ACE2输入某一时刻的大气快照，然后用它来预测六小时后、再六小时后的大气状况，依此类推。它所看到的未来包含了许多真实世界中丰富的大气事件：气旋、平流层突然变暖事件以及其他熟悉的现象。但这些机器视觉作为中期预测真的有用吗？

最近的研究表明它们是有用的。去年，英国国家气象局委托ACE2预测未来整整一个季节。他们发现，对于从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温度和降水，其准确性几乎与最好的基于纳维-斯托克斯的模拟相当。此外，传统的基于物理的模拟可能在超级计算机集群上运行数小时，而ACE2模拟在单台机器上只需两分钟。

目前尚未得到证实——并且是激烈辩论话题的是——像ACE2这样的算法能否长期保持准确性。天气预报可能关注冷锋在未来10天内如何在北美移动，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。

物理学家有充分的理由持怀疑态度。与流体动力学方程不同，神经网络只是近似物理定律。如果长时间运行它们，它们的小错误可能会开始累积。另一个问题是，神经网络擅长复制其训练数据中的复杂模式，但气候预测处理的是前所未见的事件。

"这些工具试图预测未来，"橡树岭国家实验室的计算机科学家萨拉特·斯里帕西说，他协调了美国能源部旗舰气候模型的开发。"你有多少信心？如果你的预测基于物理原理，你可能会更有信心一些。"

新的可能性艺术

尽管个别研究人员竞相开发他们偏好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。

除了像科学家对ACE2所做的那样，在真实大气数据上训练神经网络，科学家们还在物理模型的预测上训练神经网络——然后利用这些神经网络以闪电般的速度做出新的预测。与物理模型本身相比，"速度快了100到1000倍，"普里查德说。

普里查德将这种加速视为人工智能气候应用的杀手锏。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。相反，目标是获得一种统计意义上的认知：哪些可能的未来是可能发生的，哪些是罕见的，以及我们的化石燃料习惯如何扰动了这种分布。

虽然这些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，它们感觉是变革性的。即使对未来温度、降雨模式和风暴预测能力的微小改进，随着时间的推移也能累积成巨大的效益。而研究人员正在取得的不仅仅是微小的改进。

"十年后，我们对地球系统的可预测性将会有不同的认识，"普里查德说。"我所有的气候科学家同事都对可能性的艺术感到兴奋不已。"

当然，我们如何利用这些信息取决于我们自己。

当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。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地球会变热多少，危险会增加多少。但没有任何模拟能告诉我们，获得这些知识是否会激励我们追求一个不同的未来。"这是一个超越科学本身的问题，"施耐德说。"这很难预测。"

英文来源：

Climate Physicists Face the Ghosts in Their Machines: Clouds
Introduction
In October 2008, Chris Bretherton lifted off from the coast of northern Chile in a C-130 turboprop plane. It was too dark to see the sandy hills of the Atacama Desert below, but the darkness suited Bretherton just fine. The researcher wasn’t going sightseeing. Seated directly behind the pilots, he kept his focus entirely on the sky.
The plane was stuffed with instruments, and its wings bristled with sensors and other devices. Bretherton’s immediate mission was to help the pilots collect information about the ice, water vapor, and air pressure around them. 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.
The plane passed a fluffy cumulus, and Bretherton spotted a rainbowlike prism of colors. This was strange; the cloud seemed too thin to host the large droplets required to refract light in this way. “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.
In the nearly two decades since Bretherton boarded that plane, the globe has warmed by roughly half a degree Celsius. And clouds, which both reflect sunlight and trap heat, are still the biggest source of uncertainty in climate predictions. The world’s top supercomputers aren’t nearly super enough to include tiny digital clouds in the gigantic digital Earths they simulate. 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.
Over the last few years, a competition has broken out among physicists to build the next generation of these crystal balls for climate. Bretherton, now working at the Allen Institute for Artificial Intelligence (Ai2), is one prominent entrant. Tapio Schneider at the California Institute of Technology is another.
Galvanizing these new efforts is the rise of machine learning techniques categorized as artificial intelligence. Schneider leans on AI to better incorporate the effects of clouds into climate models that use physics equations to see what’s ahead. 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.
While Schneider, Bretherton, and other physicists differ in their approach, they share a sense of urgency. “Climate is changing fast,” Bretherton said. “Having a perfect model in 100 years will not be useful for solving the climate crisis.”
The Library of Fake Clouds
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. Others say 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. The latter possibility, however, could give rise to enough disaster and famine to fully destabilize human civilization. “Six degrees would be pretty frightening,” Schneider said.
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. But more than half of the variation between predictions comes from how the simulations treat clouds. “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.
In 2022, the Department of Energy tasked Frontier, then the world’s most powerful supercomputer, with running a new flagship climate model. The model was based on the physics of fluid dynamics, as calculated via a set of equations called Navier-Stokes. 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. Simulations had gone from thousands of kilometers per pixel, to hundreds, to — in this case — three.
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. “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.”
Unable to add clouds to their models directly, physicists have effectively resorted to estimating their influence. They add extra, nonphysical terms, called parameters, to the Navier-Stokes equations that indirectly capture the effects of clouds. These alternative equations are engineered to produce digital atmospheric currents that bend and curl in the ways that a truly cloudy model would. In a laborious process, researchers tweak these factors until the models produce accurate predictions based on past data.
But data is patchy, so physicists also let their intuition guide them. In the end, it’s tough to know whether one model’s parameters are better than another’s. “You have to guess a little bit,” Matheou said.
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. He hoped to automate the process and make it less subjective by training machines to pick the best parameters possible. 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.
Researchers like Bretherton can afford to fly planes through real clouds only so often. So cloud physicists turn to the next best thing: a Navier-Stokes simulation called a large-eddy simulation. “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.
The catch is that generating an LES also doesn’t come cheap: It takes a formidable amount of computational power. 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. So a few years ago, Schneider approached scientists at Google for help.
Sheide Chammas and his collaborators at Google coded an LES algorithm from scratch to run on custom computer chips called tensor processing units. They ran the code on thousands of these chips, churning out simulation after simulation. Ultimately, they developed a library of over 8,000 digital clouds native to 500 locations in the Pacific Ocean during all four seasons. “Sheide’s library will be game-changing,” Schneider said. “We’ve never had anything like it.”
Schneider and other CLIMA researchers have now trained an algorithm on this digital menagerie and used it to configure new cloud parameters. That was one of a number of improvements that Schneider believes will make CLIMA’s global climate model the leading next-generation model.
As of this winter, the model is finally up and running. 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. “It is more accurate than other models in key metrics — and with room for further improvement,” he said.
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.
Predicting a Century of Weather
Marveling through the windows of propeller planes, Bretherton developed an appreciation for — and apprehension about — the complexity of clouds. 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. Perhaps, he eventually concluded, clouds contained too much richness to be imitated with parameters, even ones based on high-resolution large eddy simulations. 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. Soon after, he found validation in climate’s sister field of weather.
Weather simulations resemble climate simulations. 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. In 2018 and 2020, however, physicists and computer scientists teamed up to pioneer a new strategy.
Hyun Kang/ORNL, E3SM, U.S. Department of Energy
They were inspired by video generation, a task already mastered by computer scientists. 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. By looping this process, the algorithm could generate whole videos.
Weather forecasters wondered if they might do the same. 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?
At first, the answer was “not really.” But by 2022, multiple groups were proving that answer wrong. “No one expected the weather enterprise to transform as rapidly as it has,” said Mike Pritchard, director of climate simulation research at Nvidia. Today AI weather forecasts are roughly 10% more accurate than physics-based weather simulations.
Throughout this weather revolution, Bretherton and his collaborators were building similar tools for forecasting the climate. 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). This data incorporates the effects of real clouds on the real atmosphere, and so ACE2 makes forecasts that also reflect that influence. Similar to CLIMA, with its improved parameters, ACE2 smuggles in clouds indirectly.
Mathew Maltrud/Los Alamos National Laboratory
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. 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. But are these machine visions actually useful as mid-term forecasts?
Recent work suggests that they are. Last year, the United Kingdom’s national meteorological service tasked ACE2 with peering one full season into the future. 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. 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.
What’s thus far unproved — and the topic of fierce debate — is whether algorithms like ACE2 can keep up over the long term. 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.
Physicists have good reasons for skepticism. Unlike fluid dynamics equations, neural networks only approximate the laws of physics. If you run them for a long time, their small errors can begin to pile up. 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.
“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. “How much confidence do you have? If [your predictions are] based on physical principles, you might have a bit more.”
The New Art of the Possible
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.
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. Compared to the physics models themselves, “it’s 100 to 1,000 times faster,” Pritchard said.
Pritchard sees this speedup as the killer AI climate app. That’s because the goal of climate forecasting isn’t to predict the exact state of the Earth in 30 years. 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.
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. Even incremental improvements to how well we can anticipate future temperatures, rainfall patterns, and storms can add up to big benefits over time. And researchers are making more than incremental improvements.
“We’ll have a different notion for how predictable the Earth system is in 10 years,” Pritchard said. “All my climate-scientist colleagues are buzzing with the art of the possible.”
Of course, what we do with that information is up to us.
Current climate models clearly show that our carbon emissions are making the planet a hotter, more dangerous place. 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. But no simulation can tell us whether gaining that knowledge will spur us to aim for a different future. “It’s a question that goes beyond just the science of it,” Schneider said. “It’s hard to predict.”