20241118|人工智能有望改善气候模型（上）

The diplomatic ructions at COP29, the United Nations climate conference currently under way in the Azerbaijani capital of Baku, are based largely on computer models.

目前，第29届联合国气候变化大会正在阿塞拜疆首都巴库举行，其外交争端主要基于计算机模型。

Some model what climate change might look like; others the cost of mitigating it. No model is perfect.

一些模型模拟气候变化的趋势；另一些模型模拟缓解气候变化的成本。没有一个模型是完美的。

Those modelling climate trends and impacts are forced to exclude many things,

那些模拟气候趋势和影响的人被迫排除许多因素，

either because the underlying scientific processes are not yet understood or because representing them is too computationally costly.

要么是因为尚未理解其背后的科学过程，要么是因为表示这些过程的计算成本太高。

This results in significant uncertainty in the results of simulations, which comes with real-world consequences.

这导致模拟结果存在很大的不确定性，并带来现实世界的后果。

Delegates' main fight in Baku, for example, will be over how much money poor countries should be given to help them decarbonise, adapt or recover.

例如，代表们在巴库的主要争论将是应该向贫穷国家提供多少资金来帮助它们脱碳、适应或恢复。

The amount needed for adaptation and recovery depends on factors such as sea-level rise and seasonal variation that climate modellers still struggle to predict with much certainty.

适应和恢复所需的金额取决于海平面上升和季节性变化等因素，气候模型专家仍然难以准确预测这些因素。

As negotiations become ever more specific, more accurate projections will be increasingly important.

随着谈判变得越来越具体，更准确的预测将变得越来越重要。

The models that carry most weight in such discussions are those run as part of the Coupled Model Intercomparison Project (CMIP),

在这些讨论中，最有分量的模型是耦合模型比对计划(CMIP)中运行的模型，

an initiative which co-ordinates over 100 models produced by roughly 50 teams of climate scientists from around the world.

该计划协调了来自世界各地的大约50个气候科学家团队制作的100多个模型。

All of them attempt to tackle the problem in the same way: splitting up the world and its atmosphere into a grid of cells,

所有这些模型都试图以同样的方式解决问题：将世界及其大气分成一个网格，

before using equations representing physical processes to estimate what the conditions in each cell might be and how they might change over time.

然后使用表示物理过程的方程来估计每个网格中的条件可能是什么以及它们可能如何随时间变化。

When CMIP started in 1995, most models used cells that were hundreds of kilometres wide-

当CMIP于1995年启动时，大多数模型使用的网格宽度为数百公里——

meaning they could make useful predictions about what might happen to a continent, but not necessarily to individual countries.

这意味着它们可以对一个大陆可能发生的情况做出有用的预测，但不一定对单个国家可能发生的事情做出有用的预测。

Halving the size of cells requires roughly ten times more computing power; today's models, thousands of times more powerful, can simulate cells of around 50km per side.

将网格大小减半需要大约十倍的计算能力；今天的模型功能强大了数千倍，可以模拟每边约50公里的网格。

Clever computational tricks can make them more detailed still.

巧妙的计算技巧可以使它们更加详细。

They have also grown better at representing the elaborate interactions at play between the atmosphere, oceans and land-

它们还能更好地表示大气、海洋和陆地之间复杂的相互作用——

such as how heat flows through ocean eddies or how soil moisture changes alongside temperature.

例如热量如何通过海洋涡流流动，或者土壤湿度如何随着温度而变化。

But many of the most complex systems remain elusive.

但许多最复杂的系统仍然难以捉摸。

Clouds, for example, pose a serious problem, both because they are too small to be captured in 50km cells and because even small changes in their behaviour can lead to big differences in projected levels of warming.

例如，云层就构成了一个严重的问题，因为它们太小，无法在50公里的单元内捕捉到，而且即使它们的行为发生微小变化，也会导致预测的变暖水平出现巨大差异。

Better data will help. But a more immediate way to improve the climate models is to use artificial intelligence (AI).

更好的数据会有所帮助。但改进气候模型更直接的方法是使用人工智能(AI)。

Model-makers in this field have begun asserting boldly that they will soon be able to overcome some of the resolution and data problems faced by conventional climate models and get results more quickly, too.

该领域的模型制作者已经开始大胆宣称，他们很快就能克服传统气候模型面临的一些分辨率和数据问题，并更快地获得结果。

Engineers from Google have been among the most bullish.

谷歌的工程师是最乐观的人之一。

Neuralgcm, the company's leading ai weather and climate model, has been trained on 40 years of weather data and has already proved itself to be as good at forecasting the weather as the models for and by which these data were originally compiled.

该公司领先的人工智能天气和气候模型Neuralgcm已经接受了40年的天气数据训练，并且已经证明自己在预测天气方面与最初编制这些数据的模型一样出色。