Artificial intelligence is helping improve climate models – Crypto News

No model is perfect. Those modelling climate trends and impacts are forced to exclude many things, either because the underlying scientific processes are not yet understood or because representing them is too computationally costly. This results in significant uncertainty in the results of simulations, which comes with real-world consequences. 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. 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. As negotiations become ever more specific, more accurate projections will be increasingly important.

The models that carry most weight in such discussions are those run as part of the Coupled Model Intercomparison Project (CMIP), an initiative which co-ordinates over 100 models produced by roughly 50 teams of climate scientists from around the world. All of them attempt to tackle the problem in the same way: splitting up the world and its atmosphere into a grid of cells, before using equations representing physical processes to estimate what the conditions in each cell might be and how they might change over time.

When CMIP started in 1995, most models used cells that were hundreds of kilometres wide—meaning they could make useful predictions about what might happen to a continent, but not necessarily to individual countries. 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.

Clever computational tricks can make them more detailed still. They have also grown better at representing the elaborate interactions at play between the atmosphere, oceans and land—such as how heat flows through ocean eddies or how soil moisture changes alongside temperature. But many of the most complex systems remain elusive. 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.

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

Engineers from Google have been among the most bullish. 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. In a paper published in Nature in July, Google claimed its model will soon be able to make projections over longer timescales faster, and using less power, than existing climate models. With additional training, the researchers also reckon NeuralGCM will be able to offer more certainty in important areas like shifts in monsoons and tropical cyclones.

This optimism, say the researchers, comes from the unique abilities of machine-learning tools. Where existing models sidestep intractable physics problems by using approximation, NeuralGCM’s creators claim it can be guided by spotting patterns in historical data and observations. These claims sound impressive, but are yet to be evaluated. In a preprint posted online in October, a team of modellers from the Lawrence Livermore National Laboratory in California noted that NeuralGCM will remain limited until it incorporates more of the physics at play on land.

Others are more sceptical that AI methods used in short-term weather forecasting can be successfully applied to the climate. “Weather and climate are both based on physics,” says Gavin Schmidt, a climate scientist who runs NASA’s Goddard Institute for Space Studies, but pose different modelling challenges. For one thing, the available data are rarely of the same quality. For weather forecasting, huge swathes of excellent data are generated every day and, therefore, able to continuously validate the previous day’s predictions. Climate models do not enjoy the same luxury. In addition, they face the challenge of simulating conditions more extreme than any previously observed, and over centuries rather than days.

AI can nonetheless help improve climate models by addressing another major source of uncertainty: human behaviour. Until now, this has been overcome by codifying different social and political choices into sets of fixed scenarios which can each then be modelled. This method makes evaluations possible, but is inflexible and often vague. With the help of AI, existing tools known as emulators can customise conventional models to suit their end users’ needs. Such emulators are now used by cities planning infrastructure projects, by insurers assessing risk and by agricultural firms estimating changes in crop yields.

Unlike models such as Google’s NeuralGCM, which is trained on the same weather data as today’s top climate models, emulators are typically trained on the outputs of full-scale climate models. This allows them to piggyback on improvements to the models themselves—both the new physics they are able to model and the ways in which they extrapolate beyond historical data. One such emulator, developed by the Commonwealth Scientific Industrial Research Organisation in Australia in 2023, for example, was capable of adjusting predictions linked to future emissions levels one million times faster than the model it was trained on.

Reducing the uncertainties in climate models and, perhaps more important, making them more widely available, will hone their usefulness for those tasked with the complex challenge of dealing with climate change. And that will, hopefully, mean a better response.

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