Guest essay by Eric Worrall

A Team of Scientists plans to use AI to improve climate modelling of sub-grid scale phenomena such as turbulence and clouds. But there is no reason to think an AI will have any more luck than human climate modellers.

International Collaboration Will Use Artificial Intelligence to Enhance Climate Change Projections

MARCH 23, 2021 10:18 PM AEDT

A team of scientists, backed by a $10 million grant from Schmidt Futures, will work to enhance climate-change projections by improving climate simulations using artificial intelligence. 

$10 million effort, backed by Schmidt Futures, to be led by NYU Courant Researcher

A team of scientists, backed by a $10 million grant from Schmidt Futures, will work to enhance climate-change projections by improving climate simulations using artificial intelligence (AI).

Led by Laure Zanna, a professor at New York University’s Courant Institute of Mathematical Sciences and NYU’s Center for Data Science, the international team will leverage advances in machine learning and the availability of big data to improve our understanding and representation in existing climate models of vital atmospheric, oceanic, and ice processes, such as turbulence or clouds. The deeper understanding and improved representations of these processes will help deliver more reliable climate projections, the scientists say.

“Despite drastic improvements in climate model development, current simulations have difficulty capturing the interactions among different processes in the atmosphere, oceans, and ice and how they affect the Earth’s climate; this can hinder projections of temperature, rainfall, and sea level,” explains Zanna, part of the Courant Institute’s Center for Atmosphere Ocean Science and a visiting professor at Oxford University. “AI and machine-learning tools excel at extracting complex information from data and will help bolster the accuracy of our climate simulations and predictions to better inform the work of policymakers and scientists.”

Due to the complexity of the atmosphere, ocean, and ice systems, scientists rely on computer simulations, or climate models, to describe their evolution. These models divide up the climate system into a series of grid boxes, or grid cells, to mimic how the ocean, atmosphere, and ice are changing and interacting with one another. However, the number of grid boxes chosen is limited by computer power; currently, climate models for multi-decade projections use grid box sizes measuring approximately 50 km to 100 km (roughly 30 to 60 miles). Consequently, processes that happen on scales that are smaller than the grid cell–clouds, turbulence, and ocean mixing–are not well captured.

Read more:

Top marks for admitting model temperature projections struggle to capture important processes. Clouds, storms, ocean mixing and turbulence are likely the reason open ocean surface temperatures in the tropics are capped at 30c.

But why do I think the AI approach will struggle to improve on human efforts?

The reason is decades of effort to improve understanding the global climate has not answered basic questions, like how much does global temperature change in response to adding more CO2, and human brains are far more powerful than any AI.

AIs work best when the solution is easy to approach, when a gentle slope of improving results provides a strong indication to the AI that it is making progress.

A gentle slope guides the AI towards the optimum solution.

But I do not think this is a good description of the climate system. The lack of progress over the last three decades, despite thousands of intelligent people dedicating years of their lives to the effort, implies the solution to better climate modelling is very difficult to find. Outside the narrow range of correct solutions there is likely a vast wilderness of poor quality answers, with very little indication of which direction the AI needs to travel to discover a high quality solution.

Either that, or there is something fundamental missing from the theory, and a high quality solution will not be possible until the missing piece of the puzzle is found.

Even a powerful AI struggles to search a multi-dimensional problem space when the correct answer is poorly signposted – there are many more ways to be wrong than right.

via Watts Up With That?

March 24, 2021 at 12:46PM