Early warning systems need alerts for non-rainfall triggers: climate scientist Tapio Schneider

New Delhi, Early warning systems need to be extended beyond rainfall events to include triggers of disasters such as the Nepal's flash floods even a few minutes of warning can help save the lives of people downstream, says climate scientist Tapio Schneider.

Schneider, a professor of environmental science and engineering at California Institute of Technology in the US, was at Ashoka University to address students and participate in a panel discussion.

Nepal continues to reel under the aftermath of August 26 flash floods, triggered by a high-altitude ice and rock collapse. The disaster sent debris flowing from Tibet through central Nepal, leaving over 1,200 people dead and thousands more missing.

"Early warning systems are important. We probably need to expand them beyond events triggered by rainfall to such events so that you can have at least a few minutes of warning for people downstream and save some lives that way," Schneider told PTI in an interview.

He added that permafrost is melting as the climate warms and one can expect more such events "even though any individual event will have different proximate causes".

The climate scientist added that the last few years have seen a global warming of 1.5 degrees Celsius and it is "inevitable" that the world will see a decade of 1.5 degrees Celsius warming.

Schneider also leads the 'Climate Modelling Alliance', a coalition of scientists, engineers and applied mathematicians from Caltech, Massachusetts Institute of Technology and NASA, building a new Earth system model.

He noted that the team of experts wanted to "exploit modern computing architectures and use data more extensively".

"...to inform the small-scale processes in the model because that's where all the uncertainties come from. are widely divergent and the problem is the small-scale processes that you need to represent better and that's where we invested most effort," he said.

India could expect to see more effects of global warming as efforts to improve air quality intensify, even though it has already seen more extreme hot days and more extreme precipitation, Schneider said.

He added, however, "More data will be helpful for our model, for all models. I think where one big opportunity now lies for places like Ashoka is that climate modelling, weather prediction and assessing climate risk has been confined to rich countries."

"It doesn't have to be that way anymore. You can run climate models on relatively affordable GPU resources anywhere else. You can do it with university teams," Schneider said.

On the issue of how rainfall is hard to predict in India, he suggested that AI models that are much cheaper to run than other large-scale ones can be developed in university groups and deployed with India Meteorological Department's data.

"The extensive monsoon rainfall record IMD has, for example. Use those data to make AI models better," the Caltech professor said.

Schneider also responded to a question related to a recent announcement of the US-based AI company OpenAI claiming to have solved a longstanding open problem of the Navier-Stokes equation in mathematics.

If confirmed, the solution would be the second of the seven renowned Millenium Prize Problems, each of which offers USD 1 million in reward to a winner. The development has divided mathematicians, with some applauding the achievement and others terming it an existential crisis for the maths community.

When asked if and how AI can contribute to research, Schneider said the output of machines as of now is messy and requires a human to turn it into genuine knowledge, but they may get better at providing explanations and eventually may accelerate research.

The scientist added however that the point of a mathematical proof is not just to give a yes-or-no answer, its point is to "enhance the global mathematical knowledge" and "enlarge the canon of what we understand about math and that needs to remain true".

"AI can help there. Right now, the output of the AI systems is pretty messy and not easy to comprehend so, it requires human work to make this into something that contributes," Schneider said.

"... They might get better at producing better explanations and then I would say, 'it's an accelerator to progress in math, just as it's an accelerator to progress for what we do in climate modelling already,'" the climate scientist concluded.

This article was generated from an automated news agency feed without modifications to text.