How Big Data Might Curb Climate Change

More groups are using the emerging field of climate informatics to better understand global warming.
Mar 22, 2017 7:50 AM ET

Regardless of how much impact you believe humans have caused our climate—be that a little or a lot—global warming is an observable and empirical fact

But we don't necessarily have to wait until everyone agrees on a quantifiable cause to better understand how to prepare for, adapt to, and maybe even curb a hotter climate. In fact, the emerging field of climate informatics is doing just that—right now.

Consider the work of Claire Monteleoni from George Washington University. Together with her team of data scientists, she applies machine learning principles to accelerate our understanding of how the climate has changed. For instance, her group recently discovered that storms are increasingly moving towards the pole, "another indication that the climate is changing."

What that really means is that your local weather will change even more, including how much precipitation you get, whether your area will experience more droughts or floods, and how that will impact your local ecology and environment.

But more than just measuring and understanding the average temperature increases and related changes they cause, climate informatics also aims to study extreme weather systems, which have increased in number as the globe has slowly warmed over the last 100 years. 

Big data is also being used to better inform New York City on how its residents pull from one of the largest electrical grids in the world, Monteleoni says. And the Environmental Protection Agency is using climate informatics to enforce regulations and monitor which companies are compliant with emissions standards.

To further our understanding, influence future decisions, and create greater agreement on the causes of and reaction to global warming, the United Nations recently announced a climate change challenge to collect more data. Similarly, iNaturalist exists to help crowdsource recorded observations and discuss important findings.

Then there's the Madingley Model, free to download source code to help inform decision-makers about the impacts of their choices on biodiversity, ecosystems, and different scenarios of human development. "Unlike most other models of biodiversity, Madingley provides a rich understanding of why such responses might occur," the website asserts.

Much of the above, however, mostly speaks to increasing our understanding of climate change and developing community adaptation strategies. Is it really possible to expect big data and machine learning to actually curb the effects of global warming?

"Yes, I think so," says Monteleoni. "Citizen science can certainly help, but we still need experts to set up systems and overcome technical challenges. It's not just the data. Expertise must be used when applying models and algorithms to the data."

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