Last year was the hottest on record. Around the world, we are witnessing more extreme events—from massive forest fires to floods to “hot tub’” ocean temperatures—with devastating consequences for human life and our planet’s biodiversity. The economic costs are already tremendously high. Despite mounting feelings of eco-anxiety, my colleagues and I firmly believe that there is still much that can be done to save our planet.
There is still a lot of uncertainty in climate projections; shortcomings in both climate process understanding and computing capacities limit the accuracy of climate projections needed for optimal adaptation and resilience to climate change. Using AI helps to narrow down the uncertainties in climate modeling that can, in turn, be used to optimize adaptation, and potentially maximize equitable allocation of resources and inform actionable decision-making. In fact, AI applications to address a range of climate and environmental problems are rapidly expanding.
While we absolutely must mitigate greenhouse gas emissions to slow climate change, we also need accurate information about what is going to happen so we can better plan and adapt to the changes that are already baked into the global climate system: with current policies unchanged, we’re on course to exceed 2 degrees Celsius—over 3.5 degrees Fahrenheit—of warming this century.
Columbia University, where I’m a faculty member at the Fu Foundation School of Engineering and Applied Science and at the Climate School, along with the University at Albany, State University of New York and Esri have just released an all-encompassing Landscape Assessment of AI for Climate and Nature that was commissioned by and in collaboration with the Bezos Earth Fund. The report collates current AI applications and opportunities across several climate change mitigation, adaptation and nature sectors, and is part of a package accompanying the Bezos Earth Fund $100 million AI for Climate and Nature Grand Challenge, aimed at leveraging AI to combat climate change and protect nature. The Grand Challenge is a call to action, aimed at funding AI solutions in biodiversity conservation, power grid optimization, sustainable proteins and other innovative ideas.
Since 2021, when I became co-director of an NSF Science and Technology Center at Columbia University dedicated to improving climate projections by merging physical modeling with AI, I have been working with colleagues around the world to use AI to make our climate projections more accurate and provide a modern cloud platform for climate data.
The current generation of large AI models consumes tremendous resources. If our hope is that this newly transcendent technology will combat—rather than hasten—climate change and the destruction of nature, it is imperative that our society moves quickly to foster and fund cross-disciplinary collaborations to put the capabilities of AI tools in the hands of domain experts. We also must reimagine AI benchmarks to reflect these priorities and pursue standards of data accessibility by encouraging open data, codes and models to drive algorithmic and application developments while preserving privacy.
It is essential to invest in educational training and workforce development to support AI specialists with joint expertise in sectoral domains. Our NSF-funded center, LEAP, is already responding to this call to action, equipping the next generation of scientists with skill sets at the interface of climate science and AI and with a modern climate data exploration platform. Many colleagues are adopting a holistic approach to AI education, integrating foundational and applied AI concepts into curricula across disciplines to prepare graduates for a rapidly changing world.
Further, we must ensure that individuals and communities across the globe have equal opportunity to access the information and computation necessary to use AI tools in pursuit of their current and future well-being as it relates to climate change. This is relevant at the local scale, applying solutions that can have an impact and building the workforce in historically marginalized and disadvantaged communities most impacted by climate change, and at the global scale, investing in AI infrastructure, initiatives and education in the Global South where most of the next generation of this planet’s population is being born.
Robust technology governance frameworks to safeguard ethical, sustainable, trustworthy, safe and secure AI deployment are also needed. Already, governing bodies across the world, such as in the U.S., E.U. and U.N. General Assembly, are beginning to enact such policies. They must support governmental funding supporting AI for good.
Climate change is here. So is the era of AI. It is still possible to achieve more justice and prosperity on a thriving planet if we strategically apply our science, education and innovation towards these objectives.
We are, after all, in this together.
This story was originally published by Columbia Engineering.
Pierre Gentine is the Maurice Ewing and J. Lamar Worzel Professor of Geophysics in the Departments of Earth and Environmental Engineering at Columbia Engineering and Earth Environmental Sciences, a member of the Data Science Institute and director of the Learning the Earth with Artificial Intelligence and Physics (LEAP) Center. Gentine is also a professor of Earth and environmental sciences and of climate at Columbia Climate School.
Views and opinions expressed here are those of the authors, and do not necessarily reflect the official position of the Columbia Climate School, Earth Institute or Columbia University.
