FROM THE FIELD
A Leap Toward a Sustainable Earth: Q+A with Climate Expert Pierre Gentine
This story was originally published by Columbia Engineering.
A critical hurdle in advancing climate forecasts are limitations in the predictive models themselves, an area where artificial intelligence (AI) can play a key role. Pierre Gentine, professor of geophysics in Columbia University’s Departments of Earth and Environmental Engineering and Earth Environmental Sciences, is pioneering new methodologies leveraging the power of AI with physical knowledge so that global climate models can benefit from a more refined, and more realistic, use of machine learning. As director of an innovative new center devoted to game-changing solutions to address climate change, Gentine and his collaborators are building better, more predictive climate models to ensure a safer planet, for today and well into the future. He explains more about this work in the video and Q&A below.
Video credit: Jane Nisselson
The Learning the Earth with Artificial Intelligence and Physics (LEAP) center launched in 2021. What has been the core mission of the center?
LEAP’s mission is to increase the reliability, utility, and reach of climate projections by integrating climate and data science. Our primary strategy is to improve near-term climate projections by merging physical modeling with machine learning across a continuum, from expertise in climate science and climate modeling to cutting-edge machine learning algorithms. This will really help both the climate and data sciences communities; climate scientists and modelers struggle to fully integrate the plethora of existing datasets into their models, and while machine learning algorithms such as ChatGPT have been good at emulating, they are not so good at extrapolating or predicting extremes. By combining both approaches, we hope that LEAP will trigger a significant advancement in data science algorithms applied to physical problems. The center is incorporating physics and causal mechanisms into machine learning algorithms for better generalization and extrapolation, while optimally using the wealth of data available to climate science, in order to better predict the future.
Can you touch on some of the key projects LEAP has been focusing on? Any breakthroughs?
LEAP is working on different aspects of climate science and data science, covering not only research but also knowledge transfer and education. Some of the recent research breakthroughs in our center have shown that by using AI, we can discover new previously unknown physics (of clouds or ocean turbulence). We hope to use this “new” physics in climate models to improve their accuracy, especially with extremes. Essentially we’re using vast amounts of data, such as those from satellites, so that we can refine climate models and their evaluation and improve our predictions. It’s also critical that we share this information with the public and private sectors in a user-friendly way.
We also hope to break some of historic silos with climate research — climate research doesn’t translate easily to the public or to the private sectors, as it is very technical and difficult to use. We are creating a cloud platform where we can provide climate data more widely, and also engage with our colleagues in the field and beyond to see what is actually useful to them. For instance, a business will want to know how much the frequency of flooding or heat waves will change in the future so they could adapt their business. With LEAP, we expect to be able to refine our models so that we can help offer more precise predictions.
There has been a lot of attention given to AI, and AI is currently being applied in your climate research and others. How has AI revolutionized climate modeling, and what’s the state of that today?
Over the last five years, there has been an explosion in the use of AI to better understand climate models and to better represent physical processes (such as clouds, ocean, and terrestrial carbon cycle or ocean turbulence). The next big push is how to integrate those AI algorithms within climate models that have historically used empirical equations.
The information we are predicting in the future is really uncertain. And there are many reasons for that, including the sheer complexity of all of those processes that we are trained to do when we build climate models. So we are focused on reducing and narrowing those uncertainties to give everyone, from policy makers to business leaders to educators, accurate climate projections to inform their own decision-making. For instance in the agricultural sector, being able to provide precise information on future climate could heavily impact crop productivity and yield.
The goal is to improve climate modeling so that we can say how many days a drought may last, or what the likelihood of flooding is in New York City or any other specific low-lying area. These are questions that are really critical. Right now, the range of estimates is just so dramatic that it’s challenging to actually implement plans. We need to act now on this problem, and really then begin to fight climate change.
What excites you the most about where the field is headed and how LEAP’s work will impact our future?
We are witnessing a true transformation and it’s centered around data — and the use of observation and simulation data to answer new hypotheses or questions that could not be addressed until now. Of course, as for any new field, we still need to be cautious and ensure that the results are sound and reproducible, but I am quite excited to see where the field is heading and witness the incredible pace of advances. I think that we are really witnessing a revolution in the climate sciences.
On this Earth Day 2023, what would you like to tell your children about how we are investing in our planet, how important climate change research is, and what you hope to leave to them and future generations?
What I tell my three kids is to think about others. We need to use fewer resources, to limit our footprint, to recycle, and to emit less. In other words, we need to mitigate climate change. But we also need to adapt. Climate change is now part of our everyday lives, as evidenced by the explosion in the number — and intensity — of recent extreme events (droughts, floods, etc). It’s clear we need to change as a society. This change has to be embraced by everyone and thus we need widespread support (government, policies) so that the entire country can adapt to those changes, not just a small fraction of our society. Otherwise, we will have failed as a society and country. Social justice and climate change are tightly connected — we need both to achieve our goals.
Great insights on the need for accurate measurements of climate and physical risk. Chubb, the largest P&C (Property & Casualty) insurer in the world, announced that will only ensure Oil and Gas projects if they cut methane emissions but who has been monitoring those emissions and other metrics at project level ? They will also stop insuring in the WPAs (World Protected Areas) but how do they determine if a project or a company asset is in a WPA ? Where will they get the baseline data from, how do you benchmark against competition / other projects ?
One big opportunity in ESG and ECP is using technology is to marry different geospatial base data sets and come up with new ways of thinking about environmental climate and physical risk…which is what RS Metrics has created with Google Cloud:
Happy to share more !