To address the challenges and opportunities around using ML/AI to advance Earth system science, the National Academies convened a workshop in February 2022 that brought together Earth system experts, ML/AI researchers, social and behavioral scientists, ethicists, and decision makers to discuss approaches to improving understanding, analysis, modeling, and prediction. This publication summarizes the workshop discussions and themes that emerged throughout the meeting.

But as in all AI applications, the use of machine learning and other … are finding to creatively apply artificial intelligence in weather, …

Artificial intelligence holds vast potential to help solve a number of challenging problems in Earth system science, from improving prediction of severe weather events to increasing the efficiency of climate models. But as in all AI applications, the use of machine learning and other techniques in environmental science has the potential to introduce biases that could deepen inequities. The authors of a new paper published in the journal Environmental Data Science argue that researchers must develop ethical, responsible, and trustworthy approaches to applying AI in Earth system science to ensure that unintentional consequences do not worsen environmental and climate injustice. "It's really exciting to see all the ways researchers are finding to creatively apply artificial intelligence in weather, climate, and other environmental science research," said David John Gagne, a scientist at the National Center for Atmospheric Research (NCAR) and a paper co-author. "But we have a responsibility to ensure that we don't cause more harm than good."

The extensive evidence feeding into the report includes observations collected from across land, ocean and atmosphere, as well as numerous simulations from the latest generation of climate models. However, in recent years, climate scientists have another tool available to them thanks to rapid advances in the development of artificial intelligence (AI) and, particularly, machine learning. In contrast to models that follow a set of explicit and pre-defined rules, machine learning aims towards building systems that can learn and infer such rules based on patterns in data. As a result, a new line of climate research is emerging that aims to complement and extend the use of observations and climate models. The overall goal is to tackle persistent challenges of climate research and to improve projections for the future.

Like superheroes capable of seeing through obstacles, environmental regulators may soon wield the power of all-seeing eyes that can identify violators anywhere at any time, according to a new Stanford University-led study. The paper, published the week of April 19 in Proceedings of the National Academy of Sciences (PNAS), demonstrates how artificial intelligence combined with satellite imagery can provide a low-cost, scalable method for locating and monitoring otherwise hard-to-regulate industries. Go to the web site to view the video. Brick production, a major industry in South Asia, is a source of pollution that threatens health. Regulating brick kilns is difficult because there is no database of kiln locations.

The role of deep learning in science is at a turning point, with weather, climate, and Earth systems modeling emerging as an exciting application area for physics-informed deep learning that can more effectively identify nonlinear relationships in large datasets, extract patterns, emulate complex physical processes, and build predictive models. "Deep learning has had unprecedented success in some very challenging problems, but scientists want to understand exactly how these models work and why they do the things they do," said Karthik Kashinath, a computer scientist and engineer in the Data & Analytics Services Group (DAS) at the National Energy Research Scientific Computing Center (NERSC) who has been deeply involved in NERSC's research and education efforts in this area. "A key goal of deep learning for science is how do you design and train a neural network so that it can capture accurately the complexity of the processes it seeks to model, emulate, or predict, and we're developing ways to infuse physics and domain knowledge into these neural networks so that they obey the laws of nature and their results are explainable, robust, and trustworthy." We caught up with Kashinath following the Artificial Intelligence for Earth System Science (AI4ESS) Summer School, a week-long virtual event hosted in June by the National Center for Atmospheric Research (NCAR) and the University Corporation for Atmospheric Research (UCAR) that was attended by more than 2,400 researchers from around the world. Kashinath was involved in organizing and presenting at the event, along with David John Gagne and Rich Loft of NCAR.

How does the Earth system function? The Earth system is incredibly complex, and understanding how it works is important for the survival of our species. Earth system science is an area of knowledge that has advanced rapidly in recent years. So, too, has artificial intelligence. To learn how the latter helps the former, we spoke with Markus Reichstein, who heads the Max Planck Institute's Biogeochemical Integration Department.

You are free to share this article under the Attribution 4.0 International license. A new technique can predict the final size of wildfires from the moment of ignition, researchers report. Built around a machine learning algorithm, the model can help forecast whether a wildfire will be small, medium, or large by the time it has run its course--knowledge useful to those in charge of allocating scarce firefighting resources. "A useful analogy is to consider what makes something go viral in social media," says lead author Shane Coffield, a doctoral student in earth system science at the University of California, Irvine. "We can think about what properties of a specific tweet or post might make it blow up and become really popular--and how you might predict that at the moment it's posted or right before it's posted." The researchers applied that thinking to a hypothetical situation in which dozens of fires break out simultaneously.

How does the Earth system function? The Earth system is incredibly complex, and understanding how it works is important for the survival of our species. Earth system science is an area of knowledge that has advanced rapidly in recent years. So, too, has artificial intelligence. To learn how the latter helps the former, we spoke with Markus Reichstein, who heads the Max Planck Institute's Biogeochemical Integration Department.

In the past decades mainly static attributes have been investigated using machine learning approaches, such as the distribution of soil properties from the local to the global scale. For some time now, it has been possible to tackle more dynamic processes by using more sophisticated deep learning techniques. This allows for example to quantify the global photosynthesis on land with simultaneous consideration of seasonal and short term variations. "From a plethora of sensors, a deluge of Earth system data has become available, but so far we've been lagging behind in analysis and interpretation," explains Markus Reichstein, managing director of the Max Planck Institute for Biogeochemistry in Jena, directory board member of the Michael-Stifel-Center Jena (MSCJ) and first author of the publication. "This is where deep learning techniques become a promising tool, beyond the classical machine learning applications such as image recognition, natural language processing or AlphaGo," adds co-author Joachim Denzler from the Computer Vision Group of the Friedrich Schiller University Jena (FSU) and member of MSCJ.