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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
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Image

Machine Learning
and Artificial Intelligence
to Advance Earth
System Science

Opportunities and Challenges

_____

Rachel Silvern, Rapporteur

Board on Atmospheric Sciences and Climate

Board on Earth Sciences and Resources

Ocean Studies Board

Division on Earth and Life Studies

Board on Mathematical Sciences and Analytics

Computer Science and Telecommunications Board

Division on Engineering and Physical Sciences

Proceedings of a Workshop

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
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THE NATIONAL ACADEMIES PRESS 500 Fifth Street, NW Washington, DC 20001

This activity was supported by contracts between the National Academy of Sciences and the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and the National Science Foundation. Any opinions, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect the views of any organization or agency that provided support for the project.

International Standard Book Number-13: 978-0-309-68853-6
International Standard Book Number-10: 0-309-68853-1
Digital Object Identifier: https://doi.org/10.17226/26566

Additional copies of this publication are available from the National Academies Press, 500 Fifth Street, NW, Keck 360, Washington, DC 20001; (800) 624-6242 or (202) 334-3313; http://www.nap.edu.

Copyright 2022 by the National Academy of Sciences. All rights reserved.

Printed in the United States of America

Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges. Washington, DC: The National Academies Press. https://doi.org/10.17226/26566.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×

The National Academy of Sciences was established in 1863 by an Act of Congress, signed by President Lincoln, as a private, nongovernmental institution to advise the nation on issues related to science and technology. Members are elected by their peers for outstanding contributions to research. Dr. Marcia McNutt is president.

The National Academy of Engineering was established in 1964 under the charter of the National Academy of Sciences to bring the practices of engineering to advising the nation. Members are elected by their peers for extraordinary contributions to engineering. Dr. John L. Anderson is president.

The National Academy of Medicine (formerly the Institute of Medicine) was established in 1970 under the charter of the National Academy of Sciences to advise the nation on medical and health issues. Members are elected by their peers for distinguished contributions to medicine and health. Dr. Victor J. Dzau is president.

The three Academies work together as the National Academies of Sciences, Engineering, and Medicine to provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. The National Academies also encourage education and research, recognize outstanding contributions to knowledge, and increase public understanding in matters of science, engineering, and medicine.

Learn more about the National Academies of Sciences, Engineering, and Medicine at www.nationalacademies.org.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×

Consensus Study Reports published by the National Academies of Sciences, Engineering, and Medicine document the evidence-based consensus on the study’s statement of task by an authoring committee of experts. Reports typically include findings, conclusions, and recommendations based on information gathered by the committee and the committee’s deliberations. Each report has been subjected to a rigorous and independent peer-review process and it represents the position of the National Academies on the statement of task.

Proceedings published by the National Academies of Sciences, Engineering, and Medicine chronicle the presentations and discussions at a workshop, symposium, or other event convened by the National Academies. The statements and opinions contained in proceedings are those of the participants and are not endorsed by other participants, the planning committee, or the National Academies.

Rapid Expert Consultations published by the National Academies of Sciences, Engineering, and Medicine are authored by subject-matter experts on narrowly focused topics that can be supported by a body of evidence. The discussions contained in rapid expert consultations are considered those of the authors and do not contain policy recommendations. Rapid expert consultations are reviewed by the institution before release.

For information about other products and activities of the National Academies, please visit www.nationalacademies.org/about/whatwedo.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×

PLANNING COMMITTEE ON MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE TO ADVANCE EARTH SYSTEM SCIENCE: OPPORTUNITIES AND CHALLENGES—A WORKSHOP

L. RUBY LEUNG (NAE)1(Chair), Pacific Northwest National Laboratory

ANN BOSTROM, University of Washington

PATRICK HEIMBACH, University of Texas at Austin

AMY MCGOVERN, University of Oklahoma

DIEGO MELGAR, University of Oregon

AARTI SINGH, Carnegie Mellon University

LAURE ZANNA, New York University

National Academies of Sciences, Engineering, and Medicine Staff

RACHEL SILVERN, Program Officer

KYLE ALDRIDGE, Program Assistant

RITA GASKINS, Administrative Coordinator

ROB GREENWAY, Program Associate

LINNEA SABY, Christine Mirzayan Science & Technology Fellow (until November 2021)

___________________

1 NAE, National Academy of Engineering

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×

BOARD ON ATMOSPHERIC SCIENCES AND CLIMATE

MARY GLACKIN (Chair), The Weather Company, an IBM Business (retired)

CYNTHIA S. ATHERTON, Heising-Simons Foundation

CECILIA BITZ, University of Washington

JOHN C. CHIANG, University of California, Berkeley

BRADLEY R. COLMAN, The Climate Corporation

BART E. CROES, California Air Resources Board (retired)

ROBERT B. DUNBAR, Stanford University

EFI FOUFOULA-GEORGIOU (NAE)2, University of California, Irvine

PETER C. FRUMHOFF, Union of Concerned Scientists

VANDA GRUBIŠIĆ, National Center for Atmospheric Research

ROBERT KOPP, Rutgers, The State University of New Jersey

L. RUBY LEUNG (NAE), Pacific Northwest National Laboratory

JONATHAN MARTIN, University of Wisconsin—Madison

AMY MCGOVERN, University of Oklahoma

JONATHAN PATZ, University of Wisconsin—Madison

J. MARSHALL SHEPHERD (NAS/NAE), University of Georgia

ALLISON STEINER, University of Michigan

DAVID W. TITLEY, U.S. Navy (ret.), Pennsylvania State University

ARADHNA TRIPATI, University of California, Los Angeles

DUANE E. WALISER, Jet Propulsion Laboratory

ELKE WEBER, Princeton University

National Academies of Sciences, Engineering, and Medicine Staff

AMANDA STAUDT, Senior Board Director

APURVA DAVE, Senior Program Officer

LAURIE GELLER, Senior Program Officer

APRIL MELVIN, Senior Program Officer

AMANDA PURCELL, Senior Program Officer

STEVEN STICHTER, Senior Program Officer

ALEX REICH, Program Officer

RACHEL SILVERN, Program Officer

PATRICIA RAZAFINDRAMBININA, Associate Program Officer

RITA GASKINS, Administrative Coordinator

BRIDGET MCGOVERN, Research Associate

AMY MITSUMORI, Research Associate

ROB GREENWAY, Program Associate

KYLE ALDRIDGE, Program Assistant

LINDSAY MOLLER, Program Assistant

SABAH RANA, Program Assistant

___________________

2 NAE, National Academy of Engineering; NAS, National Academy of Sciences.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×

Acknowledgments

This Proceedings of a Workshop was reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and critical comments that will assist the National Academies of Sciences, Engineering, and Medicine in making each published proceedings as sound as possible and to ensure that it meets the institutional standards for quality, objectivity, evidence, and responsiveness to the charge. The review comments and draft manuscript remain confidential to protect the integrity of the process.

We thank the following individuals for their review of this proceedings:

Erin K. Chiou, Arizona State University

Tyler Kloefkorn, American Mathematical Society

L. Ruby Leung (NAE), Pacific Northwest National Laboratory

Amy McGovern, University of Oklahoma

Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the content of the proceedings nor did they see the final draft before its release. The review of this proceedings was overseen by William B. Gail, Google. He was responsible for making certain that an independent examination of this proceedings was carried out in accordance with standards of the National Academies and that all review comments were carefully considered. Responsibility for the final content rests entirely with the rapporteur and the National Academies.

Page viii Cite
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×

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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×

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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
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Page viii Cite
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26566.
×
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The Earth system - the atmospheric, hydrologic, geologic, and biologic cycles that circulate energy, water, nutrients, and other trace substances - is a large, complex, multiscale system in space and time that involves human and natural system interactions. Machine learning (ML) and artificial intelligence (AI) offer opportunities to understand and predict this system. Researchers are actively exploring ways to use ML/AI approaches to advance scientific discovery, speed computation, and link scientific communities.

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. Participants also explored educational pathways, responsible and ethical use of these technologies, and opportunities to foster partnerships and knowledge exchange. This publication summarizes the workshop discussions and themes that emerged throughout the meeting.

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