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
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
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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.
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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
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.
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.
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Contents
Overview of State-of-the-Art Use of ML/AI for Earth System Science
Emerging Approaches for Using, Interpreting, and Integrating ML/AI for Earth System Science
Emerging Approaches for Using and Interpreting ML/AI
Emerging Opportunities from Social and Human Engineered Systems
Challenges and Risks of Using ML/AI for Earth System Science
Workforce Development Capacity and Skill Sets
Challenges and Opportunities for Earth Science Technology and Data
Identifying Future Opportunities to Accelerate Progress
Using ML/AI for Data-Driven Decision Making
Emerging ML/AI Approaches and Opportunities for Multidisciplinary Collaboration
Responsible and Ethical Use of ML/AI Approaches
Educating the Workforce at the Intersection of ML/AI and Earth System Science
Using ML/AI for Data-Driven Decision Making
Funding Opportunities to Accelerate Progress
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