Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both and quantifies uncertainties. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.
© 2017 American Geophysical Union. Received 30 AUG 2017; Accepted 23 NOV 2017; Accepted article online 30 NOV 2017. We gratefully acknowledge financial support by Charles Trimble, by the Office of Naval Research (grant N00014-17-1-2079), and by the President's and Director's Fund of Caltech and the Jet Propulsion Laboratory. We also thank V. Balaji, Michael Keller, Dan McCleese, and John Worden for helpful discussions and comments on drafts, and Momme Hell for preparing Figure 3. The program code used in this paper is available at climate-dynamics.org/publications/. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
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