Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations. Issue 24 (29th December 2017)

• Record Type: Journal Article
• Authors: Schneider, Tapio; Lan, Shiwei; Stuart, Andrew; Teixeira, João
• Is Part Of: Geophysical research letters. Volume 44:Issue 24(2017)
• Page Start: 12, 396
• Page End: 12, 417
• Publication Date: 2017-12-29
• Subjects: Earth system models -- parameterizations -- data assimilation -- machine learning -- Kalman inversion -- Markov chain Monte Carlo; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1002/2017GL076101
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: 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. Key Points: Earth system models (ESMs) and their parameterization schemes can be radically improved by data assimilation and machine learning ESMs can integrate and learn from global observations from space and from local high‐resolution simulations Ensemble Kalman inversion and Markov chain Monte Carlo methods show promise as learning algorithms for ESMs