Monday, 8 January 2018: 3:15 PM
Room 4ABC (ACC) (Austin, Texas)
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 machine learning and data assimilation make it possible to integrate global observations and targeted high-resolution simulations in an Earth system modeling framework that systematically learns from both. Here we propose a blueprint for such an Earth system modeling framework. 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 observations and simulations. We illustrate learning algorithms for Earth system models with a 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.
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