Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations

By Tapio Schneider, Shiwei Lan, Andrew M. Stuart, João Paulo Ramos Teixeira. 2017

375 citations recorded by the source corpus.

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.

Subfields: Atmospheric Science, Global and Planetary Change, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/c14c26310a14036fbe780f8d9474f07346dee9c7

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Citations in the corpus

Deep learning to represent subgrid processes in climate models (2018, background): “Recently, it has been argued (49) that machine learning should be used to learn the parameters or parametric functions within a traditional parameterization framework—for instance learning the entrainment rate of a convection parameterization, potentially using a neural network—rather than the full ”
Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events (2018, methodology): “An alternative approach to leveraging high-resolution modeling or observations would be to use them to optimize parameters while still retaining a physically-based subgrid model [cf. Emanuel and ŽivkovićRothman, 1999; Schneider et al., 2017].”
ClimaX: A foundation model for weather and climate (2023, neutral): “In contrast, there has been a steady rise in data-driven approaches for forecasting of atmospheric variables, especially for meteorological applications (Grover et al., 2015; Dueben & Bauer, 2018; Weber et al., 2020; Scher & Messori, 2019; Scher, 2018; Kashinath et al., 2021; Schultz et al., 2021; R”
Tackling Climate Change with Machine Learning (2019, methodology): “ul if they are closely integrated into existing scientiﬁc models. This has been emphasized, again and again, by authors who have laid future paths for artiﬁcial 39 intelligence within climate science [477, 484, 485, 495, 500, 514]. New models need to leverage existing knowledge to make good predicti”
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks (2019, background): “…applicable across climate science and ML problems, which requires deep collaboration for synergistic advancements in both disciplines (Monteleoni et al., 2013; Joppa, 2017; Racah et al., 2017; Schneider et al., 2017; Gil et al., 2018; Hwang et al., 2018; Karpatne et al., 2018; Rasp et al., 2018).”
Machine learning and artificial intelligence to aid climate change research and preparedness (2019, background): “For example, tuning of parameters in climate models using observational or high-resolution model data is often laboriously performed by hand, suggesting the potential for utilizing ML (Schneider et al 2017).”
Physics-informed machine learning: case studies for weather and climate modelling (2021, background): “[90] propose a blueprint for using ML to integrate observations and high-resolution simulations in Earth system modelling that systematically learn from both and quantifies uncertainties.”
Neural general circulation models for weather and climate (2023, background): “However, we will probably need alternative training strategies 38,39 to learn important processes for climate with subtle impacts on weather timescales, such as a cloud feedback.”
Confronting the Challenge of Modeling Cloud and Precipitation Microphysics (2020, methodology): “There are some commonalities with the climate model parameterization approach proposed by Schneider et al. (2017), who similarly advocated for parameter learning within a well‐defined physical framework.”
Universal Differential Equations for Scientific Machine Learning (2020, methodology): “jl, including large software infrastructures such as the MIT-CalTech CLiMA climate modeling system [116] and the QuantumOptics.”