Climate goals and computing the future of clouds
By Tapio Schneider, João Paulo Ramos Teixeira, Chris Bretherton, F. Brient, K. Pressel, Christoph M. Schär, A. P. Siebesma. 2017
310 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/7e1948d803e19ede1b9043698ab234d33c6f6825
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In Atmospheric Science
In Global and Planetary Change
Citations in the corpus
- Deep learning to represent subgrid processes in climate models (2018, background): “These inaccuracies manifest themselves in stubborn model biases (5? , 6) and large uncertainties about how much the earth will warm as a response to increased greenhouse gas concentrations (1, 7, 8).”
- Fifty Years of Research on the Madden‐Julian Oscillation: Recent Progress, Challenges, and Perspectives (2020, methodology): “This approach involves fitting a statistical model to the output of relatively expensive physical models (e.g., CRMs) that more faithfully represent the subgrid processes (Brenowitz & Bretherton, 2018; Gentine et al., 2018; O'Gorman & Dwyer, 2018; Rasp et al., 2018; Schneider et al., 2017).”
- Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems. (2019, background): “—Convective parametrization for climate modeling: The representation of subgrid-scale processes in coarse-scale, numerical models of the atmosphere, referred to as subgrid parametrization, is a large source of error and uncertainty in numerical weather and climate prediction [29,30].”
- Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, background): “Fundamentally re-engineering atmospheric parameterization schemes, such as cloud and boundary layer parameterizations, will become a necessity as atmosphere models, within the next decade, reach horizontal grid spacings of 1–10 km and begin to resolve deep convection [Schneider et al., 2017].”
- Machine learning–accelerated computational fluid dynamics (2021, unknown): “S imulation of complex physical systems described by nonlinear partial differential equations (PDEs) is central to engineering and physical science, with applications ranging from weather (1, 2) and climate (3, 4) and engineering design of vehicles or engines (5) to wildfires (6) and plasma physics ”
- Neural operators for accelerating scientific simulations and design (2023, unknown): “However, current numerical methods require massive computing power to resolve the physics at finer scales where the effects of storms and cloud turbulence can be faithfully reproduced, and even the world’s most powerful supercomputers do not suffice [3].”
- FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators (2022, background): “In order to achieve this throughput at 1 m resolution for the requirements listed above, however, weather and climate models need to be on the order of 100 billion times faster [25].”
- Cloud feedback mechanisms and their representation in global climate models (2017, unknown): “A recent further development, made possible by steady increases in computing power, involves the use of LES rather than cloud-resolving models as a substitute for GCM parameterizations.(16,169) First results suggest encouraging improvements in the representation of boundary-layer clouds (C.”