Highlights of Physics of Climate (GPC) Talks @ APS 2020 March Meeting
American Physics Society (APS) March meeting is one of the largest physics meetings in the world. In 2020, the meeting was canceled due to concerns over the rapid spread of COVID-19.
To help the community quickly catch up on the work to be presented in this meeting, Paper Digest Team processed all talk abstracts, and generated one highlight sentence (typically the main topic) for each. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each talk. This article is on the talks related to Physics of Climate (GPC).
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TABLE : Physics of Climate (GPC)
| Title | Authors | Highlight | Session | |
|---|---|---|---|---|
| 1 | Data Assimilation and Uncertainty Quantification in the Geosciences | Restrepo, Juan | The inherent uncertainties of model and data are taken into account using a Bayesian framework. | Session 1: Predictability of the Climate System |
| 2 | Climate Change and Climate Variability: A Unified Framework | Ghil, Michael | The concepts and methods of the NDS and RDS approach will be introduced and will be illustrated using a stochastically perturbed version of the Lorenz (1963) convection model. | Session 1: Predictability of the Climate System |
| 3 | Quantifying uncertainty in climate predictability using perturbed physics ensembles and climate model emulation | Dagon, Katherine; Sanderson, Benjamin; Fisher, Rosie; Lawrence, David | In this work we use a machine learning algorithm to build an emulator for the land surface component of a climate model. | Session 1: Predictability of the Climate System |
| 4 | Earth System Modeling 2.0: Toward Data-Informed Climate Models With Quantified Uncertainties | Schneider, Tapio | This talk will cover key new concepts in the ESM, including turbulence, convection, and cloud parameterizations and fast and efficient algorithms for assimilating data and quantifying uncertainties. | Session 1: Predictability of the Climate System |
| 5 | Bayesian Inference for Climate prediction | van Leeuwen, Peter | We will discuss potential solutions based on existing techniques, and alternative ideas based on so-called particle flows.The latter are fully nonlinear while combining the strong points of smoothers and filters mentioned above, and have the potential to make substantial strides forwards towards better climate prediction. | Session 1: Predictability of the Climate System |