Bulletin of the American Physical Society
74th Annual Meeting of the APS Division of Fluid Dynamics
Volume 66, Number 17
Sunday–Tuesday, November 21–23, 2021; Phoenix Convention Center, Phoenix, Arizona
Session A31: Nonlinear Dynamics: General & Chaos
8:00 AM–9:57 AM,
Sunday, November 21, 2021
Room: North 232 ABC
Chair: Wenbo Tang, Arizona State University
Abstract: A31.00006 : Reservoir Computing as a Tool for Climate Predictability Studies*
9:05 AM–9:18 AM
Presenter:
Balu Nadiga
(Los Alamos Natl Lab)
Author:
Balu Nadiga
(Los Alamos Natl Lab)
We demonstrate that Reservoir Computing (RC), a form of machine learning suited for learning in the context of chaotic dynamics, provides an alternative nonlinear approach that comprehensively outperforms the LIM approach. We do this in the example setting of predicting sea surface temperature in the North Atlantic in the pre-industrial control simulation of a popular Inter-governmental Panel for Climate Change (IPCC) class earth system model (Community Earth System Model version 2) so that we can compare the performance of the new RC based approach with the traditional LIM approach both when learning data is plentiful and when such data is more limited. The improved perdictive skill of the RC approach over the LIM approach in both these settings reiterates the use of the new machine learning approach in future climate predictability studies. Additionally, the potential of the RC approach to capture the structure of the climatological attractor and to continue the evolution of the system on the attractor in a realistic fashion long after the ensemble average has stopped tracking the reference trajectory is highlighted by considering the RC approach in the context of the Lorenz '63 system.
Finally, comparisons to other feedforward and recurrent deep learning methods are made and a broader perspective on the use of machine learning in understanding climate predictability is offered.
*1. The Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling (EESM) program of the U.S. Department of Energy's Office of Science under the HiLAT-RASM project2. Los Alamos National Lab's LDRD program3. DOE's SciDAC-4 project “NonHydrostatic Dynamics with Multi-Moment Characteristic Discontinuous Galerkin Methods.”
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