LW
Larry White
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2 records found
1
Conference paper
(2022)
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Jennifer Sleeman, David Chung, Caroline Tang, Anshu Saksena, Larry White, Chace Ashcraft, Jay Brett, Anand Gnanadesikan, Yannis Kevrekidis, Marisa Hughes, Thomas Haine, Marie Aude Pradal, Renske Gelderloos
We propose a hybrid Artificial Intelligence (AI) climate modeling approach that enables climate modelers in scientific discovery using a climate-targeted simulation methodology based on a novel combination of deep neural networks and mathematical methods for modeling dynamical systems. The simulations are grounded by a neuro-symbolic language that both enables question answering of what is learned by the AI methods and provides a means of explainability. We describe how this methodology can be applied to the discovery of climate tipping points and, in particular, the collapse of the Atlantic Meridional Overturning Circulation (AMOC). We show how this methodology is able to predict AMOC collapse with a high degree of accuracy using a surrogate climate model for ocean interaction. We also show preliminary results of neuro-symbolic method performance when translating between natural language questions and symbolically learned representations. Our AI methodology shows promising early results, potentially enabling faster climate tipping point related research that would otherwise be computationally infeasible.
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We propose a hybrid Artificial Intelligence (AI) climate modeling approach that enables climate modelers in scientific discovery using a climate-targeted simulation methodology based on a novel combination of deep neural networks and mathematical methods for modeling dynamical systems. The simulations are grounded by a neuro-symbolic language that both enables question answering of what is learned by the AI methods and provides a means of explainability. We describe how this methodology can be applied to the discovery of climate tipping points and, in particular, the collapse of the Atlantic Meridional Overturning Circulation (AMOC). We show how this methodology is able to predict AMOC collapse with a high degree of accuracy using a surrogate climate model for ocean interaction. We also show preliminary results of neuro-symbolic method performance when translating between natural language questions and symbolically learned representations. Our AI methodology shows promising early results, potentially enabling faster climate tipping point related research that would otherwise be computationally infeasible.
AMOC Dynamics
Can a Box Model Explain a Global Model?
Abstract
(2022)
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Genevieve Jay Brett, Larry White, Anand Gnanadesikan, Marie Aude Pradal, R. Gelderloos, Thomas Haine, Yannis Kevrekidis, Jennifer Sleeman
This study examines the applicability of the Gnanadesikan four-box model of the Atlantic Meridional Overturning Circulation to a global earth system model. The Community Earth System Model 2 (CESM2) is used to quantify the extent to which pycnocline depth and AMOC strength as predicted by the four-box model from regional densities and Southern Ocean fluxes match CESM2 values. The dynamics of a reduced meridional density difference driving a deepening pycnocline and weakening AMOC are qualitatively matched between the 4-box model and CESM2. We will quantify the fraction of variability in the CESM2 Large Ensemble of historical and SSP3-7.0 runs that is explained by the 4-box model; initial results suggest about two-thirds of the variability in AMOC is explained and over 90% of the variability in the pycnocline depth. This analysis may be repeated for any CMIP6 model, and a comparison of SSP5-8.5 across a handful of models is included as a demonstration.
...
This study examines the applicability of the Gnanadesikan four-box model of the Atlantic Meridional Overturning Circulation to a global earth system model. The Community Earth System Model 2 (CESM2) is used to quantify the extent to which pycnocline depth and AMOC strength as predicted by the four-box model from regional densities and Southern Ocean fluxes match CESM2 values. The dynamics of a reduced meridional density difference driving a deepening pycnocline and weakening AMOC are qualitatively matched between the 4-box model and CESM2. We will quantify the fraction of variability in the CESM2 Large Ensemble of historical and SSP3-7.0 runs that is explained by the 4-box model; initial results suggest about two-thirds of the variability in AMOC is explained and over 90% of the variability in the pycnocline depth. This analysis may be repeated for any CMIP6 model, and a comparison of SSP5-8.5 across a handful of models is included as a demonstration.