ABSTRACT

This INSPIRE award is partially funded by the Physical Oceanography Program in the Division of Ocean Sciences in the Directorate for Geosciences, and the Mathematical Physics program in the Division of Physics, the Computational Mathematics Program in the Division of Mathematical Sciences and the Condensed Matter and Materials Theory Program in the Division of Materials Research, all in the Directorate for Mathematical and Physical Sciences.

Intellectual Merit: The natural variability of the climate system is primarily understood through observations, climate modeling, and analysis. Natural climate variability takes the form of large-scale, coherent patterns such as El-Nino and the North Atlantic Oscillation. There is currently no fundamental theory to explain or predict the natural variability of the climate system. The climate system is driven by energy from incoming solar radiation, which is then released back to space as outgoing long-wave radiation. The climate system, apart from anthropogenic forcing, is approximately in a thermodynamically non-equilibrium steady-state. Recent progress within the physics community has led to a new and deeper understanding of natural variability in non-equilibrium steady-states. Non-equilibrium steady-states have energetic pathways that channel their natural variability into well-define lifecycles. These lifecycles are manifested as the patterns of natural variability seen in the climate system.

This INSPIRE project will create a new interdisciplinary collaboration between physicists and climate scientists developing and applying theories of non-equilibrium fluctuations to the problem of understanding and predicting climate variability. This project leverages a technical linkage between these two communities. Linear Gaussian Models (LGMs) are among the simplest models that display non-equilibrium steady-state fluctuations. LGMs have independently been extensively studied by both the physics and climate communities, and have demonstrated predictive skill for some climate phenomena. LGMs allow the immediate application of results from non-equilibrium physics to climate phenomena, and provide a focus for new developments in non-equilibrium physics.

This INSPIRE project will develop a new approach to understanding the natural variability of climate and introduce the climate system as a new application area for statistical physics. The context of non-equilibrium steady-states provides a new theoretical framework for understanding the patterns of natural variability. New diagnostics, based on the phase-space dynamics of the patterns, will be developed and applied to a hierarchy of models and data. Study of models with different constant carbon dioxide concentrations, and models with anthropogenic forcing will provide insight into how the natural variability of Sea Surface Temperature and ocean heat content will evolve under climate change and how well-represented these processes are in present climate models. New theoretical work in non-equilibrium physics will extend current theory in directions motivated by climate variability. The project has two threads: 1) the application of non-equilibrium theory to a hierarchy of sea surface temperature and upper ocean heat content datasets; and 2) theoretical work to advance non-equilibrium physics in directions that are important for the climate system.

Broader Impacts: The proposed work will serve as a template for application of statistical physics to other patterns of natural climate variability. The natural variability of Linear Gaussian Models can provide a null hypothesis for the entire spectrum of natural climate variability, and will provide insight even in situations where it fails. The project will serve to cross-fertilize the physics and climate communities and provide new directions for research. Finally, the project will cross-train one graduate student and one post-doctoral researcher in climate modeling, statistical physics, and climate data analysis, and will support the intellectual growth of a young faculty member.

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