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Browsing by Author "Rugenstein, Maria, committee member"

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    Detecting forced change within combined climate fields using explainable neural networks
    (Colorado State University. Libraries, 2021) Rader, Jamin, author; Barnes, Elizabeth, advisor; Rugenstein, Maria, committee member; Witt, Jessica, committee member
    Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single- and multi-variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer-wise relevance propagation, a neural network visualization tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These "indicator patterns" vary in time and between climate models, providing a template for investigating inter-model differences in the time evolution of the forced response. This work demonstrates how neural networks and their visualization tools can be harnessed to identify patterns of the forced signal within combined fields.
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    Relative oriented class groups of quadratic extensions
    (Colorado State University. Libraries, 2024) O'Connor, Kelly A., author; Pries, Rachel, advisor; Achter, Jeffrey, committee member; Shoemaker, Mark, committee member; Rugenstein, Maria, committee member
    In 2018 Zemková defined relative oriented class groups associated to quadratic extensions of number fields L/K, extending work of Bhargava concerning composition laws for binary quadratic forms over number fields of higher degree. This work generalized the classical correspondence between ideal classes of quadratic orders and classes of integral binary quadratic forms to any base number field of narrow class number 1. Zemková explicitly computed these relative oriented class groups for quadratic extensions of the rationals. We consider extended versions of this work and develop general strategies to compute relative oriented class groups for quadratic extensions of higher degree number fields by way of the action of Gal(K/Q) on the set of real embeddings of K. We also investigate the binary quadratic forms side of Zemková's bijection and determine conditions for representability of elements of K. Another project comprising work done jointly with Lian Duan, Ning Ma, and Xiyuan Wang is included in this thesis. Our project investigates a principal version of the Chebotarev density theorem, a famous theorem in algebraic number theory which describes the splitting of primes in number field extensions. We provide an overview of the formulation of the principal density and describe its connection to the splitting behavior of the Hilbert exact sequence.
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    Understanding the role of ocean dynamics in climate variability
    (Colorado State University. Libraries, 2021) Patrizio, Casey R., author; Thompson, David, advisor; Randall, David, advisor; Rugenstein, Maria, committee member; Rugenstein, Jeremy, committee member; Small, Richard, committee member
    The ocean plays a key role in regulating Earth's mean climate, both because of its massive heat capacity, but also its heat transport by slow-moving circulations and other dynamics. In principle, fluctuations in such ocean heat transport can influence the variability in the climate, by impacting the sea-surface temperature (SST) variability and in turn the atmospheric variability through surface heat exchange, but this is incompletely understood, particularly in the extratropics. The goal of this dissertation is to clarify the role of ocean dynamics in climate variability, first focusing on the role of ocean dynamics in SST variability across the global oceans (Chapters 1 and 2), and then on the impact of midlatitude ocean-driven SST anomalies on the atmospheric circulation (Chapter 3). In Chapter 1, the contributions of ocean dynamics to ocean-mixed layer temperature variance are quantified on monthly to multiannual timescales across the globe. To do so, two methods are used: 1) a method in which monthly ocean heat transport anomalies are estimated directly from a state-of-the-art ocean state estimate spanning 1992-2015; and 2) a method in which they are estimated indirectly using the energy budget of the mixed layer with monthly observations of SSTs and air-sea heat fluxes between 1980-2017. Consistent with previous studies, both methods indicate that ocean dynamics contribute notably to mixed layer temperature variance in western boundary current regions and tropical regions on monthly to interannual timescales. However, in contrast to previous studies, the results also suggest that ocean dynamics reduce the variance of Northern Hemisphere mixed layer temperatures on timescales longer than a few years. In Chapter 2, the role of ocean dynamics in midlatitude SST variability is further understood using Hasselmann's model of climate variability, wherein midlatitude SST anomalies are driven entirely by atmospheric processes. Motivated by the results of Chapter 1, here Hasselmann's climate model is extended to include the forcing and damping of SST variability by ocean processes, which are estimated indirectly from monthly observations. It is found that the classical Hasselmann model driven only by observed surface heat fluxes generally produces midlatitude SST power spectra that are too red compared to observations. Including ocean processes in the model reduces this discrepancy by decreasing the low-frequency SST variance and increasing the high-frequency SST variance, leading to a whitening of the midlatitude SST spectra. This happens because ocean forcing increases the midlatitude SST variance across many timescales but is outweighed by ocean damping at timescales > 2 years, particularly away from the western boundary currents. It is also shown that the whitening of midlatitude SST variability by ocean dynamical processes operates in NCAR's Community Earth System Model (CESM). In the final chapter, the atmospheric circulation response to midlatitude ocean-forced SST anomalies is explored. In particular, the extended Hasselmann model is used to isolate the oceanic and atmospheric-forced components of the observed SST variability in the Kuroshio-Oyashio Extension (KOE) region. The associated atmospheric circulation anomalies are diagnosed by lagged-regression of monthly sea-level pressure (SLP) anomalies onto the KOE-averaged SST anomalies, and their oceanic and atmospheric-forced components. Consistent with previous studies, a large-scale SLP pattern is found to lag the KOE SST anomalies by one month. Here it is shown that this pattern is linked to the oceanic-forced component of the SST variability, but not the atmospheric-forced component. The results hence suggest that the midlatitude ocean dynamical processes in the North Pacific influence the variability of the large-scale atmospheric circulation.
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    When is the unpredictable (slightly more) predictable? An assessment of opportunities for skillful decadal climate prediction using explainable neural networks
    (Colorado State University. Libraries, 2023) Gordon, Emily M., author; Barnes, Elizabeth A., advisor; Hurrell, James W., committee member; Rugenstein, Maria, committee member; Anderson, Charles, committee member
    Predicting climate variability on decadal (2-10 year) timescales can have huge implications for society because it can provide better estimates of both global trends as well as regional climate variability for crucial, actionable lead times. The key to skillful decadal prediction is understanding and predicting oceanic variability. However, predictable signals in the ocean can be masked by the inherent noise in the system, and therefore, skillful prediction on decadal timescales is challenging. Machine learning, with its ability to extract nonlinear signals from large sets of noisy data, has been shown a powerful tool for predicting and understanding processes across weather and climate applications. In this dissertation, I explore applications of machine learning to decadal prediction. First, I present a machine learning approach to predicting the Pacific decadal oscillation (PDO) with artificial neural networks (ANNs) within the Community Earth System Model version 2 (CESM2) pre-industrial control simulation. Predicting PDO transitions and understanding the associated mechanisms has proven a critical but challenging task in climate science. As a form of decadal variability, the PDO is associated with both large- scale climate shifts and regional climate predictability. I show that ANNs predict PDO persistence and transitions at lead times of 12 months onward. Using layer-wise relevance propagation to investigate the ANN predictions, I demonstrate that the ANNs utilize oceanic patterns that have been previously linked to predictable PDO behavior. ANNs recognize a build-up of ocean heat content in the off-equatorial western Pacific 12–27 months before a transition occurs. The ANNs also distinguish transition mechanisms between positive-to-negative sign transitions, and negative-to-positive transitions. Secondly, I demonstrate a technique for incorporating an uncertainty estimate into the prediction of a regression neural network, allowing the identification of predictable sea surface temperature (SST) anomalies on decadal timescales in the CESM2 pre-industrial control simulation. Predictability in SSTs can be masked by unpredictable variability, and one approach to extracting predictable signals is to investigate state-dependent predictability – how differences in prediction skill depend on the initial state of the system. I leverage the network's prediction of uncertainty to examine state-dependent predictability in SSTs by focusing on predictions with the lowest uncertainty. In particular, I study two regions of the global ocean–the North Atlantic and North Pacific–and find that skillful initial states identified by the neural network correspond to particular phases of low frequency variability in the North Pacific and North Atlantic oceans. Finally, I examine the potential role of predictable internal variability in a future, warmer climate by designing an interpretable neural network that can be decomposed to examine the relative contributions of external forcing and internal variability to future regional decadal SST trend predictions. I show that there is additional prediction skill to be garnered from internal variability in the CESM2 Large Ensemble in the near-term climate (2020-2050), even in a relatively high forcing future scenario. This predictability is especially apparent in the North Atlantic, North Pacific and Tropical Pacific Oceans as well as in the Southern Ocean. I further investigate how prediction skill covaries across the ocean and find three regions with distinct coherent prediction skill driven by internal variability. SST trend predictability is found to be associated with consistent patterns of interannual and decadal variability for the grid points within each region.