Theses and Dissertations from UMD

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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

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Subject: search.filters.subject.Climate Modeling

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    Combining Physics-based Modeling, Machine Learning, and Data Assimilation for Forecasting Large, Complex, Spatiotemporally Chaotic Systems
    (2023) Wikner, Alexander Paul; Ott, Edward; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    We consider the challenging problem of forecasting high-dimensional, spatiotemporally chaotic systems. We are primarily interested in the problem of forecasting the dynamics of the earth's atmosphere and oceans, where one seeks forecasts that (a) accurately reproduce the true system trajectory in the short-term, as desired in weather forecasting, and that (b) correctly capture the long-term ergodic properties of the true system, as desired in climate modeling. We aim to leverage two types of information in making our forecasts: incomplete scientific knowledge in the form of an imperfect forecast model, and past observations of the true system state that may be sparse and/or noisy. In this thesis, we ask if machine learning (ML) and data assimilation (DA) can be used to combine observational information with a physical knowledge-based forecast model to produce accurate short-term forecasts and consistent long-term climate dynamics. We first describe and demonstrate a technique called Combined Hybrid-Parallel Prediction (CHyPP) that combines a global knowledge-based model with a parallel ML architecture consisting of many reservoir computers and trained using complete observations of the system's past evolution. Using the Kuramoto-Sivashinsky equation as our test model, we demonstrate that this technique produces more accurate short-term forecasts than either the knowledge-based or the ML component model acting alone and is scalable to large spatial domains. We further demonstrate using the multi-scale Lorenz Model 3 that CHyPP can incorporate the effect of unresolved short-scale dynamics (subgrid-scale closure). We next demonstrate how DA, in the form of the Ensemble Transform Kalman Filter (ETKF), can be used to extend the Hybrid ML approach to the case where our system observations are sparse and noisy. Using a novel iterative scheme, we show that DA can be used to obtain training data for successive generations of hybrid ML models, improving the forecast accuracy and the estimate of the full system state over that obtained using the imperfect knowledge-based model. Finally, we explore the commonly used technique of adding observational noise to the ML model input during training to improve long-term stability and climate replication. We develop a novel training technique, Linearized Multi-Noise Training (LMNT), that approximates the effect of this noise addition. We demonstrate that reservoir computers trained with noise or LMNT regularization are stable and replicate the true system climate, while LMNT allows for greater ease of regularization parameter tuning when using reservoir computers.
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    AEROSOL-CLOUD-CLIMATE INTERACTIONS DUE TO CARBONACEOUS AEROSOLS
    (2022) Gohil, Kanishk; Asa-Awuku, Akua A; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Aerosols can affect the net radiation budget and global climate of the Earth either “directly” – through their radiative properties, or “indirectly” – through their cloud-forming abilities by acting as Cloud Condensation Nuclei (CCN). The interactions between aerosols and clouds are the most significant sources of uncertainty in the overall radiative forcing from due to a lack of understanding related to the droplet formation mechanism of aerosols. These uncertainties are majorly associated with the carbonaceous aerosols present in the atmosphere, notably due to their compositional diversity, vastly variable physicochemical properties, and unique water uptake characteristics. In this dissertation, new lab-based measurement techniques and computational methods have been developed to resolve the CCN activity and water uptake behavior of pure and mixed carbonaceous aerosol particles.The first part of this dissertation accomplishes two goals: 1. The development and application of a new CCN measurement method, and 2. The formulation of a new computational framework for CCN activity analysis of aerosols. The results in this dissertation demonstrate the significance of size-resolved morphology and dissolution properties of aerosol particles in improving their CCN activity analysis under varying ambient conditions. Furthermore, these results suggest that in the future, more comprehensive CCN analysis frameworks can be developed by explicitly treating other physical and chemical properties of the aerosols to further improve their CCN activity analysis. The second part of this dissertation focuses on large-scale analysis. The CCN analysis framework is implemented into a climate model to quantify the water uptake behavior of carbonaceous aerosols, and then study the subsequent variabilities associated with the physical and radiative properties of ambient aerosols and clouds. Statistical techniques are also developed in this work for chemical characterization of ambient aerosols. The characterization results show large regional compositional variations in ambient aerosol populations. These results also suggest that the knowledge of chemical species is necessary to quantify the water uptake properties of the aerosol population.
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    Quantification of the Past and Future Anthropogenic Effect on Climate Change Using the Empirical Model of Global Climate, an Energy Balance Multiple Linear Regression Model
    (2020) Hope, Austin Patrick; Salawitch, Ross J; Canty, Timothy P; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The current episode of global warming is one of, if not the, biggest challenge to modern society as the world moves into the 21st century. Rising global temperatures due to anthropogenic emissions of greenhouse gases are causing sea level rise, extreme heat waves, droughts and floods, and other major social and economic disruptions. To prepare for and potentially reverse this warming trend, the causes of climate change must not only be understood, but thoroughly quantified so that we can attempt to make reasonable predictions of the future rise in global temperature and its associated consequences. The project described in this dissertation seeks to use a simple model of global climate, utilizing an energy balance and multiple linear regression approach, to provide a quantification of historical temperature trends and use that knowledge to provide probabilistic projections of future temperature. By considering many different greenhouse gas and aerosol emissions scenarios along with multiple possibilities for the role of the ocean in the climate system and the extent of climate feedbacks, I have determined that there is a 50% probability of keeping global warming beneath 2 °C if society can keep future emissions on the pathway suggested by the RCP 4.5 scenario, which includes moderately ambitious emissions reductions policies, and a 67% probability of keeping global warming beneath 1.5 °C if society can keep emissions in line with the very ambitious RCP 2.6 scenario. These probabilities are higher, e.g. more optimistic, than similar probabilities for the same scenarios given by the most recent IPCC assessment report. Similarly, we find larger carbon budgets than those from GCM analyses for any warming limitation target and confidence level, e.g. the EM-GC predicts a total carbon budget of 710 GtC for limiting global warming to 1.5 °C with 95% confidence. The results from our simple climate model suggest that the difference in future temperatures is related to an overestimation of recent warming by the IPCC global climate models. We postulate that this difference is partially due to an overestimation of cloud feedback processes in the global climate models. Importantly, though, I also reaffirm the consensus that anthropogenic emissions are driving current warming trends, and discuss both the effects of shifting the energy sector toward increase methane emissions and the timeline we have for emitting the remainder of our carbon budget – less than a decade if we wish to prevent global warming from exceeding the 1.5 °C threshold with 95% certainty.