2026-09-19T11:15:54Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/125782 2025-05-13 col_2142_5131 col_2142_17362 com_2142_5130 com_2142_17361 com_2142_8903

application/pdf en text Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01 The student, Robert Garrett, accepted the attached license on 2024-07-03 at 12:08. The student, Robert Garrett, submitted this Dissertation for approval on 2024-07-03 at 12:41. This Dissertation was approved for publication on 2024-07-07 at 16:15. DSpace SAF Submission Ingestion Package generated from Vireo submission #20929 on 2025-02-04 at 21:25:21 Methods for comparison and analysis of spatiotemporal fields Garrett, Robert Charles 2024-07-07 Li, Bo Li, Bo Douglas, Jeffrey A Shand, Lyndsay Harris, Trevor A Spatial Statistics Functional Data Analysis Dynamic Linear Model Climate Model Evaluation eng This dissertation develops three methods for spatiotemporal fields data, each designed to address research topics in climate science. The first two methods are similarity measures for evaluating the differences between climate models and observational datasets. The last method is a multivariate spatiotemporal model which characterizes the joint evolution of observed climate processes. Chapter 2 introduces the spherical convolutional Wasserstein distance (SCWD) to more comprehensively measure differences between climate models and observational data. This new similarity measure accounts for spatial variability using convolutional projections and quantifies local differences in the distribution of climate variables. We apply SCWD to evaluate the historical model outputs of the Coupled Model Intercomparison Project (CMIP) members by comparing them to observational and reanalysis data products. Additionally, we investigate the progression from CMIP phase 5 to phase 6 and find modest improvements in the phase 6 models regarding their ability to produce realistic climatologies. Chapter 3 introduces the sliced elastic distance, a new metric which considers potential time misalignment between climate models and observational data. Sliced elastic distance decomposes differences in the local evolution of climate processes into shape differences (amplitude), timing variability (phase), and bias (translation). We apply the sliced elastic distance to rank CMIP phase 6 precipitation models by their similarity to observational data at both global and regional scales. Using intermediate calculations from our method, we perform an in-depth phase analysis of the Indian summer monsoon to identify timing biases in the onset and retreat of the monsoon season in each CMIP6 model. Finally, Chapter 4 introduces a novel multivariate space-time dynamic model to quantify relationships in the joint evolution of atmospheric processes. This model captures spatial variation using a flexible set of basis functions for which the coefficients are allowed to vary in time through a vector autoregressive (VAR) structure. The model is cast in a Bayesian dynamic linear model (DLM) framework and estimated using a customized MCMC sampling approach. We apply this model to study the relationship between aerosols, radiation, and temperature following the 1991 Mt. Pinatubo eruption and highlight when such a model is advantageous over simpler univariate models. 2024-08 Thesis https://hdl.handle.net/2142/125782 Copyright 2024 Robert Garrett Statistics Statistics University of Illinois at Urbana-Champaign Ph.D. Dissertation