Browsing by Author "Hurrell, James, committee member"
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Item Open Access Diagnosing the angular momentum fluxes that drive the quasi-biennial oscillation(Colorado State University. Libraries, 2023) Hughes, Ann-Casey, author; Randall, David A., advisor; Hurrell, James, committee member; Oprea, Iuliana, committee memberThe quasi-biennial oscillation (QBO) is a descending pattern of alternating easterly and westerly equatorial stratospheric winds that is produced by the upward transport of momentum in multiple types of atmospheric waves. The discovery of the QBO and its role in the global circulation are discussed. The angular momentum budget of the QBO is analyzed using ERA-Interim isentropic analyses. We explain the benefits of isentropic coordinates and angular momentum as tools for analyzing atmospheric motion. We diagnose vertical motion utilizing continuity, allowing direct computation of the angular momentum fluxes due to vertical motion. The angular momentum fluxes due to unresolved convectively generated gravity waves are computed as a residual. These results are discussed with the goal of improving the representation of sub-grid scale motions in numerical models. We also discuss these results within the context of the reliability of reanalysis datasets and the downsides to treating reanalysis data as observations. We also revisit and discuss the seasonal dependence of the QBO transition.Item Open Access Modeling and simulation to investigate the electrification potential of medium- and heavy-duty vehicle fleets(Colorado State University. Libraries, 2023) Trinko, David A., author; Bradley, Thomas H., advisor; Quinn, Jason C., committee member; Simske, Steven, committee member; Hurrell, James, committee memberThis project involves developing and integrating new modeling tools to simulate the dynamics of electric medium- and heavy-duty fleet vehicle adoption. A technical and economic modeling tool, combining a data-driven hardware cost model with a cost-optimal charging strategy microsimulation, enables tailored analysis of the costs and benefits of electrifying individual fleets. Next, a novel text synthesis process, applied to a curated corpus of literature, quantifies trade-offs between technical, economic, and other factors in the fleet vehicle procurement decision. The outcomes of these tasks combine with knowledge from recent literature on fleet decision processes to specify the vehicle procurement model used by fleets in an agent-based model of the medium- and heavy-duty electric vehicle market. This model embodies an especially disaggregated approach to adoption modeling, internalizing factors and dynamics that conventional adoption models externalize. In particular, explicitly modeling the formation and diffusion of opinions among agents enables experiments that conventional models cannot support. Demonstrations show, for example, that increasing the extent of interactions between populations with different proclivities to electric vehicles has an asymmetrical outcome. High-proclivity electric vehicle adoption is generally unaffected as interactions increase, but low-proclivity adoption is accelerated. By representing individual fleets' requirements and costs at a high level of detail, incorporating an adoption decision model informed by a wide body of empirical research, and broadening the array of variables and dynamics available for experimentation, this integrated model offers a new way to understand the urgent challenge of eliminating emissions from the most emissions-intensive transportation sectors.Item Embargo Quantitative health impact assessments as a tool for exploring public health dimensions of environmental exposures(Colorado State University. Libraries, 2024) Dean, Daniel, author; Rojas-Rueda, David, advisor; Anderson, G. Brooke, advisor; Peel, Jennifer, committee member; Hurrell, James, committee memberPublic health is influenced by a population's built and natural environment in both negative (e.g., natural disasters or ongoing stress from heat) and positive (for instance, heat-moderating effects of vegetation) ways, as well as interactively with behavioral and social dynamics. One framing of policy priorities and urban resilience is a "triad" consisting of exposure reduction (limiting the extent to which community members are exposed to environmental hazards—including "ambient" ones like stressful temperatures), vulnerability reduction (mitigating the impacts of sustained hazards), and hazard reduction (actively reducing the frequency or intensity of hazards) (Hoegh-Guldberg et al. 2018). Because any such measures carry tradeoffs in financial and other resources, it is important that policymakers and other stakeholders weigh comparative benefits of potential environmental hazards or interventions with consistent, quantifiable metrics. In this body of work, we applied quantitative health impact assessments, an epidemiology framework that provides a valuable tool here, allowing researchers to project health outcome changes for a population of interest given predicted changes in a relevant exposure and using epidemiological evidence, including exposure-response functions (exposure-response functions), which link exposure and health outcomes. In this body of work, we use HIAs to explore three different resilience-relevant systems spanning a range of intervention types, environmental systems, and spatiotemporal scales: Project 1: Health Impacts of Future Tropical Cyclones in the Eastern United States: While tropical cyclones are among the most damaging natural disasters faced by the United States, the temporal and spatial rarity of these events impedes traditional frequency-based estimates for public health and related risk projections, leading to potential oversights in risk characterization. In addition, mortality associated with tropical cyclones may not be readily apparent between delayed onset and indirect causes (e.g. stress, disrupted medical care, infections), meaning that immediate mortality counts often underestimate full attributable mortality. In this project, we performed a pilot quantitative health impact assessment designed to address aspects of these limitations. First, we tested extending the historical tropical storm dataset using a pool of 10,000 simulated, or "synthetic" tropical cyclone seasons from the widely used and open-source STORM algorithm, trained from and intended to represent the "gold standard" of historical International Best Tracks Archive for Climate Stewardship (IBTrACS) data. To the extent that STORM represents real-world conditions, this vastly expanded 'sample population provided information on potential tropical cyclone exposure risk than would be possible from historical data alone. For the second challenge of accounting for delayed and indirect attributable mortality, we combined the synthetic data with a recently-developed exposure-response function: an integrated Bayesian causal-predictive model trained on Medicare Claims data (simplified to Americans aged 65 and older), featuring an integrated model approach to combine a whole-population causal inference model for central trends with county-specific predictive models for give county-specific estimates. This model also tracked up to 21 days of "lag time" in health outcomes after a TC exposure to capture delayed mortality. This combination of methodologies promises a comprehensive, county level picture of tropical cyclone-associated all-cause mortality risk among older adults. This approach provided insights including regions of the country at the greatest risk for tropical cyclone-related exposures among older adults. However, as our study represented a new application of the STORM algorithm (in particular, our emphasis focusing on post-landfall behavior of tropical cyclones), we also assessed the level of agreement between STORM and the historical dataset, finding some discrepancies including lower overall frequency, and considerably 'smoother' spatial distribution in exposures; some discrepancies were in line with previously noted limitations. This project used recent innovations in atmospheric science and epidemiology modeling to explore the utility of a quantitative health impact assessment framework for present-day risk and could inform policy and planning decisions in terms of tropical cyclone preparedness and response measures. Project 2: Health impacts of Urban Tree Canopy policy scenarios in Denver and Phoenix: We explored potential health impacts (in terms of all-cause mortality, stroke, and dementia) of standing policy goals in Denver, Colorado and Phoenix, Arizona, for increasing the urban tree canopy coverage in these relatively arid cities. We projected health benefits (in terms of reduced attributable all-cause mortality, stroke, and dementia incidence) at a census block group level using several existing exposure-response functions based on the widely used Normalized Difference Vegetation Index (NDVI). Because the cities expressed policy goals in terms of percentage urban tree canopy, we generated predictive models to "translate" between this metric used in policy goals and the NDVI metric. We modeled the public health impacts of proposed real-world policies for near-future policy interventions in the form of increasing urban tree canopy, using current populations, and modeling an "overnight" change in exposure, with policy scenario benefits modeled for populations in year 2020, rather than demographic projections for the 2030 (Phoenix) and 2050 (Denver) dates in the policy goal timelines. We also considered socioeconomic dimensions by using the census-based Social Vulnerability Index to trace the equity of current UTC and NDVI exposures, as well as of potential benefits. We determined that each city could, by reaching its standing policy goals, could avert hundreds of all-cause mortality cases, with even a partial attainment scenario (halfway between current and desired UTC levels) having appreciable benefits, with roughly half the captured mortality prevention; with respect to equity of UTC access, more-vulnerable communities in the cities saw lower access to current canopy cover, and consequently greater potential per-capita benefits under successful intervention scenarios. Project 3: Health Impacts of Future Temperature Extremes Under a Solar Climate Intervention Scenario. In this project, we explored potential all-cause mortality implications of a proposed climate intervention effort intended to counteract anthropogenic warming, modeling the years 2050-2060 under alternate climate scenarios. Specifically, we projected temperature-associated mortality under a stratospheric aerosol injection (SAI) intervention scenario, as well as a corresponding scenario of "middle-of-the-road" climate change. We used a study population of 65-and-older Americans in eight major US cities (Seattle, Chicago, New York, Philadelphia, Los Angeles, Phoenix, Houston, and Miami) spanning a range of local climates. We built our analysis on widely used models and the shared socioeconomic pathway platform, allowing our two scenarios to be compatible, differing only in the SAI intervention itself. We focused on two age groups (65-75, and 75+) to reflect elevated heat- and cold-associated mortality risks among this population, finding broadly similar trends between age groups. We explored city-specific exposure-response functions for the temperature-mortality association, using a widely used modeling, comparing the anticipated number of cold- and heat-related deaths under each scenario, and highlighted tradeoffs for either policy scenario, finding considerable heterogeneity in trends between these cities. To make our analysis more specific to the mid-21st century, we incorporated existing estimates for population growth and mortality rate changes based on the same climate modeling scenarios as the SAU exposure scenarios. We observed dramatic variability in minimum mortality temperatures and temperature-attributable mortality between cities and found that SAI was not associated with decisive reductions in all-cause mortality among either age group. While SAI did effectively reduce heat-attributable mortality, lower cold-attributable mortality under the warmer, non-SAI scenario counterbalanced this effect, yielding a weak net impact in central tendencies. This observation could help inform planning and resilience efforts as far as types of temperature-related stress under each scenario, as well as provide insights for larger cost-benefit analyses for the overall proposition of SAI. Together, these projects demonstrated how quantitative health impact assessments can help form a methodological foundation for exploring epidemiology and resilience-relevant systems. The variety of projects covered demonstrated the utility of this methodology in a variety of spatial scales, ranging from census block groups (comparable to neighborhoods) in Project 2 to county-level characterizations of tropical cyclone-associated risk for much of the eastern United States in Project 1. We also explored a range of time periods, ranging from Project 1's focus on characterizing tropical cyclone risk representative of the past several decades (as represented by the STORM resampling algorithm), through our attempts to explicitly model mid-21st century populations and temperature-related mortality trends using both climate and demographic projections. The modularity of the quantitative health impact assessment framework enabled our projects to leverage of existing research and datasets for low-cost, comparatively rapid assessments, as well as to lay infrastructure for future research and introduce several specific innovations in their respective designs.Item Open Access The role of Earth system interactions in large-scale atmospheric circulation and climate(Colorado State University. Libraries, 2023) Yook, Simchan, author; Thompson, David W. J., advisor; Ravishankara, A. R., committee member; Hurrell, James, committee member; Ebert-Uphoff, Imme, committee memberThe complex interactions among different components of the Earth system play a key role in governing the climate variability through various physical processes. For example, an interaction between the fluctuations in one component of the Earth system and associated variations in another component of the Earth system can either amplify or dampen the climate variability depending on the nature of their two-way feedback mechanisms. Thus, understanding the role of various physical interactions among components of the Earth system is critical to understand the changes in climate as well as to reduce the uncertainty in future climate projections. This dissertation focuses on discovering the key processes and interactions among different components of the Earth system on the climate variability using observations and model hierarchies. In Part 1, the interactions between the atmospheric circulation and western North Pacific SST anomalies are explored in two sets of simulations: 1) a simulation run on a coupled atmosphere-ocean general circulation model (GCM), and 2) a simulation forced with prescribed, time-evolving SST anomalies over the western North Pacific. The results support the interpretation of the observed lead/lag relationships between western North Pacific Sea Surface Temperature (SST) anomalies and the atmospheric circulation, and provide numerical evidence that SST variability over the western North Pacific has a demonstrable effect on the large-scale atmospheric circulation throughout the North Pacific sector. In Part 2, the role of moist lapse rate in altering the temperature variability under climate change is explored. To reduce the complexity of the problem, the changes in the temperature variance under global warming are first analyzed in the simplest version of model hierarchy: a single column Rapid Radiative Transfer Model with a simplified convective adjustment. Similar analyses were repeated with varying model hierarchies with additional complexities: a global general circulation model in global Radiative Convective Equilibrium (RCE) setting with fixed SST, and fully coupled Earth system models. The results highlight the role of moist lapse rate as a potential constraint for climate variability in the tropical atmosphere simulated by different model hierarchies. In Part 3, the effects of coupled chemistry-climate interactions on the amplitude and structure of stratospheric temperature variability are quantified in two numerical simulations: A "free running" simulation that includes fully coupled chemistry-climate interactions; and a "specified chemistry" version of the model forced with prescribed chemical composition. The results indicate that the inclusion of coupled chemistry-climate interactions increases the internal variability of temperature by a factor of ~two in the lower tropical stratosphere through dynamically driven ozone-temperature feedbacks. The results highlight the fundamental role of two-way feedbacks between the atmospheric circulation and chemistry in driving climate variability in the lower stratosphere. In Part 4, the effects of coupled chemistry-climate interactions on the large-scale atmospheric circulation are further explored based on two observational case studies of the Antarctic ozone holes of 2020 and 2021. The 2020 and 2021 were marked by two of the largest Antarctic ozone holes on record. It has been demonstrated that the ozone holes of 2020 and 2021 were associated with large changes in the atmospheric circulation consistent with the climate impacts of Antarctic ozone depletion. The ozone holes were also unusual for their associations with aerosol burdens due to two extraordinary events: the Australian wildfires of early 2020 and the eruption of La Soufriere in 2021. The results provide suggestive evidence that injections of both wildfire smoke and volcanic emissions into the stratosphere can lead to hemispheric-scale changes in surface climate. This dissertation provides a detailed look at the complex aspects of the coupled interactions among different components of the Earth system and their roles on climate variability and large-scale dynamics. To clarify the role of the different physical processes contributing to the climate responses, this study performed a comprehensive analysis based on observations as well as a series of numerical experiments run on different configurations of climate model hierarchies. The findings herein improve our understanding of different Earth system interactions and their influences on global climate and large-scale atmospheric dynamics.