Doctor of Philosophy with a Major in Earth and Atmospheric Sciences

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Doctor of Philosophy with a Major in Earth and Atmospheric Sciences

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https://hdl.handle.net/1853/73611

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School of Earth and Atmospheric Sciences

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Modeling the emission of energetic neutral atoms at Titan

(Georgia Institute of Technology, 2024-11-14) Tippens, Tyler Franklin

Saturn's largest moon Titan orbits near the outer edge of the planet's magnetosphere, where conditions vary erratically on timescales ranging from years to tens of minutes. Magnetospheric plasma that rotates with the planet's magnetic field continuously overtakes the moon. Titan's ionosphere causes this magnetic field to pile up and drape around the moon, forming a localized induced magnetosphere. Our present characterization of Titan's induced magnetosphere is largely based on plasma and magnetic field data collected in situ, along the one-dimensional trajectory of Cassini during these flybys. However, it is difficult to place such measurements within the context of the full three-dimensional interaction due to the rapid oscillation of Saturn's magnetosphere. Charge exchange between energetic magnetospheric ions and the moon's neutral atmosphere generates energetic neutral atoms (ENAs), which can be imaged in a manner largely analogous to traditional photography. The Cassini spacecraft took numerous such "photos" of Titan's ENA signature across 126 close flybys. ENA images constitute snapshots of the moon's entire interaction region, observed simultaneously, making these remote measurements advantageous in such a variable environment. It remains challenging to interpret such observations, however, as ENA images contain information on the ambient energetic ion distribution, the electromagnetic environment near Titan, and the moon's atmosphere. A successful disentangling of these influences, which does not yet exist, would provide a major scientific advantage over in-situ measurements alone. Specifically, understanding exactly how the energetic ion dynamics in Titan's induced magnetosphere shape ENA observations is key to deciphering the information embedded in ENA images taken by Cassini. For this purpose, we have developed two new simulation codes which calculate ENA production and detection. Each of our models utilizes a different detection scheme for capturing modeled ENA emissions: first, a hypothetical spherical detector which reveals the entire ENA population, and second, a point-like detector with a finite field of view closely resembling that of Cassini's ENA camera. Using the first scheme, we identify a "belt" of elevated ENA flux that forms a great circle in the plane perpendicular to the ambient magnetospheric field vector. Field line draping attenuates the intensity of ENA emissions into this belt, but does not strongly alter the belt morphology. Using the second model, we generate and compare over 1000 synthetic ENA images of Titan's magnetospheric interaction with data from several Cassini flybys. We find that both the ambient field vector and field line draping can strongly influence the observed ENA signature, and the visibility of the moon's plasma interaction in ENA images is highly dependent on the viewing geometry.
Dynamics and Observational Implication of Close-in Exoplanets

(Georgia Institute of Technology, 2024-07-28) Chen, Chen

With more than five thousand exoplanets discovered, it unveiled significantly diverse orbital configurations, contrasting with those in our Solar Systems and confronting classical understanding of planet formation. For instance, the prevalence of observed close-in exoplanets that lie within the orbit of Mercury up to ~0.01 AU brings up questions about their formation, as their current orbit distances can be within or close to the dust sublimation zone. These planets could initially formed at a further distance, and then migrate inward. However, why and how the configuration of these close-in systems differs from the Solar System is still puzzling. Investigating their dynamical and physical properties can help us gain deeper insights into the planetary system formation and evolution beyond the Solar System and better understand their habitability. Under this context, this thesis presents the characterization of the dynamics and the identification of the physical properties for close-in systems. In the first work, I focused on the dynamics of ultra-short-period planet (USP), which is defined as the planet orbiting its host star shorter than one day. The USPs orbit in close proximity to their stars within the sublimation zone. This extreme object typically has a larger period ratio and higher mutual inclination with its outer companion, comparing to other systems without it. To characterize the dynamics of USP systems, I utilized secular simulations and developed an analytical method to investigate the mutual inclination evolution of the USP system. The stellar oblateness (J2) plays an important role in the dynamics. It can excite the mutual inclination between planets by precessing their orbital angular momentum at different rates, and it decreases with time due to magnetic braking. Therefore, we focused on the dynamical effects of J2. I successfully identified the formation channel of the Kepler-653 system with different initial conditions. The result suggests that either USP planets formed early and needed significant inclinations or they formed late when their host stars rotated slower (smaller J2). In the second work, I characterized the physical properties of the close-in planets by employing deep learning techniques to predict the parameters of exoplanets. Most planets are discovered from transit method without the measurement of their masses. However, mass is important to better understand the composition and formation mechanism of planets. One way to determine the mass is through transit timing variation (TTV). The TTV encodes rich dynamical information as it is contributed by perturbations from planets, and provide a powerful method to estimate planetary masses and orbital parameters. The traditional Markov Chain Monte Carlo (MCMC) method incorporates TTV to predict the planetary properties, however, MCMC is computationally expensive, and highly sensitive to the prior distribution. Especially, when the system is with only one planet transit, the properties of non-transit planet are even harder to obtain. Deep learning techniques are able to tackle these challenges. It can predict the exact values of the properties, and there is no need to consider the specific prior distribution. Therefore, I designed a deep learning model to determine the orbital parameters and mass of non-transit planet with transit information as input, focusing on single transit planetary system. The deep learning model I trained gives an overall fractional error of ~1% on the predictions of the testing set. I also utilized the model to make predictions on the real system, Kepler-88. This work can contribute to the design of observational missions aiming to search companions of single transiting systems.
Influence of Magnetic Field Line Draping on Charged Particle Irradiation of Europa's Surface Ice

(Georgia Institute of Technology, 2024-01-17) Addison, Peter

Europa, the smallest of the Galilean moons of Jupiter, orbits within its parent planet's inner magnetosphere. When the Galileo spacecraft visited Europa in the late 1990s, its magnetometer measured signatures consistent with a secondary magnetic field centered at the moon. It was found that such an induced field could only be generated by a highly-conducting, liquid water layer locked beneath the moon's icy crust. The presence of this subsurface ocean has since made Europa one of the most promising locations to search for extraterrestrial life. Analysis of the ocean is, however, thwarted by the 10s to 100s of kilometer thick ice shell under which it is encased, and any investigation is (for now) limited to the moon’s surface. Unfortunately, the surface is exposed to a harsh radiation environment. At its location within Jupiter's magnetosphere, Europa is located within a region of dense, energetic magnetospheric plasma which hammers down on the surface. This charged particle bombardment makes the upper surface uninhabitable to any organic signatures, drives surface chemistry, generates the moon's dilute exosphere by ejecting neutral material from the surface, and is potentially harmful to spacecraft. Characterizing the intensity and spatial distribution of this charged particle irradiation is therefore critical not only to understanding the evolution of Europa's surface and exosphere, but is also of utmost importance to spacecraft safety. The impact locations of charged magnetospheric particles onto Europa's surface is determined by the dynamics of these particles both in Jupiter's global magnetosphere and in the moon's local electromagnetic fields. The dense plasma within Jupiter's equatorial plasma sheet continually washes over Europa's orbital trailing hemisphere. This flowing plasma interacts with the induced field from Europa's subsurface ocean, as well as electric currents within the moon's ionosphere, drastically warping the background Jovian field. Such perturbations may deflect particles and shield the moon's surface, or focus irradiation onto regions which previous studies have determined to be relatively "safe". In order to develop a comprehensive picture of magnetospheric particle irradiation at Europa and its effect on the surface, we combine a three-dimensional hybrid model of the moon's perturbed electromagnetic environment with a relativistic particle tracer in order to map how the field perturbations affect the irradiation patterns. We calculate ion and electron irradiation patterns, energy deposition, and sputtering rates, and compare our results with observations from both the Galileo spacecraft and Hubble Space Telescope. We find that the electromagnetic field perturbations strongly affect the irradiation patterns of magnetospheric ions, but not of electrons, and that exogenic particle irradiation is essential to explain several observed features of Europa’s surface and exosphere.
Next Generation Earthquake Monitoring: Harnessing Deep Learning for Enhanced Seismic Phase Detection and Association

(Georgia Institute of Technology, 2023-12-11) Chuang, Yuling Lindsay

As seismology enters the era of big data, the exponential growth in data volume and processing needs surpasses the capacity of traditional seismic monitoring workflows. The recent success of machine learning applications across various scientific domains has made a paradigm shift in image processing and simple task automation. Within this context, this thesis presents a modern earthquake monitoring workflow with deep learning integrated into different fronts. In the first study, I utilized a deep learning phase picker - EQTransformer and a template matching method for foreshock discovery. I performed a detailed study of the foreshock sequence preceding the 2010 magnitude 6.7 Yushu, Qinghai earthquake in the Tibetan plateau and successfully identified 120 foreshocks with magnitude ranging from -0.7 to 1.6. The foreshock sequence started with an magnitude 4.6 foreshock that occurred approximately 2 hours before at a fault plane roughly perpendicular to the mainshock rupture zone. The observations suggest that extensional step-overs and conjugate faults along major strike-slip faults play an important role in generating short-term foreshock sequences. In the second and third studies, I introduced a novel phase association and location framework tailored for a global-scale seismic monitoring network. The global seismic phase association remains a challenging task due to several factors, such as an inhomogeneous sparse seismic network, the high volume of phase arrivals (comprising both true and false picks), and the large solution space inherent for the global scale. I crafted a framework to tackle these challenges by combining an ensemble deep learning locator, advanced sampling strategies, beam search, and an OcTree grid search algorithm. Through comprehensive evaluations with synthetic and real-world datasets, I demonstrated the framework's effectiveness in associating seismic phases, even in scenarios with multiple events, noise, and overlapping events. During a 9-day trial period in May 2010, this framework recovered up to 93 \% of the events cataloged in the analyst-curated Unconstrained Global Event Bulletin (UGEB) catalog when applied to the phase arrival dataset at the International Data Centre (IDC), while effectively handling up to 88 \% of false picks, despite only using P-waves. Finally, I presented an integration of full waveforms and velocity models through an auto-encoder network and an Eikonet-style deep-learning surrogate model. This work contributes to the modern earthquake monitoring workflow by leveraging deep learning across various aspects of seismic research in the era of big data.
Uncertainties in Projections of Tropical Precipitation and Atmospheric Circulation and Their Remote Impacts

(Georgia Institute of Technology, 2023-12-10) Lu, Kezhou

My doctoral work focuses on understanding anthropogenic responses of precipitation and atmospheric circulation by employing both statistical methods and climate models of varying levels of complexity. My research has two main goals: (1) to understand the forced response of tropical air-sea interactions across different time scales and their subtropical impact, and (2) to investigate the reasons underlying the uncertainties in climate models when simulating tropical and extra-tropical climate. My dissertation research comprises four individual projects. For my first project, I have explored the mechanism of how the Walker circulation (WC) responds to CO2 forcing across different time scales. The WC, a significant tropical atmospheric circulation spanning both horizontally and vertically, plays a crucial role in the tropical climate and is closely related to phenomena such as the Madden–Julian Oscillation and El Nino-Southern Oscillation. The prevailing consensus suggests that the long-term weakening of the WC is primarily driven by the sea surface temperature (SST) warming caused by increased greenhouse gases, while the fast response of the WC appears largely independent of changes in SST. However, my findings indicate that the air-sea interactions play a substantial role. By analyzing data output from Coupled Model Intercomparison Project Phase 5 (CMIP5) under abrupt4xCO2 scenarios, models with a stronger air-sea coupling in the equatorial Pacific are discovered to simulate an initial strengthening of the WC following the external forcing, which contrasts with the long-term response. Conversely, models characterized by weaker air-sea coupling simulate a monotonically weakening of the WC. My results suggest that the inter-model discrepancy in the WC changes is associated with then uncertainty in the fast component. My second project focuses on understanding the summer North Pacific subtropical high (NPSH). As part of the planetary wave system, the NPSH integrates both tropical and extra-tropical impacts on the monsoons and typhoons over East Asia and hydroclimate over California. Given its considerable socioeconomic significance, reliable future projections of the NPSH are crucial for preparing adaptation plans. However, state-of-the-art climate models exhibit diverging responses of the NPSH to anthropogenic CO2 forcing. This project has revealed that model variability in the future projection of the summer NPSH originates from both inter-model SST-driven and non-inter-model SST-driven uncertainties in the tropical precipitation. Specifically, I investigate the connection between the tropical precipitation and the NPSH by modifying the diabatic heating in both a baroclinic stationary wave model and a comprehensive climate model, i.e. the Community Atmospheric Model version 5 (CAM5). Drawing upon the knowledge acquired from the previous two chapters, my third project has explored how the tropical air-sea interaction and the summer NPSH are influenced by anthropogenic forcing, as well as their interplay. I have discovered that the inter-model spread in projecting the fast changes of the WC directly contributes to the inter-model spread in tropical SST responses. By analyzing CMIP5 and CMIP6 data under abrupt4xCO2 scenario, models with a stronger tropical equatorial Pacific air-sea coupling simulate a strengthening of the WC and a La Nina-like central Pacific cooling. And this La Nina-like SST anomaly induces anomalous tropical precipitation and further modulates the NPSH via the Matsuno-Gill wave response. During the collaboration with my advisor on the fourth project, we have found considerable inter-basin variations in the future projection of the tropical Hydrological sensitivity (HS) regardless of how SST warms. I have further demonstrated the remote impact of the inter-basin discrepancy in HS on land precipitation and surface temperature by understanding the corresponding tropical-extra-tropical teleconnections. Specifically, I have analyzed the atmospheric circulation response induced by tropical precipitation with and without inter-basin discrepancy in HS by conducting diabatic heating adjustment experiments in CAM5.
Insights into Ozone and PM2.5 Pollution: A Case Study in Spring China and Trend Analysis across the Continental United States

(Georgia Institute of Technology, 2023-12-06) Chong, Kezhen

Ground-level Ozone (O3) and fine particulate matters (PM2.5) are two major pollutants, produced through complex photochemical processes involving nitrogen oxides (NOx=NO+NO2), volatile organic compounds (VOCs), and various radicals. Understanding this chemical system is crucial for effective mitigation strategies. This thesis leverages model simulations, and comprehensive ground- and satellite-based observations to gain insights into the underlying photochemistry of O3 and PM2.5 formation. This dissertation begins by exploring three observation-based pathways associated with nitrous acid (HONO) production, highlighting intrinsic relationships between NO2, particulate nitrate (pNO3) and nitric acid (HNO3). Our results reveal varying implications for O3 production. The conversion of HONO from pNO3 enhances regional O3 production, while the conversion of HONO from NO2 can reduce O3 sensitivity to NOx changes in polluted eastern China. Secondly, the comparison between satellite and ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements validates the use of satellite in assessing air pollution patterns, with better agreement observed for NO2 compared to formaldehyde (HCHO). Nonetheless, the TROPOspheric Monitoring Instrument (TROPOMI) HCHO still shows promising improvements compared to two Ozone Monitoring Instrument (OMI) products. Meanwhile, a bias ~30% between two OMI NO2 products are linked to the discrepancies between scattering weight profiles in two retrieval algorithms. In addition, regional effects, seasonal and historical trends of secondary organic carbon (SOC) across the continental United States (CONUS) through 2005-2020 are investigated using organic carbon (OC) and elemental carbon (EC) data from the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network. We divided CONUS into six regions according to the correlations of OC concentrations among different sites. The regional mean secondary fractions vary from 22% to 40% and are consistent with co-located values reported by previous studies. With a consistent peak-in-summer seasonal pattern across all six regions, controlling factors for summertime SOC production for each region are investigated through a stepwise multiple linear regression. Furthermore, despite decreasing trends of anthropogenic emissions, as well as that of primary OC, significant decreasing trends in SOC are found only in eastern US in winter, and the southeast (SE) in summer. Accordingly, annual mean SOC fractions have been found to be significantly increasing except for SE. As anthropogenic emissions continue to decrease, SOC will most likely account for increasingly larger fractions of OC and PM2.5.
Characterization of Water-soluble Metals in Urban Aerosols and Comparative Analysis of PM2.5 Oxidative Potential in a Subarctic City

(Georgia Institute of Technology, 2023-12-06) Yang, Yuhan

Epidemiological studies have established a link between fine particulate matter (PM2.5) mass and adverse health-related issues. Particle oxidative potential (OP), referring to the redox ability of PM, is a possible unifying concept that connects a host of adverse health effects, but has not been well studied in subarctic regions. Furthermore, there is a notable absence of comparative investigations regarding OP across cities with distinct sources, as well as between indoor and outdoor environments. In this dissertation, we investigated the OP of PM2.5 in wintertime Fairbanks, Alaska, which is a subarctic city with episodic severe PM2.5 concentrations during winter associated with large residential heating emissions, and compared it to Atlanta and Los Angeles. Approximately 40 23.5-hr filter samples were collected during the 2022 winter heating season and analyzed for OP via the dithiothreitol-depletion (OPDTT) and hydroxyl-generation (OPOH) assays. Multivariate linear regression analysis, correlations with source tracers, and contrast between cold and warmer events indicated that OPDTT was sensitive to copper, elemental carbon and organic aerosol from residential wood burning, and OPOH to iron and organic aerosol from vehicles. Fairbanks exhibited higher PM2.5 mass concentrations than Atlanta and Los Angeles, while OPDTT levels were similar between Fairbanks and Atlanta. Los Angeles had the highest OPDTT and OPOH levels. Differences were due to contrasting emissions from biomass burning and vehicles. Indoor PM2.5 at the residential site was also investigated with OPDTT and compared to outdoor levels. Indoor perturbation experiments showed intrinsic OPDTT (OPDTT per PM2.5 mass, a measure of toxicity) was highly dependent on the type of activity; pellet stove emissions had high toxicity while cooking emissions were substantially lower. These findings demonstrate the importance of sources and specific aerosol components, such as transition metals and organic species, providing valuable insights beyond PM2.5 mass concentration in assessing air quality. While OP of organic species has been investigated in a number of studies, the linkage between transition metals and aerosol adverse health effect is not very well understood. One possibility is that transition metals, especially water-soluble redox-active transition metals, such as iron and copper ions in PM, can catalyze the production of reactive oxygen species (ROS) in vivo, leading to oxidative stress and therefore are important contributors to aerosol OP and have a stronger association with adverse health outcomes than PM2.5 mass. In addition, soluble metals and metal nanoparticles are readily transported to the brain from the olfactory mucosa and have been associated with reduced cognitive function and dementia. However, water-soluble metals are operationally defined (as those species in the aqueous filter extract that can penetrate through a 0.45 µm syringe filter) and water-soluble species have not been well characterized. In this dissertation, we developed robust liquid spectrophotometric methods for measuring total and soluble Fe and Cu with a relatively inexpensive analytical system. These methods were applied to 24-hour filter samples collected throughout the year 2017 in urban Atlanta. The water-soluble components were further characterized by ultrafiltration, which showed that roughly 85% of both the Fe and Cu in the water-soluble fraction was composed of dissolved species or colloidal particles smaller than nominally 4 nm. The solubility and ultrafiltration components of Fe are possibly associated with both acid-promoted dissolution process and complexation by organic ligands, such as oxalate; oxalate-Fe ligand was mainly found in the smallest ultrafiltrate size (< nominally 2 nm), whereas pH cycling could lead to the formation of colloidal particles with sizes between nominally 2-4 nm. Size distributions of metals of PM samples collected at a roadside site in urban Atlanta showed a shift in water-soluble metals to smaller particle size relative to the size of total metal, moving to more acidic aerosol. The shift was inversely related to the solubility of the metal such that less soluble metals like iron were at smaller particle sizes than more soluble metals like copper, consistent with the large role of particle pH on the dissolution of highly insoluble emitted species. This thesis provides new insights into the specific components of PM2.5 that may be most detrimental to human health.
Geochemical Constraints on The Formation of Authigenic Clay Minerals During Reverse Weathering

(Georgia Institute of Technology, 2023-08-25) Zhao, Simin

The formation of authigenic clay minerals from biogenic silica (bSi) in marine sediments, termed reverse weathering (RW), describes the formation of cation-rich aluminosilicate clay phases at the expense of bSi and detrital lithogenic minerals with concurrent sequestration of cations (e.g., Fe2+, Mg2+, and K+), consuming alkalinity and releasing CO2. The RW process acts as a globally significant sink for Si and other elements such as Fe. It is an important process for regulating ocean pH and atmospheric CO2 over geologic time scales. However, the formation mechanism, kinetics, and phases of authigenic clay during RW has long puzzled the geochemical community and much remains unknown, owing to the intrinsic challenges to trace and characterize authigenic clay formation during RW. This dissertation develops a framework of understanding for authigenic clay formation in the RW process by systematically investigating the potential geochemical triggers, identifying the product phases, and unrevealing the reaction rates and mechanisms under well-controlled laboratory conditions. Dissolved Fe2+ and Al were identified as triggers for authigenic clay formation from bSi. The formation mechanism was identified to occur through (1) dissolution of Fe and Al minerals and bSi for dissolved Fe2+, Al, and dissolved Si (DSi) inputs, and (2) reprecipitation of these species via homogeneous or heterogenous precipitation and transformation. Authigenic clay formation rates, mechanism, and steps were constrained by initial DSi concentration, dissolved Fe2+ and Al concentration, and the presence of substrates. The authigenic phase type and crystallinity are constrained by reactant type and concentration (e.g., solid Al mineral phase for sufficient Al supply or limited sporadic dissolved Al input spikes, dissolved Fe2+ concentration), the presence of substrates, and reaction time. The observed authigenic phase includes Fe-bearing mica, berthierine, chamosite, and Fe-Al layered double hydroxide, allophane-like Al-Si gel precursor phase, hisingerite-like Fe(II)-Si gel precursor phase, and Fe-smectite. The results shed light on authigenic clay formation during RW and provide bases to refine Earth system models and quantify the impacts of RW in regulating global biogeochemical cycles and sinks, CO2 dynamics, and global climate.
Observations of peroxyacyl nitrates in polluted and remote troposphere

(Georgia Institute of Technology, 2023-08-01) Lee, Young Ro

Emissions of volatile organic compounds (VOCs) and their photooxidation with nitrogen oxides (NOx) play a significant role in atmospheric chemistry and have substantial effects on air quality. Understanding these processes in the ambient environments is a challenge, in part, due to uncertainties in emission sources and the complex chemical evolution of emissions. This dissertation leverages a comprehensive suite of ground-based and airborne observations to investigate the impacts of VOCs-NOx photochemistry on atmospheric trace gas compositions, both in heavily polluted and pristine environments. In particular, this work focuses on observations of peroxyacyl nitrates (PANs) to provide a detailed diagnosis of photochemistry in the regions discussed throughout the dissertation. East Asian countries such as South Korea and China have experienced severe air pollution problems. In this work, extensive observations of primary and secondary pollutants were conducted in two locations: a remote ground site in the Yellow River Delta, China, during the Ozone Photochemistry and Export from China Experiment (OPECE) in 2018, and a petrochemical producing region in South Korea during the Korea-United States Air quality (KORUS-AQ) campaign in 2016. Our findings during the field observations indicated that both regions are characterized by heterogeneous VOC composition with substantial emissions of alkenes and aromatics. Photooxidation of these VOCs led to efficient ozone production in a radical-limited environment. In addition, elevated levels of peroxyacetic nitric anhydride (PAN), as well as rarely measured homologs such as peroxybenzoic nitric anhydride (PBzN) and peroxyacrylic nitric anhydride (APAN), illustrated the unique atmospheric chemistry in East Asian environments. This dissertation presents global-scale airborne observations of PAN from the NASA DC-8 research aircraft during the ATmospheric Tomography (ATom) campaign. The focus of this investigation was on PAN observations in remote tropospheric regions such as over the Pacific and Atlantic Oceans. We found that PAN over remote oceans is significantly influenced by relatively simple sources including anthropogenic and biomass burning emissions. Notably, biomass burning has a dominant and persistent impact on the global distribution of PAN. Based on a diagnostic evaluation using observations, this work suggests that accurate model treatment of biomass burning can improve prediction of PAN in the remote troposphere. Lastly, the characterization of a low-activity 210Po ion source with an initial activity of 1.5 mCi was performed for use with iodide-chemical ionization mass spectrometry (I--CIMS). We demonstrated that the low activity source is a viable substitute of higher activity radioactive source, offering advantages in terms of reduced regulatory burden during storage and shipping. The performance of the low activity source is illustrated using airborne measurements of PANs during the ATom campaign.
Utilizing Subsurface-Dwelling Foraminifera for Quantitative Paleoclimate Reconstructions

(Georgia Institute of Technology, 2023-07-27) Lakhani, Karim Q.

Foraminifera are useful tools for paleoclimatology (how the climate was different in the past) with many proxies for key ocean variables in their shells. Subsurface-dwelling foraminifera have been underutilized in paleoclimate due to their inherent habitat uncertainty; much research with these species has been qualitative in the past. I outline a methodology for using these species quantitatively for paleoclimate reconstruction and apply it to the Last Glacial Maximum (LGM). Firstly, I compile a database of foraminiferal data to quantitively estimate the error in their habitats. Using these uncertainty estimates, I describe a regression method to estimate an ocean profile that can reconstruct important features in the ocean such as the thermocline. Using five species of foraminifera that live in the surface, subsurface, and bottom of the ocean, this method recreates large scale features in the thermocline across the Tropical Pacific as well as changes between the Holocene and LGM at published core sites. Finally, I apply this method to a dataset of LGM foraminiferal data for sites across the Tropical Pacific. After filling in the data gaps for these sites, I find that there were heterogenous changes in the Tropical Pacific thermocline, with no change in the Western Pacific thermocline and a deepening at some sites in the Eastern Pacific. Looking along a transect in the Western Pacific, I find that there are structural differences between the profiles estimated from LGM data and the Holocene climatology, suggesting a change in atmospheric circulation during the past. By improving the ability to utilize these recorders of subsurface ocean conditions, we can better understand how climate was different in the past and how well climate models can recreate those differences.