Harmful algal blooms have been a persistent and growing concern along the California coast. The diatom Pseudo-nitzschia produces the neurotoxin domoic acid, which accumulates in shellfish and can cause Amnesic Shellfish Poisoning in mammals upon consumption, posing serious risks to both wildlife and human health. Despite the ecological and public health significance of domoic acid events, the oceanographic drivers that trigger and sustain these blooms remain poorly understood. This study examines the influence of coastal upwelling, the process by which nutrient-rich deep water is transported to the surface, on the likelihood of a domoic acid event. Using in situ domoic acid measurements and the Biologically Effective Upwelling Transport Index (BEUTI), the probability of an event was calculated at each latitude, and 1–14 day offsets were assessed to capture delayed bloom responses to upwelling forcing. Results reveal spatially concentrated positive correlations in the central California Current, with lag analyses suggesting peak domoic acid response occurring several days after upwelling events. These findings help clarify the mechanistic link between upwelling dynamics and domoic acid production, with direct implications for harmful algal bloom forecasting and coastal resource management.

Mono Lake is a hypersaline, closed basin system that records late Holocene lake level fluctuations. Mars’ Jezero Crater, an ancient lake basin, can be analyzed by studying existing shorelines such as Mono Lake. Current evidence suggests Jezero experienced periods of closed-basin activity, which makes Mono Lake a relevant area to study as it contains a sedimentary record that has not been washed away. We compare the two to analyze fluctuation in stratigraphic and sedimentary change over time. Closed basin systems serve as strong recorders of sedimentary history because nothing is washed away by fluvial activity, every rise and fall in lake level is recorded as distinct layers in each environment’s stratigraphic record. Both environments are mineral rich, containing lots of carbonate deposits. Ground Penetrating Radar (GPR) sends electromagnetic waves into the surface to detect lithologic variation in sediments, changes in composition, and other structures not easily detectable using other methods. Existing GPR data collected at Mono Lake shorelines will be imaged, processed, and analyzed using a Python application. By comparing the sedimentology and history of Mono Lake shorelines, because all of this data is preserved in the stratigraphic record, we have a good foundation to interpret of the history and characteristics of Jezero Crater.

Earth’s polar caps are of particular interest to space physics research due to the effects of the solar wind, a stream of charged particles flowing continuously out from the sun. High energy electrons from the solar wind called polar rain travel along high latitude open field lines, precipitating in Earth’s polar cap ionosphere. In my research, I sought to determine if polar rain parameters measured from the Defense Meteorological Satellite Program (DMSP), a lineage of sun-synchronous polar orbiting satellites at 830 km altitude, could be related to bulk solar wind parameters measured by satellites at the L1 orbital position. Using data from both instrumentation platforms, I conducted numerous analyses of solar wind and polar rain data using statistical methods such as canonical-correlation analysis (CCA). I have both solidified the arguments put forth by previous literature and uncovered potentially novel trends in the course of my investigation. The ultimate goal of this research is to develop an empirical model of the polar rain based on solar wind parameters. Intimate understanding of these kinds of relationships are vital to an increasingly technological society, as vital communication systems, electric power grids, and space systems are all at risk of solar disturbances.

Oceanic mesoscale energy dominates the upper ocean’s kinetic energy, playing an important role in air-sea interaction, tides, and turbulent mixing. Large-scale ocean circulation provides around 1TW of energy input to mesoscale energy and dissipates through processes such as internal wave breaking and quadratic bottom drag. Due to sparse bottom velocity measurements, previous studies have assumed simple ocean vertical structures from satellite-measured surface velocity to predict bottom flow. This results in poorly constrained estimations of bottom drag dissipation, between 0.2-0.8TW. In this study, we aim to train a machine learning model to predict bottom flow from the ocean surface, by considering local ocean dynamics. We use temperature, salinity, and velocity features from Global Ocean Physics Reanalysis (GLORYS), and apply longitude, latitude, and bathymetry as geographical features in a supervised Random Forest algorithm. Results show a global prediction skill R-squared of 0.3. In regions of strong currents, there is a higher prediction skill than in low energy regions, suggesting a weak surface to bottom correlation in the abyssal ocean. Implementing neural network models or adding coarse-graining spatial structure in the future could improve the prediction. By improving feature selection and model complexity, we may better predict bottom drag dissipation and increase our understanding of the ocean energy budget.

Quasi-stationary Atmospheric Rivers (QSARs) are fundamental components of the atmospheric circulation that influence the persistence and intensity of regional weather, particularly in the midlatitudes. This research investigates the phenomenological effects of these waves on extreme precipitation across the Northern Hemisphere. It focuses on the spatial relationship between wave location and latitude to precipitation extremes. Using atmospheric reanalysis datasets, the study tracks the location of extreme precipitation events relative to the Pacific and Atlantic QSARs across a latitudinal range. The results demonstrate a latitudinal dependency in the positioning of these extremes. At mid-latitudes, extreme precipitation occurs predominantly upstream of the wave. However, as one moves toward higher latitudes, there is a clear transition where the precipitation maxima shift to be downstream of the wave. This research is built on the known effects of potential vorticity and local wave activity on column water vapor transport and is an area for continued discovery for this research. The significance of this project lies in its contribution to meteorological forecasting and climate risk assessment. By identifying the shifting spatial footprint of extreme weather relative to planetary waves, this work enhances our ability to predict the locations most vulnerable to extreme precipitation as atmospheric patterns become more stagnant in a warming world.