Thesis Defense Seminar - EvaLynn Jundt

COASTAL AND MARINE SYSTEM SCIENCE PROGRAM
DEPARTMENT OF PHYSICAL AND ENVIRONMENTAL SCIENCES
TEXAS A&M UNIVERSITY-CORPUS CHRISTI

SUBJECT: Multiple Linear Regression Models for the Estimation of pH and Aragonite Saturation State in the Northwestern Gulf of Mexico

MAJOR ADVISOR: Dr. Xinping Hu

ABSTRACT

The ocean plays a vital role in making up 70% of the Earth’s surface, producing over half of oxygen globally and absorbing approximately 30% of anthropogenic CO2 since the industrial revolution. Ocean acidification (OA) is a direct threat to many organisms living in the oceans across the globe, yet the state of carbonate chemistry and the rate of OA vary in different parts of the world’s oceans. Although current data suggests that the open Gulf of Mexico (GOM) surface waters have relatively high pH (> 8) and aragonite saturation state (ΩArag > 3), the GOM could still experience ecological impacts of OA. In addition, the combination of increasing atmospheric CO2, ocean upwelling, and increasing terrestrial nutrient export may acidify the coastal waters even further. Acidifying ocean waters have decreasing ΩArag, posing serious threats to calcifying organisms, affecting their populations, growth patterns, and shell integrity or skeletal density. The GOM is home to the northernmost tropical coral reefs as well as prominent shellfish industry. Historical water column carbonate measurements are scarce, so the progression of OA in the GOM is poorly understood. Research regarding OA in the GOM is needed to manage and protect these resources. In the literature, multiple linear regression (MLR) models have been created to fill data gaps in areas such as the Gulf of Alaska, the Southern Ocean, Sea of Japan, and coasts of the northeastern and northwestern United States. Prior to this study, no such model existed for the GOM. By creating a model that is built upon the relationships between commonly measured hydrographic properties (salinity, temperature, pressure, and dissolved oxygen (DO)) and pH as well as ΩArag, data gaps can be filled in areas that do not have sufficient sampling coverage. In this study, I created robust statistical models for the estimation of ΩArag and pH in the northwestern GOM (NWGOM) from latitudes 27.1-29˚N and longitudes 91.5-95˚W. The calibration data used in the models included depth, salinity, temperature, and DO collected from four cruises that took place in July 2007, July 2017, and April and August of 2021. The models predict ΩArag with R2 ≥ 0.98, RMSE ≤ 0.14 and pH with R2 ≥ 0.93, RMSE ≤ 0.02 for four different scenarios including different subsets of the data. The data used to create the models was also used to create contour plots that show variations of ΩArag and pH over the timeframe of the study from 2007 to 2021. Relatively low ΩArag (ΩArag ≤ 2) was present in the depths ≥ 180 m. The depth range of the water column between ΩArag = 1.5-2.0 decreased over this period. The depths for ΩArag = 2 and ΩArag = 1.1 varied ±20 and ±50 m respectively, while the depth for ΩArag = 1.5 shoaled 50 m from 2007 to 2021. Depth profiles for pH revealed consistent patterns over all four cruises with highest values over the shelf and upper 125 m and minimum values around 500 m. The pH = 7.9 horizon remained around 265 m for all cruises, while the pH = 8.0 horizon varied 10 m (from 2007 to 2021). Over the shelf, the highest and lowest pH values were 0.0356 and 0.0133 units lower in 2021 than in 2007 respectively. This resulted in the range of pH values experienced narrowing by 0.0223 and transitioning to lower pH values overall. These MLR models are valuable tools for reconstructing ΩArag and pH data where direct chemical observations are absent but hydrographical information is available. These models can be applied to the NWGOM within a 10-year time span, although observations of potential shifts in circulation and water masses as well as additional anthropogenic CO2 uptake should be monitored.

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