Date of Award

2024

Degree Type

Thesis

Degree Name

Master of Science in Oceanography

Specialization

Chemical Oceanography

Department

Oceanography

First Advisor

Hongjie Wang

Abstract

The Pacific-Arctic Region (PAR) is highly vulnerable to ocean acidification (OA) due to its low buffer capacity, carbonate concentration, and the regionally-amplified effects of climate change. Although it experiences the highest rates of OA globally, the existing literature lacks observation-based surface decadal OA rates for the PAR, primarily due to large data gaps. To address these limitations, we aggregated open-source carbonate datasets and established spatially-dependent relationships to predict surface total alkalinity (TA) using salinity and temperature (R2=0.93, MAE = 23 μmol kg-1). We then applied these relationships to gridded sea surface salinity and temperature products to obtain monthly surface TA fields. The TA fields were coupled with the MPI-SOM-FFN surface pCO2 dataset (doi: 10.7289/v5z899n6) to obtain monthly 1°x 1° surface pH, ΩAr, and dissolved inorganic carbon fields from 1993-2021 for the entire PAR, yielding the first gapless gridded Arctic carbonate system dataset to date. This dataset indicated that the Southern PAR acidified at rates comparable to the global average, predominantly due to the absorption of anthropogenic CO2. In contrast, the Bering Sea shelf exhibited basification, likely a result of increased primary productivity. The Northern PAR exhibited acidification rates 2-4x greater than the global rate due to reduced TA linked to sea ice melt. Our findings suggest that continued warming will likely exacerbate surface acidification in regions experiencing a shift from year-round multi-year ice cover to a seasonal ice pack. While local processes such as primary productivity can temporarily counteract OA, whether they can compensate for rising anthropogenic CO2 levels is unclear. This highlights the complexity of predicting future ocean acidification trends and underscores the importance of advanced models that integrate both climatic and biological factors, enabling accurate forecasts of impacts on marine ecosystems in these highly sensitive regions.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.