Anthropogenic and Climatic Contributions to Observed Carbon System Trends in the Northeast Pacific
Open Access
- 8 July 2021
- journal article
- research article
- Published by American Geophysical Union (AGU) in Global Biogeochemical Cycles
- Vol. 35 (7)
Abstract
The ocean absorbs anthropogenic carbon, slowing atmospheric CO2 increase but driving ocean acidification. Long‐term changes in the carbon system are typically assessed from single‐point time series or from hydrographic sections spaced by decades. Using higher resolution observations (1–3 year−1) from the Line P time series, we investigate processes modulating trends in the carbon system of the northeast subarctic Pacific. Dissolved inorganic carbon (DIC) and apparent oxygen utilization (AOU) from 1990 to 2019 reveal substantial trends over most of the upper water column along the 1,500 km coastal to open ocean transect. At the surface, an increasing trend in salinity‐normalized DIC (sDIC33) (+0.5 ± 0.4 μmol kg−1 yr−1) is associated with a decrease in pH (0.01–0.02 decade−1) and a decrease in aragonite saturation state (0.04–0.08 decade−1). These observed trends are driven by anthropogenic CO2 uptake, partially offset by trends in surface salinity or temperature. Stratification associated with recent marine heat waves appears to have caused anomalously low surface pCO2. sDIC33 trends of similar magnitude were found below the seasonal thermocline on the 26.7–26.8 isopycnals (150–300 m), which are ventilated in the western Pacific. Roughly, a third (20%–50%) of the subsurface sDIC33 trend is driven by increased remineralization, likely caused by long‐term decreases in ventilation in the western Pacific. Bidecadal oscillations in the ventilation of the 26.7–26.8 isopycnals arising from the Lunar Nodal Cycle cause oscillations in sDIC33 and AOU at the offshore end of our transect. We trace the oscillations to alternating periods of higher anthropogenic carbon uptake or higher carbon remineralization.Keywords
Funding Information
- Marine Environmental Observation Prediction and Response Network
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