Abstract
Seasonal variability of the aragonite saturation state (ΩAR) in the upper (50 m and 100 m depths) North Pacific Ocean (NPO) was investigated using multiple linear regression (MLR). The MLR algorithm derived from a high-quality carbon data set accurately predicted the ΩAR of evaluation data sets (three time series stations and P02 section) with acceptable uncertainty (<0.1 ΩAR). The algorithm was combined with seasonal climatology data, and the estimated ΩAR varied in the range of 0.4-0.6 in the midlatitude western NPO, with the largest variation found for the tropical eastern NPO. These marked variations were largely controlled by seasonal changes in vertical mixing and thermocline depth, both of which determine the degree of entrainment of CO2-rich corrosive waters from deeper depths. Our MLR-based subsurface ΩAR climatology is complementary to surface climatology based on pCO2 measurements.
Original language | English |
---|---|
Pages (from-to) | 4498-4506 |
Number of pages | 9 |
Journal | Geophysical Research Letters |
Volume | 42 |
Issue number | 11 |
DOIs | |
Publication status | Published - 2015 Jun 16 |
Keywords
- North Pacific Ocean
- aragonite saturation state
- fossil fuel CO2
- mutiple linear regression
- ocean acidification
ASJC Scopus subject areas
- Geophysics
- Earth and Planetary Sciences(all)