Detecting regional modes of variability in observation-based surface ocean pCO2
We use a neural network-based estimate of the sea surface partial pressure of CO 2 (pCO 2 ) derived from measurements assembled within the Surface Ocean CO 2 Atlas to investigate the dominant modes of pCO 2 variability from 1982 through 2015. Our analysis shows that detrended and deseasonalized sea...
| Published in: | Geophysical Research Letters |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/21.11116/0000-0003-3900-D http://hdl.handle.net/21.11116/0000-0003-5FC2-8 |
| Summary: | We use a neural network-based estimate of the sea surface partial pressure of CO 2 (pCO 2 ) derived from measurements assembled within the Surface Ocean CO 2 Atlas to investigate the dominant modes of pCO 2 variability from 1982 through 2015. Our analysis shows that detrended and deseasonalized sea surface pCO 2 varies substantially by region and the respective frequencies match those from the major modes of climate variability (Atlantic Multidecadal Oscillation, Pacific Decadal Oscillation, multivariate ENSO index, Southern Annular Mode), suggesting a climate modulated air-sea exchange of CO 2 . We find that most of the regional pCO 2 variability is driven by changes in the ocean circulation and/or changes in biology, whereas the North Atlantic variability is tightly linked to temperature variations in the surface ocean and the resulting changes in solubility. Despite the 34-year time series, our analysis reveals that we can currently only detect one to two periods of slow frequency oscillations, challenging our ability to robustly link pCO 2 variations to climate variability. ©2019. American Geophysical Union. All Rights Reserved. |
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