Detecting Regional Modes of Variability in Observation-Based Surface Ocean pCO(2)

We use a neural network-based estimate of the sea surface partial pressure of CO2 (pCO(2)) derived from measurements assembled within the Surface Ocean CO2 Atlas to investigate the dominant modes of pCO(2) variability from 1982 through 2015. Our analysis shows that detrended and deseasonalized sea s...

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Published in:Geophysical Research Letters
Main Authors: Landschuetzer, Peter, Ilyina, Tatiana, Lovenduski, Nicole S.
Format: Text
Language:English
Published: Amer Geophysical Union
Subjects:
geo
Online Access:https://doi.org/10.1029/2018GL081756
https://archimer.ifremer.fr/doc/00675/78733/80972.pdf
https://archimer.ifremer.fr/doc/00675/78733/
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spelling fttriple:oai:gotriple.eu:10670/1.xr8r0o 2023-05-15T17:34:30+02:00 Detecting Regional Modes of Variability in Observation-Based Surface Ocean pCO(2) Landschuetzer, Peter Ilyina, Tatiana Lovenduski, Nicole S. https://doi.org/10.1029/2018GL081756 https://archimer.ifremer.fr/doc/00675/78733/80972.pdf https://archimer.ifremer.fr/doc/00675/78733/ en eng Amer Geophysical Union doi:10.1029/2018GL081756 10670/1.xr8r0o https://archimer.ifremer.fr/doc/00675/78733/80972.pdf https://archimer.ifremer.fr/doc/00675/78733/ other Archimer, archive institutionnelle de l'Ifremer Geophysical Research Letters (0094-8276) (Amer Geophysical Union), 2019-03 , Vol. 46 , N. 5 , P. 2670-2679 envir geo Text https://vocabularies.coar-repositories.org/resource_types/c_18cf/ fttriple https://doi.org/10.1029/2018GL081756 2023-01-22T18:23:37Z We use a neural network-based estimate of the sea surface partial pressure of CO2 (pCO(2)) derived from measurements assembled within the Surface Ocean CO2 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 CO2. 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. Plain Language Summary In our study we show that there is a link between the amount of carbon in the surface ocean and natural climate variability. We find that this variability is very different between different oceanic regions, but most of the observed variability is on decadal timescales and longer. Current data products therefore do not extend long enough in time to robustly detect long-term oscillations of the surface ocean carbon content. Text North Atlantic Unknown Pacific Geophysical Research Letters 46 5 2670 2679
institution Open Polar
collection Unknown
op_collection_id fttriple
language English
topic envir
geo
spellingShingle envir
geo
Landschuetzer, Peter
Ilyina, Tatiana
Lovenduski, Nicole S.
Detecting Regional Modes of Variability in Observation-Based Surface Ocean pCO(2)
topic_facet envir
geo
description We use a neural network-based estimate of the sea surface partial pressure of CO2 (pCO(2)) derived from measurements assembled within the Surface Ocean CO2 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 CO2. 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. Plain Language Summary In our study we show that there is a link between the amount of carbon in the surface ocean and natural climate variability. We find that this variability is very different between different oceanic regions, but most of the observed variability is on decadal timescales and longer. Current data products therefore do not extend long enough in time to robustly detect long-term oscillations of the surface ocean carbon content.
format Text
author Landschuetzer, Peter
Ilyina, Tatiana
Lovenduski, Nicole S.
author_facet Landschuetzer, Peter
Ilyina, Tatiana
Lovenduski, Nicole S.
author_sort Landschuetzer, Peter
title Detecting Regional Modes of Variability in Observation-Based Surface Ocean pCO(2)
title_short Detecting Regional Modes of Variability in Observation-Based Surface Ocean pCO(2)
title_full Detecting Regional Modes of Variability in Observation-Based Surface Ocean pCO(2)
title_fullStr Detecting Regional Modes of Variability in Observation-Based Surface Ocean pCO(2)
title_full_unstemmed Detecting Regional Modes of Variability in Observation-Based Surface Ocean pCO(2)
title_sort detecting regional modes of variability in observation-based surface ocean pco(2)
publisher Amer Geophysical Union
url https://doi.org/10.1029/2018GL081756
https://archimer.ifremer.fr/doc/00675/78733/80972.pdf
https://archimer.ifremer.fr/doc/00675/78733/
geographic Pacific
geographic_facet Pacific
genre North Atlantic
genre_facet North Atlantic
op_source Archimer, archive institutionnelle de l'Ifremer
Geophysical Research Letters (0094-8276) (Amer Geophysical Union), 2019-03 , Vol. 46 , N. 5 , P. 2670-2679
op_relation doi:10.1029/2018GL081756
10670/1.xr8r0o
https://archimer.ifremer.fr/doc/00675/78733/80972.pdf
https://archimer.ifremer.fr/doc/00675/78733/
op_rights other
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container_title Geophysical Research Letters
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