Can Empirical Algorithms Successfully Estimate Aragonite Saturation State in the Subpolar North Atlantic?
The aragonite saturation state (ΩAr) in the subpolar North Atlantic was derived using new regional empirical algorithms. These multiple regression algorithms were developed using the bin-averaged GLODAPv2 data of commonly observed oceanographic variables [temperature (T), salinity (S), pressure (P),...
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Frontiers Media S.A.
2017
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Online Access: | https://doi.org/10.3389/fmars.2017.00385 https://doaj.org/article/7c89cb53bd1144d7b9af6cfe6a08f7f5 |
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ftdoajarticles:oai:doaj.org/article:7c89cb53bd1144d7b9af6cfe6a08f7f5 2023-05-15T17:06:15+02:00 Can Empirical Algorithms Successfully Estimate Aragonite Saturation State in the Subpolar North Atlantic? Daniela Turk Michael Dowd Siv K. Lauvset Jannes Koelling Fernando Alonso-Pérez Fiz F. Pérez 2017-12-01T00:00:00Z https://doi.org/10.3389/fmars.2017.00385 https://doaj.org/article/7c89cb53bd1144d7b9af6cfe6a08f7f5 EN eng Frontiers Media S.A. http://journal.frontiersin.org/article/10.3389/fmars.2017.00385/full https://doaj.org/toc/2296-7745 2296-7745 doi:10.3389/fmars.2017.00385 https://doaj.org/article/7c89cb53bd1144d7b9af6cfe6a08f7f5 Frontiers in Marine Science, Vol 4 (2017) aragonite saturation state empirical algorithms autonomous sensors commonly observed oceanic variables GLODAPv2 subpolar North Atlantic Science Q General. Including nature conservation geographical distribution QH1-199.5 article 2017 ftdoajarticles https://doi.org/10.3389/fmars.2017.00385 2022-12-31T12:09:51Z The aragonite saturation state (ΩAr) in the subpolar North Atlantic was derived using new regional empirical algorithms. These multiple regression algorithms were developed using the bin-averaged GLODAPv2 data of commonly observed oceanographic variables [temperature (T), salinity (S), pressure (P), oxygen (O2), nitrate (NO3-), phosphate (PO43-), silicate (Si(OH)4), and pH]. Five of these variables are also frequently observed using autonomous platforms, which means they are widely available. The algorithms were validated against independent shipboard data from the OVIDE2012 cruise. It was also applied to time series observations of T, S, P, and O2 from the K1 mooring (56.5°N, 52.6°W) to reconstruct for the first time the seasonal variability of ΩAr. Our study suggests: (i) linear regression algorithms based on bin-averaged carbonate system data can successfully estimate ΩAr in our study domain over the 0–3,500 m depth range (R2 = 0.985, RMSE = 0.044); (ii) that ΩAr also can be adequately estimated from solely non-carbonate observations (R2 = 0.969, RMSE = 0.063) and autonomous sensor variables (R2 = 0.978, RMSE = 0.053). Validation with independent OVIDE2012 data further suggests that; (iii) both algorithms, non-carbonate (MEF = 0.929) and autonomous sensors (MEF = 0.995) have excellent predictive skill over the 0–3,500 depth range; (iv) that in deep waters (>500 m) observations of T, S, and O2 may be sufficient predictors of ΩAr (MEF = 0.913); and (iv) the importance of adding pH sensors on autonomous platforms in the euphotic and remineralization zone (<500 m). Reconstructed ΩAr at Irminger Sea site, and the K1 mooring in Labrador Sea show high seasonal variability at the surface due to biological drawdown of inorganic carbon during the summer, and fairly uniform ΩAr values in the water column during winter convection. Application to time series sites shows the potential for regionally tuned algorithms, but they need to be further compared against ΩAr calculated by conventional means to fully assess ... Article in Journal/Newspaper Labrador Sea North Atlantic Directory of Open Access Journals: DOAJ Articles Irminger Sea ENVELOPE(-34.041,-34.041,63.054,63.054) Frontiers in Marine Science 4 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
aragonite saturation state empirical algorithms autonomous sensors commonly observed oceanic variables GLODAPv2 subpolar North Atlantic Science Q General. Including nature conservation geographical distribution QH1-199.5 |
spellingShingle |
aragonite saturation state empirical algorithms autonomous sensors commonly observed oceanic variables GLODAPv2 subpolar North Atlantic Science Q General. Including nature conservation geographical distribution QH1-199.5 Daniela Turk Michael Dowd Siv K. Lauvset Jannes Koelling Fernando Alonso-Pérez Fiz F. Pérez Can Empirical Algorithms Successfully Estimate Aragonite Saturation State in the Subpolar North Atlantic? |
topic_facet |
aragonite saturation state empirical algorithms autonomous sensors commonly observed oceanic variables GLODAPv2 subpolar North Atlantic Science Q General. Including nature conservation geographical distribution QH1-199.5 |
description |
The aragonite saturation state (ΩAr) in the subpolar North Atlantic was derived using new regional empirical algorithms. These multiple regression algorithms were developed using the bin-averaged GLODAPv2 data of commonly observed oceanographic variables [temperature (T), salinity (S), pressure (P), oxygen (O2), nitrate (NO3-), phosphate (PO43-), silicate (Si(OH)4), and pH]. Five of these variables are also frequently observed using autonomous platforms, which means they are widely available. The algorithms were validated against independent shipboard data from the OVIDE2012 cruise. It was also applied to time series observations of T, S, P, and O2 from the K1 mooring (56.5°N, 52.6°W) to reconstruct for the first time the seasonal variability of ΩAr. Our study suggests: (i) linear regression algorithms based on bin-averaged carbonate system data can successfully estimate ΩAr in our study domain over the 0–3,500 m depth range (R2 = 0.985, RMSE = 0.044); (ii) that ΩAr also can be adequately estimated from solely non-carbonate observations (R2 = 0.969, RMSE = 0.063) and autonomous sensor variables (R2 = 0.978, RMSE = 0.053). Validation with independent OVIDE2012 data further suggests that; (iii) both algorithms, non-carbonate (MEF = 0.929) and autonomous sensors (MEF = 0.995) have excellent predictive skill over the 0–3,500 depth range; (iv) that in deep waters (>500 m) observations of T, S, and O2 may be sufficient predictors of ΩAr (MEF = 0.913); and (iv) the importance of adding pH sensors on autonomous platforms in the euphotic and remineralization zone (<500 m). Reconstructed ΩAr at Irminger Sea site, and the K1 mooring in Labrador Sea show high seasonal variability at the surface due to biological drawdown of inorganic carbon during the summer, and fairly uniform ΩAr values in the water column during winter convection. Application to time series sites shows the potential for regionally tuned algorithms, but they need to be further compared against ΩAr calculated by conventional means to fully assess ... |
format |
Article in Journal/Newspaper |
author |
Daniela Turk Michael Dowd Siv K. Lauvset Jannes Koelling Fernando Alonso-Pérez Fiz F. Pérez |
author_facet |
Daniela Turk Michael Dowd Siv K. Lauvset Jannes Koelling Fernando Alonso-Pérez Fiz F. Pérez |
author_sort |
Daniela Turk |
title |
Can Empirical Algorithms Successfully Estimate Aragonite Saturation State in the Subpolar North Atlantic? |
title_short |
Can Empirical Algorithms Successfully Estimate Aragonite Saturation State in the Subpolar North Atlantic? |
title_full |
Can Empirical Algorithms Successfully Estimate Aragonite Saturation State in the Subpolar North Atlantic? |
title_fullStr |
Can Empirical Algorithms Successfully Estimate Aragonite Saturation State in the Subpolar North Atlantic? |
title_full_unstemmed |
Can Empirical Algorithms Successfully Estimate Aragonite Saturation State in the Subpolar North Atlantic? |
title_sort |
can empirical algorithms successfully estimate aragonite saturation state in the subpolar north atlantic? |
publisher |
Frontiers Media S.A. |
publishDate |
2017 |
url |
https://doi.org/10.3389/fmars.2017.00385 https://doaj.org/article/7c89cb53bd1144d7b9af6cfe6a08f7f5 |
long_lat |
ENVELOPE(-34.041,-34.041,63.054,63.054) |
geographic |
Irminger Sea |
geographic_facet |
Irminger Sea |
genre |
Labrador Sea North Atlantic |
genre_facet |
Labrador Sea North Atlantic |
op_source |
Frontiers in Marine Science, Vol 4 (2017) |
op_relation |
http://journal.frontiersin.org/article/10.3389/fmars.2017.00385/full https://doaj.org/toc/2296-7745 2296-7745 doi:10.3389/fmars.2017.00385 https://doaj.org/article/7c89cb53bd1144d7b9af6cfe6a08f7f5 |
op_doi |
https://doi.org/10.3389/fmars.2017.00385 |
container_title |
Frontiers in Marine Science |
container_volume |
4 |
_version_ |
1766061288714141696 |