Can empirical algorithms successfully estimate aragonite saturation state in the subpolar North Atlantic?
17 pages, 4 tables, 8 figures 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...
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Online Access: | http://hdl.handle.net/10261/306570 https://doi.org/10.3389/fmars.2017.00385 |
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ftcsic:oai:digital.csic.es:10261/306570 2024-02-11T10:05:36+01:00 Can empirical algorithms successfully estimate aragonite saturation state in the subpolar North Atlantic? Turk, Daniela Dowd, MIchael Lauvset, Siv K. Koelling, Jannes Alonso Pérez, Fernando Pérez, Fiz F. European Commission Ministerio de Economía y Competitividad (España) 2017 http://hdl.handle.net/10261/306570 https://doi.org/10.3389/fmars.2017.00385 en eng Frontiers Media #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/MINECO//CTM2013-41048-P/ES/OBSERVACION BIENAL DEL CARBONO, ACIDIFICACION, TRANSPORTE Y SEDIMENTACION EN EL ATLANTICO NORTE/ Publisher's version https://doi.org/10.3389/fmars.2017.00385 Sí Frontiers in Marine Science 4: 385 (2017) http://hdl.handle.net/10261/306570 doi:10.3389/fmars.2017.00385 2296-7745 open Aragonite saturation state Empirical algorithms Autonomous sensors Commonly observed oceanic variables GLODAPv2 Subpolar North Atlantic artículo 2017 ftcsic https://doi.org/10.3389/fmars.2017.00385 2024-01-16T11:40:34Z 17 pages, 4 tables, 8 figures 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 (NO−3 ), phosphate (PO3−4 ), 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 ... Article in Journal/Newspaper Labrador Sea North Atlantic Digital.CSIC (Spanish National Research Council) Irminger Sea ENVELOPE(-34.041,-34.041,63.054,63.054) Frontiers in Marine Science 4 |
institution |
Open Polar |
collection |
Digital.CSIC (Spanish National Research Council) |
op_collection_id |
ftcsic |
language |
English |
topic |
Aragonite saturation state Empirical algorithms Autonomous sensors Commonly observed oceanic variables GLODAPv2 Subpolar North Atlantic |
spellingShingle |
Aragonite saturation state Empirical algorithms Autonomous sensors Commonly observed oceanic variables GLODAPv2 Subpolar North Atlantic Turk, Daniela Dowd, MIchael Lauvset, Siv K. Koelling, Jannes Alonso Pérez, Fernando Pérez, Fiz F. 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 |
description |
17 pages, 4 tables, 8 figures 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 (NO−3 ), phosphate (PO3−4 ), 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 ... |
author2 |
European Commission Ministerio de Economía y Competitividad (España) |
format |
Article in Journal/Newspaper |
author |
Turk, Daniela Dowd, MIchael Lauvset, Siv K. Koelling, Jannes Alonso Pérez, Fernando Pérez, Fiz F. |
author_facet |
Turk, Daniela Dowd, MIchael Lauvset, Siv K. Koelling, Jannes Alonso Pérez, Fernando Pérez, Fiz F. |
author_sort |
Turk, Daniela |
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 |
publishDate |
2017 |
url |
http://hdl.handle.net/10261/306570 https://doi.org/10.3389/fmars.2017.00385 |
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_relation |
#PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/MINECO//CTM2013-41048-P/ES/OBSERVACION BIENAL DEL CARBONO, ACIDIFICACION, TRANSPORTE Y SEDIMENTACION EN EL ATLANTICO NORTE/ Publisher's version https://doi.org/10.3389/fmars.2017.00385 Sí Frontiers in Marine Science 4: 385 (2017) http://hdl.handle.net/10261/306570 doi:10.3389/fmars.2017.00385 2296-7745 |
op_rights |
open |
op_doi |
https://doi.org/10.3389/fmars.2017.00385 |
container_title |
Frontiers in Marine Science |
container_volume |
4 |
_version_ |
1790602701466238976 |