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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-12-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fmars.2017.00385/full |
| _version_ | 1819058340873895936 |
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| author | Daniela Turk Daniela Turk Michael Dowd Siv K. Lauvset Siv K. Lauvset Jannes Koelling Fernando Alonso-Pérez Fiz F. Pérez |
| author_facet | Daniela Turk Daniela Turk Michael Dowd Siv K. Lauvset Siv K. Lauvset Jannes Koelling Fernando Alonso-Pérez Fiz F. Pérez |
| author_sort | Daniela Turk |
| collection | DOAJ |
| 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 their validity and performance. |
| first_indexed | 2024-12-21T13:53:39Z |
| format | Article |
| id | doaj.art-7c89cb53bd1144d7b9af6cfe6a08f7f5 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-12-21T13:53:39Z |
| publishDate | 2017-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-7c89cb53bd1144d7b9af6cfe6a08f7f52022-12-21T19:01:37ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452017-12-01410.3389/fmars.2017.00385304460Can Empirical Algorithms Successfully Estimate Aragonite Saturation State in the Subpolar North Atlantic?Daniela Turk0Daniela Turk1Michael Dowd2Siv K. Lauvset3Siv K. Lauvset4Jannes Koelling5Fernando Alonso-Pérez6Fiz F. Pérez7Department of Oceanography, Dalhousie University, Halifax, NS, CanadaLamont-Doherty Earth Observatory, Columbia University, Palisades, NY, United StatesDepartment of Mathematics and Statistics, Dalhousie University, Halifax, NS, CanadaUni Research Klima, Bjerknes Centre for Climate Research, Bergen, NorwayGeophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Bergen, NorwayScripps Institution of Oceanography, La Jolla, CA, United StatesDepartamento de Oceanografía, Instituto de Investigaciones Marinas (CSIC), Vigo, SpainDepartamento de Oceanografía, Instituto de Investigaciones Marinas (CSIC), Vigo, SpainThe 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 their validity and performance.http://journal.frontiersin.org/article/10.3389/fmars.2017.00385/fullaragonite saturation stateempirical algorithmsautonomous sensorscommonly observed oceanic variablesGLODAPv2subpolar North Atlantic |
| spellingShingle | Daniela Turk Daniela Turk Michael Dowd Siv K. Lauvset Siv K. Lauvset Jannes Koelling Fernando Alonso-Pérez Fiz F. Pérez Can Empirical Algorithms Successfully Estimate Aragonite Saturation State in the Subpolar North Atlantic? Frontiers in Marine Science aragonite saturation state empirical algorithms autonomous sensors commonly observed oceanic variables GLODAPv2 subpolar North Atlantic |
| title | 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_short | 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 |
| topic | aragonite saturation state empirical algorithms autonomous sensors commonly observed oceanic variables GLODAPv2 subpolar North Atlantic |
| url | http://journal.frontiersin.org/article/10.3389/fmars.2017.00385/full |
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