Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring
The Biogeochemical-Argo (BGC-Argo) program is building a network of globally distributed, sensor-equipped robotic profiling floats, improving our understanding of the climate system and how it is changing. These floats, however, are limited in the number of variables measured. In this study, we trai...
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ftdatacite:10.48550/arxiv.2111.00126 2023-05-15T18:25:02+02:00 Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring Park, Ellen Kim, Jae Deok Aoki, Nadege Cao, Yumeng Melody Arefeen, Yamin Beveridge, Matthew Nicholson, David Drori, Iddo 2021 https://dx.doi.org/10.48550/arxiv.2111.00126 https://arxiv.org/abs/2111.00126 unknown arXiv Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Machine Learning cs.LG Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences article-journal Article ScholarlyArticle Text 2021 ftdatacite https://doi.org/10.48550/arxiv.2111.00126 2022-03-10T13:27:40Z The Biogeochemical-Argo (BGC-Argo) program is building a network of globally distributed, sensor-equipped robotic profiling floats, improving our understanding of the climate system and how it is changing. These floats, however, are limited in the number of variables measured. In this study, we train neural networks to predict silicate and phosphate values in the Southern Ocean from temperature, pressure, salinity, oxygen, nitrate, and location and apply these models to earth system model (ESM) and BGC-Argo data to expand the utility of this ocean observation network. We trained our neural networks on observations from the Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP) and use dropout regularization to provide uncertainty bounds around our predicted values. Our neural network significantly improves upon linear regression but shows variable levels of uncertainty across the ranges of predicted variables. We explore the generalization of our estimators to test data outside our training distribution from both ESM and BGC-Argo data. Our use of out-of-distribution test data to examine shifts in biogeochemical parameters and calculate uncertainty bounds around estimates advance the state-of-the-art in oceanographic data and climate monitoring. We make our data and code publicly available. : 6 pages, 4 figures Article in Journal/Newspaper Southern Ocean DataCite Metadata Store (German National Library of Science and Technology) Southern Ocean |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
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language |
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topic |
Machine Learning cs.LG Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences |
spellingShingle |
Machine Learning cs.LG Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences Park, Ellen Kim, Jae Deok Aoki, Nadege Cao, Yumeng Melody Arefeen, Yamin Beveridge, Matthew Nicholson, David Drori, Iddo Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring |
topic_facet |
Machine Learning cs.LG Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences |
description |
The Biogeochemical-Argo (BGC-Argo) program is building a network of globally distributed, sensor-equipped robotic profiling floats, improving our understanding of the climate system and how it is changing. These floats, however, are limited in the number of variables measured. In this study, we train neural networks to predict silicate and phosphate values in the Southern Ocean from temperature, pressure, salinity, oxygen, nitrate, and location and apply these models to earth system model (ESM) and BGC-Argo data to expand the utility of this ocean observation network. We trained our neural networks on observations from the Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP) and use dropout regularization to provide uncertainty bounds around our predicted values. Our neural network significantly improves upon linear regression but shows variable levels of uncertainty across the ranges of predicted variables. We explore the generalization of our estimators to test data outside our training distribution from both ESM and BGC-Argo data. Our use of out-of-distribution test data to examine shifts in biogeochemical parameters and calculate uncertainty bounds around estimates advance the state-of-the-art in oceanographic data and climate monitoring. We make our data and code publicly available. : 6 pages, 4 figures |
format |
Article in Journal/Newspaper |
author |
Park, Ellen Kim, Jae Deok Aoki, Nadege Cao, Yumeng Melody Arefeen, Yamin Beveridge, Matthew Nicholson, David Drori, Iddo |
author_facet |
Park, Ellen Kim, Jae Deok Aoki, Nadege Cao, Yumeng Melody Arefeen, Yamin Beveridge, Matthew Nicholson, David Drori, Iddo |
author_sort |
Park, Ellen |
title |
Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring |
title_short |
Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring |
title_full |
Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring |
title_fullStr |
Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring |
title_full_unstemmed |
Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring |
title_sort |
predicting critical biogeochemistry of the southern ocean for climate monitoring |
publisher |
arXiv |
publishDate |
2021 |
url |
https://dx.doi.org/10.48550/arxiv.2111.00126 https://arxiv.org/abs/2111.00126 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.48550/arxiv.2111.00126 |
_version_ |
1766206189293535232 |