Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design
<p>Numerical models of ocean biogeochemistry are becoming the major tools used to detect and predict the impact of climate change on marine resources and to monitor ocean health. However, with the continuous improvement of model structure and spatial resolution, incorporation of these addition...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-04-01
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| Series: | Biogeosciences |
| Online Access: | https://bg.copernicus.org/articles/20/1405/2023/bg-20-1405-2023.pdf |
| _version_ | 1827968371220545536 |
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| author | A. Mignot H. Claustre H. Claustre G. Cossarini F. D'Ortenzio F. D'Ortenzio E. Gutknecht J. Lamouroux P. Lazzari C. Perruche S. Salon R. Sauzède V. Taillandier V. Taillandier A. Teruzzi |
| author_facet | A. Mignot H. Claustre H. Claustre G. Cossarini F. D'Ortenzio F. D'Ortenzio E. Gutknecht J. Lamouroux P. Lazzari C. Perruche S. Salon R. Sauzède V. Taillandier V. Taillandier A. Teruzzi |
| author_sort | A. Mignot |
| collection | DOAJ |
| description | <p>Numerical models of ocean biogeochemistry are becoming the major tools used to detect
and predict the impact of climate change on marine resources and to monitor
ocean health. However, with the continuous improvement of model structure
and spatial resolution, incorporation of these additional degrees of freedom
into fidelity assessment has become increasingly challenging. Here, we
propose a new method to provide information on the model predictive skill in a concise
way. The method is based on the conjoint use of a <span class="inline-formula"><i>k</i></span>-means clustering
technique, assessment metrics, and Biogeochemical-Argo (BGC-Argo) observations. The <span class="inline-formula"><i>k</i></span>-means
algorithm and the assessment metrics reduce the number of model data points
to be evaluated. The metrics evaluate either the model state accuracy or the
skill of the model with respect to capturing emergent properties, such as the deep
chlorophyll maximums and oxygen minimum zones. The use of BGC-Argo
observations as the sole evaluation data set ensures the accuracy of the
data, as it is a homogenous data set with strict sampling methodologies and
data quality control procedures. The method is applied to the Global Ocean Biogeochemistry Analysis and Forecast system of the Copernicus Marine
Service. The model performance is evaluated using the model efficiency
statistical score, which compares the model–observation misfit with the
variability in the observations and, thus, objectively quantifies whether the
model outperforms the BGC-Argo climatology. We show that, overall, the model
surpasses the BGC-Argo climatology in predicting pH, dissolved inorganic
carbon, alkalinity, oxygen, nitrate, and phosphate in the mesopelagic and
the mixed layers as well as silicate in the mesopelagic layer. However,
there are still areas for improvement with respect to reducing the model–data misfit for
certain variables such as silicate, pH, and the partial pressure of CO<span class="inline-formula"><sub>2</sub></span>
in the mixed layer as well as chlorophyll-<span class="inline-formula"><i>a</i></span>-related, oxygen-minimum-zone-related, and particulate-organic-carbon-related metrics. The method proposed
here can also aid in refining the design of the BGC-Argo network, in
particular regarding the regions in which BGC-Argo observations should be enhanced to
improve the model accuracy via the assimilation of BGC-Argo data or
process-oriented assessment studies. We strongly recommend increasing the
number of observations in the Arctic region while maintaining the existing
high-density of observations in the Southern Oceans. The model error in
these regions is only slightly less than the variability observed in
BGC-Argo measurements. Our study illustrates how the synergic use of
modeling and BGC-Argo data can both provide information about the performance of models
and improve the design of observing systems.</p> |
| first_indexed | 2024-04-09T18:20:31Z |
| format | Article |
| id | doaj.art-f8022a0182dd464faa28b14ea4020dd3 |
| institution | Directory Open Access Journal |
| issn | 1726-4170 1726-4189 |
| language | English |
| last_indexed | 2024-04-09T18:20:31Z |
| publishDate | 2023-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Biogeosciences |
| spelling | doaj.art-f8022a0182dd464faa28b14ea4020dd32023-04-12T10:52:12ZengCopernicus PublicationsBiogeosciences1726-41701726-41892023-04-01201405142210.5194/bg-20-1405-2023Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system designA. Mignot0H. Claustre1H. Claustre2G. Cossarini3F. D'Ortenzio4F. D'Ortenzio5E. Gutknecht6J. Lamouroux7P. Lazzari8C. Perruche9S. Salon10R. Sauzède11V. Taillandier12V. Taillandier13A. Teruzzi14Mercator Ocean International, 31400 Toulouse, FranceLaboratoire d'Océanographie de Villefranche, CNRS, Sorbonne Université, 06230 Villefranche-sur-Mer, FranceInstitut de la Mer de Villefranche, CNRS, Sorbonne Université, 06230 Villefranche-sur-Mer, FranceNational Institute of Oceanography and Applied Geophysics – OGS, 34010 Trieste, ItalyLaboratoire d'Océanographie de Villefranche, CNRS, Sorbonne Université, 06230 Villefranche-sur-Mer, FranceInstitut de la Mer de Villefranche, CNRS, Sorbonne Université, 06230 Villefranche-sur-Mer, FranceMercator Ocean International, 31400 Toulouse, FranceMercator Ocean International, 31400 Toulouse, FranceNational Institute of Oceanography and Applied Geophysics – OGS, 34010 Trieste, ItalyMercator Ocean International, 31400 Toulouse, FranceNational Institute of Oceanography and Applied Geophysics – OGS, 34010 Trieste, ItalyInstitut de la Mer de Villefranche, CNRS, Sorbonne Université, 06230 Villefranche-sur-Mer, FranceLaboratoire d'Océanographie de Villefranche, CNRS, Sorbonne Université, 06230 Villefranche-sur-Mer, FranceInstitut de la Mer de Villefranche, CNRS, Sorbonne Université, 06230 Villefranche-sur-Mer, FranceNational Institute of Oceanography and Applied Geophysics – OGS, 34010 Trieste, Italy<p>Numerical models of ocean biogeochemistry are becoming the major tools used to detect and predict the impact of climate change on marine resources and to monitor ocean health. However, with the continuous improvement of model structure and spatial resolution, incorporation of these additional degrees of freedom into fidelity assessment has become increasingly challenging. Here, we propose a new method to provide information on the model predictive skill in a concise way. The method is based on the conjoint use of a <span class="inline-formula"><i>k</i></span>-means clustering technique, assessment metrics, and Biogeochemical-Argo (BGC-Argo) observations. The <span class="inline-formula"><i>k</i></span>-means algorithm and the assessment metrics reduce the number of model data points to be evaluated. The metrics evaluate either the model state accuracy or the skill of the model with respect to capturing emergent properties, such as the deep chlorophyll maximums and oxygen minimum zones. The use of BGC-Argo observations as the sole evaluation data set ensures the accuracy of the data, as it is a homogenous data set with strict sampling methodologies and data quality control procedures. The method is applied to the Global Ocean Biogeochemistry Analysis and Forecast system of the Copernicus Marine Service. The model performance is evaluated using the model efficiency statistical score, which compares the model–observation misfit with the variability in the observations and, thus, objectively quantifies whether the model outperforms the BGC-Argo climatology. We show that, overall, the model surpasses the BGC-Argo climatology in predicting pH, dissolved inorganic carbon, alkalinity, oxygen, nitrate, and phosphate in the mesopelagic and the mixed layers as well as silicate in the mesopelagic layer. However, there are still areas for improvement with respect to reducing the model–data misfit for certain variables such as silicate, pH, and the partial pressure of CO<span class="inline-formula"><sub>2</sub></span> in the mixed layer as well as chlorophyll-<span class="inline-formula"><i>a</i></span>-related, oxygen-minimum-zone-related, and particulate-organic-carbon-related metrics. The method proposed here can also aid in refining the design of the BGC-Argo network, in particular regarding the regions in which BGC-Argo observations should be enhanced to improve the model accuracy via the assimilation of BGC-Argo data or process-oriented assessment studies. We strongly recommend increasing the number of observations in the Arctic region while maintaining the existing high-density of observations in the Southern Oceans. The model error in these regions is only slightly less than the variability observed in BGC-Argo measurements. Our study illustrates how the synergic use of modeling and BGC-Argo data can both provide information about the performance of models and improve the design of observing systems.</p>https://bg.copernicus.org/articles/20/1405/2023/bg-20-1405-2023.pdf |
| spellingShingle | A. Mignot H. Claustre H. Claustre G. Cossarini F. D'Ortenzio F. D'Ortenzio E. Gutknecht J. Lamouroux P. Lazzari C. Perruche S. Salon R. Sauzède V. Taillandier V. Taillandier A. Teruzzi Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design Biogeosciences |
| title | Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design |
| title_full | Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design |
| title_fullStr | Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design |
| title_full_unstemmed | Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design |
| title_short | Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design |
| title_sort | using machine learning and biogeochemical argo bgc argo floats to assess biogeochemical models and optimize observing system design |
| url | https://bg.copernicus.org/articles/20/1405/2023/bg-20-1405-2023.pdf |
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