Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design
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Published:2023-04-12
Issue:7
Volume:20
Page:1405-1422
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ISSN:1726-4189
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Container-title:Biogeosciences
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language:en
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Short-container-title:Biogeosciences
Author:
Mignot Alexandre, Claustre Hervé, Cossarini Gianpiero, D'Ortenzio Fabrizio, Gutknecht Elodie, Lamouroux Julien, Lazzari PaoloORCID, Perruche CoralieORCID, Salon Stefano, Sauzède Raphaëlle, Taillandier Vincent, Teruzzi AnnaORCID
Abstract
Abstract. 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 k-means clustering
technique, assessment metrics, and Biogeochemical-Argo (BGC-Argo) observations. The k-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 CO2
in the mixed layer as well as chlorophyll-a-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.
Funder
Horizon 2020 Agence Nationale de la Recherche European Research Council
Publisher
Copernicus GmbH
Subject
Earth-Surface Processes,Ecology, Evolution, Behavior and Systematics
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