Publication Date:
2022-12-05
Description:
Data from profiling floats in the Black Sea revealed complex temporal and spatial relationships between physical variables and oxygen, chlorophyll and the backscattering coefficient at 700 nm, as well as some limits in understanding the details of biogeochemistry dynamics. To account for different interdependences between physical and biogeochemical properties, a feedforward backpropagation neural network (NN) was used. This NN learns from data recorded by profiling floats and predicts biogeochemical states using physical measurements only. The performance was very high, particularly for oxygen, but it decreased when the NN was applied to older data because the interrelationships between the physical and biogeochemical properties have changed recently. The biogeochemical states reconstructed by the NN using physical data produced by a coupled physical–biogeochemical operational model were better than the biogeochemical outputs of the same coupled model. Therefore, the use of data from profiling floats, physical properties from numerical models and NNs appears to be a powerful approach for reconstructing the 4D dynamics of the euphotic zone. Basin‐wide patterns and temporal variabilities in oxygen, backscattering coefficient and chlorophyll were also analyzed. Of particular interest is the reconstruction of short‐lived biogeochemical features, particularly in coastal anticyclone areas, which are difficult to observe with available floats at the basin scale.
Description:
Plain Language Summary:
This study addresses the biogeochemical dynamics of the euphotic layer in the Black Sea. Observations are provided from profiling floats, and the observed biogeochemical parameters include oxygen, the backscattering coefficient at 700 nm and chlorophyll‐a. Data analysis showed complex temporal and spatial relationships between physical and biogeochemical variables and some limits in understanding the details of biogeochemical dynamics. A feedforward backpropagation neural network was developed, which can be considered an input–output mapping in which the neurons combine the input data in such a way that the output can be considered a nonlinear combination of input data. When applied to older data, the reconstruction performance decreases, suggesting a change in the dependency of biogeochemical characteristics on physical drivers caused by known climate change. A comparison with simulations from a coupled operational biogeochemical model shows that the neural network outperforms the numerical model. The newly proposed method, combining data from profiling floats, physical properties from numerical models and a backpropagation neural network, allows us to reconstruct the 4D dynamics of the euphotic layer over the period 2013–2020.
Description:
Key Points:
Machine learning helps identify fundamental biogeochemical mechanisms in the Black Sea.
A feedforward backpropagation neural network performs better than a coupled physical‐biogeochemical model.
Data from profiling floats, physical data from numerical models and machine learning enabled the analysis of 4D biogeochemical dynamics.
Description:
MASRI
Description:
National Roadmap for Scientific Infrastructure
Description:
European Horizon 2020 project DOORS
Description:
https://resources.marine.copernicus.eu/product-detail/BLKSEA_MULTIYEAR_PHY_007_004/INFORMATION
Description:
https://resources.marine.copernicus.eu/product-detail/BLKSEA_REANALYSIS_BIO_007_005/INFORMATION
Description:
http://www.coriolis.eu.org/Data-Products/Data-selection
Description:
https://zenodo.org/record/6860705
Keywords:
ddc:551
;
biogeochemistry
;
neural networks
;
profiling floats
;
euphotic zone
Language:
English
Type:
doc-type:article
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