EGU24-20729, updated on 11 Mar 2024
https://doi.org/10.5194/egusphere-egu24-20729
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Combining Neural Networks and Data Assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model

Carolina Amadio1, Anna Teruzzi1, Gloria Pietropolli1,2, Luca Manzoni2, Gianluca Coidessa1, and Gianpiero Cossarini1
Carolina Amadio et al.
  • 1OGS Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Italy (camadio@ogs.it)
  • 2Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste H2bis Building, Via Alfonso Valerio 12/1, 34127 Trieste, Italy

Biogeochemical-Argo (BGC-Argo) float profiles provide substantial information for key vertical biogeochemical dynamics and successfully integrated in biogeochemical models via data assimilation approaches. Although results on the BGC-Argo assimilation are encouraging, data scarcity remains a limitation for their effective use in operational oceanography. To address availability gaps in the BGC-Argo profiles, an Observing System Experiment (OSE), that combines Neural Network (NN) and Data Assimilation (DA), has been performed here. NN was used to reconstruct nitrate profiles starting from oxygen profiles and associated Argo variables (pressure, temperature, salinity), while a variational data assimilation scheme (3DVarBio) has been upgraded to integrate BGC-Argo and reconstructed observations in the Copernicus Mediterranean operational forecast system (MedBFM). To ensure high quality of oxygen data, a post-deployment quality control method has been developed with the aim of detecting and eventually correcting potential sensors drift. The Mediterranean OSE features three different setups: a control run without assimilation; a multivariate run with assimilation of BGC-Argo chlorophyll, nitrate, and oxygen; and a multivariate run that also assimilates reconstructed observations. The general improvement of skill performance metrics demonstrated the feasibility in integrating new variables (oxygen and reconstructed nitrate). Major benefits have been observed in reproducing specific BGC process-based dynamics such as the nitracline dynamics, primary production and oxygen vertical dynamics. The assimilation of BGC-Argo nitrate corrects a generally positive bias of the model in most of the Mediterranean areas, and the addition of reconstructed profiles makes the corrections even stronger. The impact of enlarged nitrate assimilation propagates to ecosystem processes (e.g., primary production) at basin wide scale, demonstrating the importance of BGC profiles in complementing satellite ocean colour assimilation.

How to cite: Amadio, C., Teruzzi, A., Pietropolli, G., Manzoni, L., Coidessa, G., and Cossarini, G.: Combining Neural Networks and Data Assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20729, https://doi.org/10.5194/egusphere-egu24-20729, 2024.