Submitted as: methods for assessment of models
04 Apr 2023
Submitted as: methods for assessment of models |  | 04 Apr 2023
Status: this preprint is currently under review for the journal GMD.

An along-track biogeochemical Argo modelling framework, a case study of model improvements for the Nordic Seas

Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen

Abstract. We present a framework that links in situ observations from the biogeochemical-Argo (BGC-Argo) array to biogeochemical models. The framework allows a minimized technical effort to construct a Lagrangian type 1D modelling experiment along BGC-Argo tracks. We utilize the Argo data in two ways; (1) drive the model physics, (2) evaluate the model biogeochemistry. BGC-Argo physics data is used to nudge the model physics closer to observations to reduce the errors in biogeochemistry stemming from physics errors. This allows us to target model biogeochemistry and by using the Argo biogeochemical dataset, we identify potential sources of model errors, introduce changes to model formulation, and validate model configurations. We present experiments for the Nordic Seas and showcase how we identify potential BGC-Argo buoys to model, prepare forcing, design experiments and approach model improvement and validation. We used ECOSMO II(CHL) model as the biogechemical component and focused on chlorophyll a. The experiments revealed that ECOSMO II(CHL) required improvements during low-light conditions, as the comparison to BGC-Argo reveals that ECOSMO II(CHL) simulates a late spring bloom and does not represent the deep chlorophyll maximum formation in summer periods. We modified the productivity and chlorophyll a relationship and statistically documented decreased bias and error in the revised model using BGC-Argo data. Our results reveal that nudging the model T and S closer to BGC-Argo data reduces errors in biogeochemistry, and we suggest a relaxation time-period of 1–10 days. The BGC-Argo data coverage is ever growing and the framework is a valuable asset for improving models in 1D-model efficiently and transfer the configurations to 3D-model with a wide range of focus from operational, regional/global and climate scale.

Veli Çağlar Yumruktepe et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-25', Anonymous Referee #1, 15 May 2023
  • CC1: 'Comment on gmd-2023-25', Martí Galí, 24 May 2023
  • RC2: 'Comment on gmd-2023-25', Anonymous Referee #2, 29 May 2023

Veli Çağlar Yumruktepe et al.

Veli Çağlar Yumruktepe et al.


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Short summary
We present a framework that links biogeochemical-Argo data to models. We utilize Argo dataset to identify sources of model errors, improve and validate model configurations. We imitate the observed physical conditions along the biogeochemical-Argo tracks and focus on the biogeochemical model formulations and parameterizations. We showcase the framework for the Nordic Seas and focus on improvements towards model chlorophyll-a and production dynamics.