Controls on carbon export in the subtropical North Atlantic

Yumruktepe, Veli Çağlar; Salihoğlu, Barıș; Neuer, Susanne

doi:10.1016/j.pocean.2020.102380

Cited by 2 publications

(1 citation statement)

References 99 publications

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“…Capturing such variability plays an important role in understanding the ecosytem dynamics of regions where the seasonal and annual productivity is highly dependent on the strength and duration of vertical mixing events due to nutrient limitation such as the subtropical North Atlantic (Siegel et al, 1999;Neuer et al, 2007;Helmke et al, 2010;Yumruktepe et al, 2020).…”

Section: Discussion On the Relaxation Time-scalesmentioning

confidence: 99%

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

Yumruktepe

Mousing

Tjiputra

et al. 2023

Preprint

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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.

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Section: Discussion On the Relaxation Time-scalesmentioning

confidence: 99%

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

Yumruktepe

Mousing

Tjiputra

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas

Yumruktepe,

Mousing,

Tjiputra

et al. 2023

Geosci. Model Dev.

View full text Add to dashboard Cite

Abstract. We present a framework that links in situ observations from the Biogeochemical Argo (BGC-Argo) array to biogeochemical models. The framework minimizes the technical effort required to construct a Lagrangian-type 1D modelling experiment along BGC-Argo tracks. We utilize the Argo data in two ways: (1) to drive the model physics and (2) to evaluate the model biogeochemistry. BGC-Argo physics data are used to nudge the model physics closer to observations to reduce the errors in the biogeochemistry stemming from physics errors. This allows us to target the model biogeochemistry and, by using the Argo biogeochemical dataset, we identify potential sources of model errors, introduce changes to the 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 use the ECOSMO II(CHL) model as the biogeochemical component and focus on chlorophyll a. The experiments reveal that ECOSMO II(CHL) requires 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 layer formation in summer periods. We modified the productivity and chlorophyll a relationship and statistically documented decreased bias and error in the revised model when using BGC-Argo data. Our results reveal that nudging the model temperature and salinity closer to BGC-Argo data reduces errors in biogeochemistry, and we suggest a relaxation time period of 1–10 d. The BGC-Argo data coverage is ever-growing and the framework is a valuable asset, as it improves biogeochemical models by performing efficient 1D model configurations and evaluation and then transferring the configurations to a 3D model with a wide range of use cases at the operational, regional/global and climate scales.

show abstract

Controls on carbon export in the subtropical North Atlantic

Cited by 2 publications

References 99 publications

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

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

An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas

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