Publication Date

8-2-2026

Document Type

Article

Publication Title

Journal of Advances in Modeling Earth Systems

Volume

18

Issue

8

DOI

10.1029/2025MS004976

Abstract

We present a data-assimilative regional ocean biogeochemical model, ASTE-BGC, which simulates the physical and biogeochemical state of the North Atlantic Ocean from 2002 to 2017. Model physics are provided by a physical state estimate (ASTE), which assimilates O(109) in situ and satellite-based observations over the model domain and time period. Model biogeochemistry is simulated by a medium-complexity biogeochemical module (BLING), which simulates nine prognostic biogeochemical tracers and three ecological classes. Through a three-step optimization process which combines informal adjustments and formal mathematical optimization, we minimize model-data misfit between a “spun-up” simulated biogeochemical state and O(105) BGC-Argo and ship-based bottle data of O2, NO3, PO4, dissolved inorganic carbon (DIC), and alkalinity by 43%. First, we move away from the traditional biogeochemical model spin-up process to initialize these five biogeochemical variables directly from the GLODAPv2.2016b 1° × 1° mapped climatology (22% misfit reduction). Second, we tune the empirical relationship between solar irradiance and photosynthesis to delay the simulated spring phytoplankton bloom (19% misfit reduction). Finally, we use a Green's Functions approach to formally optimize seven BLING biogeochemical model parameters using sensitivity experiments (2% misfit reduction). This process leads to demonstrable improvements in all regions, particularly in the subpolar North Atlantic. We compare the unconstrained and optimized models against independent satellite data, global ocean biogeochemical models, and observation-based products to further demonstrate improvements in simulated O2, surface ocean pCO2, and chlorophyll-a in the Labrador Sea. We conclude by offering our perspective on the challenges of biogeochemical data assimilation and the future work needed to advance this field.

Funding Number

2019625

Funding Sponsor

National Science Foundation

Keywords

data assimilation, in situ observations, Labrador Sea, North Atlantic Ocean, ocean biogeochemical modeling, parameter optimization

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Department

Moss Landing Marine Laboratories

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