Ocean biogeochemical modelling

• Published in: Nature Reviews Methods Primers
• Main Authors: Fennel, Katja, Mattern, Jann Paul, Doney, Scott C., Bopp, Laurent, Moore, Andrew M., Wang, Bin, Yu, Liuqian
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer Nature 2022
• Subjects: Ocean acidification
• Online Access: https://repository.hkust.edu.hk/ir/Record/1783.1-121315; https://doi.org/10.1038/s43586-022-00154-2; http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=2662-8449&rft.volume=2&rft.issue=1&rft.date=2022&rft.spage=&rft.aulast=Fennel&rft.aufirst=&rft.atitle=Ocean+biogeochemical+modelling&rft.title=Nature+Reviews+Methods+Primers; http://www.scopus.com/record/display.url?eid=2-s2.0-85138712234&origin=inward

Ocean biogeochemical models describe the ocean’s circulation, physical properties, biogeochemical properties and their transformations using coupled differential equations. Numerically approximating these equations enables simulation of the dynamic evolution of the ocean state in realistic global or regional spatial domains, across time spans from years to centuries. This Primer explains the process of model construction and the main characteristics, advantages and drawbacks of different model types, from the simplest nutrient–phytoplankton–zooplankton–detritus model to the complex biogeochemical models used in Earth system modelling and climate prediction. Commonly used metrics for model-data comparison are described, alongside a discussion of how models can be informed by observations via parameter optimization or state estimation, the two main methods of data assimilation. Examples illustrate how these models are used for various practical applications, ranging from carbon accounting, ocean acidification, ocean deoxygenation and fisheries to observing system design. Access points are provided, enabling readers to engage in biogeochemical modelling through practical code examples and a comprehensive list of publicly available models and observational data sets. Recommendations are given for best practices in model archiving. Lastly, current limitations and anticipated future developments and challenges of the models are discussed. © 2022, Springer Nature Limited.