Data assimilation of physical and chlorophyll a observations in the California Current System using two biogeochemical models

Jann Paul Mattern, Hajoon Song, Christopher A. Edwards, Andrew M. Moore, Jerome Fiechter

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Biogeochemical numerical models coupled to physical ocean circulation models are commonly combined with data assimilation in order to improve the models’ state or parameter estimates. Yet much still needs to be learned about important aspects of biogeochemical data assimilation, such as the effect of model complexity and the importance of more realistic model formulations on assimilation results. In this study, 4D-Var-based state estimation is applied to two biogeochemical ocean models: a simple NPZD model with 4 biogeochemical variables (including 1 phytoplankton, 1 zooplankton) and the more complex NEMURO model, containing 11 biogeochemical variables (including 2 phytoplankton, 3 zooplankton). Both models are coupled to a 3-dimensional physical ocean circulation model of the U.S. west coast based on the Regional Ocean Modelling System (ROMS). Chlorophyll satellite observations and physical observations are assimilated into the model, yielding substantial improvements in state estimates for the observed physical and biogeochemical variables in both model formulations. In comparison to the simpler NPZD model, NEMURO shows a better overall fit to the observations. The assimilation also results in small improvements for simulated nitrate concentrations in both models and no apparent degradation of the output for other unobserved variables. The forecasting skill of the biogeochemical models is strongly linked to model performance without data assimilation: for both models, the improved fit obtained through assimilation degrades at similar relative rates, but drops to different absolute levels. Despite the better performance of NEMURO in our experiments, the choice of model and desired level of complexity should depend on the model application and the data available for assimilation.

Original languageEnglish
Pages (from-to)55-71
Number of pages17
JournalOcean Modelling
Volume109
DOIs
Publication statusPublished - 2017 Jan 1

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Oceanography
  • Geotechnical Engineering and Engineering Geology
  • Atmospheric Science

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