Submitted as: development and technical paper
18 Nov 2022
Submitted as: development and technical paper | 18 Nov 2022
Status: this preprint is currently under review for the journal GMD.

Data assimilation sensitivity experiments in the East Auckland Current system using 4D-Var

Rafael Santana1,2,3, Helen Macdonald2, Joanne O'Callaghan1, Brian Powell4, Sarah Wakes3, and Sutara H. Suanda5 Rafael Santana et al.
  • 1The University of Auckland, Department of Physics, Auckland, 1010, New Zealand
  • 2National Institute of Water and Atmospheric Research, Wellington, 6021, New Zealand
  • 3University of Otago, Department of Mathematics & Statistics, Dunedin, 9016, New Zealand
  • 4Department of Oceanography, University of Hawai’i, Honolulu, HI 96822, United States
  • 5University of North Carolina Wilmington, Wilmington, NC, 28403, United States

Abstract. This study analyses data assimilative numerical simulations in an eddy-dominated western boundary current: the East Auckland Current (EAuC). The goal is to assess the impact of assimilating surface and subsurface data into a model of the EAuC. We used the Regional Ocean Modelling System (ROMS) in conjunction with the 4-dimensional variational (4D-Var) data assimilation scheme to incorporate sea surface height (SSH) and temperature (SST), and subsurface temperature, salinity, and velocities from three moorings located at the upper, mid and lower continental slope using a 7-day assimilation window. Assimilation of surface fields (SSH and SST) reduced SSH root mean square deviation (rmsd) in relation to the non-assimilative (NoDA) run. The inclusion of velocity subsurface data reduced SSH rmsd up- and downstream of the moorings. By improving the representation of the mesoscale eddy field, data assimilation increased complex correlation between modelled and observed velocity in all experiments. However, the inclusion of temperature and salinity slightly decreased the velocity complex correlation. The assimilative experiments had smaller SST rmsd in comparison to the NoDA run. The lack of sub-surface temperature for assimilation led to larger errors (>1 °C) around 100 m in relation to the NoDA run. Comparisons to independent Argo data showed similar results. Withholding subsurface temperature forces near-surface average negative temperature increments that are corrected by increased net heat flux at the surface which does not affect waters at 100 m depth. Assimilation of mooring temperature generates increments to the initial conditions that reduces 100 m water temperature rmsd. Larger positive wind stress curl was generated in experiments that assimilated subsurface temperature data. Positive wind stress curl generates convergence and downwelling which is another way of correcting the upper thermocline cold bias. The larger positive wind stress curl might also be responsible for decreased velocity correlation in the experiments that assimilated temperature and salinity. The few moored CTDs (8) had little impact in correcting salinity, however, using doubled decorrelation length scales of tracers and a 2-day assimilation window improved model salinity in comparison to independent Argo data. In addition, the results were similar to the global reanalysis HYCOM-NCODA which assimilates Argo profiles and was used as boundary condition. HYCOM-NCODA had near zero velocity complex correlation on the mid-slope, whereas all reanalyses showed improved results which highlights the benefit of downscaling to a regional model of the EAuC.

Rafael Santana et al.

Status: open (until 13 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2022-270', Astrid Kerkweg, 28 Nov 2022 reply
    • AC1: 'Reply on CEC1', Rafael Santana, 29 Nov 2022 reply

Rafael Santana et al.

Rafael Santana et al.


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Short summary
We show the importance of assimilating subsurface temperature and velocity data in a model of the East Auckland Current. Assimilation of velocity increased the representation of large oceanic vortexes. Assimilation of temperature is needed to correctly simulate temperatures around 100 m depth, which is the most difficult region to simulate in ocean models. Our simulations showed improved results in comparison to the US Navy global model and highlight the importance of regional models.