Sun, R., S. Sanikommu, A. C. Subramanian, M. R.
Mazloff, B. D. Cornuelle, G. Gopalakrishnan, A. J. Miller and
I. Hoteit, 2024:
Enhanced regional ocean ensemble data assimilation
through atmospheric coupling in the SKRIPS
model.
Ocean Modelling, in press.
Abstract.
We investigate the impact of ocean data assimilation using the Ensemble Adjustment
Kalman Filter (EAKF) from the Data Assimilation Research Testbed (DART) on the
oceanic and atmospheric states of the Red Sea. Our study extends the ocean data
assimilation experiment performed by Sanikommu et al. (2020) by utilizing the SKRIPS
15 model coupling the MITgcm ocean model and the Weather Research and Forecasting (WRF)
atmosphere model. Using a 50-member ensemble, we assimilate satellite-derived sea
surface temperature and height and in-situ temperature and salinity profiles every three days
for one year, starting January 01 2011. Atmospheric data are not assimilated in the
experiments. To improve the ensemble realism, perturbations are added to the WRF model
using several physics options and the stochastic kinetic energy backscatter (SKEB) scheme.
Compared with the control experiments using uncoupled MITgcm with ECMWF
ensemble forcing, the EAKF ensemble mean oceanic states from the coupled model are better
or insignificantly worse (root-mean-square errors are 30% to -2% smaller), especially
when the atmospheric model uncertainties are accounted for with stochastic perturbations.
We hypothesize that the ensemble spreads of the air-sea fluxes are better
represented in the downscaled WRF ensembles when uncertainties are well accounted for, leading
to improved representation of the ensemble oceanic states from the new experiments
with the coupled model. This indicates the ocean model assimilation will be improved
with coupled models and relaxes the need for operational centers to provide atmospheric
ensembles to drive ocean forecasts. Although the feedback from ocean to atmosphere is
included in this two-way regional coupled configuration, we find no significant effect of
ocean data assimilation on the ensemble mean latent heat flux and 10-m wind speed over
the Red Sea. This suggests that the improved skill using the coupled model is not from
the two-way coupling, but from downscaling the ensemble atmospheric forcings (one-way
coupled) to drive the ocean model.
Preprint (pdf)