Abstract
A series of observing system simulation experiments (OSSEs) have been carried out using the Met Office global Forecasting Ocean Assimilation Model (FOAM) to provide insights on the current and future design of the in situ observing network for ocean monitoring and forecasting. Synthetic observations are generated from a Nature Run (NR) that represents the true ocean state in the experiments. These observations are assimilated in FOAM and the results are compared to the NR to assess the impact of the observations, as well as assessing the effectiveness of the data assimilation system. The NR and FOAM based OSSEs have different resolutions and are driven by different surface forcing. The results show that assimilating observations equivalent to the current observing system allows the system to produce a realistic representation of the ocean state. Additional Argo profiles in some of the Western Boundary Current (WBC) regions and along the Equator improve the performance of FOAM by reducing the root mean square error (RMSE) against the Nature Run by ~10% for temperature and salinity fields in the upper ocean. Assimilating additional Deep Argo floats leads to ~20% RMSE reduction in basin scale regions and the reduction rate is up to 80% in the Labrador Sea below 2500m. An experiment withdrawing mooring profiles indicates the impact of moorings is localised and on average the analysis shows ~5% degradation without the mooring observations. The additional Argo profiles in the WBC regions and deep ocean also have impacts on the representation of the Ocean Heat Content (OHC) and the Atlantic Meridional Overturning Circulation (AMOC), with the deep Argo observations correcting the model drift in OHC below 2000m. The results highlight the necessity of a well-designed and coordinated in situ observing network globally, as well as requirements for future model and assimilation developments to achieve the best use of the additional in situ observations.
Highlights
Observations play an important role in initializing ocean models for various applications, from operational near-real time ocean forecasting to decadal predictions for climate studies (Fujii et al, 2019)
The Backbone statistics were compared to those calculated using the Free Run (FR), where the differences indicate the influence from assimilating the synthetic observations
This paper assessed the impact of assimilating observations in the Forecasting Ocean Assimilation Model (FOAM) ocean forecasting system from an observing system simulation experiments (OSSEs) perspective
Summary
Observations play an important role in initializing ocean models for various applications, from operational near-real time ocean forecasting to decadal predictions for climate studies (Fujii et al, 2019). Assessing the Impact of Expanding the Argo Array coverage of in situ ocean observations has improved significantly since 2000 with the deployment of the Argo array (Fu et al, 2018; Argo, 2020). The Horizon 2020 AtlantOS project aimed to provide a better understanding of the requirements for in situ observations to improve the monitoring and prediction of the Atlantic Ocean (Visbeck et al, 2015). The main focus of the project is the Atlantic Ocean, the participating European ocean forecasting centers (Mercator Ocean International, Met Office, CLS, and CMCC) have set up initiatives to provide assessment and evaluation of the impact of in situ observing networks on monitoring and forecasting systems in the global ocean
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