Abstract
A coordinated effort based on Observing System Simulation Experiments (OSSEs) has been carried out for the first time by four European ocean forecasting centers in order to provide insights on the present and future design of the in situ Atlantic Ocean observing system from a monitoring and forecasting perspective. This multi-system approach is based on assimilating synthetic data sets, obtained by sub-sampling in space and time an eddy-resolving unconstrained simulation, named the Nature Run. To assess the ability of a given Atlantic Ocean observing system to constrain the ocean model state, a set of assimilating experiments were performed using four global eddy-permitting systems. For each set of experiments, different designs of the in situ observing system have been assimilated, such as implementing a global drifter array equipped with a thermistor chain down to 150 m depth or extending a part of the global Argo array in the deep ocean. While results from the four systems show similarities and differences, the comparison of the experiments with the Nature Run generally demonstrates a positive impact of the different extra observation networks on the temperature and salinity fields. The spread of the multi-system simulations, combined with the sensitivity of each system to the evaluated observing networks, allowed us to discuss the robustness of the results and their dependence on the specific analysis system. By helping define and test future observing systems from an integrated observing system view, the present work is an initial step toward better-coordinated initiatives for supporting the evolution of the ocean observing system and its integration within ocean monitoring and forecasting systems.
Highlights
Over the past three decades, the development of space-based and in situ observing technologies have significantly increased the number of surface and subsurface ocean observations
The originality of this study lies in the assimilation of exactly the same synthetic
which are deduced by sub-sampling the Nature Run
Summary
Over the past three decades, the development of space-based and in situ observing technologies have significantly increased the number of surface and subsurface ocean observations. The H2020 AtlantOS project brings together scientists, stakeholders and industries from around the Atlantic Ocean, in order to provide a multinational framework for more and better-coordinated efforts in observing, understanding and predicting the Atlantic Ocean (Visbeck et al, 2015) This timely project is part of a larger process recently initiated by the oceanographic community, to define a better-coordinated and sustainable in situ observing system in preparation for the OceanObs’ conference, similar to what has been done for tropical oceans (e.g., Cravatte et al, 2016). The reliability of an OSSE system to correctly provide observation impact assessment partly lies in defining appropriate errors, which were included in the synthetic observations This includes two types of errors: (i) a representation error, which is vertically and horizontally correlated and mostly related to variability due to unresolved or poorly resolved processes of the analysis and forecast system or the statistical merging technique (e.g., inertial waves); and (ii) a random instrumental error, which is due to the uncertainty of measurement. Uncorrelated instrumental errors are added to each observation depending on the observation type, following a Gaussian distribution with the standard deviation given by the instrumental uncertainty (Table 3)
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