Abstract
This study investigated the effectiveness of concentrated observations for ocean state estimation in a region remote from the observation site. I executed a twin observing system simulation experiment (OSSE) for the North Pacific region, using an ocean data synthesis system, to examine how the potential effectiveness is for a well-defined criterion, the representativeness of the subsurface salinity minimum corresponding to North Pacific Intermediate Water (NPIW). The results of the OSSE show that data synthesis confined to the region corresponding to the recent origin of the NPIW (35°N–53°N, 130°E–170°E) can affect the modeled extent of the NPIW in the central Pacific at 35°N, 180°. The interannual variability of the NPIW is not well reproduced in terms of the standard deviation value (std), only by the data input in the origin region. The root mean square difference between the “true” and the synthesized field is twice larger than the std although there the representativeness of the scale of salinity minimum is improved by about one-third of the difference between the “true” and “first-guess” fields in a snapshot. These results imply that combinations of concentrated and other in situ observations should be required for the dynamic state estimation of the NPIW.
Highlights
Ocean state estimation based on a synthesis of observational data and model results is a powerful approach to better understand climate change, with the use of a smoothing method [1]
Observations are among the most important factors determining the quality of an ocean state estimate, but global ocean observations are limited by practical considerations such as the cost of deploying observation instruments
The ocean general circulation model (OGCM) applied to this system is based on version 3 of the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model (MOM) [12]
Summary
Ocean state estimation based on a synthesis of observational data and model results is a powerful approach to better understand climate change, with the use of a smoothing method [1]. Observations are among the most important factors determining the quality of an ocean state estimate, but global ocean observations are limited by practical considerations such as the cost of deploying observation instruments. Subject to such constraints, an effective ocean observing system is required to support climate change research. Kohl and Stammer [3] have shown that an adjoint sensitivity analysis is useful for determining the optimal observing system for a regional sea. Their scheme is promising for application to the global ocean. Halliwell et al [5] carefully constructed one such OSSE system in a domain of the Gulf of Mexico and obtained credible observing system impact assessment
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