JpGU-AGU Joint Meeting 2020

Presentation information

[E] Poster

A (Atmospheric and Hydrospheric Sciences ) » A-OS Ocean Sciences & Ocean Environment

[A-OS17] Climate variability and predictability on subseasonal to decadal timescales

convener:Takashi Mochizuki(Department of Earth and Planetary Sciences, Kyushu University), V Ramaswamy(NOAA GFDL), Yushi Morioka(Japan Agency for Marine-Earth Science and Technology)

[AOS17-P10] Predictability study on the seasonal reduction of the upstream Kuroshio transport and related targeted observation application.

*Kun Zhang1,2 (1.Institute of Oceanology, Chinese Academy of Sciences, 2.Center for Ocean Mega-Science, Chinese Academy of Sciences)

Keywords:the Kuroshio, predictability, targeted observation, the CNOP appraoch

With the Regional Ocean Modeling System (ROMS), we realistically simulated the upstream Kuroshio transport (UKT) variations, especially for the transport reduction in autumn. Then the effects of the optimal initial errors estimated by the conditional nonlinear optimal perturbation (CNOP) approach on predicting one seasonal UKT reduction event were investigated. The CNOP-type initial errors are located around (128°E, 17°N) horizontally and in the upper 1000 m vertically. The CNOP greatly affects the UKT by developing into a westward eddy-like structure. Eddy-energetic analysis indicates that the errors obtain energy from the background state through both barotropic and baroclinic instabilities and that the latter plays a more important role. Based on the spatial distribution of the CNOP, the sensitive area for improving the prediction of the UKT reduction event was subsequently identified. Within this sensitive area, we further explored the CNOP-based adaptive observation system design by considering the impacts of two factors: the number of observation sites and observation distance. Observing system simulation experiments (OSSEs) indicate that improving the initial conditions can effectively improve the UKT prediction. Compared to the random or conventional observation system, the CNOP-based observation system always exhibits better performance. Optimal CNOP-based observation networks are established using six or eight observation sites and observation distances of 140 or 165 km, which produce a mean prediction improvement of 43.1%. This preliminary work is expected to provide guidance on future observations in the upstream Kuroshio region and to help realistic UKT predictions.