SCIENCE CHINA Earth Sciences, Volume 60, Issue 5: 866-875(2017) https://doi.org/10.1007/s11430-016-9020-8

Identifying the sensitive area in adaptive observation for predicting the upstream Kuroshio transport variation in a 3-D ocean model

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  • ReceivedJan 16, 2017
  • AcceptedFeb 20, 2017
  • PublishedMar 24, 2017


Using the conditional nonlinear optimal perturbation (CNOP) approach, sensitive areas of adaptive observation for predicting the seasonal reduction of the upstream Kuroshio transport (UKT) were investigated in the Regional Ocean Modeling System (ROMS). The vertically integrated energy scheme was utilized to identify sensitive areas based on two factors: the specific energy scheme and sensitive area size. Totally 27 sensitive areas, characterized by three energy schemes and nine sensitive area sizes, were evaluated. The results show that the total energy (TE) scheme was the most effective because it includes both the kinetic and potential components of CNOP. Generally, larger sensitive areas led to better predictions. The size of 0.5% of the model domain was chosen after balancing the effectiveness and efficiency of adaptive observation. The optimal sensitive area OSen was determined accordingly. Sensitivity experiments on OSen were then conducted, and the following results were obtained: (1) In OSen, initial errors with CNOP or CNOP-like patterns were more likely to yield worse predictions, and the CNOP pattern was the most unstable. (2) Initial errors in OSen rather than in other regions tended to cause larger prediction errors. Therefore, adaptive observation in OSen can be more beneficial for predicting the seasonal reduction of UKT.

Funded by

Strategic Priority Research Program of the Chinese Academy of Sciences(XDA11010303)

National Natural Science Foundation of China (NSFC)(41230420,41306023 ,&, 41421005,the National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406401)


The authors are grateful to the two anonymous reviewers for their valuable comments and constructive suggestions. This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA11010303), the National Natural Science Foundation of China (Grant Nos. 41230420, 41306023 & 41421005) and the National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406401). The authors gratefully acknowledge the support of K. C. Wong Foundation.


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  • Figure 1

    Distributions of non-dimensional vertically integrated energy of CNOP in the upper 1000 m.

  • Figure 2

    Snapshots of selected sensitive areas determined by different energy schemes and sensitive area sizes; (a1)-(a3) denote the sensitive areas determined using the KE scheme and horizontal sizes of 0.3%, 0.5% and 0.7% of the model domain, respectively. Similarly, (b1)-(b3) and (c1)-(c3) denote the sensitive areas determined using the PE and TE schemes, respectively.

  • Figure 3

    Percentages of grids shared by Sen_KEs, Sen_PEs and Sen_TEs for different sensitive area sizes.

  • Figure 4

    (a) Prediction benefits of performing ideal hindcasting experiments in identified sensitive areas, (b) prediction benefit increments of enlarging Sen_TEs from 0.1% to 0.9%.

  • Figure 5

    Domains of the compared areas (R1 to R6). The dotted region denotes the identified sensitive area OSen.

  • Figure 6

    The UKT changes at the prediction time caused by the random initial errors in OSen. The red column denotes the mean of the absolute UKT changes. The dashed lines denote UKT changes of 6000 and −6000 m3 s-1.

  • Figure 7

    Scatter diagrams of UKT changes and the correlation coefficients between CNOP-OSen and initial errors in OSen: (a) for random initial errors and (b) for special-pattern initial errors. The green line is the regression line.

  • Figure 8

    The UKT changes (unit: m3 s‒1) at the prediction time caused by the random initial errors in each compared areas (R1 to R6). The red column denotes the mean of the absolute UKT changes. The dashed lines denote UKT changes of 6000 and −6000 m3 s‒1.

  • Table 1   Statistics of the absolute UKT changes caused by random initial errors in OSen and the compared areas (R1 to R6)


    Number of random errors (m3 s−1)

    Maximum (m3 s−1)

    Mean (m3 s−1)

    Median (m3 s−1)




































    Mean of all random initial errors in R1‒R6


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