Skip to main content
Log in

The fastest growing initial error in prediction of the Kuroshio Extension state transition processes and its growth

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

In this study, the predictability of the Kuroshio Extension (KE) transition processes is explored from an error growth perspective. The fastest growing initial errors (FGIEs) are obtained through the conditional nonlinear optimal perturbation approach within a reduced-gravity shallow-water model forced by steady winds, which provides a fairly realistic simulation of the KE low-frequency variability of intrinsic origin. The large amplitudes of the FGIEs for both the transitions from a typical low-energy state to a typical high-energy state (LH) and the opposite transition (HL), are found mainly in the Kuroshio large meander region south of Japan and in the KE region. The FGIE grows more rapidly for the HL process than for the LH process, implying that the HL transition process may be more difficult to predict. The evolution processes of the FGIEs and the related mechanisms are revealed by investigating the evolution of the potential vorticity anomalies caused by the FGIEs. The dominant physical processes governing the FGIE growth are found to be different for the LH and HL processes. For the LH process, the evolution is mainly governed by linear advection processes and interfacial friction, while for the HL process, in addition to these two processes, the nonlinear advection process also plays a vital role in the evolution. This indicates that nonlinear intrinsic oceanic processes affect considerably the error growth, especially in the HL transition process, suggesting that the intrinsic processes should be carefully considered when exploring the predictability and forecast of the KE low-frequency variability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  • Birgin EG, Martinez JM, Raydan M (2000) Nonmonotone spectral projected gradient methods on convex sets. SIAM J Optim 10:1196–1211

    Google Scholar 

  • Douglass EM, Jayne SR, Bryan FO, Peacock S, Maltrud M (2012) Kuroshio pathways in a climatologically forced model. J Oceanogr 68:625–639

    Google Scholar 

  • Duan WS, Yu Y, Xu H, Zhao P (2013) Behaviors of nonlinearities modulating the El Niño events induced by optimal precursory disturbances. Clim Dyn 40:1399–1413

    Google Scholar 

  • Duan WS, Li XQ, Tian B (2018) Towards optimal observational array for dealing with challenges of El Niño-Southern Oscillation predictions due to diversities of El Niño. Clim Dyn 51:3351–3368

    Google Scholar 

  • Farrell BF (1990) Small error dynamics and the predictability of flows. J Atmos Sci 47:2409–2416

    Google Scholar 

  • Frankignoul C, Sennechael N, Kwon YO, Alexander MA (2011) Influence of the meridional shifts of the Kuroshio and the Oyashio Extensions on the atmospheric circulation. J Clim 24:762–777

    Google Scholar 

  • Gentile V, Pierini S, de Ruggiero P, Pietranera L (2018) Ocean modelling and altimeter data reveal the possible occurrence of intrinsic low-frequency variability of the Kuroshio Extension. Ocean Model 131:24–39

    Google Scholar 

  • Kelly KA, Small RJ, Samelson RM, Qiu B, Joyce TM, Kwon Y, Cronin MF (2010) Western boundary currents and frontal air–sea interaction: Gulf Stream and Kuroshio Extension. J Clim 23:5644–5667

    Google Scholar 

  • Kramer W, Dijkstra HA, Pierini S, Van Leeuwen PJ (2012) Measuring the impact of observations on the predictability of the Kuroshio Extension in a shallow-water model. J Phys Oceanogr 42:3–17

    Google Scholar 

  • Kwon YO, Joyce TM (2013) Northern Hemisphere winter atmospheric transient eddy heat fluxes and the Gulf Stream and Kuroshio–Oyashio Extension variability. J Clim 26:9839–9859

    Google Scholar 

  • Liu X, Mu M, Wang Q (2018) The nonlinear optimal triggering perturbation of the Kuroshio large meander and its evolution in a regional ocean model. J Phys Oceanogr 48:1771–1786

    Google Scholar 

  • Miller AJ, Chai F, Chiba S, Moisan JR, Neilson DJ (2004) Decadal-scale climate and ecosystem interactions in the North Pacific ocean. J Oceanogr 60:163–188

    Google Scholar 

  • Mu M, Duan WS, Wang B (2003) Conditional nonlinear optimal perturbation and its applications. Nonlinear Process Geophys 10:493–501

    Google Scholar 

  • Mu M, Duan WS, Chen DK, Yu WD (2015) Target observations for improving initialization of high-impact ocean-atmospheric environmental events forecasting. Natl Sci Rev 2:226–236

    Google Scholar 

  • Mu M, Duan WS, Tang YM (2017) The predictability of atmospheric and oceanic motions: retrospect and prospects. Sci China Earth Sci 60:2001–2012

    Google Scholar 

  • Nishikawa H, Yasuda I, Itoh S (2011) Impact of winter-to-spring environmental variability along the Kuroshio jet on the recruitment of Japanese sardine. Fish Oceanogr 20:570–582

    Google Scholar 

  • Nonaka M, Sasaki H, Taguchi B, Nakamura H (2012) Potential predictability of interannual variability in the Kuroshio Extension jet speed in an eddy-resolving OGCM. J Clim 25:3645–3652

    Google Scholar 

  • O’Reilly CH, Czaja A (2015) The response of the Pacific storm track and atmospheric circulation to Kuroshio Extension variability. Q J R Meteorol Soc 141:52–66

    Google Scholar 

  • Pierini S (2005) A model study of the spectral structure of boundary-driven Rossby waves and related altimetric implications. J Phys Oceanogr 35:218–231

    Google Scholar 

  • Pierini S (2006) A Kuroshio Extension system model study: decadal chaotic self-sustained oscillations. J Phys Oceanogr 36:1605–1625

    Google Scholar 

  • Pierini S (2008) On the crucial role of basin geometry in double-gyre models of the Kuroshio Extension. J Phys Oceanogr 38:1327–1333

    Google Scholar 

  • Pierini S (2014) Kuroshio Extension bimodality and the North Pacific oscillation: a case of intrinsic variability paced by external forcing. J Clim 27:448–454

    Google Scholar 

  • Pierini S (2015) A comparative analysis of Kuroshio Extension indices from a modeling perspective. J Clim 28:5873–5881

    Google Scholar 

  • Pierini S, Dijkstra HA (2009) Low-frequency variability of the Kuroshio Extension. Nonlinear Process Geophys 16:665–675

    Google Scholar 

  • Pierini S, Dijkstra HA, Riccio A (2009) A nonlinear theory of the Kuroshio Extension bimodality. J Phys Oceanogr 39:2212–2229

    Google Scholar 

  • Pierini S, Dijkstra HA, Mu M (2014) Intrinsic low-frequency variability and predictability of the Kuroshio current and of its extension. Adv Oceanol Limnol 5:1–44

    Google Scholar 

  • Qiu B (2002) The Kuroshio Extension system: its large-scale variability and role in the midlatitude ocean–atmosphere interaction. J Oceanogr 58:57–75

    Google Scholar 

  • Qiu B (2003) Kuroshio Extension variability and forcing of the Pacific decadal oscillations: responses and potential feedback. J Phys Oceanogr 33:2465–2482

    Google Scholar 

  • Qiu B, Chen S (2005) Variability of the Kuroshio Extension jet, recirculation gyre, and mesoscale eddies on decadal time scales. J Phys Oceanogr 35:2090–2103

    Google Scholar 

  • Qiu B, Chen S, Schneider N, Taguchi B (2014) A coupled decadal prediction of the dynamic state of the Kuroshio Extension system. J Clim 27:1751–1764

    Google Scholar 

  • Seager R, Kushnir Y, Naik NH, Cane MA, Miller J (2001) Wind-driven shifts in the latitude of the Kuroshio–Oyashio Extension and generation of SST anomalies on decadal timescales. J Clim 14:4249–4265

    Google Scholar 

  • Stewart RH (2008) Introduction to physical oceanography. Texas A & M University, College Station, p 353

    Google Scholar 

  • Taguchi B, Xie SP, Schneider N, Nonaka M, Sasaki H, Sasai Y (2007) Decadal variability of the Kuroshio Extension: observations and an eddy-resolving model hindcast. J Clim 20:2357–2377

    Google Scholar 

  • Taguchi B, Qiu B, Nonaka M, Sasaki H, Xie SP, Schneider N (2010) Decadal variability of the Kuroshio Extension: mesoscale eddies and recirculation. Ocean Dyn 60:673–691

    Google Scholar 

  • Tang Y, Kleeman R, Miller S (2006) ENSO predictability of a fully coupled GCM model using singular vector analysis. J Clim 19:3361–3377

    Google Scholar 

  • Tokinaga H, Tanimoto Y, Xie SP, Sampe T, Tomita H, Ichikawa H (2009) Ocean frontal effects on the vertical development on clouds over the western North Pacific: in situ and satellite observations. J Clim 22:4241–4260

    Google Scholar 

  • Usui N, Tsujino H, Nakano H, Fujii Y (2008) Formation process of the Kuroshio large meander in 2004. J Geophys Res 113:C08047. https://doi.org/10.1029/2007JC004675

    Article  Google Scholar 

  • Wang Q, Mu M, Dijkstra HA (2013) The similarity between optimal precursor and optimally growing initial error in prediction of Kuroshio large meander and its application to targeted observation. J Geophys Res 118:869–884

    Google Scholar 

  • Wang S, Liu Z, Pang C, Liu H (2016) The decadally modulating eddy field in the upstream Kuroshio Extension and its related mechanisms. Acta Oceanol Sin 35:9–17

    Google Scholar 

  • Wang Q, Tang Y, Pierini S, Mu M (2017) Effects of singular-vector-type initial errors on the short-range prediction of Kuroshio Extension transition processes. J Clim 30:5961–5983

    Google Scholar 

  • Wills SM, Thompson DWJ (2018) On the observed relationships between wintertime variability in Kuroshio–Oyashio Extension sea surface temperatures and the atmospheric circulation over the North Pacific. J Clim 31:4669–4681

    Google Scholar 

  • Yang Y, Liang XS, Qiu B, Chen S (2017) On the decadal variability of the eddy kinetic energy in the Kuroshio Extension. J Phys Oceanogr 47:1169–1187

    Google Scholar 

  • Yu PL, Zhang LF, Zhong QJ (2019) Contrasting relationship between the Kuroshio Extension and the East Asian summer monsoon before and after the late 1980s. Clim Dyn 52:929–950

    Google Scholar 

  • Zu Z, Mu M, Dijkstra HA (2016) Optimal initial excitations of decadal modification of the Atlantic meridional overturning circulation under the prescribed heat and freshwater flux boundary conditions. J Phys Oceanogr 46:2029–2047

    Google Scholar 

Download references

Acknowledgements

The authors thank three anonymous reviewers for their valuable comments. This study was supported by the National Natural Science Foundation of China (41576015), the Qingdao National Laboratory for Marine Science and Technology (QNLM2016ORP0107), the Fundamental Research Funds for the Central Universities (2020B00114), the National Programme on Global Change and Air-Sea interaction (GASI-IPOVAI-06) and the National Natural Science Foundation of China (41490644 and 41490640).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qiang Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: Derivation of the evolution equation of the PV anomaly

Appendix: Derivation of the evolution equation of the PV anomaly

To obtain the evolution equation of the PV anomaly, we first derive the governing equation for the PV based on the shallow-water model. Taking the curl of Eqs. (1a) and (1b), we obtain

$$\begin{aligned} \frac{\partial \zeta }{\partial t} + \zeta \nabla \cdot \varvec{V} + \varvec{V} \cdot \nabla \zeta + f\nabla \cdot \varvec{V} + v\frac{\partial f}{\partial y} & = curl\left( {A_{H} \nabla^{2} u,A_{H} \nabla^{2} v} \right) \\ & \quad + curl\left( { - \gamma u\sqrt {u^{2} + v^{2} } , - \gamma v\sqrt {u^{2} + v^{2} } } \right) + curl\left( {\frac{\tau }{\rho h},0} \right), \\ \end{aligned}$$
(12)

where \(\varvec{V} = \left( {u,v} \right)\). For simplicity, we denote \({\text{AH}} = curl\left( {A_{H} \nabla^{2} u,A_{H} \nabla^{2} v} \right)\), \({\text{FR}} = curl\left( { - \gamma u\sqrt {u^{2} + v^{2} } , - \gamma v\sqrt {u^{2} + v^{2} } } \right)\), \({\text{WS}} = curl\left( {\frac{\tau }{\rho h},0} \right)\) and rearrange Eq. (12) as follows,

$$\frac{{D\left( {\zeta + f} \right)}}{dt} + \left( {\zeta + f} \right)\nabla \cdot \varvec{V} = {\text{AH}} + {\text{FR}} + {\text{WS}},$$
(13)

where \(D/dt\) denotes material derivative.

According to Eq. (1c), we have

$$\nabla \cdot \varvec{V} = - \frac{1}{h}\frac{Dh}{dt}.$$
(14)

Substituting Eq. (14) into Eq. (13), we can obtain the following PV evolution equation

$$\frac{\partial q}{\partial t} = - \varvec{ V} \cdot \nabla q + \frac{\text{AH}}{h} + \frac{\text{FR}}{h} + \frac{\text{WS}}{h}.$$
(15)

For the reference state, the evolution equation becomes,

$$\frac{{\partial q_{r} }}{\partial t} = - \varvec{ V}_{r} \cdot \nabla q_{r} + \frac{{{\text{AH}}_{r} }}{{h_{r} }} + \frac{{{\text{FR}}_{r} }}{{h_{r} }} + \frac{{{\text{WS}}_{r} }}{{h_{r} }},$$
(16)

where the subscript \(r\) denotes the reference state. Accordingly, for the state obtained after perturbing the initial reference state with the FGIE, the PV equation is,

$$\begin{aligned} \frac{\partial \left( {q_{r} + q^{\prime}} \right)}{\partial t} & = - \left( {\varvec{V}_{r} + \varvec{V}^{\prime}} \right) \cdot \nabla \left( {q_{r} + q^{\prime}} \right) \\&\quad+ \frac{{{\text{AH}}_{p} }}{{h_{r} + h^{\prime}}} + \frac{{{\text{FR}}_{p} }}{{h_{r} + h^{\prime}}} + \frac{{{\text{WS}}_{p} }}{{h_{r} + h^{\prime}}}, \end{aligned}$$
(17)

where the prime denotes the anomaly caused by the FGIE and the subscript \(p\) represents the term calculated after superimposing the FGIE on the reference state.

Subtracting Eq. (16) from Eq. (17), we obtain the equation governing the evolution of the PV anomaly as follows

$$\frac{{\partial q^{\prime}}}{\partial t} = - \varvec{V}_{r} \cdot \nabla q^{\prime} - \varvec{V}^{\prime} \cdot \nabla q_{r} - \varvec{V}^{\prime} \cdot \nabla q^{\prime} + \frac{{{\text{AH}}_{p} }}{{h_{r} + h^{\prime}}} - \frac{{{\text{AH}}_{r} }}{{h_{r} }} + \frac{{{\text{FR}}_{p} }}{{h_{r} + h^{\prime}}} - \frac{{{\text{FR}}_{r} }}{{h_{r} }} + \frac{{{\text{WS}}_{p} }}{{h_{r} + h^{\prime}}} - \frac{{{\text{WS}}_{r} }}{{h_{r} }}.$$
(18)

Hence, the expression of each term in the right-hand side of Eq. (11) is

$${\text{LADV}}1 = - \varvec{V}_{r} \cdot \nabla q^{\prime},$$
(19)
$${\text{LADV}}2 = - \varvec{V}^{\prime} \cdot \nabla q_{r} ,$$
(20)
$${\text{NADV}} = - \varvec{V}^{\prime} \cdot \nabla q^{\prime},$$
(21)
$${\text{VISC}} = \frac{{{\text{AH}}_{p} }}{{h_{r} + h^{\prime}}} - \frac{{{\text{AH}}_{r} }}{{h_{r} }},$$
(22)
$${\text{FRIC}} = \frac{{{\text{FR}}_{p} }}{{h_{r} + h^{\prime}}} - \frac{{{\text{FR}}_{r} }}{{h_{r} }},$$
(23)
$${\text{WIND}} = \frac{{{\text{WS}}_{p} }}{{h_{r} + h^{\prime}}} - \frac{{{\text{WS}}_{r} }}{{h_{r} }}.$$
(24)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Q., Mu, M. & Pierini, S. The fastest growing initial error in prediction of the Kuroshio Extension state transition processes and its growth. Clim Dyn 54, 1953–1971 (2020). https://doi.org/10.1007/s00382-019-05097-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-019-05097-1

Navigation