Decadal Variability, Impact, and Prediction of the Kuroshio Extension System

Decadal Variability, Impact, and Prediction of the Kuroshio Extension System

B. Qiu1, S. Chen1, N. Schneider1 and B. Taguchi2

1. Dept of Oceanography, University of Hawaii, USA

2. Earth Simulator Center, JAMSTEC, Japan

Climate Implications of Frontal Scale Air-Sea Interaction Workshop, Boulder, 5-7 August 2013

AVISO satellite altimeter data: 10/1992-present

• Observed decadal changes in the Kuroshio Extension system • Forced vs. intrinsic KE variability and impact on SST front• KE index and its prediction

Outlines

Low-frequency circulation

modulations

Mesoscale eddy fluctuations

Altimeter-derived KE paths: Oscillations between stable and unstable states

Stable yrs: 1993-94, 2002-04, 2010-2013 Unstable yrs: 1996-2001, 2006-08

Altimeter-derived KE paths: Oscillations between stable and unstable states

Yearly-averaged SSH field in the region surrounding the KE system

Various dynamic properties representing the decadal KE variability

Various dynamic properties representing the decadal KE variability

Question 1: What causes the transitions between the stable and unstable dynamic states of the KE system?

Decadal KE variability lags the PDO index by 4 yrs ( r=0.67 )

• Center of wind forcing is in eastern half of North Pacific basin

• + PDO generates negative local SSHAs through Ekman divergence, and vice versa

L

H

KE <∂h> SSH field

145E 165E155E135E L

SSHA along 34°N PDO index

center of PDO forcing

H

L

SSHA along 34°N PDO index/AL pressure

center of PDO forcing

SSH variability vs wind forcing

• Large-scale SSH changes are governing by linear vorticity dynamics:

• Given the wind forcing, SSH changes can be found along the Rossby wave paths:

cR: observed value

wind stress: NCEP reanalysis

H

H

L

L

Wind-forced SSHA along 34°N SSHA along 34°N PDO index/AL pressure

center of PDO forcing

H

L

H

KE strength of 142°-180°E (Taguchi et al. 2007)

See also Nonaka et al. (2006), Pierini et al. (2009)

Question 2: What is the relative importance of the PDO-related wind forcing vs. the internal oceanic eddy forcing?

Quantifying wind- vs. eddy-forced KE variability in a 1.5-layer reduced-gravity North Pacific Ocean model

• Governing equations:

where is velocity vector and is upper layer thickness.

• denotes the external wind forcing; given by interannually varying, or climatological, monthly NCEP data

• denotes the oceanic eddy forcing; inferred from the weekly AVISO anomalous SSH data

• North Pacific domain: 0°-50°N, 120°E-75°W

• Horizontal eddy viscosity: Ah = 1,000 m2/s

Observed KE properties Modeled KE properties wind forcing only

Modeled KE properties clim. wind + eddy forcing

• Wind forcing (only) case underestimates KE position/intensity changes and misses eddy signals

• Eddy + clim. wind forcing case fails to capture the decadal KE signals

Observed KE properties Modeled KE properties wind + eddy forcing

Modeled KE properties clim. wind + eddy forcing

• Combined forcing by time-varying wind and eddy flux divergence generates the observed, decadal modulations of the KE system

KE dynamic state affects regional SST and cross-front SST gradient

JF rms 850mb v’T’ of stormtrack variability (Nakamura et al. 2004)

JFM rms net surface heat flux variabilityRMS SSH variability (>2yrs)

By carrying a significant amount of warm water from the tropics,

KE is the region of intense air-sea interaction

850mb v’T’ and Q’ regressed to the KE dynamic state in 1977-2012

When KE is stable, more heat release from O to A and a northward shift of stormtracks

JF rms 850mb v’T’ of stormtrack variability (Nakamura et al. 2004)

JFM rms net surface heat flux variability

Question 3: Can the decadally-modulating KE dynamic state

be predicted in advance?

A composite index quantifying the KE dynamic state: the KE index

KE index : average of the 4 dynamic properties

(normalized)

Regression between the KE index and the AVISO SSH anomaly field

KE index: represented well by SSH anomalies in

31-36°N, 140-165°E

Predicting KE index becomes equivalent to

predicting SSH anomalies in this key

box

Implications:

1. Prediction with Rossby wave dynamics

h1(x, t) = hobs [ x+cR(t-t0), t0 ] where

KE index and x-t SSHAs

hobs(x, t0) : initial SSHAs

cR : Rossby wave speed

The basis for long-term KE index prediction rests on 2 processes:

1. Oceanic adjustment in mid-latitude North Pacific is via slow, baroclinic Rossby waves

Examples of decadal KE index predictions and verifications

Mean square skill of the predicted KE index

• Compared to damped persistence, wave-carried SSHAs increase predictive skill with a 2~3 yr lead (Schneider and Miller 2001)

KEI>0

KE index-regressed curl field (tropical influence removed)

stormtracks

The basis for long-term KE index prediction rests on 2 processes:

2. The KE decadal variability affects the basin-scale wind stress curl field

NCEP mean wind stress curl field

NCEP mean wind stress curl field

KE index-regressed curl field (tropical influence removed)

Other studies of wind stress curl responses to KE variability

wind curl > 0 SSHA < 0

KEI>0stormtracks wind curl > 0

SSHA < 0

SSHA < 0RW propagation

half of the oscillation cycle: ~5 yrs in the N Pacific basin

KEI>0

The reason behind enhanced predictive skill with the 4~6 yr lead: delayed negative feedback mechanism

1. Prediction with Rossby wave dynamics

h1(x, t) = hobs [ x+cR(t-t0), t0 ]

2. Prediction with Rossby wave dynamics + KE feedback to wind forcing

h2(x, t) = h1(x, t) +

∫ b[ x+cR(t’-t0) ] K(t’) dt’ t t0

where

KE index and x-t SSHAs

hobs(x, t0) : initial SSHAs

cR : Rossby wave speed

where

b(x) : feedback coefficient

K(t) : forecast KE index

Examples of decadal KE index predictions and verifications

Mean square skill of the predicted KE index

• Compared to damped persistence, wave-carried SSHAs increase predictive skill with a 2~3 yr lead (Schneider and Miller 2001)

• Additional skill is gained with the 4~6 yr lead by considering the wind forcing due to KE feedback

Summary KE dynamic state (i.e. EKE level, path, and jet/RG strengths) is

dominated by decadal variations. It impacts the broad-scale cross-front SST gradient.

SSH anomaly signals in 31-36°N, 140-165°E provide a good proxy for the decadally-varying KE index.

A positive KE index induces overlying-high and downstream- low pressure anomalies. This feedback favors a coupled mode with a ~10 yr timescale.

Oscillatory nature of this mode enhances predictability

Compared to Rossby wave dynamics, inclusion of the KE-feedback wind forcing increases predictive skill with a lead time from 2~3 to 4~5 yrs.

Due to the persistent negative PDO phase, a prolonged stable KE dynamic state (until 2017) is predicted.

KE dynamic state (i.e. EKE level, path, and jet/RG strengths) is dominated by decadal variations. It impacts the broad-scale cross-front SST gradient.

SSH anomaly signals in 31-36°N, 140-165°E provide a good proxy for the decadally-varying KE index.

A positive KE index induces overlying-high and downstream- low pressure anomalies. This feedback favors a coupled mode with a ~10 yr timescale.

Oscillatory nature of this mode enhances predictability

Compared to Rossby wave dynamics, inclusion of the KE-feedback wind forcing increases predictive skill with a lead time from 2~3 to 4~5 yrs.

Due to the persistent negative PDO phase, a prolonged stable KE dynamic state (until 2017) is predicted.

2013-2023 KE index forecast based on 2012 AVISO SSH data

Yearly SSH anomaly field in the North Pacific Ocean

+ +

-

-

/ : PDO index+ -

Stable Dynamic State

Unstable Dynamic State

• Reducing EKE level

• Strengthening RG and northerly KE jet

• Incoming positive SSHA

• Ekman conver-gence in the east

– PDO

+ PDO• Ekman

divergence in the east

• Incoming negative SSHA

• Weakening RG and southerly KE jet

• Enhancing local EKE level

• Eddy-eddy interaction re-enhances the RG

Schematic of the decadally-modulating Kuroshio Extension system

N.B. Nonlinearity important; phase changes paced by external wind forcing

Pierini et al. (2009, JPO)

Bimodal decadal KE variability can be purely nonlinearly driven