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Convection process in the North Pacific from ARGO data. 1 Eunjeong Lee, Yign Noh, 2 Bo Qiu 1 Department of atmospheric sciences, Yonsei University 2 Department of Oceanography, University of Hawaii. Department of Atmospheric Sciences, Yonsei University. Contents. Objective About ARGO

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convection process in the north pacific from argo data

Convection process in the North Pacific from ARGO data

1Eunjeong Lee, Yign Noh, 2Bo Qiu

1Departmentof atmospheric sciences, Yonsei University

2Department of Oceanography, University of Hawaii

Department of Atmospheric Sciences, Yonsei University

contents
Contents
  • Objective
  • About ARGO
  • Data analysis

- ARGO

- NCEP/NCAR reanalysis I

  • Results

PartⅠ - Response of the ocean to the surface cooling

- Deepening of MLD

- In the KE region (Eddy process)

PartⅡ - Correlation

- Efficiency (deepening, heating & cooling)

  • Conclusion

Department of Atmospheric Sciences, Yonsei University

objective
Objective
  • Analysis of the convection process in the upper ocean to the

atmospheric forcing using ARGO data

    • How are the variations of the MLD and SST related to the surface

forcing?

 i) Understanding the air-sea interaction in the North Pacific

ii) Providing the information for the parameterization of

convection in the mixed layer model

Department of Atmospheric Sciences, Yonsei University

about argo
About ARGO
  • Processing
  • Position of float
  • Profile data, metadata, trajectories and technical data
  • An ascending profile with measurements

(e.g. pressure, temperature, salinity)

  • Data mode (e.g. R : Real time, D : Delayed mode, A : adjusted values)
  • Quality control (e.g. 1:good data, 4:bed data, 9:missing value)

Department of Atmospheric Sciences, Yonsei University

data analysis argo
Data analysis : ARGO
  • 27 different locations
  • Area : North Pacific

(130-240˚E, 20-60˚N)

    • 0-500m depth
  • Period : 2001 - 2007
  • Data processing
    • - use data with quality control 1 or 2.
    • - semi-monthly averaging
    • - mixed layer depth : z = z [T(0)-0.5]

Department of Atmospheric Sciences, Yonsei University

slide6
Data analysis : NCEP data
  • NCEP reanlaysis I – surface heat flux, SST
  • Surface heat flux
    • (+) : direction from atmosphere to ocean
  • Data processing
    • semi-monthly averaging
    • 2001-2007 data on an average compare with each year value

Department of Atmospheric Sciences, Yonsei University

part response of the ocean to the surface cooling
Location

Mean

Each year

Heat Flux

SST

MLD

Part Ⅰ : Response of the ocean to the surface cooling
  • 150-155˚E, 28-30˚N
  • 135-140˚E, 28-30˚N

2005-2006년

2005-2006년

Reduce

Department of Atmospheric Sciences, Yonsei University

part deepening of mld in winter
slower deepening

faster deepening

Part Ⅰ : Deepening of MLD in winter

135-140˚E, 28-30˚N

Location

2004-2005년

2005-2006년

Heat Flux

SST

MLD

Department of Atmospheric Sciences, Yonsei University

part deepening of mld in winter1
Insensitive to

surface heat flux

Part Ⅰ :Deepening of MLD in winter

175-180˚W, 42-44˚N

Location

2003-2004년

2002-2003년

Heat Flux

SST

MLD

Department of Atmospheric Sciences, Yonsei University

the ke region
The KE region
  • Near 145˚E, 35˚N
  • High T & S (20°, 34.5‰), 50~300m/s velocity
  • Eddy process

Department of Atmospheric Sciences, Yonsei University

slide11
The KE region

PDO index

EKE level

[Qiu et al.(2008)]

  • Center of action of wind forcing is in the eastern half of the N Pacific basin
  • Positive (negative) phase of PDO generates – (+) local SSH through Ekman divergence (convergence)

Department of Atmospheric Sciences, Yonsei University

part eddy process
Part Ⅰ :Eddy process

145-150˚E, 32-34˚N

Location

2004-2005년

2005-2006년

Heat Flux

SST

MLD

Department of Atmospheric Sciences, Yonsei University

part eddy process1
Part Ⅰ :Eddy process

Kuroshio Extension

  • 155-160˚E, 32-34˚N

2005-2006년

Location

the effect of internal ocean dynamics is important

→ eddy process

Due to eddy

processing

MLD

Eddy processing

interrupt

MLD deepening

Department of Atmospheric Sciences, Yonsei University

part relation
No horizontal advectionPart Ⅱ :Relation

d(MLD)=MLD(t+15)-MLD(t)

  • 130-135˚W, 50-52˚N

d(MLD)&Qdt

d(SST)&Qdt

SST&MLD

Department of Atmospheric Sciences, Yonsei University

part relation1
Part Ⅱ :Relation
  • 155-160˚E, 32-34˚N

d(MLD)&Qdt

SST&MLD

d(SST)&Qdt

horizontal advection

Department of Atmospheric Sciences, Yonsei University

part correlation
-0.1

-0.05

-0.6

-0.5

-0.4

-0.3

-0.2

0.05

0.1

0.2

0.3

0.4

0.5

0.6

-0.8

0

0.8

Part Ⅱ : Correlation

summer

  • d(MLD) & Qdt

MAY & JUN

JUL & AUG

d(SST)Qdt

  • Strong heating→strong stratification
  • → shallower MLD
  • Weak correlation in the KE region duo to advection effect
  • Already shallow MLD
  • → no longer weakening of MLD

Department of Atmospheric Sciences, Yonsei University

part correlation1
-0.1

-0.05

-0.6

-0.5

-0.4

-0.3

-0.2

0.05

0.1

0.2

0.3

0.4

0.5

0.6

-0.8

0

0.8

Part Ⅱ : Correlation

Earlysummer

  • d(MLD) & Qdt

Weak eddy

Strong eddy

Department of Atmospheric Sciences, Yonsei University

part correlation2
-0.1

-0.05

-0.6

-0.5

-0.4

-0.3

-0.2

0.05

0.1

0.2

0.3

0.4

0.5

0.6

-0.8

0

0.8

Part Ⅱ : Correlation

winter

  • d(MLD) & Qdt

NOV & DEC

JAN & FEB

d(SST)Qdt

  • Strong cooling →weak stratification
  • → rapid convective deepening
  • Weak correlation in the KE region duo to advection effect
  • Already deep MLD
  • → no longer deepening of MLD
  • : Maximum MLD is insensitive to surface cooling

Department of Atmospheric Sciences, Yonsei University

part correlation3
-0.1

-0.05

-0.6

-0.5

-0.4

-0.3

-0.2

0.05

0.1

0.2

0.3

0.4

0.5

0.6

-0.8

0

0.8

Part Ⅱ : Correlation

Early winter

  • d(MLD) & Qdt

Weak eddy

Strong eddy

Department of Atmospheric Sciences, Yonsei University

part deepening efficiency
Part Ⅱ : Deepening efficiency
  • Deepening efficiency

Department of Atmospheric Sciences, Yonsei University

part deepening efficiency1
Stronger efficiency than NOV& DEC except KE region

-0.02

-0.01

-0.005

-0.004

-0.001

0

0.001

11

0.003

12

0.004

0.005

0.007

0.01

0.02

-0.007

-0.003

-0.002

0.002

Part Ⅱ : Deepening efficiency

winter

  • Deepening efficiency

NOV & DEC

JAN & FEB

  • Overall positive efficiency
  • Negative efficiency in the KE region

Department of Atmospheric Sciences, Yonsei University

part heating cooling efficiency
Part Ⅱ : Heating(Cooling) efficiency
  • Heating(Cooling) efficiency

Department of Atmospheric Sciences, Yonsei University

part heating cooling efficiency1
-0.02

-0.01

-0.005

-0.004

-0.001

0

0.001

11

0.003

12

0.004

0.005

0.007

0.01

0.02

-0.007

-0.003

-0.002

0.002

Part Ⅱ : Heating(Cooling) efficiency

summer

  • Heating efficiency

MAY & JUN

JUL & AUG

Advection + heating effect

Low heating efficiency

Department of Atmospheric Sciences, Yonsei University

part heating cooling efficiency2
Advection + heating effect

Advection + cooling effect

-0.02

-0.01

-0.005

-0.004

-0.001

0

0.001

11

0.003

12

0.004

0.005

0.007

0.01

0.02

-0.007

-0.003

-0.002

0.002

Part Ⅱ : Heating(Cooling) efficiency

winter

  • Cooling efficiency

NOV & DEC

JAN & FEB

Low cooling efficiency

Department of Atmospheric Sciences, Yonsei University

conclusion
Conclusion
  • The response of the ocean mixed layer and sea surface temperature to surface forcing in the Pacific was investigated by analyzing ARGO data.
  • The d(SST) and d(MLD) has high correlation and efficiency with surface cooling except in the KE region in early summer and winter.
  • The d(MLD) are more sensitive to the surface heat flux in late summer and winter.
  • The initial convective deepening shows large variability, but the maximum MLD does not show much variability.
  • In the KE region,
  • - the MLD increase is interrupted by mesoscale eddies
  • - the heat transport by the Kuroshio is important to determine SST

Department of Atmospheric Sciences, Yonsei University

slide26
Reference
  • Argo Data Management Team (2004), Argo quality control manual, version 2.0b, p. 23, Argo Data Manage., Toulouse, France.
  • Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, R. Jenne, and D. Joseph (1996), The NCEP/NCAR 40-Year Reanalysis Project, Bull. Am. Meteorol. Soc., 77, 437- 471.
  • Qiu, B., S. Chen, and P. Hacker (2007), Effect of mesoscale eddies on Subtropical Mode Water variability from the Kuroshio Extension System Study (KESS), J. Phys. Oceanogr., 37, 982-1000.

Department of Atmospheric Sciences, Yonsei University

thank you

Thank you

Department of Atmospheric Sciences, Yonsei University

slide29
(a) Upstream KE path length (141-153°E)

(b) Eddy kinetic energy (141-153°E, 32-38°N)

Stable yrs: 1993-94, 2002-04

Unstable yrs: 1996-2001, 2006-07

ad