Multi-Perturbation Methods forEnsemble Prediction of the MJO

Seoul National University

A paper appearing in Climate Dynam-ics (2013)

In-Sik Kang and Pyong-Hwa Jang

Post Processing

Predictability Research

Prediction

Initialization

Atmosphere LandOcean

CGCM

OceanModel

AtmosphericModel

Intraseasonal Prediction System

2

3

Boreal Winter Boreal Summer

ABOM EC GFDL NCEP CMCC ECMWF JMA

Ensemble number 10 10 10 5 5 15 6

Ref. CLIVAR/ISVHEIntraseasonal Variability Hindcast Experiment

Corr

ela

tion

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

Corr

ela

tion

5 10 15 20 25 30 35

Leadtime (DAY)

RMM index

4

SNU CGCM

MJO simulation

Initialization/Perturbation methods

MJO Ensemble Prediction Results

CONTENTS

5

Model Description References

AGCM SNU AGCM

T42, 21 levels (2.8125X2.8125)SAS cumulus convection2-stream k-distribution radiationBonan (1996) land sur-face

Kim (1999)Kang et al. (2002)Kang et al. (2004)Kim et al. (2003) Lee et al. (2003)

OGCM SNU OGCM

MOM2.2 + Mixed Layer Model1/3o lat. x 1o lon. over tropics(10S-10N), Vertical 32 levels

Noh and Kim (1999)Noh et al. (2003a) Noh et al (2003b)Kim et al. (2004) Noh et al. (2004) Noh et al. (2005)

CGCM SNU CGCM SNU AGCM + MOM2.2

-1-day interval exchange- Ocean : SST- Atmosphere : Heat, Salt, Mo-mentum Flux- No Flux Correction is applied

M M

H H

K S ql

K S ql

Vertical Eddy Viscosity:

Vertical Eddy Diffusivity:

,M HS S: empirical Constantwhere

2 / 2q : TKE

l : the length scale of turbulence

Mixed Layer Model Coupling Strategy

SNU CGCM

6

CNTL CGCM

Bias

Annual mean SST

• Convective momentum trans-port

• Diurnal coupling• Tokioka constraint

(alpha=0.1)• Auto conversion time scale

(3200s)

Improved CGCM

Bias

Annual mean SST

Improvement, Ham et al. (2012) Climate Dyna-mics

7

Performance of SNU CGCM ver.2

Power Spectrum (averaged 15S-15N),summer (May-Oct)

OLR, OBS OLR, SNUCGCM

U850, OBS U850, SNUCGCM

Performance of SNU CGCM

8

Phase composites of velocity potential at 200 hPa

OBS SNU CGCM

9

SNU CGCM V.2

SNU AGCM + MOM2.2T42, 20 levels resolution

Coupled GCM

GODAS- SST & salinity Nudging

(relaxation time: 5 day)

ERA interim- U, V, T, Q, Ps Nudging

(relaxation time: 6hr)

Ocean

Atmosphere

Initialization

+

Data for Nudg-ing

Perturbation method

Lagged average fore-cast

(LAF)

Breeding Method

Empirical Singular Vector

Intra-seasonal Prediction System

10

ATM: ERA interim (T,U,V,Q,Ps)

OCN: GODAS (temperature, salinity)

Nudging

Initialization Prediction

LAF

Breeding

4 ensemble members

(6hr lag)

breeding interval : 1 day, 3 days, 5 days

BD (+) perturbation

BD (-) perturbation

3(breeding intervals) × 2(mirror im-ages)

= 6 ensemble members

Rescaling factor (percentage) : 10%

L()= : VP200, U200, U850: VP200, U200, U850

ESV

ESV (+) perturbation

ESV (-) perturbation

2 ensemble members

Performance of SNU CGCM

11

EV of the combined EOF of 20-100 day filteredsummer (May-Oct)

OLRU200U850

12

Boreal Summer season

STD of VP200 perturbation

(b) Bred Perturbation 3%

(a) Bred Perturbation 0.3% (c) Bred Perturbation 10%

- Characteristics of Bred perturbations

Initial Perturbation - Breeding

13

14

Bred Vector 1day Bred Vector 5day

Unit : ×105 (m²/s)

Bred Vector 3day1st mode

2nd mode

1st mode

2nd mode

1st mode

2nd mode

Initial Perturbation - Breeding

- Characteristics of Bred perturbations

EOF of perturbations

15

Bred Vector mode1

Bred Vector mode2

• Breeding rescaling factor : 10%

• Breeding interval : 5 day

- EOF modes of VP200 perturbation- Winter

(X 1e+6)

Characteristics of Bred perturbation To be up-date

16

Final VP200 anomaly is on the east of initial VP200 anomaly

Eastward propagating mode

Observa-tion

Model

Initial Perturbation - ESV

- Singular mode

17

Initial Perturbation - ESV

- Singular mode of ESV

0

1

2

3

Singular value

1st mode

2nd mode

3rd mode

4th mode

5th mode

5lag 10lag 15lag 20lag

18

Initial Perturbation

- EOF of initial perturbation

Total 360 summer cases Include all MJO phase

Summer : 12 < LAF(4) + BD1day(2) + BD3day(2) + BD5-

day(2) + ESV(2)> cases × 18 cases/year × 20 years

= 4320 predictions

19

- Outline

1 May11 May

21 Oct

45 Days Integration

whole

SU

MM

ER

Ensemble Prediction

- Correlation skill of Real-time Multivariate MJO (RMM) Index

20

LAF [4]

BD1day+ BD3day [4]BD1day [2]

BD1day+ BD3day+BD5day [6]BD5day [2]BD3day [2]

Ensemble Prediction* The parenthesis refers ensemble members

Corr

ela

tion

Coeffi

cie

nt

Lead Days

- Correlation skill of Real-time Multivariate MJO (RMM) Index

21

Ensemble Prediction* The parenthesis refers ensemble members

ESV [2]LAF [4]

22

- Correlation skill of Real-time Multivariate MJO Index

LAF (4)

ESV (2) ALL (12)

BD1day+BD3day+ BD5day(6)

: 4 ensemble mem-bers with 6 hours lag intervals

Multi-Perturbation Ensemble* The parenthesis refers ensemble members

23

- Correlation skill of RMM Index for each MJO phase

Multi-Perturbation Ensemble* The parenthesis refers ensemble members

* The parenthesis refers ensemble members

(a) 5 day

(b) 10 day

(c) 15 day

24

- Correlation Skill of U850 for lead times

MP (12)= LAF (4) + BRED (6) + ESV (2)

Multi-Perturbation Ensemble

25

Summary

1. Various perturbation methods (LAF, Breed-ing, ESV) produce different unstable modes and different perturbations.

2. MJO predictability is not sensitive to the perturbation methods.

3. Multi-perturbation ensemble prediction sys-tem is slightly better than that of any pertur-bation method.

26

Thank You

27

Multi-Perturbation Ensemble

- The ensemble spread of 200-hPa zonal wind averaged 15°S-15°N

LAFBDESV

28

Initial Perturbation

- Variance of initial perturbation of VP200 Unit : ×105 (m²/s)

(a) LAF (b) BD

(c) ESV

29

- Correlation skill of RMM Index for each MJO amplitude

Multi-Perturbation Ensemble* The parenthesis refers ensemble members

MP predic-tions

LAF predic-tions

30

Boreal Winter sea-son

31

Inter-comparison of models

* The parenthesis refers ensemble members

LAF (4)

Intra-Seasonal Prediction – Results

- Correlation skill of Real-time Multivariate MJO Index

To be up-date