role of atmosphere-ocean interaction and seasonal predictability international workshop on...

Role of Atmosphere-Ocean Role of Atmosphere-Ocean InteractionInteraction

And Seasonal PredictabilityAnd Seasonal Predictability

International Workshop on Variability and Predictability of the Earth Climate System, 26-27 Jan 2005, Japan

In-Sik Kang and Kyung JinIn-Sik Kang and Kyung Jin

Climate Environment System Research CenterClimate Environment System Research CenterSeoul National UniversitySeoul National University

Contents Contents

I. Limitation of dynamic predictability in tier-two system

II. Local atmosphere-ocean interaction

Ⅲ. Local and remote influence in coupled system

Ⅳ. Examination of predictability in tier-one vs. tier-two

Prescribe SST as boundary condition

Atmosphere Atmosphere

OceanSST Prediction

Current activities of seasonal prediction: Tier-2 vs. Tier-1Current activities of seasonal prediction: Tier-2 vs. Tier-1

Tier-two system Tier-one system

SST is prescribed as boundary condition

Atmosphere-ocean interaction is embodied

SST predictionsystem

CGCMComponent

Feature

AGCM

Experimental Design and Participated Models Experimental Design and Participated Models

CLIVAR Asian-Australian Monsoon Atmospheric GCM Intercomparison Project CLIVAR Asian-Australian Monsoon Atmospheric GCM Intercomparison Project

Group Country Numerics Convection ParameterizationCOLA USA R40L18 Relaxed Arakawa-Schubert (RAS, Moorthi and Suarez, 92) DNM Russia 4o×5o, L21 Betts (86)

GEOS USA 2o×2.5o, L43 RAS (Moorthi and Suarez, 92) GFDL USA T42L18 RAS (Moorthi and Suarez, 92) IAP China R15L9 MCA (Manabe et al., 65)IITM India 2.5o×3.75o, L19 Mass flux penetrative convection scheme (Gregory and Rowntree, 90) MRI Japan 4o×5o, L15 Arakawa-Schubert, Tokioka et al. (88)

NCAR USA T42L18 Mass flux scheme (Zhang and McFarlane, 95)NCEP USA T42L28 RAS (Moorthi and Suarz, 92) SNU Korea T31L20 Simplified Arakawa-Schubert

SUNY USA 4ox 5o, L17 Modified Arakawa-Schubert

Institute Model Resolution Experiment Type Ensemble MemberJMA JMA T63L40 SMIP 10KMA GDAPS T106L21 SMIP 10NCEP NCEP T62L28 SMIP 10

NASA/NSIPP NSIPP 2ox2.5o L43 AMIP 9SNU GCPS T63L21 SMIP 10

APEC Climate Network (APCN) participantsAPEC Climate Network (APCN) participants

- 10 ensemble simulations from Nov1996 to Aug98

- 21 year simulation from 1979 to 1999

DJF97/98 Precipitation Anomaly for Each Model EnsembleDJF97/98 Precipitation Anomaly for Each Model Ensemble

CLIVAR Asian-Australian Monsoon Atmospheric GCM Intercomparison Project CLIVAR Asian-Australian Monsoon Atmospheric GCM Intercomparison Project

Climate signals caused by external forcing

Intrinsic transients due to natural variability

Forced Variance Free Variance Signal-to-noise

N

i

n

jiij XX

nN 1 1

2)()1(

1

N

ii XX

N 1

2)(1

1 Theoretical limit of

predictability

Analysis of Variance of 21-yr JJA Rainfall in Tier-Two systemsAnalysis of Variance of 21-yr JJA Rainfall in Tier-Two systems

Forced Variance Error Variance Forced/Error Variance

Error Variance of 21-yr JJA Rainfall in Tier-Two systemsError Variance of 21-yr JJA Rainfall in Tier-Two systems

AGCMs show systematic error over the western North Pacific during summer.

Area averaged correlation coefficientsArea averaged correlation coefficients

El Nino region (10oS-5oN, 80oW-180oW)

Western North Pacific (5-30oN, 110-150oE)

Predictability of JJA Precipitation in Tier-Two systemsPredictability of JJA Precipitation in Tier-Two systems

Correlation with JJA observed and simulated rainfall during 1979-99Correlation with JJA observed and simulated rainfall during 1979-99

(5 model composite)

Wrong model physics?Absence of air-sea

interaction?

Systematic error in tier-two system

Model Inability Modeling Strategy

Local Air-sea interactionLocal Air-sea interaction

Observed and simulated air-sea interaction

Local air-sea interaction processes

Climate Environment System Research CenterClimate Environment System Research CenterClimate Environment System Research CenterClimate Environment System Research Center

JJA SST-rainfall relationshipJJA SST-rainfall relationship

Correlation between JJA precipitation and SST during 1979-1999

(a) MME

(d) NCEP

(b) JMA

(e) NSIPP

(c) KMA

(f) SNU

Air-Sea InteractionAir-Sea Interaction

Lead-lag correlation between SST and rainfall pentad data during 1982-1999

Rainfall lead Rainfall lag> -20 -15 -10 -5 0 +5 +10 +15 +20 <

Only more than 95% significance level is shaded

Atmosphere forces the ocean where the correlation coefficients between rainfall and SST show negative.

JJA

-30 -20 -10 0 +10 +20 +30

days

Rainfall lead Rainfall lag

Western North Pacific (5-30N, 110-150E)

95% significance level

Seasonal March of Air-Sea Interaction and PredictabilitySeasonal March of Air-Sea Interaction and Predictability

(a) Observation

Correlation between observed and simulated rainfall

Month

La

titu

de

Time-latitude cross section averaged over 110-150oE during 1979-99

(b) SNU AGCM

Correlation between rainfall and SST

Contour denotes 95% significance level.

Experimental DesignExperimental Design

Atmosphere Atmosphere Atmosphere

Ocean(Full dynamics)

Perfect boundary Perfect boundary conditioncondition

Local air-sea Local air-sea interactioninteraction

Fully coupled Fully coupled systemsystem

SST

Slab ocean (No dynamics and

advection)

SSTObserved SST

heat flux, wind stress, fresh water flux

heat flux

AGCM(1950-1999, 4runs)

Mixed layer model+ AGCM(50 yrs, 4runs)

CGCM(75 yrs)

Experiment Integration Period

Runs

Resolution

Boundary Conditions Properties

AGCM 1950~1999(50 years) 4 T31L21

GISST and OISST and Sea

ice

Prefect boundary condition with observed SST

Mixed-layer Model

50 years 4 T31L21Climatological cycle OISST and Sea ice

Local air-sea interaction With slab ocean mixed-layer model

(Waliser et al. 1999)

CGCM 75 years 1 T42L21 NoFully coupled system

T42 SNU AGCM v2 (Kim, 1999)+MOM2.2 (Pacanowski et al., 1993)

Model Resolution Note

SNU AGCM T42L21 (2.8125oX2.8125o) No flux correction

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

Ocean mixed layer model (Noh and Kim, 1999)

CGCM

Mixed-layer AGCM

Model Note

SNU AGCM T31L21 (3.75oX3.75o)

Slab ocean mixed-layer model

• Fixed depth slab ocean mixed-layer model without ocean dynamics and advection • Anomaly coupling per each time step (Waliser et al. 1999)

Model DescriptionModel Description SNU AGCM

Model Dynamics Physics

SNUAGCM

Spectral model using semi-implicit

method

• 2-stream k-distribution radiation scheme (Nakajima and Tanaka 1986)• Simplified Arakawa-Schubert cumulus convection scheme based on RAS scheme (Moorthi and Suarez 1992)• Orographic gravity-wave drag (McFarlane 1987)• Bonan’s land surface model (Bonan 1996)• Mon-local PBL/vertical diffusion (Holtslag and Boville 1993)• Diffusion-type shallow convection

THC

F

dt

Td

p

H : mixed layer depth = 50 m : density of sea water = 1022 kg/m3

Cp : heat capacity of sea water = 4000 J/kg·k : damping factor = (150day)-1

Model SST equation

Observation

Mixed Layer Model

Correlation between SST and PrecipitationCorrelation between SST and Precipitation

AGCM

JJA Atmosphere-Ocean InteractionJJA Atmosphere-Ocean Interaction

CGCM

Perfect boundary condition

Local air-sea

interaction without

dynamics

Air-sea interaction and Ocean dynamics

StrategyStrategy

Local air-sea interaction

• Thermodynamic processes • Except tropical eastern Pacific mixed-layer ocean model

Local air-sea interaction Remote forcing+


• Ocean dynamic processes • Tropical eastern Pacific Observed SST

Part Ⅰ

Part Ⅱ

What regulate the direction of air-sea interaction?

Part Ⅲ Fully coupled system Tier-two systemvs.

Influence on the extratropical circulation variability

Examination of real predictability

Consideration of radiative fluxesConsideration of radiative fluxes

COA anomalies by rainfall in mixed-layer model during 50 yearsCOA anomalies by rainfall in mixed-layer model during 50 years

(a) Surface short-wave fluxJJA DJF

(b) Surface long-wave flux

(c) (a) minus (b)

(d) Surface short-wave flux

(f) Surface long-wave flux

(g) (d) minus (f)

Positive for downward flux

COA = CORRELATION[A,B]*σB (Kang et al. 2001 JMSJ)

To measure an actual magnitude of quantity of B related to the reference data A

Consideration of radiative forcing Consideration of radiative forcing

JJA climatological cloud cover and ratiJJA climatological cloud cover and ratio of radiative fluxes o of radiative fluxes

Climatological total cloud cover

Rat

io o

f su

rfac

e lo

ng

-wav

e /

sh

ort

-wav

e fl

ux

Western North Pacific (5-30N, 110-170E) Eastern Pacific (15S-15N, 180E-80W) North Pacific (30-70N, 140E-120W)

Over the cloud heavy region having small climatological cloud cover such as western North Pacific, the ratio of surface long-wave flux by short-wave flux related with rainfall has smaller value than cloud free region. Rainfall cools the ocean surface well due to strong radiative cooling over those regions.

Y axis is ratio of radiative fluxes(COA of long-wave/short-wave flux)

Consideration of net surface fluxes Consideration of net surface fluxes

COA anomalies by rainfall in mixed-layer model during 50 yearsCOA anomalies by rainfall in mixed-layer model during 50 years

JJA DJF(a) Surface radiative flux (d) Surface radiative flux

(b) Surface latent heat flux (e) Surface latent heat flux

(c) (a) minus (b) (f) (d) minus (e)

Latent heat flux prevail Radiative flux prevail

Rainfall SSTSST

Summer hemisphere

Radiative flux > Latent heat flux

radiative cooling

Winter Hemisphere(DJF 10-30oN North Pacific, JJA Southern Indian Ocean)Radiative flux < Latent heat flux

Winter Hemisphere(DJF 30-50oN North Pacific)Radiative flux < Latent heat flux evaporative cooling

• Contour denotes net surface flux anomalies• Positive for downward flux

Opposite sign

Same sign

Shortwave flux has an important role to decrease the SST anomalies associated with increasing rainfall in summer hemisphere.

Except the region where ocean dynamics is important such as central and eastern Pacific, thermodynamic processes may work

AGCM cannot simulate the interaction atmosphere forces the ocean

Thermodynamic Processes of Local Air-Sea InteractionThermodynamic Processes of Local Air-Sea Interaction

Local air-sea interaction


Part Ⅰ

What regulate the direction of air-sea interaction?

Local and Remote Response in Local and Remote Response in Coupled SystemCoupled System

Characteristics of extratropical North Pacific variability as the air-sea coupled mode

Influence on the extratropical predictability


Consideration of predictability Consideration of predictability using coupled systemusing coupled system

Low potential predictability due to internal dynamics different from tropics Strong modal characteristics of SST anomalies North Pacific Ocean has a rich spectrum of interannual to interdecadal climate variability (Wallace et al. 1993; Trenberth and Hurrel 1994; Latif and Barnett 1996; Jin 1997).

Local coupling can influence on the atmospheric variability?

Tropical SST Anomaly

North Pacific SST Anomaly

Extratropicalcirculation

over North PacificDownstreamLocal air-sea

interaction

Remote influence

Influence from tropics and extratr

opics




Part Ⅱ

For the focus on the summertime extatropical North Pacific

Observed North Pacific modeObserved North Pacific mode

(a) 1st mode of EOF (b) PC time series (North Pacific Index)

(c) Lag Cor [NPI(JJA), NINO3.4(JJA- )]

ENSO Impact It has identical interannual variability different from NINO3.4 index, even though it has negative lag relation with previous spring NINO3.4 index.

Origin of North Pacific SST variability Air-sea coupled feedback (Frankignoul 1985; Norris et al. 1998; Lau et al. 2003) Tropical remote forcing (Pan and Oort 1990; Lau and Nath 2001) Stochastic atmospheric forcing (Blade, 1997; Barsugli and Battisti 1998) Delayed feedback provided by slow ocean dynamics (Latif and Barnet, 1996; Pierce et al. 1999)

Influence on the adjacent climate Summertime teleconnection patterns linking the rainfall anomalies over the North American to those of the East Asian monsoon and North Pacific SST are suggested by many authors (Nitta 1987; Huang and Sun 1992; Latif and Barnet 1996; Livezey and Smith, 1999; Lau and Weng 2000)

Experimental DesignExperimental Design

Observed SST

Interactive Ocean

AMIPAMIP(GOGA, Global (GOGA, Global Ocean Global Ocean Global Atmosphere)Atmosphere)

TOGA-MLTOGA-ML(Tropical Ocean (Tropical Ocean

Global Global Atmosphere-Mixed Atmosphere-Mixed

Layer)Layer)

MLML(Mixed Layer)(Mixed Layer)

AGCM(1950-1999, 4runs)

Mixed layer model (50 yrs, 4runs)

Extratropics

Tropics

Observed SST(Perfect

boundarycondition)

Mixed layer model+ Tropical SST

(1950-1999, 4runs)

Interactive Ocean(Local air-sea

interaction withimperfect SST)

+

Experiment Integration Period

Runs

Resolution

Boundary Conditions Properties

AMIP 1950~1999(50 years) 4 T31L21 GISST and OISST

and Sea icePrefect boundary condition

with observed SST

TOGA-ML 1950~1999(50 years) 4 T31L21 GISST and OISST

and Sea ice

Local air-sea interaction over extratropics + perfect tropics

With slab ocean mixed-layer model(Waliser et al. 1999)

ML 50 years 4 T42L21Climatological cycle of OISST and Sea

ice

Local air-sea interaction over whole globe

With slab ocean mixed-layer model(Waliser et al. 1999)

Observed and Simulated North Pacific modeObserved and Simulated North Pacific mode

TOGA-ML

Observation

NPI (North Pacific Index) is defined as the PC time series of 1st EOF mode of the 9-yr high filtered SST anomalies over the North Pacific

Simulated North Pacific local mode in TOGA-ML run shows similar relationship with ENSO, even though the interannual variability of NPI is different from observed with 0.3 correlation coefficients.

ML

Lag Cor [NPI(JJA), NINO3.4(JJA- )]

ObservationTOGA-MLML

NINO lead NINO lag

Most realistic reproducibility of North Pacific mode is simulated in TOGA-ML case with tropical forcing and local air-sea interaction.

7cases positive minus negative composite differences

Observed and Simulated North Pacific modeObserved and Simulated North Pacific mode

Observation AMIP ML

500 hPa geopotential height anomalies

Only local air-sea interaction

Perfect boundary condition

Local couplingTropical influence

TOGA-ML

7cases El Nino minus La Nina composite differences7cases El Nino minus La Nina composite differences

Observed and Simulated ENSO modeObserved and Simulated ENSO mode

Observation TOGA-ML

TOGA-ML minus AMIP

500 hPa geopotential height anomalies

AMIP

Most of AGCMs underestimate the intensity of PNA (Kang et al. 2003). Difference charts primarily portray the amplification of the signals: Affirmative characteristics of coupled system.

• Air-sea coupling effectively reduces the thermal damping of the atmosphere, thus amplifying the variability and enhancing the temporal persistence of extratropical atmospheric signals (Blade 1997; Barsugli and Battisti 1999; Lau and Nath 2001).

Observed and Simulated ENSO modeObserved and Simulated ENSO mode

Local coupling improves the amplitude and pattern of circulation over the North Pacific and the downstream, even though extratropical SST is imperfect.

Pattern Correlation with observed composite differencesPattern Correlation with observed composite differences

Extratropical Northern Hemisphere (0-360oE, 30-80oN)

Change of partial influence in coupled systemChange of partial influence in coupled system

Interactive ocean over extratropics enhances the local negative relationship.

Coupled system alleviates the overestimated remote influence in AMIP.

(a) Local SST

(b) NINO 3.4

(c) Local SST

(d) NINO 3.4

(e) Local SST

(f) NINO 3.4

Observation TOGA-ML

Partial Correlation between JJA SST and PrecipitationPartial Correlation between JJA SST and PrecipitationAMIP

Partial Correlation (Edward, 1979)

Calculate the partial effect of local SST and NINO 3.4 SST on the precipitation anomalies by removing relationship between local and NINO3.4 SST

223

213

2313123,12

11 RR

RRRR

Increased Potential Predictability: Perfect Model Correlation

North Pacific (120-280oE, 30-80oN)

North America (240-300oE, 30-60oN, land)

Perfect Model Correlation- Considering one member of the ensemble as an observation and making spatial correlation between the model observation and the ensemble mean of the other members. - Theoretical predictability limit using a hypothetical perfect model with no systematic error.

Perfect model pattern correlation of composite differencesPerfect model pattern correlation of composite differences

Local coupling increase the upper limit of theoretical potential predictability of atmospheric variability during ENSO years.




Part Ⅱ

Influence on the extratropical circulation variability

Influence of Air-sea Interaction Influence of Air-sea Interaction on the Real Predictabilityon the Real Predictability

The North Pacific SST variability has coupled feedback mechanism required both air-sea interaction and tropics-extratropics interaction. Accordingly, both local coupling and remote forcing is needed to simulation of circulation variability associated with this mode.

During ENSO years when strong remote influence and local air-sea interaction works together, the intensity and predictability of PNA is increased by local coupling.

In additions, PNA is potentially more predictable by increase of forced variance in coupled system. during ENSO years.

Without coupled process, the exact reproduction of extratropical atmospheric circulation such as PNA is impossible.

Examination of PredictabilityExamination of Predictabilityin Tier-One vs. Tier-Twoin Tier-One vs. Tier-Two

SNU SMIP/HFP (tier-two) vs. DEMETER (tier-one)


Model Experiments: Tier-one vs. Tier-two

Tier-one systemTier-one system Tier-two systemTier-two system Upper limit of Tier-two systemUpper limit of Tier-two system

DEMETER of 7 CGCMs SMIP2/HFP of SNU AGCM SMIP2 of SNU AGCM

Investigate seasonal real predictability based on the observed initial condition

and fully coupled GCM

Investigate seasonal real predictability based on the observed initial condition and predicted boundary

condition

Investigate seasonal potential predictability based

on the observed initial condition and observed

boundary condition

4 month x 20 year (1980-1999), 9 ensembles



7 CGCMs (CERFACS, ECMWF, INGV, LODYC, Meteo-France, Max-Plank Institute, UK

Met Office)

Development of European Multimodel Ensemble system for seasonal-to-interannual

prediction

Description of DEMETER (Tier-one Prediction System)

Development of European Multimodel Ensemble system for seasonal-to-interannual prediction One-tier prediction system using CGCM 9 ensemble members of 7 models 1980-1999 forecast

Institute AGCM Resolution OGCM Resolution

Atmosphere initial

conditionsEnsemble

generation

CERFACS ARPEGE T6331 Levels OPA 8.2 2.0x2.0

31 Levels ERA-40 Windstress and SST perturbations

ECMWF IFS T9540 Levels

HOPE-E 1.4x0.3-1.429 Levels ERA-40 Windstress and SST pe

rturbations

INGV ECHAM-4 T4219 Levels OPA 8.1 2.0x0.5-1.5

31 Levels

CoupledAMIP-typeexperiment

Windstress and SST perturbations

LODYC IFS T9540 Levels OPA 8.2 2.0x2.0


Meteo-France ARPEGE T6331 Levels OPA 8.0 182GPx152GP


MPI ECHAM-5 T4219 Levels MPI-OM1 2.5x0.5-2.5

23 Levels

Coupled run relaxed to

observed SSTs

Atmosphericconditions from the coupled initialization run (lagged method)

UK Met Office HadAM3 2.5x3.75

19 Levels

GloSea OGCM based on Had

CM3

1.25x0.3-12540 Levels ERA-40 Windstress and SST pe

rturbations

DEMETER CGCM Description

Tier 2 : SNU SST prediction system

3 month lead forecast

Tier 1 : DEMETER

Prediction skill – Correlation with observation of JJA SST

Prediction skill – Correlation with observation of JJA rainfall

Tier 2 : SNU AGCM

Tier 1 : DEMETER

Real predictability: Tier two vs. Tier one Real predictability: Tier two vs. Tier one

Pattern correlation of JJA rainfall anomalies during 1980-1999Pattern correlation of JJA rainfall anomalies during 1980-1999

Western North Pacific region (5-30oN, 110-150oE)

Global domain (60oS-80oN, 0-360oE)

Tier-one: 7 CGCMs average from DEMETER Each CGCMTier-two: SNU AGCM SMIP/HFP with predicted SSTTier-two (upper limit): SNU AGCM SMIP with observed SST

20 yrs mean

0.26

-0.04

20 yrs mean

Real predictability: Tier two vs. Tier one Real predictability: Tier two vs. Tier one





Real predictability: Tier two vs. tier one Real predictability: Tier two vs. tier one





20 yrs mean0.380.26

0.28-0.04

20 yrs mean

Real predictability: Tier two vs. tier one Real predictability: Tier two vs. tier one





20 yrs mean0.380.260.33

0.28-0.04-0.10

20 yrs mean

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