1. Role of Atmosphere-Ocean Interaction And Seasonal Predictability International Workshop on Variability and Predictability of the Earth Climate System, 26-27 Jan 2005, Japan In-Sik Kang and Kyung Jin Climate Environment System Research Center Seoul National University

2. 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

3. Prescribe SST as boundary condition Atmosphere Ocean SST Prediction Current activities of seasonal prediction: Tier-2 vs. Tier-1 Tier-two systemTier-one system SST is prescribed as boundary condition Atmosphere-ocean interaction is embodied SST prediction system CGCM Component Feature AGCM

4. Experimental Design and Participated Models  CLIVAR Asian-Australian Monsoon Atmospheric GCM Intercomparison Project GroupCountryNumericsConvection Parameterization COLA USAR40L18Relaxed Arakawa-Schubert (RAS, Moorthi and Suarez, 92) DNM Russia4 o ×5 o, L21Betts (86) GEOS USA2 o ×2.5 o, L43RAS (Moorthi and Suarez, 92) GFDL USAT42L18RAS (Moorthi and Suarez, 92) IAP ChinaR15L9MCA (Manabe et al., 65) IITM India2.5 o ×3.75 o, L19Mass flux penetrative convection scheme (Gregory and Rowntree, 90) MRI Japan4 o ×5 o, L15Arakawa-Schubert, Tokioka et al. (88) NCAR USAT42L18Mass flux scheme (Zhang and McFarlane, 95) NCEP USAT42L28 RAS (Moorthi and Suarz, 92) SNUKoreaT31L20Simplified Arakawa-Schubert SUNY USA4 o x 5 o, L17 Modified Arakawa-Schubert InstituteModelResolutionExperiment TypeEnsemble Member JMA T63L40SMIP10 KMAGDAPST106L21SMIP10 NCEP T62L28SMIP10 NASA/NSIPPNSIPP2 o x2.5 o L43AMIP9 SNUGCPST63L21SMIP10  APEC Climate Network (APCN) participants - 10 ensemble simulations from Nov1996 to Aug98 - 21 year simulation from 1979 to 1999

5. DJF97/98 Precipitation Anomaly for Each Model Ensemble CLIVAR Asian-Australian Monsoon Atmospheric GCM Intercomparison Project CLIVAR Asian-Australian Monsoon Atmospheric GCM Intercomparison Project

6. Climate signals caused by external forcing Intrinsic transients due to natural variability Forced VarianceFree Variance Signal-to-noise Theoretical limit of predictability Analysis of Variance of 21-yr JJA Rainfall in Tier-Two systems

7. Forced VarianceError Variance Forced/Error Variance Error Variance of 21-yr JJA Rainfall in Tier-Two systems  AGCMs show systematic error over the western North Pacific during summer.

8. Area averaged correlation coefficients El Nino region (10 o S-5 o N, 80 o W-180 o W) Western North Pacific (5-30 o N, 110-150 o E) Predictability of JJA Precipitation in Tier-Two systems Correlation 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 InabilityModeling Strategy

9. Local Air-sea interaction Observed and simulated air-sea interaction Local air-sea interaction processes Climate Environment System Research Center

10. JJA SST-rainfall relationship Correlation between JJA precipitation and SST during 1979-1999 (a) MME (d) NCEP (b) JMA (e) NSIPP (c) KMA (f) SNU

11. Air-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

12. Seasonal March of Air-Sea Interaction and Predictability (a) Observation Correlation between observed and simulated rainfall Month Latitude Time-latitude cross section averaged over 110-150 o E during 1979-99 (b) SNU AGCM Correlation between rainfall and SST Contour denotes 95% significance level.

13. Experimental Design Atmosphere Ocean (Full dynamics) Perfect boundary condition Local air-sea interaction Fully coupled system SST Slab ocean (No dynamics and advection) SST Observed 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 RunsResolution Boundary Conditions Properties AGCM 1950~1999 (50 years) 4T31L21 GISST and OISST and Sea ice Prefect boundary condition with observed SST Mixed- layer Model 50 years4T31L21 Climatological cycle OISST and Sea ice Local air-sea interaction With slab ocean mixed-layer model (Waliser et al. 1999) CGCM 75 years1T42L21No Fully coupled system T42 SNU AGCM v2 (Kim, 1999)+MOM2.2 (Pacanowski et al., 1993)

14. ModelResolutionNote SNU AGCM T42L21 (2.8125 o X2.8125 o )No flux correction MOM2.2 OGCM 1/3 o lat. x 1 o lon. over tropics(10S-10N), Vertical 32 levels Ocean mixed layer model (Noh and Kim, 1999)  CGCM  Mixed-layer AGCM ModelNote SNU AGCM T31L21 (3.75 o X3.75 o ) 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 Description  SNU AGCM ModelDynamicsPhysics SNU AGCM 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 H : mixed layer depth = 50 m  : density of sea water = 1022 kg/m 3 C p : heat capacity of sea water = 4000 J/kg·k  : damping factor = (150day) -1  Model SST equation

15. Observation Mixed Layer Model Correlation between SST and Precipitation AGCM JJA Atmosphere-Ocean Interaction CGCM Perfect boundary condition Local air-sea interaction without dynamics Air-sea interaction and Ocean dynamics

16. Strategy Local air-sea interaction Thermodynamic processes Except tropical eastern Pacific  mixed-layer ocean model Local air-sea interactionRemote forcing + Thermodynamic processes Except tropical eastern Pacific  mixed-layer ocean model Ocean dynamic processes Tropical eastern Pacific  Observed SST Part Ⅰ Part Ⅱ What regulate the direction of air-sea interaction? Part Ⅲ Fully coupled systemTier-two system vs. Influence on the extratropical circulation variability Examination of real predictability

17. Consideration of radiative fluxes COA anomalies by rainfall in mixed-layer model during 50 years (a) Surface short-wave flux JJA 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

18. Consideration of radiative forcing JJA climatological cloud cover and ratio of radiative fluxes Climatological total cloud cover Ratio of surface long-wave / short-wave flux 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)

19. Consideration of net surface fluxes COA 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 SST Summer hemisphere Radiative flux > Latent heat flux  radiative cooling Winter Hemisphere (DJF 10-30 o N North Pacific, JJA Southern Indian Ocean) Radiative flux < Latent heat flux Winter Hemisphere (DJF 30-50 o N North Pacific) Radiative flux < Latent heat flux  evaporative cooling Contour denotes net surface flux anomalies Positive for downward flux Opposite sign Same sign

20.  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 Interaction Local air-sea interaction Thermodynamic processes Except tropical eastern Pacific  mixed-layer ocean model Part Ⅰ What regulate the direction of air-sea interaction?

21. Local and Remote Response in Coupled System Characteristics of extratropical North Pacific variability as the air-sea coupled mode Influence on the extratropical predictability Climate Environment System Research Center

22. Consideration of predictability using 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 Extratropical circulation over North Pacific Downstream Local air-sea interaction Remote influence Influence from tropics and extratropics Local air-sea interactionRemote forcing + Thermodynamic processes Except tropical eastern Pacific  mixed-layer ocean model Ocean dynamic processes Tropical eastern Pacific  Observed SST Part Ⅱ For the focus on the summertime extatropical North Pacific

23. Observed North Pacific mode (a) 1 st 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)

24. Experimental Design Observed SST Interactive Ocean AMIP (GOGA, Global Ocean Global Atmosphere) TOGA-ML (Tropical Ocean Global Atmosphere-Mixed Layer) ML (Mixed Layer) AGCM (1950-1999, 4runs) Mixed layer model (50 yrs, 4runs) Extratropics Tropics Observed SST (Perfect boundary condition) Mixed layer model + Tropical SST (1950-1999, 4runs) Interactive Ocean (Local air-sea interaction with imperfect SST) + Experiment Integration Period RunsResolution Boundary Conditions Properties AMIP 1950~1999 (50 years) 4T31L21 GISST and OISST and Sea ice Prefect boundary condition with observed SST TOGA-ML 1950~1999 (50 years) 4T31L21 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 years4T42L21 Climatological cycle of OISST and Sea ice Local air-sea interaction over whole globe With slab ocean mixed-layer model (Waliser et al. 1999)

25. Observed and Simulated North Pacific mode TOGA-ML Observation  NPI (North Pacific Index) is defined as the PC time series of 1 st 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-  )] Observation TOGA-ML ML NINO lead NINO lag

26.  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 mode Observation AMIP ML 500 hPa geopotential height anomalies Only local air- sea interaction Perfect boundary condition Local coupling Tropical influence TOGA-ML

27. 7cases El Nino minus La Nina composite differences Observed 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).

28. Observed 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 differences Pattern Correlation with observed composite differences Extratropical Northern Hemisphere (0-360 o E, 30-80 o N)

29. Change 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 ObservationTOGA-ML Partial Correlation between JJA SST and Precipitation AMIP 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

30. Increased Potential Predictability: Perfect Model Correlation North Pacific (120-280 o E, 30-80 o N) North America (240-300 o E, 30-60 o N, 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 differences Perfect model pattern correlation of composite differences  Local coupling increase the upper limit of theoretical potential predictability of atmospheric variability during ENSO years.

31. Local air-sea interactionRemote forcing + Thermodynamic processes Except tropical eastern Pacific  mixed-layer ocean model Ocean dynamic processes Tropical eastern Pacific  Observed SST Part Ⅱ Influence on the extratropical circulation variability Influence of Air-sea Interaction on 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.

32. Examination of Predictability in Tier-One vs. Tier-Two SNU SMIP/HFP (tier-two) vs. DEMETER (tier-one) Climate Environment System Research Center

33. Model Experiments: Tier-one vs. Tier-two Tier-one system Tier-one system Tier-two system Tier-two system Upper limit of Tier-two system Upper limit of Tier-two system DEMETER of 7 CGCMsSMIP2/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 4 month x 21 year (1979- 1999), 6 ensembles 7 month x 21 year (1979- 1999), 10 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

34. 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 InstituteAGCMResolutionOGCMResolution Atmosphere initial conditions Ensemble generation CERFACS ARPEGE T63 31 Levels OPA 8.2 2.0x2.0 31 Levels ERA-40 Windstress and SST perturbations ECMWF IFS T95 40 Levels HOPE-E1.4x0.3-1.4 29 Levels ERA-40 Windstress and SST perturbations INGV ECHAM-4 T42 19 Levels OPA 8.1 2.0x0.5-1.5 31 Levels Coupled AMIP-type experiment Windstress and SST perturbations LODYC IFS T95 40 Levels OPA 8.2 2.0x2.0 31 Levels ERA-40 Windstress and SST perturbations Meteo-France ARPEGET63 31 Levels OPA 8.0 182GPx152GP 31 Levels ERA-40 Windstress and SST perturbations MPI ECHAM-5T42 19 Levels MPI-OM1 2.5x0.5-2.5 23 Levels Coupled run relaxed to observed SSTs Atmospheric conditions from the coupled initialization run (lagged method) UK Met Office HadAM3 2.5x3.75 19 Levels GloSea OGCM based on HadCM3 1.25x0.3-125 40 Levels ERA-40 Windstress and SST perturbations  DEMETER CGCM Description

35. Tier 2 : SNU SST prediction system 3 month lead forecast Tier 1 : DEMETER Prediction skill – Correlation with observation of JJA SST

36. Prediction skill – Correlation with observation of JJA rainfall Tier 2 : SNU AGCM Tier 1 : DEMETER

37. Real predictability: Tier two vs. Tier one Pattern correlation of JJA rainfall anomalies during 1980-1999 Western North Pacific region (5-30 o N, 110-150 o E) Global domain (60 o S-80 o N, 0-360 o E) Tier-one: 7 CGCMs average from DEMETER Each CGCM Tier-two: SNU AGCM SMIP/HFP with predicted SST Tier-two (upper limit): SNU AGCM SMIP with observed SST 20 yrs mean 0.26 -0.04 20 yrs mean

38. Real predictability: Tier two vs. Tier one Pattern correlation of JJA rainfall anomalies during 1980-1999 Western North Pacific region (5-30 o N, 110-150 o E) Global domain (60 o S-80 o N, 0-360 o E) Tier-one: 7 CGCMs average from DEMETER Each CGCM Tier-two: SNU AGCM SMIP/HFP with predicted SST Tier-two (upper limit): SNU AGCM SMIP with observed SST

39. Real predictability: Tier two vs. tier one Pattern correlation of JJA rainfall anomalies during 1980-1999 Western North Pacific region (5-30 o N, 110-150 o E) Global domain (60 o S-80 o N, 0-360 o E) Tier-one: 7 CGCMs average from DEMETER Each CGCM Tier-two: SNU AGCM SMIP/HFP with predicted SST Tier-two (upper limit): SNU AGCM SMIP with observed SST 20 yrs mean 0.38 0.26 0.28 -0.04 20 yrs mean

40. Real predictability: Tier two vs. tier one Pattern correlation of JJA rainfall anomalies during 1980-1999 Western North Pacific region (5-30 o N, 110-150 o E) Global domain (60 o S-80 o N, 0-360 o E) Tier-one: 7 CGCMs average from DEMETER Each CGCM Tier-two: SNU AGCM SMIP/HFP with predicted SST Tier-two (upper limit): SNU AGCM SMIP with observed SST 20 yrs mean 0.38 0.26 0.33 0.28 -0.04 -0.10 20 yrs mean