Content of Lectures Content of Lectures Lecture 1: Current status of Climate models Lecture 2: Improvement of AGCM focused on MJO Lecture 3: Multi-model Seasonal Prediction Lecture 4: Seasonal Preditability Climate Modeling and Prediction In-Sik Kang Seoul National University

Current Status of Climate Models Current Status of Climate Models In-Sik Kang Climate Environment System Research Center Seoul National University Lecture 1

Procedure What is the climate model? What is the climate model? Part Ⅰ : AGCM General performance of state-of-the-art AGCMs General performance of state-of-the-art AGCMs Inherent limitation of two-tier strategy using AGCM Inherent limitation of two-tier strategy using AGCM Part Ⅱ : CGCM Current status of CGCMs Current status of CGCMs Efforts for development of CGCM Efforts for development of CGCM Part Ⅲ : Climate System Model Future perspective on the climate model Future perspective on the climate model

What is the Climate Model ? The general circulation model (AGCM) is the model close to the real atmospheric state of the whole Earth, which has been developed since middle of the 20th century. As the AGCM can reproduce the real atmospheric condition in the planetary scale, it is the most useful equipment of experiment and climate prediction. Recently, the concept of global climate model considering the condition of ocean and vegetation as well as atmosphere, has been established. Oceanic System Atmospheric System Environmental System Environmental System Unification Integrated Climate and Environment Model Modeling The integrated climate and environment model requires construction, development and improvement of oceanic general circulation model (OGCM) and environmental model in addition to AGCM. EnvironmentalModel AGCM OGCM

Three-dimension hydrostatic primitive equations on sphere with sigma coordinate Vorticity and Divergence equations Mass continuity equation Hydrostatic equation Thermodynamic energy equation Moisture conservation equation Dynamics of Atmosphere Structure of Atmospheric General Circulation Model Dynamics Three-dimension hydrostatic primitive equations on sphere with sigma coordinate Physics of Atmosphere and Land Surface Cumulus Convection Large Scale Condensation Radiation Land Surface Process Planetary Boundary Layer Gravity Wave Drag Cumulus convection Shallow convection Large-scale condensation Gravity wave drag Planetary boundary layer Radiation Land surface Physics U, V, T, q, ql

General Performance of State-of-the-art AGCMs Climate Environment System Research Center Lecture 1: Current status of climate models Global Atmospheric Anomalies associated with ENSO Global Atmospheric Anomalies associated with ENSO Climatological Monsoon Variabilities Climatological Monsoon Variabilities Monsoon Variabilities during 97/98 El Niño Monsoon Variabilities during 97/98 El Niño Inherent Limitation of Two-tier Strategy using AGCM Inherent Limitation of Two-tier Strategy using AGCM

Experimental Design and Participated Models Experiment Integration Period Boundary Conditions Initial Conditions SSTSea ice ’97~98 Ensemble Experiments 1Sep1996 ~31Aug1998 Weekly Mean OISST(NCEP) AMIP II Climatological Cycle 10 Member Ensemble Simulations with Different Initial Conditions Observed SST Run (AMIP II) 1Jan1979 ~31Aug1998 AMIP II Monthly dataOISST after Mar1996 CLIVAR Asian-Australian Monsoon Atmospheric GCM Intercomparison Project CLIVAR Asian-Australian Monsoon Atmospheric GCM Intercomparison Project ño The AGCM intercomparison program was initiated by the CLIVAR/Asian–Australian Monsoon Panel to evaluate a number of current atmospheric GCMs in simulating the global climate anomalies associated with the recent El Niño. Experimental Design Experimental Design Models Participated Models Participated 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

Monsoon Predictability: Climatological JJA Precipitation

Two Categories of AGCMs following to Basic State 10ºN-20ºN Latitudinal Mean of Rainfall Variability Indian Monsoon region Western North Pacific Monsoon region Red Series Blue Series JJA Precipitation (shading )and JJA Precipitation (shading )and 850 hPa Streamfunction (contour) (c) Composite (DNM, IAP, MRI, NCAR) (b) Composite (COLA, GEOS, IITM,SNU) (a) CMAP Observation

1 st Mode of EOF for Climatological MJJAS Precipitation

Pattern correlation for each EOF mode for MJJAS precipitation The pattern correlations between the eigenvectors of individual models and the observed counter parts All correlation values of the model composite are quite high. But most of the models have a large value of correlation only for the first eigenvector but not for the higher modes. The pattern correlations between the eigenvectors of individual models and the observed counter parts All correlation values of the model composite are quite high. But most of the models have a large value of correlation only for the first eigenvector but not for the higher modes.

SOI = SLP anomaly difference over two regions [145 o W-155 o W, 5 o S-5 o N] – [125 o E-135 o E, 5 o S-5 o N] Evolution of El Niño and SOI Indices (a) NINO3.4 INDEX EL-NINO WINTER EL-NINO SUMMER WIN TER SUMMER (b) SST anomaly DJF97/98 (c) Observed and Simulated SOI indices

Precipitation Anomalies for Each Summer and Winter Precipitation Anomalies for Each Summer and Winter Model Composite CMAP Observation Pattern Correlation Corr[CMAP,Model] for each model

Fig. 6. Distribution of precipitation anomaly during the 97/98winter. (a) is for the CMAP observation, and the rest of the figures are the ensemble mean of each model.

Current Predictability: Pattern Correlation and RMS of Rainfall (b) Root-mean-square (a) Pattern Correlation Monsoon-ENSO region: 60 o E-90 o W, 30 o S-30 o N DJF96/97 JJA97 DJF97/98JJA98 DJF96/97 JJA97 DJF97/98 JJA98

DJF hPa Geopotential Height Anomalies Precipitation 200hPa Geopotential height PNA Correlation PNA Normalized RMS PNA region: 180 o E-60 o W, o N Correlation vs. RMS Precipitation vs. Circulation

Tropical SST Anomaly Anomalous Tropical Convection Subtropical Jet change Forced Rossby wave Transient activity change Extratropical Circulation Improvement of physical parameterization : PBL, Convection. Advances in the computing power : High resolution Eddy StreamfunctionTransient vorticity forcing Improvement of Predictability following to ENSO Simulation Old Model Recent Model

Current Monsoon Predictability: Pattern Correlation (a) El-Nino region JJA97 DJF97/98JJA98 DJF96/97 (b) Monsoon region JJA97 DJF97/98JJA98 DJF96/97 (c) Southeast Asian and Western North Pacific (d) The rest of the Asian-Australian Monsoon domain JJA97 DJF97/98JJA98 JJA97 DJF97/98JJA98 El-Nino region (160 o E-80 o W, 30 o S-30 o N) Monsoon region ( o E, 30 o S-30 o N) Southeast Asian and Western North Pacific region ( o E, 5-30 o N) Correlation between CMAP and models for JJA97/98

(a) JJA (b) JJA (c) DJF (d) DJF Observation5 Model Composite Cause of Low Predictability: Atmosphere-Ocean Interaction Correlation between JJA SST and Precipitation during InstituteModelResolutionExperiment TypeEnsemble Member JMA T63L40SMIP)10 KMAGDAPST106L21SMIP10 NCEP T62L28SMIP10 NASA/NSIPPNSIPP2 o x2.5 o L43AMIP9 SNUGCPST63L21SMIP10

(a) Observation ( )(b) AGCM ( ) (c) Mixed layer model (16 years)(d) CGCM (50 years)  No ENSO  Only local air- sea interaction Correlation between JJA SST and Precipitation Improved Simulation using Coupled System over WNP

Precipitation Climatology During Boreal Summer Observation (CMAP) CGCM(Ver.2)AGCM

Current Status of CGCMs Climate Environment System Research Center Lecture 1: Current status of climate models Present the problem of state-of-the-art CGCMs through CGCM Intercomparison Project (CMIP) Present the problem of state-of-the-art CGCMs through CGCM Intercomparison Project (CMIP)

Coupled Model Intercomparison Project (CMIP) Participating Model Participating Model Under the auspices of the Working Group on Coupled Modeling (WGCM) The PCMDI supports CMIP by helping WGCM to determine the scope of the project. CMIP has received model output from the pre-industrial climate simulations ("control runs") and 1% per year increasing-CO2 simulations. Under the auspices of the Working Group on Coupled Modeling (WGCM) The PCMDI supports CMIP by helping WGCM to determine the scope of the project. CMIP has received model output from the pre-industrial climate simulations ("control runs") and 1% per year increasing-CO2 simulations.

CMIP: SST Climatology - Warm Bias at Eastern Edge of the Equatorial Pacific - Too strong Cold tongue - Kuroshio Extension region  Common Problems in CGCM Simulations

CMIP: Precipitation Climatology - Zonal Mean Precipitation Double ITCZ

CMIP: Vertical Structure of Zonal Current along the Equator  Common Problems in CGCM Simulations - Mostly simulate weak equatorial undercurrents - Strong easterly surface currents - Some models have a critical problem to simulate oceanic vertical structure

CMIP: Interannual SST Variability - Weak Interannual variability in the eastern Pacific - Relatively strong in the central-western Pacific. - Better interannual variability seems to be connected to better vertical ocean structure simulation except BCM case  Common Problems in CGCM Simulations

Coupled GCMAGCMOGCM Coupling Strategy CES CGCM (Ver. 1) CES AGCM T31, 21 levels (3.75X3.75) MOM3 OGCM Uneven Grid (3 lon. X 1 lat. near equator) 1-day Mean Exchange (SST, Heat Flux, Wind stress, Fresh Water Flux) No Flux Correction CES CGCM (Ver. 2) CES AGCM T42, 21 levels (2.8125X2.8125) MOM2.2 OGCM + Ocean mixed layer model Uneven Grid (1 lon. X 1/3 lat. near equator) 1-day Mean Exchange (SST, Heat Flux, Wind stress, Fresh Water Flux) No Flux Correction Development of CES Coupled GCM Mixed Layer Model Vertical Eddy Viscosity: Vertical Eddy Diffusivity: : empirical Constantwhere : TKE l : the length scale of turbulence Noh and Kim (1999)  To simulate correct vertical ocean structure

SST Climatology Observation CGCM with MLMCGCM without MLM

a) Observationb) CGCM without MLM Vertical Structure of Ocean Temperature 1 o S-1 o N mean b) CGCM with MLM

Vertical Structure of Zonal Current along the Equator 1 o S-1 o N mean a) Observationb) CGCM without MLMc) CGCM with MLM

Observation Interannual SST Variability CGCM with MLMCGCM without MLM

Effect of Horizontal Diffusion a) Observationb) Strong Diffusionc) Weak Diffusion EXP_strong (CNTL) EXP_weak Horizontal Mixing for Momentum Notes  When horizontal diffusion is strong  Weak Equatorial Undercurrent  Strong Equatorial Surface Current  Westward extension of cold tongue  Weak SST zonal gradient  Weak Interannual Variability

Effect of Horizontal Diffusion Strong Diffusion Weak Diffusion SST Climatology Interannual Variability

ENSO Variability in the CGCM with MLM Year  NINO3.4 SST  Linear Regression with respect to NINO3.4 SST  SST Anomalies along the Equator