Content of LecturesLecture 1: Current status of Climate modelsLecture 2: Improvement of AGCM focused on MJOLecture 3: Multi-model Seasonal PredictionLecture 4: Seasonal PreditabilityClimate Modeling and Prediction

In-Sik KangSeoul National University

Current Status of Climate Models In-Sik Kang

Climate Environment System Research CenterSeoul National UniversityLecture 1Climate Environment System Research Center

Procedure What is the climate model?Part : AGCM General performance of state-of-the-art AGCMs Inherent limitation of two-tier strategy using AGCM Part : CGCM Current status of CGCMs Efforts for development of CGCMPart : Climate System 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.Integrated Climate and Environment Model

Structure of Atmospheric General Circulation ModelDynamics

Three-dimension hydrostatic primitive equations on sphere with sigma coordinate

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

Experimental Design andParticipated Models CLIVAR Asian-Australian Monsoon Atmospheric GCM Intercomparison Project The AGCM intercomparison program was initiated by the CLIVAR/AsianAustralian Monsoon Panel to evaluate a number of current atmospheric GCMs in simulating the global climate anomalies associated with the recent El Nio. Experimental Design Models Participated

Monsoon Predictability: Climatological JJA Precipitation

Two Categories of AGCMs following to Basic State 10N-20N Latitudinal Mean of Rainfall VariabilityIndian MonsoonregionWestern North Pacific Monsoon regionRed SeriesBlue SeriesJJA Precipitation (shading )and 850 hPa Streamfunction (contour)(c) Composite (DNM, IAP, MRI, NCAR)(b) Composite (COLA, GEOS, IITM,SNU)(a) CMAP Observation

1st 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.

u850

0.88425610.7254650.5043399-0.2965269

0.67491210.32100470.0504237410.6542835

0.84416160.7872080.58498660.404967

0.87755420.86704130.563264-0.049364682

0.86198410.64080720.50917880.2217971

0.81585820.81928450.47214310.094350524

-0.1173572-0.37162240.0357588420.2612103

0.75565030.44869980.63875090.1157582

0.80494360.90493360.75579040.645509

0.82039130.6663390.38659470.4122365

0.89017890.88512570.76951010.6930253

1st mode

2nd mode

3rd mode

4th mode

Pattern correlation for each mode of SVD for U850

prcp

0.57957050.44997780.19698510.3471215

0.38592230.38680920.1520170.1898449

0.68981130.24826970.37772130.1842896

0.68713930.28527910.13440360.1845249

0.45462360.46947710.0162162090.2503924

0.72243580.45472230.0810204970.1462904

0.22144850.43495490.40457480.074436501

0.11474890.38852840.3861360.045623373

0.61850390.65645060.60929610.4031093

0.65258990.70295270.18531530.1966341

0.76049990.69304710.62309130.4669239

1st mode

2nd mode

3rd mode

4th mode

Sheet1

prcp1st2nd3rd4thCISOCISO1st

CMAP0.9999999110.999999911

COLA0.57957050.44997780.34712150.19698510.10783320.3120939

DNM0.38592233.87E-011.90E-011.52E-010.2598015-5.69E-02

GEOS0.68981130.24826971.84E-010.3777213-0.2731366-0.1484626

GFDL0.68713930.28527910.18452491.34E-010.2344090.2150892

IAP0.45462360.46947710.25039241.62E-02-0.17591934.72E-02

IITM0.72243580.45472230.14629048.10E-02-0.2193118-0.2603764

MRI0.22144854.35E-017.44E-020.40457480.2005076-1.93E-03

NCAR0.11474893.89E-014.56E-023.86E-012.78E-02-0.2764597

SNU0.61850390.65645060.40310930.6092961-0.23509260.5032701

SUNY0.65258990.70295270.19663410.1853153-0.1077461-0.2677587

Comp.0.76049990.69304710.46692390.6230913-0.10474190.1522722

u8501st2nd3rd4th

CMAP1111

COLA0.88425610.7254650.5043399-0.2965269

DNM0.67491210.32100475.04E-020.6542835

GEOS0.84416160.7872080.58498660.404967

GFDL0.87755420.86704130.563264-4.94E-02

IAP0.86198410.64080720.50917880.2217971vi

IITM0.81585820.81928450.47214319.44E-02

MRI-0.1173572-0.37162243.58E-020.2612103

NCAR0.75565030.44869980.63875090.1157582

SNU0.80494360.90493360.75579040.645509

SUNY0.82039130.6663390.38659470.4122365

Comp.0.89017890.88512570.76951010.6930253

Sheet2

Sheet3

SOI = SLP anomaly difference over two regions [145oW-155oW, 5oS-5oN] [125oE-135oE, 5oS-5oN]Evolution of1997-98 El Nio and SOI Indices (a) NINO3.4 INDEX(b) SST anomaly DJF97/98(c) Observed and Simulated SOI indices

Precipitation Anomalies for Each Summer and Winter Model CompositeCMAP Observation

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 CorrelationMonsoon-ENSO region:60oE-90oW, 30oS-30oN

DJF97-98 200hPa Geopotential Height AnomaliesPrecipitation200hPa Geopotential heightPNA CorrelationPNA Normalized RMSPNA region: 180oE-60oW, 20-80oNCorrelation vs. RMSPrecipitation vs. Circulation

Tropical SST AnomalyImprovement of physical parameterization : PBL, Convection.Advances in the computing power : High resolutionImprovement of Predictability following to ENSO Simulation

Current Monsoon Predictability: Pattern CorrelationEl-Nino region (160oE-80oW, 30oS-30oN)Monsoon region (40-160oE, 30oS-30oN)Southeast Asian and Western North Pacific region (80-150oE, 5-30oN)Correlation between CMAP and models for JJA97/98

(a) JJA (b) JJA (c) DJF (d) DJF Observation5 Model CompositeCause of Low Predictability: Atmosphere-Ocean InteractionCorrelation between JJA SST and Precipitation during 1979-1999

InstituteModelResolutionExperiment TypeEnsemble MemberJMAJMAT63L40SMIP)10KMAGDAPST106L21SMIP10NCEP NCEPT62L28SMIP10NASA/NSIPPNSIPP2ox2.5o L43AMIP9SNUGCPST63L21SMIP10

(a) Observation (1979-2001)(b) AGCM (1979-2001)(c) Mixed layer model (16 years)(d) CGCM (50 years) No ENSO Only local air-sea interactionCorrelation between JJA SST and PrecipitationImproved Simulation using Coupled System over WNP

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

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

Coupled Model Intercomparison Project (CMIP) 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.

Sheet1

Atmospheric modelOceanic modelAtmospheric resolutionOCEAN resolutionFlux adjust

MRI2MRI/JMA98Bryan-Cox Primitive eq. codeT42(2.8X2.8),L302.0X2.5,L23H,W,M

GFDL_R30GFDLGFDL MOM 1.1R30(2.25X3.75),L141.875X2.25,L18H,W

CSIRO Mk2CSIRO 9-level agcmBryan-Cox Primitive eq. codeR21(3.2X5.6),L93.2X5.6,L21H,W,M

HadCM3Unified modelBryan-Cox Primitive eq. code2.5X3.75,L191.25X1.25,L20.

HadCM2Unified modelBryan-Cox Primitive eq. code2.5X3.75,L192.5X3.75,L20H,W

CCCma CGCM1GCM2GFDL MOM1.1T32(3.8X3.8),L101.8X1.8,L29H,W

DOE PCMCCM3LANL POPT42(2.8X2.8),L180.67X0.67,L32.

CSM 1.0CCM3.0NCOM1.1T42(2.8X2.8),L182.0X2.4,L45.

ECHO-gECHAMHOPE-gT30(3.75X3.75),L19T42(2.8X2.8),L20H,W

ECHAM4/OPYC3ECHAMOcean isoPYCnal GCMT42(2.8X2.8),L192.8X2.8,L11H,W

Sheet2

Sheet3

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-

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

Development of CES Coupled GCMMixed Layer ModelVertical Eddy Viscosity:Vertical Eddy Diffusivity:: empirical Constantwhere: TKEl : the length scale of turbulenceNoh and Kim (1999) To simulate correct vertical ocean structure

Coupled GCMAGCMOGCMCoupling StrategyCES CGCM (Ver. 1)CES AGCMT31, 21 levels (3.75X3.75)MOM3 OGCMUneven Grid(3 lon. X 1 lat. near equator)1-day Mean Exchange(SST, Heat Flux, Wind stress, Fresh Water Flux)No Flux CorrectionCES CGCM(Ver. 2)CES AGCMT42, 21 levels (2.8125X2.8125)MOM2.2 OGCM + Ocean mixed layer modelUneven Grid(1 lon. X 1/3 lat. near equator)1-day Mean Exchange(SST, Heat Flux, Wind stress, Fresh Water Flux)No Flux Correction

SST ClimatologyObservationCGCM with MLMCGCM without MLM

a) Observationb) CGCM without MLMVertical Structure of Ocean Temperature1oS-1oN meanb) CGCM with MLM

Vertical Structure of Zonal Current along the Equator1oS-1oN meana) Observationb) CGCM without MLMc) CGCM with MLM

ObservationInterannual SST VariabilityCGCM with MLMCGCM without MLM

Effect of Horizontal Diffusiona) Observationb) Strong Diffusionc) Weak DiffusionEXP_strong (CNTL)EXP_weakHorizontal Mixing for MomentumNotes 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 DiffusionStrong DiffusionWeak DiffusionSST ClimatologyInterannual Variability

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

Correlation of rainfall and geopotential heightEach model precipitaiton anomalyComparison of climate models having different system also back up theses results.These are all SNU AGCM and this is coupled with slab ocean and this is fully coupled CGCM.Coupled systems mimic the realistic negative relationship clearly different from AMIP.Even in the slab ocean model case only having local air-sea interaction without any advection or ocean dynamics,the negative relation is shown so clearly as the seasonal characteristics in the summer hemisphere.