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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.