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 Content of Lectures

 Content of Lectures. Climate Modeling and Prediction In-Sik Kang Seoul National University. 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. Lecture 1.

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 Content of Lectures

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  1.  Content of Lectures Climate Modeling and Prediction In-Sik Kang Seoul National University 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

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

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

  4. Modeling AGCM Environmental Model AtmosphericSystem OGCM Environmental System OceanicSystem Unification • The integrated climate and environment model requires construction, development and improvement of oceanic general circulation model (OGCM) and environmental model in addition to AGCM. 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. Integrated Climate and Environment Model • Recently, the concept of global climate model considering the condition of ocean and vegetation as well as atmosphere, has been established.

  5. Dynamics of Atmosphere Physics of Atmosphere and Land Surface • 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 • Radiation • Cumulus Convection U, V, T, q, ql Physics Radiation • Large Scale Condensation Cumulus convection Shallow convection • Gravity Wave Drag Large-scale condensation Gravity wave drag • Planetary Boundary Layer Planetary boundary layer Land surface • Land Surface Process Structure of Atmospheric General Circulation Model Dynamics Three-dimension hydrostatic primitive equations on sphere with sigma coordinate

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

  7. Experimental Design and Participated Models • CLIVAR Asian-Australian Monsoon Atmospheric GCM Intercomparison Project The AGCM intercomparison program wasinitiated by the CLIVAR/Asian–Australian MonsoonPanel to evaluate a number of current atmosphericGCMs in simulating the global climate anomalies associatedwith the recent El Niño. • Experimental Design • Models Participated

  8. Monsoon Predictability: Climatological JJA Precipitation

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

  10. 1st Mode of EOF for Climatological MJJAS Precipitation

  11. 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.

  12. EL-NINO WINTER EL-NINO SUMMER WINTER SUMMER Evolution of 1997-98 El Niño and SOI Indices (a) NINO3.4 INDEX (c) Observed and Simulated SOI indices (b) SST anomaly DJF97/98 SOI = SLP anomaly difference over two regions [145oW-155oW, 5oS-5oN] – [125oE-135oE, 5oS-5oN]

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

  14. 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.

  15. DJF96/97 JJA97 DJF97/98 JJA98 DJF96/97 JJA97 DJF97/98 JJA98 Current Predictability: Pattern Correlation and RMS of Rainfall (a) Pattern Correlation (b) Root-mean-square Monsoon-ENSO region: 60oE-90oW, 30oS-30oN

  16. DJF97-98 200hPa Geopotential Height Anomalies Correlation vs. RMS PNA Normalized RMS PNA Correlation Precipitation vs. Circulation 200hPa Geopotential height Precipitation PNA region: 180oE-60oW, 20-80oN

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

  18. (a) El-Nino region DJF97/98 DJF97/98 JJA98 JJA98 JJA97 JJA97 DJF96/97 JJA97 DJF97/98 JJA98 (c) Southeast Asian and Western North Pacific (b) Monsoon region DJF96/97 JJA97 DJF97/98 JJA98 (d) The rest of the Asian-Australian Monsoon domain Current Monsoon Predictability: Pattern Correlation Correlation between CMAP and models for JJA97/98 El-Nino region (160oE-80oW, 30oS-30oN) Monsoon region (40-160oE, 30oS-30oN) Southeast Asian and Western North Pacific region (80-150oE, 5-30oN)

  19. Cause of Low Predictability: Atmosphere-Ocean Interaction Correlation between JJA SST and Precipitation during 1979-1999 Observation 5 Model Composite (a) JJA (b) JJA (c) DJF (d) DJF

  20. Improved Simulation using Coupled System over WNP Correlation between JJA SST and Precipitation (a) Observation (1979-2001) (b) AGCM (1979-2001) (c) Mixed layer model (16 years) (d) CGCM (50 years) • No ENSO • Only local air-sea interaction

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

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

  23. Coupled Model Intercomparison Project (CMIP) • 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. Participating Model

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

  25. Zonal Mean Precipitation Double ITCZ CMIP: Precipitation Climatology -

  26. 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

  27. CMIP: Interannual SST Variability • Common Problems in CGCM Simulations • 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

  28. Development of CES Coupled GCM Mixed Layer Model Noh and Kim (1999) • To simulate correct vertical ocean structure Vertical Eddy Viscosity: Vertical Eddy Diffusivity: where : empirical Constant : TKE l : the length scale of turbulence

  29. SST Climatology Observation CGCM without MLM CGCM with MLM

  30. Vertical Structure of Ocean Temperature 1oS-1oN mean a) Observation b) CGCM without MLM b) CGCM with MLM

  31. Vertical Structure of Zonal Current along the Equator 1oS-1oN mean a) Observation b) CGCM without MLM c) CGCM with MLM

  32. Interannual SST Variability Observation CGCM without MLM CGCM with MLM

  33. Effect of Horizontal Diffusion Notes Horizontal Mixing for Momentum • When horizontal diffusion is strong • Weak Equatorial Undercurrent • Strong Equatorial Surface Current • Westward extension of cold tongue • Weak SST zonal gradient • Weak Interannual Variability EXP_strong (CNTL) EXP_weak a) Observation b) Strong Diffusion c) Weak Diffusion

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

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

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