Predictability of Seasonal Prediction Perfect prediction Correlation skill

Predictability of Seasonal Prediction Perfect prediction Correlation skill of Nino 3. 4 index Theoretical limit (measured by perfect model correlation) Anomaly correlation Predictability gap Actual predictability (from DEMETER) Gap between theoretical limit & actual predictability 1. Deficiency due to Initial conditions 2. Model imperfectness Forecast lead month

Statistical correction (SNU AGCM) Before Bias correction After Bias Correction Predictability over equatorial region is not improved much

Is post-processing a fundamental solution? Forecast error comes from model imperfectness poor initial conditions Model improvement is essential to achieve to predictability limit Good initialization process

Issues in Climate Modeling (Toward a new Integrated Climate Prediction System) Climate Dynamics Laboratory Seoul National University Sung-Bin Park Yoo-Geun Ham Dong min Lee Daehyun Kim Yong-Min Yang Ildae Choi Won-Woo Choi

MJO prediction ECMWF forecast (VP 200, Adrian Tompkins) Analysis Forecast Lack of MJO in climate models : Barrier for MJO prediction

Physical parameterization Problem Solution Problem Simplified Arakawa-Schubert cumulus convection scheme Loose convection (a) Control criteria suppress the well developed largescale eastward waves Impact of Cumulus Parameter (b) Modified Minimum entrainment constraint Relaxed Arakawa-Schubert scheme Absence of cloud microphysics Prognostic clouds & Cloud microphysics (Mc. RAS) Large-scale condensation scheme Influence of Cloud. Radiation Interaction (a) RAS Relative Humidity (a) Control Criterion Cloud-radiation interaction suppress the eastward waves Layer-cloud precipitation time scale (b) Mc. RAS Unrealistic precipitation occurs over the warm but dry (b) Modified region

Basic Flow of model development Next Generation Climate Model Multi-Scale Prediction System (Global Cloud Resolving Model) Initialization Ocean/land/Air Fully Coupled Model (Unified moist processes) Air-Sea Coupling Convection Ocean (Sea ice) Coupling Land Surface and Ocean Radiation Cloud Microphysics Interaction with Hydrometer CRM Coupling convection and boundary layer Turbulence Characteristics Boundary Layer Coupling Land Surface and boundary layer Coupling Radiation and Aerosol Interaction with Hydrometer Surface flux & Cloud Land surface Aerosol Coupling Land Surface and Aearosol

Problem : cloud top sensitivity to environmental moisture Prescribed profile Updraft mass flux Pressure Single column model (SNUGCM) Cloud resolving model (CNRM CRM) 30% 50% 70% 90% RH(%) Pressure 30% 50% 70% 90% * temperature and specific humidity are nudged with 1 hour time scale kg m/s

Problem : cloud top sensitivity to environmental moisture Single column model (Parameterization) Updraft mass flux Moistening Pressure 30% 50% 70% 90% Pressure Relative humidity kg m/s kg/kg/day % Cloud top is insensitive to environmental moisture Detrainment occurs at the similar level regardless of environmental moisture Too high relative humidity produced

Effect of turbulence on convection Delayed convection relative to the turbulence initiation Cloud liquid water Turbulent kinetic energy t = 0 hr t = +10 hr

Delaying effect 1. Heat transport by turbulence Accumulation of MSE above turbulence raising instability (t = t 1) Convection initiated (t = t 2) Convection and boundary layer turbulence coupling 2. Interactions between hydrometeors Heat exchange between hydrometeors (t = t 3) deep convection Microphysical cloud model Moistening Latent/ sensible heat t = t 0 Transport by turbulence t = t 1 Convection initiation t = t 2 Interaction between hydrometeor s t = t 3 time

Future Plans II Cloud microphysics GCM Water vapor Precipitation f(Mass flux) Cloud water Validation Implementation of cloud microphysics CRM Prognostic equations of cloud microphysics Cloud ice Snow Rain Graupe l

Future Plans II Improvement of Land Surface process Transpiration by moisture stress Heterogeneity of land data Desborough (1997) Arola and Lettenmaier(1996) * Update land surface using high resolution data Surface runoff Canopy layer * USGS/EROS 1 km vegetation type River network Routing Surface layer Root zone Liston and Wood (1994) Ground runoff Soil moisture with river routing Deep soil Source for fresh water in coupled model

Works have been done: Aerosol Module Frame Aerosol module in Climate model Direct Aerosol Indirect Radiation Climate Cloud microphysics Radiation Advection Diffusion Gravity settling Wet deposition Mobilization Dry deposition

Development of Fully Coupled Model (Unified moist processes) Air-Sea Coupling Ocean (Sea ice)

Development of Global Cloud Resolving Model Next Generation Model (Global Cloud Resolving Model) Air-Sea Coupling Ocean (Sea ice) Global Simulation with 20 -30 km (Ideal boundary condition/spherical coordinate) CRM

Resolution dependency n 3 hourly precipitation 100 km resolution 300 km resolution 20 km resolution

Description of CRM Cloud resolving model GCE-Goddard Cumulus Ensemble Good tool for evaluation of the interaction in nature Good substitution of observation Updraft mass flux GCE simulation SNUAGCM single column The Goddard Cumulus Ensemble (GCE) model was created at 1993 by model Wei-Kuo Tao. 1. Non-hydrostatic Explicit interaction between boundary layer turbulence and convection 2. Sophisticated representation of microphysical processes

Physics process of CRM Cloud and Radiation interaction Tao et al. (1996) Cloud-Top cooling Physic s proces s of CRM Clear region cooling Cloud-Base warming Stratiform Convective Turbulent kinetic energy Tao and Simpson (1993) process • Attempt to parameterization flux of prognostic quantities due to unresolved eddies (1. 5 order scheme) where stability shear diffusion dissipation

Future plan: global cloud resolving model Maximize realism of model simulation Physics complexity Coarse resolution test of CRM Goal High resolution CRM On-going work Low resolution GCM Don e High resolution GCM resolutio n Global cloud resolving model can be the best tool to simulate the nature.

Future plan: global cloud resolving model GCE Global Test Dynamics and Physics of GCE Non-hydrostatics dynamical core, Physics test to step by step DYNAMICS MICROPHYSICS SURFACE FLUX Aqua-Planet simulation Consider surface flux of ocean type first Test simulation of 25 km resolution (1536 x 768) Hybrid approach of explicit or parameterized convection scheme RADIATION Example, NICAM (Japan)

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