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Predictability of Seasonal Prediction. Perfect prediction. Correlation skill of Nino3.4 index. Theoretical limit (measured by perfect model correlation). Predictability gap. Actual predictability (from DEMETER). Anomaly correlation. Gap between theoretical limit & actual predictability

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Predictability of Seasonal Prediction


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slide1

Predictability of Seasonal Prediction

Perfect prediction

Correlation skill of Nino3.4 index

Theoretical limit

(measured by perfect model correlation)

Predictability gap

Actual predictability

(from DEMETER)

Anomaly correlation

Gap between theoretical limit & actual predictability

1. Deficiency due to Initial conditions

2. Model imperfectness

Forecast lead month

slide2

Statistical correction (SNU AGCM)

Before Bias correction

After Bias Correction

Predictability over equatorial region is not improved much

slide3

Is post-processing a fundamental solution?

model imperfectness

poor initial conditions

Forecast error comes from

Model improvement

Good initialization process

is essential to achieve to predictability limit

slide4

Issues in Climate Modeling

  • (Towarda 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

slide5

MJO prediction

ECMWF forecast (VP200, Adrian Tompkins)

Analysis

Forecast

Lack of MJO in climate models

: Barrier for MJO prediction

slide6

(a) Control

Simplified Arakawa-Schubert cumulus convection scheme

Relaxed Arakawa-Schubert scheme

(b) Modified

(b) Modified

(a) Control

Impact of Cumulus Parameter

(a) RAS

(b) McRAS

Large-scale condensation scheme

Influence of Cloud- Radiation Interaction

Physical parameterization

Problem

Solution

Solution

Problem

Loose convection criteria suppress the well developed large-scale eastward waves

Unrealistic precipitation occurs over the warm but dry region

Minimum entrainment constraint

Relative Humidity Criterion

Absence of cloud microphysics

Prognostic clouds

& Cloud microphysics

(McRAS)

Cloud-radiation interaction suppress the eastward waves

Layer-cloud precipitation time scale

slide7

Basic Flow of model development

Multi-Scale

Prediction System

Next Generation Climate Model

(Global Cloud Resolving Model)

Fully Coupled Model

(Unified moist processes)

Initialization

Ocean/land/Air

Air-Sea Coupling

Convection

Radiation

Cloud

Microphysics

Interaction with

Hydrometer

Ocean

(Sea ice)

CRM

Coupling convection

and boundary layer

Coupling Radiation

and Aerosol

Turbulence

Characteristics

Interaction with

Hydrometer

Coupling

Land Surface

and Ocean

Surface flux &

Cloud

Boundary Layer

Aerosol

Land surface

Coupling Land Surface

and boundary layer

Coupling Land

Surface and Aearosol

slide8

Problem : cloud top sensitivity to environmental moisture

Prescribed profile

  • Updraft mass flux

Cloud resolving model

(CNRM CRM)

Single column model

(SNUGCM)

Pressure

30% 50%70% 90%

Pressure

30% 50%70% 90%

RH(%)

* temperature and specific humidity are nudged with 1 hour time scale

kg m/s

kg m/s

slide9

Problem : cloud top sensitivity to environmental moisture

  • Single column model (Parameterization)

Updraft mass flux

Moistening

Relative humidity

30% 50%

70% 90%

30% 50%70% 90%

Pressure

Pressure

Pressure

kg m/s

kg/kg/day

%

Detrainment occurs at the similar level regardless of environmental moisture

Cloud top is insensitive to environmental moisture

Too high relative humidity produced

slide10

Effect of turbulence on convection

Delayed convection relative to the turbulence initiation

Cloud liquid water

Turbulent kinetic energy

t = +10hr

t = 0hr

slide11

Delaying effect

  • Heat transport by turbulence
    • Accumulation of MSE above turbulence  raising instability (t = t1)
    • Convection initiated (t = t2)
    •  Convection and boundary layer turbulence coupling
  • Interactions between hydrometeors
    • Heat exchange between hydrometeors (t = t3)  deep convection
    •  Microphysical cloud model

Moistening

Interaction between hydrometeors

Latent/

sensible heat

Transport by turbulence

Convection initiation

time

t = t0

t = t1

t = t2

t = t3

slide12

Future Plans II Cloud microphysics

GCM

Water vapor

Precipitation f(Mass flux)

Cloud water

Cloud ice

Validation

Implementation of cloud microphysics

Snow

Rain

Graupel

CRM

Prognostic equations of cloud microphysics

slide13

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

River network Routing

Surface layer

* USGS/EROS 1km vegetation type

Liston and Wood (1994)

Root zone

Ground runoff

Soil moisture with river routing

Deep soil

Source for fresh water in coupled model

slide14

Works have been done: Aerosol Module Frame

Aerosol module in Climate model

Direct

Radiation

Aerosol

Climate

Cloud microphysics

Indirect

Radiation

Advection

Diffusion

Gravity settling

Wet deposition

Mobilization

Dry deposition

slide15

Development of Fully Coupled Model

Fully Coupled Model

(Unified moist processes)

Unified moist processes

Air-Sea Coupling

Ocean

(Sea ice)

Cloud

Microphysics

Convection

Radiation

Coupling convection

and boundary layer

Coupling Radiation

and Aerosol

CRM

Boundary Layer

Aerosol

Coupling Land

Surface and Aearosol

Land surface

Coupling Land Surface

and boundary layer

slide16

Development of Global Cloud Resolving Model

Next Generation Model

(Global Cloud Resolving Model)

Air-Sea Coupling

Ocean

(Sea ice)

Global Simulation with 20-30km

(Ideal boundary condition/spherical coordinate)

CRM

slide17

100km resolution

Resolution dependency

  • 3 hourly precipitation

300km resolution

20km resolution

slide18

Description of CRM GCE-Goddard Cumulus Ensemble

Cloud resolving model

  • Good tool for evaluation of the interaction in nature
  • Good substitution of observation

Updraft mass flux

GCE simulation

The Goddard Cumulus Ensemble (GCE) model was created at 1993 by Wei-Kuo Tao.

SNUAGCM single column model

  • Non-hydrostatic
    • Explicit interaction between boundary layer turbulence and convection
  • Sophisticated representation of microphysical processes
    • Physically based representation of precipitation process
slide19

Physics process of CRM

Cloud and Radiation interaction

Tao et al. (1996)

Cloud-Top cooling

Physics process of CRM

Clear region cooling

Clear region cooling

Cloud-Base warming

Stratiform

Convective

Turbulent kinetic energy process

Tao and Simpson (1993)

  • Attempt to parameterization flux of prognostic quantities due to unresolved eddies (1.5 order scheme)

where

stability

shear

diffusion

dissipation

slide20

Future plan: global cloud resolving model

Maximize realism of model simulation

Physics complexity

Goal

Coarse resolution test of CRM

High resolution CRM

On-going work

Done

Low resolution GCM

High resolution GCM

resolution

Global cloud resolving model can be the best tool to simulate the nature.

slide21

GCE

DYNAMICS

MICROPHYSICS

SURFACE FLUX

RADIATION

  • Future plan: global cloud resolving model

Global Test Dynamics and Physics of GCE

Aqua-Planet simulation

Test simulation of 25km resolution (1536 x 768)

Non-hydrostatics dynamical core, Physics test to step by step

Consider surface flux of ocean type first

Hybrid approach of explicit or parameterized convection scheme

Example, NICAM (Japan)