Assessing IPCC AR4 model simulation of present and future changes in extreme indices

Speaker: Tung Yu-ShiangAdviser: Chen Cheng-Ta

Outline Motivation Data Definition of Extreme Rainfall Indices Validation of Rainfall Extremes Simulation

in Present Climate Attribution of the model bias Future Climate Projection

Motivation and Aims of Study Extreme rainfall events have much more significant

impact on human society and nature environment. Can current generation of climate models (such as in

IPCC AR4 data archive) properly simulate extreme rainfall (after considering the effect of spatial scale of model gridded output)?

What leads to the model bias in precipitation extreme indices?

Future projections of changes in extreme rainfall indices from IPCC models

What are the major contributions to the projected change? Is the change in basic thermodynamic condition (moisture supply) enough to explain?

DataObservation data CMAP 1981-2000 monthly precipitation GPCP 1997-2007 daily precipitation Gridded daily rainfall analysis based on gauge data

(Aphrodite, Yatagai et al, 2007)Model-simulation data IPCC AR4 data archive 15 models (daily rainfall available and

resolution higher than T42): daily precipitation from1981-2000 period of 20c3m run and from 2081-2100 period of A1B scenario run

Considering the impact of spatial scale on gridded model rainfall, we conservatively interpolate all data to T42 resolution before identify rainfall extremes.

IPCC AR4 models list(1)Model ID Center/

CountryResolutio

n(lon×lat)

Stratiform parameterization

Convective parameterization

1. bccr_bcm2_0 BCCR/Norway 2.8°×~2.8°Statistical cloud scheme for stratiform clouds based on Ricard and Royer (1993).

Mass-flux convective scheme with Kuo-type closure based on Bougeault (1985)

2. cccma_cgcm3_1_t63

CCCma/Canada 2.8°×~2.8°Precipitation occurs whenever the local relative humidity is supersaturated

Zhang and McFarlane (1995) scheme

3. cnrm_cm3 CNRM/France 2.8°×~2.8° Statistical cloud scheme of Ricard and Royer (1993)

Mass flux convective scheme with Kuo-type closure of Bougeault (1985)

4. csiro_mk3_0 CSIRO/Australia 1.88°×~1.88°

Stratiform cloud condensate scheme from Rotstayn(2000)

Bulk mass flux convection scheme with stability dependent closure (Gregory and Rowntree 1990)

5. csiro_mk3_5 CSIRO/Australia 1.88°×~1.88° Same as CSIRO-Mk3.0 Same as CSIRO-Mk3.0

6. gfdl_cm2_0 GFDL/USA 2.5°×2.0°Microphysics (Rotstayn et al. 2000) and macrophysics (Tiedtke 1993)

Relaxed Arakawa–Schubert (Moorthi and Suarez 1992)

7. gfdl_cm2_1 GFDL/USA 2.5°×2.0° Same as GFDL-CM2.0 Same as GFDL-CM2.0

8. iap_fgoals1_0_g IAP/China 2.8°×~2.8°Precipitation occurs whenever the local relative humidity is supersaturated

Mass-flux scheme designed by Zhang and McFarlane(1995)

9. ingv_echam4 INGV/Italy 1.125°×~1.125°

Sundquist(1978) type prognostic scheme for stratiform fractional clouds

Shallow, mid-level, and deep cumulus convection with Tiedke (1989) mass flux scheme and adjustment closure for deep convection as described by Nordeng (1996)

IPCC AR4 models list(2)Model ID country Resolution

(lon×lat)Stratiform parameterization

Convective parameterization

10. miroc3_2_hiresCCSR/NIES/

FRCGC/Japan

1.125°×~1.125°

Prognostic total water scheme based on Le Treut and Li (1991) with the second indirect effect of aerosols based on Berry (1967)

Prognostic closure of Arakawa–Schubert based on Pan and Randall (1998) with the empirical suppression condition based on Emori et al. (2001)

11. miroc3_2_medresCCSR/NIES/

FRCGC/Japan

2.8°×~2.8° Same as MIRO3.2-hires Same as MIRO3.2-hires

12. mpi_echam5 MPI/Germany

1.88°×~1.88°

Prognostic equations for the water phases (vapor, liquid, ice), bulk cloud microphysics (Lohmann and Roeckner, 1996)

Mass flux scheme for shallow, mid-level and deep convection (Tiedtke, 1989) with modifications for deep convection according to Nordeng (1994).

13. mri_cgcm2_3_2a MRI/Japan 2.8°×~2.8°Precipitation occurs whenever the local relative humidity is supersaturated

Arakawa-Schubert scheme based on Randall and Pan (1993) with some modifications

14. ncar_ccsm3_0 NCAR/USA 1.4°×~1.4°Prognostic condensate and precipitation parameterization (Zhang et al. 2003)

Simplified Arakawa and Schubert (1974) (cumulus ensemble) scheme developed by Zhang and McFarlane (1995)

15. ncar_pcm1 NCAR/USA 2.8°×~2.8°Precipitation occurs whenever the local relative humidity is supersaturated

Same as CCSM3

Definition of Extreme indexExpert Team (ET) on climate change detection and

indices (ETCCDI) RX1day Highest 1-day precipitation amount(mm) Let RRij be the daily precipitation amount for day i of

period j. Then maximum 1-day values for period j are: RX1day j =max(RRij) SDII Simple daily intensity index(mm/wet day) Let RRwj be the daily precipitation amount for wet day w

(RR ≧ 1.0 mm) of period j. Then the mean precipitation amount at wet days is given by:

Regional RX1day comparison for observation data

• Eaten Asia station data RX1day is similar with GPCP(1997-2007, 11 years)

Eaten Asia station RX1day different with GPCP on north India.

(Aphrodite)

(mm)

RX1day (20th)

RX1day (20th)-Taylor DiagramJJA

DJF

Model bias comparisonmean precipitation RX1day

SDII (20th)

(mm/wet day)

Wet day frequency(RR1)

Days/year

Model bias comparison

SDII Wet day frequency

Extreme indices zonal mean(20th)RX1day Mean precipitation

• For RX1day, models have much spread in tropical region

• For mean precipitation, models are very similar with mean precipitation, but in south tropical, models over estimated.

SDII Wet day frequency

• For SDII, GPCP is higher than ensemble mean

• GPCP have less wet days frequency than most model

Future climate

RX1day increase percentage

%

Projected change in mean total precipitable water

Zonal mean change in Rx1day

temperature

Total precipitable water

• RX1day increase in tropical region

• Temperature increase in north high latitude region, total precipitation has the same result.

Zonal mean change SDII wet day frequency

mean precipitation

Thanks for your listening

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