Siegfried Schubert, Max Suarez, Philip Pegion, Randal Koster and Julio Bacmeister

Potential Predictability of Drought and Pluvial Conditions over the Central United States on

Interannual to Decadal Time Scales

Siegfried Schubert, Max Suarez, Philip Pegion,

Randal Koster and Julio Bacmeister

Global Modeling and Assimilation Office

Earth Sciences Directorate

29th Annual Climate Diagnostics and Prediction WorkshopMadison, Wisconsin18-22 October 2004

Problem and Approach

Does the predictability of Great Plains precipitation change on inter-annual and longer time scales? If so - why?

Examine the spread of an ensemble of century-long simulations forced with observed SSTs

AGCM: NSIPP-1 (NASA S-I Prediction Project)

Climatology and Skill (Bacmeister et al. 2000, Pegion et al. 2000, Schubert et al. 2002)Great Plains drought (Schubert et al. 2003; 2004)Global grid point dynamical core, 4rth Order (Suarez and Takacs 1995)Relaxed Arakawa-Schubert Convection (Moorthi and Suarez 1992)Shortwave/Longwave Radiation (Chou et al. 1994, 1999)Mosaic interactive land model (Koster and Suarez 1992, 1996)1st Order PBL Turbulence Closure (Louis et al. 1982)

C20C AGCM runs with Specified SST

HadISST and sea ice dataset (1902-1999)22 ensemble members - same SST, different ICs

(14 with fixed CO2, 8 with time varying CO2)

Model resolution: 3 degree latitude by 3.75 degree longitude (34 levels)

Idealized AGCM runs forced with composite SST patterns

Observations

Model ensemble mean

C20C runs

CO2 runs in blue

Quantities

- ensemble mean

2 - intra-ensemble variance

()2 - intra-ensemble coefficient of variation

Great Plains Precipitation (Normalized , Normalized 2 )

Great Plains Precipitation (Normalized , Normalized 2 )

Great Plains Precipitation (Normalized , Normalized )

Great Plains Precipitation (Normalized , Normalized )

JFM 0.11 -.33 0.02 -.15 .37

FMA 0.03 -.53 0.02 -.35 .71

MAM -.26 -.67 -.12 -.41 .75

AMJ -.55 -.76 -.23 -.38 .67

MJJ -.52 -.73 -.23 -.33 .53

JJA -.39 -.73 -.12 -.26 .45

JAS -.08 -.71 .04 -.26 .49

ASO 0.33 -.53 .19 -.29 .59

SON 0.54 -.46 .38 -.30 .70

OND 0.56 -.38 .32 -.30 .70

NDJ 0.41 -.28 .27 -.13 .61

DJF 0.19 -.23 .00 -.11 .23

(,) ((,) (,nino3) ((,nino3) (,nino3)

Summary of

Correlations

• Results show that periods of less rain have greater relative variability than periods of more rain– implies that droughts are less predictable than

pluvial conditions

• How do the SST influence precipitation variability in the Great Plains?– atmospheric variability– land/atmosphere coupling

Correlation Between Ensemble Mean () GP Precipitation and SST

Correlation between SST and GP Precipitation

Correlation between SST and GP Precipitation

Composites based on Great Plains Precipitation

200mb Z Composites Based On Largest/Smallest Values of

Coefficient of Variation of GP Precipitation

Largest Smallest

Difference in Composites of of 200mb Z

Dimensionless

Difference in Composites of of Evaporation

Model Runs with Idealized SST

• Focus on AMJ• Force model with 2 composite SST patterns

– Positive: GP precip > +1 STD– Negative: GP precip < +1 STD

• 100 ensemble members (March 1 - June30) for each composite

• Initial soil moisture conditions are from AMIP runs• Repeat both sets of runs with fixed soil moisture

(fixed beta)

SST Forcing Fields

°C

GP precip > +1 STD

GP precip < +1 STD

Differences in Idealized Runs-Precipitation

Fixed BetaInteractive soil

Differences in Idealized Runs-Evaporation

Fixed BetaInteractive soil

Soil Moisture

From C20C Runs

W W

E

E

W (soil moisture)

Idealized run -1stdIdealized run +1std

Interactive soil Fixed Beta

Idealized run +1std

Idealized run -1std

C20C runs

Conclusions and Implications

• In the Great Plains, simulated droughts are less predictable than pluvial conditions

• Differences in ensemble spread are associated with changes in the strength of the atmosphere/land coupling

• Should also be true in other “hot spots”• Future work - seasonality, model dependence,

other regions (e.g. SW US), SST uncertainty

JJA Land-Atmosphere Coupling Strength, Averaged Across AGCMs