# Presentation: 911059

## 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 Workshop
- Madison, Wisconsin
- 18-22 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

- CO2 runs in blue

## Quantities

- - ensemble mean
- 2 - intra-ensemble variance
- ()2 - intra-ensemble coefficient of variation

## Slide 7

## Slide 8

## Slide 9

## Slide 10

## Summary of

- 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)

- 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

- 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 Ensemble Mean () GP Precipitation and SST

## Correlation between SST and GP Precipitation  

- Correlation between SST and GP Precipitation 

## Correlation between SST and GP Precipitation  

- Correlation between SST and GP Precipitation 

## Composites based on Great Plains Precipitation  

## Largest

- 200mb Z Composites Based On Largest/Smallest Values of
-  Coefficient of Variation of GP Precipitation

- Smallest

## 	Difference in Composites of  of 200mb Z 

- 	Difference in Composites of  of 200mb Z

- Dimensionless

## 	Difference in Composites of  of Evaporation 

- 	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)

## °C

- SST Forcing Fields

- GP precip    >    +1 STD

- GP precip  <    +1 STD

## Differences in Idealized Runs-Precipitation

- Fixed Beta

- Interactive soil

## Differences in Idealized Runs-Evaporation

- Fixed Beta

- Interactive soil

## Soil Moisture

- From C20C Runs

## W (soil moisture)

******W**

******W**

******E**

******E**

- Idealized run -1std

- Idealized run +1std

## Idealized run +1std

- Interactive soil

- Fixed Beta

- 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

**JJA Land-Atmosphere Coupling Strength, Averaged Across AGCMs**