Potential Predictability of Drought and Pluvial Conditions over the Central United States on Interannual to Decadal Time Scales

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Potential Predictability of Drought and Pluvial
Conditions over the Central United States on
Interannual to Decadal Time Scales
29th Annual Climate Diagnostics and Prediction
Workshop Madison, Wisconsin 18-22 October 2004

• Siegfried Schubert, Max Suarez, Philip Pegion,
• Randal Koster and Julio Bacmeister
• Global Modeling and Assimilation Office
• Earth Sciences Directorate

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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

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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
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C20C runs
Model ensemble mean
Observations
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CO2 runs in blue
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Quantities
m - ensemble mean s2 - intra-ensemble
variance (s/m)2 - intra-ensemble coefficient of
variation
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(s2,m) ((s/ m)2,m) (s2,nino3) ((s/
m)2,nino3) (m,nino3)
JFM 0.11 -.33 0.02 -.15 .37 FMA 0.03 -.53 0.02 -.3
5 .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 O
ND 0.56 -.38 .32 -.30 .70 NDJ 0.41 -.28 .27 -.13 .
61 DJF 0.19 -.23 .00 -.11 .23
Summary of Correlations
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• 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

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Correlation Between Ensemble Mean (m) GP
Precipitation and SST
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Correlation between SST and GP Precipitation
(s/m)2
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Correlation between SST and GP Precipitation
(s/m)2
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Composites based on Great Plains Precipitation
(s/m)2
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200mb Z Composites Based On Largest/Smallest
Values of Coefficient of Variation of GP
Precipitation
Largest
Smallest
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Difference in Composites of (s/m)2 of 200mb Z
Dimensionless
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Difference in Composites of (s/m)2 of
Evaporation
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Model Runs with Idealized SST

• Focus on AMJ
• Force model with 2 composite SST patterns
• Positive GP precip (s/m)2 gt 1 STD
• Negative GP precip (s/m)2 lt 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)

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SST Forcing Fields
GP precip (s/m)2 gt 1 STD
GP precip (s/m)2 lt 1 STD
C
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Differences in Idealized Runs-Precipitation
Fixed Beta
Interactive soil
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Differences in Idealized Runs-Evaporation
Interactive soil
Fixed Beta
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From C20C Runs
Soil Moisture
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Idealized run 1std
Idealized run -1std
DE
DE
DW
DW
W (soil moisture)
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Idealized run 1std
Idealized run -1std
C20C runs
Fixed Beta
Interactive soil
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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

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JJA Land-Atmosphere Coupling Strength, Averaged
Across AGCMs