1. Alfredo Ruiz-Barradas Sumant Nigam
North American Hydroclimate Variability in IPCC’s 20th Century Climate Simulations
Alfredo Ruiz-Barradas
Sumant Nigam
Department of
Atmospheric and Oceanic Science
University of Maryland
2006 Joint Assembly Meeting
Baltimore, Maryland
May 23, 2006

2. Motivation
To better know the structure and mechanisms of precipitation variability in state-of-the-art climate models
At issue:
Model validation
Relative contributions of local (evaporation) and remote (moisture fluxes) water sources

3. Outline The data sets. Annual Cycle of Precipitation
Precipitation variability over the Great Plains.
Structure of hydroclimate fields and their relative contributions associated with precipitation anomalies.
The surface temperature signature.
Conclusions

4. Data Sets US-Mexico retrospective precipitation analysis:1951-1998.
North American Regional Reanalysis (NARR):
Simulations of the Climate of the 20th Century:
Historical Forcing:
Solar irradiance
Volcanic and anthropogenic aerosols (optical depth)
Ozone, Carbon Dioxide
Other well mixed greenhouse gases
Resolution:
Horizontal: R30 (96x80) Gaussian grid
Vertical: 17-pressure levels

5. MODELS

6. MODELS

7. Precipitation: Annual Cycle & Annual Mean (1979-1998)
The annual cycle diminishes and
occurs earlier in the summer months
from tropical Mexico to central US
Weak maximum over
southern US in winter/spring
months
Maximum over
northwestern US
In January
Models do well
over US northwest
in winter
But seem to have some
problems over
southeastern and central
US in spring and summer

8. JJA Precipitation
Climatology

9. Precipitation: JJA STD
What makes the
observed and
Simulated STD?
Great Plains
Precipitation Index is
defined over the
area of maximum
STD in observations
US-MEX
0.91 mm/day
0.63 mm/day
1.00 mm/day
min
1.12 mm/day
0.92 mm/day
Max
0.72 mm/day
0.78 mm/day

10. Histogram of Precipitation Events According to GPP Indices (1951-1998)
0.91 mm/day
76<0
68>0
1.00 mm/day
0.63 mm/day
Small STD due to
a concentration of
small events
77<0
67>0
67<0
77>0
Large STD
due to extreme
events
1.12 mm/day
0.92 mm/day
75<0
69>0
72<0
72>0
0.72 mm/day
0.78 mm/day
73<0
71>0
73<0
71>0

11. Regressed Precipitation Anomalies on JJA GPP Indices
0.89 mm/day
1.04 mm/day
0.63 mm/day
1.13 mm/day
0.92 mm/day
0.73 mm/day
0.78 mm/day

12. Regressed Convective
Precipitation on
GPP Indices

13. Regressed Moisture Flux Anomalies on JJA GPP Indices
0.66 mm/day
0.45 mm/day
0.46 mm/day
0.40 mm/day
0.83 mm/day
0.91 mm/day
0.66 mm/day

14. Regressed Evaporation Anomalies on JJA GPP Indices
0.22 mm/day
0.69 mm/day
0.04 mm/day
0.59 mm/day
0.14 mm/day
-0.04 mm/day
0.06 mm/day
CI=1/3 of that in P & MFC

15. So far, we have the following picture regarding the
relative controls of precipitation variability:
Evaporation dominates over Moisture Flux Convergence:
CCSM3, GFDL-CM2.1
Moisture Flux Convergence largely dominates over Evaporation:
GISS-EH, MIROC3.2(hires)
Moisture Flux Convergence dominates over Evaporation:
NARR/US-MEX
PCM,UKMO-HadCM3
Is precipitation recycling the same in CCSM3 and GFDL-CM2.1?

16. Autocorrelation of GPP Indices
Standard Errors
Significance at the
0.05 level
CCSM3 & GFDL-CM2.1
do not recycle
precipitation in
the same way!!

17. Regressed Surface Temperature Anomalies on JJA GPP Indices
Large evaporation anomalies
have implications on the
surface energy balance
and so on surface
temperature, the variable of
choice when analyzing warming
scenarios
-0.8 K
-2.4 K
-0.3 K
-1.9 K
-0.9 K
-0.0 K
-0.7 K

18. Conclusions
NARR/US-Mexico data sets suggest that remote water sources (moisture fluxes) dominate over local water sources (evaporation) in the generation of interannual rainfall variability over the Great Plains during the warm-season.
Three different hierarchy of process in models:
MFC >> E: MIROC3.2(hires), GISSEH, GISS-ER
MFC > E: UKMO-HadCM3, PCM
E > MFC: CCSM3, GFDL-CM2.1, GFDL-CM2.0, GISS-AOM
Deficient simulation of moisture pathways feeding the Great Plains.
In consequence: regional hydroclimate simulations and predictions remain challenging for global models, at least
in the context of variability over the Great Plains.