Diagnosis of North American Hydroclimate Variability in IPCC

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Diagnosis of North American Hydroclimate
Variability in IPCCs Climate SimulationsAlfredo
RuizBarradas1 and Sumant NigamUniversity of
Maryland----o---- Breckenridge, CO
June 19-21, 2007

1alfredo_at_atmos.umd.edu
Abstract The annual cycle of precipitation, as
well as interannual variability of North American
hydroclimate during summer months are analyzed in
IPCCs coupled simulations of the 20th century
climate. The state-of-the-art general circulation
models, participating in the 4th Assessment
Report for the IPCC, included in the present
study are the Americans CCSM3, PCM, GISS-EH, and
GFDL-CM2.1 the British UMKO-HadCM3, and the
Japanese MIROC3.2(hires). Data sets with proven
high quality such as NCEPs North American
Regional Reanalysis (NARR), and CPCs US-Mexico
(US-MEX) precipitation analysis are used as
targets for simulations. While models capture
winter precipitation very well over the US
northwest, they are challenged over the US
southeast in the same season, and over central US
and Mexico during summer. Models potential in
simulating interannual hydroclimate variability
over North America during the warm-season is
varied and limited to the central US. Models like
PCM, and in particular UKMO-HadCM3, exhibit
reasonably well the observed distribution and
relative importance of remote versus local
contributions to precipitation variability over
the region. However, in models like CCSM3 and
GFDL-CM2.1 local contributions dominate over
remote ones, in contrast with warm-season
observations. The significance of SST linkages,
in the context of interannual variability of
precipitation over the Great Plains, is
highlighted by a coherent basin-scale structure
resembling the Pacific Decadal variability
pattern in observations the UKMO-HadCM3 model is
the one that best reproduces such SST structure.
Figure 4 Correlation between Julys GPP
anomalies with May, June, July and August monthly
precipitation anomalies, 1951-1998. Error bars in
June represent the standard error when
calculating correlations.
Figure 5 Warm-season regressions of GPP Indices
on precipitation anomalies from NARR and model
simulations. Contour interval is 0.3 mm/day.
Figure 1 First harmonic of climatological
precipitation (vectors), and mean annual
precipitation (background) in NARR and model
simulations, 1979-1998. Vectors pointing to the
north indicate a maximum on 1 July. Only 1, 2, 3,
4, 6, 9 mm/day isohyets are displayed.
Figure 2 Standard deviation of monthly summer
(JJA) precipitation in NARR and model
simulations. Contour interval is 0.3 mm/day. The
blue box defines the Great Plains region
(90-100W,35-45N).
Figure 3 Histogram of precipitation events over
the Great Plains region, as portrayed by the
Great Plains Precipitation (GPP) Index during
summer months in US-MEX analysis and model
simulations, 1951-1998.

• Summary
• Climatological winter precipitation is well
  simulated over the US northwest, but not over the
  southeast during the same season, or central US
  during summer.
• .Large precipitation variability in models arises
  as consequence of the occurrence of rare extreme
  wet or dry events. On the other hand, reduced
  precipitation variability is consequence of the
  lack of those extreme events, and the increased
  number of small wet and dry events.
• .The relative importance of processes
  contributing to the generation of interannual
  variability of precipitation is varied among
  models. PCM, and in particular UKMO-HadCM3,
  exhibit reasonably well the observed distribution
  and relative importance of remote versus local
  contributions to precipitation variability over
  the region. In CCSM3 and GFDL-CM2.1 local
  contributions dominate over remote ones, in
  contrast with observations in the other extreme
  are models like GISS-EH and MIROC3.2(hires).
• CCSM3, and GFDL-CM2.1 both prioritize the local
  recycling of precipitation over convergence of
  remote moisture fluxes, however, the land-surface
  memory in GFDL-CM2.1 is stronger than in CCSM3.
  In both models large negative air temperature
  anomalies arise as consequence of the strong
  recycling of precipitation.
• A coherent basin-scale correlation structure,
  resembling the Pacific Decadal variability
  pattern in SST observations, is associated with
  GPP variability the UKMO-HadCM3 model is the one
  that best reproduces such SST structure.

Figure 9 Warm-season SST correlations of
smoothed GPP Indices from US-MEX analysis/Hadley
and model simulations. Contour interval is 0.1.
The index was smoothed using a 1-2-1 filter on
seasonal mean anomalies.
Figure 8 Warm-season regressions of GPP Indices
on surface air temperature anomalies from NARR
and model simulations. Contour interval is 0.3K.
Figure 7 Warm-season regressions of GPP Indices
on evaporation anomalies from NARR and model
simulations. Contour interval is 0.1 mm/day.
Figure 6 Warm-season regressions of GPP Indices
on vertically integrated moisture fluxes and
associated convergence anomalies from NARR and
model simulations. Contour interval is 0.3 mm/day.
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References

• Ruiz-Barradas, A. S. Nigam, 2006 IPCC's 20th
  Century Climate Simulations Varied
  Representations of North American Hydroclimate
  Variability. J. Climate, 19, 4041-4058.
• Ruiz-Barradas. A., S. Nigam, 2006 Great Plains
  hydroclimate variability the view from North
  American Regional Reanalysis.
• J. Climate, 19, 3004-3010.
• Nigam, S., and A. Ruiz-Barradas, 2006 Seasonal
  Hydroclimate Variability over North America in
  Global and Regional Reanalyses and AMIP
  Simulations A Mixed Assessment. J. Climate, 19,
  815-837.
• Ruiz-Barradas, A., S. Nigam, 2005 Warm-season
  rainfall variability over the US Great Plains in
  observations, NCEP and ERA-40 reanalyses, and
  NCAR and NASA atmospheric model simulations.
• J. Climate , 18, 1808-1829.