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Dynamical Downscaling of NASA/GISS ModelE:
Continuous, Multi-Year WRF Runs
Tanya L. Otte1, Jared H. Bowden1, Christopher G. Nolte1, Martin J. Otte1, Jonathan E. Pleim1,
Jerold A. Herwehe1, Greg Faluvegi2, and Drew T. Shindell2
1 U.S. EPA, Research Triangle Park, North Carolina
2 NASA/Goddard Institute for Space Studies, New York,
9th Annual CMAS Conference, Chapel Hill, NC
11 October 2010
Office of Research and Development
National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division
New York
Why are we performing this research?
Regional Impacts of
Climate Change
Global Climate
- Comprehensive science
- Emissions scenarios
- Multi-century data
- Coarse resolution
Large, well-established programs
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Regional Climate Modeling
a.k.a. Downscaling
- Effects on air quality and
environment
- Assessment, mitigation
- Detailed local land cover,
topography, etc.
Our interests
What is “dynamical downscaling”?
Global climate model (GCM) creates
coarse gridded future climate with
world-wide coverage.
GCM
RCM
Regional climate model (RCM)
generates gridded higher-resolution
climate predictions over focal area.
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More detail in local effects from:
- scale-appropriate physics
- topography & land/water interfaces
- urban areas (population centers)
- precipitation patterns
Our Research Problem…Simplified
Freedom of RCM to
develop smaller-scale
processes
Constraint of RCM
toward GCM
delicate balance
Keeps RCM climate
consistent with GCM
Allows RCM climate
to deviate from GCM
Decreases variability
Increases variability
More constraint toward GCM
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Less constraint toward GCM
RCM to be constrained to GCM using nudging.
Downscaling Configuration
• NASA/GISS ModelE (6 years, ca. 2000)
– 1 of 3 U.S. GCMs in IPCC AR5
– Coupled atmosphere-ocean model creates GCM forecast
– 2.0° x 2.5°, 6-h fields, 40 s-p layers up to 0.1 hPa
– Use native vertical layers in downscaling
– Validate WRF vs. ModelE
• WRFv3.2
– 108-km domain, 34 layers, model top at 50 hPa
– CAM LW and SW Radiation
– WSM6 Microphysics
– Yonsei University PBL
– NOAH Land-Surface Model
– Grell Cumulus Parameterization
– Various analysis and spectral nudging configurations
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Domains and Topography
ModelE
(cropped to WRF domain and projection)
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WRF at 108-km
Multi-year downscaled simulations (even without nudging)
can capture interannual variability from the GCM.
Monthly Area-Averaged 2-m Temperature Distribution (Plains) for “2002-2007”
ModelE (“obs”)
No Nudging
Increased range
“2002” “2003” “2004” “2005” “2006” “2007”
mean
maximum
95th %ile
90th %ile
75th %ile
50th %ile
25th %ile
10th %ile
5th %ile
minimum
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Analysis Nudging
Spectral Nudging
Multi-year WRF runs using ModelE have persistent and
systematic regional and seasonal biases, even with nudging.
Monthly Area-Averaged 2-m Temperature Difference by Region
Midwest
Northwest
2002
2003
2004
2005
2006
2007
Northeast
No Nudging
nearly zero annual bias
4 Nudging Options
Southwest
Plains
annual periodicity of biases
annual periodicity of biases
Jan.
distinct cold bias
Southeast
annual periodicity of biases
Apr.
Sept. Sept. Sept. Sept. Sept. Sept.
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Result of inconsistencies in GCM/RCM physics or errors in modeling physical processes?
Corroborate with multi-year reanalysis runs?
Multi-year WRF runs using ModelE can broadly capture monthto-month trends in precipitation within different regions.
Monthly Area-Averaged Accumulated Precipitation Difference by Region
Northwest
2002
2003
2004
2005
Southwest
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2006
Midwest
Northeast
Plains
Southeast
2007
Subtle changes in nudging strategy can have a large impact on
results.
Plains U.S. region
Precipitation
Analysis
Nudging
(Sens. 1)
It’s not just about
whether or not
nudging is used but
how it is used.
2-m Temperature
JFMAMJJASOND
JFMAMJJASOND
Precipitation
ModelE
Analysis
Nudging
(Sens. 2)
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WRF
2-m Temperature
JFMAMJJASOND
JFMAMJJASOND
One size (configuration) will not fit all (regions and
applications).
6-Year Monthly Error from 17 Nudging Sensitivities
2-m Temperature Difference
(Plains U.S. region)
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Strategy may ultimately be dictated by application and region(s) of interest.
Next Steps
• Analysis of upper-air and column-averaged fields
• Space-time analysis
• Additional nudging sensitivities, as warranted by analysis
• Nested (36-km) runs and nesting strategies
• Longer time slices (and climate change)
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