eric salath jisao climate impacts group university of washington rick steed uw yongxin zhang cig,...
DESCRIPTION
150-km GCM High resolution is needed for regional studies Washington Oregon Idaho Cascade Range Rocky Mountains Snake Plain Olympics Global models typically have km ( mi.) resolution Cannot distinguish Eastern WA from Western WA No Cascades No land cover differencesTRANSCRIPT
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Eric SalathéJISAO Climate Impacts Group
University of Washington
Rick Steed UWYongxin Zhang CIG, NCARCliff Mass UW
Regional Climate Modeling and Projected Changes in Extreme Precipitation
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Downscaling and Regional Climate Modeling
12-50 km or~7-32 mi
Statistical Downscaling • Maps the climate change signal from a global model onto the observed patterns• Computationally efficient• Can tune to observed climate• Preserves uncertainty in Global Climate Models• Cannot represent fine-scale patterns of climate change
Regional Climate Models (“Dynamic Downscaling”) • Extend the physical modeling of the climate system to finer spatial scales• Computationally demanding• Cannot correct bias in global model• Adds to uncertainty from Global Climate Models
Global ClimateModel
6-hourlyMonthly
100-200 km
6 km or~3.7 mi
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150-km GCM
High resolution is needed for regional studies
Washington
OregonIdaho
Cas
cade
Ran
ge
Rocky Mountains
Snake Plain
Olympics
Global models typically have 100-200 km (62-124 mi.) resolution
•Cannot distinguish Eastern WA from Western WA
•No Cascades
•No land cover differences
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High resolution is needed for regional studies (cont’d)
Washington
OregonIdaho
Cas
cade
Ran
ge
Rocky Mountains
Snake Plain
Olympics
Regional models typically have 12-50 km (7-32 mi) resolution
• 12 km WRF at UW/CIG
• Can represent major topographic features
• Can simulate small extreme weather systems
• Represent land surface effects at local scales
12-km WRF
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Why do we want to simulate the regional climate?
Process studies Topographic effects on temperature and
precipitation Extreme weather Attribution of observed climate change Land-atmosphere interactions
Climate Impacts Applications Streamflow and flood statistics Water supply Ecosystems Human health Air Quality
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Regional Climate Modeling at CIG
WRF Model ECHAM5 A1B forcing 36-km (~32 mi) grid spacing CCSM3 A2 forcing 20-km (~12 mi) grid spacing
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Emissions Scenarios
IPCC Emissions Scenarios for Climate Projections
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Statistical Downscaling CCSM3Fall difference between 1990s and 2040s
Low spatial detail for climate change signal
°C %
Temperature Precipitation (%change)
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WRF “Dynamic Downscaling” CCSM3
Temperature Precipitation (%change)
Fall difference between 1990s and 2040s
High spatial detail for climate change signal
%
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Extreme Precipitation: Global Models
Change from 1980–1999 to 2080–2099 in the intensity of precipitation
largest increase areas already experiencing heavy precipitation
Change in mm
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Extreme Precipitation: Regional Models
Change from 1970-2000 to 2030-2060 in the intensity of precipitation
largest increase on windward slopes of Cascades, Columbia basinsmall increase or decrease along Cascade crest
Change in mm
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Historic Trends in Extreme Precipitation (1970-2000)
Precip Intensity
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ECHAM5-WRF Northeast Washington: Pend Oreille at Boundary Dam
Trends in intensity
Not the light events
But the big events
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ECHAM5-WRF North Cascades: Skagit at Mt Vernon
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ECHAM5-WRF North Cascades:Skagit at Diablo Dam
Trends are lostin the variability
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Summary of Precipitation Intensity
Station 1970-1999Standard Deviation
Change 2020s
Change 2040s
Skagit Diablo Dam 14.79 1.51 -0.34 0.41
Skagit Mt Vernon 11.47 0.96 0.31 1.11
Ross Newhalem 10.06 0.90 -0.28 0.22
Baker Concrete 17.14 1.61 -0.18 0.97
Sauk 17.05 1.73 -0.50 0.67
Box Canyon 7.43 0.61 0.11 0.43
Boundary 7.50 0.68 0.15 0.47
Sea Tac 5.97 0.65 0.00 0.08
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Summary of Precipitation Intensity
• Interannual variability is very large and dominates in the near future
• Increased precipitation intensity emerges at a few locations by mid century