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
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
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
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
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
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
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
Emissions Scenarios
IPCC Emissions Scenarios for Climate Projections
Statistical Downscaling CCSM3Fall difference between 1990s and 2040s
Low spatial detail for climate change signal
°C %
Temperature Precipitation (%change)
WRF “Dynamic Downscaling” CCSM3
Temperature Precipitation (%change)
Fall difference between 1990s and 2040s
High spatial detail for climate change signal
%
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
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
Historic Trends in Extreme Precipitation (1970-2000)
Precip Intensity
ECHAM5-WRF Northeast Washington: Pend Oreille at Boundary Dam
Trends in intensity
Not the light events
But the big events
ECHAM5-WRF North Cascades: Skagit at Mt Vernon
ECHAM5-WRF North Cascades:Skagit at Diablo Dam
Trends are lostin the variability
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
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