1. Eric Salathé JISAO Climate Impacts Group University of Washington Rick Steed UW Yongxin Zhang CIG, NCAR Cliff Mass UW Regional Climate Modeling and Projected Changes in Extreme Precipitation

2. 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 Climate Model Regional Climate Model (WRF) Statistical Downscaling (BCSD) Bias Correction Stat Downscale 6-hourlyMonthly Time Disaggregation Hourly Output Daily Output 100-200 km 6 km or ~3.7 mi

3. 150-km GCM High resolution is needed for regional studies Washington Oregon Idaho Cascade Range 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

4. High resolution is needed for regional studies (cont’d) Washington Oregon Idaho Cascade Range 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

5. 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

6. 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

7. Emissions Scenarios IPCC Emissions Scenarios for Climate Projections

8. Statistical Downscaling CCSM3 Fall difference between 1990s and 2040s Low spatial detail for climate change signal °C % Temperature Precipitation (%change)

9. WRF “Dynamic Downscaling” CCSM3 Temperature Precipitation (%change) Fall difference between 1990s and 2040s High spatial detail for climate change signal %

10. 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

11. Extreme Precipitation: Regional Models Change from 1970-2000 to 2030-2060 in the intensity of precipitation largest increase on windward slopes of Cascades, Columbia basin small increase or decrease along Cascade crest Change in mm

12. Historic Trends in Extreme Precipitation (1970- 2000) Precip Intensity

13. ECHAM5-WRF Northeast Washington: Pend Oreille at Boundary Dam Trends in intensity Not the light events But the big events

14. ECHAM5-WRF North Cascades: Skagit at Mt Vernon

15. ECHAM5-WRF North Cascades: Skagit at Diablo Dam Trends are lost in the variability

16. Summary of Precipitation Intensity Station1970-1999 Standard Deviation Change 2020s Change 2040s Skagit Diablo Dam14.791.51-0.340.41 Skagit Mt Vernon11.470.960.311.11 Ross Newhalem10.060.90-0.280.22 Baker Concrete17.141.61-0.180.97 Sauk17.051.73-0.500.67 Box Canyon7.430.610.110.43 Boundary7.500.680.150.47 Sea Tac5.970.650.000.08

17. 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