Determining the Local Implications of Global Warming

1. Determining the Local Implications of Global Warming Professor Clifford Mass, Eric Salathe, Patrick Zahn, Richard Steed University of Washington

2. Project Support • King County • Seattle City Light • EPA STAR Program • NOAA • Climate Impacts Group

3. Questions What are the implications of global warming for the Northwest? How will our mountains and land-water contrasts alter the story? Are there some potential surprises?

4. Regional Climate Prediction • To understand the impact of global warming, one starts with global circulation models (GCMs) that provide a view of the large-scale flow of the atmosphere. • GCMs are essentially the same as global weather prediction models but are run with much coarser resolution and allow the composition of the atmosphere to vary in time (e.g., more CO2) • Even leading GCMs only describe features roughly 500 km or larger in scale.

5. Northwest weather is dominated by terrain and land-water contrasts of much smaller scale. • In order to understand the implications of global changes on our weather, downscaling of the GCM predictions considering our local terrain and land use is required.

6. Annual Precipitation

7. Downscaling

8. Downscaling • The traditional approach to use GCM output is through statistical downscaling, which finds the statistical relationship between large-scale atmospheric structures and local weather. • Statistical downscaling either assumes current relationships will hold or makes simplifying assumptions on how local weather works.

9. Downscaling Such statistical approaches may be a reasonable start, but may give deceptive or wrong answers… since the relationships between the large scale atmospheric flow and local weather might change in the future.

10. Downscaling • There is only one way to do this right… running full weather forecasting models at high resolution over extended periods, with the large scale conditions being provided by the GCMs….this is called dynamical downscaling. • Such weather prediction models have very complete physics and high resolution, so they are capable of handling any “surprises”

11. Example of Potential Surprises • Might western Washington be colder during the summer under global warming? • Reason: interior heats up, pressure falls, marine air pushes in from the ocean • Might the summers be wetter? • Why? More thunderstorms due to greater surface heating.

12. Downscaling • Computer power and modeling approaches are now powerful enough to make dynamical downscaling realistic. • Takes advantage of the decade-long work at the UW to optimize weather prediction for our region.

13. UW Regional Climate Simulations • Makes use of the same weather prediction model that we have optimized for local weather prediction: the MM5. • 10-year MM5 model runs nested in the German GCM (ECHAM). • MM5 nests at 135km, 45km, and 15 km model grid spacing.

15. MM5 Model Nesting • 135, 45, 15 km MM5 domains • Need 15 km grid spacing to model local weather features.

16. Regional Modeling • Ran this configuration over several ten-year periods: • 1990-2000-to see how well the system is working • 2020-2030, 2045-2055, 2090-2100

17. Details on Current Study: GCM • European ECHAM model with resolution roughly equivalent to having grid points spaced ~ 210 km apart. Can resolve features of roughly 850 km size or more. • IPCC climate change scenario A2 -- aggressive CO2 increase (doubling by 2050) IPCC Report, 2001 IPCC Report, 2001

18. ECHAM Global Climate System Model

19. Global Forcing: Surface Temperature

20. First things first • But to make this project a reality we needed to conquer some significant technical hurtles. • Example: diagnosing and predicting future deep soil temperatures • Example: requirements for acquiring GCM output every 6 h and storing massive amounts of output. • Evaluating the 1990-2000 simulations

21. Evaluating of Model Fidelity • We have carefully evaluated how well the GCM and the MM5 duplicated the 1990-2000 period. • We previously had run the system using another GCM…the Parallel Climate Model…with unsatisfactory results….crazy cold waves during the winter. • ECHAM Model appears far better…but not perfect.

23. Too Cold • Cold episodes occurred 1-2 times per winter with temperature getting unrealistically cold (below 10F) in Puget Sound: • Also a general cold bias to minima • Better than previous attempts.

24. Why Cold Outbreaks? • Widespread surges of arctic air originate in ECHAM5, likely owing to poorly-resolved terrain (Cascades and Rockies). • Extreme cold air inherited by MM5. • Results from previous experiments with lower-resolution (T42) GCM indicate that higher resolution reduces frequency and severity of unrealistic cold events.

25. Evaluation of Future Runs Because there are some biases in the GCM runs, results for future decades (2020s, 2040s, and 2090s) will be evaluated against the ECHAM5-MM5 1990-2000 baseline

26. Now, The Future

36. Why Such Strong Warming on Mountain Slopes..Particularly in Spring? • Probable Answer: Snow melt resulting in more solar heating.

37. Change in Water Of Snowpack (%)

40. Snow and Ice Reflect Much of The Incoming Solar Radiation Solar Radiation Now

41. Global Warming Causes Snow level to Rise Resulting In Absorption of Solar Energy on Melted Slopes Solar Radiation Future =WARMING

43. Why Cooling West of Cascades in Spring? • Low clouds due to more onshore flow from off the cool, cloud Pacific. • The Montereyization of the western lowlands!