1. Regional Impacts of day-to-day changes in the large scale Pacific North America (PNA) pattern: observations and prospects for skillful 7-14 day lead-time weather risk forecasts Craig Brown, M.S. Thesis UW Atmospheric Sciences Advisers: David Battisti, Nate Mantua and Ed Sarachik

2. Research Goals For selected stations, quantify the extent of “extreme weather” sensitivity to changes in the large scale PNA circulation pattern (freeze events, heavy rain and snow, wind gusts, extreme temperatures) –Use Thompson and Wallace (1999) approach with AO examine the potential for extending deterministic weather forecasts into skillful risk probability forecasts at lead times up to 14 days

3. Tracking the PNA pattern Wallace and Gutzler (1981): with standardized 500mb height anomalies … PNA(t) = 1/4*(Hawaii - Aleutians + Alberta - Florida) PNA pattern

4. Seasonal mean PNA index Created by Todd Mitchell, UW JISAO

5. Daily PNA index Frequency distributionsPNA index values St devs.

6. Properties of the daily PNA index Gaussian Impressive persistence, with an e- folding time scale ~7 days Well-correlated with heights at each of the 4 “centers of action” Distribution of the daily PNA index 1948-1999

7. Impacts of the day-to-day PNA variability Composite reanalysis fields to gain insights into what PNA variations at day-to-day time scales might be doing to surface weather

8. PNA 500 mb ht pattern: a dominant mode of cool season intraseasonal-to-interannual variability PNA + PNA -

9. Composite surface wind fields for +/- PNA PNA + PNA -

10. Composite surface wind and surface temperature anomaly fields for +/- PNA PNA + PNA -

11. Craig’s Extreme Event Analysis At 50 stations, daily data for 5 weather parameters, 1948-1999 –Tmin, Tmax, Snowfall, Precipitation, and peak wind gusts Using Histograms: –Identify extreme events: >1.5 and 2.0 std devs –Compare event counts for PNA +/- days –Map the ratios of event counts for PNA-/PNA+ days

12. Histograms of daily Oct-Mar Tmax at Stampede Pass, WA All days Avg=34.7 F PNA < -1 Avg=29.2 F PNA > +1 Avg=39.7 F

13. Histograms of daily Oct-Mar Tmin at Juneau, AK All days Avg=26 F PNA < -1 Avg=18 F PNA > +1 Avg=33 F

14. Daily PNA and Peak wind gusts

15. Daily PNA and extreme high precipitation events

16. Daily PNA and extreme high temperatures

17. Daily PNA and extreme low temperatures

18. Daily PNA and number of freeze events (Tmin < 0)

19. Daily PNA and extreme snowfall events

20. Seattle snowfall and the daily PNA

21. PNA and PNW weather extremes summary Days with PNA < -1 Increased frequency of extreme cold temperatures, freeze events, heavy precipitation, low elevation snowfall, and high wind gusts Days with PNA > +1 Increased frequency of extremely warm winter temperatures Reduced frequency of extreme cold, freezes, low elevation snowfall, heavy precip., and high wind gusts

22. Ensemble PNA forecasts and probabilistic risk assessments Lorenz told us the “butterflies” will trash the skill of deterministic weather forecasts at lead times >14 days, no exceptions! Due to the robust nature of PNA variability, and its strong tendency for persistence, it is the most predictable atmospheric pattern at lead times of 6- 10 days (Renwick and Wallace (1995)). Ensemble PNA forecasts, based on the output of global weather prediction models, show promising skill at lead times of 7-to-14 days

23. NCEP 11 member ensemble forecasts for the daily PNA index derived from Medium Range Forecast (MRF) model output

24. PNA prediction skill Forecast lead time in days

25. PNA-based risk forecast -2 -1 0 1 2 Predicted probability Seattle Snowfall (inches) Predicted probability 0 1 2 3 4 5 6 7 8 Step 1: Ensemble PNA forecast Step 2: Probability risk forecast

26. Filling the holes in the existing prediction system MM5 GFS PNA, AO/NAO, AAO Ensembles Intraseasonal … Madden-Julian Oscillation --> PNA ? Forecast Lead-Time Interannual ENSO, QBO, PDO

27. fair weather Fall 2002 and the PNA