2. The Global Wind Oscillation April-May 2007 Edward Berry NOAA/NWS Dodge City, Kansas Klaus Weickmann NOAA/ESRL/PSD, Boulder, Colorado High Plains Conference Hastings, NE August 16 th, 2007

3. > WB(2007) proposed a Global Synoptic Dynamic Model of subseasonal atmospheric variabilty (GSDM) > GSDM considers the interaction of 3-4 different time scales including the MJO recurrence time > Purpose of GSDM is to extend current thinking beyond the MJO > The Global Wind Oscillation (GWO) objectively represents a non-oscillatory component of the GSDM Background

4. Outline of Talk * Review the GSDM * Introduce the GWO * Case Study * Summary * Risk Assessment Plots

5. There is no Cookbook!!!

6. Subseasonal Forecasting Tools –Numerical Models ensembles (multi-model) ocean coupled –Statistical Models composites regressions –Statistical-Dynamical linear inverse models analogues of forecasts –Global Synoptic-Dynamic Model (GSDM) Weather Climate Linkage –Regime transitions –Extreme events eddies convection mountains

7. What is the GSDM?   Evolutionary framework for weather- climate diagnosis and forecasting   Core subseasonal time scales - fast, slow and “quasi-o”; also ENSO, etc.   Physical Processes – –Tropical Convection (tropical OLR modes) – –Momentum Transports (eddy vs. HC/FC) – –Torques (Mountain and friction)   New way to evaluate model predictions   Used to keep pulse on evolving climate state: from synoptic storms to decadal shifts

8. GLOBAL SYNOPTIC DYNAMIC MODEL (GSDM) Each stage shows spatial structure of 3-4 time scales Wave energy dispersion favors high impact weather across USA Plains Below normal temperatures possible across central and eastern USA High Impact weather event possible along USA west coast Heavy precipitation event possible Southwest USA

9. What is the GWO? Quasi-phase space plot of normalized AAM against normalized AAM tendency Similar to MJO phase space plots such as WH(2004) Objectively represents portion of GSDM most closely linked with AAM variations – Tropical Convection (tropical OLR modes) implicitly – Momentum Transports (eddy vs. HC/FC) – Torques (Mountain and friction) Captures behaviors such as poleward propagation of zonal mean wind anomalies Provides considerable independent information on variations of the global circulation

10. Stage 1 Stage 2 Stage 3 Stage 4 Relative AAM Tendency Relative AAM 4/3 5/11 4/9 5/16 4/26 5/26 4/16 5/22

11. Case Study: Week-2 Forecast for 18-25 May 2007  Western USA Trough when models forecasted a ridge  Severe storms across the Plains  GWO provided additional predictive insight

13. MAY 11

14. #1 #2

15. E E E E E E E E W W W W W E #1 #2 4/9 5/16 4/3 5/11 4/26 5/26 4/16 5/22

16. H H H H H H L L L L L L H L H H L H H L H L H L L H H H L H H H H H L May 7 May 11 250mb Daily Mean Vector Wind Anomalies

17. L L L L H H H H H H H H L H H L L L May 15 May 19 H H 250mb Daily Mean Vector Wind Anomalies

18. H H H L L H L L L H L H H H L May 23 250mb Daily Mean Vector Wind Anomalies

20. PDF for AMJ GSDM Stage 2 vs. 4 Western USA 850 Ta Probs ordinate Std dev abscissa Stage 2 Stage 4 - 4.25 +4.25 - 1.75

21. GSDM provides link between weather and climate GWO is objective representing a portion of the GSDM Risk assessment maps based on the GSDM are being developed Present efforts include HydroMet Testbed for west coast floods and CPC Global Tropical HA Subseasonal forecasts (weeks 1-4) must be probabilistic and verified accordingly Summary and Conclusions

22. QUESTIONS?

23. Summary and Conclusions GSDM provides link between weather and climate GSDM provides link between weather and climate GWO is objective representing a portion of the GSDM GWO is objective representing a portion of the GSDM Risk assessment maps based on the GSDM are being developed Risk assessment maps based on the GSDM are being developed Present efforts include HydroMet Testbed for west coast floods and CPC Global Tropical HA Present efforts include HydroMet Testbed for west coast floods and CPC Global Tropical HA Subseasonal forecasts (weeks 1-4) must be probabilistic and verified accordingly Subseasonal forecasts (weeks 1-4) must be probabilistic and verified accordingly

25. . 11May.

29. W W E W W E W W E #1 #2 4/35/11 4/9 5/16 4/26 5/26 4/16 5/22

30. #1 #2 4/3 4/9 5/11 5/16 4/26 5/26 4/16 5/22

31. #1 #2 4/3 4/9 5/11 5/16 4/26 5/26 4/16 5/22