Does mesoscale instability control sting jet variability?

1. Does mesoscale instability control sting jet variability? Neil Hart, Suzanne Gray and Peter Clark Martínez-Alvarado et al 2014, MWR

2. Instability and Predictability Baroclinic Instability Convective Instability Symmetric Instability ~1km ~30mins ~100km ~6hours ~1000km ~day

3. St Jude Forecast:Global Ensemble Courtesy: ECMWF

4. St Jude Forecast: MetOffice 4km Courtesy: MetOffice

5. IR satellite image at 0600 UTC St Jude Forecast: MetOffice UKV Courtesy: MetOffice & EUMETSAT

6. Why mesoscale instability? • Browning 2004 hypothesized that Conditional Symmetric Instability (CSI) in the cloud head cloud is an explanation for the fingering seen in satellite imagery at the tip of some cloud heads • The resulting slantwise circulation would see ascent into the cloud head with descent near the cloud head tip • This hypothesizes a mechanism for sting jet descents, as seen in • 1987 Great Storm Fig. 14 Browning 2004, QJRMS

7. Why mesoscale instability? T -10hrs Shading is number of pressure levels between 800hPa and 600hPa, that have CSI (MPVS*<0) Blue circle indicates position of air parcels manually identified as part of the sting jet descent Moist Baroclinic LC1 experiment Fig. 7 Baker et al 2013, QJRMS

8. Why mesoscale instability? T -6hrs Shading is number of pressure levels between 800hPa and 600hPa, that have CSI (MPVS*<0) Blue circle indicates position of air parcels manually identified as part of the sting jet descent Moist Baroclinic LC1 experiment Fig. 7 Baker et al 2013, QJRMS

9. Why mesoscale instability? T -2hrs Shading is number of pressure levels between 800hPa and 600hPa, that have CSI (MPVS*<0) Blue circle indicates position of air parcels manually identified as part of the sting jet descent Moist Baroclinic LC1 experiment Fig. 7 Baker et al 2013, QJRMS

10. Friedhelm, Robert and Ulli Friedhelm 8 Dec ‘11 Robert 27 Dec ’11 Ulli 3 Jan ‘12 Identified with DSCAPE diagnostic applied to ERA-Interim (after Martínez-Alvarado 2012) Smart & Browning 2013 Martínez-Alvarado et al 2014, MWR

11. Cyclone Robert Courtesy: EUMETSAT, Sat24.com

12. Methodology Friedhelm 8 Dec ‘11 Robert 27 Dec ’11 Ulli 3 Jan ‘12 Produce 24 member ensemble simulations of each storm Compute back trajectories from low-level jet region of each member Cluster analysis to classify trajs. to identify descending airstreams Explore link between these descents and CSI across ensemble

13. Model Setup • MetOffice Unified Model vn8.2 • MOGREPS-Global ETKF • 24 Init. Pert. Members • (Bowler et al, 2008) • MOGREPS-Regional • N. Atl. & Europe Domain • 12km, 70 Levels • All storms initialised at 18 UTC • the day before maximum intensity • Results analysed further are T+10 to T+24 forecasts

14. Synoptic Overview

15. Synoptic Overview Small spread in synoptic scale evolution between ensemble members: Good, since can now focus on mesoscale differences

16. Compute Back Trajectories Control Run from Cyclone Robert ensemble

17. Compute Back Trajectories Cloud Top Temperature 850hPa 45m/s Isotach Control Run from Cyclone Robert ensemble

18. Compute Back Trajectories Trajectories Computed with Lagranto (Wernli & Davies, 1997) Control Run from Cyclone Robert ensemble

19. Classification of Airstreams Identify class means that descend Cluster Class Mean Trajectories: Each trajectory described by x,y, P, θw for 5 hours preceding arrival in low-level jet Use Relative Humidity to remove descents that started outside cloud head Ward’s Hierarchical Clustering Algorthim

20. Classification of Airstreams Identify class means that descend Use Relative Humidity to remove descents that started outside cloud head Ward’s Hierarchical Clustering Algorthim

21. Classification of Airstreams Identify class means that descend Use Relative Humidity to remove descents that started outside cloud head

22. Classification of Airstreams Use Relative Humidity to remove descents that started outside cloud head

23. Classification of Airstreams

24. Classification of Airstreams Each Class contains a population of individual trajectories that arrive at given time. Next slide Size of these populations are gathered for all descent classes at all times for each ensemble member

25. # of Traj. Arriving in LLJ 1600

26. # of Traj. Arriving in LLJ 1600 Majority of of ensemble members have peak in # trajs at 12UTC

27. Ensemble Sensitivity Control run 281K θw 850hpa Control run cloud head X Interpret as change in # trajs for 1 s.d change in CSI metric

28. Ensemble Sensitivity X Methodology after Torn & Hakim 2008

29. Ensemble Sensitivity X Methodology after Torn & Hakim 2008

30. Ensemble Sensitivity X Methodology after Torn & Hakim 2008

31. Ensemble Sensitivity X Methodology after Torn & Hakim 2008

32. Conclusions • Consistent synoptic development across ensemble • Considerable variability in mesoscale wind features • Demonstrated method to classify descending airstreams • Large variability in number of descending trajectories across ensemble • Does mesoscale instability control sting jet variability? • Strength of sting jet descent is associated • with CSI in the cloud head • (in Robert as simulated with MetUM)

33. Cyclone Friedhelm

34. Cyclone Friedhelm Comparison to Martinez- Alvarado et al 2014 manual classification

35. Ensemble Sensitivity # trajs CSI across ensemble If correlation > threshold (0.5 used here), good!

36. Ensemble Sensitivity # trajs ∆y ∆x CSI across ensemble Calculate Gradient

37. Ensemble Sensitivity # trajs ∆y ∆x CSI across ensemble Ens. Sensitivity = ∆y (∆x = 1 s.d.)