1. ISU Atmospheric Component Update – Part I Justin Glisan Iowa State University

2. Update PhD work completed last semester! Dissertation title: Arctic Daily Temperature and Precipitation Extremes: Observed and Simulated Behavior – Composed of three papers – Will be submitted to J. Clim. and JGR Postdoctoral work on NSF extremes project 2

3. PhD Research Questions Are there certain atmospheric circulation regimes favorable for extreme events? Does seasonality and geography affect extremes? Can WRF simulate well Arctic extreme and spatially wide-spread events? What is the effect of spectral nudging on extremes? 3

4. Case Study 1: Effects of spectral nudging on temperature and precipitation simulations 4

5. RACM Domain and Analysis Regions 5

6. Case Study 1 Background Long and short PAW simulations were run on the RACM domain A systematic, atmosphere-deep circulation bias formed within the northern Pacific storm track Various remedies tested, but with little success Spectral or interior nudging was introduced

7. Hypothesis A set of short simulations was run using the WRF default nudging strength with promising results This case study examines the effects of a range of nudging strengths on temperature and precipitation means and extremes We hypothesize that too much interior nudging can smooth out extreme events while leaving mean behavior observationally consistent

8. Case Study Setup PAW six-member ensemble on RACM Two study months: – January and July 2007 – Simulations begun in December and June, with first three weeks discarded for spin-up Four analysis regions selected to study geographical effects of nudging on means and extremes – 2-m T: 1 st, 5 th, 50 th, 95 th, and 99 th percentiles – Daily precipitation: 50 th, 95 th, and 99 th percentiles

9. Nudging Coefficient Table 9

10. Tukey HSD Rank Matrix Compares the means of all possible pairs in the nudging coefficient pool – Including applicable observation sets – Also includes ANOVA Calculates how large the mean difference among group members must be for any two members to be significantly related 10

11. January Precipitation 11 *Coefficients that are significantly related are connected by a box. Alaska Analysis Region - Tukey HSD Rank Matrix

12. July Precipitation 12 Alaska Analysis Region - Tukey HSD Rank Matrix

13. January 2m-Temperature 13 1 st 2 nd 3 rd 4 th 5 th 6 th 7 th 8 th 9 th 10 th Double Full Half Quarter Eighth Sixteenth 128th Zero EI NCDC 1 st 2 nd 3 rd 4 th 5 th 6 th 7 th 8 th 9 th 10 th Double Full Half Quarter Eighth Sixteenth 128th Zero EI NCDC Alaska Analysis Region - Tukey HSD Rank Matrix

14. July 2m-Temperature January 6th, 2012Glisan Ph.D. Seminar – Iowa State University14 1 st 2 nd 3 rd 4 th 5 th 6 th 7 th 8 th 9 th 10 th Double Full Half Quarter Eighth Sixteenth 128th Zero EI NCDC 1 st 2 nd 3 rd 4 th 5 th 6 th 7 th 8 th 9 th 10 th Double Full Half Quarter Eighth Sixteenth 128th Zero EI NCDC Alaska Analysis Region - Tukey HSD Rank Matrix

15. Conclusions Winter behavior more sensitive to nudging Improve Cold Season Mean and Extreme Behavior – Stronger SN for precipitation – Weaker SN for surface temperatures Improve Warm Season Mean and Extreme Behavior – Weaker SN for precipitation – Stronger SN for surface temperatures 15 Optimal range for pan-Arctic simulations: 1/8 th – 1/16 th the WRF default

16. Case Study 2: WRF Summer extreme daily precipitation over the CORDEX Arctic 16

17. CORDEX Arctic Domain 17

18. Case Study 2 Setup 19-year, six-member ensemble simulation Summer season (JAS), defined by climatological sea ice minimum Four analysis regions over North America Daily precipitation analysis – Mean behavior – Individual extreme events – Spatially wide-spread extreme events 18

19. Analysis Regions 19 CE CW AS AN

20. Frequency vs. Intensity Grid point daily events (> 2.5 mm) pooled separately for PAW and NCDC observations Extremes defined at the 95 th and 99 th percentiles Histograms normalized to account for differences in spatial sampling 20

21. Frequency vs. Intensity for WC 21

22. Simultaneity of Extremes We define simultaneous extremes as 25 or more concurrent grid point events NCDC scaled to match model resolution Plots give an indication of the spatial scale of the extremes 22

23. Simultaneity of Events 23

24. Extreme Composites From the simultaneity plot, we extract days matching our wide-spread criterion Using the EI and PAW output, we construct composites of pertinent surface and atmospheric fields – Diagnose relevant physical conditions conducive for wide-spread extremes – Anomaly plots also used to show how extremes depart from climatology – Are PAW and obs. consistent in their treatment of circulation behavior? 24

25. 25 MSLP [hPa] 850-hPa Winds [ms -1 ] 500-hPa Geopotential Heights [gpm] ERA-InterimPan-Arctic WRF

26. 26 Figure 1: (top left) Composited summer extreme precipitation [mm-d -1 ] and (top right) location occurrence [%] of spatially widespread extreme events. (bottom) Convective contribution anomaly [%] of total daily precipitation during extreme event days for Western Canada. Figure 2: (left) Composited Convective Available Potential Energy anomaly [J-kg -1 ] and (right) Level of Free Convection anomaly [m] for Western Canada.

27. Summer Conclusions o The model produces well the physical causes of extremes, despite lower precipitation intensity o Similar physical consistency between model and observations appears for all analysis regions (not shown) o Orographic processes producing a majority of widespread extreme events in all analysis regions except Western Canada o Convective processes contribute significantly to widespread extreme precipitation in Western Canada 27

28. Future Work The use of SOMs to better understand seasonally dominant circulation features Produce future climate simulations with PAW – Determine if contemporary causes of extreme behaviors are present and if not, how and why they evolve in a warming climate – Force PAW with GCM BCs to determine how extreme events may be altered 28