ISU Atmospheric Component Update – Part I

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

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?

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

5. RACM Domain and Analysis Regions

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: 1st, 5th, 50th, 95th, and 99th percentiles • Daily precipitation: 50th, 95th, and 99th percentiles

9. Nudging Coefficient Table

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

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

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

13. January 2m-Temperature Alaska Analysis Region - Tukey HSD Rank Matrix

14. July 2m-Temperature Alaska Analysis Region - Tukey HSD Rank Matrix Glisan Ph.D. Seminar – Iowa State University

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 • Optimal range for pan-Arctic simulations: • 1/8th– 1/16th the WRF default

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

17. CORDEX Arctic Domain

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

19. Analysis Regions CE AN AS CW

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

21. Frequency vs. Intensity forWC

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

23. Simultaneity of Events

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?

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

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

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