Christopher Selman

1. Diurnal Variations in the Southeastern United States Christopher Selman Center for Ocean-Atmospheric Prediction Studies Florida State University

2. Objectives • Highlight the importance of diurnal rainfall • Highlight the importance of global atmospheric reanalysis package and convection scheme choice in downscaling • Investigate physical behavior of the model • Propose development of a next-generation data set which incorporates these ideas and improves upon existing data

3. Overview Section 1: Why diurnal? Section 2: Downscaling via the RSM Section 3: Verifying against Obs. Section 4: Analysis of summer climate The RSM Fractional Variance Characteristics of SEUS JJA In the Southeast Reanalysis packages Choosing the right setup Impact of vegetation Impact on seasonal rain Convective schemes

4. Section 1: Why Diurnal?

5. Diurnal Variability in the Southeast • Topography • Sea breezes • Land surface feedbacks - Irrigation - Urban Heat Islands - Vegetative feedbacks?

6. Showing the Importance • Diurnal rains represent a large portion of total seasonal rainfall - As seen in (Bastola & Misra 2012, Selman et al. 2013) • Myriad private firms, public firms, and individuals rely on strong rainfall - Usage restrictions, “water wars” • Easy to plan on climatological scale, but what about near-term extreme events?

7. Results from Observation NCEP STAGE IV “Fraction of total seasonal rainfall explained by diurnal rainfall” DJF MAM JJA SON Bastola & Misra (2012)

8. Section 2: Downscaling via the RSM

9. Downscaling • Downscaling is the process of taking coarse resolution data (e.g., global climate model) and numerically transforming the data to a finer resolution for use by science and industry alike • Dynamical downscaling - Uses physics parameterizations to model the atmosphere - Allows us to explore physical ties between phenomena

10. Reanalysis Packages • National Center for Environmental Prediction Department of Energy Reanalysis 2 (NCEP-R2) • European Center for Medium-Range Weather Forecasts 40-Year Reanalysis (ERA40) • Twentieth Century Reanalysis Project (20CR) • Different data assimilation techniques • Different resolution • No benefit in downscaling by a factor of three

11. The RSM • The Regional Spectral Model (Kanamaru & Kanamitsu 2007) - High-resolution, regional model - Scale-selective Bias Correction eliminates need for nested domains - Integrations calculated from 1989 to 1999 • CLARReS10 / CLAREnCE10 / FLAReS1.0

12. Convection Schemes • Schematics that govern the formation of cumulus clouds within a physical model • We have chosen to compare the Relaxed Arakawa-Schubert (RAS) scheme, and the Kain-Fritsch 2 (KF2) scheme - Differentiated by closure scheme - RAS: Mass flux - KF2: Convectively Available Potential Energy

13. Section 3: Validating against Observation

14. Observation Packages • National Centers for Environmental Prediction STAGE IV rainfall analysis - Timeframe of 2003-2010 - Quality controlled, multi-sensor analyses - (No satellite data) • Ratio of variance of diurnal amplitude of precipitation to variance of average daily accumulated precipitation Fractional Variance

15. STAGEIV 20CR - RAS ERA40 - RAS NCEPR2-RAS

16. STAGEIV 20CR - KF ERA40 - KF NCEPR2-KF

17. Fractional Variance • From these plots, we see that NCEP-R2 generally gives us good results - 20CR underestimates variability - ERA40 is a solid second place • RAS outperforms KF in this regard - When does the rain fall? - Huge impact on hydrology - Especially in summer!

18. Time of JJA PMAX NCEPR2-RAS WINNER NCEPR2-KF

19. Section 4: Analysis of summer climate

20. Characteristics of the Southeast

21. Characteristics of the Southeast • Separation between TMAX and PMAX - Why are they not at the same time? - Have to “punch through” the boundary layer, which is ballooning upward with T increase - Layer is “punched through” by moisture convergence - At some point, a threshold is crossed, and convergence is able to punch through the boundary layer, and convection is initiated • Is this physically represented within our model?

22. Characteristics of the Southeast Temp (Needleaf Evergreen)

23. Characteristics of the Southeast Temp PBL

24. Characteristics of the Southeast Temp MSC PBL

25. Characteristics of the Southeast Temp MSC Precip PBL

26. Characteristics of the Southeast Temp (Perennial Groundcover)

27. Characteristics of the Southeast Temp PBL

28. Characteristics of the Southeast Temp PBL MSC

29. Characteristics of the Southeast Temp Precip PBL MSC

30. Surface Vegetation • Our land surface model categorizes vegetation into twelve types - Six of which are present in the Southeast - They include cultivations, broadleaf deciduous, etc. - Each surface can be characterized by roughness lengths, heat capacity, etc… • Do these different vegetative surfaces seem to play a role in dictating diurnal variability?

31. They don’t! Diurnal Amplitude (mm) Elevation (m) Albedo of Surface (frac)

32. …latitude does, though! Diurnal Amplitude (mm) Elevation (m) Latitude (deg)

33. Why? • It seems that latitude has a primary influence on diurnal precipitation • There is also a noticeable influence from elevation - Attributable to model bias (Meinke 2007) • Surface type may yet have a secondary effect - What matters more is how that land is being used (Dai et al. 2006) - Non-static characteristics of land are important too!

34. Conclusions

35. Conclusions • Reanalysis package and convection scheme choice play a significant role in simulating diurnal rains • Physics of diurnal variability is captured in simulation (even in low soil moisture conditions) • Latitude, not vegetation type, has first order impact on diurnal rainfall

36. What’s left? • Adding irrigation to the model - Southeast: not a heavy irrigator - Present use has an impact on hydrology & climate - Vigor of irrigation • Adding urban centers to model - Land surface feedbacks hugely important - How does incorporating urban heat islands or changing surface characteristics impact local climate?

37. Questions?