1. Module 17 MM5: Climate Simulation BREAK

2. Regional Climate Simulation for the Pan-Arctic using MM5 William J. Gutowski, Jr., Helin Wei, Charles Vörösmarty, Balazs Fekete & Steven Frolking Iowa State University University of New Hampshire

3. Regional Climate Simulation for the Pan-Arctic using MM5 Focus Land-Atmosphere Coupling in Pan-Arctic Hydrologic Cycle

4. NCAR/Penn State Non-hydrostatic MM5 (V2) Grell cumulus convection Mixed-phase microphysics CCM2 radiation Blackadar high resolution PBL NCAR Land Surface Model (LSM) Simple Thermodynamic Sea Ice Model Model

5. Period: 1 Oct. 1985 - 30 Sep. 1986 Sensitivity runs: Oct 85 or July 86 Computation: DOE/Ames ALICE (32-nodes) NCAR C90 (4-nodes) [2.5 cpu-hr/month] NCAR Blackforest Model domain (grid: 51 x 91; 120 km)

6.  Historical Arctic Rawinsonde Archive (HARA)  NCEP reanalysis (upper air)  TOVS  sfc. (skin) temperature  vertical temp profile  PBL stratification  Xie-Arkin precipitation  Polar Radiation Flux Project (PRF)  The Arctic 2-Meter Air Temperature data set Observational comparisons

7. Asian Arctic Watershed North American Arctic Watershed European Arctic Watershed Analysis Sectors = selected HARA sites Central Arctic Ocean

8. Cloud calibration (Oct. 1985 & July 1986) ExperimentCC algorithmResult (non-convective) 1RH > 75% (MM5 std.)too much 2RH > 98%too much 3CLW > 10 -2 kg/m 3 too little CIW > 5. 10 -3 kg/m 3 => CC=75% 4CLW > 10 -2 kg/m 3 ~ OK CIW > 5. 10 -3 kg/m 3 => CC=90%

9. Surface incident solar radiation [W/m 2 ] Polar Rad. Flux Obs. MM5/LSM

10. Cloud calibration - Precipitation simulation (July 1986)

11. Cloud calibration - Precipitation simulation (Oct. 1985)

12. 500 hPa Heights (Dec85 - Jan86 - Feb86) NCEPMM5

13. 500 hPa Heights (Mar - Apr - May 86) NCEPMM5

14. 500 hPa Heights (Jun - Jul - Aug 86) NCEPMM5

15. 500 hPa Heights (Oct85 - Nov85 - Sep86) NCEPMM5

16. latitude 45N 65N 85N RMS Differences (NCEP - simulation) (a) Z(500 hPa) (b) u(850 hPa) (c) v(850 hPa) (a) 45N 65N 85N 45N 65N 85N OCT JAN APR JUL 12 10 8 [m/s] 6 4 2 0 (b) (c) 12 10 8 [m/s] 6 4 2 0 200 150 [m] 100 50 0

17. 500 hPa Heights: RMS Difference vs. Time (MM5-NCEP) [m] 150 100 50 0 1 OCT 1 NOV

18. 850 hPa Wind: RMS Difference vs. HARA MM5/LSM NCEP U component V component

19. 850 hPa Wind: RMS Difference vs. HARA MM5/LSM NCEP U component V component

20. Obs. MM5/LSM 2-Meter Air Temperature

21. Obs. MM5/LSM Stratification Parameter:  (950 hPa) -  (900 hPa)

22. Precipitatble Water: MM5 vs. NCEP/NCAR Reanalysis & Sat. Obs.

23. Precipitation: MM5 vs. Xie-Arkin

24. Global, Composite Runoff  0.5˚ climatology  Composite based on Observed river discharge 0.5˚ river network (STN-30p) Climatology-driven water balance model River Networking & Runoff

25. Mackenzie River Global, Composite Runoff

26. Hudson Hope Gauging Station

27. 1. Water Balance Model driven by climatological precip. & temp. computes runoff in 0.5˚ grids 2. Runoff vs. discharge 3. Correct runoff in 0.5˚ grids by discharge

28. Surface Runoff [mm/month ] ______________ Post-processing assumption: No infiltration over frozen soil UNH-CLIM MM5/LSM

29. What is error vs. region size? Averaging Grids: Compute average error variance for...

30. What is error vs. region size? Averaging Grids: Compute average error variance for grids...

31. What is error vs. region size? Averaging Grids: Compute average error variance for grids of different spacings

32. What is error vs. region size? Average RMS difference scaled by amplitude of field’s annual cycle

33. Module 17 MM5: Climate Simulation Examples of MM5 climate simulation:  North America - good near-surface simulation - shortcomings of narrow boundary zone zone - generic errors in precipitation bias - shortcomings of gridded observations  Arctic - benefits of variety of observations - importance of cloud cover to all simulation - shortcomings of reanalyses - scale-dependence of errors