1. ICTP Regional Climate, 2-6 June 20031 Sensitivity to convective parameterization in regional climate models Raymond W. Arritt Iowa State University, Ames, Iowa USA

2. ICTP Regional Climate, 2-6 June 20032 Acknowledgments Zhiwei Yang PIRCS organizing team: William J. Gutowski, Jr., Eugene S. Takle, Zaitao Pan PIRCS Participants funding from NOAA, EPRI, NSF

3. ICTP Regional Climate, 2-6 June 20033 Overview Survey of convective parameterizations Sensitivity to specification of closure parameters in the RegCM2 implementation of the Grell scheme Sensitivity to the choice of cumulus parameterization in regional climate simulations using MM5

4. ICTP Regional Climate, 2-6 June 20034 Survey of some commonly used convective parameterizations in regional models Kuo-Anthes –RegCM2, RAMS, MM5 Kain-Fritsch –MM5, RAMS (being implemented) Grell –RegCM2, MM5 Betts-Miller –Eta, MM5

5. ICTP Regional Climate, 2-6 June 20035 Survey of cumulus parameterization methods History and variants Mode of action: –What is the fundamental assumption linking the grid scale and cumulus scale? Cloud model, trigger, etc.

6. ICTP Regional Climate, 2-6 June 20036 Kuo-Anthes scheme Originally developed by Kuo (1965) with refinements by Anthes (1974) Mode of action: –assume convection is caused by moisture convergence (this is wrong!) –moisture convergence into a column is partitioned between column moistening and precipitation –thermodynamic profiles are relaxed toward a moist adiabat over a time scale 

7. ICTP Regional Climate, 2-6 June 20037 Partitioning of moisture convergence in the Kuo scheme column moistening = b × moisture convergence precipitation = (1-b) × moisture convergence Anthes: parameter b varies (inversely) with column relative humidity moisture convergence

8. ICTP Regional Climate, 2-6 June 20038 Grell scheme Simplification of the Arakawa and Schubert (1974) scheme –there is only a single dominant cloud type instead of a spectrum of cloud types Mode of action: –convective instability is produced by the large scale (grid scale) –convective instability is dissipated by the small scale (cumulus scale) on a time scale  –there is a quasi-equilibrium between generation and dissipation of instability

9. ICTP Regional Climate, 2-6 June 20039 Grell scheme Lifting depth trigger: –vertical distance between the lifted condensation level and the level of free convection becomes smaller than some threshold depth  p –default  p = 150 mb in RegCM2 and default  p = 50 mb in MM5 LFC LCL pppp

10. ICTP Regional Climate, 2-6 June 200310 Kain-Fritsch scheme Refinement of the approach by Fritsch and Chappell (1980, J. Atmos. Sci.) –the only scheme originally developed for mid- latitude mesoscale convective systems Mode of action: Instantaneous convective instability (CAPE) is consumed during a time scale  –makes no assumptions about relation between grid-scale destabilization rate and convective- scale stabilization rate

11. ICTP Regional Climate, 2-6 June 200311 Kain-Fritsch scheme Trigger: Parcel at its lifted condensation level can reach its level of free convection –a parcel must overcome negative buoyancy between LCL and LFC –a temperature perturbation is added that depends on the grid-scale vertical velocity Detailed and flexible cloud model: –updrafts and downdrafts, ice phase –entrainment and detrainment using a buoyancy sorting function

12. ICTP Regional Climate, 2-6 June 200312 Entrainment and detrainment in the Kain-Fritsch scheme negatively buoyant parcels are detrained positively buoyant parcels are entrained mix cloud and environmental parcels, then evaluate buoyancy

13. ICTP Regional Climate, 2-6 June 200313 Betts-Miller scheme based mainly on tropical maritime observations, e.g., GATE –variant Betts-Miller-Janjic used in the Eta model mode of action: when convective instability is released, grid-scale profiles of T and q are relaxed toward equilibrium profiles –equilibrium profiles are slightly unstable below freezing level –basic version of the scheme has different equilibrium profiles for land and water; this can cause problems (see Berbery 2001)

14. ICTP Regional Climate, 2-6 June 200314 Questions Within a given cumulus parameterization scheme, how sensitive are results to specification of the closure parameters? Within a given regional climate model, how sensitive are results to the choice of cumulus parameterization scheme?

15. ICTP Regional Climate, 2-6 June 200315 Sensitivity to closure parameters Perform an ensemble of simulations each using a different value for a closure parameter or parameters –must truly be an adjustable parameter; e.g., don’t vary gravitational acceleration or specific heat –parameter value should be reasonable; e.g., convective time scale can't be too long Present study: in the Grell scheme of RegCM2, vary  p (lifting depth threshold for trigger)  (time scale for release of convective instability)

16. ICTP Regional Climate, 2-6 June 200316 Closure parameter ensemble matrix 150 mb125 mb100 mb75 mb50 mb 7200 s 5400 s 3600 s 1800 s 600 s  pp

17. ICTP Regional Climate, 2-6 June 200317 Test cases Two strongly contrasting cases over the same domain: –drought over north-central U.S. (15 May - 15 July 1988) –flood over north-central U.S. (1 June - 31 July 1993) output archived at 6-hour intervals initial and boundary conditions from NCEP/NCAR Reanalysis

18. ICTP Regional Climate, 2-6 June 200318 Verification measures Root-mean-square error –compute RMSE at each grid point in the target region (north-central U.S. flood area) and average Number of days that each parameter combination was within the 5 best (lowest RMSE) of the 25 combinations –attempts to show consistency with which the parameter combinations perform

19. ICTP Regional Climate, 2-6 June 200319 Flood case: RMS precipitation error (mm) over the north-central U.S. 150 mb125 mb100 mb75 mb 50 mb 7200 s 129108114113 131 5400 s 121122119116 111 3600 s 122129121114 115 1800 s 125127121123 114 600 s 157154128130 137 low values of  p tend to perform well

20. ICTP Regional Climate, 2-6 June 200320 Drought case: RMS precipitation error (mm) over the north-central U.S. 150 mb125 mb100 mb75 mb50 mb 7200 s 7978736575 5400 s 7085847062 3600 s 7784817776 1800 s 85881179660 600 s 7162675773

21. ICTP Regional Climate, 2-6 June 200321 Flood case: number of days for which each ensemble member was among the 5 members with lowest RMSE 150 mb125 mb100 mb75 mb50 mb 7200 s2321171317 5400 s1413 1221 3600 s7108 20 1800 s85547 600 s911121015

22. ICTP Regional Climate, 2-6 June 200322 Drought case: number of days for which each ensemble member was among the 5 members with lowest RMSE 150 mb125 mb100 mb75 mb50 mb 7200 s149102022 5400 s1412101215 3600 s157967 1800 s51381016 600 s191417129

23. ICTP Regional Climate, 2-6 June 200323 Variability with different convective schemes: A mixed-physics ensemble How much variability can be attributed to differences in physical parameterizations? Perform a number of simulations each using different cloud parameterizations: –convective parameterization: Kain-Fritsch, Betts- Miller, Grell –shallow convection on or off

24. ICTP Regional Climate, 2-6 June 200324 Mixed-physics ensemble MeanSpread

25. ICTP Regional Climate, 2-6 June 200325 Multi-model ensemble (PIRCS-1B) MeanSpread

26. ICTP Regional Climate, 2-6 June 200326 Area-averaged precipitation in the north-central U.S. Mixed PhysicsMulti-Model (PIRCS 1B)

27. ICTP Regional Climate, 2-6 June 200327 Preliminary findings Results can be sensitive to choice of closure parameters –best value of closure parameter varies depending on the situation: it is not realistic to expect a single best value Use of different cumulus parameterizations produced about as much variability as use of completely different models: –Beware of statements such as “MM5 (RAMS, RegCM2 etc.) has been verified...” without reference to the exact configuration! –There may be potential for this variability to aid in generating ensemble forecasts: it is easier to run one model with different parameterizations than to run a suite of different codes

28. ICTP Regional Climate, 2-6 June 200328 Preliminary findings