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Sensitivity to convective parameterization in regional climate models

Sensitivity to convective parameterization in regional climate models. Raymond W. Arritt Iowa State University, Ames, Iowa USA. Acknowledgments. Zhiwei Yang PIRCS organizing team: William J. Gutowski, Jr., Eugene S. Takle, Zaitao Pan PIRCS Participants funding from NOAA, EPRI, NSF.

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Sensitivity to convective parameterization in regional climate models

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  1. Sensitivity to convective parameterization in regional climate models Raymond W. Arritt Iowa State University, Ames, Iowa USA ICTP Regional Climate, 2-6 June 2003

  2. Acknowledgments • Zhiwei Yang • PIRCS organizing team: William J. Gutowski, Jr., Eugene S. Takle, Zaitao Pan • PIRCS Participants • funding from NOAA, EPRI, NSF ICTP Regional Climate, 2-6 June 2003

  3. 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 ICTP Regional Climate, 2-6 June 2003

  4. 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 ICTP Regional Climate, 2-6 June 2003

  5. 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. ICTP Regional Climate, 2-6 June 2003

  6. 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 t ICTP Regional Climate, 2-6 June 2003

  7. 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 ICTP Regional Climate, 2-6 June 2003

  8. 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 t • there is a quasi-equilibrium between generation and dissipation of instability ICTP Regional Climate, 2-6 June 2003

  9. Grell scheme • Lifting depth trigger: • vertical distance between the lifted condensation level and the level of free convection becomes smaller than some threshold depth Dp • default Dp = 150 mb in RegCM2 and default Dp = 50 mb in MM5 LFC Dp LCL ICTP Regional Climate, 2-6 June 2003

  10. 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 t • makes no assumptions about relation between grid-scale destabilization rate and convective-scale stabilization rate ICTP Regional Climate, 2-6 June 2003

  11. 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 ICTP Regional Climate, 2-6 June 2003

  12. Entrainment and detrainment in the Kain-Fritsch scheme mix cloud and environmental parcels, then evaluate buoyancy positively buoyant parcels are entrained negatively buoyant parcels are detrained ICTP Regional Climate, 2-6 June 2003

  13. 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) ICTP Regional Climate, 2-6 June 2003

  14. 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? ICTP Regional Climate, 2-6 June 2003

  15. 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 Dp (lifting depth threshold for trigger) t (time scale for release of convective instability) ICTP Regional Climate, 2-6 June 2003

  16. Closure parameter ensemble matrix Dp t ICTP Regional Climate, 2-6 June 2003

  17. 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 ICTP Regional Climate, 2-6 June 2003

  18. 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 ICTP Regional Climate, 2-6 June 2003

  19. 150 mb 125 mb 100 mb 75 mb 50 mb 7200 s 129 108 114 113 131 5400 s 121 122 119 116 111 3600 s 122 129 121 114 115 1800 s 125 127 121 123 114 600 s 157 154 128 130 137 low values of Dp tend to perform well Flood case: RMS precipitation error (mm) over the north-central U.S. ICTP Regional Climate, 2-6 June 2003

  20. 150 mb 125 mb 100 mb 75 mb 50 mb 7200 s 79 78 73 65 75 5400 s 70 85 84 70 62 3600 s 77 84 81 77 76 1800 s 85 88 117 96 60 600 s 71 62 67 57 73 Drought case: RMS precipitation error (mm) over the north-central U.S. ICTP Regional Climate, 2-6 June 2003

  21. 150 mb 125 mb 100 mb 75 mb 50 mb 7200 s 23 21 17 13 17 5400 s 14 13 13 12 21 3600 s 7 10 8 10 20 1800 s 8 5 5 4 7 600 s 9 11 12 10 15 Flood case: number of days for which each ensemble member was among the 5 members with lowest RMSE ICTP Regional Climate, 2-6 June 2003

  22. 150 mb 125 mb 100 mb 75 mb 50 mb 7200 s 14 9 10 20 22 5400 s 14 12 10 12 15 3600 s 15 7 9 6 7 1800 s 5 13 8 10 16 600 s 19 14 17 12 9 Drought case: number of days for which each ensemble member was among the 5 members with lowest RMSE ICTP Regional Climate, 2-6 June 2003

  23. 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 ICTP Regional Climate, 2-6 June 2003

  24. Mixed-physics ensemble Mean Spread ICTP Regional Climate, 2-6 June 2003

  25. Multi-model ensemble (PIRCS-1B) Mean Spread ICTP Regional Climate, 2-6 June 2003

  26. Area-averaged precipitation in the north-central U.S. Mixed Physics Multi-Model (PIRCS 1B) ICTP Regional Climate, 2-6 June 2003

  27. 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 ICTP Regional Climate, 2-6 June 2003

  28. Preliminary findings ICTP Regional Climate, 2-6 June 2003

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