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

ICTP Regional Climate, 2-6 June 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 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 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 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 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 

ICTP Regional Climate, 2-6 June 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 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

ICTP Regional Climate, 2-6 June 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

ICTP Regional Climate, 2-6 June 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

ICTP Regional Climate, 2-6 June 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 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

ICTP Regional Climate, 2-6 June 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 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 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)

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

ICTP Regional Climate, 2-6 June 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 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 Flood case: RMS precipitation error (mm) over the north-central U.S. 150 mb125 mb100 mb75 mb 50 mb 7200 s s s s s low values of  p tend to perform well

ICTP Regional Climate, 2-6 June Drought case: RMS precipitation error (mm) over the north-central U.S. 150 mb125 mb100 mb75 mb50 mb 7200 s s s s s

ICTP Regional Climate, 2-6 June 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 s s s s s

ICTP Regional Climate, 2-6 June 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 s s s s s

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

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

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

ICTP Regional Climate, 2-6 June 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 Preliminary findings