ICTP Regional Climate, 2-6 June 2003 1

Sensitivity to convective Sensitivity to convective parameterization in regional parameterization in regional

climate modelsclimate models

Raymond W. ArrittIowa State University, Ames, Iowa USA

ICTP Regional Climate, 2-6 June 2003 2

AcknowledgmentsAcknowledgments

• 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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OverviewOverview

• 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

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Survey of some commonly used Survey of some commonly used convective parameterizations in convective parameterizations in regional modelsregional models

• Kuo-Anthes– RegCM2, RAMS, MM5

• Kain-Fritsch– MM5, RAMS (being implemented)

• Grell– RegCM2, MM5

• Betts-Miller– Eta, MM5

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Survey of cumulus Survey of cumulus parameterization methodsparameterization methods

• History and variants• Mode of action:

– What is the fundamental assumption linking the grid scale and cumulus scale?

• Cloud model, trigger, etc.

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Kuo-Anthes schemeKuo-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

acQ

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Partitioning of moisture Partitioning of moisture convergence in the Kuo schemeconvergence 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

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Grell schemeGrell 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

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Grell schemeGrell 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

LFCLFCLCLLCL

pp

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Kain-Fritsch schemeKain-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

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Kain-Fritsch schemeKain-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

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Entrainment and detrainment in Entrainment and detrainment in the Kain-Fritsch schemethe Kain-Fritsch scheme

negatively buoyant parcels are detrained

positively buoyant parcels are entrained

mix cloud and environmental parcels, then evaluate buoyancy

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Betts-Miller schemeBetts-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)

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QuestionsQuestions

• 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?

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Sensitivity to closure parametersSensitivity 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, varyp (lifting depth threshold for trigger)

(time scale for release of convective instability)

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Closure parameter ensemble Closure parameter ensemble matrixmatrix

150 mb 125 mb 100 mb 75 mb 50 mb

7200 s

5400 s

3600 s

1800 s

600 s

p

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Test casesTest 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

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Verification measuresVerification 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

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Flood case: RMS precipitation error Flood case: RMS precipitation error (mm) over the north-central U.S.(mm) over the north-central U.S.

  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 low values of p tend to perform wellp tend to perform well

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Drought case: RMS precipitation error Drought case: RMS precipitation error (mm) over the north-central U.S.(mm) over the north-central U.S.

  150 mb 125 mb 100 mb 75 mb 50 mb

7200 s79 78 73 65 75

5400 s70 85 84 70 62

3600 s77 84 81 77 76

1800 s85 88 117 96 60

600 s71 62 67 57 73

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Flood case: number of days for which Flood case: number of days for which each ensemble member was among each ensemble member was among the 5 members with lowest RMSEthe 5 members with lowest RMSE

  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

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Drought case: number of days for which Drought case: number of days for which each ensemble member was among the each ensemble member was among the 5 members with lowest RMSE5 members with lowest RMSE

  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

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Variability with different convective Variability with different convective schemes: A mixed-physics ensembleschemes: 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

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Mixed-physics ensembleMixed-physics ensembleMean Spread

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Multi-model ensemble (PIRCS-1B)Multi-model ensemble (PIRCS-1B)Mean Spread

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Area-averaged precipitation in the Area-averaged precipitation in the north-central U.S.north-central U.S.

Mixed Physics Multi-Model (PIRCS 1B)

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Preliminary findingsPreliminary 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

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Preliminary findingsPreliminary findings