Transcript Comparison of ensemble methods in a nested regional

Sensitivity to convective
parameterization in regional
climate models
Raymond W. Arritt
Iowa State University, Ames, Iowa USA
ICTP Regional Climate, 2-6 June 2003
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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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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
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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
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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.
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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
a 
Qc 
t
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Partitioning of moisture
convergence in the Kuo scheme
column moistening
= b × moisture convergence
precipitation
= (1-b) × moisture convergence
moisture
convergence
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Anthes: parameter b varies
(inversely) with column relative
humidity
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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
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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
Dp
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LFC
LCL
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Kain-Fritsch scheme
• Refinement of the approach by Fritsch and
Chappell (1980, J. Atmos. Sci.)
– the only scheme originally developed for midlatitude 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 convectivescale stabilization rate
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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
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Entrainment and detrainment in
the Kain-Fritsch scheme
mix cloud and
environmental
parcels, then
evaluate buoyancy
positively
buoyant parcels
are entrained
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negatively
buoyant parcels
are detrained
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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)
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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?
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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)
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Closure parameter ensemble
matrix
t
Dp 150 mb 125 mb 100 mb
75 mb
50 mb
7200 s
5400 s
3600 s
1800 s
600 s
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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
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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
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Flood case: RMS precipitation error
(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 Dp tend to perform well
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Drought case: RMS precipitation error
(mm) over the north-central U.S.
7200 s
5400 s
3600 s
1800 s
600 s
150 mb
125 mb
100 mb
75 mb
50 mb
79
78
73
65
75
70
85
84
70
62
77
84
81
77
76
85
88
117
96
60
71
62
67
57
73
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Flood case: number of days for which
each ensemble member was among
the 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
each ensemble member was among the
5 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
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, BettsMiller, Grell
– shallow convection on or off
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Mixed-physics ensemble
Mean
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Spread
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Multi-model ensemble (PIRCS-1B)
Mean
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Spread
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Area-averaged precipitation in the
north-central U.S.
45
40
35
30
25
20
15
10
5
0
Ensemble Mean
run2
run4
run6
run8
run10
Multi-Model (PIRCS 1B)
450
run1
run3
run5
run7
run9
400
Precipitation (mm)
Precipitation (cm)
Mixed Physics
350
300
250
ClimRA M S
DA RLAM
M M 5-A NL
P ROM ES
CRCM
HIRHA M
M M 5-B A TS
RegCM 2
RSM -NCEP
SweCLIM -ECM WF
M o del A verage
OB S-VEM A P
RSM -Scripps
SweCLIM -NCEP
OB S-HIGGINS
200
150
100
50
0
1-Jun
1-Jul
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31-Jul
1-Jun
11-Jun
21-Jun
1-Jul
11-Jul
21-Jul
31-Jul
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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
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Preliminary findings
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