towards a mcica representation of cloud-radiation interactions in the ecmwf model
DESCRIPTION
Towards a McICA representation of cloud-radiation interactions in the ECMWF model. Radiation: J.-J. Morcrette Cloud processes: Adrian Tompkins. McICA. In long seasonal runs and high-resolution 10-day forecasts How do the model survive noise in radiative heating rate? - PowerPoint PPT PresentationTRANSCRIPT
ECMWFRadiation and Clouds: Towards McICA? 20060608 1
Towards a McICA representation of cloud-radiation interactions
in the ECMWF model
Radiation: J.-J. Morcrette
Cloud processes: Adrian Tompkins
ECMWFRadiation and Clouds: Towards McICA? 20060608 2
McICA
In long seasonal runs and high-resolution 10-day forecastsHow do the model survive noise in radiative heating rate?How do the model survive noise in layer cloud fraction?
Tests with 31x10-day FC at TL319L60 from 20010401 to 20010501Tests with 4-month simulations at TL95 L60 for same period
control (control)random perturbation within Gaussian distribution (the relevant
quantity x -> x (1+*ran)=2 CF (1-CF) applied on x = CF (random1)=1.5 CF |HRtot| applied on x = HR (random2)=2 CF sqrt (HRLW
2+HRSW2) applied on x = HR (random3)
ECMWFRadiation and Clouds: Towards McICA? 20060608 3
McICA: How does the model survive radiative noise?
NH SH
Tropics India
Arabia E. Asia
Anomaly correlation Z 1000 hPa
NH SH
Tropics India
Arabia E.Asia
Anomaly correlation Z 500 hPa
TL319 L60 31 x 10-day FCs
ECMWFRadiation and Clouds: Towards McICA? 20060608 4
McICA: How does the model survive radiative noise?
NH SH
Tropics Tropics
NH SH
Arabia E. Asia Arabia E. Asia
IndiaIndia
Mean error T 850 hPa Mean error T 200 hPa
TL319 L60 31 x 10-day FCs
ECMWFRadiation and Clouds: Towards McICA? 20060608 5
McICA: Hoes does the model deal with radiative noise?
Systematic perturbation:
Re +1 mDe +10 m
TL95 L60 starting24-hour apart from20010401 to 2001030
Results averaged over JJA
Ref=Control Systematic perturbation
Difference Perturb-Control Student t-test
ECMWFRadiation and Clouds: Towards McICA? 20060608 6
McICA: Hoes does the model deal with radiative noise?
Systematic perturbation:
Re +0.1 mDe +1 m
Randomperturbation:
random3
Difference Perturb.-Control
Difference Random-Control
t-test
t-test
ECMWFRadiation and Clouds: Towards McICA? 20060608 7
McICA: How does the model survive radiative noise?
For each variable, first column is differenceSecond is area with difference significant at > 95% levelThird is area with difference significant at > 97.5 % level
No particular problem in either forecast or long run modeThe McICA approach can then be used (Pincus et al., 2004, JGR)
OLR ASW STR SSRControl 247.1 0.950 0.975 226.5 0.950 0.975 -54.9 0.950 0.975 140.1 0.950 0.975Re1/De10 1.3 17.1 11.1 3.0 24.0 18.4 -0.4 11.6 7.0 3.2 36.8 28.3Re0.1/De1 0.1 4.7 2.3 0.5 3.3 1.7 -0.1 6.2 3.6 0.5 9.2 4.3Random3 0.0 4.7 2.1 0.1 2.7 1.4 0.0 4.7 2.4 0.1 7.4 3.3
ECMWFRadiation and Clouds: Towards McICA? 20060608 8
What is McICA?
Monte-Carlo Independent Column Approximation
The CKD approach for 1-D PPH columns is
The ICA approach for domain averages is (ICA: Independent Column Approx.)
Combining (1) and (2) gives
Assuming clear- and cloudy-sky columns of gas, and if there are Nc cloudy
columns, (3) can be written as
K
k
knkn Fc1
,F Correlated-k distributed absorption coefficientsas in RRTM
N
n
nN 1
F1
F
(1)
(2)
N
n
K
k
knkFcN 1 1
,1
F (3)
Nc
n
K
k
cldknk
NcN
n
K
k
clrknk FcFc
N 1 1,
1 1,
1F
ECMWFRadiation and Clouds: Towards McICA? 20060608 9
What is McICA?
Which can be simplified to
The hypothesis is that can be given by
In which case, it follows (see Barker’s May 2002 presentation) that
cldcclrc
Nc
n
K
k
cldknk
cc
K
k
clrknkc
AA
FcN
AFcA
FF)1(
1)1(F
1 1,
1,
cldF
K
k
cldkNcrdmkFc
1},,...,1{
K
k
Nc
n
cldknk
c
cldKNcKKNc
cldKKK
cldNcNc
cldTcld
FcN
FcfFcfFcfFcfT
E
1 1,
,,,1,11,11,1,111,1
1
......1
limF
The model is unbiased in the ICA sense, so for T=K * Nc large enough, an unbiased value can be obtained using a different random cloud profile for each k-coefficient
ECMWFRadiation and Clouds: Towards McICA? 20060608 10
McICA: Tests with 1-D radiation code: LW
North Slope of Alaska
OLR SDLW Differences McICA-Ref
South Great Plains
ECMWFRadiation and Clouds: Towards McICA? 20060608 11
McICA: Tests with 1-D radiation code: LW
Trop. West Pacific: Manus
Trop. West Pacific: Nauru
OLR SDLW Differences McICA-Ref
ECMWFRadiation and Clouds: Towards McICA? 20060608 12
It does work in the LW : not yet in SW!
ARM-TWP NauruOLR
SDLW
Box100
McICA
ARM-SGPOLR
SDLW
Box100
McICA
ECMWFRadiation and Clouds: Towards McICA? 20060608 13
ECMWF Plans: Statistical Scheme
These explicitly specify the probability density function (PDF) for the total water qt (and sometimes also temperature)
sq
ttstc dqqPDFqqq )()(
qt
x
q
sq
qt
PD
F(q
t)
qs
Cloud cover is integral under
supersaturated part of PDF
sq
tt dqqPDFC )(Assumes no
supersaturation
LOTS OF ISSUES FOR IMPLEMENTATION: contact Adrian for his thoughts!!!
ECMWFRadiation and Clouds: Towards McICA? 20060608 14
Can use PDF information consistently in other schemes: Radiation, microphysics…
Example of use (with Rob Pincus): Use “cloud generator” to split cloudy column into many subcolumns to investigate effect of subgrid variability on ECMWF microphysics
ECMWFRadiation and Clouds: Towards McICA? 20060608 15
What do we expect?
qliq
Warm rain autoconversion
dqliq
dtSundqvist
Range of values
If subcloud variability is
ignored
Taking variability
into account
Lower autoconversion if subgrid variability neglected hence expect higher mean cloud thickness
ECMWFRadiation and Clouds: Towards McICA? 20060608 16
Instead: Sensitivity opposite to expected effect. Dominated by ice microphysics (q0.16 ice to snow) and accretion terms – i.e. Complex, esp. with multiphase microphysics