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The Stochas+c Mul+cloud Model (SMCM) as part of an opera+onal convec+on
parameterisa+on in a comprehensive GCM
Karsten Peters1,2, Chris+an Jakob1, Benjamin Möbis2,1
EGU GA 2015, Session AS1.13, 17 Apr 2015
SMCM development: Boualem Khouider and Andrew J Majda
1ARC Centre of Excellence for Climate System Science, Monash University, Australia 2Max Planck Ins+tut für Meteorologie, Hamburg Germany
Rainfall in current generation GCMs (IPCC AR5 status) CMIP5 precip vs GPCP (IPCC AR5 (2013))
Projected precip change 2081-‐2100 w.r.t. 1986-‐2005, RCP8.5 scenario (IPCC AR5 (2013))
The future of convection schemes?
Our sugges(on
i. stochas+c framework, ii. which incorporates memory effects and iii. convec+ve organisa+on/aggrega+on
Such a tool already exists….
The Stochastic Multi-Cloud Model (SMCM) (Khouider et al 2010)
- divides a large-‐scale domain into independent sub-‐domains
- predicts an ensemble of congestus, deep and stra+form cloud
Ø transi+ons determined by stochas(c Markov-‐Chain process
Ø area frac(ons per cloud type - driven by 2 external large-‐scale variables
Ø C as “convec+on” (e.g. CAPE or ω500)
Ø D as “dryness” (e.g. RH500)
The Stochastic Multi-Cloud Model (SMCM) (Khouider et al 2010)
SMCM produces sensible tropical convec+ve features in idealized theore(cal studies and compared to observa(ons
Khouider et al (2010), Frenkel et al (2012,2013), Peters et al (2013),Khouider (2014), Deng et al (2015)
- divides a large-‐scale domain into independent sub-‐domains
- predicts an ensemble of congestus, deep and stra+form cloud
Ø transi+ons determined by stochas(c Markov-‐Chain process
Ø area frac(ons per cloud type - driven by 2 external large-‐scale variables
Ø C as “convec+on” (e.g. CAPE or ω500)
Ø D as “dryness” (e.g. RH500)
C: “convec+ve propensity”
D: 500 hPa “dryness”
Sta+s+cs similar between model vs. observa+ons
C determined by ω at 500 hPa D determined by 2x(1 -‐ RH) at 500 hPa
SMCM vs observations
cf. Peters et al (2013)
mean variability
SMCM
observa+o
ns
C: “convec+ve propensity”
D: 500 hPa “dryness”
Sta+s+cs similar between model vs. observa+ons
✔ C determined by ω at 500 hPa D determined by 2x(1 -‐ RH) at 500 hPa cf. Peters et al (2013)
mean variability
SMCM
observa+o
ns
SMCM vs observations
The SMCM as part of an existing scheme (ECHAM6.1)
Host model Dynamics
Physics
convec(on scheme
diagnosis
parcel ascent
closure
+mestep + 1
ECHAM uses a mass-‐flux convec+on parameterisa+on based on Tiedtke (1989) and Nordeng (1994), all simula+ons performed at T63L47 resolu+on
The SMCM as part of an existing scheme (ECHAM6.1)
ECHAM uses a mass-‐flux convec+on parameterisa+on based on Tiedtke (1989) and Nordeng (1994), all simula+ons performed at T63L47 resolu+on
Host model Dynamics
Physics
+mestep + 1
SMCM RH500 w500
fd, used for deep convec+ve cloud base mass flux Ø fd * 1 m/s
The currently opera+onal CAPE closure is not used! Through the SMCM, we know how much deep convec+on there is given the large scale environment
convec(on scheme
diagnosis
parcel ascent, closure
“How does the SMCM couple to the convection scheme?” Experiment setup: AMIP, one year spinup (2002), then one week output @(mesteps. Here: 4 January 2004, loca+on: 148E,1S (random pick)
SMCM
REF
How does the SMCM version of ECHAM6.1 behave in a climate simula+on?
The SMCM in 30yr AMIP simulations Total precipita+on
Convec+ve precipita+on
Large-‐scale precipita+on
SMCM
– REF
ECHAM(REF) -‐ GPCP
ECHAM(SMCM) -‐ GPCP
ECHAM(SMCM) – ECHAM(REF) ECHAM(SMCM) -‐ ERAI ECHAM(REF) -‐ ERAI
The SMCM in 30yr AMIP simulations
Weaker convec+on in SMCM -‐> moister BL and at the same +me drier lower and middle tropical troposphere Slightly reduces bias compared to ERA-‐Interim
Specific humidity
RMSE: 2.5E-‐4 RMSE: 2.8E-‐4
Hovmoeller diagrams of total precipita+on, May – August 2003, 10S-‐10N
The SMCM in 30yr AMIP simulations
Summary ECHAM SMCM setup results in
Ø more temporally coherent deep convec+ve rainfall (tropics) Ø more consistent triggering of shallow vs deep convec+on
Ø an overall reduc(on of global mean precipita(on Ø reduced bias w.r.t. GPCP
Ø reduced moist bias in the lower troposphere (w.r.t. ERA-‐I)
Ø increased convec(ve organiza(on in the tropics
Outlook Ø implementa+on into most recent ECHAM (vn6.3) Ø include further SMCM simulated cloud area frac+ons Ø include convec+ve interac+ons (Khouider (2014))
The SMCM has the poten+al to provide the backbone of next genera+on convec+on
parameterisa+ons
References
- Deng, Q., B. Khouider, and A. Majda, 2015: The MJO in a Coarse-‐Resolu+on GCM with a Stochas+c Mul+cloud Parameteriza+on, J. Atmos. Sci., DOI: 10.1175/JAS-‐D-‐14-‐0120.1
- Frenkel, Y., A. Majda, and B. Khouider, 2012: Using the stochas+c mul+cloud model to improve tropical convec+ve parameteriza+on: A paradigm example. J. Atmos. Sci., 69, 1080–1105.
- Frenkel, Y., A. Majda, and B. Khouider, 2013: Stochas+c and determinis+c mul+cloud parameteriza+ons for tropical convec+on. Clim. Dyn., 41:1527–1551 DOI 10.1007/s00382-‐013-‐1678-‐z
- Khouider B, Biello J, Majda A. 2010. A stochas+c mul+cloud model for tropical convec+on. Commun. Math. Sci. 8(1): 187–216.
- Khouider, B., 2014: A coarse grained stochas+c mul+-‐type par+cle interac+ng model for tropical convec+on: Nearest neighbour interac+ons. Commun. Math. Sci., 12, 1379-‐1407
- Nordeng T. 1994. Extended versions of the convec+ve parametriza+on scheme at ecmwf and their impact on the mean and transient ac+vity of the model in the tropics. In: Tech. Memo., 206, European Centre for Medium-‐Range Weather Forecasts, Reading, U.K.
- Peters K, Jakob C, Davies L, Khouider B, Majda AJ. 2013. Stochas+c Behavior of Tropical Convec+on in Observa+ons and a Mul+cloud Model. J. Atmos. Sci. 70(11): 3556–3575, doi:10.1175/JAS-‐D-‐13-‐031.1
- Tiedtke M. 1989. A comprehensive mass flux scheme for cumulus parameteriza+on in large-‐ scale models. Mon. Wea. Rev. 117(8): 1779–1800.