View
50
Download
2
Category
Tags:
Preview:
DESCRIPTION
A new physics package for the next version of MIROC. Masahiro Watanabe CCSR, University of Tokyo hiro@ccsr.u-tokyo.ac.jp. May 27, 2008. Team “MIROC-physics” in KAKUSHN project S. Watanabe 1 , T. Takemura 2 , M. Chikira 1 , T. Ogura 3 , T. Mochizuki 1 , - PowerPoint PPT Presentation
Citation preview
A new physics package for the next version of MIROC
A new physics package for the next version of MIROC
Masahiro WatanabeCCSR, University of Tokyohiro@ccsr.u-tokyo.ac.jp
May 27, 2008
Team “MIROC-physics” in KAKUSHN projectS. Watanabe1, T. Takemura2, M. Chikira1, T. Ogura3, T. Mochizuki1, K. Sudo4, T. Nishimura1, M. Watanabe5, S. Emori3, and M. Kimoto5
1: FRCGC/JAMSTEC, 2: RIAM/Kyushu Univ, 3: NIES, 4: Nagoya Univ, 5: CCSR/Univ of Tokyo
Climate change simulation by MIROC3.2 @ AR4
Climate change simulation by MIROC3.2 @ AR4
Global mean SAT – change from the end of 18th centuryGlobal mean SAT – change from the end of 18th century
Year
Full forcing (Natual + Anthropogenic)
Year
Anthropogenic forcingOnly
Year
Natural forcing Only(Solar + Volcano)
Glo
bal
mea
n S
AT
an
om
aly
(oC
)
Year
No forcing
Glo
bal
mea
n S
AT
an
om
aly
(oC
)
Observation Model ensemble mean
Further development of MIROCFurther development of MIROC
MIROC3.2 has presented as good ability as other MIROC3.2 has presented as good ability as other state-of-the-art CGCMs in simulating climate and itstate-of-the-art CGCMs in simulating climate and its variabilitys variability
Why we need to update it?Why we need to update it? We We knowknow the model still contains large uncertainty the model still contains large uncertainty
(model is (model is tunabletunable even if it generates realistic clim even if it generates realistic climate)ate)
Forthcoming CGCMs must be more robust as highForthcoming CGCMs must be more robust as higher accuracy will be required in AR5er accuracy will be required in AR5
CloudsClouds might be the key might be the key
Atmospheric component of MIROCAtmospheric component of MIROC
MIROC3.2 MIROC4.1Dynamical core Spectral + semi-Lagrangian sc
heme (Lin & Rood 1996)Spectral+ semi-Lagrangian scheme (Lin & Rood 1996)
Coordinate Eta (hybrid sigma) Eta (hybrid sigma)
Cloud Diagnostic (LuTreut & Li 1991) + Simple water/ice partition
Prognostic PDF (Watanabe et al. 2008) + Ice microphysics (Wilson & Ballard 1999)
Turbulence Level 2.0 (Mellor & Yamada 1982)
Level 2.5 (Nakanishi & Niino 2004)
Convection Prognostic A-S + critical RH(Pan & Randall 1998, Emori et al. 2001)
Prognostic A-S + critical RHwith water/ice partition
Radiation 2-stream DOM 37ch (Nakajima et al. 1986)
2-stream DOM 111ch (Sekiguchi et al. 2008?)
Aerosols simplified SPRINTARS(Takemura et al. 2002)
full SPRINTARS + prognostic CCN
Land submodel MATSIRO MATSIRO mosaic
Hybrid prognostic cloud (HPC) scheme Hybrid prognostic cloud (HPC) scheme
Large-scale condensation (LSC) Assume a subgrid-scale distribution of qt’ or s=aL(qt’-LTl’) ? Predict condensate amount and cloud?
Tompkins (2005) Prognostic equations for PDF variance & skewness Quasi-reversible operator between grid quantities & PDF
HPC-DU HPC-ST
Basis PDFs (varying skewness)
cloud water [g/kg]
clou
d fr
acti
on
C-qc relationship
cloud water [g/kg]
clou
d fr
acti
on
C-qc relationship
( ) , ( ) ( ) , ( , )c c
c c c l t s lQ Q
C G s ds q Q s G s ds Q a q q T p
Similar approach:Tompkins (2002, JAS)Wilson & Gregory (2003, QJ)
Single column model testSingle column model test
A-S, prognostic cloud scheme + simple cloud physics 12hr integration from Weisman & Klemp (1982) profile
Cloud mass flux
qc & qi Precipitation rate
Variance & skewness
McCf
V
S > 0
convectivestratiform
cumulus detrainment ⇒ V, S+ precipitation/snowfall ⇒ S-
0 1 2 3 4 [hr]
ice cloud
anvil
Snapshot of qc at hour 96 in NICAMCloud water at z=835m
Collaboration with NIES
How can we verify predicted PDF moments?
How can we verify predicted PDF moments?
Comparison w/ GCRM: 1-week integration from Dec. 25, 2006• GCRM (named NICAM) w/ 3.5km grid, realistic topography • MIROC atmosphere w/ T42
6400 points on avg. in a T42 grid diagnosis for the PDF moments
GCRMPDF variance
AGCM
8.3km
8.3km835m
835m
PDF variance
Improvement with HPCImprovement with HPC
ISCCP AGCM HPC AGCM HPC-ORG
ORG HPC
Annual-mean cloud water & cloud fraction along 10°S
* Low-cloud is yet insufficient over continents * Better representation of low clouds over the cold tongue
Annual-mean low cloud
Watanabe et al. (2008)
Higher-order turbulence closureHigher-order turbulence closurework done by M. Chikira (FRCGC)work done by M. Chikira (FRCGC)
From Level 2.0 (Mellor-Yamada 1982) to Level 2.5/3.0 (Nakanishi-Niino 2004)
• Evaluation of MLS locally (changing in space and in time)• Advection of TKE and other turbulent variables• Coupling with cloud scheme
Zonal and annual mean master length scales
Lev2.0 Lev2.5
Annual mean PBL height [m]
Lev2.0
Diff.
Lev2.5
Higher-order turbulence closureHigher-order turbulence closure
850hPa specific humidity
Annual mean clim. Bias
work done by M. Chikira (FRCGC)work done by M. Chikira (FRCGC)
Lev2.0
Lev2.5
ERA40
• MIROC3.2 has suffered from a low-level dry bias <- insufficient mixing
• Predicting TKE significantly improves the boundary layer structure -> reduced the dry bias
22
turb.
2 2
2
2 2
t l
l
L
Lt
Lq
t t
q q
t
a
Vt
V
• Turbulent variance/covariance can directly be used for predicting subgrid-scale PDF variance -> tighter coupling between turbulence & cloud processes
Sophisticated ice-cloud microphysicsSophisticated ice-cloud microphysicswork done by T. Ogura (NIES)work done by T. Ogura (NIES)
Ice
Cloud microphysics in MIROC3.2
Wilson and Ballard (1999)
Cloud microphysics in MIROC4.1
Liquid
Cloud liquid/ice fraction
ΔT2x=6.3K
Rotstayn et al. (2000)
Airborne measurements
Cloud liquid/ice fraction
ΔT2x=4.0K
• In MIROC3.2 climate sensitivity has largely been affected by a parameter for cloud liquid/ice partition
Coupling HPC-ice microphysics with cumuli
Coupling HPC-ice microphysics with cumuli
**** **
● ●●* * *
● ●●●
melting layer
cloud ice
cloud liquid
mixed-phase ice/liquid
MSE & total water budgets in A-S -> vapor, liquid and ice partitioned inside the cumulus with reference to temperature and saturation deficit in the cumulus tower
ice nucleation/deposition/fallout
change in the PDF variance and skewness
any type of cloud fractionis then calculated with HPC
/ / ( )cch t M h z D h h
/ / ( )ct c t t tq t M q z D q q
Preliminary model performancePreliminary model performance
T42L20 Atmos. + 0.5x1.4deg Ocean (corresponding to MIROC-mid)
qv 850hPa Dec-Feb clim., MIROC4.1 qv 850hPa Dec-Feb clim. bias NEWNEW
ORGORG
[kg/kg]
Preliminary model performancePreliminary model performance
T42L20 Atmos. + 0.5x1.4deg Ocean (corresponding to MIROC-mid)
Annual-mean precipitation
NEWNEW ORGORGObsObs
Preliminary model performancePreliminary model performance
T42L20 Atmos. + 0.5x1.4deg Ocean (corresponding to MIROC-mid)
SST Dec-Feb climatology, MIROC4.1 SST Dec-Feb climatology bias NEWNEW
ORGORG
[K]
SST interannual variability
Preliminary model performancePreliminary model performance
T42L20 Atmos. + 0.5x1.4deg Ocean (corresponding to MIROC-mid)
Annual-mean x & subsurface T
100E 60W
Obs
ORGNEW
100E 60W
SummarySummary
From diagnostic to prognostic schemesFrom diagnostic to prognostic schemes
Stronger coupling between subgrid-scale Stronger coupling between subgrid-scale processes processes
Better representation of climatology, Better representation of climatology, variability and climate sensitivity? variability and climate sensitivity?
Yes, we do hope so!Yes, we do hope so!
Major part of the atmospheric physics schemes was renewed in MIROC4.1
Concerns: characteristic timescale and difficulty in deriving diagnostic equations
Concerns: physical consistency, but errors in one scheme may be distributed
Recommended