A new physics package for the next version of MIROC Masahiro Watanabe CCSR, University of Tokyo May 27, 2008 Team “MIROC-physics” in KAKUSHN project S. Watanabe 1, T. Takemura 2, M. Chikira 1, T. Ogura 3, T. Mochizuki 1, K. Sudo 4, T. Nishimura 1, M. Watanabe 5, S. Emori 3, and M. Kimoto 5 1: FRCGC/JAMSTEC, 2: RIAM/Kyushu Univ, 3: NIES, 4: Nagoya Univ, 5: CCSR/Univ of Tokyo

Climate change simulation by AR4 Global mean SAT – change from the end of 18 th century Year Full forcing (Natual + Anthropogenic) Year Anthropogenic forcing Only Year Natural forcing Only (Solar + Volcano) Global mean SAT anomaly ( o C) Year No forcing Global mean SAT anomaly ( o C) ObservationModel ensemble mean

Further development of MIROC MIROC3.2 has presented as good ability as other state-of-the-art CGCMs in simulating climate and its variability MIROC3.2 has presented as good ability as other state-of-the-art CGCMs in simulating climate and its variability Why we need to update it? Why we need to update it? We know the model still contains large uncertainty (model is tunable even if it generates realistic climate) We know the model still contains large uncertainty (model is tunable even if it generates realistic climate) Forthcoming CGCMs must be more robust as higher accuracy will be required in AR5 Forthcoming CGCMs must be more robust as higher accuracy will be required in AR5 Clouds might be the key Clouds might be the key

Atmospheric component of MIROC MIROC3.2MIROC4.1 Dynamical coreSpectral + semi-Lagrangian scheme ( Lin & Rood 1996) CoordinateEta (hybrid sigma) CloudDiagnostic (LuTreut & Li 1991) + Simple water/ice partition Prognostic PDF (Watanabe et al. 2008) + Ice microphysics (Wilson & Ballard 1999) TurbulenceLevel 2.0 (Mellor & Yamada 1982) Level 2.5 (Nakanishi & Niino 2004) ConvectionPrognostic A-S + critical RH (Pan & Randall 1998, Emori et al. 2001) Prognostic A-S + critical RH with water/ice partition Radiation2-stream DOM 37ch (Nakajima et al. 1986) 2-stream DOM 111ch (Sekiguchi et al. 2008?) Aerosolssimplified SPRINTARS (Takemura et al. 2002) full SPRINTARS + prognostic CCN Land submodelMATSIROMATSIRO mosaic

Hybrid prognostic cloud (HPC) scheme  Large-scale condensation (LSC) Assume a subgrid-scale distribution of q t ’ or s=a L (q t ’-  L T l ’) ? Predict condensate amount and cloud? Tompkins (2005) Prognostic equations for PDF variance & skewness Quasi-reversible operator between grid quantities & PDF HPC-DUHPC-ST Basis PDFs (varying skewness) cloud water [g/kg] cloud fraction C-q c relationship cloud water [g/kg] cloud fraction C-q c relationship Similar approach: Tompkins (2002, JAS) Wilson & Gregory (2003, QJ)

Single column model test  A-S, prognostic cloud scheme + simple cloud physics  12hr integration from Weisman & Klemp (1982) profile Cloud mass flux qc & qiqc & qi Precipitation rate Variance & skewness Mc Cf V S > 0 convective stratiform cumulus detrainment ⇒ V, S+ precipitation/snowfall ⇒ S [hr] ice cloud anvil

Snapshot of q c at hour 96 in NICAM Cloud water at z=835m Collaboration with NIES 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/ T points on avg. in a T42 grid diagnosis for the PDF moments GCRM PDF variance AGCM 8.3km 835m PDF variance

Improvement with HPC ISCCPAGCM HPCAGCM HPC-ORG ORGHPC 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 closure 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 closure 850hPa specific humidity Annual mean clim. Bias 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 Turbulent variance/covariance can directly be used for predicting subgrid-scale PDF variance -> tighter coupling between turbulence & cloud processes

Sophisticated ice-cloud microphysics 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 ΔT 2x =6.3K Rotstayn et al. (2000) Airborne measurements Cloud liquid/ice fraction ΔT 2x =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 ＊ ＊ ＊ ＊＊ ＊ ● ●● ＊＊ ＊ ● ● ●● 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 fraction is then calculated with HPC

Preliminary model performance T42L20 Atmos x1.4deg Ocean (corresponding to MIROC-mid) qv 850hPa Dec-Feb clim., MIROC4.1 qv 850hPa Dec-Feb clim. bias NEW ORG [kg/kg]

Preliminary model performance T42L20 Atmos x1.4deg Ocean (corresponding to MIROC-mid) Annual-mean precipitation NEW ORG Obs

Preliminary model performance T42L20 Atmos x1.4deg Ocean (corresponding to MIROC-mid) SST Dec-Feb climatology, MIROC4.1 SST Dec-Feb climatology bias NEW ORG [K]

SST interannual variability Preliminary model performance T42L20 Atmos x1.4deg Ocean (corresponding to MIROC-mid) Annual-mean  x & subsurface T 100E60W Obs ORG NEW 100E60W

Summary From diagnostic to prognostic schemes Stronger coupling between subgrid-scale processes Better representation of climatology, variability and climate sensitivity? 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