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A new physics package for the next version of MIROC

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 ,

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A new physics package for the next version of MIROC

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  1. 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. 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

  2. Climate change simulation by MIROC3.2 @ AR4 Global mean SAT – change from the end of 18th century Anthropogenic forcing Only Full forcing (Natual + Anthropogenic) Year Year Global mean SAT anomaly (oC) Global mean SAT anomaly (oC) No forcing Natural forcing Only (Solar + Volcano) Year Year Observation Model ensemble mean

  3. Further development of MIROC • 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? • 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 • Clouds might be the key

  4. Atmospheric component of MIROC MIROC3.2 MIROC4.1

  5. C-qcrelationship cloud fraction cloud water [g/kg] C-qcrelationship Basis PDFs (varying skewness) HPC-DU HPC-ST cloud fraction cloud water [g/kg] Hybrid prognostic cloud (HPC) scheme • Large-scale condensation (LSC) • Assume a subgrid-scale distribution of qt’ or s=aL(qt’-aLTl’) ? • Predict condensate amount and cloud? • Prognostic equations for PDF variance & skewness • Quasi-reversible operator between grid quantities & PDF Tompkins (2005) Similar approach: Tompkins (2002, JAS) Wilson & Gregory (2003, QJ)

  6. Single column model test • A-S, prognostic cloud scheme + simple cloud physics • 12hr integration from Weisman & Klemp (1982) profile Cloud mass flux Variance & skewness anvil S > 0 Cf Mc V Precipitation rate qc&qi convective ice cloud stratiform cumulus detrainment ⇒ V, S+ precipitation/snowfall ⇒ S- 0 1 2 3 4 [hr]

  7. PDF variance PDF variance Snapshot ofqcat hour 96 in NICAM GCRM 8.3km 835m Cloud water at z=835m AGCM 8.3km 835m 6400 points on avg. in a T42 grid diagnosis for the 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 Collaboration with NIES

  8. Improvement with HPC Annual-mean low cloud ISCCP AGCM HPC AGCM HPC-ORG Annual-mean cloud water & cloud fraction along 10°S ORG HPC Watanabe et al. (2008) * Low-cloud is yet insufficient over continents * Better representation of low clouds over the cold tongue

  9. Annual mean PBL height [m] Lev2.0 Lev2.5 Diff. 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

  10. Higher-order turbulence closure work done by M. Chikira (FRCGC) 850hPa specific humidity • MIROC3.2 has suffered from • a low-level dry bias • <- insufficient mixing Annual mean clim. Bias Lev2.0 • Predicting TKE significantly • improves the boundary layer • structure • -> reduced the dry bias Lev2.5 • Turbulent variance/covariance • can directly be used for predicting • subgrid-scale PDF variance • -> tighter coupling between • turbulence & cloud processes ERA40

  11. Cloud microphysics in MIROC4.1 Wilson and Ballard (1999) Sophisticated ice-cloud microphysics work done by T. Ogura (NIES) Cloud microphysics in MIROC3.2 Cloud liquid/ice fraction Cloud liquid/ice fraction • In MIROC3.2 climate • sensitivity has largely • been affected by • a parameter for cloud • liquid/ice partition Airborne measurements Ice ΔT2x=6.3K ΔT2x=4.0K Liquid Rotstayn et al. (2000)

  12. ＊ ＊ ＊ ＊ ＊ ＊ cloud ice mixed-phase ice/liquid melting layer ＊ ＊ ＊ ● ● ● cloud liquid ● ● ● ● Coupling HPC-ice microphysics with cumuli 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

  13. qv 850hPa Dec-Feb clim. bias qv 850hPa Dec-Feb clim., MIROC4.1 NEW ORG [kg/kg] Preliminary model performance T42L20 Atmos. + 0.5x1.4deg Ocean (corresponding to MIROC-mid)

  14. Preliminary model performance T42L20 Atmos. + 0.5x1.4deg Ocean (corresponding to MIROC-mid) Annual-mean precipitation Obs NEW ORG

  15. Preliminary model performance T42L20 Atmos. + 0.5x1.4deg Ocean (corresponding to MIROC-mid) SST Dec-Feb climatology bias SST Dec-Feb climatology, MIROC4.1 NEW ORG [K]

  16. SST interannual variability 100E 60W Preliminary model performance T42L20 Atmos. + 0.5x1.4deg Ocean (corresponding to MIROC-mid) Annual-mean tx & subsurface T NEW ORG Obs 100E 60W

  17. Summary Major part of the atmospheric physics schemes was renewed in MIROC4.1 • From diagnostic to prognostic schemes • Stronger coupling between subgrid-scale processes • Better representation of climatology, variability and climate sensitivity? Yes, we do hope so! Concerns: characteristic timescale and difficulty in deriving diagnostic equations Concerns: physical consistency, but errors in one scheme may be distributed

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