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CMIP3 Subtropical Stratocumulus Feedback Interpreted T hrough a Mixed-Layer Model

Office of Science. CMIP3 Subtropical Stratocumulus Feedback Interpreted T hrough a Mixed-Layer Model. Peter Caldwell, Yunyan Zhang, and Steve Klein Contact: caldwell19@llnl.gov Lawrence Livermore National Lab AGU Meeting, SF 12 / 8 /11. UCRL: LLNL-PRES -484756. Approach:.

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CMIP3 Subtropical Stratocumulus Feedback Interpreted T hrough a Mixed-Layer Model

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  1. Office of Science CMIP3 Subtropical Stratocumulus Feedback Interpreted Through a Mixed-Layer Model Peter Caldwell, Yunyan Zhang, and Steve Klein Contact: caldwell19@llnl.gov Lawrence Livermore National Lab AGU Meeting, SF 12/8/11 Prepared by LLNL under Contract DE-AC52-07NA237344. UCRL: LLNL-PRES-484756

  2. Approach: Mixed Layer Model (MLM) Cloud fraction, LWP, etc CMIP model output (or reanalysis) • mixed-layer model (JClim 2009) Drizzle damps mixing Turbulence keep qt and sl well-mixed in boundary layer Get from GCM output: daily SST, surface pressure, winds, free-tropospheric T, q, and subsidence, advection of BL T and q qt=qv+ql sl=cpT+gz-Lql 3. Calculate cloud fraction as % of time cloudy MLM solution is found (Zhang et al, JClim 2009) 2. Run MLM to equilibrium using GCM model forcing for each day zi Strong LW cooling at cloud top destabilizes BL Entrainment warms, dries BL We use years 1980-2000 from 20c3m as “current climate” and 2080-2100 from sresA1B as “future climate” Ocean

  3. Motivating Questions: • How much inter-model spread is due to cloud physics (local) vs dynamics (large-scale)? • How will cloudiness change? • What is the physical mechanism for change? • What is the impact of variability change?

  4. Current-Climate Results ⇒ Suggests that poor simulation by GCMs due to problems w/ physics parameterization rather than with large-scale conditions MLM LOW cloud fraction (%) GCM TOTAL cloud fraction (%) • MLM reproduces observed cloud amount and sensitivity to forcing • GCMs don’t.

  5. Climate Change Signal • MLM does not reduce inter-model spread • MLM predicts 1-3% increase in cloud fraction • fixing cloud physics is not sufficient for reducing inter-model spread.

  6. What Causes Intermodel Differences? • EIS is a great predictor of MLM cloud fraction at all timescales. • but current-climate dcld/dEIS is not a good predictor of future change (not shown) Correlation between MLM cloud fraction and EIS 1 0.5 0 -0.5 -1 changes in EIS, cldfrac across models cross-model current-climate EIS vscldfrac current-climate within-model temporal correlations CA Peru Canary Australia Namibia

  7. What Causes Intermodel Differences? • Correlation with other forcing variables is weaker. Correlation between MLM cloud fraction and forcing variables 1 0.5 0 -0.5 -1 Inter-model, Δcldfrac Inter-model, 20C Within-model, 20C q+ T+ RH+ EIS q advect T advect dθ+/dz divergence surf wspd

  8. Mean Changes in Large-scale Forcing Multi-Model Mean Change in MLM-driving Quantities • GCMs robustly predict EIS increases Bold = 8/10 models agree on sign

  9. Do Variability Changes Matter? California Peru Canary Namibia Australia • Variability changes increase cloud change in Canary region, decrease in Australia, and otherwise have no systematic effect.

  10. Motivating Questions: • How much inter-model spread is due to cloud physics (local) vs dynamics (large-scale)? • How will cloudiness change? • What is the physical mechanism for change? • What is the impact of variability change? GCM problems due to cloud physics, but large-scale differences preclude agreement anyways MLM predicts low cloud increases, the opposite of GCMs Cloud fraction increases are due to EIS increases (robustly predicted by CMIP3 models) Variability changes have some regional effect, but no systematic impact.

  11. Thanks! Contact: caldwell19@llnl.gov

  12. Why clear sky? • Decoupling dominates.

  13. What are Forcing Variability Changes? • Variance changes scale with mean value changes • BL height variability decreases

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