CMIP Low Cloud Feedback Interpreted T hrough a Mixed-Layer Model

1. CMIP Low Cloud Feedback Interpreted Through a Mixed-Layer Model Peter Caldwell, Steve Klein, and Yunyan Zhang (LLNL) XinQu, Alex Hall (UCLA) Lawrence Livermore National Lab, CA UW AtmosPhy and Chem Seminar, 1/31/11 Prepared by LLNL under Contract DE-AC52-07NA237344. UCRL: LLNL-PRES-????

2. Motivation: Importance of Low Cloud Annual-Average Low Cloud Amount Net Cloud Radiative Forcing* Low clouds cover large regions of the globe and have the strongest cloud radiativeforcing (CRF). * Cloud radiative forcing = clear-sky flux – observed flux (both at top of atmosphere)

3. Motivation: GCM Uncertainty E. Pacific (30S-30N, 150W-60W) 4.5 4.0 3.5 3.0 2.5 2.0 All Tropics (30S-30N) Fig: Greyscale=cloud feedback parameter (dCRF/dTsurf) from Lauer et al, (2010, Jclim). Red=Equilibrium global climate sensitivity from IPCC AR4 Table 8.2. Equilib. Climate Sensitivity (K) Local Feedback Parameter (W m-2 K-1) Tropical stratocumulus (Sc) feedback is a good predictor of tropical cloud feedback (compare grey bars)and a decent predictor of global climate feedback (grey vs red bars). Current-generation GCMs show little agreement in Sc cloud forcing.

4. Why do GCMs Have Trouble? Low clouds are totally under-resolved… and are controlled by complex interactions between turbulence, microphysics, and radiation… which are difficult to parameterize. CAM3 vertical grid (mb) 847 zi typical cloud thickness in SE Pacific 921 975 Fig: Vertical grid for the CAM3 GCM in CMIP3 (in green) compared to a typical cloud thickness in the SE Pacific. 1005 1020 Ocean

5. What to do?!? (outline of my talk) • Observational constraints on models? • Are GCMs really giving a consistent picture? The case for CMIP-forced limited-area models. • Previous limited-area studies • Cloud fraction from a mixed-layer model? • Open questions about forcing • Preliminary results • Summary/Conclusions • Clarification on radiativefeedbacks (if time)

6. What can current-climate variability tell us about future low cloud change? Work by XinQu and Alex Hall (UCLA) A: cgcm3.1 T47 B: cgcm3.1 T63 C: gfdl cm2.0 D: gfdl cm2.1 E: giss model er F: iap fgoals1.0.g G: inmcm3.0 H: miroc3.2 medres I: ncar ccsm3.0 J: ncar pcm1 K: giss model eh L: ingv echam4 M: mri cgcm2.3.2a N: ipsl cm4 O: mpi echam5 P: bccr bcm2.0 Q: csiro mk3.5 free-troposphere integrated moisture Model Change (%) model climate change response Frominterannual variability Change predicted from Internal Variability (%) Fig: Change in SE Pacific low cloud fraction from CMIP3 models and as predicted from their LTS and q internal variability (A1B 2080-2100 – 2000-2020). LTS=lower tropospheric stability: θ700 - θsurf

7. What can current-climate variability tell us about future low cloud change? • Internal variability is a very good indicator of model response • assuming differences in dcld/dX dominate (more on this later) • Hinted at by Bony and Dufresne (GRL 2005) A: cgcm3.1 T47 B: cgcm3.1 T63 C: gfdl cm2.0 D: gfdl cm2.1 E: giss model er F: iap fgoals1.0.g G: inmcm3.0 H: miroc3.2 medres I: ncar ccsm3.0 J: ncar pcm1 K: giss model eh L: ingv echam4 M: mri cgcm2.3.2a N: ipsl cm4 O: mpi echam5 P: bccr bcm2.0 Q: csiro mk3.5 Model Change (%) Change predicted from Internal Variability (%) Fig: Change in SE Pacific low cloud fraction from CMIP3 models and as predicted from their LTS and q internal variability (A1B 2080-2100 – 2000-2020).

8. What can current-climate variability tell us about future low cloud change? But previous studies found LTS to be a poor predictor of future change (e.g. Lauer et al. 2010)!!! Explanation: the slope and y-intercept don’t fit a 1:1 line... ⇒internal variability is a poor magnitude predictor, but a good predictor of relative strength. A: cgcm3.1 T47 B: cgcm3.1 T63 C: gfdl cm2.0 D: gfdl cm2.1 E: giss model er F: iap fgoals1.0.g G: inmcm3.0 H: miroc3.2 medres I: ncar ccsm3.0 J: ncar pcm1 K: giss model eh L: ingv echam4 M: mri cgcm2.3.2a N: ipsl cm4 O: mpi echam5 P: bccr bcm2.0 Q: csiro mk3.5 Model Change (%) Change predicted from Internal Variability (%) Fig: Change in SE Pacific low cloud fraction from CMIP3 models and as predicted from their LTS and q internal variability (A1B 2080-2100 – 2000-2020).

9. What can current-climate variability tell us about future low cloud change? Conclusions: • Intermodel spread in low cloud response (equal to uncertainty?) would be reduced if models got current-climate variability right! (used by Clement et al. 2009, Science) • A more nuanced view of LTS/climate feedback is needed A: cgcm3.1 T47 B: cgcm3.1 T63 C: gfdl cm2.0 D: gfdl cm2.1 E: giss model er F: iap fgoals1.0.g G: inmcm3.0 H: miroc3.2 medres I: ncar ccsm3.0 J: ncar pcm1 K: giss model eh L: ingv echam4 M: mri cgcm2.3.2a N: ipsl cm4 O: mpi echam5 P: bccr bcm2.0 Q: csiro mk3.5 Model Change (%) Change predicted from Internal Variability (%) Fig: Change in SE Pacific low cloud fraction from CMIP3 models and as predicted from their LTS and q internal variability (A1B 2080-2100 – 2000-2020).

10. What can current-climate variability tell us about future low cloud change? Questions: • Why does dcld/dLTS behave differently for climate change than for current-climate variability? • direct CO2 effect plays role • What physics cause cloudiness (a very nonlinear quantity) to be linearly related to LTS? • Zhang et al (JClim 2009) explores this A: cgcm3.1 T47 B: cgcm3.1 T63*** C: gfdl cm2.0 D: gfdlcm2.1*** E: giss model er F: iap fgoals1.0.g G: inmcm3.0 H: miroc3.2 medres I: ncar ccsm3.0 J: ncar pcm1 K: giss model eh L: ingv echam4 M: mri cgcm2.3.2a N: ipsl cm4 O: mpi echam5 P: bccr bcm2.0 Q: csiro mk3.5 Model Change (%) Change predicted from Internal Variability (%) Fig: Change in SE Pacific low cloud fraction from CMIP3 models and as predicted from their LTS and q internal variability (A1B 2080-2100 – 2000-2020).

11. Internal Variability or Forcing Differences? = + + model-independent or small terms + + = These sum to the “internal variability-predicted change” on prev slide LTS generally contributes to cloud increase; qv impact varies. Differences between models are dominated by dcld/dX perturbations – forcing changes are relatively consistent.

12. Guiding Idea: If CMIP models agree about changes to the Sc-driving conditions, forcing a single local model by boundary conditions from many CMIP models may provide a consistent picture of low cloud changes.

13. Local Models of Low Cloud Feedback: Early Work • Tropics are divided into low cloud and deep convective boxes. • Free tropospheric conditions are determined from observed tropical relations: • T assumed horizontally uniform in tropics and set by deep convection to a moist adiabat. • Relative humidity assumed invariant to climate change • subsidence chosen to balance radiative cooling • Examples: Betts and Ridgeway (JAS 1989), Pierrehumbert (JAS 1995), Larson et al. (JClim 1999) Fig: Conceptual framework from Larson et al (1999) These models focused on tropical circulation and generally had crude low cloud parameterizations

14. Local Models of Low Cloud Feedback: Recent Work A spate of recent studies ignore Sc→deep convective coupling and use similar free-tropospheric approximations to drive more complex boundary-layer cloud models • Zhang + Bretherton (JClim 2008): Neg low cloud feedback in SCAM3 due to unphysical interaction between PBL, shallow and deep convection • Caldwell + Bretherton (JClim 2009): Neg Sc feedback in MLM due to decreased subsidence and increased LTS • Blossey, Bretherton, + Wyant (JAMES 2009): Neg shallow Cu feedback in 2D CRM runs forced by ctl and +2K SP-CAM simulations due to increased LTS • Xu et al (JAS 2010): Neg Sc and shallow Cu feedback in 3D CRM runs • Lauer et al (JClim 2010): Pos low cloud feedback in iRAM regional model. Run with forcings from 3 different GCMs and got similar answers

15. Local Models of Low Cloud Feedback: Recent Work A spate of recent studies ignore Sc→deep convective coupling and use similar free-tropospheric approximations to drive more complex boundary-layer cloud models • Zhang + Bretherton (JClim 2008): Neg low cloud feedback in SCAM3 due to unphysical interaction between PBL, shallow, and deep convection • Caldwell + Bretherton (JClim 2009): Neg Sc feedback in MLM due to increased subsidence and LTS • Blossey, Bretherton, + Wyant (JAMES 2009): Neg shallow Cu feedback in 2D CRM runs forced by ctl and +2K SP-CAM simulations due to increased LTS • Xu et al (JAS 2010): Neg Sc and shallow Cu feedback in 3D CRM runs • Lauer et al (JClim 2010): Pos low cloud feedback in iRAM regional model. Run with forcings from 3 different GCMs and got similar answers Points: 1: Chris is prolific 2: Most studies show neg feedback 3: Reasons for cloud changes are still unclear

16. Our Approach: • Run a local model forced by a large variety of CMIP boundary conditions • Requires a computationally-efficient model • provides a lens to explore inter-GCM forcing consistency

17. Our Model: • Mixed layer model (MLM) forced by daily-varying advection, free-tropospheric conditions, and SST from global mode • For each day and each atmospheric column, MLM is run to equilibrium. • Cloud fraction is the proportion of time a cloudy equilibrium is obtained. qt=qv+ql sl=cpT+gz-Lql • mixed-layer model zi Strong LW cooling at cloud top destabilizes BL Entrainment warms, dries BL MLM Schematic (JClim 2009) Entrainment parameterized following Lewellen2 (JAS 1998) w/ wind-shear term Criteria for a cloudy equilibrium: MLM reaches equilibrium in 200 days MLM predicts cloudy solution divergence (proportional to subsidence) > 0.5x10-6 s-1 BL top < 2 km (A TKE-based measure would be better but didn’t work…) (JClim 2009) Assumed to keep qt and sl well-mixed in boundary layer Ocean

18. Results from Z09: When forced by ERA40 for 1990-2001, this model reproduces the observed geographical distribution of cloud and the observed low-cloud vs. LTS relationship. - Obs Sept-Nov low cloud observed from ISCCP and from the model. Red boxes denote the 6 Sc regions identified in Klein + Hartmann (JClim 1993) and used below. • Results ok, but spurious high values found at equator and near coast • The observed LTS vs cloud fraction relation is very well reproduced • because LTS and w>0 (no-cld) times are anti-correlated • Liquid water path is overpredicted • (not shown) LTS (K) Modeled and observed relation between LTS and cloud fraction using each season for each region as a datapoint.

19. Open Questions about Forcing: • How will LTS change? • This is partially a statement about the geographical pattern of SST change since θ700 is controlled by ITCZ SST and θsurf is controlled by Sc SST. • How will horizontal advection change? • Wyant et al (JAMES 2009) found temperature advection to be unchanged and moisture advection to increase. • This is expected for uniform SST increase – what about realistic SST? • How will subsidence (ω) change? • Theory suggests that ω will decrease, but Wyant et al found this to only occur far above the BL (see pic). • Increased near-BL radiative cooling due to increased Sc counteracted the expected decrease! • How will free-tropospheric moisture change? • roughly constant RH is expected, but small changes could matter… We said earlier that GCMs largely agree onforcings. True for these particular issues? Subsidence for current-climate and +2K SST runs from Wyant et (2009) Concern: will GCM Sc imprint onto MLM Sc by influencing SST?

20. MLM Forcing from CMIP3 • T and LTS increases (and T increases more @ higher z) • 850 mbqv generally increases, often by < constant RH prediction • 850 mbgeopotential increases, consistent with thermal expansion • dry advection increases and there is a hint of increased cold advection • Divergence generally decreases Change in MLM forcings between 20th century (1990-2000) and A1B (2090-2100) runs. Green bars are average over regions and models (China excluded), red xs are model-averages for each region, and black dots are individual models.

21. Variability Changes in CMIP3 Forcings • Variability in moisture and moisture advection seems to increase • Variability in divergence largely decreases • Signals are generally noisy Change in the standard deviation of MLM forcings between 20th century (1990-2000) and A1B (2090-2100) runs. Green bars are average Δσ for all regions and models (China excluded), red xs are model-average Δσ for each region, and black dots are Δσ for individual models.

22. Preliminary MLM Results: A: cgcm3.1 T47 B: cgcm3.1 T63*** C: gfdl cm2.0 D: gfdlcm2.1*** E: giss model er F: iap fgoals1.0.g G: inmcm3.0 H: miroc3.2 medres I: ncar ccsm3.0 J: ncar pcm1 K: giss model eh L: ingv echam4 M: mri cgcm2.3.2a N: ipsl cm4 O: mpi echam5 P: bccr bcm2.0 Q: csiro mk3.5 Model Change (%) Current and future cloud fraction from MLM and directly from GCM (calculated from monthly 3d data using random overlap on σ levels > 0.7). Using GCM total cloud fraction instead gives similar answers). Change predicted from Internal Variability (%) • MLM cldfrac too low • GCM cldfrac too high (b/c appropriate data not available?) • MLM improves the seasonal cycle

23. Preliminary MLM Results • MLM does not reduce inter-model spread in these cases • MLM results ≈ upward translation of GCM results • because direct CO2 effect not included? • results in negative low cloud feedback • due to MLM LWP increase with increased BL depth? • Method of defining GCM low cloud makes quantitative, not qualitative difference Change in low cldfrac (2090-2100 minus 1990-2000) from the MLM and direct from the GCMs. GCM bars use random overlap, dots indicate total cloud values.

24. Needed Improvements/Future Work • MLM currently projects all diabaticforcings onto surface or BL top (makes TKE unrealistic) • Criteria for cloud existence is ad hoc • Equilibrium assumption is unrealistic (e.g. causes cloud too close to coast) • Should do 20yr runs, use just A1B to avoid jump due to 20c3m→A1B transition, and run for more models • Explore effect of GCM cloud imprinting onto subsidence and SST used by MLM • Explore forcing differences in more detail (PDFs)

25. Conclusions • Current-climate variability is a good predictor of the relative strength of CMIP3 climate response • improving simulation of current climate would reduce intermodel spread • internal variability is not a good quantitative predictor of climate change (direct CO2 effect + ??? important) • Intermodel spread in low cloud response is larger than spread in several important quantities for driving clouds • but perhaps other quantities with less intermodel agreement are also important since MLM spread wasn’t reduced?

26. Connections to CGILS: CGILS is an ongoing intercomparison of climate change predictions from a variety of local-area models forced by reanalysis and composite GCM output. focus regions • CGILS looks at multi-model response to one forcing scenario, we look at single-model response to multiple forcings. • Approaches are complementary • We can investigate • which forcings are important/uncertain • how GCM biases imprint themselves upon local models

27. I expect to run out of time here • Next section is mostly to show people in individual meetings....

28. Clarifying Feedback Mechanisms Several pathways for low-cloud feedback have been suggested. Which are important? • Adiabatic LWP at fixed cloud depth increases • Has weak effect (Caldwell + Bretherton ’09, Wyant et al ’09) • Increased LTS reduces entrainment warming, drying • Decreased subsidence means deeper BL for given entrainment rate, thickening cloud • BL radiative cooling affects turbulent moisture transport, changing cloudiness

29. Source of BL Radiative Cooling Change Run RRTMG radiation on Caldwell et al ’09 thermodynamic profiles for CTL and +2K conditions to clarify how and why warming changes cloud-top cooling. • Using diurnally-averaged radiation doesn’t affect div (white dot) • Different story for SWCF (Blossey et al, 2009) • Breaking change into individual components works surprisingly well (black stars) Change (from current-climate) in BL-integrated radiative cooling due to changing various quantities to their +2K value. Cloud boundaries and cloud liquid water are held fixed.

30. Source of BL Radiative Cooling Change • Suggests a decrease in div (and therefore turbulence) • Free-tropospheric and BL impacts cancel, so net change is controlled by the direct CO2 effect. • Free-tropospheric effects are dominated by Planck response, while BL effects are most influenced by emissivity increase. Change (from current-climate) in BL-integrated radiative cooling due to changing various quantities to their +2K value. Cloud boundaries and cloud liquid water are held fixed.

31. Nonlinearities in BL Rad Div • Theimportance of RH changes decreases at higher RH. • Free trop RH traps BL rad and decreases the effective emission level, reducing BL cooling • LWP changes don’t affect BL div for LWP>40 g/m2. • Still important for SWCF

32. Clear-Sky BL Radiative Changes • Colder, moister BL in Sc means BL actually warms radiatively in absence of cloud • Despite very different absolute magnitudes, clear-sky response is similar to cloudy response.

33. Extra Slides

34. How does Sc work? qt=qv+ql sl=cpT+gz-Lql Free Troposphere Subsiding warm air + cold SST = strong inversion 847 zi Strong LW cooling at cloud top destabilizes BL Entrainment warms, dries BL 921 Boundary Layer (BL) Condensation/ evaporation warm/cool BL 975 Entrainment drying → large LHF sl(0) ≈ cpSST →SHF small 1005 1020 Ocean

35. Is EIS Response Closer to a 1:1 Line? Comparison using EIS instead of LTS as a predictor. LTS-based comparison from previous slide. Current-climate EIS variations are not a better predictor of climate change response.

36. Justification for Using Diurnal Ave Insolation Method for getting diurnal radiation properties: Calculate insolation-weighted diurnal average cos(zenith angle) following Hartmann (1994) eq 2.18. Compute an effective solar constant which gives the diurnally-averaged insolation when used with the diurnal-average cos(zenith angle). Why 12 hrly is so low: SW warming opposes LW cooling during day BL Div High vals during night not captured w/ 2 times. time

37. Free TroposphericRad Divergence • xxx

38. Model inputs from CMIP3 • Subsidence: Using 1000 mb divergence following Z09 (bad?) derived from 1000 mb winds • wind-derived and direct ERA output correlated at 0.93 and means differ by <5%. • Geopotential: Integrating hydrostatic+ideal gas equations (W+H eq 2.24) upward from coarse CMIP output levels. • For ERA, this calculation is correlated with direct output at 0.99 and has a mean error of 2m. • BL moisture gradient: calculated by assuming 80% near surface relative humidity. • For ERA data, this approximation caused no mean error, a slight reduction in variance, and was correlated with direct output at 0.88. • Advection of Cloud Top: Currently assuming = 0 (need to fix) • Tried using minimal model of Caldwell+Bretherton (2009) but it was too noisy.