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Peter Caldwell, Steve Klein, and Yunyan Zhang (LLNL) Xin Qu, Alex Hall (UCLA) Lawrence Livermore National Lab, CA UW Atmos Phy and Chem Seminar, 1/31/11.

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Presentation on theme: "Peter Caldwell, Steve Klein, and Yunyan Zhang (LLNL) Xin Qu, Alex Hall (UCLA) Lawrence Livermore National Lab, CA UW Atmos Phy and Chem Seminar, 1/31/11."— Presentation transcript:

1 Peter Caldwell, Steve Klein, and Yunyan Zhang (LLNL) Xin Qu, Alex Hall (UCLA) Lawrence Livermore National Lab, CA UW Atmos Phy and Chem Seminar, 1/31/11 Peter Caldwell, Steve Klein, and Yunyan Zhang (LLNL) Xin Qu, Alex Hall (UCLA) Lawrence Livermore National Lab, CA UW Atmos Phy and Chem Seminar, 1/31/11 Prepared by LLNL under Contract DE-AC52-07NA UCRL: LLNL-PRES-????

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

3 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). 2.Current-generation GCMs show little agreement in Sc cloud forcing Equilib. Climate Sensitivity (K) Local Feedback Parameter (W m -2 K -1 ) E. Pacific (30S-30N, 150W-60W) All Tropics (30S-30N) Fig: Greyscale=cloud feedback parameter (dCRF/dT surf ) from Lauer et al, (2010, Jclim). Red=Equilibrium global climate sensitivity from IPCC AR4 Table 8.2.

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

5 1.Observational constraints on models? 2.Are GCMs really giving a consistent picture? The case for CMIP-forced limited-area models. 1.Previous limited-area studies 2.Cloud fraction from a mixed-layer model? 3.Open questions about forcing 4.Preliminary results 3.Summary/Conclusions 4.Clarification on radiative feedbacks (if time) 1.Observational constraints on models? 2.Are GCMs really giving a consistent picture? The case for CMIP-forced limited-area models. 1.Previous limited-area studies 2.Cloud fraction from a mixed-layer model? 3.Open questions about forcing 4.Preliminary results 3.Summary/Conclusions 4.Clarification on radiative feedbacks (if time)

6 Work by Xin Qu and Alex Hall (UCLA) From interannual variability LTS=lower tropospheric stability: θ θ surf 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 – ). A: cgcm3.1 T47 B: cgcm3.1 T63 C: gfdl cm2.0 D: gfdl cm2.1 E: giss model e r F: iap fgoals1.0.g G: inmcm3.0 H: miroc3.2 medres I: ncar ccsm3.0 J: ncar pcm1 K: giss model e h 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 climate change response

7 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) 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) 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 – ). A: cgcm3.1 T47 B: cgcm3.1 T63 C: gfdl cm2.0 D: gfdl cm2.1 E: giss model e r F: iap fgoals1.0.g G: inmcm3.0 H: miroc3.2 medres I: ncar ccsm3.0 J: ncar pcm1 K: giss model e h L: ingv echam4 M: mri cgcm2.3.2a N: ipsl cm4 O: mpi echam5 P: bccr bcm2.0 Q: csiro mk3.5

8 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 – ). A: cgcm3.1 T47 B: cgcm3.1 T63 C: gfdl cm2.0 D: gfdl cm2.1 E: giss model e r F: iap fgoals1.0.g G: inmcm3.0 H: miroc3.2 medres I: ncar ccsm3.0 J: ncar pcm1 K: giss model e h L: ingv echam4 M: mri cgcm2.3.2a N: ipsl cm4 O: mpi echam5 P: bccr bcm2.0 Q: csiro mk3.5 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 dont fit a 1:1 line... internal variability is a poor magnitude predictor, but a good predictor of relative strength. 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 dont fit a 1:1 line... internal variability is a poor magnitude predictor, but a good predictor of relative strength.

9 Conclusions: 1.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) 1.A more nuanced view of LTS/climate feedback is needed Conclusions: 1.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) 1.A more nuanced view of LTS/climate feedback is needed 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 – ). A: cgcm3.1 T47 B: cgcm3.1 T63 C: gfdl cm2.0 D: gfdl cm2.1 E: giss model e r F: iap fgoals1.0.g G: inmcm3.0 H: miroc3.2 medres I: ncar ccsm3.0 J: ncar pcm1 K: giss model e h L: ingv echam4 M: mri cgcm2.3.2a N: ipsl cm4 O: mpi echam5 P: bccr bcm2.0 Q: csiro mk3.5

10 Questions: 1.Why does dcld/dLTS behave differently for climate change than for current- climate variability? – direct CO2 effect plays role 2.What physics cause cloudiness (a very nonlinear quantity) to be linearly related to LTS? – Zhang et al (JClim 2009) explores this Questions: 1.Why does dcld/dLTS behave differently for climate change than for current- climate variability? – direct CO2 effect plays role 2.What physics cause cloudiness (a very nonlinear quantity) to be linearly related to LTS? – Zhang et al (JClim 2009) explores this 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 – ). A: cgcm3.1 T47 B: cgcm3.1 T63*** C: gfdl cm2.0 D: gfdl cm2.1*** E: giss model e r F: iap fgoals1.0.g G: inmcm3.0 H: miroc3.2 medres I: ncar ccsm3.0 J: ncar pcm1 K: giss model e h L: ingv echam4 M: mri cgcm2.3.2a N: ipsl cm4 O: mpi echam5 P: bccr bcm2.0 Q: csiro mk3.5

11 These sum to the internal variability-predicted change on prev slide = = + model- independent or small terms = = 1.LTS generally contributes to cloud increase; q v impact varies. 2.Differences between models are dominated by dcld/dX perturbations – forcing changes are relatively consistent.

12 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 Tropics are divided into low cloud and deep convective boxes. Free tropospheric conditions are determined from observed tropical relations: 1.T assumed horizontally uniform in tropics and set by deep convection to a moist adiabat. 2.Relative humidity assumed invariant to climate change 3.subsidence chosen to balance radiative cooling Examples: Betts and Ridgeway (JAS 1989), Pierrehumbert (JAS 1995), Larson et al. (JClim 1999) These models focused on tropical circulation and generally had crude low cloud parameterizations Fig: Conceptual framework from Larson et al (1999)

14 A spate of recent studies ignore Scdeep convective coupling and use similar free-tropospheric approximations to drive more complex boundary-layer cloud models 1.Zhang + Bretherton (JClim 2008): Neg low cloud feedback in SCAM3 due to unphysical interaction between PBL, shallow and deep convection 2.Caldwell + Bretherton (JClim 2009): Neg Sc feedback in MLM due to decreased subsidence and increased LTS 3.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 4.Xu et al (JAS 2010): Neg Sc and shallow Cu feedback in 3D CRM runs 5.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 A spate of recent studies ignore Scdeep convective coupling and use similar free-tropospheric approximations to drive more complex boundary-layer cloud models 1.Zhang + Bretherton (JClim 2008): Neg low cloud feedback in SCAM3 due to unphysical interaction between PBL, shallow, and deep convection 2.Caldwell + Bretherton (JClim 2009): Neg Sc feedback in MLM due to increased subsidence and LTS 3.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 4.Xu et al (JAS 2010): Neg Sc and shallow Cu feedback in 3D CRM runs 5.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 1.Run a local model forced by a large variety of CMIP boundary conditions a.Requires a computationally-efficient model b.provides a lens to explore inter-GCM forcing consistency 1.Run a local model forced by a large variety of CMIP boundary conditions a.Requires a computationally-efficient model b.provides a lens to explore inter-GCM forcing consistency

17 mixed- layer model (JClim 2009) q t =q v +q l zizi Ocean s l =c p T+gz-Lq l Strong LW cooling at cloud top destabilizes BL Entrainment warms, dries BL Assumed to keep q t and s l well-mixed in boundary layer Entrainment parameterized following Lewellen 2 (JAS 1998) w/ wind-shear term (JClim 2009) 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. 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. MLM Schematic Criteria for a cloudy equilibrium: 1.MLM reaches equilibrium in 200 days 2.MLM predicts cloudy solution 3.divergence (proportional to subsidence) > 0.5x10 -6 s -1 4.BL top < 2 km (A TKE-based measure would be better but didnt work…) Criteria for a cloudy equilibrium: 1.MLM reaches equilibrium in 200 days 2.MLM predicts cloudy solution 3.divergence (proportional to subsidence) > 0.5x10 -6 s -1 4.BL top < 2 km (A TKE-based measure would be better but didnt work…)

18 When forced by ERA40 for , 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. LTS (K) Modeled and observed relation between LTS and cloud fraction using each season for each region as a datapoint. 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)

19 1.How will LTS change? – This is partially a statement about the geographical pattern of SST change since θ 700 is controlled by ITCZ SST and θ surf is controlled by Sc SST. 2.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? 3.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! 4.How will free-tropospheric moisture change? – roughly constant RH is expected, but small changes could matter… We said earlier that GCMs largely agree on forcings. 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 T and LTS increases (and T increases higher z) 850 mb q v generally increases, often by < constant RH prediction 850 mb geopotential increases, consistent with thermal expansion dry advection increases and there is a hint of increased cold advection Divergence generally decreases T and LTS increases (and T increases higher z) 850 mb q v generally increases, often by < constant RH prediction 850 mb geopotential 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 20 th century ( ) and A1B ( ) 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 Change in the standard deviation of MLM forcings between 20 th century ( ) and A1B ( ) 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. Variability in moisture and moisture advection seems to increase Variability in divergence largely decreases Signals are generally noisy Variability in moisture and moisture advection seems to increase Variability in divergence largely decreases Signals are generally noisy

22 Model Change (%) Change predicted from Internal Variability (%) A: cgcm3.1 T47 B: cgcm3.1 T63*** C: gfdl cm2.0 D: gfdl cm2.1*** E: giss model e r F: iap fgoals1.0.g G: inmcm3.0 H: miroc3.2 medres I: ncar ccsm3.0 J: ncar pcm1 K: giss model e h L: ingv echam4 M: mri cgcm2.3.2a N: ipsl cm4 O: mpi echam5 P: bccr bcm2.0 Q: csiro mk3.5 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). MLM cld frac too low GCM cld frac too high (b/c appropriate data not available?) MLM improves the seasonal cycle

23 Change in low cld frac ( minus ) from the MLM and direct from the GCMs. GCM bars use random overlap, dots indicate total cloud values. 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 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

24 1.MLM currently projects all diabatic forcings onto surface or BL top (makes TKE unrealistic) 2.Criteria for cloud existence is ad hoc 3.Equilibrium assumption is unrealistic (e.g. causes cloud too close to coast) 4.Should do 20yr runs, use just A1B to avoid jump due to 20c3mA1B transition, and run for more models 5.Explore effect of GCM cloud imprinting onto subsidence and SST used by MLM 6.Explore forcing differences in more detail (PDFs) 1.MLM currently projects all diabatic forcings onto surface or BL top (makes TKE unrealistic) 2.Criteria for cloud existence is ad hoc 3.Equilibrium assumption is unrealistic (e.g. causes cloud too close to coast) 4.Should do 20yr runs, use just A1B to avoid jump due to 20c3mA1B transition, and run for more models 5.Explore effect of GCM cloud imprinting onto subsidence and SST used by MLM 6.Explore forcing differences in more detail (PDFs)

25 1.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) 2.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 wasnt reduced? 1.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) 2.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 wasnt reduced?

26 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 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 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

27 Next section is mostly to show people in individual meetings....

28 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 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 Several pathways for low-cloud feedback have been suggested. Which are important?

29 Using diurnally- averaged radiation doesnt affect div (white dot) – Different story for SWCF (Blossey et al, 2009) Breaking change into individual components works surprisingly well (black stars) Using diurnally- averaged radiation doesnt 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. 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.

30 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. 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 The importance of RH changes decreases at higher RH. Free trop RH traps BL rad and decreases the effective emission level, reducing BL cooling LWP changes dont affect BL div for LWP>40 g/m 2. Still important for SWCF The importance of RH changes decreases at higher RH. Free trop RH traps BL rad and decreases the effective emission level, reducing BL cooling LWP changes dont affect BL div for LWP>40 g/m 2. Still important for SWCF

32 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

34 How does Sc work? Free Troposphere Boundary Layer (BL) q t =q v +q l zizi Ocean s l =c p T+gz-Lq l s l (0) c p SST SHF small Entrainment drying large LHF Subsiding warm air + cold SST = strong inversion Strong LW cooling at cloud top destabilizes BL Entrainment warms, dries BL Condensation/ evaporation warm/cool BL

35 LTS-based comparison from previous slide. Comparison using EIS instead of LTS as a predictor. Current-climate EIS variations are not a better predictor of climate change response.

36 Method for getting diurnal radiation properties: 1.Calculate insolation- weighted diurnal average cos(zenith angle) following Hartmann (1994) eq 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: BL Div time SW warming opposes LW cooling during day High vals during night not captured w/ 2 times.

37 xxx

38 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 Advection of Cloud Top: Currently assuming = 0 (need to fix) – Tried using minimal model of Caldwell+Bretherton (2009) but it was too noisy.


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