Peter Caldwell, Steve Klein, and Yunyan Zhang (LLNL) Xin Qu, Alex Hall (UCLA) Lawrence Livermore...

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CMIP Low Cloud Feedback Interpreted Through a Mixed- Layer Model 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- 07NA237344. UCRL: LLNL-PRES-????

Transcript of Peter Caldwell, Steve Klein, and Yunyan Zhang (LLNL) Xin Qu, Alex Hall (UCLA) Lawrence Livermore...

Page 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.

CMIP Low Cloud Feedback Interpreted Through a Mixed-Layer Model

Peter Caldwell, Steve Klein, and Yunyan Zhang (LLNL)Xin Qu, Alex Hall (UCLA)

Lawrence Livermore National Lab, CAUW Atmos Phy and Chem Seminar, 1/31/11

Prepared by LLNL under Contract DE-AC52-07NA237344. UCRL: LLNL-PRES-????

Page 2: 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.

Motivation: Importance of Low Cloud

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).

Page 3: 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.

Motivation: GCM Uncertainty

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.

4.5

4.0

3.5

3.0

2.5

2.0

Equi

lib. C

limat

e Se

nsiti

vity

(K)

Loca

l Fee

dbac

k Pa

ram

eter

(W m

-2 K

-1) E. Pacific (30S-30N, 150W-60W)

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.

Page 4: 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.

Why do GCMs Have Trouble?

zi

Ocean

10201005

975

921

847

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.

Fig: Vertical grid for the CAM3 GCM in CMIP3 (in green) compared to a typical cloud thickness in the SE Pacific.

Page 5: 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.

What to do?!? (outline of my talk)

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 studies2. Cloud fraction from a mixed-layer model?3. Open questions about forcing4. Preliminary results

3. Summary/Conclusions4. Clarification on radiative feedbacks (if time)

Page 6: 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.

What can current-climate variability tell us about future low cloud change?

Work by Xin Qu and Alex Hall (UCLA)

From interannual variability

LTS=lower tropospheric stability: θ700 - θsurf

Mod

el C

hang

e (%

)

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).

A: cgcm3.1 T47B: cgcm3.1 T63C: gfdl cm2.0D: gfdl cm2.1E: giss model e rF: iap fgoals1.0.gG: inmcm3.0H: miroc3.2 medresI: ncar ccsm3.0J: ncar pcm1K: giss model e hL: ingv echam4M: mri cgcm2.3.2aN: ipsl cm4O: mpi echam5P: bccr bcm2.0Q: csiro mk3.5

Δcld =∂cld

∂LTS*ΔLTS +

∂cld

∂qv

*Δq

free-troposphere integrated moisturemodel climate change response

Page 7: 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.

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)

Mod

el C

hang

e (%

)

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).

A: cgcm3.1 T47B: cgcm3.1 T63C: gfdl cm2.0D: gfdl cm2.1E: giss model e rF: iap fgoals1.0.gG: inmcm3.0H: miroc3.2 medresI: ncar ccsm3.0J: ncar pcm1K: giss model e hL: ingv echam4M: mri cgcm2.3.2aN: ipsl cm4O: mpi echam5P: bccr bcm2.0Q: csiro mk3.5

Page 8: 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.

Mod

el C

hang

e (%

)

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).

A: cgcm3.1 T47B: cgcm3.1 T63C: gfdl cm2.0D: gfdl cm2.1E: giss model e rF: iap fgoals1.0.gG: inmcm3.0H: miroc3.2 medresI: ncar ccsm3.0J: ncar pcm1K: giss model e hL: ingv echam4M: mri cgcm2.3.2aN: ipsl cm4O: mpi echam5P: bccr bcm2.0Q: csiro mk3.5

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.

Page 9: 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.

What can current-climate variability tell us about future low cloud change?

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)

2. A more nuanced view of LTS/climate feedback is needed

Mod

el C

hang

e (%

)

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).

A: cgcm3.1 T47B: cgcm3.1 T63C: gfdl cm2.0D: gfdl cm2.1E: giss model e rF: iap fgoals1.0.gG: inmcm3.0H: miroc3.2 medresI: ncar ccsm3.0J: ncar pcm1K: giss model e hL: ingv echam4M: mri cgcm2.3.2aN: ipsl cm4O: mpi echam5P: bccr bcm2.0Q: csiro mk3.5

Page 10: 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.

What can current-climate variability tell us about future low cloud change?

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

Mod

el C

hang

e (%

)

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).

A: cgcm3.1 T47B: cgcm3.1 T63***C: gfdl cm2.0D: gfdl cm2.1***E: giss model e rF: iap fgoals1.0.gG: inmcm3.0H: miroc3.2 medresI: ncar ccsm3.0J: ncar pcm1K: giss model e hL: ingv echam4M: mri cgcm2.3.2aN: ipsl cm4O: mpi echam5P: bccr bcm2.0Q: csiro mk3.5

Page 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.

Internal Variability or Forcing Differences?

These sum to the “internal variability-predicted change” on prev slide

= +€

∂cld

∂X

⎝ ⎜

⎠ ⎟′ΔX

∂cld

∂XΔ ′ X

model-independent

or small terms

∂cld

∂X* ΔX +

∂cld

∂X

⎝ ⎜

⎠ ⎟′Δ ′ X

∂cld

∂X*ΔX +

+

+=

1. LTS generally contributes to cloud increase; qv impact varies.2. Differences between models are dominated by dcld/dX

perturbations – forcing changes are relatively consistent.

Page 12: 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.

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.

Page 13: 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.

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:

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)

Page 14: 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.

A spate of recent studies ignore Sc→deep convective coupling and use similar free-tropospheric approximations to drive more complex boundary-layer cloud models

Local Models of Low Cloud Feedback: Recent Work

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

Page 15: 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.

A spate of recent studies ignore Sc→deep convective coupling and use similar free-tropospheric approximations to drive more complex boundary-layer cloud models

Local Models of Low Cloud Feedback: Recent Work

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

Page 16: 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.

Our Approach:

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

Page 17: 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.

Our Model:

• mixed-layer model

(JClim 2009)

qt=qv+ql

zi

Ocean

sl=cpT+gz-Lql

Strong LW coolingat cloud top

destabilizes BL

Entrainment warms, dries BL

Assumed to keep qt and sl well-mixed in boundary layer

Entrainment parameterized following Lewellen2 (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.

MLM Schematic

Criteria for a cloudy equilibrium:1. MLM reaches equilibrium in 200 days2. MLM predicts cloudy solution3. divergence (proportional to

subsidence) > 0.5x10-6 s-1

4. BL top < 2 km(A TKE-based measure would be better but didn’t work…)

Page 18: 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.

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.

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)

Page 19: 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.

Open Questions about Forcing:1. 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.

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?

Page 20: 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.

MLM Forcing from CMIP3 • T and LTS increases (and

T increases more @ higher z)• 850 mb qv 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 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.

Page 21: 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.

Variability Changes in CMIP3 Forcings

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.

• Variability in moisture and moisture advection seems to increase• Variability in divergence

largely decreases• Signals are generally

noisy

Page 22: 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.

Preliminary MLM Results:M

odel

Cha

nge

(%)

Change predicted from Internal Variability (%)

A: cgcm3.1 T47B: cgcm3.1 T63***C: gfdl cm2.0D: gfdl cm2.1***E: giss model e rF: iap fgoals1.0.gG: inmcm3.0H: miroc3.2 medresI: ncar ccsm3.0J: ncar pcm1K: giss model e hL: ingv echam4M: mri cgcm2.3.2aN: ipsl cm4O: mpi echam5P: bccr bcm2.0Q: 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

Page 23: 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.

Preliminary MLM Results

Change in low cld frac (2090-2100 minus 1990-2000) 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

Page 24: 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.

Needed Improvements/Future Work

1. MLM currently projects all diabatic forcings onto surface or BL top (makes TKE unrealistic)

2. Criteria for cloud existence is ad hoc3. 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 20c3m→A1B 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)

Page 25: 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.

Conclusions1. 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 wasn’t reduced?

Page 26: 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.

Connections to CGILS:

• 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

Page 27: 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.

I expect to run out of time here

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

Page 28: 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.

Clarifying Feedback Mechanisms

• 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?

Page 29: 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.

Source of BL Radiative Cooling Change

• 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.

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.

Page 30: 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.

• 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.

Source of BL Radiative Cooling Change

Page 31: 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.

Nonlinearities in BL Rad Div

•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 don’t affect BL div for LWP>40 g/m2.• Still important for SWCF

Page 32: 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.

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.

Page 33: 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.

Extra Slides

Page 34: 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.

How does Sc work?F

ree

Trop

osph

ere

Bou

ndar

y L

ayer

(B

L)

qt=qv+ql

zi

Ocean

sl=cpT+gz-Lql

sl(0) ≈ cp SST→SHF small

Entrainmentdrying →large LHF

Subsiding warm air + cold SST

= strong inversion

Strong LW coolingat cloud top

destabilizes BL

Entrainment warms, dries BL

Condensation/evaporationwarm/cool

BL10201005

975

921

847

Page 35: 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.

Is EIS Response Closer to a 1:1 Line?

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.

Page 36: 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.

Justification for Using Diurnal Ave Insolation

Method for getting diurnal radiation properties:

1. Calculate insolation-weighted diurnal average cos(zenith angle) following Hartmann (1994) eq 2.18.

2. 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 D

iv

time

SW warming opposes LW cooling during day

High vals during night not captured w/ 2 times.

Page 37: 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.

Free Tropospheric Rad Divergence

• xxx

Page 38: 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.

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.