Subtropical low cloud feedback in a superparameterized GCM - a mechanism and a CRM column analogue

Peter N. Blossey

Matthew C. Wyant

Christopher S. Bretherton

Department of Atmospheric Sciences

University of Washington

(thanks also to Marat Khairoutdinov and CMMAP)

Clouds in a superparameterized GCM• Superparameterization - a climate model with a small

cloud-resolving model (CRM) running in place of the normal physical parameterizations in every grid column.

• Computationally expensive, but may simulate turbulent clouds (especially deep convection) more realistically.

• SP-CAM (Khairoutdinov and Randall 2005) uses 2D CRMs with 32x30 gridpoints,x = 4 km - under-resolves boundary-layer Cu, Sc.

• Wyant et al. (2006) examined SPCAM cloud response to an idealized climate warming by comparing 3.5-year simulations with control SSTs vs. SST+2K.

• Is the cloud response:– physically understandable?– sensitive to grid resolution?

SPCAM has reasonable net CRF and low clouds

• Patterns good; not enough offshore stratocumulus; ‘bright’ trades/ITCZ.

LTS = 700 - 1000 - correlated to net CRF over

subtropical oceans.- Natural separator between

subtropical cloud regimes.

Use LTS for Bony-type cloud regime sorting’ to analyze subtropical (30S-30N) oceanic low cloud response

+2K cloud/CRF changes

• SWCF trends dominate net low cloud response.

• Low cloud increases in subtropics, summer high-latitude.

• LTS increases over all ocean regions.

Typical vertical structure in trades (SE Pac)

• Cloud fraction and inversion strength increase together.• Net CRF (not shown) proportional to cloud fraction.

Inversion strengthensand LTS increases

Subsidence changesare location-dependent.

LTS-sorted low-latitude ocean cloud response

• 10-20% relative increase in low cld fraction/condensate across all high-LTS (cool-SST, subsiding) regimes.

high LTSsubsidence

low LTS

warm SST cold SST

high LTSsubsidence

low LTS

Other LTS-ordered fields

diversechanges

1-2% moister PBL

more PBLrad cool

low LTS low LTShigh LTS high LTS

high SST high SST low SSTlow SST

Conceptual model of SP-CAM trade ‘Cu’ feedbacks

Possible issues:• SP-CAM under-resolution• Sensitive to GHG & warming scenario since radiatively-driven.

Radiative Mechanism

Higher SST More

absolute humidity

More clouds

More radiative cooling

More convection

Column Analogue for SP-CAM low-cld feedbacks

(1) Calculate MMF composite for LTS decile (e.g. 80-90%).

(2) Use composite , horizontal advective T/q tendencies and SST. Nudge to composite winds. A realistic wind direction profile is also needed (RICO).

(3) Allow mean subsidence to adjust to local diabatic cooling to keep SCM T profile close to SP-CAM sounding. (More on next slide.)

(4) Nudge moisture above surface layer to counteract effects of sporadic deep convection and detraining high cloud in SP-CAM composite forcings.

(5) Run to a statistically-steady state.Key assumption 1: (like Zhang&Breth 2008, Caldwell&Breth 2008)

- Regime-mean +2K cloud response can be recovered from

regime-mean profile/advective tendency changes.

• In low latitudes, the free-tropospheric temperature profile is remotely forced by deep convection over the warm parts of the tropics.

• Weak temperature gradient approximation (WTG): Stratified adjustment (compensating vertical motions) prevents build-up of local temperature anomalies.

• Our new WTG formulation for column modeling builds on Caldwell & Bretherton (2008); related to approaches used by Mapes (2004), Raymond & Zeng (2005),Kuang (2008).

• Compared to existing approaches, it has the advantage of a clear derivation from a relevant physical model applicable to quasi-steady dynamics.

Key assumption 2: Vertical Velocity Feedbacks

• Assume small perturbation to a reference state.• The linear, damped, hydrostatic, quasi-steady momentum and

mass conservation equations in pressure coordinates give:

Vertical Velocity Feedbacks (Derivation)

amu* − fv* =−∂φ* ∂x

amv* + fu* =0

∂φ* ∂p=−RdTv

* p

∂u* ∂x+∂ * ∂p=0

∂∂p

am−1 f 2 +am

2( )∂ *

∂p=−

Rdp

∂2Tv*

∂x2

∂∂p

am−1 f 2 +am

2( )∂ ′∂p

≈Rdk

2

p′Tv

• These equations can be combined to relate * to Tv*:

• Assuming sinusoidal pertubations in x of wavenumber k:

A horizontal length scale , where k=(2), and momentum-damping rate am are needed. We choose =650km and am=1/(2 days) w/ am vertically uniform.

LTS80-90 forcings and profiles

Hor. advection

winds

,q profiles; SST

+ q nudging

averaging period

1 d−1,0 d−1,

p≤550hPaat surface

⎧⎨⎩

−u ⋅∇s −u ⋅∇q

ctrl+2K

0 ,ω0 + ′ω

Results• CRM has deeper moist layer, but similar +2K cloud response.• Mean and +2K cld response depend a bit on setup details, wind shear.

CRM

SP-CAM

Cu-layer radiative forcing/nudging• Radiative heating change in the same sense in CRM as in SP-CAM, though not as strong.• Vertical velocity feedback ′ is small compared to SP-CAM 0, has little change in +2K run.• Q nudging small compared to vadv.

SP-CAM

CRM

Vertical Advection

CRM Q nudge

LES resolution (x=100 m, z=40 m, Nx=512) • Large reduction in mean low cloud and SW cloud forcing.• +2K low cloud change similar in magnitude but different in structure.

LES

CRM

Interpretation

4 km makes Cu clouds too weak and broad• Excessive Cu needed to flux water up to inversion.

LES

CRM

Conclusions

• Subtropical boundary-layer cloud increases dramatically in SP-CAM simulations with 2 K warmer SST.

• Tropospheric warming increases the clear-sky radiative cooling of the moist Cu layer, driving more Cu cloud.

• A column CRM analogue suggests that SP-CAM mean cloud are greatly overestimated due to coarse CRM resolution. The structure of the +2K cloud changes depends on resolution.

• LES column analogues show promise for studying greenhouse+aerosol effects on boundary-layer clouds; further research needed into the optimal formulation of large-scale dynamical feedbacks on the column.

• See poster later today for a static version of this talk.