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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.