1. 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)

2. 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?

3. 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

4. +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.

5. Typical vertical structure in trades (SE Pac) Cloud fraction and inversion strength increase together. Net CRF (not shown) proportional to cloud fraction. Inversion strengthens and LTS increases Subsidence changes are location-dependent.

6. 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 LTS subsidence low LTS warm SSTcold SST high LTS subsidence low LTS

7. Other LTS-ordered fields diverse changes 1-2% moister PBL more PBL rad cool low LTS high LTS high SST low SST

8. 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

9. 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.

10. 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

11. 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) 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 a m are needed. We choose  =650km and a m =1/(2 days) w/ a m vertically uniform.

12. LTS80-90 forcings and profiles Hor. advection winds ,q profiles; SST + q nudging averaging period ctrl +2K

13. 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

14. 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

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

16. Interpretation 4 km makes Cu clouds too weak and broad Excessive Cu needed to flux water up to inversion. LES CRM

17. 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.