A Mechanism for Low Cloud Response in SP-CAM

1. A Mechanism for Low Cloud Response in SP-CAM Matthew C. Wyant Christopher S. Bretherton Peter Blossey Department of Atmospheric Sciences University of Washington (thanks also to Marat Khairoutdinov and CMMAP) Wyant et al.(2008) submitted to JAMES, July, 2008

2. Overview • Why do we care about low cloud changes? • In SP-CAM, tropical low cloudiness increases by 10-20% as the SST is increased by 2K. What causes this change? • Does a CO2 increase affect low clouds differently? • What are some future directions for research on low-cloud feedbacks in CMMAP?

3. What are the advantages of superparameterization in studying cloud feedbacks? • Many clouds are formed by turbulent circulations. These circulations may be resolved in a superparameterized model but must be parameterized in a GCM. • Aerosol and microphysical processes can thereby also be incorporated more realistically. • Higher resolutions are possible within the subgrid cloud-resolving model (CRM) of a superparameterized GCM than in a global CRM (e.g. by using 2-D CRM and/or a CRM domain smaller than the parent GCM column).

4. SP-CAM Climate Model (prototype-MMF) • SP-CAM is “superparameterized”- contains a CRM running in every grid column replacing convective parameterizations. • Uses CAM3 as its host GCM (Khairoutdinov and Randall, 2005) with 2.8° x 2.8° grid. • Uses System for Atmospheric Modeling (SAM) 2D CRM (Khairoutdinov and Randall 2003). • 32 sub-columns in each CAM column (4km horizontal resolution) • 28-level vertical resolution • 5 category bulk microphysics, temperature diagnostic for phase and ice habit • CAM3 Radiation

5. Cloud Forcing • Change in net downward radiative flux at the top of atmosphere due to clouds: SWCF = SW↓net - SW ↓net clear LWCF = - (LW ↑ - LW ↑clear) Net cloud forcing = SWCF + LWCF • Positive net cloud forcing → Clouds warm climate system

6. Climate Sensitivity () Ts = G Global-mean change in outgoing radiation at the top of the atmosphere Change in global mean surface temperature

7. Climate Sensitivity for +2K SSTlDTs/G

8. Low cloud feedbacks and climate sensitivity Stratocumulus Deep Convection High-sensitivity Low-sensitivity ΔCloud Forcing (W m-2 K-1) Subsidence Rate ω @ 500mb (mb day-1) Cloud forcing sensitivity from 15 coupled GCMs in a 2xCO2 experiment binned in 30N-30S by subsidence rate ω (Bony & Dufresne, 2005). Red values are from 8 high-sensitivity models, blue are for the remaining 7 low-sensitivity models.

9. +2K Cloud and Cloud Forcing changes • SWCF trends dominate net cloud forcing because of low-cloud response. • Low cloud increases in subtropics, summer high-latitude.

10. Lower tropospheric stability LTS = 700hPa - 2m Correlated with subtropical marine stratus cloud cover (Klein & Hartmann 1993) In observations and models

11. SPCAM has reasonable net CRF and low clouds • Patterns good; not enough offshore stratocumulus; ‘bright’ trades/ITCZ. • Excessive subtropical coastal stratofogulus (poor vertical resolution?) • In most areas, clouds have plausible vertical distribution.

12. Analysis Approach • Use 2.5 - 5 year simulations with specified SST, and analyze monthly climatologies. • Present-day SST, CO2 is the control experiment. • Compare with SST +2K run. • Compare with 4xCO2 run, with SST unchanged. • Focus on tropical (30N – 30S) oceans. • Sort column-months of the large-scale grid using lower tropospheric stability.

13. high LTS cold SST subsidence low LTS warm SST ascent 80-90%

14. LTS-sorted low-latitude ocean cloud response warm SST cold SST high LTS subsidence low LTS high LTS subsidence low LTS • 10-20% relative increase in low cld fraction/condensate across all high-LTS (cool-SST, subsiding) regimes. • This is responsible for SP-CAM’s negative tropical low-cloud feedback.

15. Typical vertical structure in trades (SE Pac) • Cloud fraction and inversion strength increase together. • Net cloud liquid (not shown) proportional to cloud fraction. • Little change in PBL depth Inversion strengthens and LTS increases Subsidence changes are location-dependent.

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

17. Conceptual model of SP-CAM trade ‘Cu’ feedbacks 80-90% LTS Radiative Mechanism Higher SST More absolute humidity More radiative cooling More convection More clouds Mechanism could be sensitive to GHG and warming scenario since radiatively-driven.

18. 4xCO2 experiment setup • Increase CO2 while keeping SST constant. • Complements +2K SST experiment by focusing on the effects of radiative changes. • Gregory and Webb (2008) found this approach useful in studying the rapid response of cloud forcing to CO2 increase. • An updated version of SP-CAM is used. • 2 ½ year integrations are used with the first ½ year discarded. • Though the duration is short, the main results hold in each of the final two years.

19. Control ∆ 4 x CO2 Radiative Heating Cloud

20. Control ∆ 4 x CO2 ω RH

21. ∆ Radiative Heating 80-90% LTS

22. Increased CO2 Reduced LW Cooling in and above BL Shallower BL Less BL Convection Reduced LWP

23. 50-100% LTS Comparison

24. Conclusions • Subtropical boundary-layer cloud increases dramatically in SP-CAM simulations with +2K warmer SST, more-so than in most other conventional GCMs • Tropospheric warming increases the clear-sky radiative cooling of the moist trade-cumulus layer, driving more trade-cumulus cloud. This further increases the radiative cooling. • In experiment with 4xCO2, the cloud response is weaker. With reduced clear-sky radiative cooling, cloud height is lowered and liquid water is reduced. • In a fully coupled CO2 experiment we speculate that low cloud would increase, though perhaps less than what one would expect from the SST change alone.

25. Using a Cloud Resolving Model (CRM) understand and test SP-CAM • Use regime-composite large-scale forcing from SP-CAM output to force ‘single-column’ CRM simulations. • We focus on high-LTS bins with suppressed deep convection (70-80% and 80-90%) and trade-cumulus and stratocumulus

26. θ RH CLOUD SWCF LES resolution (x=100 m, z=40 m, Nx=512) SP-CAM CRM LES

27. Summary of CRM Experiments • Steady-state CRM experiments at SP-CAM resolution are able to reproduce many features of composite SP-CAM profiles and low-cloud response. • Better horizontal and vertical resolution leads to lower cloud fraction and different cloud structure. • Cloud feedbacks are reduced in LES with improved resolution.

28. Future Directions • Examine low-cloud feedback mechanism further in existing SP-CAM runs (aquaplanet), and future SP-CAM runs with finer horizontal and vertical resolution. • Consider alternative model configurations (e.g. embedded mini-LES, adaptive vertical grid (Marchand)). • Continue work on single-column analogue CRM experiments. • Find minimum resolution needed to accurately simulate BL-cloud feedbacks. • Apply method to different cloud regimes (stratocumulus, deep cumulus) and forcings (e.g. aerosols). • Add synoptic variability to forcing. • Study feedbacks in future SP-CAM runs utilizing improved physics (e.g. double-moment Morrison microphysics, RRTMG radiation, higher-order turbulence closures).

29. Extra Slides

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

31. Comparison of regime sorting methods over tropical (30N-30S) oceans warm cold neutral stable subsidence ascent

32. Comparison of Tropical Clouds with ISCCP Wyant et al (2006)

33. Comparing GCM Feedbacks 2xCO2 experiment with 12 Coupled models (Soden and Held 2006)

34. averaging period

35. Column Analogue for SP-CAM low-cld feedbacks • Calculate SP-CAM composite for LTS decile (e.g. 80-90%). • Use composite , horizontal advective T/q tendencies and SST. Nudge to composite winds. A realistic wind direction profile is also needed (RICO). • Allow mean subsidence to adjust to local diabatic cooling to keep SCM T profile close to SP-CAM sounding. • Nudge moisture above surface layer to counteract effects of sporadic deep convection and detraining high cloud in SP-CAM composite forcings. • Run to a statistically-steady state (average over days 20-60).

36. 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.

37. Key assumption 2:Vertical Velocity Feedbacks • 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.

38. Vertical Velocity Feedbacks (Derivation) • Assume small perturbation to a reference state. • The linear, damped, hydrostatic, quasi-steady momentum and mass conservation equations in pressure coordinates give: • 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.

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

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

41. CRM Vertical Velocity Feedback • Vertical velocity feedback  is small compared to SP-CAM 0, has little change in +2K run.

42. CRM Cu-layer forcing/nudging Rad Heating T Vertical Advection Q Vertical Advection Q Nudging • Q nudging small compared to Q vertical advection

43. Radiative Heating Radiative cooling also stronger in +2K CRM (though less so than SP-CAM)

44. +2K cloud/CRF changes • SWCF trends dominate net Þ low cloud response. • Low cloud increases in subtropics, summer high-latitude. • LTS increases over all ocean regions.