Goal: Isolate SP-CAM low cloud response in a simpler setting and examine its resolution sensitivity. Key assumptions: (like Zhang & Bretherton 2008, Caldwell & Bretherton 2008) 1.Regime-mean +2K cloud response can be recovered from regime-mean profile/advective tendency changes. 2.In low latitudes, strong nonlocal dynamical feedbacks counteract changes in column temperature profile. These feedbacks are included here by allowing mean subsidence to adjust to the column diabatic cooling to keep the temperature profile close to the SP-CAM composite, using an approach that builds on that of Caldwell and Bretherton (2008). Here,  =  0 +  where  0 is from SP-CAM and  responds to column cooling. Additional humidity relaxation is applied above the cumulus layer. (NEW: CHANGE FROM POSTER AT GCSS MEETING.) + q nudging Subtropical low cloud feedback in a superparameterized GCM - a mechanism and CRM column analogue Matthew C. Wyant, Christopher S. Bretherton, and Peter N. Blossey Department of Atmospheric Sciences, University of Washington, Seattle USA The SP-CAM superparameterized GCM predicts more low cloud in a warmer climate (1)What is the physical mechanism? (2)Is this cloud increase sensitive to the coarse resolution of the embedded CRMs? Introduction 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. Nevertheless, SP-CAM has a boundary-layer cloud climatology that is comparably good to other GCMs. Comparing Figs. 1a vs.1b, its biggest bias is too much trade cumulus and too little stratocumulus. Fig. 1c shows lower tropospheric stability LTS =   1000, which is well correlated to observed net CRF over the subtropical oceans (Klein and Hartmann 1993) and is a natural separator between subtropical cloud regimes. In low latitudes, free tropospheric  is nearly uniform and LTS tends to be anticorrelated with SST. LTS-sorting illuminates +2K low cloud increase Thanks...to Marat Khairoutdinov for the SP-CAM runs and for providing SAM, and to CMMAP and NSF for financial support. Conclusions Subtropical boundary-layer cloud increases dramatically in SP-CAM with warmer SST. Warming increases radiative destabilization of Cu layer, driving more convective cloud. CRM analogue  SP-CAM low cloud overestimated due to CRM under-resolution. We are about to submit this work to JAMES - one of us or check the JAMES-D website if you would like a copy of the manuscript. 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. Low cloud increased over the subtropical oceans and high-latitudes, implying a strong negative global cloud-radiative feedback dCRF/dSST = W m -2 K -1 (Fig. 1d-e). Fig. 1f shows a correlated 1-2 K LTS increase across low latitudes. Fig. 1: (a-b) SPCAM vs. observed net cloud radiative forcing; (c) Observed LTS, (d-f) +2K SPCAM changes. (a) (b) (d) (e) (c) (f) CRM column analogue for SPCAM cloud response Use percentiles of monthly-mean LTS to sort the low latitude (30S-30N) oceans (LLO) into boundary-layer cloud regimes: LTS better correlated with observed net CRF over LLO (r = 0.59) than are  (500 hPa), SST, or EIS. It also correlates strongly with inversion height and boundary-layer vertical structure. Use percentiles since LTS is not climate-invariant. A given low cloud fraction is associated with higher LTS in a warmer climate in which the tropical free-tropospheric temperature follows a more stable moist adiabat. Fig. 2a-d shows LTS-sorting applied to the SP-CAM mean climatology. As in  (500 hPa)-binning (Bony et al. 2004; Wyant et al. 2006) the Hadley- Walker circulation is evident, with low LTS (high SST) corresponding to extensive deep convective cloud, deep humidity, rising motion, and less radiative cooling. The high LTS (low SST) regions have little cloud or humidity above the moist boundary layer. Radiative cooling (especially strong in the boundary layer) drives mean subsidence. diverse changes 1-2% moister PBL low LTS high LTS warm SST cold SST warm SST cold SST ~25% relative increase in low cloud cover for high LTS! more PBL rad cool Fig. 2: Left panels (a-d): Climatology of SP-CAM LLO column- months sorted into 5-percent bins of LTS. Right panels (e-h) show +2K changes in sorted climatology. Purple panels show 80-90th LTS percentiles, which show the low cloud changes well and are used for further analysis. The +2K changes in the sorted climatology (Figs. 2e-h) show that in the high-LTS, subsiding regions, the strong low cloud increases are accompanied by more radiative cooling, slightly more PBL relative humidity, but no systematic changes in lower-tropospheric subsidence. More PBL radiative cooling will destabilize the PBL and force more cumulus convection and perhaps more associated cloud. Fig. 3c shows that the +2K radiative cooling increase is mainly a clear-sky response to the increased absolute PBL humidity (and hence emissivity) associated with a warmer temperature profile (Fig. 3a). The small +2K increases in relative humidity do not strongly affect the radiative cooling changes. RH and cloud changes are viewed as positive feedbacks on the clear-sky radiative cooling increases. Radiative mechanism for +2K cloud response Suggested mechanism of SP-CAM low cloud increase Possible issues SP-CAM under-resolution Sensitive to  GHG & warming scenario since radiatively-driven. Atmosphere warmer and moister Cu layer destabilized by stronger radiative cooling More Cu clouds Large-scale dynamics also provide important feedbacks. The free- tropospheric temperature profile is remotely forced by deep convection over the warm parts of the tropics. Changes in diabatic cooling drive subsidence changes, which we view here as a local dynamical feedback. Winds and large-scale horizontal T, RH advection show negligible +2K changes, and don’t appear important to the low cloud changes. Fig. 3: LTS percentile changes of key variables. Temperature (a) and radiative cooling (c) profile changes are central to our cloud increase mechanism. (a) (b) (c) (d) (e) (f) (g) (h) (a) (b) (c) (d) Method: 1.Make composite forcings/profiles for a cloud regime defined with percentiles of LTS over low-lat ocn column-months, for ctrl and SST+2K SP- CAM runs. 2.Configure SAM6.5 CRM to use identical microphys, radiation, resolution, domain orientation as in SP-CAM. 3.Run CRM to steady-state, look at +2K cloud differences LES (  x=100 m,  z=40 m, N x =512): Large reduction in mean cloud. Similar +2K change in low cloud, but with different BL structure. Hor. Advection winds LTS80-90 ,q profiles; SST averaging period ctrl +2K CRM column analogue for SPCAM - philosophy CRM: Deeper moist layer, but similar +2K cloud response. Mean & +2K cloud response depend a bit on wind shear, forcing details. CRM analogue results SP-CAM LTS80-90 composites References Bony, S., J. DuFresne, H. Le Treut, J.-J. Morcrette, and C. Senior 2004: On dynamic and thermodynamic components of cloud changes. Climate Dyn., 22, Caldwell, P., and C. S. Bretherton, 2008: Large eddy simulation of the diurnal cycle in Southeast Pacific stratocumulus. J. Climate, submitted. Khairoutdinov, M. F., and D. A. Randall, 2003: Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities. J. Atmos. Sci., 60, Khairoutdinov, M., D. Randall, and C. DeMott, 2005: Simulations of the atmospheric general circulation using a cloud-resolving model as a superparameterization of physical processes. J. Atmos. Sci., 62, Klein, S., and D. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Climate, 6, Wyant, M. C., M. Khairoutdinov, and C. S. Bretherton, 2006: Climate sensitivity and cloud response of a GCM with a superparameterization. Geophys. Res. Lett., 33, L06714, doi: /2005GL Zhang, M., and C. S. Bretherton, 2008: Mechanisms of low cloud climate feedback in idealized single- column simulations with the Community Atmospheric Model (CAM3). J. Climate, in press.