The Newsletter of the multi-institution Climate Process Team on Low-Latitude Cloud Feedbacks on Climate Sensitivity outlines their significant findings to date:
1. Clouds have a strong negative-feedback cooling effect on climate in both the tropics and extra-tropics
2. A warmer climate enhances [increases] boundary layer clouds resulting in increasing negative-feedback
3. Due to this strong negative-feedback, global climate sensitivity is only 0.41 K/(W m-2) - HALF the 0.8 K/(W/m2) + assumed by the IPCC
The Climate Process Team on Low-Latitude Cloud Feedbacks on Climate Sensitivity (cloud CPT) includes three climate modeling centers, NCAR, GFDL, and NASA’s Global Modeling and Assimilation Office (GMAO), together with 8 funded external core PIs led by Chris Bretherton of the University of Washington (UW). Its goal has been to reduce uncertainties about the feedback of low-latitude clouds on climate change as simulated in atmospheric general circulation models (GCMs). To coordinate this multi-institution effort, we have hired liaison scientists at NCAR and GFDL, had regular teleconferences and annual meetings, and developed special model output datasets for group analysis. The cloud CPT web site http://www.atmos.washington.edu/~breth/CPT-clouds.html provides links to all its publications and activities. The cloud CPT has had many interesting subplots; here we focus on two of interesting recent results and its future plans. The results showcase a key CPT strategy - gaining insight from the
use of several complementary modeling perspectives on the cloud feedbacks problem.
Two recent findings of the cloud CPT:
(1) The world’s first superparameterization climate sensitivity results show strong negative cloud feedbacks driven by enhancement of boundary layer clouds in a warmer climate.
Superparameterization is a recently developed form of global modeling in which the parameterized moist physics in each grid column of an AGCM is replaced by a small cloud-resolving model (CRM). It holds the promise of much more realistic simulations of cloud fields associated with moist convection and turbulence. Superparameterization is computationally expensive, but multiyear simulations are now feasible. The Colorado State University and UW cloud CPT groups collaborated on the first climate sensitivity analysis of a superparameterized AGCM (Wyant et al. 2006b). The Khairoutdinov-Randall (2001, 2005) superparameterized CAM3, hereafter CAM-SP, was used. Each CRM in CAM-SP has the same vertical levels as CAM3, 4 km horizontal resolution, and one horizontal dimension with 32 horizontal gridpoints.
Following Cess et al. (1989), climate sensitivity was assessed by examining the TOA radiative response to a uniform SST increase of 2K, based on the difference between control and +2K 3.5 year CAMSP simulations. Fig. 2 compares the results to standard versions of the NCAR CAM3, GFDL AM2 and GMAO AGCMs. All these models have similar clear-sky responses, so we just plot the +2K changes in longwave (greenhouse) and shortwave (albedo) cloud radiative forcings (ΔLWCF and ΔSWCF). Since ΔSWCF tends to be larger than ΔLWCF. boundary-layer cloud changes (which have little greenhouse effect compared to their albedo enhancement) appear to
be particularly important. The CAM-SP shows strongly negative net cloud feedback in both the tropics and in the extratropics, resulting in a global climate sensitivity of only 0.41 K/(W m-2), at the low end of traditional AGCMs (e.g. Cess et al. 1996), but in accord with an analysis of 30- day SST/SST+2K climatologies from a global aquaplanet CRM run on the Earth Simulator (Miura et al. 2005). The conventional AGCMs differ greatly from each other but all have less negative net cloud forcings and correspondingly larger climate sensitivities than the
superparameterization. The coarse horizontal and vertical resolution of CAM3-SP means that it highly under-resolves the turbulent circulations that produce boundary layer clouds. Thus, one should interpret its predictions with caution. With this caveat, cloud feedbacks are arguably more naturally simulated by superparameterization than in conventional AGCMs [conventional climate models], suggesting a compelling need to better understand the differences between the results from these two approaches.
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