Talk-Polywell.org

One of the comments to the CA article shows the simplicity and obviousness of the existence of negative feedback in one of our most common weather events. Willis Eschenbach writes:

Cloud positive feedback is one of the most foolish and anti-common sense claims of the models.

This is particularly true of cumulus and cumulonimbus, which increase with the temperature during the day, move huge amounts of energy from the surface aloft, reflect huge amounts of energy to space, and fade away and disappear at night.

“Superparameterization” is described by the Climate Process Team on Low-Latitude Cloud Feedbacks on Climate Sensitivity in an online meeting report (Bretherton, 2006) as:

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.

Clouds have, of course, been the primary source of uncertainty in climate models since the 1970s. Some of the conclusions from cloud parameterization studies are quite startling.

The Climate Process Team on Low-Latitude Cloud Feedbacks on Climate Sensitivity reported that:

The world’s first superparameterization climate sensitivity results show strong negative cloud feedbacks driven by enhancement of boundary layer clouds in a warmer climate.

These strong negative cloud feedbacks resulted in a low climate sensitivity of only 0.41 K/(W m-2), described as being at the “low end” of traditional GCMS (i.e. around 1.5 deg C/doubled CO2.):

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