A new paper published in Geophysical Research Letters demonstrates the pathetic state of climate modelling of clouds. According to the paper, the only way for the modellers to reproduce cloud effects on observed temperatures was to use microphysical properties of clouds that were the most divergent from satellite observations. In other words, to "tune" the model to reproduce one observation [temperature] results in detuning and non-reproduction of another observation [cloud microphysical properties]. The models thus remain in a state of infancy regarding clouds, one of the most important parameters required for climate projections. A mere 1 to 2% cloud modelling error can alone account for global warming or cooling, and these computer modelling games are nowhere close to achieving such a level of accuracy.
The models also spectacularly fail on many other vital components of climate.
From the latest edition of the NIPCC Report:
Suzuki, K., Golaz, J.-C. and Stephens, G.L. 2013. Evaluating cloud tuning in a climate model with satellite observations. Geophysical Research Letters 40: 4464-4468.
According to Suzuki et al. (2013), "climate models contain various uncertain parameters in the formulations of parameterizations for physical processes," and they say that "these parameters represent 'tunable knobs' that are typically adjusted to let the models reproduce realistic values of key-observed climate variables."
Against this backdrop, Suzuki et al. examined "the validity of a tunable cloud parameter, the threshold particle radius triggering the warm rain formation in a climate model." And the model they chose for this purpose was the Geophysical Fluid Dynamics Laboratory (GFDL) Coupled Climate Model version 3 (CM3), because it is known that alternate values of that model's tunable cloud parameter that fall within its real-world range of uncertainty "have been shown to produce severely different historical temperature trends due to differing magnitudes of aerosol [cloud] indirect forcing."
The results of the three researchers' analysis indicated that "the simulated temperature trend best matches [the] observed trend when the model adopts the threshold radius that worst reproduces satellite-observed microphysical statistics and vice versa." Of this finding the three researchers state, "this inconsistency between the 'bottom-up' process-based constraint and the 'top-down' temperature trend constraint implies the presence of compensating errors in the model." And they note that "if this behavior is not a peculiarity of the GFDL CM3, the contradiction may be occurring in other climate models as well," which is not what one would want to find.