The Outlook for Modeling Clouds (Adequately) ... is Still Cloudy
Lauer, A. and Hamilton, K. 2013. Simulating clouds with global climate models: A comparison of CMIP5 results with CMIP3 and satellite data. Journal of Climate 26: 3823-3845.
After conducting their several analyses, Lauer and Hamilton concluded that "the simulated cloud climate feedbacks activated in global warming projections differ enormously among state-of-the-art models," informing us that "this large degree of disagreement has been a constant feature documented for successive generations of GCMs from the time of the first Intergovernmental Panel on Climate Change assessment through the CMIP3 generation models used in the fourth IPCC assessment." And they add that "even the model-simulated cloud climatologies for present-day conditions are known to depart significantly from observations and, once again, the variation among models is quite remarkable (e.g., Weare, 2004; Zhang et al., 2005; Waliser et al., 2007, 2009; Lauer et al., 2010; Chen et al., 2011)."
As for some other specifics, the two researchers determined that (1) "long-term mean vertically integrated cloud fields have quite significant deficiencies in all the CMIP5 model simulations," that (2) "both the CMIP5 and CMIP3 models display a clear bias in simulating too high LWP [liquid water path] in mid-latitudes," that (3) "this bias is not reduced in the CMIP5 models," that (4) there have been "little to no changes in the skill of reproducing the observed LWP and CA [cloud amount]," that (5) "inter-model differences are still large in the CMIP5 simulations," and that (6) "there is very little to no improvement apparent in the tropical and subtropical regions in CMIP5."
In closing, Lauer and Hamilton indicate there is "only very modest improvement in the simulated cloud climatology in CMIP5 compared with CMIP3," and they sadly state that even this slightest of improvements "is mainly a result of careful model tuning rather than an accurate fundamental representation of cloud processes in the models."
So, the outlook for adequately modeling clouds and cloud processes, after all these years of trying, must still be characterized as cloudy.
Chen, W.-T., Woods, C.P., Li, J.-L.F., Waliser, D.E., Chern, J.-D., Tao, W.-K., Jiang, J.H. and Tompkins, A.M. 2011. Partitioning CloudSat ice water content for comparison with upper tropospheric ice in global atmospheric models. Journal of Geophysical Research 116: 10.1029/2010JD015179.
Lauer, A., Hamilton, K., Wang, Y., Phillips, V.T.J. and Bennartz, R. 2010. The impact of global warming on marine boundary layer clouds over the eastern Pacific - A regional model study. Journal of Climate 23: 5844-5863.
Waliser, D.E., Seo, K.-W., Schubert, S. and Njoku, E. 2007. Global water cycle agreement in the climate models assessed in the IPCC AR4. Geophysical Research Letters 34: 10.1029/2007GL030675.
Waliser, D.E., Li, J.-L.F., Woods, C.P., Austin, R.T., Bacmeister, J., Chern, J., Del Genio, A., Jiang, J.H., Kuang, Z., Meng, H., Minnis, P., Platnick, S., Rossow, W.B., Stephens, G.L., Sun-Mack, S., Tao, W.-K., Tompkins, A.M., Vane, D.G., Walker, C. and Wu, D. 2009. Cloud ice: A climate model challenge with signs and expectations of progress. Journal of Geophysical Research 114: 10.1029/2008JD010015.
Weare, B.C. 2004. A comparison of AMIP II model cloud layer properties with ISCCP D2 estimates. Climate Dynamics 22: 281-292.
Zhang, M.H., Lin, W.Y., Klein, S.A., Bacmeister, J.T., Bony, S., Cederwall, R.T., Del Genio, A.D., Hack, J.J., Loeb, N.G., Lohmann, U., Minnis, P., Musat, I., Pincus, R., Stier, P., Suarez, M.J., Webb, M.J., Wu, J.B., Xie, S.C., Yao, M.-S. and Zhang, J.H. 2005. Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. Journal of Geophysical Research 110: 10.1029/2004JD005021.