Lee, T., Waliser, D.E., Li, J.-L.F., Landerer, F.W. and Gierach, M.M. 2013. Evaluation of CMIP3 and CMIP5 wind stress climatology using satellite measurements and atmospheric reanalysis products. Journal of Climate 26: 5810-5826.
The authors write that "the reliability of future climate projections using climate models depends heavily on the fidelity of the climate models," and they note in this regard that "the latter can be assessed by evaluating the ability of the climate models to simulate the present climate using available observations," citing Pierce et al. (2006), Gleckler et al. (2008), Waliser et al. (2009) and Su et al. (2013), which is something that should be obvious to all rational people.
What was done
Lee et al. tell us that "wind stress measurements from the Quick Scatterometer (QuikSCAT) satellite and two atmospheric reanalysis [NCEP-1 and ERA-Interim] products were used to evaluate the annual mean and seasonal cycle of wind stress simulated by phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5)."
What was learned
The five researchers state that (1) "generally speaking, there is a lack of significant improvement of CMIP5 over CMIP3," that (2) "the CMIP ensemble-average zonal wind stress has eastward biases at mid-latitude westerly wind regions (30°-50°N and 30°-50°S, with CMIP being too strong by as much as 55%)," that (3) there are "westward biases in subtropical-tropical easterly wind regions (15°-25°N and 15°-25°S)," that (4) there are "westward biases at high-latitude regions (poleward of 55°S and 55°N)" that "correspond to too strong anticyclonic (cyclonic) wind stress curl over the subtropical (subpolar) ocean gyres," that (5) "in the equatorial Atlantic and Indian Oceans, CMIP ensemble zonal wind stresses are too weak and result in too small of an east-west gradient of sea level," that (6) "in the equatorial Pacific Ocean, CMIP zonal wind stresses are too weak in the central and too strong in the western Pacific," and that (7) "the CMIP [models] as a whole overestimate the magnitude of seasonal variability by almost 50% when averaged over the entire global ocean."
What it means
Even the most up-to-date CMIP5 models are still a long, long way from where they need to be for mankind to place much faith in what they predict in the way of CO2-induced global warming and its imagined negative consequences.
Gleckler, P.J., Taylor, K.E. and Doutriaux, C. 2008. Performance metrics for climate models. Journal of Geophysical Research 113: 10.1029/2007JD008972.
Pierce, D.W., Barnett, T.P., Fetzer, E.J. and Gleckler, P.J. 2006. Three-dimensional tropospheric water vapor in coupled climate models compared with observations from the AIRS satellite system. Geophysical Research Letters 33: 10.1029/2006GL027060.
Su, H., Jiang, J.H., Zhai, C., Perun, V.S., Shen, J.T., Del Genio, A., Nazarenko, L.S., Donner, L.J., Horowitz, L., Seman, C., Morcrette, C., Petch, J., Ringer, M., Cole, J., von Salzen, K., Mesquita, M., Iversen, T., Kristjansson, J.E., Gettelman, A., Rotstayn, L., Jeffrey, S., Dufresne, J.-L., Watanabe, M., Kawai, H., Koshiro, T., Wu, T., Volodin, E.M., L'Ecuyer, T., Teixeira, J. and Stephens, G.L. 2013. Diagnosis of regime-dependent cloud simulation errors in CMIP5 models using "A-Train" satellite observations and reanalysis data. Journal of Geophysical Research 118: 2762-2780.
Waliser, D.E., Li, J.-L., 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.Reviewed 23 October 2013