In an eye-popping article published in Environmental Research Letters, Ramirez-Villegas et al. (2013) write that "future outlooks of agricultural production and food security are contingent on the skill of GCMs [global climate models] in reproducing seasonal rainfall and temperatures," citing Berg et al. (2010), Ines et al. (2011) and Lobell et al. (2012). And, hence, they proceeded to determine just how well current GCMs are able to do so, by "assessing the skill of 24 CMIP3 and 26 CMIP5 GCMs in five regions of the tropical world (the Andes, West Africa, East Africa, Southern Africa and South Asia)," which they selected "due to their vulnerability to climate change."

This assessment focused on four key variables that exert significant control on crops: mean temperature, daily temperature extremes (i.e., diurnal temperature range), precipitation, and wet-day frequency," the data for which they obtained from the University of East Anglia Climatic Research Unit (New et al., 2002), World Clim (Hijmans et al., 2005), various sources of weather stations, and the ERA-40 reanalysis (Uppala et al., 2005)."

When all was said and done, the four researchers discovered that "climatological means of seasonal mean temperatures depict mean errors between 1 and 18°C (2-130% with respect to mean), whereas seasonal precipitation and wet-day frequency depict larger errors, often offsetting observed means and variability beyond 100%." In fact, they found that "no single GCM matches observations in more than 30% of the areas for monthly precipitation and wet-day frequency, 50% for diurnal range and 70% for mean temperatures."

However, all was not lost, for there were some "improvements" in mean climate skill: "5-15% for climatological mean temperatures, 3-5% for diurnal range and 1-2% in precipitation." And so it was that Ramirez-Villegas et al. concluded that "at these improvement rates, we estimate that at least 5-30 years of CMIP work is required to improve regional temperature simulations and at least 30-50 years for precipitation simulations, for these to be directly input into impact models," all of which makes us wonder why we should place any confidence at all in current GCMs.

Sherwood, Keith and Craig Idso

References
Berg, A., Sultan, B. and de Noblet-Ducoudre, N. 2010. What are the dominant features of rainfall leading to realistic large-scale crop yield simulations in West Africa? Geophysical Research Letters 37: 10.1029/2009GL041923.

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 165-178.

Ines, A.V.M., Hansen, J.W. and Robertson, A.W. 2011. Enhancing the utility of daily GCM rainfall for crop yield prediction. International Journal of Climatology 31: 2168-2182.

Lobell, D.B., Sibley, A. and Ivan Ortiz-Monasterio, J. 2012. Extreme heat effects on wheat senescence in India. Nature Climate Change 2: 186-189.

New, M., Lister, D., Hulme, M. and Makin, I. 2002. A high-resolution data set of surface climate over global land areas. Climate Research 21: 1-25.

Uppala, S.M., Kallberg, P.W., Simmons, A.J., Andrae, U., Costa Bechtold, V. da, Fiorino, M., Gibson, J.K., Haseller, J., Hernandez, A., Kelly, G.A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R.P., Andersson, E., Arpe, K., Balmaseda, M.A., Beljaars, A.C.M., Berg, L., van d. Berg, L., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Habemann, S., Holm, E., Hoskins, B.J., Isaksen, L., Janssen, P.A.E.M., Jenne, R., Mcnally, A.P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N.A., Saunders, R.W., Simon, P., Sterl, A., Trenberth, K.E., Untch, A., Vasiljevic, D., Viterbo, P. and Woollen, J. 2005. The ERA-40 re-analysis. Quarterly Journal of the Royal Meteorological Society 131: 2961-3012.