Creating Global Climate Models for Agricultural Impact Research
Ramirez-Villegas, J., Challinor, A.J., Thornton, P.K. and Jarvis, A. 2013. Implications of regional improvement in global climate models for agricultural impact research. Environmental Research Letters 8: 10.1088/1748-9326/8/2/024018.
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.
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