We assessed three important criteria of forecastability—simplicity, certainty, and variability. Climate is complex due to many causal variables and their variable interactions. There is uncertainty about causes, effects, and data. Using evidence-based (scientific) forecasting principles, we determined that a naïve "no change" extrapolation method was the appropriate benchmark. To be useful to policy makers, a proposed forecasting method would have to provide forecasts that were substantially more accurate than the benchmark. We calculated benchmark forecasts against the UK Met Office Hadley Centre's annual average thermometer data from 1850 through 2007. For 20- and 50-year horizons the mean absolute errors were 0.18°C and 0.24°C. The accuracy of forecasts from our naïve model is such that even perfect forecasts would be unlikely to help policy makers. We nevertheless evaluated the Intergovernmental Panel on Climate Change's 1992 forecast of 0.03°C-per-year temperature increases. The small sample of errors from ex ante forecasts for 1992 through 2008 was practically indistinguishable from the naïve benchmark errors. To get a larger sample and evidence on longer horizons we backcast successively from 1974 to 1850. Averaged over all horizons, IPCC errors were more than seventimes greater than errors from the benchmark. Relative errors were larger for longer backcast horizons.