Annual rankings of global temperature are widely cited by media and the general public, not only to place the most recent year in a historical perspective, but also as a first-order metric of recent climate change that is easily digestible by the general public. Moreover, all annual NOAAGlobalTemp anomalies from 1880 (the earliest reading available) through the mid-1970s are well below anomalies of the top 10 warmest years in Table 1, even when considering the uncertainty of the NOAAGlobalTemp time series values. While we expect the algorithm\u27s performance to be largely independent of any changes made to the way that NOAAGlobalTemp (or any other annual global temperature time series) is calculated, we do envision monitoring the algorithm\u27s performance and proposing future fine tuning of the algorithm if warranted. Similarly, the AR with trend extension approach (and the AR without trend extension approach to a lesser extent) appears to slightly outperform the AR+ENSO approach in terms of simulation error and prediction interval width, but again the differences are not statistically significant
