Multi-model detection and attribution of extreme temperature changes

Recent studies have detected anthropogenic influences due to increases in greenhouse gases on extreme temperature changes during the latter half of the 20th century at global and regional scales. Most of the studies, however, were based on a limited number of climate models, and also separation of anthropogenic influence from natural factors due to changes in solar and volcanic activities remains challenging at regional scales. Here we conduct optimal fingerprinting analyses using 12 climate models integrated under anthropogenic-only forcing or natural plus anthropogenic forcing. We compare observed and simulated changes in annual extreme temperature indices of coldest night and day (TNn and TXn) and warmest night and day (TNx and TXx) from 1951-2000. Spatial domains from global mean to continental and sub-continental regions are considered and standardization of indices is employed for better intercomparisons between regions and indices. The anthropogenic signal is detected in global and northern continental means of all four indices, albeit less robustly for TXx, consistent with previous findings. The detected anthropogenic signals are also found to be separable from natural forcing influence at the global scale and to a lesser extent at continental and sub-continental scales.