Influence of sea surface temperature variability on global temperature and precipitation extremes

The HadISST1 data set was used to categorize seasonal patterns of observed global sea surface temperature (SST) variability between 1870 and 2006 using the method of Self-Organizing Maps (SOM). Eight patterns represented the majority of global SST variations associated with the El Nino-Southern Oscillation (ENSO). Time series of the eight patterns exhibited periods with "preferred" SST states since the late 19th century, i.e., when one or more patterns occurred more frequently than in other periods. The eight patterns were used to investigate the global land-based response of observed extreme temperature and precipitation indices from the HadEX data set to different nodes of SST variability between 1951 and 2003. Results showed very strong statistically significant opposite temperature and precipitation extremes associated with the first pattern (strong La Nina) and the last pattern (strong El Nino). Extreme maximum temperatures were significantly cooler during strong La Nina events than strong El Nino events over Australia, southern Africa, India, and Canada while the converse was true for United States and northeastern Siberia. These responses were larger when global warming was retained. Even intermediate patterns representing a shift from a weak El Nino to a weak La Nina with associated variability in the North Atlantic were linked with statistically significant increases in warm nights and warm days particularly across Scandinavia and northwest Russia. While the link between precipitation extremes and global SST patterns was less spatially coherent, there were large areas across North America and central Europe, which showed statistically significant differences in the response to opposite phases of the El Nino-Southern Oscillation. These results confirm that the variability of global SST anomaly patterns is important for the modulation of extreme temperature and precipitation globally.