Bootstrap estimated seasonal potential predictability of global temperature and precipitation

Abstract

Potential predictability of seasonal mean temperature and precipitation is assessed using a moving blocks bootstrap method. The bootstrap method allows the potential predictability of seasonal means to be assessed even for autocorrelated, highly non-Gaussian, intermittent data. The results reveal that the largest fraction of predictable variance for both temperature and precipitation occur mainly over the tropics where El Nio/Southern Oscillation dominates the interannual variability. Statistically significant potential predictability also is found in extratropics for temperature, particularly over most oceans and appreciable land areas. The potential predictability of temperature is generally smaller over land than over ocean and displays a significant annual cycle. Potential predictability of precipitation displays spotty and less continuous spatial patterns over extratropical regions and also undergoes a significant annual cycle. The potential predictability estimates are generally consistent with previous studies, but some inconsistency is also observed, such as the lack of significant potential predictability for temperature over North American winter. Copyright 2011 by the American Geophysical Union.