Changes in Internal Variability of Annual Mean Precipitation Simulated in a High Emissions Scenario

We present a new methodology for testing changes in internal variability over geographic regions while accounting for covarying temporal and spatial relationships. Changes in variability imply changes in the distribution of an examined variable, or non-stationarity, which has implications for extreme events in the tails of the distribution. We also discuss an implication of detecting and attributing climate change when variability also changes. The methodology presented here is derived from the Kullback-Leilber Divergence, which itself is fundamental to a wide range of applications including information theory. We present on how the method can be used to assess the field significance of a map quantifying local changes in variance. The method is invariant to linear transformation and therefore avoids certain problems that arise with spatial aggregation techniques that have been used in previous studies. We apply our methodology to the CMIP5 RCP8.5 emissions scenario for 7 global climate models to quantify the changes in internal variability of annual mean precipitation. We will show that models predict significant changes in spatio-temporal patterns of the internal variability of annual mean precipitation in response to global warming.