Autocorrelation - A Simple Diagnostic for Tropical Precipitation in Global Kilometer-Scale Climate Models

We propose the lag 1 autocorrelation of daily precipitation as a simple diagnostic of tropical precipitation in climate models. This metric generally has a relatively uniform distribution of positive values over the tropics. However, selected land regions are characterized by exceptionally low autocorrelation values. Low values correspond to the dominance of high-frequency variance in precipitation. Consistent with previous work, we show that CMIP6 climate models overestimate the autocorrelation. Global kilometer-scale models capture the observed autocorrelation pattern when deep convection is explicitly simulated. When a deep convection parameterization is used, the autocorrelation increases across the tropics, suggesting that land surface-atmosphere interactions are not responsible for the changes in precipitation variability. Furthermore, an accurate simulation of convectively coupled equatorial waves does not necessarily lead to a correct representation of the autocorrelation, and vice versa. This suggests other driving processes for the autocorrelation pattern.