Intercomparison of daily precipitation persistence in multiple global observations and climate models

Abstract

Daily precipitation persistence is affected by various atmospheric and land processes and provides complementary information to precipitation amount statistics for understanding the precipitation dynamics. In this study, daily precipitation persistence is assessed in an exhaustive ensemble of observation-based daily precipitation datasets and evaluated in global climate model (GCM) simulations for the period of 2001–2013. Daily precipitation time series are first transformed into categorical time series of dry and wet spells with a 1 mm d ^−1 precipitation threshold. Subsequently, P _dd ( P _ww ), defined as the probability of a dry (wet) day to be followed by another dry (wet) day is calculated to represent daily precipitation persistence. The analysis focuses on the long-term mean and interannual variability (IAV) of the two indices. Both multi-observation and multi-model means show higher values of P _dd than P _ww . GCMs overestimate P _ww with a relatively homogeneous spatial bias pattern. They overestimate P _dd in the Amazon and Central Africa but underestimate P _dd in several regions such as southern Argentina, western North America and the Tibetan Plateau. The IAV of both P _dd and P _ww is generally underestimated in climate models, but more strongly for P _ww . Overall, our results highlight systematic model errors in daily precipitation persistence that are substantially larger than the already considerable spread across observational products. These findings also provide insights on how precipitation persistence biases on a daily time scale relate to well-documented persistence biases at longer time scales in state-of-the-art GCMs.

Keywords

• daily precipitation
• precipitation persistence
• climate models
• CMIP5