Southeastern United States summer rainfall framework and its implication for seasonal prediction

Abstract

A new rainfall framework is constructed to describe the complex probability distribution of southeastern United States (SE US) summer (June–July–August) rainfall, which cannot be well represented by traditional kernel fitting methods. The new framework is based on the configuration of a three-cluster finite normal mixture model and is realized by Bayesian inference and a Markov Chain Monte Carlo (MCMC) algorithm. The three rainfall clusters reflect the probability distribution of light, moderate, and heavy rainfall in summer, and are linked to different climate factors. The variation of light rainfall intensity is likely associated with the combined effects of La Niña and the tri-pole sea surface temperature anomaly (SSTA) over the North Atlantic. Heavy rainfall concurs with a ‘horseshoe-like’ SSTA over the North Atlantic. In contrast, moderate rainfall is less correlated with the SSTA and likely caused by atmospheric internal dynamics. Rainfall characteristics and their linkages with SSTAs help improve seasonal predictions of regional climate. Such a new framework has an important implication in understanding the response of regional hydrology to climate variability and climate change; and our study suggest that it can be extended to other regions and seasons with similar climate.

Keywords

• southeastern United States (SE US) summer rainfall
• Finite Normal Mixture Model
• Markov Chain Monte Carlo (MCMC)
• sea surface temperature anomaly (SSTA)