Attribution of Linear and Nonlinear Cross-timescale Interference between ENSO and the MJO and Influences on Seasonal Rainfall Patterns

There is demand from decision makers to improve forecast skill of anomalous precipitation events, which would provide leaders with the means to support their communities. This study investigates anomalous rainfall events that are often caused by cross-timescale interactions of large-scale climate modes of variability, such as the Madden Julian Oscillation (MJO) and El Niño-Southern Oscillation (ENSO). First, this research focuses on furthering the understanding of how to identify the independent and joint nonlinear rainfall signals contributed by (in this case) the MJO and ENSO based on observations and, second, identifies if rainfall signals can be explained through a linear or nonlinear cross-timescale interference analysis. Here, composites of rainfall anomalies are created from separate (“independent”) and joint ENSO and MJO occurrences. The independent composites suggest that it is possible to identify the individual signatures from several climate drivers on observed rainfall. However, joint analyses expose distinct ENSO- and MJO-related rainfall distributions that cannot be seen when an independent composite analysis is conducted. Generally speaking, global precipitation variability can be a result of both linear and nonlinear cross-timescale interactions of these (and other) modes of climate variability, depending on their magnitude and when and where the interaction occurs. The methodology is a completely general approach and can be used to analyze other climate drivers or modes of variability at global or regional scales.