Monthly Modulations of ENSO Teleconnections: Implications for Potential Predictability in North America

Using a high-resolution atmospheric general circulation model simulation of unprecedented ensemble size, we examine potential predictability of monthly anomalies under El Nino-Southern Oscillation (ENSO) forcing and background internal variability. This study reveals the pronounced month-to-month evolution of both the ENSO forcing signal and internal variability. Internal variance in upper-level geopotential height decreases (similar to 10%) over the North Pacific during El Nino as the westerly jet extends eastward, allowing forced signals to account for a greater fraction of the total variability, and leading to increased potential predictability. We identify February and March of El Nino years as the most predictable months using a signal-to-noise analysis. In contrast, December, a month typically included in teleconnection studies, shows little to no potential predictability. We show that the seasonal evolution of SST forcing and variability leads to significant signal-to-noise relationships that can be directly linked to both upper-level and surface variable predictability for a given month. The stark changes in forced response, internal variability, and thus signal-to-noise across an ENSO season indicate that subseasonal fields should be used to diagnose potential predictability over North America associated with ENSO teleconnections. Using surface air temperature and precipitation as examples, this study provides motivation to pursue windows of forecast opportunity in which statistical skill can be developed, tested, and leveraged to determine times and regions in which this skill may be elevated.

Automated summary

Using a high-resolution atmospheric general circulation model simulation, this study investigates the impact of ENSO on monthly anomalies and reveals the evolving relationship between internal variability and ENSO forced signals. Signal-to-noise analysis identifies February and March of ENSO years as the most predictable months, while December shows little to no potential predictability.