Impacts of Madden–Julian Oscillation on Storm-Track Activity, Surface Air Temperature, and Precipitation over North America

The Madden-Julian Oscillation (MJO) is a dominant mode of intraseasonal tropical variability. Many previous studies found that the MJO can act as a diabatic heating source and modify the circulation in the extratropics. In this study, the intraseasonal variability of storm track activity, surface air temperature and precipitation over North America associated with the MJO during boreal winter have been investigated. A lag composite strategy that considers different MJO phases and different lag days have been developed. The results highlight regions over which the MJO has significant impact on surface weather.

The lag composite strategy shows that surface air temperature has the largest MJO-related variability over eastern US. A north-south shift of the storm track activity associated with the MJO is also found over North America. This shift is consistent with the temperature gradient change induced by MJO. In many regions over western, central and southeastern US, the MJO-related precipitation signal is also consistent with nearby storm track activity, as most of the winter precipitation in the midlatitude is due to cyclones. An MJO-related north-south shift of precipitation has also been found near the west coast of North America, with the precipitation over California being consistent with the MJO-related storm track activity over eastern Pacific. The results suggest that MJO may modulate atmospheric river events over eastern Pacific. MJO related temperature and storm track anomalies are also found near Alaska.

Given that significant impacts can be found out to near 30 days lag with respect to the MJO phases, and the MJO phases can be predicted out to about four weeks in advance, we hypothesize that these MJO related impacts may be predictable out to the subseasonal to seasonal (S2S) time scale. Currently we are assessing how well these MJO related impacts are predicted in S2S and SubX reforecast data.