A document by Kirsten J. Mayer. Click on the document to view its contents.

Here we explore the relative contribution of the Madden-Julian Oscillation (MJO) and El Niño Southern Oscillation (ENSO) to midlatitude subseasonal predictive skill of upper atmospheric circulation over the North Pacific, using an inherently interpretable neural network applied to pre-industrial control runs of the Community Earth System Model version 2. We find that this interpretable network generally favors the state of ENSO, rather than the MJO, to make correct predictions on a range of subseasonal lead times and predictand averaging windows. Moreover, the predictability of positive circulation anomalies over the North Pacific is comparatively lower than that of their negative counterparts, especially evident when the ENSO state is important. However, when ENSO is in a neutral state, our findings indicate that the MJO provides some predictive information, particularly for positive anomalies. We identify three distinct evolutions of these MJO states, offering fresh insights into opportune forecasting windows for MJO teleconnections.

Subseasonal timescales (~2 weeks - 2 months) are known for their lack of predictability, however, specific Earth system states known to have a strong influence on these timescales can be harnessed to improve prediction skill (known as “forecasts of opportunity”). As the climate continues warming, it is hypothesized these states may change and consequently, their importance for subseasonal prediction may also be impacted. Here, we examine changes to midlatitude subseasonal prediction skill provided by the tropics under anthropogenic warming using artificial neural networks to quantify skill. The network is tasked to predict the sign of the 500hPa geopotential height for historical and future time periods in the CESM2-LE across the Northern Hemisphere at a 3 week lead using tropical precipitation. We show prediction skill changes substantially in key midlatitude regions and these changes appear linked to changes in seasonal variability with the largest differences in accuracy occurring during forecasts of opportunity.