The Madden-Julian Oscillation (MJO) is a promising target for improving sub-seasonal weather forecasts. Current forecast models struggle to simulate the MJO due to imperfect convective parameterizations and mean state biases, degrading their forecast skill. Previous studies have estimated a potential MJO predictability 5-15 days higher than current forecast skill, but these estimates also use models with parameterized convection. We perform a perfect-model predictability experiment using a superparameterized global model, in which the convective parameterization is replaced by a cloud resolving model. We add a second silent cloud resolving component to the control simulation that independently calculates convective-scale processes using the same large-scale forcings. The second set of convective states are used to initialize forecasts, representing uncertainty on the convective scale. We find a potential predictability of the MJO of 35-40 days in boreal winter using a single-member ensemble forecast.

Various indices have been defined to characterize the phase and amplitude of the Madden-Julian oscillation (MJO). One widely used index is the Outgoing Longwave Radiation (OLR) based MJO index (OMI), which is calculated using the spatial pattern of 30-96-day eastward-filtered OLR. The EOFs used to calculate the OMI in observations are prone to degeneracy and exhibit oscillations on the order of 10-20 days, despite initial filtering of the OLR. We propose a simple modification to the OMI that involves aligning the EOFs between neighboring days while retaining the spatial pattern described by the EOFs. This rotation method is implemented as a postprocessing step of the current OMI calculation and cleanly removes the spurious oscillations and degeneracy issues seen in the standard method. A similar rotation procedure can be implemented in calculations of other MJO indices.