Search

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. Plain Language Summary: The Madden‐Julian Oscillation is a convective signal in the tropics that has the potential to improve 10‐40‐day weather forecasts. Current weather forecast models struggle to simulate the MJO, leading to a lower forecast skill than many studies estimate could be possible. We use a model with a comparatively good representation of the MJO that calculates convection information with a cloud permitting model. We modify this multiscale model's structure to generate MJO forecasts for its own MJO. Results from these forecasts suggest that the MJO in this model could be predictable up to 35–40 days using a single‐member ensemble forecast, which is 5–10 days longer than current state‐of‐the‐art forecasts. Key Points: A novel approach is used to conduct perfect‐model predictability experiments using a superparameterized global modelA single ensemble member model with superparameterized convection finds a potential Madden‐Julian Oscillation predictability of 35–40 daysResulting predictability estimates are comparable to those from current state‐of‐the‐art multiple ensemble member forecasting models [ABSTRACT FROM AUTHOR]

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 empirical orthogonal functions (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 procedure 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. Plain Language Summary: Characterization of the Madden‐Julian oscillation (MJO) is important for subseasonal weather forecasting. However, the indices often used to characterize the MJO are projected onto a basis that includes unphysical day‐to‐day variations and noise that arise from the mathematical procedure used to calculate the basis, rather than the atmosphere itself. We propose a modified postprocessing procedure to the standard method that removes spurious oscillations in the index basis while maintaining the broad structures of the original index. Key Points: Current methods used to calculate Madden‐Julian oscillation (MJO) indices result in degeneracy and unphysical oscillatory behaviorA simple projection and rotation algorithm is derived as a postprocessing step to current MJO index calculationsAdding a simple rotation results in a slowly varying index basis that retains the broad structure of the original index [ABSTRACT FROM AUTHOR]