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  • Author or Editor: D. E. Waliser x
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D. E. Waliser, K. M. Lau, W. Stern, and C. Jones

The objective of this study is to estimate the limit of dynamical predictability of the Madden–Julian oscillation (MJO). Ensembles of “twin” predictability experiments were carried out with the NASA Goddard Laboratory for the Atmospheres (GLA) atmospheric general circulation model (AGCM) using specified annual cycle SSTs. Initial conditions were taken from a 10-yr control simulation during periods of strong MJO activity identified via extended empirical orthogonal function (EOF) analysis of 30–90-day bandpassed tropical rainfall. From this analysis, 15 cases were chosen when the MJO convective center was located over the Indian Ocean, Maritime Continent, western Pacific Ocean, and central Pacific Ocean, respectively, making 60 MJO cases in total. In addition, 15 cases were selected that exhibited very little to no MJO activity. Two different sets of small random perturbations were added to these 75 initial states. Simulations were then performed for 90 days from each of these 150 perturbed initial conditions. A measure of potential predictability was constructed based on a ratio of the signal associated with the MJO, in terms of rainfall or 200-hPa velocity potential (VP200), and the mean-square error between sets of twin forecasts. This ratio indicates that useful predictability for this model's MJO extends out to about 25–30 days for VP200 and to about 10–15 days for rainfall. This is in contrast to the timescales of useful predictability associated with persistence forecasts or forecasts associated with daily “weather” variations, which in either case extend out only to about 10–15 days for VP200 and 8–10 days for rainfall. The predictability measure shows modest dependence on the phase of the MJO, with greater predictability for the convective phase at short (< ~5 days) lead times and for the suppressed phase at longer (> ~15 days) lead times. In addition, the predictability of intraseasonal variability during periods of weak MJO activity is significantly diminished compared to periods of strong MJO activity. The implications of these results as well as their associated model and analysis caveats are discussed.

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F. Vitart, C. Ardilouze, A. Bonet, A. Brookshaw, M. Chen, C. Codorean, M. Déqué, L. Ferranti, E. Fucile, M. Fuentes, H. Hendon, J. Hodgson, H.-S. Kang, A. Kumar, H. Lin, G. Liu, X. Liu, P. Malguzzi, I. Mallas, M. Manoussakis, D. Mastrangelo, C. MacLachlan, P. McLean, A. Minami, R. Mladek, T. Nakazawa, S. Najm, Y. Nie, M. Rixen, A. W. Robertson, P. Ruti, C. Sun, Y. Takaya, M. Tolstykh, F. Venuti, D. Waliser, S. Woolnough, T. Wu, D.-J. Won, H. Xiao, R. Zaripov, and L. Zhang


Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days).

The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been considered for a long time as a “desert of predictability.” In particular, this database will help identify common successes and shortcomings in the model simulation and prediction of sources of subseasonal to seasonal predictability. For instance, a preliminary study suggests that the S2S models significantly underestimate the amplitude of the Madden–Julian oscillation (MJO) teleconnections over the Euro-Atlantic sector. The S2S database also represents an important tool for case studies of extreme events. For instance, a multimodel combination of S2S models displays higher probability of a landfall over the islands of Vanuatu 2–3 weeks before Tropical Cyclone Pam devastated the islands in March 2015.

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