KAI-CHIH TSENGBarnes E.AMaloney E.2022-11-112022-11-11202008948755https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079869871&doi=10.1175%2fJCLI-D-19-0512.1&partnerID=40&md5=28faceee817d3c2131b3b5bb032532b8https://scholars.lib.ntu.edu.tw/handle/123456789/624825The Madden–Julian oscillation (MJO) is one of the most important sources of predictability on subseasonal to seasonal (S2S) time scales. Many previous studies have explored the impact of the present state of the MJO on the future evolution and predictability of extratropical weather patterns. What is still unclear, however, is the importance of the accumulated influence of past MJO activity on these results. In this study, the importance of past MJO activity in determining the future state of extratropical circulations is examined by using a linear baroclinic model (LBM) and one of the simplest machine learning algorithms: logistic regression. By increasing the complexity of the logistic regression model with additional information about the past activity of the MJO, it is demonstrated that the past 15 days play a dominant role in determining the state of MJO teleconnections more than 15 days into the future. This conclusion is supported by numerical LBM simulations. It is further shown that the past 15 days of additional information are only important for some MJO phases/lead times and not others, and the physical basis for this result is explored. © 2020 American Meteorological Society.[SDGs]SDG13[SDGs]SDG14Learning algorithms; Logistic regression; Machine learning; Baroclinic models; Extratropical; Extratropical circulation; Logistic Regression modeling; Midlatitudes; Teleconnections; Time-scales; Weather patterns; Climatologyjournal article10.1175/JCLI-D-19-0512.12-s2.0-85079869871