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Chidong Zhang

As a prime example of intraseasonal variability, the Madden–Julian Oscillation affects— and is pivotal to predicting—both weather and climate. The conceptual separation of weather and climate is deeply rooted in our daily experience, as Herbertson (1901) put it: “Climate is what on an average we may expect, weather is what actually we get.” 1 Translated into a scientific language, weather is a state of the atmosphere at a particular instance and climate is a set of statistics of an ensemble

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Kunio Yoneyama, Chidong Zhang, and Charles N. Long

A field campaign in the Indian Ocean region collected unprecedented observations during October 2011–March 2012 to help advance knowledge of physical processes of the MJO—especially its convective initiation—and improve its prediction. View from Addu Atoll showing a mix of convective and cirroform clouds. From time to time, the tropical atmosphere feels the pulses of extraordinary strong deep convection and rainfall that repeat every 30–90 days. They come from the Madden–Julian oscillation (MJO

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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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J. Gottschalck, M. Wheeler, K. Weickmann, F. Vitart, N. Savage, H. Lin, H. Hendon, D. Waliser, K. Sperber, M. Nakagawa, C. Prestrelo, M. Flatau, and W. Higgins

The U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group (MJOWG) has taken steps to promote the adoption of a uniform diagnostic and set of skill metrics for analyzing and assessing dynamical forecasts of the MJO. Here we describe the framework and initial implementation of the approach using real-time forecast data from multiple operational numerical weather prediction (NWP) centers. The objectives of this activity are to provide a means to i) quantitatively compare skill of MJO forecasts across operational centers, ii) measure gains in forecast skill over time by a given center and the community as a whole, and iii) facilitate the development of a multimodel forecast of the MJO. The MJO diagnostic is based on extensive deliberations among the MJOWG in conjunction with input from a number of operational centers and makes use of the MJO index of Wheeler and Hendon. This forecast activity has been endorsed by the Working Group on Numerical Experimentation (WGNE), the international body that fosters the development of atmospheric models for NWP and climate studies.

The Climate Prediction Center (CPC) within the National Centers for Environmental Prediction (NCEP) is hosting the acquisition of the forecast data, application of the MJO diagnostic, and real-time display of the standardized forecasts. The activity has contributed to the production of 1–2-week operational outlooks at NCEP and activities at other centers. Further enhancements of the diagnostic's implementation, including more extensive analysis, comparison, illustration, and verification of the contributions from the participating centers, will increase the usefulness and application of these forecasts and potentially lead to more skillful predictions of the MJO and indirectly extratropical and other weather variability (e.g., tropical cyclones) influenced by the MJO. The purpose of this article is to inform the larger scientific and operational forecast communities of the MJOWG forecast effort and invite participation from additional operational centers.

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Tim Li, Lu Wang, Melinda Peng, Bin Wang, Chidong Zhang, William Lau, and Hung-Chi Kuo

A study published in Chinese in 1963 documented a 40–50-day oscillation in the Asian monsoon region, eight years earlier than its discovery by Madden and Julian in the early 1970s. Madden and Julian (1971, hereafter MJ71) unveiled a 40–50-day oscillation in the tropospheric zonal wind using radiosonde observation at a single station (i.e., Canton Island) in the central Pacific. This oscillation was later connected to a broad global tropical circulation using observations from multiple stations

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Kathy Pegion, Ben P. Kirtman, Emily Becker, Dan C. Collins, Emerson LaJoie, Robert Burgman, Ray Bell, Timothy DelSole, Dughong Min, Yuejian Zhu, Wei Li, Eric Sinsky, Hong Guan, Jon Gottschalck, E. Joseph Metzger, Neil P Barton, Deepthi Achuthavarier, Jelena Marshak, Randal D. Koster, Hai Lin, Normand Gagnon, Michael Bell, Michael K. Tippett, Andrew W. Robertson, Shan Sun, Stanley G. Benjamin, Benjamin W. Green, Rainer Bleck, and Hyemi Kim

ACC of 0.0, 0.12, and 0.5. SUBSEASONAL SOURCES OF PREDICTABILITY. Subseasonal predictability is likely influenced by a number of modes of climate variability that vary on time scales of weeks, including the Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 ) or the North Atlantic Oscillation (NAO; Hurrell et al. 2010 ). Several studies have suggested these modes may be predictable on subseasonal time scales, and present potential sources of predictability, allowing for the

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Mitchell W. Moncrieff, Duane E. Waliser, Martin J. Miller, Melvyn A. Shapiro, Ghassem R. Asrar, and James Caughey

cyclones and easterly waves; (iv) ensembles of mesoscale systems (superclusters) embedded within planetary-scale phenomena, such as convectively coupled equatorial waves ( Kiladis et al. 2009 ), the Madden–Julian oscillation (MJO; Madden and Julian 1972 ), the monsoons ( Li 2010 ), and the El Niño–Southern Oscillation (ENSO; McPhaden 2004 ); and (v) the iconic long-term pattern of persistent convection known as the intertropical convergence zone (ITCZ). Elements of some of these phenomena are

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Nicholas J. Weber and Clifford F. Mass

. 2010 ). Thus, the poor simulation of tropical convection in contemporary global models can hamper subseasonal atmospheric prediction in both the tropics and extratropics. Several studies have shown that current NWP systems struggle to properly simulate organized tropical convection ( Brunet et al. 2010 ) and its observed intraseasonal (2–128 days) variability ( Lin et al. 2006 ). In particular, the Madden–Julian oscillation (MJO), an eastward-propagating intraseasonal tropical convective phenomenon

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Philip J. Klotzbach, Carl J. Schreck III, Gilbert P. Compo, Steven G. Bowen, Ethan J. Gibney, Eric C. J. Oliver, and Michael M. Bell

temperatures will be referred to as SSTs throughout the remainder of this manuscript. All SST time series generated from the combination of these two datasets (e.g., Niño-3.4, Atlantic Main Development Region SSTs, relative SSTs) rank correlate at ∼0.9 with similar SST time series calculated from NOAA’s Extended Reconstructed SST, version 5 (ERSSTv5; Huang et al. 2017 ). Madden–Julian oscillation index. The Madden–Julian oscillation (MJO) is the dominant mode of tropical convective variability on

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Duane E. Waliser, Mitchell W. Moncrieff, David Burridge, Andreas H. Fink, Dave Gochis, B. N. Goswami, Bin Guan, Patrick Harr, Julian Heming, Huang-Hsuing Hsu, Christian Jakob, Matt Janiga, Richard Johnson, Sarah Jones, Peter Knippertz, Jose Marengo, Hanh Nguyen, Mick Pope, Yolande Serra, Chris Thorncroft, Matthew Wheeler, Robert Wood, and Sandra Yuter

capabilities in this area leaves us disadvantaged in simulating and/or predicting prominent phenomena of the tropical atmosphere, such as the intertropical convergence zone (ITCZ), El Niño–Southern Oscillation (ENSO), monsoons and their active/break periods, the Madden–Julian oscillation (MJO), easterly waves and tropical cyclones, subtropical stratus decks, and even the diurnal cycle. Furthermore, tropical climate and weather disturbances strongly inf luence stratospheric–tropospheric exchange and the

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