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Michelle L. L’Heureux, Ken Takahashi, Andrew B. Watkins, Anthony G. Barnston, Emily J. Becker, Tom E. Di Liberto, Felicity Gamble, Jon Gottschalck, Michael S. Halpert, Boyin Huang, Kobi Mosquera-Vásquez, and Andrew T. Wittenberg

Abstract

The El Niño of 2015/16 was among the strongest El Niño events observed since 1950 and took place almost two decades after the previous major event in 1997/98. Here, perspectives of the event are shared by scientists from three national meteorological or climate services that issue regular operational updates on the status and prediction of El Niño–Southern Oscillation (ENSO). Public advisories on the unfolding El Niño were issued in the first half of 2015. This was followed by significant growth in sea surface temperature (SST) anomalies, a peak during November 2015–January 2016, subsequent decay, and its demise during May 2016. The life cycle and magnitude of the 2015/16 El Niño was well predicted by most models used by national meteorological services, in contrast to the generally overexuberant model predictions made the previous year. The evolution of multiple atmospheric and oceanic measures demonstrates the rich complexity of ENSO, as a coupled ocean–atmosphere phenomenon with pronounced global impacts. While some aspects of the 2015/16 El Niño rivaled the events of 1982/83 and 1997/98, we show that it also differed in unique and important ways, with implications for the study and evaluation of past and future ENSO events. Unlike previous major El Niños, remarkably above-average SST anomalies occurred in the western and central equatorial Pacific but were milder near the coast of South America. While operational ENSO systems have progressed markedly over the past several decades, the 2015/16 El Niño highlights several challenges that will continue to test both the research and operational forecast communities.

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Annarita Mariotti, Cory Baggett, Elizabeth A. Barnes, Emily Becker, Amy Butler, Dan C. Collins, Paul A. Dirmeyer, Laura Ferranti, Nathaniel C. Johnson, Jeanine Jones, Ben P. Kirtman, Andrea L. Lang, Andrea Molod, Matthew Newman, Andrew W. Robertson, Siegfried Schubert, Duane E. Waliser, and John Albers

Abstract

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

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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

Abstract

The Subseasonal Experiment (SubX) is a multimodel subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced 17 years of retrospective (re)forecasts and more than a year of weekly real-time forecasts. The reforecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation 3 weeks ahead of time in specific regions. The SubX multimodel ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden–Julian oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated, with skillful predictions of the MJO 4 weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones, which can help emergency management and aid organizations to plan for disasters.

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Gabriele C. Hegerl, Emily Black, Richard P. Allan, William J. Ingram, Debbie Polson, Kevin E. Trenberth, Robin S. Chadwick, Phillip A. Arkin, Beena Balan Sarojini, Andreas Becker, Aiguo Dai, Paul J. Durack, David Easterling, Hayley J. Fowler, Elizabeth J. Kendon, George J. Huffman, Chunlei Liu, Robert Marsh, Mark New, Timothy J. Osborn, Nikolaos Skliris, Peter A. Stott, Pier-Luigi Vidale, Susan E. Wijffels, Laura J. Wilcox, Kate M. Willett, and Xuebin Zhang

Abstract

Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time series over land, but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols and because of the large climate variability presently limits confidence in attribution of observed changes.

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Ben P. Kirtman, Dughong Min, Johnna M. Infanti, James L. Kinter III, Daniel A. Paolino, Qin Zhang, Huug van den Dool, Suranjana Saha, Malaquias Pena Mendez, Emily Becker, Peitao Peng, Patrick Tripp, Jin Huang, David G. DeWitt, Michael K. Tippett, Anthony G. Barnston, Shuhua Li, Anthony Rosati, Siegfried D. Schubert, Michele Rienecker, Max Suarez, Zhao E. Li, Jelena Marshak, Young-Kwon Lim, Joseph Tribbia, Kathleen Pegion, William J. Merryfield, Bertrand Denis, and Eric F. Wood

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users.

The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model.

Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (www.cpc.ncep.noaa.gov/products/NMME/). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models.

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William J. Merryfield, Johanna Baehr, Lauriane Batté, Emily J. Becker, Amy H. Butler, Caio A. S. Coelho, Gokhan Danabasoglu, Paul A. Dirmeyer, Francisco J. Doblas-Reyes, Daniela I. V. Domeisen, Laura Ferranti, Tatiana Ilynia, Arun Kumar, Wolfgang A. Müller, Michel Rixen, Andrew W. Robertson, Doug M. Smith, Yuhei Takaya, Matthias Tuma, Frederic Vitart, Christopher J. White, Mariano S. Alvarez, Constantin Ardilouze, Hannah Attard, Cory Baggett, Magdalena A. Balmaseda, Asmerom F. Beraki, Partha S. Bhattacharjee, Roberto Bilbao, Felipe M. de Andrade, Michael J. DeFlorio, Leandro B. Díaz, Muhammad Azhar Ehsan, Georgios Fragkoulidis, Sam Grainger, Benjamin W. Green, Momme C. Hell, Johnna M. Infanti, Katharina Isensee, Takahito Kataoka, Ben P. Kirtman, Nicholas P. Klingaman, June-Yi Lee, Kirsten Mayer, Roseanna McKay, Jennifer V. Mecking, Douglas E. Miller, Nele Neddermann, Ching Ho Justin Ng, Albert Ossó, Klaus Pankatz, Simon Peatman, Kathy Pegion, Judith Perlwitz, G. Cristina Recalde-Coronel, Annika Reintges, Christoph Renkl, Balakrishnan Solaraju-Murali, Aaron Spring, Cristiana Stan, Y. Qiang Sun, Carly R. Tozer, Nicolas Vigaud, Steven Woolnough, and Stephen Yeager

Abstract

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

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