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Leonard F. Borchert, Wolfgang A. Müller, and Johanna Baehr


An analysis of a three-member ensemble of initialized coupled simulations with the MPI-ESM-LR covering the period 1901–2010 shows that Atlantic northward ocean heat transport (OHT) at 50°N influences surface temperature variability in the North Atlantic region for several years. Three to ten years after strong OHT phases at 50°N, a characteristic pattern of sea surface temperature (SST) anomalies emerges: warm anomalies are found in the North Atlantic and cold anomalies emerge in the Gulf Stream region. This pattern originates from persistent upper-ocean heat content anomalies that originate from southward-propagating OHT anomalies in the North Atlantic. Interannual-to-decadal SST predictability of yearly initialized hindcasts is linked to this SST pattern: when ocean heat transport at 50°N is strong at the initialization of a hindcast, SST anomaly correlation coefficients in the northeast Atlantic at lead years 2–9 are significantly higher than when the ocean heat transport at 50°N is weak at initialization. Surface heat fluxes that mask the predictable low-frequency oceanic variability that influences SSTs in the northwest Atlantic after strong OHT phases, and in the northwest and northeast Atlantic after weak OHT phases at 50°N lead to zonally asymmetrically predictable SSTs 7–9 years ahead. This study shows that the interannual-to-decadal predictability of North Atlantic SSTs depends strongly on the strength of subpolar ocean heat transport at the start of a prediction, indicating that physical mechanisms need to be taken into account for actual temperature predictions.

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Daniela I. V. Domeisen, Amy H. Butler, Kristina Fröhlich, Matthias Bittner, Wolfgang A. Müller, and Johanna Baehr


Predictability on seasonal time scales over the North Atlantic–Europe region is assessed using a seasonal prediction system based on an initialized version of the Max Planck Institute Earth System Model (MPI-ESM). For this region, two of the dominant predictors on seasonal time scales are El Niño–Southern Oscillation (ENSO) and sudden stratospheric warming (SSW) events. Multiple studies have shown a potential for improved North Atlantic predictability for either predictor. Their respective influences are however difficult to disentangle, since the stratosphere is itself impacted by ENSO. Both El Niño and SSW events correspond to a negative signature of the North Atlantic Oscillation (NAO), which has a major influence on European weather.

This study explores the impact on Europe by separating the stratospheric pathway of the El Niño teleconnection. In the seasonal prediction system, the evolution of El Niño events is well captured for lead times of up to 6 months, and stratospheric variability is reproduced with a realistic frequency of SSW events. The model reproduces the El Niño teleconnection through the stratosphere, involving a deepened Aleutian low connected to a warm anomaly in the northern winter stratosphere. The stratospheric anomaly signal then propagates downward into the troposphere through the winter season. Predictability of 500-hPa geopotential height over Europe at lead times of up to 4 months is shown to be increased only for El Niño events that exhibit SSW events, and it is shown that the characteristic negative NAO signal is only obtained for winters also containing major SSW events for both the model and the reanalysis data.

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Adrian M. Tompkins, María Inés Ortiz De Zárate, Ramiro I. Saurral, Carolina Vera, Celeste Saulo, William J. Merryfield, Michael Sigmond, Woo-Sung Lee, Johanna Baehr, Alain Braun, Amy Butler, Michel Déqué, Francisco J. Doblas-Reyes, Margaret Gordon, Adam A. Scaife, Yukiko Imada, Masayoshi Ishii, Tomoaki Ose, Ben Kirtman, Arun Kumar, Wolfgang A. Müller, Anna Pirani, Tim Stockdale, Michel Rixen, and Tamaki Yasuda
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Jochem Marotzke, Wolfgang A. Müller, Freja S. E. Vamborg, Paul Becker, Ulrich Cubasch, Hendrik Feldmann, Frank Kaspar, Christoph Kottmeier, Camille Marini, Iuliia Polkova, Kerstin Prömmel, Henning W. Rust, Detlef Stammer, Uwe Ulbrich, Christopher Kadow, Armin Köhl, Jürgen Kröger, Tim Kruschke, Joaquim G. Pinto, Holger Pohlmann, Mark Reyers, Marc Schröder, Frank Sienz, Claudia Timmreck, and Markus Ziese


Mittelfristige Klimaprognose (MiKlip), an 8-yr German national research project on decadal climate prediction, is organized around a global prediction system comprising the Max Planck Institute Earth System Model (MPI-ESM) together with an initialization procedure and a model evaluation system. This paper summarizes the lessons learned from MiKlip so far; some are purely scientific, others concern strategies and structures of research that target future operational use.

Three prediction system generations have been constructed, characterized by alternative initialization strategies; the later generations show a marked improvement in hindcast skill for surface temperature. Hindcast skill is also identified for multiyear-mean European summer surface temperatures, extratropical cyclone tracks, the quasi-biennial oscillation, and ocean carbon uptake, among others. Regionalization maintains or slightly enhances the skill in European surface temperature inherited from the global model and also displays hindcast skill for wind energy output. A new volcano code package permits rapid modification of the predictions in response to a future eruption.

MiKlip has demonstrated the efficacy of subjecting a single global prediction system to a major research effort. The benefits of this strategy include the rapid cycling through the prediction system generations, the development of a sophisticated evaluation package usable by all MiKlip researchers, and regional applications of the global predictions. Open research questions include the optimal balance between model resolution and ensemble size, the appropriate method for constructing a prediction ensemble, and the decision between full-field and anomaly initialization.

Operational use of the MiKlip system is targeted for the end of the current decade, with a recommended generational cycle of 2–3 years.

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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


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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