Quantifying signals and uncertainties in climate models is essential for climate change detection, attribution, prediction and projection [1][2][3] . Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain 4 , leading to low confidence in regional projections especially for precipitation over the coming decades 5, 6 . Furthermore, model simulations with tiny differences in initial conditions suggest that uncertainties may be largely irreducible due to the chaotic nature of the climate system 7-9 . However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate predictions of the last six decades project (GA 776613). FJDR, LPC, SW and RB also acknowledge the support from the EUCP project (GA 776613) and from the Ministerio de Economía y Competitividad (MINECO) as part of the CLINSA project (Grant No. CGL2017-85791-R). SW received funding from the innovation programme under the Marie Skĺodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433 and PO from the Ramon y Cajal senior tenure programme of MINECO. The EC-Earth simulations were performed on Marenostrum 4 (hosted by the Barcelona Supercomputing Center, Spain) using Auto-Submit through computing hours

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.

Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system "Mittelfristige Klimaprognose" (MiKlip). Among the tested methods, three tackle aspects of model-consistent initialization using the ensemble Kalman filter, the filtered anomaly initialization, and the initialization method by partially coupled spin-up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter corrects each ensemble member with the ensemble mean during model integration. And the bred vectors perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low-resolution configuration (Preop-LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop-LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the ensemble Kalman filter and filtered anomaly initialization show the most distinct improvements over Preop-LR for surface temperatures and upper ocean heat content, followed by the bred vectors, the ensemble dispersion filter, and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin-wide long-term mean and variability and with respect to the temporal evolution at the 26 • N latitude.

We analyze the quasi-biennial oscillation (QBO) variability of historical and decadal hindcast simulations of the MiKlip (Mittelfristige Klimavorhersagen) decadal prediction system using the higher resolved version of the Max Planck Institute Earth System Model. We find a realistic variability of the QBO in historical simulations when changing from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to the Coupled Model Intercomparison Project Phase 6 (CMIP6) external forcing. This agreement between the simulated and the observed QBO is improved by the initialization of decadal hindcast simulations with CMIP6 forcing in the first three lead years. In the decadal hindcast simulations, the agreement is similar to a persistence forecast in the first five lead years and higher than the persistence forecast in the later lead years. We find a strong relation between the QBO and the ozone variability in the stratosphere and conclude that the change of the ozone data from CMIP5 to CMIP6 leads to the improved QBO variability and prediction skill in our simulations.Plain Language Summary The quasi-biennial oscillation (QBO) is a climate mode in the stratosphere with the feature of reversing wind directions above the equator roughly every second year (period of 28 months). The QBO variability has worldwide implications for other climate modes like the strength of the polar vortex that influences Europe via the North Atlantic Oscillation. Previous historical simulations with our climate model show that with a high vertical resolution, the model is able to produce a QBO variability, however, with an unrealistic phase. These historical simulations need external forcing like greenhouse gases and ozone concentrations. We show that in our simulations, the QBO variability becomes realistic when we use the updated external forcing data from Coupled Model Intercomparison Project Phase 6 (CMIP6) instead of those from CMIP5. Moreover, we find evidence that the variability of the stratospheric ozone data leads to the realistic QBO variability in our climate simulations. This has implications for decadal climate predictions since-for a good climate prediction-the stratospheric ozone variability must be projected into the future.