We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Niña in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Niña. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Niño3 region is predicted to warm slightly by about 0.5 °C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the current observed record. Verification of these forecasts will provide an important opportunity to test the performance of models and our understanding and knowledge of the drivers of climate change

Many theories for the Madden‐Julian oscillation (MJO) focus on diabatic processes, particularly the evolution of vertical heating and moistening. Poor MJO performance in weather and climate models is often blamed on biases in these processes and their interactions with the large‐scale circulation. We introduce one of the three components of a model evaluation project, which aims to connect MJO fidelity in models to their representations of several physical processes, focusing on diabatic heating and moistening. This component consists of 20 day hindcasts, initialized daily during two MJO events in winter 2009–2010. The 13 models exhibit a range of skill: several have accurate forecasts to 20 days lead, while others perform similarly to statistical models (8–11 days). Models that maintain the observed MJO amplitude accurately predict propagation, but not vice versa. We find no link between hindcast fidelity and the precipitation‐moisture relationship, in contrast to other recent studies. There is also no relationship between models' performance and the evolution of their diabatic heating profiles with rain rate. A more robust association emerges between models' fidelity and net moistening: the highest‐skill models show a clear transition from low‐level moistening for light rainfall to midlevel moistening at moderate rainfall and upper level moistening for heavy rainfall. The midlevel moistening, arising from both dynamics and physics, may be most important. Accurately representing many processes may be necessary but not sufficient for capturing the MJO, which suggests that models fail to predict the MJO for a broad range of reasons and limits the possibility of finding a panacea.

[1] Multiyear climate predictions with two initialization strategies are systematically assessed in the EC-Earth V2.3 climate model. In one ensemble, an estimate of the observed climate state is used to initialize the model. The other uses estimates of observed ocean and sea ice anomalies on top of the model climatology. The ensembles show similar spatial characteristics of drift related to the biases in control simulations. As expected, the drift is less with anomaly initialization. The full field initialization overshoots to a colder state which is related to cold biases in the tropics and North Atlantic, associated with oceanic processes. Despite different amplitude of the drift, both ensembles show similar skill in multiyear global temperature predictions, but regionally differences are found. On multiyear time scales, initialization with observations enhances both deterministic and probabilistic skill scores in the North Atlantic. The probabilistic verification shows skill over the European continent.

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suggesting changing predictive skill dependent on sign and amplitude of the anomaly. The results confirm the importance of realistic sea ice initial conditions for seasonal forecasts. However, correlations do seldom exceed 0.6 indicating that Arctic sea ice variations can only explain a part of winter climate variations in northern mid and high latitudes.