Until recently, long-range forecast systems showed only modest levels of skill in predicting surface winter climate around the Atlantic Basin and associated fluctuations in the North Atlantic Oscillation at seasonal lead times. Here we use a new forecast system to assess seasonal predictability of winter North Atlantic climate. We demonstrate that key aspects of European and North American winter climate and the surface North Atlantic Oscillation are highly predictable months ahead. We demonstrate high levels of prediction skill in retrospective forecasts of the surface North Atlantic Oscillation, winter storminess, near-surface temperature, and wind speed, all of which have high value for planning and adaptation to extreme winter conditions. Analysis of forecast ensembles suggests that while useful levels of seasonal forecast skill have now been achieved, key sources of predictability are still only partially represented and there is further untapped predictability.

Seasonal-to-decadal predictions are inevitably uncertain, depending on the size of the predictable signal relative to unpredictable chaos. Uncertainties can be accounted for using ensemble techniques, permitting quantitative probabilistic forecasts. In a perfect system, each ensemble member would represent a potential realization of the true evolution of the climate system, and the predictable components in models and reality would be equal. However, we show that the predictable component is sometimes lower in models than observations, especially for seasonal forecasts of the North Atlantic Oscillation and multiyear forecasts of North Atlantic temperature and pressure. In these cases the forecasts are underconfident, with each ensemble member containing too much noise. Consequently, most deterministic and probabilistic measures underestimate potential skill and idealized model experiments underestimate predictability. However, skilful and reliable predictions may be achieved using a large ensemble to reduce noise and adjusting the forecast variance through a postprocessing technique proposed here.

No abstract

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

There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change.npj Climate and Atmospheric Science (2019)2:13 ; https://doi.

The rate of global mean surface temperature (GMST) warming has slowed this century despite the increasing concentrations of greenhouse gases. Climate model experiments [1][2][3][4] show that this slowdown was largely driven by a negative phase of the Pacific Decadal Oscillation (PDO), with a smaller external contribution from solar variability, and volcanic and anthropogenic aerosols 5,6 . The prevailing view is that this negative PDO occurred through internal variability 7-11 . However, here we show that coupled models from the Fifth Coupled Model Intercomparison Project robustly simulate a negative PDO in response to anthropogenic aerosols implying a potentially important role for external human influences. The recovery from the eruption of Mount Pinatubo in 1991 also contributed to the slowdown in GMST trends. Our results suggest that a slowdown in GMST trends could have been predicted in advance, and that future reduction of anthropogenic aerosol emissions, particularly from China, would promote a positive PDO and increased GMST trends over the coming years. Furthermore, the overestimation of the magnitude of recent warming by models is substantially reduced by using detection and attribution analysis to rescale their response to external factors, especially cooling following volcanic eruptions. Improved understanding of external influences on climate is therefore crucial to constrain near-term climate predictions.The recent slowdown in surface temperature warming is clearly seen in observed time series of 15-year global mean surface temperature (GMST) trends 8,9 that reached a peak in the 15-year period 1992-2006 followed by a sharp decline (black curves in Fig. 1a). This is seen in all of the leading observational data sets despite recent claims that the slowdown is eliminated by corrections to sea surface temperature biases 12 . A similar peak and decline is also simulated by all coupled models from the Fifth Coupled Model Intercomparison Project (CMIP5, coloured curves in Fig. 1a, with thick red curve showing the ensemble mean, see Methods and Supplementary Table 1). Similar results are obtained for different trend lengths ( Supplementary Fig. 1). The modelled trends are generally larger than observed. This is an important point that will be addressed later, but we first investigate the cause of the simulated peak and decline in GMST trends.Since internal variability is unlikely to be in phase in the CMIP5 model simulations, any common signals are likely to be externally forced. We therefore investigate additional CMIP5 model simulations that are forced by different combinations of external factors (Supplementary Table 1 and Methods). These show that the sharp peak in the trend in the period 1992-2006 is caused by natural factors (Nat, green curve in Fig. 1b), and in particular the eruption of Mount Pinatubo as pointed out previously 9,13 . The reason is straightforward: the eruption of Mount Pinatubo in 1991 caused −0.6 −0.4 −0.2 −0.0 0.2 0.4 0.6 0.8 a −0.2 0.0 0.2 0.4 b Obs. All GHGs Nat. Aero. End year...

The possibility that Arctic sea ice loss weakens mid-latitude westerlies, promoting more severe cold winters, has sparked more than a decade of scientific debate, with apparent support from observations but inconclusive modelling evidence. Here we show that sixteen models contributing to the Polar Amplification Model Intercomparison Project simulate a weakening of mid-latitude westerlies in response to projected Arctic sea ice loss. We develop an emergent constraint based on eddy feedback, which is 1.2 to 3 times too weak in the models, suggesting that the real-world weakening lies towards the higher end of the model simulations. Still, the modelled response to Arctic sea ice loss is weak: the North Atlantic Oscillation response is similar in magnitude and offsets the projected response to increased greenhouse gases, but would only account for around 10% of variations in individual years. We further find that relationships between Arctic sea ice and atmospheric circulation have weakened recently in observations and are no longer inconsistent with those in models.

No abstract