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The IPCC Fifth Assessment Report highlighted large uncertainty in European precipitation changes in the coming century. This paper investigates the sources of intermodel differences using CMIP5 model European precipitation data. The contribution of atmospheric circulation to differences in precipitation trends is investigated by applying cluster analysis to daily mean sea level pressure (MSLP) data. The resulting classification is used to reconstruct monthly precipitation time series, thereby isolating the component of precipitation variability directly related to atmospheric circulation. Reconstructed observed precipitation and reconstructions of simulated historical and projection data are well correlated with the original precipitation series, showing that circulation variability accounts for a substantial fraction of European precipitation variability. Removing the reconstructed precipitation from the original precipitation leaves a residual component related to noncirculation effects (and any small remaining circulation effects). Intermodel spread in residual future European precipitation trends is substantially reduced compared to the spread of the original precipitation trends. Uncertainty in future atmospheric circulation accounts for more than half of the intermodel variance in twenty-first-century precipitation trends for winter months for both northern and southern Europe. Furthermore, a substantial part of this variance is related to different forced dynamical responses in different models and is therefore potentially reducible. These results highlight the importance of understanding future changes in atmospheric dynamics in achieving more robust projections of regional climate change. Finally, the possible dynamical mechanisms that may drive the future differences in regional circulation and precipitation are illustrated by examining simulated teleconnections with tropical precipitation.

Persistent weather regimes in daily North Atlantic–European winter mean sea level pressure (MSLP) fields from the 140-yr Twentieth Century Reanalysis are investigated. The phase space is divided into discrete cells based on quantiles of empirical orthogonal function (EOF) principal components; the cells are thus approximately equally populated. An estimate of persistence is provided in terms of the number of different cells visited for a given trajectory duration. This technique is also applied to the well-known Lorenz63 system, which clearly exhibits two regimes, and the more complex Lorenz96 system where the regime structure is less pronounced. While the analysis identifies the two regimes of both the Lorenz63 and Lorenz96 systems, evidence for comparable regimes in the MSLP data is weaker. Recurrent weather regimes produced by k-means clustering might be expected to be clearly linked to slower-moving regions of phase space, but this is shown not to be the case. Only the region of phase space associated with the negative phase of the North Atlantic Oscillation (NAO) shows any regime-like behavior. Nevertheless, the analysis does reveal some structure to the time evolution of the atmospheric circulation—transitions between neighboring pairs of cells show a preferred direction of evolution in many cases.

We present a case study of long range forecasts for Northern Hemisphere winter 2015/2016. This winter produced the strongest El Nino event since 1997/1998 and equatorial Pacific sea surface temperature anomalies exceeded 3 °C. Other factors relevant to the Northern Hemisphere extratropical atmosphere circulation included a strong westerly phase of the Quasi-Biennial Oscillation (QBO) and very strong winds in the stratospheric polar night jet in early winter. At the surface, intense cyclonic extratropical circulation anomalies occurred in early winter in both the North Pacific and North Atlantic, consistent with known teleconnections to these phases of El Nino–Southern Oscillation, the QBO and the polar night jet. The midlatitude flow was very westerly in early winter and less westerly and sometimes northerly in late winter, when sudden stratospheric warming events also occurred. We show that initialised climate predictions were able to capture the winter mean flow pattern at seasonal lead times from well before the start of winter. In this special case, not only the winter mean flow pattern, but also some aspects of the sub-seasonal evolution were skilfully predicted. We show that the winter of 1982/1983 was closely analogous to winter 2015/2016 in both the predictable driving factors and the forecast winter circulation. This case study adds to the evidence that the north Atlantic circulation can be predictable on seasonal timescales and advance warning of the increased risk of intense rainfall and storminess which caused extreme flooding in the UK in December was possible in this case.

A method is presented for deriving weather patterns objectively over an area of interest, in this case the UK and surrounding European area. A set of 30 and eight patterns are derived through k-means clustering of daily mean sea level pressure (MSLP) data (1850–2003). These patterns have been designed for the purpose of post-processing forecast output from ensemble prediction systems and understanding how forecast models perform under different circulation types. The 30 weather patterns are designed for use in the medium-range and the eight weather patterns are designed for use in the monthly and seasonal timescales, or when there is low forecast confidence in the medium-range. Weather patterns are numbered according to their annual historic occurrences, with lower numbered patterns occurring most often. Lower numbered patterns occur more in summer (with weak MSLP anomalies) and higher numbered patterns occur more in winter (with strong MSLP anomalies). Weather patterns have been applied in a weather forecasting context, whereby ensemble members are assigned to the closest matching pattern definition. This provides a probabilistic insight into which patterns are most likely within the forecast range and summarises key aspects from the large volumes of data which ensembles provide. Verification of European Centre for Medium-Range Weather Forecasts medium-range ensemble forecasts for the set of eight weather patterns shows small forecast biases annually with some large variations seasonally. The most prominent seasonal variation shows the westerly (NAO+) pattern to over-forecast in summer and under-forecast in winter. Forecast skill was found to be better in winter than summer for most patterns.

This work was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101), the UK Public Weather Service research program, and the European Union Framework 7 SPECS project. Leon Hermanson was funded as part of his Research Fellowship by Willis as part of Willis Research Network (WRN).

Seasonal forecasting systems, and related systems for decadal prediction, are crucial in the development of adaptation strategies to climate change. However, despite important achievements in this area in the last 10 years, significant levels of skill are only generally found over regions strongly connected with the El Niño–Southern Oscillation. With the aim of improving the skill of regional climate predictions in tropical and extratropical regions from intraseasonal to interannual time scales, a new Met Office global seasonal forecasting system (GloSea4) has been developed. This new system has been designed to be flexible and easy to upgrade so it can be fully integrated within the Met Office model development infrastructure. Overall, the analysis here shows an improvement of GloSea4 when compared to its predecessor. However, there are exceptions, such as the increased model biases that contribute to degrade the skill of Niño-3.4 SST forecasts starting in November. Global ENSO teleconnections and Madden–Julian oscillation anomalies are well represented in GloSea4. Remote forcings of the North Atlantic Oscillation by ENSO and the quasi-biennial oscillation are captured albeit the anomalies are weaker than those found in observations. Hindcast length issues and their implications for seasonal forecasting are also discussed.

Summer climate in the North Atlantic–European sector possesses a principal pattern of year-to-year variability that is the parallel to the well-known North Atlantic Oscillation in winter. This summer North Atlantic Oscillation (SNAO) is defined here as the first empirical orthogonal function (EOF) of observed summertime extratropical North Atlantic pressure at mean sea level. It is shown to be characterized by a more northerly location and smaller spatial scale than its winter counterpart. The SNAO is also detected by cluster analysis and has a near-equivalent barotropic structure on daily and monthly time scales. Although of lesser amplitude than its wintertime counterpart, the SNAO exerts a strong influence on northern European rainfall, temperature, and cloudiness through changes in the position of the North Atlantic storm track. It is, therefore, of key importance in generating summer climate extremes, including flooding, drought, and heat stress in northwestern Europe. The El Niño–Southern Oscillation (ENSO) phenomenon is known to influence summertime European climate; however, interannual variations of the SNAO are only weakly influenced by ENSO. On interdecadal time scales, both modeling and observational results indicate that SNAO variations are partly related to the Atlantic multidecadal oscillation. It is shown that SNAO variations extend far back in time, as evidenced by reconstructions of SNAO variations back to 1706 using tree-ring records. Very long instrumental records, such as central England temperature, are used to validate the reconstruction. Finally, two climate models are shown to simulate the present-day SNAO and predict a trend toward a more positive index phase in the future under increasing greenhouse gas concentrations. This implies the long-term likelihood of increased summer drought for northwestern Europe.

Observed atmospheric circulation over the North Atlantic–European (NAE) region is examined using cluster analysis. A clustering algorithm incorporating a “simulated annealing” methodology is employed to improve on solutions found by the conventional k-means technique. Clustering is applied to daily mean sea level pressure (MSLP) fields to derive a set of circulation types for six 2-month seasons. A measure of the quality of this clustering is defined to reflect the average similarity of the fields in a cluster to each other. It is shown that a range of classifications can be produced for which this measure is almost identical but which partition the days quite differently. This lack of a unique set of circulation types suggests that distinct weather regimes in NAE circulation do not exist or are very weak. It is also shown that the stability of the clustering solution to removal of data is not maximized by a suitable choice of the number of clusters. Indeed, there does not appear to be any robust way of choosing an optimum number of circulation types. Despite the apparent lack of preferred circulation types, cluster analysis can usefully be applied to generate a set of patterns that fully characterize the different circulation types appearing in each season. These patterns can then be used to analyze NAE climate variability. Ten clusters per season are chosen to ensure that a range of distinct circulation types that span the variability is produced. Using this classification, the effect of forcing of NAE circulation by tropical Pacific sea surface temperature (SST) anomalies is analyzed. This shows a significant influence of SST in this region on certain circulation types in almost all seasons. A tendency for a negative correlation between El Niño and an anomaly pattern resembling the positive winter North Atlantic Oscillation (NAO) emerges in a number of seasons. A notable exception is November–December, which shows the opposite relationship, with positive NAO-like patterns correlated with El Niño.

Reconstructed daily mean sea level pressure patterns of the North Atlantic–European region are classified for the period 1850 to 2003 to explore long-term changes of the atmospheric circulation and its impact on long-term temperature variability in the central European region. Commonly used k-means clustering algorithms resulted in classifications of low quality because of methodological deficiencies leading to local optima by chance for complex datasets. In contrast, a newly implemented clustering scheme combining the concepts of simulated annealing and diversified randomization (SANDRA) is able to reduce substantially the influence of chance in the cluster assignment, leading to partitions that are noticeably nearer to the global optimum and more stable. The differences between conventional cluster analysis and the SANDRA scheme are significant for subsequent analyses of single clusters—in particular, for trend analysis. Conventional indices used to determine the appropriate number of clusters failed to provide clear guidance, indicating that no distinct separation between clusters of circulation types exists in the dataset. Therefore, the number of clusters is determined by an external indicator, the so-called dominance criteria for t-mode principal component analysis. Nevertheless, the resulting partitions are stable for certain numbers of clusters and provide meaningful and reproducible clusters. The resulting types of pressure patterns reveal pronounced long-term variability and various significant trends of the time series of seasonal cluster frequency. Tentative estimations of central European temperature changes based solely on seasonal cluster frequencies can explain between 33.9% (summer) and 59.0% (winter) of temperature variance on the seasonal time scale. However, the signs of long-term changes in temperature are correctly reproduced even on multidecadal–centennial time scales. Moreover, linear warming trends are reproduced, implying from one-third up to one-half of the observed temperature increase between 1851/52 and 2003 (except for summer, but with significant trends for spring and autumn), indicating that changes in daily circulation patterns contribute to the observed overall long-term warming in the central European region.