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The El Niño-Southern Oscillation (ENSO) has previously been shown to influence the winter North Atlantic Oscillation (NAO). In this presentation we investigate the ENSO-NAO teleconnection in historical and RCP8.5 scenario CMIP5 simulations, and show a future strengthening of the teleconnection under RCP8.5. The teleconnection strength is associated with increased tropical east Pacific rainfall variability. Stratospheric and tropospheric teleconnection pathways are examined, with both pathways having stronger links in future. The stratospheric pathway involves the Aleutian Low and the stratospheric polar vortex, with a downward influence on the NAO. This pathway is clearest in the high-top models that better resolve the stratosphere. The tropospheric pathway is driven by the Pacific subtropical jet strengthening and extending further into the Atlantic in future, generating increased baroclinicity in the Caribbean and influencing the Atlantic storm track. Our results suggest increasing influence of tropical rainfall on extratropical circulation in future.

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.

We quantify seasonal prediction skill of tropical winter rainfall in 14 climate forecast systems. High levels of seasonal prediction skill exist for year-to-year rainfall variability in all tropical ocean basins. The tropical East Pacific is the most skilful region, with very high correlation scores, and the tropical West Pacific is also highly skilful. Predictions of tropical Atlantic and Indian Ocean rainfall show lower but statistically significant scores. We compare prediction skill (measured against observed variability) with model predictability (using single forecasts as surrogate observations). Model predictability matches prediction skill in some regions but it is generally greater, especially over the Indian Ocean. We also find significant inter-basin connections in both observed and predicted rainfall. Teleconnections between basins due to El Niño–Southern Oscillation (ENSO) appear to be reproduced in multi-model predictions and are responsible for much of the prediction skill. They also explain the relative magnitude of inter-annual variability, the relative magnitude of predictable rainfall signals and the ranking of prediction skill across different basins. These seasonal tropical rainfall predictions exhibit a severe wet bias, often in excess of 20 of mean rainfall. However, we find little direct relationship between bias and prediction skill. Our results suggest that future prediction systems would be best improved through better model representation of inter-basin rainfall connections as these are strongly related to prediction skill, particularly in the Indian and West Pacific regions. Finally, we show that predictions of tropical rainfall alone can generate highly skilful forecasts of the main modes of extratropical circulation via linear relationships that might provide a useful tool to interpret real-time forecasts. © 2018 Crown copyright, Met Office Weather © 2018 Royal Meteorological Society This article is published with the permission of the Controller of HMSO and the Queen˘2019s Printer for Scotland

This article describes the UK Met Office Global Seasonal forecast system version 5 (GloSea5). GloSea5 upgrades include an increase in horizontal resolution in the atmosphere (N216–0.7°) and the ocean (0.25°), and implementation of a 3D-Var assimilation system for ocean and sea-ice conditions. GloSea5 shows improved year-to-year predictions of the major modes of variability. In the Tropics, predictions of the El Nino–Southern Oscillation are improved with reduced errors in the West Pacific. In the Extratropics, GloSea5 shows unprecedented levels of forecast skill and reliability for both the North Atlantic Oscillation and the Arctic Oscillation. We also find useful levels of skill for the western North Pacific Subtropical High which largely determines summer precipitation over East Asia.

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.

Skilful predictions of hurricane frequency have been limited to lead times of one season, and evidence for external forcing has been indirect. Simulations with nine variants of one global climate model show an influence of external forcing on hurricane frequency, and predictability on multi-year timescales.

The seasonal mean variability of the atmospheric circulation is affected by processes with time scales from less than seasonal to interannual or longer. Using monthly mean data from an ensemble of Atmospheric General Circulation Model (AGCM) realisations, the interannual variability of the seasonal mean is separated into intraseasonal, and slowly varying components. For the first time, using a recently developed method, the slowly varying component in multiple AGCM ensembles is further separated into internal and externally forced components. This is done for Southern Hemisphere 500 hPa geopotential height from five AGCMs in the CLIVAR International Climate of the Twentieth Century project for the summer and winter seasons. In both seasons, the intraseasonal and slow modes of variability are qualitatively well reproduced by the models when compared with reanalysis data, with a relative metric finding little overall difference between the models. The Southern Annular Mode (SAM) is by far the dominant mode of slowly varying internal atmospheric variability. Two slow-external modes of variability are related to El Nino-Southern Oscillation (ENSO) variability, and a third is the atmospheric response to trends in external forcing. An ENSO-SAM relationship is found in the model slow modes of variability, similar to that found by earlier studies using reanalysis data. There is a greater spread in the representation of model slow-external modes in winter than summer, particularly in the atmospheric response to external forcing trends. This may be attributable to weaker external forcing constraints on SH atmospheric circulation in winter.

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.

A multi-model set of atmospheric simulations forced by historical sea surface temperature (SST) or SSTs plus Greenhouse gases and aerosol forcing agents for the period of 1950-1999 is studied to identify and understand which components of the Asian-Australian monsoon (A-AM) variability are forced and reproducible. The analysis focuses on the summertime monsoon circulations, comparing model results against the observations. The priority of different components of the A-AM circulations in terms of reproducibility is evaluated. Among the subsystems of the wide A-AM, the South Asian monsoon and the Australian monsoon circulations are better reproduced than the others, indicating they are forced and well modeled. The primary driving mechanism comes from the tropical Pacific. The western North Pacific monsoon circulation is also forced and well modeled except with a slightly lower reproducibility due to its delayed response to the eastern tropical Pacific forcing. The simultaneous driving comes from the western Pacific surrounding the maritime continent region. The Indian monsoon circulation has a moderate reproducibility, partly due to its weakened connection to June-July-August SSTs in the equatorial eastern Pacific in recent decades. Among the A-AM subsystems, the East Asian summer monsoon has the lowest reproducibility and is poorly modeled. This is mainly due to the failure of specifying historical SST in capturing the zonal land-sea thermal contrast change across the East Asia. The prescribed tropical Indian Ocean SST changes partly reproduce the meridional wind change over East Asia in several models. For all the A-AM subsystem circulation indices, generally the MME is always the best except for the Indian monsoon and East Asian monsoon circulation indices.

We use a simple methodology to test whether a set of atmospheric climate models with prescribed radiative forcings and ocean surface conditions can reproduce twentieth century climate variability. Globally, rapid land surface warming since the 1970s is reproduced by some models but others warm too slowly. In the tropics, air-sea coupling allows models to reproduce the Southern Oscillation but its strength varies between models. We find a strong relationship between the Southern Oscillation in global temperature and the rate of global warming, which could in principle be used to identify models with realistic climate sensitivity. This relationship and a weak response to ENSO suggests weak sensitivity to changes in sea surface temperature in some of the models used here. In the tropics, most models reproduce part of the observed Sahel drought. In the extratropics, models do not reproduce the observed increase in the North Atlantic Oscillation in response to forcings, through internal variability, or as a combination of both.

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.

The latest coupled configuration of the Met Office Unified Model (Global Coupled configuration 2, GC2) is presented. This paper documents the model components which make up the configuration (although the scientific description of these components is detailed elsewhere) and provides a description of the coupling between the components. The performance of GC2 in terms of its systematic errors is assessed using a variety of diagnostic techniques. The configuration is intended to be used by the Met Office and collaborating institutes across a range of timescales, with the seasonal forecast system (GloSea5) and climate projection system (HadGEM) being the initial users. In this paper GC2 is compared against the model currently used operationally in those two systems. Overall GC2 is shown to be an improvement on the configurations used currently, particularly in terms of modes of variability (e.g. mid-latitude and tropical cyclone intensities, the Madden–Julian Oscillation and El Niño Southern Oscillation). A number of outstanding errors are identified with the most significant being a considerable warm bias over the Southern Ocean and a dry precipitation bias in the Indian and West African summer monsoons. Research to address these is ongoing.

International audience; Climate change is expected to increase winter rainfall and flooding in many extratropical regions as evaporation and precipitation rates increase, storms become more intense and storm tracks move polewards. Here, we show how changes in stratospheric circulation could play a significant role in future climate change in the extratropics through an additional shift in the tropospheric circulation. This shift in the circulation alters climate change in regional winter rainfall by an amount large enough to significantly alter regional climate change projections. The changes are consistent with changes in stratospheric winds inducing a change in the baroclinic eddy growth rate across the depth of the troposphere. A change in mean wind structure and an equatorward shift of the tropospheric storm tracks relative to models with poor stratospheric resolution allows coupling with surface climate. Using the Atlantic storm track as an example, we show how this can double the predicted increase in extreme winter rainfall over Western and Central Europe compared to other current climate projections.

The consistency of tropical tropospheric temperature trends with climate model expectations remains contentious. A key limitation is that the uncertainties in observations from radiosondes are both substantial and poorly constrained. We present a thorough uncertainty analysis of radiosonde‐based temperature records. This uses an automated homogenization procedure and a previously developed set of complex error models where the answer is known a priori. We perform a number of homogenization experiments in which error models are used to provide uncertainty estimates of real‐world trends. These estimates are relatively insensitive to a variety of processing choices. Over 1979–2003, the satellite‐equivalent tropical lower tropospheric temperature trend has likely (5–95% confidence range) been between −0.01 K/decade and 0.19 K/decade (0.05–0.23 K/decade over 1958–2003) with a best estimate of 0.08 K/decade (0.14 K/decade). This range includes both available satellite data sets and estimates from models (based upon scaling their tropical amplification behavior by observed surface trends). On an individual pressure level basis, agreement between models, theory, and observations within the troposphere is uncertain over 1979 to 2003 and nonexistent above 300 hPa. Analysis of 1958–2003, however, shows consistent model‐data agreement in tropical lapse rate trends at all levels up to the tropical tropopause, so the disagreement in the more recent period is not necessarily evidence of a general problem in simulating long‐term global warming. Other possible reasons for the discrepancy since 1979 are: observational errors beyond those accounted for here, end‐point effects, inadequate decadal variability in model lapse rates, or neglected climate forcings.

The ability of atmospheric general circulation models (AGCMs), that are forced with observed sea surface temperatures (SSTs), to simulate the Indian monsoon rainfall (IMR) variability on interannual to decadal timescales is analyzed in a multimodel intercomparison. The multimodel ensemble has been performed within the CLIVAR International ``Climate of the 20th Century'' (C20C) Project. This paper is part of a C20C intercomparison of key climate time series. Whereas on the interannual timescale there is modest skill in reproducing the observed IMR variability, on decadal timescale the skill is much larger. It is shown that the decadal IMR variability is largely forced, most likely by tropical sea surface temperatures (SSTs), but as well by extratropical and especially Atlantic Multidecadal Oscillation (AMO) related SSTs. In particular there has been a decrease from the late 1950s to the 1990s that corresponds to a general warming of tropical SSTs. Using a selection of control integrations from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3), it is shown that the increase of greenhouse gases (GHG) in the twentieth century has not significantly contributed to the observed decadal IMR variability.

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.

Northern hemisphere winter 2009/10 was exceptional for atmospheric circulation: the North Atlantic Oscillation (NAO) index was the lowest on record for over a century. This contributed to cold conditions over large areas of Eurasia and North America. Here we use two versions of the Met Office GloSea4 seasonal forecast system to investigate the predictability of this exceptional winter. The first is the then operational version of GloSea4, which uses a low top model and successfully predicted a negative NAO in forecasts produced in September, October and November 2009. The second uses a new high top model, which better simulates sudden stratospheric warmings (SSWs). This is particularly relevant for 2009/10 due to its unusual combination of a strong El Nino and an easterly quasi-biennial oscillation (QBO) phase, favouring SSW development. SSWs are shown to play an influential role in surface conditions, producing a stronger sea level pressure signal and improving predictions of the 2009/10 winter.

The UK experienced record average rainfall in winter 2013–14, leading to widespread and prolonged flooding. The immediate cause of this exceptional rainfall was a very strong and persistent cyclonic atmospheric circulation over the North East Atlantic Ocean. This was related to a very strong North Atlantic jet stream which resulted in numerous damaging wind storms. These exceptional meteorological conditions have led to renewed questions about whether anthropogenic climate change is noticeably influencing extreme weather. The regional weather pattern responsible for the extreme UK winter coincided with highly anomalous conditions across the globe. We assess the contributions from various possible remote forcing regions using sets of ocean–atmosphere model relaxation experiments, where winds and temperatures are constrained to be similar to those observed in winter 2013–14 within specified atmospheric domains. We find that influences from the tropics were likely to have played a significant role in the development of the unusual extra-tropical circulation, including a role for the tropical Atlantic sector. Additionally, a stronger and more stable stratospheric polar vortex, likely associated with a strong westerly phase of the stratospheric Quasi-Biennial Oscillation (QBO), appears to have contributed to the extreme conditions. While intrinsic climatic variability clearly has the largest effect on the generation of extremes, results from an analysis which segregates circulation-related and residual rainfall variability suggest that emerging climate change signals made a secondary contribution to extreme rainfall in winter 2013–14.