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Angular momentum is fundamental to the structure and variability of the atmosphere and therefore has an important influence on regional weather and climate. Total atmospheric angular momentum is also directly related to the rotation rate of the Earth and, hence, the length of day. However, the long-range predictability of fluctuations in the length of the day and atmospheric angular momentum is unknown. Here we show that fluctuations in atmospheric angular momentum and the length of day are predictable out to more than a year ahead and that this provides an atmospheric source of long-range predictability for surface climate. Using ensemble forecasts from a dynamical climate model, we demonstrate long-range predictability of signals in the atmospheric angular momentum field that propagate slowly and coherently polewards due to wave–mean flow interaction within the atmosphere. These predictable signals are also shown to precede changes in extratropical climate via the North Atlantic Oscillation and the extratropical jet stream. These results extend the lead time for length-of-day predictions, provide a source of long-range predictability from within the atmosphere and provide a link between geodesy and climate prediction.

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1–3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7–9may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.

The impact of strong La Niña events on January–February North Atlantic mean sea level pressure is unknown, as these events have never been observed. Using large ensembles from the Met Office decadal prediction system, examples of strong La Niña events are simulated and the Atlantic response to these is found to be a positive North Atlantic Oscillation. This is very different to the wavelike response observed and simulated for strong El Niño events. The reason for this difference is traced to the fact that the December‐January‐February mean tropospheric teleconnection of El Niño–Southern Oscillation to the North Atlantic dominates for strong El Niño events, while the stratospheric teleconnection dominates for strong La Niña events. The strength of the tropospheric pathway grows linearly and symmetrically with El Niño–Southern Oscillation. The stratospheric pathway is the source of the asymmetry between January and February surface responses to strong El Niño and strong La Niña events. The El Niño–Southern Oscillation has a large impact on January–February North Atlantic mean sea level pressure (MSLP), important for forecasting the mean climate over the United Kingdom and Northern Europe during these months. Simulations from the Met Office decadal prediction system are used to research the impact of strong negative (La Niña) events, not yet observed in nature. Really strong positive (El Niño) and negative (La Niña) events are found to have an asymmetric impact on the North Atlantic. This paper studies the reasons why. El Niño–Southern Oscillation impacts the North Atlantic via two routes—one through the tropical Atlantic in the lower atmosphere (troposphere) and one through the middle atmosphere (stratosphere) in polar latitudes. It is found that the tropospheric pathway dominates for El Niño events, leading to a wavelike response in North Atlantic MSLP, characterized by a high pressure west of the United Kingdom. However, the stratospheric pathway dominates for La Niña events leading to a different MSLP pattern, characterized by high pressure over the Azores and low pressure over Iceland. Therefore, if a really strong La Niña event were ever to occur, the United Kingdom and Northern Europe might experience a very wet winter. North Atlantic late winter response is not symmetric between strong El Ninos and strong La NinasTropospheric teleconnection grows linearly with ENSO but stratospheric response is asymmetricDominant pathway is stratospheric for strong La Nina and tropospheric for strong El Nino

Accurate long‐term flood predictions are increasingly needed for flood risk management in a changing climate, but are hindered by the underestimation of climate variability by climate models. Here, we drive a statistical flood model with a large ensemble of dynamical CMIP5‐6 predictions of precipitation and temperature. Predictions of UK winter flooding (95th streamflow percentile) have low skill when using the raw 676‐member ensemble averaged over lead times of 2–5 years from the initialization date. Sub‐selecting 20 ensemble members that adequately represent the multiyear temporal variability in the North Atlantic Oscillation (NAO) significantly improves the flood predictions. Applying this method we show positive skill in 46% of stations compared to 26% using the raw ensemble, primarily in regions most strongly influenced by the NAO. Our findings reveal the potential of decadal predictions to inform flood risk management at long lead times. Reliable predictions of flooding can help society to manage the associated risk to lives and property. Seasonal predictions of flooding over the coming months already form the basis of many operational services around the world. In contrast, decadal predictions with lead times of up to 10 years are more challenging, due to the difficulty of simulating dynamic changes in atmospheric circulation at these timescales. Here, we show that a large ensemble of climate models can predict average winter flood conditions over the UK in the next decade. The climate models underestimate the magnitude of atmospheric variability in the north Atlantic and identifying a subset of skillful climate model simulations improves the ability to predict floods. Our results suggest that decadal climate predictions may be useful in the context of flood risk management. However, the use of multiyear averages for flood prediction is still poorly studied, and therefore further work should help determine how such predictions can be used in an operational setting. Multiyear predictions of mean winter floods 2–5 years ahead are skillful across much of the UKSkill is improved by “NAO‐matching” to overcome spuriously weak modeled signalsThe higher the sensitivity of streamflow to the North Atlantic Oscillation at a given gauge, the greater the benefit of NAO‐matching for decadal flood prediction Multiyear predictions of mean winter floods 2–5 years ahead are skillful across much of the UK Skill is improved by “NAO‐matching” to overcome spuriously weak modeled signals The higher the sensitivity of streamflow to the North Atlantic Oscillation at a given gauge, the greater the benefit of NAO‐matching for decadal flood prediction

It is well established that the positive phase of El Niño Southern Oscillation (ENSO) tends to weaken the Northern Hemisphere stratospheric polar vortex (SPV), promoting a negative North Atlantic Oscillation (NAO). Pacific Decadal Variability (PDV) is characterized by a pattern of sea surface temperatures similar to ENSO, but its impacts are more uncertain: some studies suggest similar impacts of ENSO and PDV on the SPV and NAO, while others find the opposite. We use climate model experiments and reanalysis to find further evidence supporting opposite interannual and decadal impacts of Pacific variability on the extratropics. We propose that the decadal strengthening of the SPV in response to positive PDV is caused by a build‐up of stratospheric water vapor leading to enhanced cooling at the poles, an increased meridional temperature gradient and a strengthened extratropical jet. Our results are important for understanding decadal variability, seasonal to decadal forecasts and climate projections. El Niño Southern Oscillation (ENSO) dominates the year‐to‐year variability in the Pacific and is crucial for seasonal forecasts, whereas Pacific Decadal Variability (PDV) describes changes which are important in decadal predictions. The impacts of ENSO have been well studied but the impacts of PDV are more uncertain despite the pattern of sea surface temperature being very similar to ENSO. In this study we use observational reanalysis and climate models to show that positive PDV and ENSO phases have opposite impacts on stratospheric winds. We argue that this is because a positive PDV allows more water vapor to enter the stratosphere which builds up over the period of a decade. This causes a cooling over the Northern Hemisphere polar stratosphere and hence a strengthening of Northern Hemisphere polar stratospheric winds, resulting in different surface impacts. This result is important for understanding climate variability and improving climate predictions and projections. Positive phases of pacific variability weaken the stratospheric polar vortex on interannual timescales but strengthen it on a decadal basisWe hypothesize this is due to a long‐term build‐up of stratospheric water, due to an increase of tropical tropopause temperaturesImpacts of Pacific variability on north Atlantic winter sea level pressure are also different on interannual and decadal timescales Positive phases of pacific variability weaken the stratospheric polar vortex on interannual timescales but strengthen it on a decadal basis We hypothesize this is due to a long‐term build‐up of stratospheric water, due to an increase of tropical tropopause temperatures Impacts of Pacific variability on north Atlantic winter sea level pressure are also different on interannual and decadal timescales

The North Atlantic Oscillation (“NAO”) and the East Atlantic Pattern (“EAP”) dominate winter atmospheric variability over the North Atlantic. Unlike the NAO, seasonal predictability of the EAP has remained elusive. A multi‐model ensemble of seasonal predictions yields skillful forecasts of the EAP in late autumn and early winter, complementing NAO prediction skill in winter. The shift in prediction skill from EAP to NAO reflects the ability of the ensemble to forecast the evolving influence of the El Niño South Oscillation on the North Atlantic region. In early winter, the ensemble correctly forecasts the key tropical–extratropical teleconnections, resulting in skillful predictions of the EAP and western European temperatures and rainfall. However, the modeled tropical–extratropical teleconnections are weak compared to observations, contributing to a signal to noise error in predictions of the EAP. Improving the strength of such teleconnections would improve predictions of the EAP and associated surface climate. Predicting the most likely weather patterns ahead of the autumn and winter can be very useful for resilience planning. To date, skillful forecasts have only been possible for the most common pattern of winter atmospheric variability over the North Atlantic and European region, called the North Atlantic Oscillation (“NAO”), with no significant skill for the second pattern, called the East Atlantic Pattern (“EAP”). Using seasonal predictions from a range of coupled ocean—atmosphere models, we demonstrate significant skill in the EAP, in late autumn and early winter. The associated temperature and precipitation variability across western Europe is also skilfully predicted. We further show that skill shifts from the EAP to the NAO over the autumn to winter and reflects the evolving influence of the El Niño South Oscillation, a large‐scale atmosphere‐ocean feature of the tropical Pacific. However, climate models underestimate the magnitude of the predictable signal of the EAP, as found previously for the NAO. Resolving this error would further improve seasonal predictions of the late autumn and early winter period. Seasonal prediction skill of the East Atlantic Pattern and associated surface climate is demonstrated in late autumn and early winterA seasonal shift in prediction skill reflects the evolving influence of the El Niño Southern Oscillation on the North Atlantic regionModeled tropical–extratropical teleconnections are weak, contributing to a signal to noise error in East Atlantic Pattern predictions Seasonal prediction skill of the East Atlantic Pattern and associated surface climate is demonstrated in late autumn and early winter A seasonal shift in prediction skill reflects the evolving influence of the El Niño Southern Oscillation on the North Atlantic region Modeled tropical–extratropical teleconnections are weak, contributing to a signal to noise error in East Atlantic Pattern predictions

Aims. In this paper we present the first results of a detailed spectroscopic and photometric analysis of the $V = 11.7^{\rm m}$eclipsing binary ASAS J052821+0338.5.

The frequency of tropical storms in the North Atlantic region varies markedly on decadal timescales, with profound socio-economic impacts. Climate models largely reproduce the observed variability when forced by observed sea surface temperatures. However, the relative importance of natural variability and external influences such as greenhouse gases, dust, sulphate and volcanic aerosols on sea surface temperatures, and hence tropical storms, is highly uncertain. Here, we assess the effect of individual climate drivers on the frequency of North Atlantic tropical storms between 1860 and 2050, using simulations from a collection of climate models. We show that anthropogenic aerosols lowered the frequency of tropical storms over the twentieth century. However, sharp declines in anthropogenic aerosol levels over the North Atlantic at the end of the twentieth century allowed the frequency of tropical storms to increase. In simulations with a model that comprehensively incorporates aerosol effects (HadGEM2-ES; ref. ), decadal variability in tropical storm frequency is well reproduced through aerosol-induced north–south shifts in the Hadley circulation. However, this mechanism changes in future projections. Our results raise the possibility that external factors, particularly anthropogenic aerosols, could be the dominant cause of historical tropical storm variability, and highlight the potential importance of future changes in aerosol emissions.

Several studies have shown that the diet of the North American mink can vary according to habitat, season and sex (Gerell, 1967;Akande, 1972; Day & Linn, 1972; Birks & Dunstone, 1985; Dunstone & Birks, 1987). It is clear that at times, particularly in autumn and winter, both male and female mink may obtain over 50 % of their food (as fish and crustaceans) from an aquatic environment. Thus, mink will readily take to water and dive to capture prey despite the fact that they possess very few anatomical adaptations which enhance their ability to hunt underwater. Indeed, the unusually high resting metabolic rate of mustelids (Iversen, 1972) and the high energetic cost of transport during surface swimming (Williams, 1983) argue against the successful use of a diving foraging strategy. Brown & Lasiewski (1972) proposed that the adaptive advantage of the elongated body shape of mink (which is energetically expensive because of high rates of heat loss as a result of the high surface area to body volume ratio) lies in the increased ability to capture prey in confined spaces (e.g. lagomorphs in their burrows; Dunstone & Birks, 1987). Perhaps an enhanced ability to capture fish and crustaceans in underwater hides (e.g. Dunstone, 1978) compensates for their presumed locomotory inefficiency underwater.

Simple models of wave-mean flow interaction in the Northern Hemisphere winter stratosphere suggest the existence of subseasonal vacillations in the strength of the polar vortex. Here, we define a sinusoidal fit to the daily deseasonalized stratospheric wind. A suitable fixed period and amplitude for the sine waves is identified. Their mean value, equivalent to polar vortex strength, and phase, equivalent to the timing of sudden stratospheric warmings during winter, varies from year to year. These vacillations explain much of the subseasonal and interannual variability in the monthly mean vortex strength and, consistent with wave-mean flow interaction theory, their amplitude correlates positively with the magnitude of winter mean planetary wave driving. Furthermore, they allow skillful prediction of the vortex strength one month ahead. Identifying and understanding this subseasonal variability has potential implications for winter seasonal forecasts, as the December–February mean behavior may miss important subseasonal events. Simple sine wave fits capture two thirds of interannual variance in monthly Northern Hemisphere winter stratospheric polar vortex strengthAmplitude of these vacillations increases with planetary wave driving, consistent with wave-mean flow interaction theorySimple model skillfully predicts the stratospheric polar vortex strength at one month lead time

The Paris Agreement calls for efforts to limit anthropogenic global warming to less than 1.5 °C above preindustrial levels. However, natural internal variability may exacerbate anthropogenic warming to produce temporary excursions above 1.5 °C. Such excursions would not necessarily exceed the Paris Agreement, but would provide a warning that the threshold is being approached. Here we develop a new capability to predict the probability that global temperature will exceed 1.5 °C above preindustrial levels in the coming 5 years. For the period 2017 to 2021 we predict a 38% and 10% chance, respectively, of monthly or yearly temperatures exceeding 1.5 °C, with virtually no chance of the 5‐year mean being above the threshold. Our forecasts will be updated annually to provide policy makers with advanced warning of the evolving probability and duration of future warming events. The Paris Agreement calls for efforts to limit human‐induced global warming to less than 1.5 °C above preindustrial levels. Observations of global mean temperature contain both human‐induced temperature change and superimposed natural variability. Natural variability may temporarily add to the underlying human‐induced warming, leading to observed temperatures that are higher than 1.5 °C for short‐term periods. This would not necessarily exceed the Paris agreement, which is usually interpreted to refer to long‐term averages, but would give an important indication that the threshold is being approached. If exceedance occurs, policy makers will require guidance regarding how long temperatures will remain above the threshold. Here we develop a new capability to predict the likelihood that global temperature will exceed 1.5 °C above preindustrial levels in the coming 5 years. We use decadal climate predictions that are regularly produced by several international climate prediction centers. Importantly, these predictions take into account the observed present day conditions since this is essential to predict the evolution of natural variability. For the period 2017 to 2021 we predict a 38% and 10% chance, respectively, of monthly or yearly temperatures exceeding 1.5 °C, with virtually no chance of the 5‐year mean being above the threshold. We will update our forecasts every year to provide policy makers with advanced warning of the evolving probability and duration of future warming events. Early temporary excursions above 1.5 °C would provide a warning that one of the Paris Agreement thresholds is being approachedInitialized climate predictions indicate a 38% (10%) chance of at least 1 month (year) exceeding 1.5 °C in the 5 year period 2017‐2021Five‐year mean temperatures above 1.5 °C are extremely unlikely in this period

Aims. In this paper we study the feasibility of inferring the magnetic field from polarized multi line spectra using two methods: The pseudo line approachand The PCA-ZDI approach.Methods. We use multi line techniques, meaning that all the lines of a stellar spectrum contribute to obtain a polarization signature. The use of multiple lines dramatically increases the signal-to-noise-ratio of these polarizations signatures. Using one technique, the pseudo line approach, we construct the pseudo line as the mean profile of all the individual lines. The other technique, the PCA-ZDI approachproposed recently by Semel et al. (2006, ASPC, 358, 355) for the detection of polarized signals, combines principle components analysis (PCA) and the Zeeman Doppler imaging technique (ZDI). This new method has a main advantage: the polarized signature is extracted using cross correlations between the stellar spectra and functions containing the polarization properties of each line. is extracted using cross correlations between the stellar spectra and functions containing the polarization properties of each line." These functions are the principal components of a database of synthetic spectra. The synthesis of the spectra of the database are obtained using the radiative transfer equations in LTE. The profiles built with the PCA-ZDI technique are called multi Zeeman signatures.Results. The construction of the pseudo line as well as the multi Zeeman signatures is a powerful tool in the study of stellar and solar magnetic fields. The information of the physical parameters that governs the line formation is contained in the final polarized profiles. We have shown in particular using inversion codes that the magnetic field vector can be properly inferred with both approaches despite the magnetic field regime.

Aims. In this paper we study the feasibility of inferring the magnetic field from polarized multi line spectra using two methods: The pseudo line approachand The PCA-ZDI approach.