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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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

Seasonal forecasts using coupled ocean-atmosphere climate models are increasingly employed to provide regional climate predictions. For the quality of forecasts to improve, regional biases in climate models must be diagnosed and reduced. The evolution of biases as initialized forecasts drift away from the observations is poorly understood, making it difficult to diagnose the causes of climate model biases. This study uses two seasonal forecast systems to examine drifts in sea surface temperature (SST) and precipitation, and compares them to the long-term bias in the free-running version of each model. Drifts are considered from daily to multi-annual time scales. We define three types of drift according to their relation with the long-term bias in the free-running model: asymptoting, overshooting and inverse drift. We find that precipitation almost always has an asymptoting drift. SST drifts on the other hand, vary between forecasting systems, where one often overshoots and the other often has an inverse drift. We find that some drifts evolve too slowly to have an impact on seasonal forecasts, even though they are important for climate projections. The bias found over the first few days can be very different from that in the free-running model, so although daily weather predictions can sometimes provide useful information on the causes of climate biases, this is not always the case. We also find that the magnitude of equatorial SST drifts, both in the Pacific and other ocean basins, depends on the El Niño Southern Oscillation (ENSO) phase. Averaging over all hindcast years can therefore hide the details of ENSO state dependent drifts and obscure the underlying physical causes. Our results highlight the need to consider biases across a range of timescales in order to understand their causes and develop improved climate models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]