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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 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

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 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

Analysis of single forcing runs from CMIP5 (the fifth Coupled Model Intercomparison Project) simulations shows that the mid-twentieth century temperature hiatus, and the coincident decrease in precipitation, is likely to have been influenced strongly by anthropogenic aerosol forcing. Models that include a representation of the indirect effect of aerosol better reproduce inter-decadal variability in historical global-mean near-surface temperatures, particularly the cooling in the 1950s and 1960s, compared to models with representation of the aerosol direct effect only. Models with the indirect effect also show a more pronounced decrease in precipitation during this period, which is in better agreement with observations, and greater inter-decadal variability in the inter-hemispheric temperature difference. This study demonstrates the importance of representing aerosols, and their indirect effects, in general circulation models, and suggests that inter-model diversity in aerosol burden and representation of aerosol-cloud interaction can produce substantial variation in simulations of climate variability on multi-decadal timescales.

Summer rainfall in the Sahel region of Africa exhibits one of the largest signals of climatic variability and with a population reliant on agricultural productivity, the Sahel is particularly vulnerable to major droughts such as occurred in the 1970s and 1980s. Rainfall levels have subsequently recovered, but future projections remain uncertain. Here we show that Sahel rainfall is skilfully predicted on inter-annual and multi-year (that is, >5 years) timescales and use these predictions to better understand the driving mechanisms. Moisture budget analysis indicates that on multi-year timescales, a warmer north Atlantic and Mediterranean enhance Sahel rainfall through increased meridional convergence of low-level, externally sourced moisture. In contrast, year-to-year rainfall levels are largely determined by the recycling rate of local moisture, regulated by planetary circulation patterns associated with the El Niño-Southern Oscillation. Our findings aid improved understanding and forecasting of Sahel drought, paramount for successful adaptation strategies in a changing climate. Summer rainfall in the agriculturally-reliant Sahel is extremely variable, with the region particularly vulnerable to major droughts. Here, the authors investigate the mechanisms that drive Sahel summer rainfall change on inter-annual and multi-year timescales and show that Sahel rainfall can be skilfully predicted.

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. Forecasting: Large ensemble improves decadal climate predictions There is increasing demand for near-time climate predictions to provide guidance for adaptation planning at policy-relevant timescales. Although previous work has shown some skill in forecasting decadal surface temperature, it has proven more difficult to make predictions for precipitation and atmospheric circulation. By using a large, multi-model ensemble of climate models, a multi-institution team lead by Doug Smith of the Met Office Hadley Centre, UK were able to make skillful decadal predictions for near surface temperature, precipitation for the Sahel and broad swathes of Europe and Eurasia, and mean sea level pressure for many regions, with some exceptions being predictions for the South Atlantic and Southern Ocean. Further work is needed to understand whether the instances in which forecasts and observations differ are due to internal variability or external factors such as solar variability, volcanoes and anthropogenic aerosols.

Using idealised model experiments we show that the tropical Atlantic main hurricane development region (MDR) is potentially one of the most predictable regions for atmospheric variables such as precipitation and wind shear on multi‐year timescales. Similarly we also find predictability for the number of tropical storms and the position of the inter‐tropical convergence zone. Further experiments that withhold data in different parts of the ocean identify the North Atlantic sub‐polar gyre as the key region for driving the skill in the model MDR. This further highlights the importance of observing the high‐latitude North Atlantic Ocean for initialising future decadal predictions. Key Points Tropical Atlantic atmosphere has potential multi‐annual predictability The high latitude North Atlantic appears key in driving this predictability Predictability shown for model hurricanes supports recent real world results

The frequency of North Atlantic tropical storms varies markedly on decadal timescales. An analysis of climate model simulations suggests that anthropogenic aerosols lowered the frequency of tropical storms in the North Atlantic over the twentieth century. The frequency of tropical storms in the North Atlantic region varies markedly on decadal timescales 1 , 2 , 3 , 4 , with profound socio-economic impacts 5 , 6 . Climate models largely reproduce the observed variability when forced by observed sea surface temperatures 1 , 8 , 10 . 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 11 , 12 , 13 , 14 , 15 , 16 . 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 17 . 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.  18 ), 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.

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 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

We present a set of idealised model experiments that investigate the impact of assimilating different amounts of ocean and atmosphere data on decadal climate prediction skill. Assimilating monthly average sub‐surface temperature and salinity data successfully initialises the meridional overturning circulation and produces skillful predictions of global ocean heat content. However, when sea surface temperature data is assimilated alone the predictions have much less skill, particularly in the extra‐tropics. The upper 2000m temperature and salinity observations currently provided by the Argo array of floats are therefore potentially well suited to initialising decadal climate predictions. We note however that we do not attempt to simulate the actual distribution of Argo floats. Assimilating data beneath 2000m always reduces the RMSE, with the most significant improvements in the Southern Ocean. Furthermore, assimilating six hourly atmospheric observations significantly improves the forecast skill within the first year, but has little impact thereafter.

The strongest El Niño events of the past four decades were associated with large rainfall deficits in North Brazil during the December to February mature phase, leading to substantial societal and ecological impacts and influencing the global carbon cycle. While the teleconnection between El Niño and northern South America is well studied, the small number of El Niño events—and especially high magnitude ‘major’ El Niños—in the recent observational record make a robust characterisation of the response over North Brazil in today’s climate difficult. Here we use a large, initialised ensemble of global climate simulations to provide a much greater sample of North Brazil rainfall responses to recent El Niño events than is available from observations, and use this to form an assessment of the chance of unprecedented dry conditions during El Niño. We find that record low rainfall totals are possible during El Niño events in the current climate, and that as the magnitude of El Niño increases, so too does the chance of unprecedented low rainfall, reaching close to 60% for major El Niños. However, during even the largest El Niños, when the observed North Brazil response has been similar and very dry, we find rainfall rates close to normal are still possible due to internal atmospheric variability. In addition to the predictable influence of the tropical Pacific, an unpredictable influence from the extratropics appears to play a role in modulating the North Brazil rainfall response via an equatorward wave-train that propagates down the western coast of North America and across to the Caribbean. Combining forecasts of El Niño with this improved information on the underlying chance of extremely low rainfall could feed into improved assessments of risk and preparedness for upcoming droughts in Brazil.