Improved seasonal prediction of UK regional precipitation using atmospheric circulation

Baker, Laura; Shaffrey, Len; Scaife, Adam A.

doi:10.1002/joc.5382

Cited by 35 publications

(42 citation statements)

References 36 publications

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“…But such a skill is actually "concentrated" in the first few days, because the trajectories tend to converge to the climatology after 20 days. The combination of several skill scores shows that such a system is not appropriate for ensemble forecasts beyond lead times of 40 days, which is lower than what is reported by Baker et al (2018) for a meteorological forecast of the NAO. Although the forecast system is random, it contains elements of the dynamics of the atmosphere, from the choice of The diamonds indicate the reliability of CRPS (on the same scale as CRPSS).…”

Section: Discussioncontrasting

confidence: 61%

Stochastic ensemble climate forecast with an analogue model

Yiou

Déandreis²

2019

Geosci. Model Dev.

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Abstract. This paper presents a system to perform large-ensemble climate stochastic forecasts. The system is based on random analogue sampling of sea-level pressure data from the NCEP reanalysis. It is tested to forecast a North Atlantic Oscillation (NAO) index and the daily average temperature in five European stations. We simulated 100-member ensembles of averages over lead times from 5 days to 80 days in a hindcast mode, i.e., from a meteorological to a seasonal forecast. We tested the hindcast simulations with the usual forecast skill scores (CRPS or correlation) against persistence and climatology. We find significantly positive skill scores for all timescales. Although this model cannot outperform numerical weather prediction, it presents an interesting benchmark that could complement climatology or persistence forecast.

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Section: Discussioncontrasting

confidence: 61%

Stochastic ensemble climate forecast with an analogue model

Yiou

Déandreis²

2019

Geosci. Model Dev.

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“…GloSea5, has significant skill (correlation 0.62) for the wintertime NAO at 2-to 4-month lead times for a 20-year period (1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011). The GloSea5 forecasts have since been used for applications such as seasonal forecasts of regional UK precipitation (Baker et al, 2017), weather-related impacts on UK transport (Palin et al, 2016), Baltic Sea ice (Karpechko et al, 2015), and UK energy demand and supply (Clark et al, 2017). Athanasiadis et al (2017) showed that even higher skill (correlations of 0.85) can be obtained for the NAO using a multimodel ensemble (MME) of three seasonal forecast systems (GloSea5, NCEP-CFSv2, and CMCC) but found that the exact level of skill was sensitive to both the hindcast period and the NAO definition used.…”

Section: 1029/2018gl078838mentioning

confidence: 99%

An Intercomparison of Skill and Overconfidence/Underconfidence of the Wintertime North Atlantic Oscillation in Multimodel Seasonal Forecasts

Baker

Shaffrey

Sutton

et al. 2018

Geophysical Research Letters

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100

105

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Recent studies of individual seasonal forecast systems have shown that the wintertime North Atlantic Oscillation (NAO) can be skillfully forecast. However, it has also been suggested that these skillful forecasts tend to be underconfident, meaning that there is too high a proportion of unpredictable noise in the forecasts. We assess the skill and overconfidence/underconfidence of the seasonal forecast systems contributing to the EUROpean Seasonal to Interannual Prediction (EUROSIP) multimodel ensemble system. Five of the seven systems studied have significant skill for forecasting the wintertime NAO at 2‐ to 4‐month lead times. Four of these skillful systems are underconfident for forecasting the NAO. A multimodel ensemble (ensemble size 126 members) is both skillful and clearly underconfident. Underconfidence becomes more pronounced as the ensemble size increases. Certain years in the hindcast period are well forecast by all or most models. This implies that common teleconnections and drivers of the NAO are being captured by the EUROSIP seasonal forecasts.

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“…This might suggest that extreme central European rainfall winters are poorly predicted by GloSea5. However, there are hints of predictability for other patterns (Baker et al, ) and winter 2016/2017 is an extreme case to further investigate this.…”

Section: Circulation Patterns Associated With European Rainfall and Tmentioning

confidence: 99%

Predictability of European winter 2016/2017

Dunstone

Scaife

MacLachlan

et al. 2018

Atmospheric Science Letters

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Winter 2016/2017 was one of the driest on record for central Europe and the United Kingdom. This was the result of blocked atmospheric circulation with high pressure centred over North‐West Europe dominating the winter mean circulation pattern. Using large ensembles of simulated winters, we find that the observed winter 2016/2017 circulation was very similar in pattern and strength to the circulation associated with the top 10% of driest Central European winters. Here, we explore whether seasonal forecasts were able to predict this circulation pattern. Despite the fact that the observed circulation anomaly did not project on to the North Atlantic Oscillation (NAO), we find that forecasts starting in November did predict a high‐pressure anomaly over North‐Western Europe. We use two independent data sets, and methods, to probe the drivers of this circulation pattern. We find evidence for a Rossby Wave propagating out of the tropical Atlantic where there were anomalous local rainfall anomalies. This case study is another example of real‐time seasonal forecast skill for Europe and provides evidence for predictability beyond the NAO pattern.

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Improved seasonal prediction of UK regional precipitation using atmospheric circulation

Cited by 35 publications

References 36 publications

Stochastic ensemble climate forecast with an analogue model

Stochastic ensemble climate forecast with an analogue model

An Intercomparison of Skill and Overconfidence/Underconfidence of the Wintertime North Atlantic Oscillation in Multimodel Seasonal Forecasts

Predictability of European winter 2016/2017

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