Skilful seasonal predictions of Summer European rainfal

• Published in: Geophysical Research Letters
• Main Authors: Dunstone, N, Smith, D, Scaife, A, Hermanson, L, Fereday, D, O-Reilly, C, Stirling, A, Eade, R, Gordan, M, MacLachlan, C, Sheen, KL, Woolings, T, Belcher, S
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union 2018
• Subjects: Climate; Europe; Rainfall; Drought; Seasonal; Forecasting; North Atlantic
• Online Access: http://hdl.handle.net/10871/32005; https://doi.org/10.1002/2017GL076337

This is the author accepted manuscript. The final version is available from American Geophysical Union (AGU) via the DOI in this record. Year-to-year variability in Northern European summer rainfall has profound societal and economic impacts; however current seasonal forecast systems show no significant forecast skill. Here we show skilful predictions are possible (r~0.5, p<0.001) using the latest high-resolution Met Office near-term prediction system over 1960-2017. The model predictions capture both low-frequency changes (e.g. wet summers 2007-2012) and some of the large individual events (e.g. dry summer 1976). Skill is linked to predictable North Atlantic sea surface temperature variability changing the supply of water vapour into Northern Europe and so modulating convective rainfall. However, dynamical circulation variability is not well predicted in general – although some interannual skill is found. Due to the weak amplitude of the forced model signal (likely caused by missing or weak model responses) very large ensembles (>80 members) are required for skilful predictions. This work is promising for the development of European summer rainfall climate services. This work was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101), the EU FP7 SPECS project. We acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu). We also would like to thank Gerard van der Schrier and Else Van Den Besselaar for kindly providing us the pre-release E-OBS dataset version 'v16e' and further support. Model data used to create the figures are available from the authors upon request for academic use.