| Title: |
Seasonal prediction of UK mean and extreme winds |
| Authors: |
Lockwood, Julia F.; Stringer, Nicky; Hodge, Katie R.; Bett, Philip E.; Knight, Jeff; Smith, Doug; Scaife, Adam A.; Patterson, Matthew; Dunstone, Nick; Thornton, Hazel E. |
| Contributors: |
Department for Business, Energy and Industrial Strategy, UK Government |
| Source: |
Quarterly Journal of the Royal Meteorological Society ; volume 149, issue 757, page 3477-3489 ; ISSN 0035-9009 1477-870X |
| Publisher Information: |
Wiley |
| Publication Year: |
2023 |
| Collection: |
Wiley Online Library (Open Access Articles via Crossref) |
| Description: |
Recent studies have shown that seasonal forecasting systems have significant skill in predicting northern European winds and storms in winter, but other seasons have not been so extensively analysed. Given this fact, and coupled with requests from users of the Met Office 3‐month outlook for the United Kingdom (UK), we have investigated the skill in predicting seasonal (3‐month) mean UK wind speed and storms (extreme winds) with a one‐month lead‐time, throughout the year, using a large ensemble from the Met Office's seasonal prediction system, GloSea. We find that seasonal UK storms and mean wind speeds are well correlated, and therefore a single prediction of UK mean wind speed will give an indication of predicted storm counts. Skill for these predictions is highest in winter (December–February), related to predictability of the North Atlantic Oscillation. In contrast, summer (June–August) UK wind skill is not significant and furthermore appears to be negative. We find evidence, in both observations and model members, for a Rossby wave from the tropics influencing UK summer winds and forming a significant predictable component in the model ensemble mean. However, the model predictable signal appears to be out of phase with that observed leading to the negative correlation. Further investigation into summer Rossby wave generation and propagation is necessary to understand whether summer predictions could be improved. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1002/qj.4568 |
| Availability: |
https://doi.org/10.1002/qj.4568; https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.4568 |
| Rights: |
http://creativecommons.org/licenses/by-nc/4.0/ |
| Accession Number: |
edsbas.C1B31864 |
| Database: |
BASE |