%0 Journal Article %1 lockwood2019skilful %A Lockwood, Julia F. %A Thornton, Hazel E. %A Dunstone, Nick %A Scaife, Adam A. %A Bett, Philip E. %A Li, Chaofan %A Ren, Hong-Li %D 2019 %J Climate Dynamics %K china dynamics myown seasonal skill wind %R 10.1007/s00382-019-04763-8 %T Skilful seasonal prediction of winter wind speeds in China %U https://doi.org/10.1007/s00382-019-04763-8 %X We demonstrate robust skill in forecasting winter (DJF) mean 10 m wind speeds for the period 1992/3--2011/12 over south-eastern China and the South China Sea (SE China) and northern-central (NC) China, with correlations exceeding 0.8 and 0.6 respectively. High skill over these regions is seen in two independent initialised ensembles which cover different time periods. The NC China region suffers from a similar signal-to-noise problem as identified in forecasts of the North Atlantic Oscillation, where the model appears to be less predictable than the real world. In SE China, the predictability of wind speeds comes from the model's ability to predict the El Niño Southern Oscillation. In NC China, the wind speed is strongly related to two neighbouring geopotential height anomalies. Cross-validated linear regression models using the above climate indices give similar skill to using the direct model wind predictions in both regions. The model also has significant skill in predicting the strength of the Middle Eastern jet stream, which has previously been shown to be related to winter climate in central China. The model skill demonstrated here may be high enough to develop useful sector-specific seasonal forecasts, for example wind power forecasts for the energy industry, or air quality forecasts for the health sector.

@article{lockwood2019skilful, abstract = {We demonstrate robust skill in forecasting winter (DJF) mean 10 m wind speeds for the period 1992/3--2011/12 over south-eastern China and the South China Sea (SE China) and northern-central (NC) China, with correlations exceeding 0.8 and 0.6 respectively. High skill over these regions is seen in two independent initialised ensembles which cover different time periods. The NC China region suffers from a similar signal-to-noise problem as identified in forecasts of the North Atlantic Oscillation, where the model appears to be less predictable than the real world. In SE China, the predictability of wind speeds comes from the model's ability to predict the El Ni{\~{n}}o Southern Oscillation. In NC China, the wind speed is strongly related to two neighbouring geopotential height anomalies. Cross-validated linear regression models using the above climate indices give similar skill to using the direct model wind predictions in both regions. The model also has significant skill in predicting the strength of the Middle Eastern jet stream, which has previously been shown to be related to winter climate in central China. The model skill demonstrated here may be high enough to develop useful sector-specific seasonal forecasts, for example wind power forecasts for the energy industry, or air quality forecasts for the health sector.}, added-at = {2019-04-28T00:45:42.000+0200}, author = {Lockwood, Julia F. and Thornton, Hazel E. and Dunstone, Nick and Scaife, Adam A. and Bett, Philip E. and Li, Chaofan and Ren, Hong-Li}, biburl = {https://www.bibsonomy.org/bibtex/215a846b375a67f3385795f25f2ceee73/pbett}, day = 23, description = {Skilful seasonal prediction of winter wind speeds in China | SpringerLink}, doi = {10.1007/s00382-019-04763-8}, interhash = {60961907d78dd3f7012af04b944d972b}, intrahash = {15a846b375a67f3385795f25f2ceee73}, issn = {1432-0894}, journal = {Climate Dynamics}, keywords = {china dynamics myown seasonal skill wind}, month = apr, timestamp = {2019-04-28T00:45:42.000+0200}, title = {Skilful seasonal prediction of winter wind speeds in China}, url = {https://doi.org/10.1007/s00382-019-04763-8}, year = 2019 }