Skilful seasonal prediction of winter wind speeds in China

Abstract

We demonstrate robust skill in forecasting winter (DJF) mean 10 m wind speeds for the period 1992/3-2011/12 over southeastern 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.

A skilful seasonal prediction for wintertime rainfall in southern Thailand

“…In order to overcome the limitation based on the pure dynamic prediction and to perform a more skilful seasonal prediction for the winter STR, an alternative way is to construct a linear regression prediction model by the combination of the dynamic model and statistical analysis (e.g., Lu et al ., 2017; Baker et al ., 2018; Lockwood et al ., 2019; Ren and Nie, 2021). Considering the high agreement of STR between the station rainfall gauges and the GPCP grid data, the following analysis of observational data is mainly based on the station data.…”

“…The interpretation of different probabilistic prediction skills for three categorical events in the dynamic models is beyond the scope of this study, but it is an important issue that needs to be examined in the future. Moreover, for the flooding 2016/2017 winter, the BS values for above-normal event are high in station In order to overcome the limitation based on the pure dynamic prediction and to perform a more skilful seasonal prediction for the winter STR, an alternative way is to construct a linear regression prediction model by the combination of the dynamic model and statistical analysis (e.g., Lu et al, 2017;Baker et al, 2018;Lockwood et al, 2019;Ren and Nie, 2021). Considering the high agreement of STR between the station rainfall gauges and the GPCP grid data, the following analysis of observational data is mainly based on the station data.…”

Analysis and Prediction of Wind Speed Effects in East Asia and the Western Pacific Based on Multi-Source Data

“…In the study area of this paper (0 • N~55 • N), the potential height gradually increases with the increase in latitude. Locally, the wind field at 1000 hPa is directly influenced by the Mongolia-centered air pressure, and the field at 925 hPa is doubly influenced by the East Asian monsoon circulation and the Mongolia-centered air pressure [27]. Except for the areas with sufficient wind power in Figure 5a, the wind power in the western Pacific and Western Pacific areas at the same latitude is also relatively sufficient [28].…”

The U.K.–China Climate Science to Service Partnership

“…Work in CSSP has therefore examined the evolution of major ENSO events and how these are predicted with varying success by different forecast systems, as happened in 2014/15 and 2015/16 (Ineson et al 2018); how predictability varies with ENSO type between central Pacific and east Pacific events (Ren et al 2016(Ren et al , 2018Zhang et al 2019); and how predictions represent the ENSO teleconnection to the monsoon (Hardiman et al 2018). Finally on monsoon predictions, recent work in CSSP demonstrates that skillful predictions of ENSO beyond the seasonal range allow skillful predictions of monsoon rainfall on longer time scales (Dunstone et al 2020) and prediction skill for other variables and in other seasons (Lu et al 2017;Li et al 2018b;Lockwood et al 2019), including for the extratropics (Nie et al 2019(Nie et al , 2020Wu et al 2020). These findings offer further prospects of new and extended climate services.…”