EGU2020-13084
https://doi.org/10.5194/egusphere-egu2020-13084
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Predictability of the Wintertime 500 hPa Geopotential Height over Ural-Siberia in the NCEP Climate Forecast System

Meng Zou
Meng Zou
  • Sun Yat-sen University, China (823010526@qq.com)

Using hindcast and forecastdata from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2)for the period 1982-2017, we comprehensively assess the predictability of the climatology, interannual variability, and dominant modes of the wintertime 500 hPa geopotential height over Ural-Siberia (40-80°Nand 30-100°E). Although the climatic mean 500 hPa heightover Ural-Siberia simulated by NCEP CFSv2has a negative bias, especially over the eastern part of the region, NCEP CFSv2 well predicts the spatial distribution of the two major modes(EOF1 and EOF2) over this region 2 months in advance.The forecasting skill of the principal component (PC) of the two major modes,PC1 (PC2), is highest1 (0) month in advance, where the linear correlation coefficient between the predicted and observed time series reaches +0.36 (+0.67), exceeding the 95% confidence level. Conversely, the forecasting skill of PC1 (PC2) is very low 0 (1) month in advance. The main reason for the poorer(better) prediction of PC1 0 (1) month in advance is associated with a less (more) accurate response of the Eurasian teleconnection to SST anomalies over the southwestern Atlantic. For PC2, the better (poorer) prediction of PC2 0 (1) month in advance may be due to more (less) accurate responses of the stratospheric polar vortex and the Scandinavian teleconnection to the dipole SST anomalies over the North Pacific. These results are useful for evaluating the predictability of the East Asian winter climate.

How to cite: Zou, M.: Predictability of the Wintertime 500 hPa Geopotential Height over Ural-Siberia in the NCEP Climate Forecast System, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13084, https://doi.org/10.5194/egusphere-egu2020-13084, 2020