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YANG Hongqing, FAN Ke, TIAN Baoqiang, et al. 2021. Why is the November Siberian High Intensity More Predictable by NCEP-CFSv2 Model? [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(4): 697−712. DOI: 10.3878/j.issn.1006-9895.2009.20106
Citation: YANG Hongqing, FAN Ke, TIAN Baoqiang, et al. 2021. Why is the November Siberian High Intensity More Predictable by NCEP-CFSv2 Model? [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(4): 697−712. DOI: 10.3878/j.issn.1006-9895.2009.20106

Why is the November Siberian High Intensity More Predictable by NCEP-CFSv2 Model?

  • As a critical system of the East Asian winter monsoon, the Siberian high has an important impact on the winter weather and climate anomalies in Eurasia. Using the National Center for Environment Prediction-Climate Forecast System version 2 (NCEP-CFSv2), this study comprehensively evaluates the seasonal and monthly prediction of the Siberian high intensity during the winter time (November to February). Results show that the NCEP-CFSv2 model can skillfully predict the Siberian high intensity only in November, the reasons for which are that the local thermal process, dynamic process, and Siberian snow cover extent mainly affect the Siberian high intensity in November. In terms of the thermal process, the NCEP-CFSv2 can better reproduce the Siberian high intensity in November and its related surface soil temperature, upward long-wave radiation, and other thermal factors in Siberia. In terms of the dynamic process, the NCEP-CFSv2 can better reproduce the Siberian high strength in November, which is associated with the low-level tropospheric divergent circulation and the sinking movement of the upper and middle layers in the Siberian area. The model also reproduces the relationship between the snow cover extent over Siberia and the Siberian high intensity in November. The thermodynamic process of the Siberian high and snow cover extent in the area are predictability sources of the Siberian High intensity in November, and the NCEP-CFSv2 can reasonably reproduce these predictability sources in November.
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