Assessment of the response of the East Asian winter monsoon to ENSO‐like SSTAs in three U.S. CLIVAR Project models

• Published in: International Journal of Climatology
• Main Authors: Hao, Xin, Li, Fei, Sun, Jianqi, Wang, Huijun, He, Shengping
• Other Authors: National Natural Science Foundation of China
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2015
• Subjects: aleutian low
• Online Access: http://dx.doi.org/10.1002/joc.4388; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fjoc.4388; https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.4388

ABSTRACT The simulated response of the East Asian winter monsoon ( EAWM ) to ENSO ‐like sea surface temperature anomalies ( SSTAs ) forcing in three U.S. Climate Variability and Predictability (U.S. CLIVAR ) Project models ( GFDL , CCM3 , and CCM3 .5) are evaluated. The time series of the ENSO ‐like pattern significantly correlates with the EAWM index for the period 1949–2004 based on the HadISST SST data set and the NCEP atmospheric reanalysis. Their correlation coefficient is −0.32 at the 95% confidence level. The warm (cold) phase of ENSO ‐like SSTAs is associated with a weakened (strengthened) Siberian High and an eastern‐positioned (western‐positioned) Aleutian Low, anomalous southerly (northerly) wind at 850 hPa and positive (negative) surface air temperature anomalies over East Asia and western North Pacific, while 300 hPa zonal wind anomalies emerge with a reduced (accelerated) East Asian jet stream. Further model examination reveals that the simulated response over East Asia forced by warm (cold) ENSO ‐like SSTAs in CCM3 and the three‐model ensemble corresponds to the weakened (strengthened) EAWM . The spatial correlation coefficients between the three‐model ensemble and observation in the atmospheric circulation and surface air temperature range from 0.15 to 0.80 at the 99% confidence level. This success in simulating relationship between ENSO ‐like SSTAs and EWAM using three‐model ensemble proposes a new idea for the EAWM prediction.