Intraseasonal variations in winter surface air temperature over China and its prediction skill in ECMWF System5

Abstract Based on the daily average temperature data of 2374 stations in China from 1993 to 2019 and the monthly average temperature data of ECMWF System5 in winter, this paper analyses the intraseasonal variations of winter surface air temperature (SAT) over China from 1993 to 2019 by using seasonal empirical orthogonal function decomposition (S-EOF) and the prediction skill differences of the first three dominant modes in ECMWF System5. The first mode (S-EOF1) is characterized by out-of-phase changes in SAT anomalies (SATA) between December and January. Both the Siberian high (SH) and the central Pacific El Niño-Southern Oscillation (CP ENSO) can impact S-EOF1. The second mode (S-EOF2) is characterized by consistency in warm anomalies over the Northeast and Qinghai-Tibet Plateau’s variation, while the other areas are characterized by the out-of-phase change in SATA. S-EOF2 is closely related to Arctic Oscillation (AO). The third mode is characterized by alternating changes over three months in winter, which is related to the Pacific meridional mode (PMM) and the North Atlantic SST tripole (NAT). Comparing the ECMWF System5 prediction skill for the first three leading modes for the intraseasonal variations in winter SAT, the prediction skill is not high. The model did not predict the extent of warm anomalies and the intraseasonal fluctuation in S-EOF1, which may be due to the poor prediction skills for the range and intensity of the CP ENSO and the SH. The second mode has the highest prediction skill among the three modes. For the prediction skill of AO is good. The model can capture the intraseasonal reverse SATA between December and January but did not capture the reverse SATAs between January and February. Because the model does not predict obvious PMM and NAT well.