A Simulation Study on the Extreme Temperature Events of the 20th Century by Using the BCC_AGCM

Extreme temperature events are simulated by using the Beijing Climate Center Atmospheric General Circulation Model (BCC_AGCM) in this paper. The model has been run for 136 yr with the observed ex- ternal forcing data including solar insolation, greenhouse gases, and monthly sea surface temperature (SST). The daily maximum and minimum temperatures are simulated by the model, and 16 indices representing various extreme temperature events are calculated based on these two variables. The results show that the maximum of daily maximum temperature (TXX), maximum of daily minimum (TNX), minimum of daily maximum (TXN), minimum of daily minimum (TNN), warm days (TX90p), warm nights (TN90p), summer days (SU25), tropical nights (TR20), and warm spell duration index (WSDI) have increasing trends during the 20th century in most regions of the world, while the cold days (TX10p), cold nights (TN10p), and cold spell duration index (CSDI) have decreasing trends. The probability density function (PDF) of warm/cold days/nights for three periods of 1881-1950, 1951- 1978, and 1979-2003 is examined. It is found that before 1950, the cold day/night has the largest probability, while for the period of 1979-2003, it has the smallest probability. In contrast to the decreasing trend of cold days/nights, the PDF of warm days/nights exhibits an opposite trend. In addition, the frost days (FD) and ice days (ID) have decreasing trends, the growing season has lengthened, and the diurnal temperature range is getting smaller during the 20th century. A comparison of the above extreme temperature indices between the model output and NCEP data (taken as observation) for 1948-2000 indicates that the mean values and the trends of the simulated indices are close to the observations, and overall there is a high correlation between the simulated indices and the observations. But the simulated trends of FD, ID, growing season length, and diurnal temperature range are not consistent with the observations and their correlations are low or even negative. This indicates that the model is incapable to simulate these four indices although it has captured most indices of the extreme temperature events.