Homogenized Daily Mean/Maximum/Minimum Temperature Series for China from 1960-2008

Inhomogeneities in the daily mean/maximum/ minimum temperature （Tm/Tmax/Tmin） series from 1960- 2008 at 549 National Standard Stations （NSSs） in China were analyzed by using the Multiple Analysis of Series for Homogenization （MASH） software package. Typical biases in the dataset were illustrated via the cases of Beijing （B J）, Wutaishan （WT）, Urumqi （UR） and Henan （HN） stations. The homogenized dataset shows a mean warming trend of 0.261/0.193/0.344℃/decade for the annual series of Tm/Tmax/Tmin, slightly smaller than that of the original dataset by 0.006/0.009/0.007℃/decade. However, considerable differences between the adjusted and original datasets were found at the local scale. The adjusted Tmin series shows a significant warming trend almost everywhere for all seasons, while there are a number of stations with an insignificant trend in the original dataset. The adjusted Tm data exhibit significant warming trends annually as well as for the autumn and winter seasons in northern China, and cooling trends only for the summer in the middle reaches of the Yangtze River and parts of central China and for the spring in southwestern China, while the original data show cooling trends at several stations for the annual and seasonal scales in the Qinghai, Shanxi, Hebei, and Xinjiang provinces. The adjusted Tmax data exhibit cooling trends for summers at a number of stations in the mid-lower reaches of the Yangtze and Yellow Rivers and for springs and winters at a few stations in southwestern China, while the original data show cooling trends at three/four stations for the annual/autumn periods in the Qinghai and Yunnan provinces. In general, the number of stations with a cooling trend was much smaller in the adjusted Tm and Tmax dataset than in the original dataset. The cooling trend for summers is mainly due to cooling in August. The results of homogenization using MASH appear to be robust; in particular, different groups of stations with consideration of elevation led to minor effects in the results.

Variations of Frost-free Period and Its Impact on Grain Yields in Henan Province during 1961-2013

The aim of this study was to investigate the responses of frost dates to global warming and its influences on grain yields. In this study, based on the frost date series defined by daily minimum ground temperature, the spatial and temporal characteristics of first frost date （FFD）, last frost date （LFD） and frost-free period （FFP） were analyzed. The impact of extending FFP on major crop yields was also studied. The results were as follows： FFD showed a significantly delaying trend of 2.2 d/10 y, and LFD presented an advancing trend of 2.4 d/10 y. FFP extended at a rate of 4.5 d/10 y due to the later FFD and earlier LFD. The most obvious trend of FFD was in westem Henan, while the most significant trend of LFD and FFP oc- curred in south central parts of the study area. However, in eestem region, the trends of FFD, LFD and FFP were not so obvious. Major crop yield showed a sig- nificant correlation with frost-free period for Henan during 1961-2013. The yields of grain, rice, wheat, and maize increased by 79.5, 90.0, 79.5 and 70.5 kg/hm2 with FFP extending by one day.

Pressure and Temperature Feasibility of NCEP/NCAR Reanalysis Data at Mt.Everest

Mt.Everest (27°54' N,86°54' E),the highest peak,is often referred to as the earth's 'third' pole,at an elevation of 8844.43 m. Due to the difficult logistics in the extreme high elevation regions over the Himalayas,observational meteorological data are very few on Mt. Everest. In 2005,an automatic weather station was operated at the East Rongbuk glacier Col of Mt. Everest over the Himalayas. The observational data have been compared with the reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR),and the reliability of NCEP/NCAR reanalysis data has been investigated in the Himalayan region,after the reanalyzed data were interpolated in the horizontal to the location of Mt. Everest and in the vertical to the height of the observed sites. The reanalysis data can capture much of the synoptic-scale variability in temperature and pressure,although the reanalysis values are systematically lower than the observation. Furthermore,most of the variability magnitude is,to some degree,underestimated. In addition,the variation extracted from the NCEP/NCAR reanalyzed pressure and temperature prominently appears one-day lead to that from the observational data,which is more important from the standpoint of improving the safety of climbers who attempt to climb Mt. Everest peak.