分类： 地球科学 >> 地球科学史 提交时间： 2020-10-20 合作期刊: 《干旱区科学》
摘要： Playing an important role in global warming and plant growth, relative humidity (RH) has profound impacts on production and living, and can be used as an integrated indicator for evaluating the wet-dry conditions in the arid and semi-arid area. However, information on the spatial-temporal variation and the influencing factors of RH in these regions is still limited. This study attempted to use daily meteorological data during 1966–2017 to reveal the spatial-temporal characteristics of RH in the arid region of Northwest China through rotated empirical orthogonal function and statistical analysis method, and the path analysis was used to clarify the impact of temperature (T), precipitation (P), actual evapotranspiration (ETa), wind speed (W) and sunshine duration (S) on RH. The results demonstrated that climatic conditions in North Xinjiang (NXJ) was more humid than those in Hexi Corridor (HXC) and South Xinjiang (SXJ). RH had a less significant downtrend in NXJ than that in HXC, but an increasingly rising trend was observed in SXJ during the last five decades, implying that HXC and NXJ were under the process of droughts, while SXJ was getting wetter. There was a turning point for the trend of RH in Xinjiang, which occurred in 2000. Path analysis indicated that RH was negatively correlated to T, ETa, W and S, but it increased with increase of P. S, T and W had the greatest direct effects on RH in HXC, NXJ and SXJ, respectively. ETa was the factor which had the greatest indirect effect on RH in HXC and NXJ, while T was the dominant factor in SXJ.
分类： 地球科学 >> 地球科学史 提交时间： 2020-10-20 合作期刊: 《干旱区科学》
摘要： Short-term climate reconstruction, i.e., the reproduction of short-term (several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5 (CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit (CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region (TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature (MAT) and monthly accumulated precipitation (MAP) in the TMR using the delta, physical scaling (SP) and artificial neural network (ANN) methods. Performance and uncertainty during the calibration (1971–1999) and verification (1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis (RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty (positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty (negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850 (or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term (several decades) regional climate reconstruction and longer-term (100 a or more) assessments of regional climate change.
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