Abstract
As one of the most principal meteorological factors to affect global climate change and human sustainable development, temperature plays an important role in biogeochemical and hydrosphere cycle. To date, there are a wide range of temperature data sources and only a detailed understanding of the reliability of these datasets can help us carry out related research. In this study, the hourly and daily near-surface air temperature observations collected at national automatic weather stations (NAWS) in China were used to compare with the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) and the Global Land Data Assimilation System (GLDAS), both of which were developed by using the advanced multi-source data fusion technology. Results are as follows. (1) The spatial and temporal variations of the near-surface air temperature agree well between CLDAS and GLDAS over major land of China, except that spatial details in high mountainous areas were not sufficiently displayed in GLDAS; (2) The near-surface air temperature of CLDAS were more significantly correlated with observations than that of GLDAS, but more caution is necessary when using the data in mountain areas as the accuracy of the datasets gradually decreases with increasing altitude; (3) CLDAS can better illustrate the distribution of areas of daily maximum above 35 °C and help to monitor high temperature weather. The main conclusion of this study is that CLDAS near-surface air temperature has a higher reliability in China, which is very important for the study of climate change and sustainable development in East Asia.
Highlights
Global warming and climate change are currently the world’s most pressing issues [1], which is increasing the risk of extreme events, resulting in social and economic challenges [2]
(1) The spatial and temporal variations of the near-surface air temperature agree well between CLDAS and Global Land Data Assimilation System (GLDAS) over major land of China, except that spatial details in high mountainous areas were not sufficiently displayed in GLDAS; (2) The near-surface air temperature of CLDAS were more significantly correlated with observations than that of GLDAS, but more caution is necessary when using the data in mountain areas as the accuracy of the datasets gradually decreases with increasing altitude; (3) CLDAS can better illustrate the distribution of areas of daily maximum above 35 ◦C and help to monitor high temperature weather
The results show that the near-surface air temperature data quality of European Center for Medium-Range Weather Forecasts (ECMWF) was better than that of Global Forecast System (GFS) and Japan Meteorological Agency (JMA) in general, but from the assessment indicators, the performance of ECMWF, GFS and JMA in China was not as good as that of CLDAS in this study
Summary
Global warming and climate change are currently the world’s most pressing issues [1], which is increasing the risk of extreme events, resulting in social and economic challenges [2]. The relationship between global climate change and human sustainable development, corporate social responsibility, corporate governance, business performance and the excellence model are closely. The conventional station-based measurements can provide accurate values of the measured variables, these measurements can only present information on local scale [6,7]. They are unable to adequately depict spatial variations given the limited number and locations of stations [8]. Numerical model data can perform well in spatial-temporal simulation, but due to the influence of parameterization scheme, the simulation results often have certain uncertainty [9]
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