Abstract
This paper analyzes the status of existing resources through extensive research and international cooperation on the basis of four typical
global monthly surface temperature datasets including the climate research dataset of the University of East Anglia (CRUTEM3), the dataset of
the U.S. National Climatic Data Center (GHCN-V3), the dataset of the U.S. National Aeronautics and Space Administration (GISSTMP), and
the Berkeley Earth surface temperature dataset (Berkeley). China's first global monthly temperature dataset over land was developed by integrating
the four aforementioned global temperature datasets and several regional datasets from major countries or regions. This dataset contains
information from 9,519 stations worldwide of at least 20 years for monthly mean temperature, 7,073 for maximum temperature, and 6,587 for
minimum temperature. Compared with CRUTEM3 and GHCN-V3, the station density is much higher particularly for South America, Africa,
and Asia. Moreover, data from significantly more stations were available after the year 1990 which dramatically reduced the uncertainty of the
estimated global temperature trend during 1990e2011. The integrated dataset can serve as a reliable data source for global climate change
research.
Abstract
This paper analyzes the status of existing resources through extensive research and international cooperation on the basis of four typical
global monthly surface temperature datasets including the climate research dataset of the University of East Anglia (CRUTEM3), the dataset of
the U.S. National Climatic Data Center (GHCN-V3), the dataset of the U.S. National Aeronautics and Space Administration (GISSTMP), and
the Berkeley Earth surface temperature dataset (Berkeley). China's first global monthly temperature dataset over land was developed by integrating
the four aforementioned global temperature datasets and several regional datasets from major countries or regions. This dataset contains
information from 9,519 stations worldwide of at least 20 years for monthly mean temperature, 7,073 for maximum temperature, and 6,587 for
minimum temperature. Compared with CRUTEM3 and GHCN-V3, the station density is much higher particularly for South America, Africa,
and Asia. Moreover, data from significantly more stations were available after the year 1990 which dramatically reduced the uncertainty of the
estimated global temperature trend during 1990e2011. The integrated dataset can serve as a reliable data source for global climate change
research.
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