Land Surface Air Temperature Data Are Considerably Different Among BEST-LAND, CRU-TEM4v, NASA-GISS, and NOAA-NCEI

Abstract

Several groups routinely produce gridded land surface air temperature (LSAT) data sets using station measurements to assess the status and impact of climate change. The Intergovernmental Panel on Climate Change Fifth Assessment Report suggests that estimated global and hemispheric mean LSAT trends of different data sets are consistent. However, less attention has been paid to the intercomparison at local/regional scales, which is important for local/regional studies. In this study we comprehensively compare four data sets at different spatial and temporal scales, including Berkley Earth Surface Temperature land surface air temperature data set (BEST-LAND), Climate Research Unit Temperature Data Set version 4 (CRU-TEM4v), National Aeronautics and Space Administration Goddard Institute for Space Studies data (NASA-GISS), and data provided by National Oceanic and Atmospheric Administration National Center for Environmental Information (NOAA-NCEI). The mean LSAT anomalies are remarkably different because of the data coverage differences, with the magnitude nearly 0.4°C for the global and Northern Hemisphere and 0.6°C for the Southern Hemisphere. This study additionally finds that on the regional scale, northern high latitudes, southern middle-to-high latitudes, and the equator show the largest differences nearly 0.8°C. These differences cause notable differences for the trend calculation at regional scales. At the local scale, four data sets show significant variations over South America, Africa, Maritime Continent, central Australia, and Antarctica, which leads to remarkable differences in the local trend analysis. For some areas, different data sets produce conflicting results of whether warming exists. Our analysis shows that the differences across scales are associated with the availability of stations and the use of infilling techniques. Our results suggest that conventional LSAT data sets using only station observations have large uncertainties across scales, especially over station-sparse areas. In developing future LSAT data sets, the data uncertainty caused by limited and unevenly distributed station observations must be reduced.