An observation based global land monthly mean surface air temperature dataset at 0.5 x 0.5 latitude-longitude resolution for the period from 1948 to the present was developed recently at the Climate Prediction Center, National Centers for Environmental Prediction. This data set is different from some existing land surface air temperature data sets in: (1) using a combination of two large individual data sets of station observations collected from (a) the Global Historical Climatology Network version 2 (7280 stations over the globe and rich data in the older history but collecting new data with some serious time delay) and (b) the Climate Anomaly Monitoring System Network (6158 stations worldwide and stably collecting data from the real time based Global Telecommunication System after 1981), so it can be regularly updated in the near real time with plenty of stations and (2) some unique interpolation methods, such as the anomaly interpolation approach with spatially-temporally varying temperature lapse-rates derived from the observation based Reanalysis for topographic adjustment of the climatology.

The preliminary results show that the quality of this new land surface air temperature analysis is reasonably good and it can capture most common temporal-spatial features in the observed climatology and anomaly fields over both regional and global domains. The study also reveals that there are clear biases between the observed surface air temperature and the existing Reanalysis data sets, and they vary in space and seasons. Originally, this analysis was mainly developed as one of land surface meteorological forcing inputs to derive other land surface variables, such as soil moisture, evaporation, surface runoff, snow accumulation and snow melt, etc. As a byproduct, this monthly mean land surface air temperature data set can also be applied to monitor land surface air temperature variations over global land routinely and to verify the performance of model simulation and prediction.