A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series

The relationship between satellite land surface temperature (LST) and ground-based observations of 2 m air temperature (T-2m) is characterized in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along-Track Scanning Radiometer series, which has been produced within the European Space Agency GlobTemperature project (http://www.globtemperature.info/).Global LST-T-2m differences are analyzed with respect to location, land cover, vegetation fraction, and elevation, all of which are found to be important influencing factors. LSTnight (similar to 10 P.M. local solar time, clear-sky only) is found to be closely coupled with minimum T-2m (T-min, all-sky) and the two temperatures generally consistent to within +/- 5 degrees C (global median LSTnight-T-min = 1.8 degrees C, interquartile range = 3.8 degrees C). The LSTday (similar to 10 A.M. local solar time, clear-sky only)-maximum T-2m (T-max, all-sky) variability is higher (global median LSTday-T-max = -0.1 degrees C, interquartile range = 8.1 degrees C) because LST is strongly influenced by insolation and surface regime. Correlations for both temperature pairs are typically >0.9 outside of the tropics. The monthly global and regional anomaly time series of LST and T-2m-which are completely independent data sets-compare remarkably well. The correlation between the data sets is 0.9 for the globe with 90% of the CDR anomalies falling within the T-2m 95% confidence limits. The results presented in this study present a justification for increasing use of satellite LST data in climate and weather science, both as an independent variable, and to augment T-2m data acquired at meteorological stations. Plain Language Summary Surface temperatures over land have traditionally been measured at weather stations. There are many parts of the globe where there are very few stations, for example across much of Africa and Antarctica, leading to gaps in surface temperature datasets, affecting our understanding of how surface temperatures are changing, and the impacts of extreme events (e.g. heat waves). Satellites can provide temperature observations across the globe. However, satellites measure how hot the land surface temperature (LST; including the uppermost parts of e.g. trees, buildings) are to touch, whereas weather stations measure the air temperature just above the surface (T-2m). Additionally, satellite LST data may only be available in cloud-free conditions. This paper describes a comparison between T-2m and a new 17-year LST dataset. It demonstrates that LST and T-2m are often strongly related, particularly at night, but the exact relationship depends on location, land surface type, vegetation and elevation. A time-series analysis shows that the change in LST and T-2m with time is remarkably similar; giving confidence in the T-2m trends reported elsewhere in the climate change literature, as these datasets are independent. The results of this study demonstrate that LST can usefully augment T-2m observations in climate and weather science.