Diurnal to Decadal Variability in Land Surface and Air Temperature Gradient from 2002 to 2022 over the Contiguous United States

Abstract

Abstract The surface and air temperature gradient (TS00 − Tair) drives the development of the convective boundary layer and the occurrence of clouds and precipitation. However, its variability is still poorly understood due to the lack of high-quality observations. This study fills in this gap by investigating the diurnal to decadal variability in TS00 − Tair from 2002 to 2022 based on hourly observations collected at over 100 stations of the U.S. Climate Reference Network. It is found that TS00 − Tair reaches its maximum at noon with an average of 6.85°C over the contiguous United States, which decreases to 4.28°C when the soil moisture exceeds 30%. The daily minimum of TS00 − Tair has an average of −2.08°C, which generally occurs in the early evening but is postponed as the cloud fraction decreases. Moreover, while existing studies have used the near-surface soil temperature, such as the 5-cm soil temperature (TS05), to calculate TS05 − Tair, we find that TS00 − Tair and TS05 − Tair have opposite diurnal cycles, and their amplitudes differed drastically. The daily minimum of TS00 − Tair has a significant decreasing trend (−0.50° ± 0.007°C decade−1) from 2002 to 2022 due to Tair increasing at a higher rate than TS00 during the nighttime. The occurrence frequency of near-surface stable conditions (TS00 − Tair < 0) increases significantly, and the frequency of unstable conditions (TS00 − Tair > 0) decreases notably throughout the year except for winter. When it is stable, the magnitude of TS00 − Tair tends to decrease while the TS00 − Tair tends to increase when it is unstable, which is consistent with the drying condition caused by the precipitation deficit. This study provides the first observational evidence on how TS00 − Tair responds to warming. Significance Statement The impact of global warming on surface-air temperature gradients is a crucial scientific issue that needs to be addressed. These gradients determine changes in cloud and precipitation, affecting water resources. However, traditional surface temperature measurements from weather stations are of high uncertainty due to direct exposure to the insolation. Satellite retrieval of surface temperature is limited by the availability of clear sky conditions, with low accuracy for temperature gradient calculations. Despite their importance, high-accuracy data of land surface temperature are still lacking. To address this issue, the U.S. Climate Reference Network (USCRN) uses infrared radiometers to continuously monitor surface temperature with high accuracy and sampling frequency. This study reports on surface-air temperature gradients at more than 100 U.S. stations, providing insight into diurnal to decadal variability over the contiguous United States. The study also highlights the significant difference between the land surface temperature–air temperature gradient and soil temperature–air temperature gradient.