Towards a robust test on North America warming trend and precipitable water content increase

Abstract

An increase in the atmospheric moist content has been generally assumed when the lower‐tropospheric temperature (Tcol) increases, with relative humidity holding steady. Rather than using simple linear regression, we propose a more rigorous trend detection method that considers time series memory. The autoregressive moving‐average (ARMA) parameters for the time series of Tcol, precipitable water vapor (PWAV), and total precipitable water content (PWAT) from the North American Regional Reanalysis data were first computed. We then applied the Monte Carlo method to replicate the ARMA time series samples to estimate the variances of their Ordinary Least Square trends. Student's t tests showed that Tcol from 1979 to 2006 increased significantly; however, PWAV and PWAT did not. This suggests that atmospheric temperature and water vapor trends do not follow the conjecture of constant relative humidity over North America. We thus urge further evaluations of Tcol, PWAV, and PWAT trends for the globe.