Observed and Simulated Temperature–Humidity Relationships: Sensitivity to Sampling and Analysis

Abstract

Recent studies have demonstrated that the correlation between interannual variations of large-scale average temperature and water vapor is stronger and less height dependent in one GCM than in an objective analysis of radiosonde observations. To address this discrepancy, a GCM with a different approach to cumulus parameterization is used to explore the model dependence of this result, the effect of sampling biases, and the analysis scheme applied to the data. It is found that the globally complete data from the two GCMs produce similar patterns of correlation despite their fundamentally different moist convection schemes. While this result concurs with earlier studies, it is also shown that this apparent model?observation discrepancy is significantly reduced (although not eliminated) by sampling the GCM in a manner more consistent with the observations, and especially if the objective analysis is not then applied to the sampled data. Furthermore, it is found that spatial averages of the local temperature?humidity correlations are much weaker, and show more height dependence, than correlations of the spatially averaged quantities for both model and observed data. The results of the previous studies are thus inconclusive and cannot therefore be interpreted to mean that GCMs greatly overestimate the water vapor feedback.