Erroneous Relationships among Humidity and Cloud Forcing Variables in Three Global Climate Models

Abstract

Abstract Links are examined between time-averaged cloud radiative properties, particularly the longwave and shortwave components of cloud radiative forcing (CRF), and properties of the long-term averages of atmospheric soundings, in particular upper-tropospheric humidity (UTH), lower-tropospheric precipitable water (PW), and static stability (SS). The joint distributions of moisture measures and the composite or conditional mean CRF for different moisture and stability combinations are computed. This expands on previous studies that have examined cloud properties versus vertical velocity and surface temperature. These computations are done for satellite observations and for three representative coupled climate models from major modeling centers. Aside from mean biases reported previously, several departures are identified between the modeled and observed joint distributions that are qualitative and significant. Namely, the joint distribution of PW and UTH is very compact in observations but less so in models, cloud forcings are tightly related to PW in the data but to UTH in the models, and strong negative net CRF in marine stratocumulus regions occurs only for high SS and low UTH in the data but violates one or both of these restrictions in each of the models. All three errors are preliminarily interpreted as symptoms of inadequate dependence of model convective development on ambient humidity above the boundary layer. In any case, the character of the errors suggests utility for model testing and future development. A set of scalar metrics for quantifying some of the problems is presented; these metrics can be easily applied to standard model output. Finally, an examination of doubled-CO2 simulations suggests that the errors noted here are significantly affecting cloud feedback in at least some models. For example, in one model a strong negative feedback is found from clouds forming in model conditions that never occur in the observations.