Evaluation of radiative heating rate profiles in eight GCMs using A-train satellite observations

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Abstract
In this study, we take advantage of two modeling experiments and A-train satellite observations to characterize the impact of cloud biases in the vertical distribution of radiative heating rates in eight general circulation models General Circulation Models (GCMs). We compare the modeled vertical distribution of clouds against the GCM-Oriented Cloud-Aerosols Lidar and Infrared Pathfinder Satellite Observations Cloud Product (CALIPSO-GOCCP) using a simulator approach. Although the overall pattern of modeled zonal cloud frequency profiles is relatively good (r=0.92 for the multi-model mean), we show two main systematic biases in the cloud frequency profiles: a positive bias above 7km (up to 10%), particularly in the tropics; and a negative bias below 3km (up to −10%), which reaches a maximum over the stratocumulus cloud regions. Using radiative heating rate profiles calculated with constraints from CloudSat, CALIPSO and other satellite observations, we show that the excess of clouds in the upper troposphere (>7km) results in excess infrared and solar heating in the vicinity of the clouds as well as more infrared heating for the entire column below the cloud. On the other hand, the lack of clouds in the lower troposphere reduces the infrared cooling near the missing cloud levels and increases the absorption of solar radiation by water vapor below. The global radiative heating rate between 50°S and 50°N is too warm in the models (−0.81K/day vs. −1.01K/day). The representation of clouds in GCMs remains challenging, but reducing the cloud biases would lead to an improvement of the heating rate profiles, which in turn would help in improving other aspects of models’ simulations such as the dynamics, cloud feedbacks and surface-atmosphere interactions.

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