Longwave radiative heating, which has a pronounced impact on climate prediction, is represented in Global Climate Models (GCMs) by an algorithm which converts model input parameters to heating rates. Since each GCM has a unique longwave radiative heating parameterization, an intercomparison of seven frequently used algorithms designed to assess their variability to input data was performed. The algorithms' heating rate calculation, which is perhaps the most important aspect of the parameterization in that it is a principal part which the GCM actually incorporates into its climate prediction, was evaluated by subjecting each to identical input parameters and comparing the resulting output. It should be noted that the overall shape of a given heating rate profile depends strongly on the depth of the model layers over which the average conditions were determined. But since GCMs ultimately see the heating rates only at model levels, this aspect of heating rate calculations is transparent to the models themselves. For clear sky conditions, the algorithms were tested with a diverse range of input data taken from different geographic locations and seasons and with various distributions of vertical levels. Analysis of the results from these clear sky experiments indicated that heating rate profiles generated by the algorithms were similar, with maximum variations of the order of 0.5 ¿K/d. The differences in algorithm output became substantially more pronounced when clouds at one or more levels with varying thickness were introduced into the input conditions, particularly if the clouds were thicker than one model level. Indeed, for some cloud configurations the resulting profiles of heating rates appear to have no correspondence whatsoever to one another. How important these differences are to ultimate GCM climate predictions is currently under study. ¿ American Geophysical Union 1996