Because the Arctic climate is particuarly sensitive to cloud-radiative interactions, it is essential that climate models represent Arctic clouds realistically. Observations of clouds and radiative fluxes for the North Slope of Alaska are available from the Department of Energy's Atmospheric Radiation Measurement (ARM) program. Reanalysis models also calculate quantities (e.g. cloud and radiative variables) that are needed for forcing and validation purposes, especially over data-sparse areas. In this study, ARM measurements and reanalysis output for the mid-season months of January, April, July, and October are used to show that boundary layer clouds are not only the most common type of cloud observed on the North Slope of Alaska, but they are also the most poorly simulated by the reanalysis. Fog is associated with large overestimates of the cloud fraction during the cold season, while fog and low stratus are associated with large underestimates during the warm season. These results were synthesized with other data to produce a comprehensive picture of the synoptic conditions that are most commonly present when the reanalysis fails to simulate the cloud fraction. In light of the above findings, a set of qualitative diagnoses are offered regarding the specific cause for error in the model.
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