The effect of orbital sampling is one of the chief uncertainties in satellite–climate model comparisons. In the context of an ongoing activity to make satellite data more accessible for model evaluation (i.e., obs4MIPs), six variables (temperature, specific humidity, ozone, cloud water, cloud cover, and ocean surface wind) associated with six satellite instruments are evaluated for the orbital sampling effect. Comparisons are made between reanalysis and simulated satellite-sampled data in terms of bias and pattern similarity. It is found that the bias introduced by orbital sampling for long-term annual means, monthly climatologies, and monthly means is largely negligible, which is within ~3% of the standard deviation of the three quantities for most fields. The bias for 2-hPa temperature and specific humidity, while relatively large (9–10%), is within the estimated observational uncertainty. In terms of pattern similarity, cloud water and upper level specific humidity are the most sensitive to orbital sampling among the variables considered, with the magnitude of the sampling effect dependent on the spatial resolution—insignificant at 1.25° × 1.25° resolution for both. For all variables considered, orbital sampling effects are not an important consideration for model evaluation at 1.25° × 1.25° resolution. At 0.5° × 0.5°, orbital sampling is potentially important for cloud water and upper level specific humidity when evaluating model long-term annual means and monthly climatologies, and for cloud water when evaluating monthly means, all in terms of pattern similarities. Orbital sampling is not an important factor for evaluating zonal means in call cases considered.