The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system. A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise-to-noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training dataset is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere-Ocean General Circulation Model (AOGCM) simulations. The neural network utilized for training is a U-Net, a widely adopted convolutional network primarily designed for image segmentation. To assess performance, comparisons are made between outputs from two single-model initial-condition large ensembles (SMILEs), the ensemble mean, and the U-Net’s predictions. The U-Net reduces internal variability by a factor of four, although notable discrepancies are observed at the regional scale. While demonstrating effective filtering of the El Niño Southern Oscillation, the U-Net encounters challenges in areas dominated by forced variability, such as the Arctic sea ice retreat region. This methodology holds potential for extension to other physical variables, facilitating insights into the enduring changes triggered by external forcings over the long term.