Low-level clouds exert a strong control on the Arctic surface energy budget, yet their representation in regional atmospheric models remains a major source of uncertainty. We evaluate the Weather Research and Forecasting (WRF) model against observations from the Norwegian Young Sea Ice Experiment (N-ICE2015), conducted north of Svalbard from polar night to polar day. The analysis focuses on downward surface shortwave (SW^↓) and longwave (LW^↓) radiation under synchronous cloudy conditions to diagnose cloud-related radiative biases. While near-surface meteorology is generally well reproduced, pronounced seasonal radiative errors emerge. A dominance analysis based on a simplifed two-layer emission framework shows cloud emissivity, primarily controlled by liquid water path (LWP), is the leading contributor to LW^↓ errors. During spring transition, the model underestimates cloud occurrence and simulates optically too thin clouds, leading to excessive SW transmission and insuffcient LW trapping. During polar day, a marked negative SW^↓ bias develops. Radiative errors are largest for LWP below 30–40 g.m^-2, where cloud optical properties are highly sensitive to variations in liquid water content. Sensitivity experiments demonstrate that improved representations of sea ice cover and surface albedo reduce polar day SW^↓ biases, while modifying prescribed cloud droplet number concentration alters optical thickness but introduces compensating errors. Clouds diagnosed as surface-decoupled exhibit lower LWP and larger radiative biases, and this regime is overrepresented in the model. These results highlight the need for consistent representation of surface properties, boundary-layer structure and mixed-phase microphysics to improve simulations of Arctic surface radiation.