Improving Arctic surface radiation estimation using a nonlinear perturbation model with a fused multi-satellite cloud fraction dataset

Abstract. Arctic has been undergoing rapid climate change, where radiative processes are key controlling factors. However, cloud-related uncertainties remain the primary barrier to accurately estimating the radiation budget. Strong coupling between clouds and other variables complicates the isolation of cloud-related impacts, and the linear assumptions in traditional models further restrict attribution of radiation changes to cloud-related influences. This study introduces an artificial neural network model that emulates radiative components typically represented in radiative transfer or climate models. Without relying on linear assumptions, the model directly quantifies the influence of cloud fraction (CF) on radiation. Using a more accurate CF dataset, we refined the monthly downwelling shortwave radiation (DSR) estimates from Clouds and the Earth's Radiant Energy System (CERES) SYN products and further estimated all-wave net radiation (NR) from the corrected DSR. Validation against ground-based observations confirmed that the CF-corrected DSR effectively mitigated the overestimation in CERES DSR, reducing biases by up to 23 W m−2. At sites where CF underestimation exceeded 25 %, the monthly-mean bias decreased from 25.70 to 4.88 W m−2, with RMSE reduced from 40.36 to 32.60 W m−2. The estimated monthly NR also improved markedly (RMSE reduced from 34.88 to 28.90 W m−2). Under large CF underestimation (>30 %), the CERES NR nearly failed (R2=0.0182), whereas NR derived from CF-corrected DSR retained reasonable agreement (R2=0.5411). Importantly, this work produces a new NR dataset with enhanced accuracy over the Arctic, offering direct value for studies of surface energy balance, climate feedbacks, and long-term variability.