Why April Stands Out: Monthly Impacts of Internal Variability on Arctic Amplification

Observed Arctic amplification (AA), defined as the ratio of Arctic‐mean to global‐mean surface air temperature (SAT) trends, peaks in April, a feature not captured by climate models. Here, we quantify the impact of internal variability on Arctic‐mean SAT trends in March, April, and May from 1980 to 2022 using multiple approaches, including machine learning algorithms—convolutional neural networks (CNNs) and partial least squares (ML‐PLS)—as well as dynamical adjustment and wind‐nudged experiments. Across all approaches, internal variability emerges as a substantial contributor to Arctic warming in these months, and is particularly prominent in April, when internal variability accounts for 40 ± 31% of the observed Arctic‐mean SAT trend, a result consistent across the CNN and wind‐nudged estimates. For each spring month, the linear methods estimate 4%–15% smaller contributions of internal variability than CNN estimates, suggesting that nonlinear approaches may more effectively isolate the internal variability component. Removing internal variability largely reconciles the springtime monthly AA discrepancy between climate models and observations. The strong spatial correlation between the observed‐minus‐multi‐model‐mean SAT trend pattern and that derived from wind‐nudged experiments further suggests that the simulated forced trend pattern is broadly realistic. Finally, the observed springtime monthly circulation–temperature configurations are rare in coupled climate models across all three spring months, suggesting that models have difficulty reproducing the magnitude of Arctic internal variability and/or the observed patterns are a rare manifestation.