Abstract
<jats:title>Abstract</jats:title> <jats:p>Arctic amplification (AA) refers to the enhanced warming of the Arctic relative to the global average due to rising greenhouse gases, measured as the ratio of Arctic-mean to global-mean surface air temperature (SAT) trends. From 1980 to 2022, annual-mean AA reached 4.2 (Arctic defined as north of 70°N). Climate models simulate AA but fail to reproduce its magnitude. Sweeney et al. attributed much of this model–observation discrepancy to internal variability. AA shows seasonality and so does the discrepancy. Spring (March–May) shows the largest gap: Observed AA is 4.2, while the multimodel mean is 2.7. This raises several questions: 1) What role does internal variability play in observed spring AA? 2) How does simulated spring AA compare to observations when internal variability is removed? 3) If internal variability is significant, what mechanisms drive it? To address these, we adapted the machine learning algorithm from Sweeney et al., training on simulated multidecadal spring SAT and sea level pressure (SLP) trend maps. Our results show that internal variability enhanced spring Arctic warming by 37% and reduced global warming by 10%. Removing internal variability reconciles the spring AA discrepancy. The estimated internal contribution to Arctic spring warming is supported by an independent dynamical adjustment approach. We identify an atmospheric circulation pattern in observations associated with this internal warming. Observed internal Siberian SAT and SLP trends follow the simulated SAT–SLP relationship but lie at the distribution’s extreme, suggesting models generally underestimate internal variability unless the observed configuration reflects a rare real-world realization.</jats:p> <jats:sec> <jats:title>Significance Statement</jats:title> <jats:p>Climate models tend to underestimate how much faster the Arctic is warming compared to the globe, especially in spring. We show that this discrepancy is due to internal variability. Using a machine learning approach, we isolate the effect of internal variability on observed Arctic and global spring temperature trends. Once removed, the difference between observed and modeled Arctic amplification in spring largely disappears. We also identify an observed atmospheric circulation pattern linked to this springtime internal warming, but its anomaly exceeds typical model simulations, indicating underestimated internal variability in the models or a rare real-world realization. This highlights the need to better understand the mechanisms behind this internal variability to improve both future projections and model evaluation.</jats:p> </jats:sec>