Internal Variability Increased Arctic Amplification During 1980–2022

• Published in: Geophysical Research Letters
• Main Authors: Aodhan J. Sweeney, Qiang Fu, Stephen Po‐Chedley, Hailong Wang, Muyin Wang
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2023
• Subjects: Arctic amplification; machine learning; internal variability; Geophysics. Cosmic physics; QC801-809; Arctic; Climate change; Global warming
• Online Access: https://doi.org/10.1029/2023GL106060; https://doaj.org/article/33f8a74a4c054d3890168e96cb01cc70

Abstract Since 1980, the Arctic surface has warmed four times faster than the global mean. Enhanced Arctic warming relative to the global average warming is referred to as Arctic Amplification (AA). While AA is a robust feature in climate change simulations, models rarely reproduce the observed magnitude of AA, leading to concerns that models may not accurately capture the response of the Arctic to greenhouse gas emissions. Here, we use CMIP6 data to train a machine learning algorithm to quantify the influence of internal variability in surface air temperature trends over both the Arctic and global domains. Application of this machine learning algorithm to observations reveals that internal variability increases the Arctic warming but slows global warming in recent decades, inflating AA since 1980 by 38% relative to the externally forced AA. Accounting for the role of internal variability reconciles the discrepancy between simulated and observed AA.