Internal Variability Increased Arctic Amplification During 1980–2022

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 magn...

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Bibliographic Details
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
Description
Summary: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.

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