2024-01-29 パシフィック・ノースウェスト国立研究所(PNNL)
◆この研究の結果は、観測されたAAが1980年から2022年までに観測された通りで、多くの気候モデルのシミュレーションを上回っていることを理解するのに役立ちます。機械学習を用いて内部変動を分離すると、シミュレートされたAAと観測されたAAが良好な一致を示します。
＜関連情報＞
- https://www.pnnl.gov/publications/machine-learning-assesses-climate-variability-reconciles-arctic-amplification
- https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023GL106060
1980-2022年における北極域の増幅を増大させた内部変動 Internal Variability Increased Arctic Amplification During 1980–2022
Aodhan J. Sweeney, Qiang Fu, Stephen Po-Chedley, Hailong Wang, Muyin Wang
Geophysical Research Letters Published: 15 December 2023
DOI:https://doi.org/10.1029/2023GL106060
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.
Key Points
- Internally generated and externally forced temperature trends over the Arctic and globe can be partitioned using machine learning methods
- Internal variability has enhanced Arctic warming while damping global warming over 1980-2022
- Accounting for internal variability in observations reconciles discrepancies between simulated and observed Arctic Amplification
Plain Language Summary
The Arctic has been warming four times as quickly as the global mean since 1980. This so-called Arctic Amplification (AA) has unprecedented impacts on Arctic environments and livelihoods. AA is robustly simulated by climate models, but simulations rarely reproduce the observed levels of AA for 1980–2022. This may be due to a model misrepresentation of the Arctic’s sensitivity to increasing greenhouse gases. Another possibility is that the large, observed value of AA is inflated by natural fluctuations in the climate system. Here, we use machine learning to quantify the contribution of natural fluctuations to observed AA. We show that natural fluctuations have inflated AA by 38%, and thus reconcile model-observation differences and suggest that the observed large AA over 1980 to present would not persist into the future.
