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Publication Date
15 December 2023

Internal Variability Amps Up Arctic Warming

Accounting for natural climate variability using machine learning reconciles the model-versus-observation discrepancy in Arctic amplification from 1980-2022.
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An image of an iceberg

The Arctic has warmed faster than any region on Earth. Identifying the causes of this warming is critical for understanding climate change. Scientists applied a machine learning approach to quantify the influence of internal climate variability on Arctic amplification (AA) – the ratio of Arctic and global surface warming. The researchers found that natural fluctuations in the climate system that arise from internal variability have reduced global warming and accelerated Arctic warming, leading to an inflation of AA which causes disagreements between climate models and observations.


The results of this work can help researchers understand this remarkable AA over 1980-2022, which exceeds AA in most climate model simulations. Separating out internal variability with a machine learning method brings simulated and observed AA into good agreement.


Observations show the Arctic warming four times as quickly as the global mean during the satellite era (1980 to present). AA is a robust feature in state-of-the-art climate models, but model simulations rarely reproduce the observed levels of AA for 1980–2022. Observations of AA yield a ratio of Arctic to global surface warming of 3.9–4.6, which exceeds the magnitude of AA in over 90% of model ensemble members (ranging from 2.1 to 3.6). This discrepancy between models and observations raises a concern about the climate models’ capability to capture the response of the Arctic and/or global climate to external forcings. AA is a result of external forcing (e.g., from human-induced greenhouse gas emissions), but can vary widely because of multidecadal internal variability. Researchers applied a machine learning technique to disentangle the contributions of external forcing and internal variability on global and Arctic warming. After demonstrating that the method could accurately estimate the magnitude of externally forced warming and internal variability in climate models, scientists applied the approach to observational data and determined that internal variability simultaneously reduced global warming while accelerating warming in the Arctic. This manifestation of internal variability effectively increased AA. When the effect of natural, multidecadal variability is removed from the data, observations and model simulations show excellent agreement on the AA attributable to external forcing impacts.

Point of Contact
Hailong Wang
Pacific Northwest National Laboratory
Funding Program Area(s)