Disentangling forced and internal components in observed climate evolution using spatiotemporal deep learning
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arXiv: 2610.28219v1 · Summary page
Why cover this story this week?
Untangling climate change from climate variability
How much of a regional climate trend reflects external forcing, and how much comes from the climate system’s own fluctuations? A new preprint tackles that question with a deep-learning network that learns both spatial patterns and their evolution over time. Trained and tested on large ensembles of climate-model simulations, it outperformed existing approaches in those tests. Applied to observations, it suggests that internal variability dominated the development of the La Niña-like Pacific warming pattern and the associated precipitation decline over southwestern North America since 1980. It also finds that variability has modulated Arctic amplification around a forced response in which the Arctic warms about three times as much as the global average.
The distinction matters because observed trends need not continue at the same pace when internal fluctuations change direction. The analysis also identifies differences between its estimated forced response and climate-model simulations: the models produce stronger Northern Hemisphere warming, weaker intensification of subtropical high-pressure systems and smaller declines in land precipitation. These are potentially useful clues for model evaluation, although the method’s success in simulated climates does not by itself establish that its separation of the real-world record is correct.
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