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Abstract Midlatitude prediction on subseasonal timescales is difficult due to the chaotic nature of the atmosphere and often requires the identification of favorable atmospheric conditions that may lead to enhanced skill (“forecasts of opportunity”). Here, we demonstrate that an artificial neural network (ANN) can identify such opportunities for tropical‐extratropical circulation teleconnections within the North Atlantic (40°N, 325°E) at a lead of 22 days using the network's confidence in a given prediction. Furthermore, layer‐wise relevance propagation (LRP), an ANN explainability technique, pinpoints the relevant tropical features the ANN uses to make accurate predictions. We find that LRP identifies tropical hot spots that correspond to known favorable regions for midlatitude teleconnections and reveals a potential new pattern for prediction in the North Atlantic on subseasonal timescales.
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This content will become publicly available on April 28, 2026
A Regimes‐Based Approach to Identifying Seasonal State‐Dependent Prediction Skill
Abstract Subseasonal‐to‐decadal atmospheric prediction skill attained from initial conditions is typically limited by the chaotic nature of the atmosphere. However, for some atmospheric phenomena, prediction skill on subseasonal‐to‐decadal timescales is increased when the initial conditions are in a particular state. In this study, we employ machine learning to identify sea surface temperature (SST) regimes that enhance prediction skill of North Atlantic atmospheric circulation. An ensemble of artificial neural networks is trained to predict anomalous, low‐pass filtered 500 mb height at 7–8 weeks lead using SST. We then use self‐organizing maps (SOMs) constructed from 9 regions within the SST domain to detect state‐dependent prediction skill. SOMs are built using the entire SST time series, and we assess which SOM units feature confident neural network predictions. Four regimes are identified that provide skillful seasonal predictions of 500 mb height. Our findings demonstrate the importance of extratropical decadal SST variability in modulating downstream ENSO teleconnections to the North Atlantic. The methodology presented could aid future forecasting on subseasonal‐to‐decadal timescales.
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- Award ID(s):
- 2019758
- PAR ID:
- 10596348
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 130
- Issue:
- 8
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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