Explainability, uncertainty & physics
The Impact of Tropical Convective Heating on the Amundsen Sea Low in a Deep-Learning Weather Forecasting Model
In one sentence. Tropical heating experiments show Pangu-Weather reproduces the Matsuno-Gill response and Amundsen Sea Low changes, but overweights Pacific heating versus GCMs.
Abstract
Abstract Data-driven deep-learning models have shown outstanding performance in global weather forecasting. Understanding the dynamic response processes within these models is crucial for comprehending the embedded physical processes and sources of predictability. By applying the classic tropical steady heating experiment to the Pangu-Weather deep-learning model during the austral winter background state, we observe a classic Matsuno–Gill response in the tropics and planetary Rossby waves propagating to the polar regions. The results of the Pangu-Weather model are consistent with those of traditional physics-based general circulation models (GCMs): convective heating forcing in the tropical Atlantic and western Indian Ocean, and convective cooling forcing in the Maritime Continent all deepen the Amundsen Sea Low (ASL), while convective heating forcing in the western Pacific weakens the ASL. The Pangu-Weather model has learned that these tropical basins jointly and linearly regulate the atmospheric circulation around West Antarctica through Rossby waves. However, the Pangu-Weather model overestimates (underestimates) atmospheric responses of heating in the tropical Pacific (Indian and Atlantic) Ocean compared with traditional GCMs, with a much larger contribution of Pacific heating forcing than other basins in changes of the ASL. The physics learned from reanalysis data may be the source of these deep-learning models’ predictability, and the accuracy of extended-range forecasting and the potential of seasonal forecasting using deep-learning models may be influenced by overestimation or underestimation of the role of the tropical Pacific, Indian, and Atlantic Oceans.
Labels
- Topic
- Explainability, uncertainty & physics, Global medium-range NWP, Climate analysis & attribution
- Type
- Evaluation or intercomparison of existing models
- Method
- Other
- Time scale
- 3–15 days
- Domain
- Regional
- Data
- ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1007/s00376-025-4360-6 OpenAlex:W4412960551
- Added
- 2026-10-22
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