Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect
Abstract Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surf...
שמור ב:
| Principais autores: | , , |
|---|---|
| פורמט: | Artigo |
| שפה: | Inglês |
| יצא לאור: |
Wiley
2025-02-01
|
| סדרה: | Geophysical Research Letters |
| גישה מקוונת: | https://doi.org/10.1029/2024GL109581 |
| תגים: |
אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!
|