ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler
<p>The El Niño–Southern Oscillation (ENSO) is an extremely complicated ocean–atmosphere coupling event, the development and decay of which are usually modulated by the energy interactions between multiple physical variables. In this paper, we design a multivariate air–sea coupler (ASC) based o...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/3f5cd748f59e4abd8d25a37684f3fc85 |
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| Sumario: | <p>The El Niño–Southern Oscillation (ENSO) is an extremely complicated ocean–atmosphere
coupling event, the development and decay of which are usually modulated by
the energy interactions between multiple physical variables. In this paper,
we design a multivariate air–sea coupler (ASC) based on the graph using
features of multiple physical variables. On the basis of this coupler, an
ENSO deep learning forecast model (named ENSO-ASC) is proposed, whose
structure is adapted to the characteristics of the ENSO dynamics, including
the encoder and decoder for capturing and restoring the multi-scale spatial–temporal
correlations, and two attention weights for grasping the different air–sea
coupling strengths on different start calendar months and varied effects of
physical variables in ENSO amplitudes. In addition, two datasets modulated
to the same resolutions are used to train the model. We firstly tune the
model performance to optimal and compare it with the other state-of-the-art
ENSO deep learning forecast models. Then, we evaluate the ENSO forecast
skill from the contributions of different predictors, the effective lead
time with different start calendar months, and the forecast spatial
uncertainties, to further analyze the underlying ENSO mechanisms. Finally, we
make ENSO predictions over the validation period from 2014 to 2020.
Experiment results demonstrate that ENSO-ASC outperforms the other models.
Sea surface temperature (SST) and zonal wind are two crucial predictors. The
correlation skill of the Niño 3.4 index is over 0.78, 0.65, and 0.5 within the lead
time of 6, 12, and 18 months respectively. From two heat map analyses, we also discover the
common challenges in ENSO predictability, such as the forecasting skills
declining faster when making forecasts through June–July–August and the
forecast errors being more likely to show up in the western and central tropical
Pacific Ocean in longer-term forecasts. ENSO-ASC can simulate ENSO with
different strengths, and the forecasted SST and wind patterns reflect an
obvious Bjerknes positive feedback mechanism. These results indicate the
effectiveness and superiority of our model with the multivariate air–sea
coupler in predicting ENSO and analyzing the underlying
dynamic mechanisms in a sophisticated way.</p> |
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