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:	B. Mu, B. Qin, S. Yuan
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/3f5cd748f59e4abd8d25a37684f3fc85
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spelling	oai:doaj.org-article:3f5cd748f59e4abd8d25a37684f3fc852021-11-17T12:52:16ZENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler10.5194/gmd-14-6977-20211991-959X1991-9603https://doaj.org/article/3f5cd748f59e4abd8d25a37684f3fc852021-11-01T00:00:00Zhttps://gmd.copernicus.org/articles/14/6977/2021/gmd-14-6977-2021.pdfhttps://doaj.org/toc/1991-959Xhttps://doaj.org/toc/1991-9603<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>B. MuB. QinS. YuanCopernicus PublicationsarticleGeologyQE1-996.5ENGeoscientific Model Development, Vol 14, Pp 6977-6999 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Geology QE1-996.5
spellingShingle	Geology QE1-996.5 B. Mu B. Qin S. Yuan ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler
description	<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>
format	article
author	B. Mu B. Qin S. Yuan
author_facet	B. Mu B. Qin S. Yuan
author_sort	B. Mu
title	ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler
title_short	ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler
title_full	ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler
title_fullStr	ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler
title_full_unstemmed	ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler
title_sort	enso-asc 1.0.0: enso deep learning forecast model with a multivariate air–sea coupler
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/3f5cd748f59e4abd8d25a37684f3fc85
work_keys_str_mv	AT bmu ensoasc100ensodeeplearningforecastmodelwithamultivariateairseacoupler AT bqin ensoasc100ensodeeplearningforecastmodelwithamultivariateairseacoupler AT syuan ensoasc100ensodeeplearningforecastmodelwithamultivariateairseacoupler
_version_	1718425540012015616

ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler

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