The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown th...The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.展开更多
模式分辨率对气候模式的模拟效果具有重要影响。然而,当前模式开发对于垂直分辨率的重视不够。以ENSO(厄尔尼诺-南方涛动)遥相关为例,利用CESM(Community Earth System Model)模式,探究不同模式垂直分辨率设置下模式模拟的ENSO对平流层...模式分辨率对气候模式的模拟效果具有重要影响。然而,当前模式开发对于垂直分辨率的重视不够。以ENSO(厄尔尼诺-南方涛动)遥相关为例,利用CESM(Community Earth System Model)模式,探究不同模式垂直分辨率设置下模式模拟的ENSO对平流层、对流层影响的差异,评估模式垂直分辨率在气候模拟中的重要性。结果表明,提高垂直分辨率可以显著改进模式对ENSO遥相关的模拟能力。以ECMWF(European Centre for Medium-Range Weather Forecasts)第五代再分析数据集(ERA5)为参照,ENSO对纬向平均温度的影响在北半球中高纬地区冬季呈现出“负正负”的三极子模态。CESM默认的垂直分辨率设置(L66)不能模拟出这一模态,而提高模式垂直分辨率(L103)后则可以较好地模拟出这个模态。对于水平分布而言,L66模拟的ENSO在对流层的信号与再分析资料相比明显偏强,L103则可以显著改善。同时,L103对ENSO影响平流层的模拟效果也比L66有所改善。进一步分析发现,L103模拟的行星波从对流层向平流层的传播更强,更接近再分析资料。提高垂直分辨率可以改善模式对大气波活动以及平流层-对流层动力耦合的模拟,重视模式的研发。展开更多
Recent studies have shown that deep learning(DL)models can skillfully forecast El Niño–Southern Oscillation(ENSO)events more than 1.5 years in advance.However,concerns regarding the reliability of predictions ma...Recent studies have shown that deep learning(DL)models can skillfully forecast El Niño–Southern Oscillation(ENSO)events more than 1.5 years in advance.However,concerns regarding the reliability of predictions made by DL methods persist,including potential overfitting issues and lack of interpretability.Here,we propose ResoNet,a DL model that combines CNN(convolutional neural network)and transformer architectures.This hybrid architecture enables our model to adequately capture local sea surface temperature anomalies as well as long-range inter-basin interactions across oceans.We show that ResoNet can robustly predict ENSO at lead times of 19 months,thus outperforming existing approaches in terms of the forecast horizon.According to an explainability method applied to ResoNet predictions of El Niño and La Niña from 1-to 18-month leads,we find that it predicts the Niño-3.4 index based on multiple physically reasonable mechanisms,such as the recharge oscillator concept,seasonal footprint mechanism,and Indian Ocean capacitor effect.Moreover,we demonstrate for the first time that the asymmetry between El Niño and La Niña development can be captured by ResoNet.Our results could help to alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.展开更多
海洋热浪是发生在海洋上的极端高温事件,对海洋环境和生态系统具有破坏性影响。文章采用1960—2020年第五代欧洲中期天气预报中心再分析资料(European centre for medium-range weather forecasts reanalysis v5,ERA5)和英国气象局哈德...海洋热浪是发生在海洋上的极端高温事件,对海洋环境和生态系统具有破坏性影响。文章采用1960—2020年第五代欧洲中期天气预报中心再分析资料(European centre for medium-range weather forecasts reanalysis v5,ERA5)和英国气象局哈德来中心全球海冰和海洋表面温度资料集(Hadley centre global sea ie and sea surface temperature,HadISST)以及地球系统模式(community Earth system model,CESM1)北大西洋理想试验数据等,通过相关、合成分析等多种统计方法,研究了厄尔尼诺–南方涛动(El Niño-Southern Oscillation,ENSO)与次年初夏西太平洋海洋热浪年际关系的变化特征,并进一步探讨了二者关系发生年代际变化的可能成因。研究结果表明:1)ENSO与次年初夏西太平洋海洋热浪月数的年际关系具有明显的年代际变化特征,北大西洋多年代际振荡(Atantic multidecadal oscillation,AMO)是二者年际关系发生年代际变化的主要成因。当AMO处于正位相时,ENSO与次年初夏西太平洋海洋热浪存在显著的正相关关系,而当AMO处于负位相时,上述二者相关关系不再显著;2)AMO主要通过调控ENSO事件的强度进而影响西北太平洋大气环流的异常响应,从而进一步影响ENSO与次年初夏西太平洋海洋热浪之间的关系。当AMO处于负(正)位相时,相对较强(弱)的ENSO事件通过强(弱)风–蒸发–海温正反馈过程,使得ENSO事件次年初夏西北太平洋地区产生位置相对偏东(西)、强度相对偏强(弱)的异常反气旋/气旋。异常反气旋/气旋的位置和强度导致初夏西太平洋海洋热浪的分布在AMO正、负位相存在显著差异。展开更多
基金supported by the National Key R&D Program of China(Grant No.2019YFA0606703)the National Natural Science Foundation of China(Grant No.41975116)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.Y202025)。
文摘The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.
文摘模式分辨率对气候模式的模拟效果具有重要影响。然而,当前模式开发对于垂直分辨率的重视不够。以ENSO(厄尔尼诺-南方涛动)遥相关为例,利用CESM(Community Earth System Model)模式,探究不同模式垂直分辨率设置下模式模拟的ENSO对平流层、对流层影响的差异,评估模式垂直分辨率在气候模拟中的重要性。结果表明,提高垂直分辨率可以显著改进模式对ENSO遥相关的模拟能力。以ECMWF(European Centre for Medium-Range Weather Forecasts)第五代再分析数据集(ERA5)为参照,ENSO对纬向平均温度的影响在北半球中高纬地区冬季呈现出“负正负”的三极子模态。CESM默认的垂直分辨率设置(L66)不能模拟出这一模态,而提高模式垂直分辨率(L103)后则可以较好地模拟出这个模态。对于水平分布而言,L66模拟的ENSO在对流层的信号与再分析资料相比明显偏强,L103则可以显著改善。同时,L103对ENSO影响平流层的模拟效果也比L66有所改善。进一步分析发现,L103模拟的行星波从对流层向平流层的传播更强,更接近再分析资料。提高垂直分辨率可以改善模式对大气波活动以及平流层-对流层动力耦合的模拟,重视模式的研发。
基金supported by the Shanghai Artificial Intelligence Laboratory and National Natural Science Foundation of China(Grant No.42088101 and 42030605).
文摘Recent studies have shown that deep learning(DL)models can skillfully forecast El Niño–Southern Oscillation(ENSO)events more than 1.5 years in advance.However,concerns regarding the reliability of predictions made by DL methods persist,including potential overfitting issues and lack of interpretability.Here,we propose ResoNet,a DL model that combines CNN(convolutional neural network)and transformer architectures.This hybrid architecture enables our model to adequately capture local sea surface temperature anomalies as well as long-range inter-basin interactions across oceans.We show that ResoNet can robustly predict ENSO at lead times of 19 months,thus outperforming existing approaches in terms of the forecast horizon.According to an explainability method applied to ResoNet predictions of El Niño and La Niña from 1-to 18-month leads,we find that it predicts the Niño-3.4 index based on multiple physically reasonable mechanisms,such as the recharge oscillator concept,seasonal footprint mechanism,and Indian Ocean capacitor effect.Moreover,we demonstrate for the first time that the asymmetry between El Niño and La Niña development can be captured by ResoNet.Our results could help to alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.
文摘海洋热浪是发生在海洋上的极端高温事件,对海洋环境和生态系统具有破坏性影响。文章采用1960—2020年第五代欧洲中期天气预报中心再分析资料(European centre for medium-range weather forecasts reanalysis v5,ERA5)和英国气象局哈德来中心全球海冰和海洋表面温度资料集(Hadley centre global sea ie and sea surface temperature,HadISST)以及地球系统模式(community Earth system model,CESM1)北大西洋理想试验数据等,通过相关、合成分析等多种统计方法,研究了厄尔尼诺–南方涛动(El Niño-Southern Oscillation,ENSO)与次年初夏西太平洋海洋热浪年际关系的变化特征,并进一步探讨了二者关系发生年代际变化的可能成因。研究结果表明:1)ENSO与次年初夏西太平洋海洋热浪月数的年际关系具有明显的年代际变化特征,北大西洋多年代际振荡(Atantic multidecadal oscillation,AMO)是二者年际关系发生年代际变化的主要成因。当AMO处于正位相时,ENSO与次年初夏西太平洋海洋热浪存在显著的正相关关系,而当AMO处于负位相时,上述二者相关关系不再显著;2)AMO主要通过调控ENSO事件的强度进而影响西北太平洋大气环流的异常响应,从而进一步影响ENSO与次年初夏西太平洋海洋热浪之间的关系。当AMO处于负(正)位相时,相对较强(弱)的ENSO事件通过强(弱)风–蒸发–海温正反馈过程,使得ENSO事件次年初夏西北太平洋地区产生位置相对偏东(西)、强度相对偏强(弱)的异常反气旋/气旋。异常反气旋/气旋的位置和强度导致初夏西太平洋海洋热浪的分布在AMO正、负位相存在显著差异。
