Inter-basin and Multi-time Scale Interactions in generating the 2019 Extreme Indian Ocean Dipole

Inter-basin and Multi-time Scale Interactions in generating the 2019 Extreme Indian Ocean Dipole
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2019年印度洋极端偶极子的跨流域和多时间尺度相互作用

DOI:
10.1175/jcli-d-20-0760.1
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发表时间:
2021
期刊:
影响因子:
4.9
通讯作者:
Hu, Zeng-Zhen
Hu, Zeng-Zhen
中科院分区:
地球科学2区
文献类型:
--
作者:
Zhang, Lei;Han, Weiqing;Hu, Zeng-Zhen

文献摘要

相似文献

2019年发生了史无前例的印度洋极端正偶极子事件(pIOD),对印度洋沿岸国家造成了广泛的灾难性影响,包括东非洪水和澳大利亚大面积丛林大火。在这里,我们通过分析多个观测数据集并进行数值模型实验来调查 2019 年 pIOD 的原因。我们发现,2019年的pIOD是在5月由热带印度洋上空的东风爆发引发的,与北方夏季季节内振荡的干燥阶段相关,并且此后由当地的大气-海洋相互作用维持。 9月至11月,中西部热带太平洋海表温度异常(SSTA)进一步增强印度洋东风,使pIOD达到极值。连续两次源自热带印度洋的马登-朱利安振荡(MJO)事件增强了中西部热带太平洋暖海温异常。我们的结果强调了跨流域和跨时间尺度的相互作用在产生极端 IOD 事件中的重要作用。缺乏对这些相互作用的准确表示可能是使用最先进的气候预测模型预测这种极端 pIOD 的时间较短的根源。
An unprecedented extreme positive Indian Ocean dipole event (pIOD) occurred in 2019, which has caused widespread disastrous impacts on countries bordering the Indian Ocean, including the East African floods and vast bushfires in Australia. Here we investigate the causes for the 2019 pIOD by analyzing multiple observational datasets and performing numerical model experiments. We find that the 2019 pIOD was triggered in May by easterly wind bursts over the tropical Indian Ocean associated with the dry phase of the boreal summer intraseasonal oscillation, and it was sustained by the local atmosphere–ocean interaction thereafter. During September–November, warm sea surface temperature anomalies (SSTA) in the central-western tropical Pacific Ocean further enhanced the Indian Ocean’s easterly winds, bringing the pIOD to an extreme magnitude. The central-western tropical Pacific warm SSTA was strengthened by two consecutive Madden–Julian oscillation (MJO) events that originated from the tropical Indian Ocean. Our results highlight the important roles of cross-basin and cross-time-scale interactions in generating extreme IOD events. The lack of accurate representation of these interactions may be the root for a short lead time in predicting this extreme pIOD with a state-of-the-art climate forecast model.