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Seasonal predictable source of the East Asian summer monsoon rainfall in addition to the ENSO–AO
Climate Dynamics ( IF 4.6 ) Pub Date : 2022-08-18 , DOI: 10.1007/s00382-022-06461-4
Kairan Ying , Dabang Jiang , Xiaogu Zheng , Carsten S. Frederiksen , Jing Peng , Tianbao Zhao , Linhao Zhong

Improvement in the seasonal forecasting of East Asian summer monsoon rainfall (EASMR) remains a great challenge, as it is influenced by varied and complex impacts from (1) external forcings and slowly varying internal variabilities, which are potentially predictable, and (2) internal dynamics on intraseasonal time scales, which is basically unpredictable beyond a season. In this work, a (co-)variance decomposition method is applied to identify the leading potentially predictable (slow) patterns of the EASMR [the seasonal mean rainfall in the region (5°–50° N, 100°–140° E) in June–July–August] during 1979–2019 by separating the unpredictable noise (intraseasonal). We focus on the most critical predictable sources that are additional to the decaying (DC) El Niño–Southern Oscillation (ENSO), developing (DV) ENSO, and spring Arctic Oscillation (AO)—the three most important and well-recognized predictors for EASMR. We find that (1) the indices that represent the EASMR predictability related to the DC ENSO, spring AO and DV ENSO are the preceding November to March Niño1 + 2 sea surface temperature (SST), the April–May AO, and the May Niño4 SST, respectively; (2) the dominant additional predictable EASMR signals that are linearly independent of the DC ENSO, spring AO and DV ENSO have apparent relationships with the interannual variability of the SST in the western North Pacific, tropical and southern Atlantic, southern Indian, and Arctic oceans during boreal springtime, as well as the linear trend; and (3) by applying a principal component regression scheme to evaluate the EASMR predictability arising from DC/DV ENSO–AO and these additional predictors, the cross-validated fraction variance skill of the total seasonal mean EASMR is 11% (8%—land; 13%—ocean) for the former, and 15% (15%—land; 15%—ocean) for the latter, with a total of 26% that comprises more than 80% of the potential predictability of the EASMR. The considerable skill stemming from the predictors additional to DC/DV ENSO–AO indicates that they are worthy of attention in the seasonal forecasting of EASMR, especially for terrestrial areas.



中文翻译:

除 ENSO-AO 外,东亚夏季风降雨的季节性可预测来源

改进东亚夏季季风降雨 (EASMR) 的季节预报仍然是一个巨大的挑战,因为它受到来自以下方面的各种复杂影响的影响:(1) 外部强迫和缓慢变化的内部变率,这可能是可预测的,以及 (2) 内部季节内时间尺度上的动态,这在一个季节之外基本上是不可预测的。在这项工作中,应用(共)方差分解方法来识别 EASMR 的主要潜在可预测(慢)模式 [该地区的季节性平均降雨量(5°–50° N,100°–140° E)在 6-7-8 月] 在 1979-2019 期间通过分离不可预测的噪音(季内)。我们关注最关键的可预测来源,这些来源除了衰减的 (DC) 厄尔尼诺-南方涛动 (ENSO)、发展中的 (DV) ENSO,和春季北极涛动 (AO)——三个最重要和公认的 EASMR 预测因子。我们发现(1)代表与DC ENSO、春季AO和DV ENSO相关的EASMR可预测性的指标是前11月至3月Niño1+2海面温度(SST)、4-5月AO和5月Niño4 SST,分别;(2) 与 DC ENSO、春季 AO 和 DV ENSO 线性无关的主要附加可预测 EASMR 信号与西北太平洋、热带和南大西洋、南印度洋和北冰洋海温的年际变化有明显的关系在北方春季,以及线性趋势;(3) 通过应用主成分回归方案来评估由 DC/DV ENSO–AO 和这些附加预测因子引起的 EASMR 可预测性,前者总季节平均 EASMR 的交叉验证分数方差技能为 11%(8%—陆地;13%—海洋),后者为 15%(15%—陆地;15%—海洋),其中总计 26%,占 EASMR 潜在可预测性的 80% 以上。DC/DV ENSO-AO 之外的预测因子所产生的相当大的技巧表明它们在 EASMR 的季节预报中值得关注,特别是对于陆地区域。

更新日期:2022-08-18
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