The East Asian summer monsoon (EASM) has undergone significant decadal variations since the late 1970s, which greatly affects regional economic and societal activities. However, most available EASM predictions are limited to seasonal to interannual scales, with indistinctive improvements. This study significantly improves decadal prediction skills of the 10-year-averaged EASM index (EASMI) and related decadal variations of the East China summer precipitation anomalies (ECSPAs) in the 10-member decadal prediction experiments started once a year from 1960 to 2005 using a coupled climate model. These improvements are mainly attributed to the use of more realistic and well-balanced initial conditions (ICs) from a weakly coupled data assimilation system that incorporates the monthly mean atmospheric reanalysis data into the model using the dimension-reduced projection four-dimensional variational data assimilation approach, a kind of four-dimensional ensemble-variational hybrid method. The atmospheric initialization ameliorates the ICs of Pacific Decadal Oscillation (PDO) through the air-sea coupling, and thus leads to better PDO hindcasts and forecasts (under the representative concentration pathway 4.5). The improved PDO hindcasts (forecasts) result in more accurate hindcasts (forecasts) of land-sea thermal contrast, and thus have significant impacts on the EASMI. The improved decadal predictions of the EASMI then contribute to the ameliorated prediction on decadal variations of the spatial distribution of ECSPAs. This study highlights the importance of ICs for a coupled model in reasonably presenting decadal variabilities of the EASMI.

中文翻译：

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利用弱耦合数据同化方案改进东亚夏季风的年代际预测

东亚夏季风（EASM）自1970年代后期以来经历了显着的年代际变化，极大地影响了区域经济和社会活动。然而，大多数可用的 EASM 预测仅限于季节性到年际尺度，并没有明显的改进。本研究显着提高了 10 年平均 EASM 指数 (EASMI) 的年代际预测技巧和华东夏季降水距平 (ECSPAs) 的相关年代际变化，在 1960 年至 2005 年开始的每年一次的 10 成员年代际预测实验中使用耦合气候模型。这些改进主要归因于使用来自弱耦合数据同化系统的更现实和均衡的初始条件（IC），该系统使用降维投影四维变分数据同化将月平均大气再分析数据合并到模型中方法，一种四维集成变分混合方法。大气初始化通过海气耦合改善了太平洋年代际振荡 (PDO) 的 IC，从而导致更好的 PDO 后报和预报（在代表性浓度路径 4.5 下）。改进的 PDO 后报（预报）导致更准确的海陆热对比后报（预报），从而对 EASMI 产生重大影响。EASMI 的改进的年代际预测有助于改进对 ECSPA 空间分布的年代际变化的预测。本研究强调了 IC 对耦合模型在合理呈现 EASMI 的年代际变化方面的重要性。