Tracking decadal dry–wet shifts over the past millennium in China: Insights from five high-resolution lake records
Abstract
Decadal-scale dry–wet shifts exert substantial impacts on human societies and ecosystem stability. However, their long-term evolution and underlying mechanisms remain insufficiently understood, particularly during periods of centennial-scale climate fluctuation, such as the Medieval Climate Anomaly (MCA) and the Little Ice Age (LIA). Here, we analyze five well-dated, high-resolution lacustrine precipitation records (Gonghai Lake, Nvshan Lake, Jiangcuo Lake, Kusai Lake, and Sihailongwan) and, using regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD) combined with the Hilbert transform, extract decadal hydroclimate variability and quantitatively identify decadal dry–wet shifts. The results show that these shifts exhibit a pattern of regional coherence alongside local divergence across different climatic phases: dry–wet shifts are more frequent during the MCA at Gonghai, whereas they are concentrated during the LIA at Nvshan Lake, Kusai Lake, and Sihailongwan; Jiangcuo Lake shows relatively minor differences between the two periods. Notably, the frequency of decadal dry–wet shifts was positively associated with the magnitude of centennial-scale hydroclimate contrast across the five lake records, suggesting that longer-term hydroclimate background states may influence the magnitude and frequency of higher-frequency variability. These spatial differences may reflect the combined influence of the Asian monsoon, the westerlies, and local hydrological processes in alpine basin settings. Our findings highlight the importance of decadal hydroclimate variability in shaping regional climate responses. Future work should integrate multi-proxy data and climate model simulations to better constrain the underlying mechanisms and incorporate decadal variability into region-specific water resource management frameworks to improve resilience to extreme hydroclimatic events.
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Authors: Yong Luo, Xin Zhou, Wanping Shi, Hongfei Zhao, Shiwei Jiang
Institutions: University of Science and Technology of China