Details

Autor(en) / Beteiligte

• Li, Jiajia
• He, Xinguang
• Tao, Lizhi

Titel

Assessing multiscale variability and teleconnections of monthly precipitation in Yangtze River Basin based on multiscale information theory method

Ist Teil von

• Theoretical and applied climatology, 2022, Vol.147 (1-2), p.717-735

Ort / Verlag

Vienna: Springer Vienna

Erscheinungsjahr

2022

Quelle

Springer journals

Beschreibungen/Notizen

• In this study, multiscale variability of monthly precipitation in Yangtze River Basin (YRB) and subregional precipitation teleconnections with climate indices are investigated by multiscale entropy and stepwise variable selection methods. The intrinsic mode functions (IMFs) of monthly precipitation are extracted and then their multiscale entropy is calculated. Results show that the dominant IMF1 and IMF3 tell similar spatial variability patterns with obvious longitudinal zonality and significant increasing trend from west to east, while IMF2 exhibits a nonsignificant increasing variability pattern with larger coherent areal extent having relatively high variability compared to IMF1 and IMF3. Based on multiscale variability of monthly precipitation, the YRB is divided into six contiguous and distinct subregions. In each formed subregion, multiscale teleconnection analysis of regional monthly precipitation (RMP) shows that climate indices with relatively high correlations mainly appear in IMF2 and IMF3 of the RMP for all the subregions, and most of them have stronger correlations with IMF2 than IMF3. The ECS, KC, and SCS with specific lags, and NINO1 + 2 with 4-month lag are identified as strong indicators representing periodic oscillations of the RMP in the YRB. The identified climate indices can provide effective predictive information for the RMP and its dominant components. Moreover, the climate indices identified in west can better capture periodic oscillations of IMF2 than those in east basin. Also, the ability of climate indices largely depends on the variability of monthly precipitation for predicting the RMP, and is stronger for capturing temporal variations of the RMP in west region with lower variability.