摘要
厄尔尼诺—南方涛动(ENSO)对长江中游夏季降水(YRSR)的年际变化影响较大.基于CMIP6模式数据,本文预估了未来ENSO与长江中游夏季降水关系的变化.与1979~2014年相似,SSP5-8.5高排放情景下ENSO-YRSR的关系仍表现为前冬发生厄尔尼诺(拉尼娜)后,长江中游夏季降水为正异常(负异常).同时,仍然受三个物理过程影响:前冬ENSO影响次年夏季印度洋海温(ENSO-TIO SST),印度洋海温异常进而影响菲律宾对流(TIO SST-PSC),菲律宾对流对长江中游夏季降水产生影响(PSC-YRSR).例如,(1)5个CMIP6好模式的中位数和20个EC-Earth3好子集的中位数均预估ENSO-YRSR在2015~2100年大部分时段保持显著正相关关系,因为上述三个物理过程的相关关系在未来也显著.(2)30个CMIP6模式的中位数和56个EC-Earth3子集的中位数预估ENSO-YRSR关系略有增强;主要是因为上述三个物理过程在未来变强.(3)5个CMIP6好模式的中位数预估ENSO-YRSR关系仍强于30个CMIP6模式的中位数结果,主要是因为前者预估的TIO SST-PSC和PSC-YRSR关系更强.未来将关注ENSO-YRSR预估的不确定性来源.
El Nino-Southern Oscillation(ENSO)events have a strong impact on the middle reaches of Yangtze River summer rainfall(YRSR).The authors project how this impact might vary in the future.As in the historical period of1979-2014,the middle reaches of the Yangtze River are projected to experience positive(negative)precipitation anomalies in post-El Nino(post-La Ni?a)summers in 2015-2100 under SSP5-8.5,and three related physical processes—namely,ENSO-tropical Indian Ocean(TIO)sea surface temperature(SST),TIO SST-Philippine Sea convection(PSC),and PSC-YRSR relationships—continue to have an impact.First,because the above three processes are projected to be significant,the ENSO-YRSR relationship is significant at the 90%confidence level over most periods of 2015-2100 under SSP5-8.5,according to the median of five reasonable CMIP6 models and the median of 20 reasonable EC-Earth3 runs.Second,due to the aforementioned three stronger relationships,the ENSO-YRSR relationship is projected to be somewhat stronger in 2015-2100 than in 1979-2014,based on both the median of all 30 CMIP6 models and the median of all 56 EC-Earth3 runs.Third,the ENSO-YRSR relationship projected by the median of five reasonable models remains stronger than that projected by the median of all CMIP6 models,owing to the former's stronger TIO SST-PSC and PSC-YRSR relationships under SSP5-8.5.Additionally,future assessments of uncertainty of projections in ENSO-YRSR relationship are still necessary.
基金
supported by the Natural Science Foundation of Hubei Province of China[grant number 2020CFB331]
the National Key Research and Development Program of China[grant number2018YFA0605602]
the Strategic Project of the Chinese Academy of Sciences[grant number XDA19070402]。
