ENSEMBLES耦合模式对全球陆地季风区夏季降水的年代际预测能力评估

Assessment of the Decadal Prediction Skill on Global Land Summer Monsoon Precipitation in the Coupled Models of ENSEMBLES

全球季风区降水对当地社会经济、全球大尺度环流及能量循环至关重要。采用欧洲联盟ENSEMBLES计划Stream 2的年代际回报试验,评估了其对1960—2015年全球陆地季风夏季降水年代际变化的回报能力,并探讨了北半球陆地季风区夏季(NHSM)降水年代际变化可预报性的可能来源。分析发现ENSEMBLES对全球及南球陆地季风区夏季降水的年代际回报技巧不高,但其对NHSM降水具有一定的预报能力,能合理回报出观测中NHSM降水在1960年至1970s末期的减弱趋势和1990s之后的增强趋势,其缺陷在于模式中NHSM降水最小值出现在1970s末期,较之观测提前了近10年,未能回报出1980s中期至1990s初期NHSM的干旱期。mega-ENSO与大西洋多年代际振荡(AMO)是影响NHSM降水年代际变化的2个重要驱动因子。分析发现模式回报的NHSM降水与mega-ENSO、大西洋多年代际振荡(AMO)的正相关明显大于观测,能合理再现2个指数在1960年至1970s末期和1990s后的变化趋势,是模式对这2个时段内NHSM降水回报技巧的重要来源。虽然ENSEMBLES对AMO的年代际变化具有较高的回报能力(与观测的最大相关系数高达0.85),但是对mega-ENSO的回报技巧较弱,进而限制了模式1980s中期至1990s初NHSM的年代际预报技巧。因此,提高模式对mega-ENSO的预报能力,是提升NHSM降水年代际预报水平的重要途径。

Global monsoon precipitation plays a crucial role in the local social economy and global large-scale circulation and energy cycle. Using the decadal prediction output for 1960-2015 from ENSEMBLES Stream 2, the decadal hindcast skill of climate models on global land monsoon precipitation and the potential source of predictability were examined in this paper. It is found that the decadal variation of global and southern hemispheric land monsoon precipitation is not well hindcasted by ENSEMBLES. However, the Northern Hemispheric land Summer Monsoon （NHSM） precipitation in hindcast is well predicted, including the observed downward trend from 1960 to the late 1970s and upward trend since the 1990s. The main deficiency is that the minimum NHSM precipitation occured in mid-1970s, which is 10-year earlier than the observation, leading to poor prediction of NHSM precipitation from the mid-1980s to early 1990s. Mega-ENSO and Atlantic Multi-decadal Oscillation （AMO） are the two main factored that modulate the decadal variation of NHSM precipitation. The result shows that the relationships of NHSM precipitation with mega-ENSO and AMO in ENSENBLES are higher than the observation. The climate models well predicted the increase from 1960 to the late 1970s and decrease trend since the 1990s of mega-ENSO and AMO. It is the primary source of the prediction skill on NHSM changes during the two periods. Although AMO is well predicted by ENSEMBLES （ highest correlation coefficient with observation is 0.85 ）, the prediction skill of mega-EN- SO is limited, leading to poor performance in predicting NHSM precipitation from the mid-1980s to early 1990s. Thus, improving the prediction of mega-ENSO can be seen as one important method of better decadal prediction of NHSM precipitation.