第二届未来大气科学论坛

1163 / 2026-03-27 23:46:52

Disentangling Internal Variability and Forced Response in Global Land Monsoon Projection Uncertainty: Insights from Multi-Model Large Ensembles

global monsoon precipitation, projection uncertainty, internal variability, model uncertainty, time of emergence, large ensemble

主题1 天气、气候、全球变化、行星大气 > 专题1.8 季风系统的模拟评估与预测预估

Projections of the Asian–Australian, African, and American monsoons are currently challenged by considerable levels

of uncertainty, which influences the effectiveness of climate change adaptation strategies. Clarifying the uncertainty

sources is essential to reduce this uncertainty. Most previous studies have addressed this issue based on limited members in

individual models, which cannot strictly isolate the forced model response from the internal variability. Here, we first

employ the latest multi-model large ensemble (MMLE), with a total of 550 members from eight models, under very-high

emission scenarios. The results show that model uncertainty (internal variability) increases (decreases) with time for all

monsoon regions, but with notably regional disparities in their relative contributions. On the grid scale, internal variability

dominates the total uncertainty of summer precipitation changes during the near-term (2020–39) and mid-term (2040–59)

periods in most monsoon regions. For monsoon circulation, internal variability exerts an even greater influence over the

Asian–Australian monsoon region. Compared with the MMLE results, a conventional approach to isolate the forced signal

based on polynomial fitting tends to underestimate the fraction of internal variability, particularly when and where that

fraction is large. Consequently, the conventional approach overestimates the forced signal of monsoon precipitation relative

to internal noise, leading to an earlier time of emergence by about 10 years compared with that derived from the MMLE,

which is before 2050 for most monsoon regions. The results highlight the necessity of using MMLEs to quantify sources of

uncertainty in climate projections, providing important implications for improving the robustness of future climate

assessments.