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

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.