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

The Asian-Australian, African and American monsoon projections are currently challenged by the considerable uncertainty, affecting effective climate change adaptation strategies. Clarifying the uncertainty sources is essential to reduce this uncertainty. Most of the previous studies address this issue based on limited members in individual models, which cannot strictly isolate forced model response from internal variability. Here we first employ the latest multi-model large ensembles (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 of their relative contributions. On grid scale, internal variability dominates the total uncertainty of summer precipitation changes during the near-term (2020-2039) and mid-term (2040-2059) 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 forced signal based on polynomial fitting (Hawkins and Sutton, 2009; HS09 hereinafter) tends to underestimate the fraction of internal variability, particularly when and where that fraction is large. Consequently, the HS09 method overestimates the forced signal of monsoon precipitation relative to internal noise, leading to an earlier Time of Emergence (ToE) by about 10 years than that derived from 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.