Uncertainty analysis of future summer monsoon duration and area over East Asia using a multi-GCM/multi-RCM ensemble

Abstract

This study examines spatio-temporal characteristics of the summer monsoon rainy season over East Asia using six regional climate models (RCMs) participating in the “Coordinated Regional Domain Experiment” (CORDEX) East Asia Phase II project. The combination of multiple global climate models (GCMs) × RCMs framework produces a larger spread in summer monsoon characteristics than those from driving GCMs only, enabling a better quantification of uncertainty factors. The RCM simulations on average reproduce the observed summer monsoon duration and area better than the corresponding boundary GCMs, implying the added values of downscaling. Both area and duration of the East Asian summer monsoon are projected to increase by the late 21st century, more strongly in the high emission scenarios than in the low emission ones, particularly in China. Different responses between scenarios, which indicate warming mitigation benefits become significant only in the late 21st century due to large inter-simulation uncertainties. Analysis of Variance (ANOVA) results show that uncertainty in future monsoon area and duration is larger between boundary GCMs than between RCMs over East Asia and its coastal sub-regions. A strong inter-simulation relationship between RCMs and GCMs supports that the boundary GCMs substantially diversify downscaled RCM projections through different climate sensitivities. Further, the distinct sub-regional responses in future monsoon area and duration emphasize the importance of fine-resolution projections with appropriate uncertainty measures for better preparing region-specific adaptation plans.

Future Projection and Uncertainty Analysis of Wind and Solar Energy in China Based on an Ensemble of CORDEX‐EA‐II Regional Climate Simulations

“…Results show that the total uncertainties in WPD and PVP projections are mainly influenced by the effects of RCM and GCM in most regions, except for SC and SWC, where the RCM-GCM interaction also plays the important role. Furthermore, the contribution of GCM uncertainty is stronger than that of RCM in most regions, which is consistent with the findings of Lee et al (2023).…”

“…Additionally, the contribution of GCM uncertainty is stronger than that of RCM in most regions, which is consistent with Lee et al. (2023) highlighting the importance of climate sensitivity in driving GCM when generating fine‐scale outputs with multiple RCMs.…”

“…Exceptions are seen in the TP, NC and NWC regions, where the scenario uncertainty is larger than others for the long-term projection. Additionally, the contribution of GCM uncertainty is stronger than that of RCM in most regions, which is consistent with Lee et al (2023) highlighting the importance of climate sensitivity in driving GCM when generating fine-scale outputs with multiple RCMs.…”

“…Exceptions are seen over the TP, where the GCM uncertainty and emission scenarios play the significant role in WPD and PVP projection, respectively. Moreover, previous studies have suggested that the interactive effect between GCM and RCM could also contribute to the uncertainty, and they employed the analysis of variance (ANOVA) method to isolate its contribution (Christensen & Kjellström, 2020; Lee et al., 2023; Yip et al., 2011). Similarly, the ANOVA is also applied in our studies to separate the uncertainty into GCM, RCM, and interaction terms for the mid‐term and long‐term projections (Figure S11 in Supporting Information ).…”

“…A clear separation of the sources of uncertainty has the potential to narrow the uncertainty of climate projections (Hawkins & Sutton, 2009; Lee et al., 2023; Lehner et al., 2019). In this study, we employ the principle of the separation uncertainty method proposed by Hawkins and Sutton (2009) to separate and quantify the uncertainty into four sources: driving GCM, inter‐RCM variability, emission scenarios, and internal variability.…”