Evaluating the phase evolution of CMIP GCMs for agricultural climate-change impact assessments in China

• Published in: Agricultural and forest meteorology 2024-12, Vol.359, p.110282, Article 110282
• Main Authors: Yao, Linlin, Tan, Qian, Cheng, Guanhui, Wang, Shuping, Chen, Bingming
• Format: Article
• Language: English
• Subjects: Agricultural climate-change impact assessments; Contribution analysis; Data utility; Global climate model; Structural error model
• ISSN: 0168-1923
• DOI: 10.1016/j.agrformet.2024.110282

•Innovate a top-down agricultural climate-change impact assessment framework, including the quantification of climate change and yield loss on large-scale regional scales.•Apply it to nine major agricultural regions in China with significant climate change and fragile agricultural systems.•Explain the phase evolution of CMIP GCMs, to some extent, improves the reliability of agricultural climate-change impact assessments.•Verify that CMIP6 better captures spatial and temporal trends in crop yields but may still underestimate yields and growing periods in certain regions.•Reveal that agricultural management practices (irrigation/rainfed) affect simulation reliability. The performance of general circulation models (GCMs) in the Coupled Model Intercomparison Project (CMIP) critically determines the reliability of climate-change impact assessments and has continuously progressed (e.g., from CMIP3, CMIP5 to CMIP6). It remains unclear whether this progression enhances the reliability in evaluating the effects of climate change on agricultural systems at a daily resolution, particularly concerning crop production. To address this question, the study selected AquaCrop as a crop model for large-scale agricultural impact assessment due to its compatibility, robustness, and simplicity. Subsequently, the study coupled AquaCrop with multiple GCMs from different CMIP phases: 9 from CMIP3, 14 from CMIP5, and 15 from CMIP6, and attributed GCM-driven crop yield simulations to GCM biases over China. According to the modeling results, the progression enhanced the simulation performance for daily precipitation and temperature. The impacts of CMIPs on assessment results exhibited variability across temporal scales and crop types, further modulated by water management practices. Overall, crop simulations across three CMIP phases revealed a reduction in cold and water stresses, a shortened growing period (particularly evident in CMIP6), and an underestimation of yields. The evolution of CMIP phases increased spatial-temporal correlations for maize (0.61 to 0.81), wheat (0.68 to 0.77), and rice (0.63 to 0.77), without significantly reducing yield biases. Yield biases in early growth period were primarily influenced by daily temperature fluctuations, while biases in latter growth period were correlated with precipitation and maximum temperature. Irrigation mitigated the crop model's sensitivity to precise daily precipitation data compared to rainfed systems. This comprehensive anal