Vegetation plays a critical role in regulating the water cycle and mediating carbon fluxes within the climate system. It responds rapidly to climate change and is highly sensitive to climate variability. In the context of accelerating global warming, particularly in the mid-to-high latitudes of Asia where warming rates are 2—3 times the global average—vegetation dynamics are expected to undergo significant changes. However, substantial uncertainties persist in projecting future vegetation changes in this region due to model biases, limitations in the spatiotemporal resolution and consistency of satellite remote sensing datasets, and variations in parametrizations of vegetation-climate feedbacks across models. This study integrates three independent satellite-based leaf area index (LAI) datasets—GLOBMAP (Version 3), GIMMS LAI3g, and GLASS—with climate and vegetation outputs from 15 CMIP5 and 19 CMIP6 models. Using a multi-model ensemble mean (MME) framework, we systematically evaluate historical and projected vegetation characteristics in the mid-to-high latitudes of Asia.
Analysis of the satellite datasets reveals that regions with sparse vegetation exhibit higher interannual variability, while dense vegetated regions show more pronounced increasing trends in LAI.Areas of high MME mean LAI, variability, and seasonal amplitude are primarily located in woodland regions at elevations below 1 200 m. Among the datasets, GLOBMAP and GLASS exhibit stronger mutual consistency.The MME approach involves simulation performance by mitigating nonlinearities in individual model outputs. Evaluation of historical simulations indicates that both CMIP5 and CMIP6 models perform best in reproducing surface air temperature, with CMIP6 models demonstrating superior accuracy overall. CMIP6 also partially corrects the overestimation of LAI seen CMIP5 simulations. Ensemble simulations (MME) outperform individual modes in reproducing historical vegetation dynamics.
Future projections under both low- and high-emission scenarios (RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5, respectively) show consistent increases in LAI mean values, interannual variability, and seasonal amplitude, with larger changes under high-emission scenarios. Regions with higher baseline vegetation cover are projected to experience greater LAI increases. While spatial patterns of change vary, the greatest increases are projected in high-LAI regions, high-latitude zones, and East Asia. Notably, LAI increases during the warm season are more pronounced than those in the cold season, indicating enhanced seasonal growth dynamics under future warming.
This study enhances our understanding of vegetation-climate interactions in complex mid-to-high latitude ecosystems which provides key insights into model performance, vegetation sensitivity, and carbon cycle feedbacks. These findings offer a scientific basis for improving ecosystem modeling and informing regional climate adaptation and carbon management strategies.