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Earth system models (ESMs) participating in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate various components of fine particulate matter (PM2.5) as major climate forcers. Yet the model performance for PM2.5 components remains little evaluated due in part to a lack of observational data. Here, we evaluate near-surface concentrations of PM2.5 and its five main components over China as simulated by 14 CMIP6 models, including organic carbon (OC; available in 14 models), black carbon (BC; 14 models), sulfate (14 models), nitrate (4 models), and ammonium (5 models). For this purpose, we collect observational data between 2000 and 2014 from a satellite-based dataset for total PM2.5 and from 2469 measurement records in the literature for PM2.5 components. Seven models output total PM2.5 concentrations, and they all underestimate the observed total PM2.5 over eastern China, with GFDL-ESM4 (- 1.5 %) and MPI-ESM-1-2-HAM (- 1.1 %) exhibiting the smallest biases averaged over the whole country. The other seven models, for which we recalculate total PM2.5 from the available component output, underestimate the total PM2.5 concentrations partly because of the missing model representations of nitrate and ammonium. Concentrations of the five individual components are underestimated in almost all models, except that sulfate is overestimated in MPI-ESM-1-2-HAM by 12.6 % and in MRI-ESM2-0 by 24.5 %. The underestimation is the largest for OC (by - 71.2 % to - 37.8 % across the 14 models) and the smallest for BC (- 47.9 % to - 12.1 %). The multi-model mean (MMM) reproduces the observed spatial pattern for OC (R = 0.51), sulfate (R = 0.57), nitrate (R = 0.70) and ammonium (R = 0.74) fairly well, yet the agreement is poorer for BC (R = 0.39). The varying performances of ESMs on total PM2.5 and its components have important implications for the modeled magnitude and spatial pattern of aerosol radiative forcing. [ABSTRACT FROM AUTHOR]

Paddy fields serve as significant reservoirs of soil organic carbon (SOC) and their potential for terrestrial carbon (C) sequestration is closely associated with changes in SOC pools. However, there has been a dearth of comprehensive studies quantifying changes in SOC pools following extended periods of rice cultivation across a broad geographical scale. Using 104 rice paddy sampling sites that have been in continuous cultivation since the 1980s across China, we studied the changes in topsoil (0–20 cm) labile organic C (LOC I), semi‐labile organic C (LOC II), recalcitrant organic C (ROC), and total SOC. We found a substantial increase in both the content (48%) and density (39%) of total SOC within China's paddy fields between the 1980s to the 2010s. Intriguingly, the rate of increase in content and density of ROC exceeded that of LOC (I and II). Using a structural equation model, we revealed that changes in the content and density of total SOC were mainly driven by corresponding shifts in ROC, which are influenced both directly and indirectly by climatic and soil physicochemical factors; in particular temperature, precipitation, phosphorous (P) and clay content. We also showed that the δ13CLOC were greater than δ13CROC, independent of the rice cropping region, and that there was a significant positive correlation between δ13CSOC and δ13Cstraw. The δ13CLOC and δ13CSOC showed significantly negative correlation with soil total Si, suggesting that soil Si plays a part in the allocation of C into different SOC pools, and its turnover or stabilization. Our study underscores that the global C sequestration of the paddy fields mainly stems from the substantial increase in ROC pool. [ABSTRACT FROM AUTHOR]

The infestation of pine shoot beetles (Tomicus spp.) in the forests of Southwestern China has inflicted serious ecological damages to the environment, causing significant economic losses. Therefore, accurate and practical approaches to detect pest infestation have become an urgent necessity to mitigate these harmful consequences. In this study, we explored the efficiency of thermal infrared (TIR) technology in capturing changes in canopy surface temperature (CST) and monitoring forest health at the scale of individual tree crowns. We combined data collected from TIR imagery and light detection and ranging (LiDAR) using unmanned airborne vehicles (UAVs) to estimate the shoot damage ratio (SDR), which is a representative parameter of the damage degree caused by forest infestation. We compared multiple machine learning methods for data analysis, including random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), to determine the optimal regression model for assessing SDR at the crown scale. Our findings showed that a combination of LiDAR metrics and CST presents the highest accuracy in estimating SDR using the RF model (R2 = 0.7914, RMSE = 15.5685). Our method enables the accurate remote monitoring of forest health and is expected to provide a novel approach for controlling pest infestation, minimizing the associated damages caused. [ABSTRACT FROM AUTHOR]