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The greening of the Earth over the last decades is predominantly indicated by the enhancements of leaf area index (LAI). Quantifying the relative contribution of multiple determinants, especially changes in climate and in land management changes (LMC), remains an arduous challenge. To solve this problem, we develop a simple yet novel data-driven method, called the Paired Land Use Experiment (PLUE), for mesoscale analysis. Using PLUE, we analyze vegetation development of the Sanjiang Plain, a transboundary plain between China and Russia, with roughly homogeneous climate but with distinct land management practices across the border-intensified agricultural development on China side (CNSP) versus largely little-disturbed natural vegetation on Russia side (RUSP). Both CNSP and RUSP LAI show significant trends (p < 0.05), with the annual variability reaching values of 9.8 × 10−3 yr−1 and 11.3 × 10−3 yr−1, respectively. However, in CNSP, the LAI increase is concentrated in the middle of the year, especially in five 8-day periods from 26 June to 28 July. During this period, the LAI trend ofCNSP is much higher than that of RUSP, at 92.7 × 10−3 yr−1 (p < 0.01) and 43.8 × 10−3 yr −1 (p < 0.01), respectively. Meanwhile, LAI decreased in CNSP at the begging and end of the growing season. The results show that different LMC practices lead to notably different seasonal variability in vegetation changes. The PLUE method offers a new potential tool in driver identification of vegetation greenness change based on observations. We argue for the necessity of parameterizing these different LMC in Earth system models.

Ice clouds are mostly composed of different ice crystal habits. It is of great importance to classify ice crystal habits seeing as they could greatly impact single‐scattering properties of ice crystal particles. The single‐scattering properties play an important role in the study of cloud remote sensing and the Earth's atmospheric radiation budget. However, there are countless ice crystals with different shapes in ice clouds, and the task of empirical classification based on naked‐eye observations is unreliable, time consuming and subjective, which leads to classification results having obvious uncertainties and biases. In this paper, the images of ice crystals observed from airborne Cloud Particle Imager in China are used to establish an ice crystal data set called Ice Crystals Database in China, which consists of 10 habit categories containing over 7,000 images. We propose an automatic classification model of ice crystal habits, called TL‐ResNet152, which is a deep convolutional neural network based on the newly developed method of transfer learning. The results show that the TL‐ResNet152 model could achieve reliable performance in ice crystal habits classification with the accuracy of 96%, which is far more accurate than traditional classification methods. Achieving high‐precision automatic classification of ice crystal habits will help us better understand the radiation characteristics of ice clouds.

Drought has become a major threat to local sustainable development in dryland Asia, one of the largest grassland ecosystems in the world. However, empirical‐ and science‐based evidence regarding the extent of drought changes and the future trends of these changes in dryland Asia is variable and incomplete. Here, we first investigate the historical variations in drought conditions in dryland Asia, as measured by the drought intensity and arid area, using three widely used drought indices (the Palmer Drought Severity Index, the Standardized Precipitation Index, and the Standardized Precipitation Evapotranspiration Index). Then, we use Bayesian model averaging to reproduce the future drought conditions under two representative concentration pathways (RCP2.6 and RCP4.5) from the Coupled Model Intercomparison Project Phase 5 Earth system models. The Palmer Drought Severity Index, Standardized Precipitation Index, and Standardized Precipitation Evapotranspiration Index illustrate that dryland Asia has experienced an overall drying trend and an expansion of arid areas over the past 100 years (1901–2016). Both temperature and precipitation are projected to increase under both the 1.5 and 2.0 °C warming scenarios compared with the values from the reference period (1986–2005). The projected drought conditions in the 1.5 and 2.0 °C warming scenarios will worsen, especially across Kazakhstan and Northwest China. We found that the drought conditions under the 2.0 °C warming conditions will not be as severe as those under the 1.5 °C warming conditions due to the mitigating effect of the projected precipitation increase under RCP4.5. These results call for short‐term and long‐term mitigation and adaptation measurements for drought events in dryland Asia.